Air Pollution Modeling and its Application XVIII, Volume 6 (Developments in Environmental Science)

Air Pollution Modeling and Its Application XVIII Previous Volumes in this Mini-Series Volumes I–XII were included in ...

Author: Carlos Borrego | Eberhard E. Renner

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Air Pollution Modeling and Its Application XVIII

Previous Volumes in this Mini-Series Volumes I–XII were included in the NATO Challenges of Modern Society Series. AIR POLLUTION MODELING AND ITS APPLICATION I Edited by C. De Wispelaere AIR POLLUTION MODELING AND ITS APPLICATION II Edited by C. De Wispelaere AIR POLLUTION MODELING AND ITS APPLICATION III Edited by C. De Wispelaere AIR POLLUTION MODELING AND ITS APPLICATION IV Edited by C. De Wispelaere AIR POLLUTION MODELING AND ITS APPLICATION V Edited by C. De Wispelaere, Francis A. Schiermeier, and Noor V. Gillani AIR POLLUTION MODELING AND ITS APPLICATION VI Edited by Han van Dop AIR POLLUTION MODELING AND ITS APPLICATION VII Edited by Han van Dop AIR POLLUTION MODELING AND ITS APPLICATION VIII Edited by Han van Dop and Douw G. Steyn AIR POLLUTION MODELING AND ITS APPLICATION IX Edited by Han van Dop and George Kallos AIR POLLUTION MODELING AND ITS APPLICATION X Edited by Sven-Erik Gryning and Milla´n M. Milla´n AIR POLLUTION MODELING AND ITS APPLICATION XI Edited by Sven-Erik Gryning and Francis A. Schiermeier AIR POLLUTION MODELING AND ITS APPLICATION XII Edited by Sven-Erik Gryning and Nadine Chaumerliac AIR POLLUTION MODELING AND ITS APPLICATION XIII Edited by Sven-Erik Gryning and Ekaterina Batchvarova AIR POLLUTION MODELING AND ITS APPLICATION XIV Edited by Sven-Erik Gryning and Francis A. Schiermeier AIR POLLUTION MODELING AND ITS APPLICATION XV Edited by Carlos Borrego and Guy Schayes AIR POLLUTION MODELING AND ITS APPLICATION XVI Edited by Carlos Borrego and Selahattin Incecik AIR POLLUTION MODELING AND ITS APPLICATION XVII Edited by Carlos Borrego and Ann-Lise Norman

Air Pollution Modeling and Its Application XVIII

Edited by Carlos Borrego University of Aveiro Aveiro, Portugal Eberhard Renner Leibniz-Institute for Tropospheric Research Leipzig, Germany

Amsterdam – Boston – Heidelberg – London – New York – Oxford Paris – San Diego – San Francisco – Singapore – Sydney – Tokyo

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands Linacre House, Jordan Hill, Oxford OX2 8DP, UK First edition 2007 Copyright r 2007 Elsevier Ltd. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://www.elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent veriﬁcation of diagnoses and drug dosages should be made Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-444-52987-9 ISSN: 1474-8177 For information on all Elsevier publications visit our website at books.elsevier.com

Printed and bound in Italy 07 08 09 10 11 10 9 8 7 6 5 4 3 2 1

v

Contents

List of participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The members of the scientiﬁc committee for the 28th NATO/ CCMS international technical meeting on air pollution modeling and its application . . . . . . . . . . . . . . . . . . . . . . History of NATO/CCMS air pollution pilot studies . . . . . . . Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . xvii

. . . xxxiii . . . xxxv . . xxxvii

Section 1: Local and urban scale modelling 1.1.

Application and validation of FLUENT ﬂow and dispersion modelling within complex geometries Silvana Di Sabatino, Riccardo Buccolieri, Beatrice Pulvirenti and Rex Britter . . . . . . . . . . . . . . . . . . . . 3

1.2.

Turbulence, atmospheric dispersion and mixing height in the urban area, recent experimental ﬁndings Sven-Erik Gryning and Ekaterina Batchvarova . . . . . . . . . . 12

1.3.

Inverse modelling of local surface emissions with the CHIMERE-adjoint model: The case of the Paris area during the ESQUIF ﬁeld experiment L. Menut, I. Pison and N. Blond . . . . . . . . . . . . . . . . . . . . 21

1.4.

Numerical simulations of microscale urban ﬂow using the RAMS model Tamir Reisin, Orit Altaratz Stollar and Silvia Trini Castelli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

1.5.

Assessment of dust forecast errors by using lidar measurements over Rome P. Kishcha, P. Alpert, A. Shtivelman, S. O. Krichak, J. H. Joseph, G. Kallos, P. Katsafados, C. Spyrou, G. P. Gobbi, F. Barnaba, S. Nickovic, C. Perez and J. M. Baldasano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

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1.6.

Modelling urban heat island in the context of a Mediterranean city F. Santese, S. Di Sabatino, E. Solazzo and R. Britter . . . . . 55

1.7.

Evaluation of land surface scheme modiﬁcations on atmospheric transport and deposition patterns in Copenhagen metropolitan area Alexander Mahura, Alexander Baklanov, Steen Hoe, Jens H. Sorensen, Claus Petersen and Kai Sattler . . . . . . . . 64

Section 2. Regional and intercontinental modelling 2.1.

Modelling of secondary aerosols in Switzerland in summer 2003 S. Andreani–Aksoyoglu, J. Keller, A.S.H. Prevot, U. Baltensperger and J. Flemming . . . . . . . . . . . . . . . . . . . 75

2.2.

Application of the CMAQ mercury model for U.S. EPA regulatory support O. Russell Bullock Jr. and Thomas Braverman . . . . . . . . . . 85

2.3.

Multi-objective analysis to control ozone exposure Claudio Carnevale, Giovanna Finzi, Enrico Pisoni and Marialuisa Volta . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

2.4.

Atmo-rhenA: a common air quality modelling system in the Upper Rhine Valley R. Deprost, J. Bernard, E. Riviere, N. Leclerc and C. Schillinger. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

2.5.

Perturbational downscaling and its applications in air pollution and meteorological problems Eugene Genikhovich and Guy Schayes . . . . . . . . . . . . . . . 123

2.6.

Increase in nitrate deposition as a result of sulfur dioxide emission increase in Asia: Indirect acidiﬁcation Mizuo Kajino and Hiromasa Ueda . . . . . . . . . . . . . . . . . 134

2.7.

Predicted aerosol concentrations over East Asia and evaluation of relative contribution of various sources with global chemical transport model T. Kitada, Y. Shirakawa, K. Wagatani, G. Kurata and K. Yamamoto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

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2.8.

Long-term simulations of photo-oxidants and particulate matter over Europe with emphasis on North Rhine-Westphalia M. Memmesheimer, S. Wurzler, E. Friese, H. J. Jakobs, H. Feldmann, A. Ebel, C. Kessler, J. Geiger, U. Hartmann, A. Brandt, U. Pfeffer and H.P. Dorn . . . . . . . . . . . . . . . . . . . . . . . . 158

2.9.

Developing and implementing an updated chlorine chemistry into the community multiscale air quality model Golam Sarwar, Deborah Luecken and Greg Yarwood . . . . 168

2.10. Modeling assessment of the impact of nitrogen oxides emission reductions on ozone air quality in the Eastern United States: Offsetting increases in energy use P. Steven Porter, Edith Ge´go, Alice Gilliland, Christian Hogrefe, James Godowitch and S. Trivikrama Rao.. . . . . . . . . . . . . . . . . . . . . . . . . . 177 2.11. Dispersion modelling of the concentrations of the ﬁne particulate matter in Europe Mikhail Soﬁev, Erwan Jourden, Liisa Pirjola, Leena Kangas, Niko Karvosenoja, Ari Karppinen and Jaakko Kukkonen . . . . . . . . . . . . . . . . . . . . . . . . . . 189 2.12. The use of meteorological and dispersion models in stratiﬁed atmospheric boundary layers M. R. Soler, M. Bravo and S. Ortega . . . . . . . . . . . . . . . . 200 2.13. Modelling regional air quality over decades: Past and future trends in photochemical smog Robert Vautard, Sophie Szopa, Matthias Beekmann, Laurent Menut, Didier A. Hauglustaine and Laurence Rouil. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 2.14. Forecasting ozone PM2.5 in southeastern U.S. M. Talat Odman, Yongtao Hu, Michael E. Chang and Armistead G. Russell . . . . . . . . . . . . . . . . . . . . . . . . 220 2.15. Integrated observational and modelling approaches for evaluating the effectiveness of ozone control policies J. Godowitch, A. Gilliland, E. Gego, R. Draxler and S. Trivikrama Rao . . . . . . . . . . . . . . . . . . . . . . . . . . 230

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2.16. Medium-range puff growth Torben Mikkelsen, Søren Thykier-Nielsen and Steen Hoe. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 2.17. Operational evaluation of the Eastern Texas air quality (ETAQ) forecasting system based on MM5/SMOKE/CMAQ Daewon W. Byun, Meong-Do Jang, Chang-Keun Song, Soontae Kim, Fang-Yi Cheng, Ryan Perna and Hyun-CHeol Kim. . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Section 3. Data assimilation and air quality forecasting 3.1.

Improvement in particles (PM10) urban air quality mapping interpolation using remote sensing data Nuno Grosso, Francisco Ferreira and Sandra Mesquita . . . 265

3.2.

The use of ensemble weather forecast for dispersion uncertainty modelling Lennart Robertson, Andrew Jones, Francois Bonnardot and Stefano Galmarini . . . . . . . . . . . 275

3.3.

Forward and inverse modelling of radioactive pollutants dispersion after Chernobyl accident Mikhail Soﬁev, Ilkka Valkama, Carl Fortelius and Pilvi Siljamo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283

3.4.

PREV’AIR: A platform for air quality monitoring and forecasting C. Honore´, L. Menut, B. Bessagnet, F. Meleux, L. Rouı¨ l, R. Vautard, N. Poisson and V. H. Peuch . . . . . . . . . . . . . 293

3.5.

Estimation of sulphur emissions using ensemble smoothers Alina L. Barbu, Remus G. Hanea, Arnold W. Heemink, Martijn Schaap and Pierre Girardeau. . . . . . . . . . . . . . . . 301

3.6

Application of a four-dimensional variational (4DVAR) data assimilation for optimal estimation of mineral dust and CO emissions in eastern Asia Keiya Yumimoto and Itsushi Uno . . . . . . . . . . . . . . . . . . 318

Section 4: Model assessment and veriﬁcation 4.0.

A review of uncertainty and sensitivity analyses of atmospheric transport and dispersion models Steven R. Hanna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331

Contents

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4.1.

Lagrangian particle model simulation of tracer dispersion in stable low wind speed conditions D. Anfossi, S. Alessandrini, S. Trini Castelli, E. Ferrero, D. Oettl and G. Degrazia . . . . . . . . . . . . . . . . 352

4.2.

Application and sensitivity analysis of CAMx and CHIMERE air quality models in a coastal area Isabelle Coll, Guido Pirovano, Fanny Lasry, Stefano Alessandrini, Marco Bedogni, Matteo Costa, Veronica Gabusi, Laurent Menut and Robert Vautard . . . 362

4.3.

A comparison between CHIMERE, CAMx and CMAQ air quality modelling systems to predict ozone maxima during the 2003 episode in Europe: Spain case study R. San Jose´, J. L. Pe´rez and R.M. Gonza´lez . . . . . . . . . . . 374

4.4.

Final results of the model inter-comparison of very high-resolution simulations with numerical weather prediction models for eight urban air pollution episodes in four European cities B. Fay, L. Neunha¨userer, A. Baklanov, G. Bonafe´, S. Jongen, J. Kukkonen, V. Ødegaard, J. L. Palau, G. Perez-Landa, M. Rantama¨ki, A. Rasmussen, R. S. Sokhi and Y. Yu . . . . . . . . . . . . . . . . . . . . . . . . . . 383

4.5.

Uncertainty in air pollution models used for regulatory and risk assessment purposes Bernard Fisher and Robert Willows . . . . . . . . . . . . . . . . . 395

4.6.

Aerosol mass budget analysis over Berlin city area by means of the CTM REM_Calgrid Andreas Kerschbaumer, Matthias Beekmann and Eberhard Reimer . . . . . . . . . . . . . . . . . . . . . . . . . . . 406

4.7.

The use of CFD and mesoscale air quality modelling systems for urban applications: Madrid case study R. San Jose´, J. L. Pe´rez and R.M. Gonza´lez . . . . . . . . . . . 416

4.8.

Modelling the dynamics of air pollutants over the Iberian Peninsula under typical meteorological situations P. Jime´nez, O. Jorba and J.M. Baldasano . . . . . . . . . . . . . 425

Contents

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4.9.

Modelling of the July 10 STERAO storm with the RAMS model: Chemical species redistribution including gas phase and aqueous phase chemistry Maud Leriche, Sylvie Cautenet, Mary Barth and Nadine Chaumerliac . . . . . . . . . . . . . . . . . . . . . . . . . 437

4.10. A study of process contributions to ozone formation during the 2004 ICARTT period using the Eta-CMAQ forecast model over the northeastern U.S. Shaocai Yu, Rohit Mathur, Kenneth Schere, Daiwen Kang, Jonathan Pleim, Jeffrey Young and Tanya Otte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 4.11. Validation of the integrated RAMS-Hg modelling system Antigoni Voudouri and George Kallos . . . . . . . . . . . . . . . 457 4.12. Analyzing the response of a chemical transport model to emissions reductions utilizing various grid resolutions Rainer Stern, Robert J. Yamartino and Arno Graff. . . . . . 467 Section 5: Aerosols in the atmosphere 5.1. Modelling of pollen dispersion with a weather forecast modelsystem H. Vogel, B. Vogel and Ch. Kottmeier . . . . . . . . . . . . . . . 481 5.2. Aerosol forecast over the Great Lakes for a February 2005 episode Pius Lee, Jeffery McQueen, Marina Tsidulko, Mary Hart, Shobha Kondragunta, Daiwen Kang, Geoff DiMego and Paula Davidson . . . . . . . . . . . . . . . . . 492 5.3. On the contribution of the heterogeneous chemistry to nitrate concentrations over Europe based on modelling results and long-term and campaign measurements Alma Hodzic, Laurent Menut, Bertrand Bessagnet and Robert Vautard . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503 5.4. Modelling seasonal changes of aerosol compositions over Belgium and Europe Felix Deutsch, Filip Lefebre, Liliane Janssen, Jean Vankerkom and Clemens Mensink . . . . . . . . . . . . . . 514

Contents

5.5.

xi

Modelling of Saharan dust events within SAMUM: Implications for regional radiation balance and mesoscale circulation Ju¨rgen Helmert, Bernd Heinold, Ina Tegen, Olaf Hellmuth and Ralf Wolke . . . . . . . . . . . . . . . . . . . . 523

5.6. Long-term aerosol simulation for Portugal using the CHIMERE model C. Borrego, A. Monteiro, J. Ferreira, A. I. Miranda, R. Vautard and A. T. Perez. . . . . . . . . . . . . . . . . . . . . . . 534 5.7. Radiative effects of natural PMs on photochemical processes in the Mediterranean Region Marina Astitha, George Kallos, Petros Katsafados, Ioannis Pytharoulis and Nikos Mihalopoulos . . . . . . . . . . 548 5.8. Modelling of mineral dust emissions and transport with CHIMERE-DUST model: Preliminary analysis of dust events for the AMMA ﬁeld campaign L. Menut, C. Schmechtig, R. Vautard and B. Marticorena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 560 5.9. Comparison of modelled and measured aerosol optical depth over southwestern Germany R. Rinke, D. Ba¨umer, B. Vogel, St. Versick and Ch. Kottmeier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 568 5.10. On the direct aerosol forcing of nitrate over Europe: Simulations with the new LOTOS-EUROS model Martijn Schaap, Ferd Sauter and Peter Builtjes . . . . . . . . . 582 5.11. Causes for spread between global models w.r.t. Lifetime and distribution of particulate sulphate Øyvind Seland and Trond Iversen . . . . . . . . . . . . . . . . . . 592 5.12. Modelling of aerosol composition using the MARS/MUSE dispersion model A. Arvanitis, E. Debry, I. Douros and N. Moussiopoulos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601 5.13. Interpretation of new particle formation bursts in the planetary boundary layer using a high-order columnar model Olaf Hellmuth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 610

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5.14. Mixing of plumes with ambient background air: Effects of particle size variations close to the source T. Engelke, A. Hugo, E. Renner, F. Schmidt, R. Wolke and J. Zoboki . . . . . . . . . . . . . . . . . . . . . . . . . 621

Section 6: Interactions between climate change and air quality 6.1.

Examining the impact of changing climate on regional air quality over the U. S. Ellen J. Cooter, Robert Gilliam, William Benjey, Chris Nolte, Jenise Swall and Alice Gilliland. . . . . . . . . . . 633

6.2.

Analyzing the impacts of climate change on ozone and particulate matter with tracer species, process analysis, and multiple regional climate scenarios C. Hogrefe, D. Werth, R. Avissar, B. Lynn, C. Rosenzweig, R. Goldberg, J. Rosenthal, K. Knowlton and P.L. Kinney. . . . . . . . . . . . . . . . . . . . . 648

6.3.

Dimethylsulphide (DMS) ﬂux and DMS oxidation over the North Atlantic: Comparison of a top-down & bottom-up approach A.L. Norman, M.A. Wadleigh, S. Eaton, C. Burridge, C. Zaganescu, J.P. Blanchet, M. Scarratt, S. Michaud, M. Levasseur, A. Merzouk, M. Lizotte, S. Sharma and R. Leaitch . . . . . . . . . . . . . . . . . . . . . . . . 661

Section 7: Air quality and human health 7.1.

Calculations of personal exposure to particulate matter in urban areas Inga Fløisand, Herdis Laupsa, David Broday, Trond Bøhler, Werner Holla¨nder, Susanne Lu¨tzenkirchen, Christos Housiadas, Thanos Stubos and Harold McInnes . 679

7.2.

Lagrangian particle model simulation to assess air quality along the Brenner transit corridor through the Alps D. Oettl, P. Sturm, D. Anfossi, S. Trini Castelli, P. Lercher, G. Tinarelli and T. Pittini. . . . . . . . . . . . . . . . 689

7.3.

Optimum exposure ﬁelds for epidemiology and health forecasting Bill Physick, Martin Cope, Sunhee Lee and Peter Hurley . . 698

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7.4.

On inﬂuence of long-range transport of pollen grains onto pollinating seasons Pilvi Siljamo, Mikhail Soﬁev, Elena Severova, Hanna Ranta and Svetlana Polevova . . . . . . . . . . . . . . . . 708

7.5.

Air quality characterization for environmental public health tracking Timothy Watkins, Fred Dimmick, David Holland, Alice Gilliland, Vickie Boothe, Chris Paulu and Andrew Smith . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 717

Section 8: Poster session 1.

Mass ﬂux balance at an urban intersection Sandro Baldi, Matteo Carpentieri and Alan G. Robins . . . 731

2.

Wind tunnel experiments of ﬂow and dispersion in idealised urban areas Matteo Carpentieri, Andrea Corti and Paolo Giambini . . . 734

3.

Improving the Martilli’s urban boundary layer scheme: Off-line validation over different urban surfaces R. Hamdi and G. Schayes . . . . . . . . . . . . . . . . . . . . . . . . 737

4.

Wind shear distortion of concentration ﬂuctuations from an elevated source Trevor Hilderman and David J. Wilson . . . . . . . . . . . . . . 740

5.

Inter-comparison of CFD, wind tunnel and Gaussian plume models for estimating dispersion from a complex industrial site P. Jenkinson, R. Hill, E. Lutman, A. Arnott and T.G. Parker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 742

6.

Nowcasting and forecasting the street pollution dispersion for Tallinn metropolitan area Marko Kaasik, Triinu Lukk, Kuido Kartau and Tanel Dovnar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 744

7.

Wind-driven NOx removal by ﬂow-through fences with ACF (Activated Carbon Fiber): Evaluation of the fence’s efﬁciency in reduction of ambient NOx Toshihiro Kitada, Makoto Nagano, Takaaki Shimohara and Takayuki Tokairin . . . . . . . . . . . 747

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8.

Radiative transfers in CFD modelling of the urban canopy Maya Milliez, Luc Musson-Genon and Bertrand Carissimo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 750

9.

Evaluation of passive pollutants residence time in a deep street canyon by CFD simulations F. Murena and G. Favale . . . . . . . . . . . . . . . . . . . . . . . . 754

10. Modelling integrated system for urban air quality in Bologna Linda Passoni, Vanes Poluzzi, Marco Deserti, Enrico Minguzzi, Michele Stortini and Giovanni Bonafe` . . 758 11. Long-term evaluation of secondary atmospheric pollution over Italy M. P. Costa, S. Alessandrini, M. bedogni, B. Bessagnet, E. Bossi, G. Maffeis, C. Pertot, G. Pirovano and R. Vautard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 761 12. Modelling past and future trends in sulphur and nitrogen deposition in the United Kingdom A. J. Dore, M. B"as´ , M. Kryza, J. Hall, C. J. Dore, M. Vieno, K. J. Weston and M. A. Sutton . . . . . . . . . . . . 764 13. Long-term simulation and validation of ozone and aerosol in the Po Valley M. Stortini, M. Deserti, G. Bonafe and E. Minguzzi . . . . . 768 14. Biogenic emission modelling in Lithuania Vidmantas Ulevicius, Vytautas Vebra, Kestutis Senuta and Kristina Plauskaite . . . . . . . . . . . . . . . . . . . . . . . . . . 771 15. The use of a photochemical trajectory model to estimate pollution levels within the West Midlands conurbation, UK Helen L. Walker, Jacob Baker, Richard G. Derwent and Rossa G. Donovan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 774 16. Producing high-resolution spatial maps of ambient ozone concentrations in Belgium Stijn Janssen, Jef Hooyberghs, Clemens Mensink, Gerwin Dumont and Frans Fierens . . . . . . . . . . . . . . . . . 784 17. 2D variational data assimilation of near-surface chemical species Lennart Robertson and Michael Kahnert . . . . . . . . . . . . . 787

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18. Principal component and multiple linear regressions to predict ozone concentrations Soﬁa I. V. Sousa, Fernando G. Martins, Jose´ C. M. Pires, Maria C. M. Alvim-Ferraz and Maria C. Pereira . . . . . . . 790 19. Relationships between nitrogen oxide emissions from electrical generating units in the U.S. and Meteorology P. Steven Porter, S.T. Rao and E.L. Gego . . . . . . . . . . . . 793 20. Validation of the meteorological input for air quality simulations in Northern Italy Giovanni Bonafe`, Marco Deserti, Suzanne Jongen, Enrico Minguzzi and Michele Stortini . . . . . . . . . . . . . . . 795 21. The North American mercury model inter-comparison study (NAMMIS) O. Russell Bullock, Dwight Atkinson, Thomas Braverman, Ashu Dastoor, Didier Davignon, Noelle Selin, Daniel Jacoby, Kristen Lohman, Christian Seigneur, Krish Vijayaraghavan, Tom Myers, Kevin Civerolo and Christian Hogrefe. . . . . . . . . . . . . . . . . . . . . . . . . . . 798 22. Two-dimensional steady state advection-diffusion equation: An analytical solution Daniela Buske, Marco Tullio Vilhena, Davidson Moreira and Tiziano Tirabassi . . . . . . . . . . . . . . . . . . . . . 802 23. The new GIADMT approach to simulate the pollutant dispersion in the planetary boundary layer Camila Costa, Marco Tullio Vilhena, Davidson Moreira and Tiziano Tirabassi . . . . . . . . . . . . . . . . . . . . . 805 24. One-dimensional eddy diffusivities for growing turbulence in the convective boundary layer Antonio Goulart, Umberto Rizza, Davidson Moreira, Marco T. Vilhena, Gerva´sio Degrazia and Jonas Carvalho. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 808 25. Impact of meteorological factors on turbulent dispersion over complex terrain S. Saavedra, J. A. Souto and J. Vila`-Guerau de Arellano . . 811 26. An air pollution model applying a semi-analytical solution for low wind conditions Tiziano Tirabassi, Davidson Moreira, Daniela Buske and Antoˆnio Goulart . . . . . . . . . . . . . . . . 814

xvi

Contents

27. Modeling of Saharan dust events within SAMUM: On the description of the Saharan dust cycle using LM-MUSCAT Bernd Heinold, Ju¨rgen Helmert, Ina Tegen, Olaf Hellmuth and Ralf Wolke . . . . . . . . . . . . . . . . . . . . . . . . 817 28. An improved coupling scheme in the parallel modelling system LM-MUSCAT R. Wolke, M. Lieber, B. Heinold, J. Helmert, W. Schro¨der and E. Renner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 820 29. Identiﬁcation of aerosol sources in the Baikal region by receptor modeling methods Ekaterina V. Kuchmenko, Elena V. Molozhnikova, Alexandre V. Keiko and Maxim S. Zarodnyuk . . . . . . . . . 823 30. Small-scale modeling of PM deposition and re-suspension in inner part of urban area Jiri Pospisil and Miroslav Jicha . . . . . . . . . . . . . . . . . . . . 826 31. Enhanced characterization of ambient air quality to study the link between climate variability, air quality, and health C. Hogrefe, K. Knowlton, R. Goldberg, J. Rosenthal, C. Rosenzweig, B. Lynn and P.L. Kinney . . . . . . . . . . . . . 829 32. Preparatory work for optimised European air quality and health effect monitoring (EURAQHEM) T. Kuhlbusch, A. John, U. Quass, A. Hugo, A. Peters, S. von Klot, J. Cyrys and E. Wichmann . . . . . . . . . . . . . . 832 33. PM levels and their health implications in Lisbon Hugo Tente, Francisco Ferreira, Luı´ sa Nogueira, Carlos Silva Santos and Sandra Moreira. . . . . . . . . . . . . . 835 34. Spatial and temporal variation in particulate Polycyclic Aromatic Hydrocarbons (PAH) levels Menen (Belgium) and their relation with air mass trajectories Khaiwal Ravindra, Eric Wauters and Rene´ Van Grieken . . 838 Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 843 Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 865

xvii

List of participants

The 28th NATO/CCMS International Technical Meeting on Air Pollution Modeling and Its Application, Leipzig, Germany, May 15–19, 2006.

Australia Physick, Bill

CSIRO Marine and Atmospheric Research 107-121 Station Street; Private Bag 1, Aspendale, 3195 Melbourne [email protected]

Austria Herman, Friedl

Federal Research and Training Centre for Forests Seckendorf-Gudentweg 8, A-1130 Vienna [email protected]

Pechinger, Ulrike

Central Institute for Meteorology and Geodynamics, Environmental Meteorology Hohe Warte 38, A-1191 Vienna [email protected]

Uhrner, Ulrich

Institute for Internal Combustion Engines and Thermodynamics, A-Graz University of Technology Inffeldgasse 21, A-8010 Graz [email protected]

Belgium Delcloo, Andy

Royal Meteorological Institute of Belgium, Observations Ringlaan 3, 1180 Brussels [email protected]

xviii

List of Participants

Janssen, Stijn

VITO NV, Integral Environmental Studies Boeretang 200, 2400 Mol [email protected]

Khaiwal, Ravindra

University of Antwerp, Department of Chemistry Universiteitsplein 1, B-2610 Wilrijk, Antwerp [email protected]

Mensink, Clemens

VITO NV, Integral Environmental Studies Boeretang 200, 2400 Mol [email protected]

Schayes, Guy H.

Institute of Astronomy and Geophysics, Universite´ de Louvain Ch. Du Cyclotron 2, B-1348 Louvain-La-Neuve [email protected]

Brasil Buske, Daniela

UFRGS–Federal University of Rio Grande do Sue, PROMEC–Graduate Program in Mechanical Engineering Av. Dr. Carlos Barbosa, n. 1454 Aptoo 3, 90880-000, Porto Alegra/RS [email protected]

Bulgaria Batchvarova, Ekaterina

National Institute of Meteorology and Hydrology Blvd. Tzarigradsko Chaussee 66, 1784 Soﬁa [email protected]

Ganev, Kostadin

Geophysical Institute, BAS, Physics of the Atmosphere Acad. G. Bonchev, block 3, 1113 Soﬁa [email protected]

Canada Hilderman, Trevor

Coanda Research and Development Corp. IIOA-3430 Brighton Ave., V5A 3H4 Burnaby, BC [email protected]

List of Participants

xix

Kaminski, Jacek

York University, AMDAL/CRESS 4700 Keele St., M3J-1P3, Toronto, Ontario [email protected]

Norman, Ann-Lise

The University of Calgary, Department of Physics and Astronomy 2500 University Dr., N.W., T2N 1N4 Calgary [email protected]

Steyn, Douw

University of British Columbia, Department of Earth & Ocean Sciences 6339 Stores Road, V6T 1Z4 Vancouver, B.C. [email protected]

Czech Republic Brechler, Josef

Charles University, Faculty of Mathematics and Physics, Department of Meteorology & Environmental Protection V Holesˇ ovicˇka´ch 2, 18000 Prague [email protected]

Halenka, Toma´sˇ

Charles University, Faculty of Mathematics and Physics, Department of Meteorology & Environmental Protection V Holesˇ ovicˇka´ch 2, 18000 Prague [email protected]

Jicha, Miroslav

Brno University of Technology, Department Mechanical Engineering Technicka 2, 61669 Brno [email protected]

Pospisil, Jiri

Brno University of Technology, Energy Institute Technicka 2896/2, 61669 Brno [email protected]

Denmark Gryning, Sven-Erik

Risø National Laboratory, Wind Energy Department, DK-4000 Roskilde [email protected]

xx

List of Participants

Mahura, Alexander

Danish Meteorological Institute, Department of Research and Development Lyngbyvej 100, DK-2100 Copenhagen [email protected]

Thykier-Nielsen, Søren

Risø National Laboratory Wind Energy Department, DK-4000 Roskilde [email protected]

Estonia Kaasik, Marko

Institute of Environmental Physics, University of Tartu U¨likooli 18, 50090 Tartu [email protected]

Finland Jantunen, Matti

National Public Health Institute (KTL), Department of Environmental Health P.O. Box 95 (Neulaniementie 4), FI 70101 Kuopio matti.jantunen@ktl.ﬁ

Siljamo, Pilvi

Finnish Meteorological Institute, Meteorological Research Erik Palme´nin Aukio 1 (P.O. Box 503), FI 00101 Helsinki pilvi.siljamo@fmi.ﬁ

Sofiev, Mikhail

Finnish Meteorological Institute, Air Quality Erik Palme´nin Aukio 1 (P.O. Box 503), 00561 Helsinki mikhail.soﬁev@fmi.ﬁ

France Chaumerliac, Nadine

CNRS—LaMP, Universite´ Blaise Pascal 24 avenue des Landais, 63177 Aubie`re [email protected]

Deprost, Raphaelle

ASPA—Air Quality Monitoring Network of Alsace, France 5 rue de Madrid, 673000 Schiltigheim [email protected]

List of Participants

xxi

Leriche, Maud

CNRS—LaMP, Universite´ Blaise Pascal 24 avenue des Landais, 63177 Aubie`re [email protected]

Menut, Laurent

Laboratoire de Me´te´orologie Dynamique, IPSL/ CNRS E´cole Polytechnique, 91128 Palaiseau [email protected]

Milliez, Maya

CEREA (ENPC/EDF R&D) 6-8 av. Blaise Pascal, 77455 Champs sur Marne [email protected]

Vautard, Robert

LSCE/IPSL—Laboratoire CEA/CNRS/UVSQ CEDEX (Bat 701), 91191 Gif sur Yvette [email protected]

Germany Ebel, Adolf

Rheinish Institute for Environmental Research, University of Cologne Aachener Str. 209, 50931 Ko¨ln [email protected]

Fay, Barbara

Deutscher Wetterdienst (DWD) (German Weather Service) Kaiserleistr. 42, 63067 Offenbach [email protected]

Graff, Arno

Umweltbundesamt Wo¨rlitzer Platz 1, 06844 Dessau [email protected]

Gru¨tzun, Verena

Leibniz Institute for Tropospheric Research Permoserstr. 15, 04318 Leipzig [email protected]

Heinold, Bernd

Leibniz Institute for Tropospheric Research Permoserstr. 15, 04318 Leipzig [email protected]

Hellmuth, Olaf

Leibniz Institute for Tropospheric Research Permoserstr. 15, 04318 Leipzig [email protected]

xxii

List of Participants

Helmert, Ju¨rgen

Leibniz Institute for Tropospheric Research Permoserstr. 15, 04318 Leipzig [email protected]

Hugo, Achim

Institut fu¨r Energie-und Umwelttechnik IUTA e. V. Bliersheimer Str. 60, 47229 Duisburg [email protected]

Kerschbaumer, Andreas

Institut fu¨r Meteorologie, FU Berlin Carl-Heinrich-Becker-Weg 6-10, 12165 Berlin [email protected]

Klug, Werner

Mittermayerweg 21, 64289 Darmstadt [email protected]

Knoth, Oswald

Leibniz Institute for Tropospheric Research Permoserstr. 15, 04318 Leipzig [email protected]

Kotova, Lola

Max-Planck Institute for Meteorology, The Atmoshpere in the Earth System Bundesstr. 53, 20146 Hamburg [email protected]

Martens, Reinhard

Gesellschaft fu¨r Anlagen- und Reaktorsicherheit Schwertnergasse 1, 50667 Ko¨ln [email protected]

Memmesheimer, Michael

Rheinish Institute for Environmental Research, University of Cologne Aachener Str. 201, 50931 Cologne [email protected]

Reimer, Eberhard

Freie Universita¨t Berlin, Institut fu¨r Meteorologie Carl-Heinrich-Becker-Weg 6-10, 12165 Berlin [email protected]

Renner, Eberhard

Leibniz Institute for Tropospheric Research Permoserstr. 15, 04318 Leipzig [email protected]

Rinke, Rayk

Institut fu¨r Meteorologie und Klimaforschung, Forschungszentrum Karlsruhe/Universtita¨t Karlsruhe Postfach 3640, 76021 Karlsruhe [email protected]

List of Participants

xxiii

Schlink, Uwe

Umweltforschungszentrum Leipzig-Halle GmbH (UFZ), Department Expositionsforschung und Epidemiologie Permoserstr. 15, 04318 Leipzig [email protected]

Stern, Rainer

Freie Universita¨t Berlin, Institut fu¨r Meteorologie Carl-Heinrich-Becker-Weg 6-10, 12165 Berlin [email protected]

Streuber, Oliver

Peutz Consult GmbH Kolberger Str. 19, 40599 Du¨sseldorf [email protected]

Tegen, Ina

Leibniz Institute for Tropospheric Research Permoserstr. 15, 04318 Leipzig [email protected]

Torras Ortiz, Sandra

IER, Universita¨t Stuttgart, TFU Heßbru¨hlstr. 49-A, 70565 Stuttgart [email protected]

Trukenmu¨ller, Alfred

Federal Environment Agency (UBA) of Germany Wo¨rlitzer Platz 1, 06844 Dessau [email protected]

Vogel, Heike

Leibniz Institut fu¨r Meteorologie und Klimaforschung, Forschungszentrum Karlsruhe/ Universtita¨t Karlsruhe Postfach 3640, 76021 Karlsruhe [email protected]

Wolke, Ralf

Leibniz Institute for Tropospheric Research Permoserstr. 15, 04318 Leipzig [email protected]

Greece Astitha, Marina

University of Athens, Department of Applied Physics University Campus, Bldg. PHYS-V, 15784 Zografou, Athens [email protected]

List of Participants

xxiv

Douros, John

Aristotle University of Thessaloniki, Mechanical Engineering University Campus, BOX 483, 54124 Thessaloniki [email protected]

Kallos, George

University of Athens, School of Physics University Campus, Bldg. PHYS-5, 15784 Athens [email protected]

Israel Haikin, Nitsa

NRCN P.O.B. 9001, 84190 Beer-Sheva [email protected]

Kishcha, Pavel

Tel-Aviv University, Department of Geophysics and Planetary Sciences Ramat Aviv, 69978 Tel-Aviv [email protected]

Reisin, Tamir

Soreq Nuclear Research Center, Applied Physics 81800 Yavne [email protected]

Italy Anfossi, Domenico

CNR—ISAC Corso Fiume 4, 10133 Torino [email protected]

Bedogni, Marco

Agenzia Mobilita` E Ambiente, Ambiente E Energia Via Beccaria 19, 20122 Milano [email protected]

Borghi, Sergio

Osservatorio Meteorologico di Milano-Duomo Piazza Duomo 21, 20121 Milano [email protected]

Carnevale, Claudio

University of Brescia, DEA Via Branze 38, 25123 Brescia [email protected]

List of Participants

xxv

Carpentieri, Matteo

University of Florence, Dipartimento di Energetica Via S. Marta 3, 50139 Firenze [email protected]ﬁ.it

Di Sabatino, Silvana

University of Lecce, Dipartimento di Scienza dei Materiali SP Lecce—Monteroni, 73100 Lecce [email protected]

Favale, Giuseppe

University of Naples ‘‘Federico II’’, Chemical Engineering Department P. le Tecchio 80, 80100 Napoli [email protected]

Favaron, Maurizio

Servizi Territorio Via Garibaldi 21, 20092 Cinisello Balsamo [email protected]

Minguzzi, Enrico

Regional Meteorological Service of Emilia Romagna—Italy (ARPA—SIM) Viale Silvani 6, 40726 Bologna [email protected]

Murena, Fabio

University of Naples, Chemical Engineering Department P. le Tecchio 80, 80125 Napoli [email protected]

Santese, Federica

University of Lecce, Material Science Department Prov. Lecce—Monteroni—Polo Scientiﬁco, 73100 Lecce [email protected]

Trini Castelli, Silvia

Institute of Atmospheric Sciences and Climate— C.N.R. Corso Fiume 4, 10133 Torino [email protected]

Zappala`, Daniele

Osservatorio Meteorologico di Milano-Duomo Piazza Duomo 21, 20121 Milano [email protected]

xxvi

List of Participants

Japan Kajino, Mizuo

Division of Global Atmospheric Environment Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo, Japan [email protected]

Kitada, Toshihiro

Toyohashi University of Technology, Department of Ecological Engineering Tempaku-cho, 441-8580 Toyohashi [email protected]

Yumimoto, Keiya

Kyushu University, Department of Earth System Science Kasuga Park 6—1, 816-8580 Kasuga, Fukuoka [email protected]

Lithuania Ulevicius, Vidmantas

Institute of Physics, Environmental Physics and Chemistry Savanoriu 231, LT-02300 Vilnius [email protected]

Norway Fløisand, Inga

Norwegian Institute for Air Research (NILU) P.O. Box 100, 2027 Kjeller [email protected]

Seland, Øyvind

University of Oslo, Department of Geosciences P.O. Box 1022 Blindern, 0315 Oslo [email protected]

Portugal Alvim-Ferraz, Maria C.

LEPAE, Departamento de Engenharia Guinia, Fauldade de Engenharia, Universidade do Porto Dr. Roberto Frias, 4200-461 Porto [email protected]

Borrego, Carlos

Universidade de Aveiro, Departamento Ambiente e Ordenanento Campus Universita´rio de Santiago, 3810 193 Aveiro [email protected]

List of Participants

xxvii

Grosso, Nuno

New University of Lisbon, Department of Science and Environmental Engineering Campus da Caparica, Quinta da Torre, 2829-516 Caparica [email protected]

Mesquita, Sandra

New University of Lisbon, Department of Science and Environmental Engineering Campus da Caparica, Quinta da Torre, 2829-516 Caparica [email protected]

Miranda, Ana Isabel

Universidade de Aveiro, Departamento Ambiente e Ordenanento Campus Universita´rio de Santiago, 3810-193 Aveiro [email protected]

Monteiro, Maria Alexandra

Universidade de Aveiro, Departamento Ambiente e Ordenanento Campus Universita´rio de Santiago, 3810-193 Aveiro [email protected]

Tente, Hugo

New University of Lisbon, Department of Science and Environmental Engineering Campus da Caparica, Quinta da Torre, 2829-516 Caparica [email protected]

Valente, Joana

Universidade de Aveiro, Departamento Ambiente e Ordenanento Campus Universita´rio de Santiago, 3810-193 Aveiro [email protected]

Russia Genikhovich, Eugene

Main Geophysical Observatory, Department of Monitoring of Air Pollution 7 Karbyshev St., 194021 St. Petersburg [email protected]

xxviii

Kuchmenko, Ekaterina

List of Participants

Melentiev Energy Systems Institute, Siberian branch RAS Lermontova 130, 664033 Irkutsk [email protected]

Spain Baldasano, Jose M.

Barcelona Supercomputing Center (BSC), Earth Sciences Jordi Girona 29 (Ediﬁceo Nexus II), 08034 Barcelona [email protected]

San Jose, Roberto

Technical University of Madrid (UPM), Environmental Software and Modelling Group Campus de Montegancedo, ES 28660 Boadilla del Monte (Madrid) roberto@ﬁ.upm.es

Soler, Maria Rosa

University of Barcelona, Astronomy and Meteorology Avinguda Diagonal 647, 08028 Barcelona [email protected]

Souto, Jose A.

University of Santiago de Compostela, Chemical Engineering Campus sur School of Engineering, 15782 Santiago de Compostela [email protected]

Sweden Kahnert, Michael

Swedish Meteorological and Hydrological Institute (SMHI) Folkborgsva¨gen 1, 60176 Norrko¨ping [email protected]

Robertson, Lennart

Swedish Meteorological and Hydrological Institute (SMHI) Folkborgsva¨gen 1, 60176 Norrko¨ping [email protected]

List of Participants

xxix

Switzerland Andreani-Aksoyoglu, Sebnem

Paul Scherrer Institute (PSI), Laboratory of Atmospheric Chemistry 5232 Villigen PSI [email protected]

The Netherlands Barbu, Alina Lavinia

Delft University of Technology, Delft Institute of Applied Mathematics Mekelweg 4, 2628 CD Delft [email protected]

Builtjes, Peter

TNO, Environmental Quality P.O. Box 342, 7300 AH (7204 AT) Apeldoorn [email protected]

Koopmans, Ferry

Peutz bv, Industrial Department Lindenlaan 41, 6584 AC Molenhoek [email protected]

Sauter, Ferd

RIVM (Nat. Inst. of Public Health and the Environment) P.O. Box 1, 3720 BA Bilthoven [email protected]

Schaap, Martijn

TNO, Environment, Health and Safety Laan van Westenenk 501, 7300 AH Apeldoorn [email protected]

van den Akker, Stephan

Peutz bv, Industrial Department Lindenlaan 41, 6584 AC Molenhoek [email protected]

van Dop, Han

Institute for Marine and Atmospheric Research (IMAK), Utrecht University P.O. Box 80.005, 3508 TA Utrecht [email protected]

xxx

List of Participants

Turkey Incecik, Selahattin

Istanbul Technical University, Faculty of Aeronautics and Astronautics, Department of Meteorological Engineering Campus Maslak, 34469 Maslak [email protected]

United Kingdom Baldi, Sandro

University of Surrey, Fluids & Systems/School of Engineering University Campus, G02 7XH Guildford [email protected]

Dore, Anthony

Centre for Ecology & Hydrology, Atmospheric Sciences Bush Estate, EH26 9HF Penicuik, Midlothian [email protected]

Fisher, Bernard

Environment Agency, Risk and Forecasting Kings Meadow Road, RG1 8DQ Reading bernard.ﬁ[email protected]

Jenkinson, Pete

Westlakes Scientiﬁc Consulting Ltd., Air Pollution Group Westlakes Science & Technology Park, CA24 3LN Moor Row [email protected]

Shi, Ji Ping

The Environment Agency, Air Quality Modelling and Assessment Unit 29 Newport Road, CF24 0TP Cardiff [email protected]

Smith, Justin Gillett

Health Protection Agency HPA-RPD Chilton, OX11 ORQ Didcot [email protected]

Walker, Helen L.

University of Birmingham, Geography Edgbaston, B15 2TT Birmingham [email protected]

List of Participants

xxxi

USA Bullock, Russell

NOAA Air Resources Laboratory/U.S. EPA U.S. EPA, Mail Drop E243-03, 27711 Research Triangle Park, NC [email protected]

Byun, Daewon

University of Houston, Department of Geosciences 312 Science & Research Building. 1, TX 77204 Houston, Texas [email protected]

Cooter, Ellen

NOAA/Atmospheric Sciences Modeling Division (In Partnership with the U.S. Environmental Protection Agency) 109 TW Alexander Drive U.S. Environmental Protection Agency (MD- E243-4) Research Triangle Park, NC 27711 [email protected]

Hanna, Steven

Harvard University 7 Crescent Ave., 04046 Kennebunkport, Maine [email protected]

Hodzic, Alma

National Center for Atmospheric Research (NCAR) Atmospheric Chemistry Division 3450 Mitchell Lane Boulder, CO 80301 [email protected]

Hogrefe, Christian

University of Albany, Atmospheric Sciences Research Center 251 Fuller Road, 12203 Albany [email protected]

Kang, Daiwen

U.S. EPA 27711 Research Triangle Park, NC [email protected]

Lee, Pius

National Centers for Environmental Prediction USA (NOAA), DOC 5200 Auth Rd., MD 20746 Camp Springs [email protected]

xxxii

List of Participants

Odman, Mehmet Talat

Georgia Institute of Technology, Civil & Environmental Engineering 311 Ferst Drive, 30322-0512 Atlanta, GA [email protected]

Porter, P. Steven

University of Idaho, Civil Engineering 1776 Science Center, Suite 306, 83402 Idaho Falls [email protected]

Rao, S. Trivikrama

NOAA/EPA Atmospheric Sciences Modeling Division 106 TW Alexander Drive, Room E 242, 27711 Research Triangle Park, NC [email protected]

Sarwar, Golam

U.S. EPA/Atmospheric Modeling Division 106 TW Alexander Drive, Mail Drop E 243-3, 27711 Research Triangle Park, NC [email protected]

Schiermeier, Frank

U.S. NOAA (Retired) 303 Glasgow Road, 27511 Cary, NC [email protected]

Watkins, Timothy

United States Environmental Protection Agency (U.S. EPA), Ofﬁce of Research and Development MD E205-01, 27711 Research Triangle Park, NC [email protected]

Yu, Shaocai

U.S. EPA, Department AMD (E243-01) NERL, 27711 Research Triangle Park, NC [email protected]

xxxiii

The members of the scientiﬁc committee for the 28th NATO/ CCMS international technical meeting on air pollution modeling and its application

G. Schayes, Belgium D. Syrakov, Bulgaria D. Steyn, Canada S.-E. Gryning, Denmark N. Chaumerliac, France E. Renner, Germany G. Kallos, Greece D. Anfossi, Italy

P. Builtjes, The Netherlands T. Iversen, Norway C. Borrego, Portugal (Chairman) J. M. Baldasano, Spain S. Incecik, Turkey B. Fisher, United Kingdom F. Schiermeier, United States S. Trivikrama Rao, United States

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xxxv

History of NATO/CCMS air pollution pilot studies

Pilot Study on Air Pollution: International Technical Meetings (ITM) on Air Pollution Modeling and Its Application Dates of Completed Pilot Studies: 1969

–

1975

–

1980

–

1974 Air Pollution Pilot Study (Pilot Country– United States) 1979 Air Pollution Assessment Methodology and Modelling (Pilot Country–Germany) 1984 Air Pollution Control Strategies and Impact Modeling (Pilot Country–Germany)

Dates and Locations of Pilot Study Follow-Up Meetings: Pilot Country

–

February 1971

–

July 1971

–

United States (R. A. McCormick, L. E. Niemeyer) Eindhoven, The Netherlands First Conference on Low Pollution Power Systems Development Paris, France Second Meeting of the Expert Panel on Air Pollution Modelling

All of the following meetings were entitled–NATO/CCMS International Technical Meetings (ITM) on Air Pollution Modeling and Its Application. October 1972 May 1973

– –

June 1974 Pilot Country September 1975

– – –

Paris, France Third ITM Oberursel, Federal Republic of Germany Fourth ITM Roskilde, Denmark Fifth ITM Germany (Erich Weber) Frankfurt, Federal Republic of Germany, Sixth ITM

History of NATO/CCMS Air Pollution Pilot Studies

xxxvi

September 1976 September 1977 August 1978 October 1979 Pilot Country November 1980 August 1981 September 1982 September 1983 April 1985 Pilot Country April 1987

– – – – – – – – – – – –

September 1988

–

May 1990

–

September 1991 Pilot Country November 1993 November 1995

– – – –

June 1997

–

September 1998 May 2000

– –

Pilot Country October 2001

– –

May 2003 October 2004 May 2006

– – –

Airlie House, Virginia, USA, Seventh ITM Louvain-la-Neuve, Belgium, Eighth ITM Toronto, Ontario, Canada, Ninth ITM Rome, Italy, Tenth ITM Belgium (C. De Wispelaere) Amsterdam, The Netherlands, Eleventh ITM Menlo Park, California, USA, Twelfth ITM Ile des Embiez, France, Thirteenth ITM Copenhagen, Denmark, Fourteenth ITM St. Louis, Missouri, USA, Fifteenth ITM The Netherlands (Han van Dop) Lindau, Federal Republic of Germany, Sixteenth ITM Cambridge, United Kingdom, Seventeenth ITM Vancouver, British Columbia, Canada, Eighteenth ITM Ierapetra, Crete, Greece, Nineteenth ITM Denmark (Sven-Erik Gryning) Valencia, Spain, Twentieth ITM Baltimore, Maryland, USA, Twenty-First ITM Clermont-Ferrand, France, Twenty-Second ITM Varna, Bulgaria, Twenty-Third ITM Boulder, Colorado, USA (Millennium), Twenty-Fourth ITM Portugal (Carlos Borrego) Louvain-la-Neuve, Belgium, Twenty-Fifth ITM Istanbul, Turkey, Twenty-Sixth ITM Banff, Canada, Twenty-Seventh ITM Leipzig, Federal Republic of Germany, Twenty-Eighth ITM

xxxvii

Preface

In 1969, the North Atlantic Treaty Organization (NATO) established the Committee on Challenges of Modern Society (CCMS). The subject of air pollution was from the start one of the priority problems under study within the framework of various pilot studies undertaken by this committee. The organization of a periodic conference dealing with air pollution modelling and its application has become one of the main activities within the pilot study relating to air pollution. The ﬁrst ﬁve international conferences were organized by the United States as the pilot country, the second ﬁve by the Federal Republic of Germany, the third ﬁve by Belgium, the fourth four by The Netherlands, the next ﬁve by Denmark and the last four by Portugal. This volume contains the abstracts of papers and posters presented at the 28th NATO/CCMS International Technical Meeting on Air Pollution Modeling and Its Application, held in Leipzig, Germany, during May 15–19, 2006. This ITM was jointly organized by the University of Aveiro, Portugal (pilot country), and by the Leibniz-Institute for Tropospheric Research, Germany (host organization). The key topics distinguished at this ITM included: Local and urban scale modeling; Regional and intercontinental modelling; Data assimilation and air quality forecasting; Model assessment and veriﬁcation; Aerosols in the atmosphere; Interactions between climate change and air quality; Air quality and human health. The ITM was attended by 129 participants representing 27 countries from Asia, Australia, Europe as well as North and South America. Invited papers were presented by J. W Kaminski, Canada (Multiscale air quality modelling: Progress and challenges), M. Jantunen, Finland (Air pollution exposure modelling for risk assessment and for risk management) and S. R. Hanna (A review of uncertainty and sensitivity analyses of atmospheric transport and dispersion models). On behalf of the ITM Scientiﬁc Committee and as organizers and editors, we would like to thank all the participants who made the meeting so successful. Among the participants, we especially recognize the efforts of the chairpersons and rapporteurs. Finally, special thanks to the

xxxviii

Preface

sponsoring institutions University of Aveiro, Portugal, Leibniz-Institute for Tropospheric Research, Germany and the sponsoring organizations NATO Committee on the Challenges of Modern Society and GRICES (Ofﬁce for International Relations in Science and Higher Education, Portugal). A special grant was given by EURASAP (European Association for the Sciences of Air Pollution) to award a prize to young researchers for the best paper or poster. The next conference will be held in 2007 in Portugal. Eberhard Renner (Local Conference Organizer) Germany Carlos Borrego (Scientiﬁc Committee Chairperson) Portugal

Local and urban scale modelling Chairpersons: Domenico Anfossi Guy Schayes Rapporteurs: Matteo Carpentieri Ana Margarida Costa

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Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06011-1

3

Chapter 1.1 Application and validation of FLUENT ﬂow and dispersion modelling within complex geometries Silvana Di Sabatino, Riccardo Buccolieri, Beatrice Pulvirenti and Rex Britter Abstract Flow patterns around buildings have a strong inﬂuence on pollutant dispersion derived from sources placed within the urban area. Computational ﬂuid dynamics (CFD) codes are used to provide solutions to the fundamental ﬂuid dynamics equations at spatial scales smaller than the typical urban ones. In this work, dispersion of pollutant from sources near buildings forming various street canyons is studied by means of the general purpose CFD code FLUENT to investigate the inﬂuence of small geometric features on pollutant concentration distributions. Firstly, we study the effects of a complex geometry on the ﬂow near the ground by considering a ﬁnite array of rectangular and square-shaped rings of buildings with different aspect ratios. Secondly, we study transport and diffusion of pollutant within a ﬁnite array of rectangular buildings. FLUENT concentration results are validated against wind tunnel data (CEDVAL, 2002). Numerical simulations are performed using the Reynolds Averaged Nervier– Stokes (RANS) k–e turbulence model and the advection–diffusion model. The paper documents the potential of a general purpose CFD model for the simulation of pollutant dispersion close to emission sources and within complex building arrangements in an operational context. 1. Introduction

Predicting pollutant dispersion in urban areas is currently one of the most important environmental problems, especially because of the risks associated with the exposition of a large part of the population to high pollutant concentrations.

4

Silvana Di Sabatino et al.

Studies in complex building conﬁgurations have shown that a variation of small-scale features of the building geometry has a strong inﬂuence on the ﬂow and pollutant concentration distribution in street canyons. Dispersion is affected by a number of different factors: emission source characteristics, meteorological conditions, complex geometries and building arrangements within the urban area. Among all methods to study ﬂow and dispersion in urban areas, computational ﬂuid dynamics (CFD) modelling plays an important role. CFD models are useful tools for predicting air quality (Craig et al., 2001). In particular, they allow the study of ﬂow characteristics and concentration patterns in street canyons and within urban areas, interpret wind tunnel and ﬁeld data and therefore improve operational models. However, up to now most available general purpose models still need to be validated against wind tunnel or ﬁeld data to obtain conﬁdence before applying them to a particular case study. Pollutant concentration distribution at a given location is very sensitive to geometry simpliﬁcations which, however, are often applied in typical numerical dispersion modelling. The great advantage of numerical models seems to be reduced if many simpliﬁcations of the urban morphometry are made (Chauvet et al., 2001). Wind tunnel experiments have demonstrated that the street ventilation is reduced in the presence of upstream buildings. This seems to be because of the upward displacement of the ﬂow and the consequent perturbed momentum exchange between the street canyon and the outer region of the ﬂow (Kastner-Klein and Plate, 1999). Also, numerical results have indicated that surrounding building conﬁguration affects pollutant dispersion in a street canyon, therefore it should be taken into account in numerical dispersion modelling (Xie et al., 2005). This paper is an attempt to investigate if a general purpose CFD model such as FLUENT (Fluent, Inc., 2005) can be used as a practical tool for the study of pollutant dispersion distributions in complex geometry such as an urban area. The modelling of ﬂow and dispersion is based on the Reynolds Averaged Navier–Stokes (RANS) ﬂow equations. RANS has been widely used for ﬂow and pollutant dispersion modelling in street canyons, showing to perform well when compared with wind tunnel and ﬁeld data (see for example Xie et al. (2005) and Santiago et al. (2006)).

2. Description of CEDVAL experiments

In this paper, we validate FLUENT simulations with wind tunnel experiments CEDVAL (2002). We consider the following three cases.

Application and Validation of FLUENT Flow and Dispersion Modelling

5

Conﬁguration B1-1. This experiment was made in a boundary layer wind tunnel to simulate the case of dispersion of pollutants from naturally ventilated underground parking garages (Leitl and Schatzmann, 2000). A ﬁnite array of idealized building blocks with 0.1 m by 0.15 m base dimensions and 0.125 m height (three buildings crosswind and seven buildings along wind direction) are considered as shown in Fig. 1 (top, left). The aspect ratio of the street canyons resulting from the building arrangement is W ¼ H ¼ 0.8. Four ground level CO emission sources were mounted close to the building as shown in Fig. 1 (top, right). Flow and CO dispersion were measured within the street canyon, downwind the building equipped with the sources. Concentrations are shown in a dimensionless form using the following expression: K¼

C means U ref H 2 C source Q

(1)

where Cmeans is the measured tracer concentration, Csource is the tracer concentration at the source (1 in this case), Uref is the reference wind speed (undisturbed ﬂow) at 3H, H is the building height and Q the emission source strength (m3s1). Conﬁguration B1-2. In this case, a regular array of street canyons with 56 square-shaped building rings was considered. These buildings, having dimensions of 0.25 m by 0.25 m by 0.06 m, were mounted in rows of four rings across the wind tunnel. The conﬁguration is shown in Fig. 1

Figure 1. Geometry of CEDVAL experiments: B1-1 (top) and B1-2 (bottom).

Silvana Di Sabatino et al.

6

(bottom). The aspect ratio of the street canyons resulting from the building arrangements is W ¼ H ¼ 1. Flow within the street canyons was measured. Conﬁguration B1-3. This case is the same as B1-2 with a different aspect ratio of W ¼ H ¼ 3. 3. FLUENT ﬂow and dispersion modelling 3.1. Flow setup

All FLUENT simulations were carried out by considering a neutral boundary layer. The size of the computational domain used to simulate the boundary layer is 7 m by 2 m in the horizontal plane and 0.5 m in the vertical direction. The computational domain was built using hexahedral elements with ﬁner resolution close to the ground and in those regions where the plume is evolving. The overall number of computational cells used is about half million for case B1-1 and about one million for cases B1-2 and B1-3. The smallest dimensions of the elements are equal to 0.005 m while they are about 0.025 m outside the intersection. Several tests to verify grid size independence were made with increasing mesh numbers until further reﬁnements give no signiﬁcant improvements. The k–e model (Launder and Spalding, 1974) was used. Recently, Xie et al. (2005) found a good performance of the k–e model in simulating emissions from vehicle exhausts in a street canyon within an urban environment. We used the same boundary conditions as in Di Sabatino et al. (2005). Based on CEDVAL experiments, the inlet wind speed was assumed to be described by a power law proﬁle with height z: p uðzÞ z ¼ (2) uref zref where u(z) is the average wind speed at the height z above the ground, uref the reference wind speed at the reference height zref and p is the vertical wind proﬁle exponent. Turbulent kinetic energy and dissipation rate proﬁles are speciﬁed as follows: ! 2 u z k ¼ pﬃﬃﬃﬃﬃﬃ 1 (3) d Cm

¼

! 3 u z 1 d kz

(4)

Application and Validation of FLUENT Flow and Dispersion Modelling

7

where d is the boundary layer height, Cm ¼ 0.09 a coefﬁcient used to deﬁne the eddy viscosity in k–e models, u the friction velocity and k the von Karman’s constant. 3.2. Dispersion setup

CO was considered as the pollutant emitted from the sources. The advection diffusion (AD) module was used. In turbulent ﬂows, FLUENT computes the mass diffusion that satisﬁes the conservation of mass as follows: m J CO ¼ rDCO þ t rY CO (5) Sct where DCO is the diffusion coefﬁcient for CO in the mixture, mt ¼ ð1=2ÞC m k2 = is the turbulent viscosity, YCO is the mass fraction of CO, r is the mixture density. Sct ¼ mt =ð1=2Dt Þ is the turbulent Schmidt number, where Dt is the turbulent diffusivity. Sources were simulated by separating four volumes in the geometry at the required discharge position and by setting CO source terms (3 103 g s1) for these volumes. These volumes have the same dimensions of the sources used in wind tunnel experiments (CEDVAL, 2002). 4. Results 4.1. Flow field in a finite array of rectangular buildings and square-shaped rings of buildings

FLUENT ﬂow results are compared with CEDVAL data obtained from conﬁgurations B1-1, B1-2 and B1-3. Wind tunnel experiments and numerical simulations show that a recirculation ﬂow region forms within a street canyon exposed to a perpendicular ﬂow. The ability of capturing the right dimension of the vortex is very important as the vortex drives the distribution of pollutants within the street canyon. Figure 2 shows velocity vectors on the vertical plane in the middle of the street canyon for conﬁguration B1-1. The ﬁgure shows that the shape and the dimension of the vortex predicted by FLUENT is very similar to that observed by CEDVAL experiments. Both wind tunnel measurements and FLUENT predictions show that for aspect ratio W ¼ H ¼ 0.8 a single vortex forms inside the canyon. For aspect ratio equal to 1 (case B1-2), we observe a single vortex inside the street canyon, while for aspect ratio equal to 3 (case B1-3), a wake

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Figure 2. Velocity vectors from CEDVAL experiment: B1-1 (left) and FLUENT results (right).

Figure 3. Velocity vectors from FLUENT for conﬁgurations B1-2 (left) and B1-3 (right).

interference ﬂow regime is observed, as shown by Fig. 3 and as already pointed out by Johnson and Hunter (1999). Also, velocity proﬁles show a very good agreement between FLUENT and CEDVAL data as shown in Fig. 4, where dimensionless velocity proﬁles u/Uref are plotted versus dimensionless height z/H, for conﬁgurations B1-1, B1-2 and B1-3 (top, middle and bottom, respectively).

4.2. Dispersion in a finite array of rectangular buildings

Riddle et al. (2004) showed that FLUENT dispersion spread from high level point source, calculated by using algebraic Reynolds stress models (RSM) is smaller than that predicted by the well-validated integral model ADMS-Urban. We found the same results for both the point source and the line source using the standard k–e model (Di Sabatino et al., 2005).

Application and Validation of FLUENT Flow and Dispersion Modelling

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Figure 4. Comparisons between velocity proﬁles from FLUENT and from CEDVAL experiments: B1-1 (top), B1-2 (middle) and B1-3 (bottom); on three different planes: leeward (left), in the middle of the canyon (middle) and windward (right).

Therefore, we performed some sensitivity tests for the choice of the most suitable Sct in order to artiﬁcially increase plume dispersion. From the sensitivity test, the value of 0.4 resulted to be the most appropriate one. So in this paper, both the calculated results with Sct ¼ 0.4 and Sct ¼ 0.7 (the standard value) are compared with wind tunnel data (CEDVAL). Figure 5 (top) shows the dimensionless coordinate y ¼ H versus the dimensionless concentration K near the ﬂoor (z ¼ H ¼ 0.06) within the street canyon. Figure 5 (bottom) shows dimensionless concentration K versus dimensionless coordinate x ¼ H on the same vertical plane. The horizontal velocity near the ﬂoor is negative because the vortex in the canyon is clockwise. So pollutants are carried towards the leeward side and mixed in the street canyon. FLUENT results agree well with wind tunnel measurements.

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Silvana Di Sabatino et al.

Figure 5. Dimensionless CO concentration proﬁles along y for x ¼ H ¼ 1 (top) and along x for y ¼ H ¼ 1 (bottom), for conﬁguration B1-1.

5. Conclusions

In this paper, the k–e turbulence model of the CFD code FLUENT was used to simulate ﬂow and pollutant dispersion within street canyons resulting from complex building conﬁgurations. The validation of numerical results is done using a compilation of wind tunnel data available on the web (CEDVAL). The validation showed that there is a good agreement between the model and wind tunnel measurements from which we can conclude that a general purpose CFD model can be used to simulate pollutant dispersion in the atmosphere. However, the analysis has shown that a number of considerations are required before achieving the ﬁnal results, i.e., about the grid resolution, inlet conditions, discretization methods and the selection of the appropriate turbulence and dispersion models. To make our conclusions more general, we suggest that the accuracy of FLUENT results in complex building conﬁgurations should be further assessed by considering other turbulence models and by improving the modelling of pollutant emissions.

REFERENCES CEDVAL, 2002. http://www.mi.uni-hamburg.de/cedval-validation-data.427.0.html Tech. rep categories B1-1, B1-2 and B1-3.

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Chauvet, C., Kastner-Klein, P., Kovar-Panskus, A., Savory, E., Schatzmann, M., 2001. The use of wind tunnels in modelling air quality street canyons. Final Report of the TMR Research Network TRAPOS: Optimization of Modelling Methods for Trafﬁc Pollution Streets. European Commission. Craig, K.J., De Kock, D.J., Snyman, J.A., 2001. Minimizing the effect of automotive pollution in urban geometry using mathematical optimization. Atmos. Environ. 35, 579–587. Di Sabatino, S., Buccolieri, R., Pulvirenti, B., Britter, R., 2005. Flow and pollutant dispersion modelling in street canyons using Fluent and ADMS-Urban. 5th International Conference on Urban Air Quality. Valencia. Fluent, Inc., 2005. 6.2 User Manual. http://www.ﬂuent.com Johnson, G., Hunter, L., 1999. Some insights into typical urban canyon airﬂows. Atmos. Environ. 33, 3991–3999. Kastner-Klein, P., Plate, E., 1999. Wind tunnel study of concentration ﬁelds in street canyons. Atmos. Environ. 33, 3973–3979. Launder, B., Spalding, D., 1974. The numerical computation of turbulent ﬂows. Comput. Methods Appl. Mech. Eng. 3, 269–289. Leitl, B., Schatzmann, M., 2000. Physical modelling of emissions from naturally ventilated underground parking garages. Environ. Monit. Assess. 65, 221–229. Riddle, A., Carruthers, D., Sharpe, A., McHugh, C., Stocker, J., 2004. Comparisons between FLUENT and ADMS for atmospheric dispersion modelling. Atmos. Environ. 38, 1029–1038. Santiago, J., Martilli, A., Martin, F., 2006. Validation of CFD simulation of turbulent air 1ow over a regular array of cubes against wind tunnel data and 3D analysis of the 1ow. Proc. 14th Joint Conference on the Applications of Air Pollution Metereology with the Air and Waste Management Assoc., Atlanta. Xie, X., Huang, Z., Wang, J., 2005. Impact of building conﬁguration on air quality in street canyon. Atmos. Enviro. 39, 4519–4530.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06012-3

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Chapter 1.2 Turbulence, atmospheric dispersion and mixing height in the urban area, recent experimental ﬁndings Sven-Erik Gryning and Ekaterina Batchvarova Abstract The parameterisation of the complex structure of the atmosphere over urban areas has large implication in running and testing mesoscale meteorological and air pollution models. The typical size for the urban area is of the order of 20–50 km. It is subdivided into a large number of areas (neighbourhoods) reﬂecting the development of the town, such as the central inner, residential, recreation and industrial parts. The description of the ceaseless adjustments of the ﬂow in the urban area is simpliﬁed by introducing neighbourhoods that are complexly interacting with the ﬂow and forming internal boundary layers. On the level of street canyons, the roughness sublayer, the ﬂow varies in space and time. At a level of 3–5 times, the average building height the ﬂow is in equilibrium with the underlying surface, known as inertial sub-layer. Higher up, the differences in meteorological ﬁelds introduced by surface characteristics of different neighbourhoods are blended and the boundary layer is forced by area-aggregated features. Within this framework, the parameterisation of some meteorological quantities is reviewed. Based on tracer experiments, it is found that the ability to predict the crosswind spread of the plume as well as maximum concentrations in the urban environment is within a factor of 2. 1. Introduction

The dynamical effects of urban and areas with high surface roughness are traditionally modelled by only one parameter, the roughness length. This is a highly simpliﬁed approach because the roughness is not a physical length scale. It is deﬁned by extrapolation of the logarithmic wind proﬁle down to the level where the wind disappears. Therefore the surface roughness can only be deﬁned when a logarithmic wind proﬁle exists. In

Turbulence, Atmospheric Dispersion and Mixing Height

13

an urban area, the character of the surface changes between the different areas. Hence it is not obvious that a logarithmic wind proﬁle even exists and the roughness length becomes an artefact. In the real atmosphere, the surface roughness and thermal effects are combined, which adds time dependence on the various scales to the ﬂow. Figure 1 shows a sketch of an urban area with an indication of the scales and their regimes. It looks complicated but constitutes a considerable idealization. At the abrupt change between rural and urban surface, an internal boundary layer starts to form, as shown in the left panel of Fig. 1. Mixing above the city is in this case conﬁned within the internal boundary layer. The situation is also found in coastal areas. In simpliﬁed form, the growth of the neutral internal boundary layer is given in the following equation: dh u dt

(1)

A fully developed mixed layer is illustrated in the right panel of Fig. 1. In the case of neutral conditions the mixing reaches: h

0:1u f

(2)

where h is the height of the (internal) boundary layer, u* is the friction velocity, f is the Coriolis parameter and t is time. Integrating Eq. (1),

Figure 1. Schematic picture of the boundary layer structure over an urban area. The different hatchings represent the underlying surface of the various neighbourhoods. Broad spaced patterns represent the urban internal boundary layers where advection processes are important. Fine spaced patterns show the inertial sub-layers that are in equilibrium with the underlying surface and where Monin–Obukhov scaling applies. The forward slash pattern is the roughness sub-layer that is highly inhomogeneous in its vertical and horizontal structure. Between the buildings, the momentum is transformed into heat by pressure and viscous forces. Dotted pattern represents adjustment between neighbourhoods with large accelerations and shear in the ﬂow near the top of the canopy. At the height where the internal boundary layers are intermixed, the effects of the individual neighbourhoods cannot be distinguished any more—the so-called blended layer.

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Sven-Erik Gryning and Ekaterina Batchvarova

setting t ¼ x/u and comparing with Eq. (2), we obtain a rough estimate of the distance x where the internal boundary layer reaches its equilibrium height: x

0:1u f

(3)

Beyond this distance, the boundary layer is forced by the urban surface conditions only. It can be noted that the distance does not depend on surface roughness but only on wind speed and f. At a wind speed of 5 m s1 and the Coriolis parameter 104 s1, the equilibrium distance is about 5 km. The atmospheric dispersion parameters in each of the layers are vastly different, and as a consequence, the models to simulate the dispersion process are also of different nature. The complex structure of the urban atmosphere makes the simulation of the dispersion process a real challenge. Some success has been achieved for the canopy layer (forward slash pattern in the sketch) and at larger scales (blended layer). The challenge in the transition layers (broad and ﬁne space horizontal patterns) is still considerable. 2. Tracer experiments

Measurements from the Copenhagen and BUBBLE full-scale tracer experiments are used to investigate the ability to model the dispersion process in an urban area. 2.1. Copenhagen experiment

Atmospheric dispersion experiments were carried out in the Copenhagen area with tracer released from an elevated source in the urban/residential area (Gryning and Lyck, 1984). The tracer sulphur hexaﬂuoride, an inert gas tracer, was released from a tower at a height of 115 m and collected near ground level in crosswind arcs 2–6 km from the source (see Fig. 2). Data from the experiment are available in Gryning and Lyck (2002). The tracer sampling time was 1 h. The site in both the upwind and the downwind directions was mainly residential. The meteorological measurements included turbulence at the height of the tracer release, proﬁles of temperature and wind along the mast and the standard routine radiosoundings launched 4 km northeast of the tracer release point. All tracer experiments were performed during daytime in neutral to slightly convective atmospheric conditions.

Turbulence, Atmospheric Dispersion and Mixing Height

15

Figure 2. Copenhagen experiment. Left: Tracer sampling set up for the experiments in Copenhagen. Typically 20 locations in each arc situated in the actual plume direction for the individual experiments were used. Right: View from the tracer release point.

Figure 3. BUBBLE tracer experiment. Left: Tracer sampling set up for the experiments in Basel (Switzerland). Typical 12 locations in the actual plume direction for the individual experiments were used. Right: Tracer unit at roof sampling position.

2.2. BUBBLE tracer experiment

The BUBBLE tracer experiment represents the dispersion characteristics over a typical European city (Rotach et al., 2004, Batchvarova and Gryning, 2006). It was designed as an urban roof-level tracer experiment (Fig. 3). Four tracer experiments were performed, all during very unstable meteorological conditions. The tracer was released and sampled at roof level in arcs up to 1.6 km from the tracer release. A considerable amount

Sven-Erik Gryning and Ekaterina Batchvarova

16

of information on the meteorology over the urban area is available. The data for the tracer experiments are available in Gryning et al. (2005).

3. Plume dispersion and urban areas

The study is based on well-known and commonly used semi-empirical estimates that as starting point take Taylor’s famous formula for plume dispersion: t t sz ¼ sw tf z (4) sy ¼ sv tf y Ty Tz where t is travel time of the plume and fy and fz are functions of the dimensionless travel time where Ty and Tz are Lagrangian time scales for the lateral and vertical dispersion processes. The approximations fy ¼

sﬃﬃﬃﬃﬃﬃﬃﬃ!1 t 1þ 2T y

fz ¼

1þ

rﬃﬃﬃﬃﬃﬃﬃﬃ1 t 2T z

(5)

are often recommended for applied dispersion modelling (Gryning et al., 1987). For ground-level sources Ty ¼ 200 s is recommended and Ty ¼ 600 s for elevated sources and when the vertical extent of the plume is larger than 10% of the depth of the mixing layer, for unstable atmospheric conditions Tz ¼ 300 s. 3.1. Lateral dispersion

The simulation of the lateral spread is performed in two ways. Firstly, the observed sv values (Copenhagen at 115 m and BUBBLE at 31.7 m) are used. Figure 4 shows the measurements and model simulations of sy from the Copenhagen experiment, using measured values of sv (left panel) and parameterized values (right panel). In both cases the agreement with the measurements is within a factor of 2. The analysis for the BUBBLE experiments is illustrated in Fig. 5. Simulations were performed for both Ty ¼ 200 s, which is the recommended value for ground level sources, and Ty ¼ 600 s as suggested for plumes larger than 10% of the mixed layer height. Both values of Ty in combination with the observed sv provide fair estimates of the lateral spread with the better given by Ty ¼ 600 s. This suggests that over urban areas the plume behaves more like from elevated than a low-level source.

Turbulence, Atmospheric Dispersion and Mixing Height 800 y (parameterisation of v)

800 Copenhagen

y (measured v)

17

600 400 200 0 0

Copenhagen

600 400 200

200 400 600 800 Measurements of y

0 0

200 400 600 800 Measurements of y

Figure 4. Measured and modelled values of sy for the Copenhagen experiment. The left panel shows simulations based on measurements of sv at 115 m. The right panel shows simulations using parameterized values of sv, also at 115 m. The lines show the 1:1 relationship and its factor of 2 range.

800 Parameterised y (m)

Parameterised y (m)

800 600 400 200

600 400 200 0

0 0

200

400

600

Measurements of y (m)

800

0

200 400 600 800 Measurements of y (m)

Figure 5. Measured and modelled values of sy for the BUBBLE tracer experiment for Ty ¼ 600 s (ﬁlled circles) and Ty ¼ 200 s (crosses). Left panel shows simulations based on measurements of sv at 31.7 m. The right panel is obtained using parameterized values of sv, also at 31.7 m. The lines show the 1:1 relationship and its factor of 2 range.

3.2. Maximum concentrations

Here we apply a very simple modelling approach to the very complex dispersion process of the urban area. For the Gaussian plume model, the ground-level centreline concentration Cmax(x) at downwind distance x can be expressed as Q h2 exp 2 C max ðxÞ ¼ 2sz psy sz u

(6)

C max (measured v and w)

20 16 12 8 4 0 0

4

8 12 16 C max measured

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C max (parameterisation of v and w)

Sven-Erik Gryning and Ekaterina Batchvarova

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20 16 12 8 4 0 0

4

8 12 16 C max measured

20

Figure 6. Measured and modelled normalized values of the maximum concentrations for the Copenhagen experiment. The left panel shows simulations based on hourly measurements of sv and sw at 115 m. The right panel is obtained by using parameterized values of sv and sw, also at 115 m. The lines show the 1:1 relationship and its factor of 2 range.

where h is the tracer release height, and Q the release rate. Figure 6 illustrates the results from a comparison between model simulations and measured tracer concentrations from the Copenhagen experiment. The measured arc-wise maximum concentration has been compared to the modelled centreline concentration. The values of sy and sz were derived from expressions (4) and (5), respectively, using Ty ¼ 600 s and Tz ¼ 300 s. The left panel in Fig. 6 illustrates the comparison when the measured values of sv and sw were applied in the expressions for sy and sz. The right panel refers to the case when the parameterized values of sv and sw were applied. The agreement can be seen to be within a factor of 2. A somewhat similar comparison is shown for the BUBBLE tracer experiment conducted on 26 June (Fig. 7), being the only one of the four tracer experiments with a well-developed tracer plume that is covered by the tracer sampling network. Because it is a low-level release, an even more simple modelling approach as compared to the Copenhagen experiment is used. Inserting the near-ﬁeld expressions for sy and sz: x x sz ¼ sw (7) sy ¼ sv u u in the Gaussian plume formula, the ground level concentration at the centreline (maximum) Cmax at a given downwind distance x can be expressed as C max ðxÞ ¼

Qu psv sw x2

(8)

100

100

80

80

Prediction of C max

Prediction of C max

Turbulence, Atmospheric Dispersion and Mixing Height

60 40 20 0

19

60 40 20 0

0

20 40 60 80 100 Measurements of C max

0

20

40

60

80

100

Measurements of C max

Figure 7. Measured and modelled values of the maximum concentrations for the BUBBLE tracer experiment conducted on 26 June. Left: Simulations based on measurements of sv and sw at 31.7 m. Right: Simulations using parameterized values of sv and sw, also at 31.7 m.

where Q is the tracer release rate. Input to the model is measurements of sv, sw and wind speed u at a representative height for the dispersion of the plume. The maximum tracer concentrations are drawn from two arcs about 700 and 1200 m from the tracer source. Input to the model is half-hourly observations of sv, sw and the wind speed at a height of 31.7 m. The lines show the 1:1 relationship and its factor of 2 range. Concentration units are in ng m3 with a tracer release rate of 0.0503 g s1. Figure 7 shows the comparison between the maximum observed and predicted half-hourly tracer concentration in the two sampling arcs during the experiment conducted on 26 June. It can be seen that 11 out of 12 half-hourly concentrations fall within the range of a factor of 2. 4. Concluding remarks

We have applied simple models for the lateral and vertical atmospheric dispersion in an urban environment and found an agreement of about a factor of 2 between model results and measurements. This result is very promising when considering the complex structure of the urban boundary layer.

ACKNOWLEDGMENT

The authors are thankful to the BUBBLE community, and especially to Dr. Mathias Rotach (Meteo Swiss), Dr. Roland Vogt and Andreas

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Christen (University of Basel) for the discussions on the tracer experiment results. This study was supported by NATO (ESP.EAP.EV 981781) and the EU FP6 ACCENT Network of Excellence. REFERENCES Batchvarova, E., Gryning, S.-E., 2006. Progress in urban dispersion studies. Theor. Appl. Climatol. 84, 57–67. Gryning, S.-E., Batchvarova, E., Rotach, M., Christen, A., Vogt, R., 2005. Roof-level SF6 tracer experiments in the city of Basel. Verlag Institut fu¨r Atmospha¨re und Klima ETH, Zu¨rich. (Zu¨rcher Klima-Schriften, 83) p. 97. Gryning, S.-E., Holtslag, A.A.M., Irwin, J.S., Sivertsen, B., 1987. Applied dispersion modelling based on meteorological scaling parameters. Atmos. Environ. 21, 79–89. Gryning, S.-E., Lyck, E., 1984. Atmospheric dispersion from elevated sources in an urban area: Comparison between tracer experiments and model calculations. J. Climate Appl. Meteorol. 23, 651–660. Gryning, S.-E., Lyck, E., 2002. The Copenhagen tracer experiments: Reporting of measurements. Risø-R-1054(rev.1)(EN) p. 74 (only available on the Internet: www. risoe.dk/rispubl/VEA/ris-r-1054_rev1.htm). Rotach, M.W., Gryning, S.-E., Batchvarova, E., Christen, A., Vogt, R., 2004. Pollutant dispersion close to an urban surface—BUBBLE tracer experiment. Meteorol. Atmos. Phys. 87, 39–56.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06013-5

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Chapter 1.3 Inverse modeling of local surface emissions with the CHIMERE-adjoint model: The case of the Paris area during the ESQUIF ﬁeld experiment L. Menut, I. Pison and N. Blond Abstract The aim of this study is to deﬁne, test and apply a methodology to optimize surface emissions of anthropogenic species such as NOx at a local scale by using routine measurements of the air quality monitoring networks (measurements available in numerous areas and for long periods). A new methodology in which analyzed maps of concentrations obtained through a kriging technique are used as constraints for the inversion is proposed. The methodology is tested and applied to the Paris area because (i) the kriging technique has been developed for and ﬁrst applied to this area because of the fully developed AIRPARIF measurement network and (ii) the layout of the area is representative of a simple type of large urbanized area. The NOx emission ﬂuxes were particularly studied because (i) they are ozone precursors with large uncertainties, particularly in their 24-h time proﬁle (Vautard et al., 2003) and (ii) they are directly linked to NO concentrations that are measured by AIRPARIF and can be analyzed by kriging.

1. Modeling system

The principle of the proposed inversion methodology is displayed on Fig. 1. The optimized concentrations provided by the inverse are used to perform a new kriging analysis. The new analyzed concentrations are then used as constraints for a second inversion. This gives the possibility of reﬁning the results of the optimization by iterating several kriging-inversion cycles. The cost function to be minimized is in the form of J(e) ¼ (ysimya)T 1 R (ysimya) where ya contains the analyzed concentrations, corresponding

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L. Menut et al.

Figure 1. Flowchart of the modeling system.

to each simulated concentration in ysim, and R is taken to be diagonal since the kriging technique does not provide the extra-diagonal terms. Note that information on the ﬁrst-guess inventory is included in the analyzed concentrations. Therefore, this formulation of J avoids the problem of estimating a ‘‘background’’ matrix. Our modeling system is based on CHIMERE chemistry-transport model (Vautard et al., 2001) and its adjoint (Menut et al., 2000a; Menut, 2003). The adjoint approach was chosen because it makes it easy to take the trajectory of the model into account and perform 4D-integration. The kriging technique used here is called ‘‘INK’’ and has been developed by Blond et al. (2003). At any given hour, for any location s, the analysis ya(s) is a correction of the CHIMERE simulation yb(s) by a linear combination of the innovations yo(sk)yb(sk), where sk, k ¼ 1,y,K are the locations of the K measurement values yo(sk) provided by the network monitoring stations. The INK technique and CHIMERE are used by the Paris area air quality network, AIRPARIF, to produce daily maps of air quality forecasts (www.airparif.org). The minimizer called by the inverse code is N1QN3 (Gilbert and Lemare´chal, 1989).

Inverse Modeling of Local Surface Emissions

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2. The pollution event of the 7th of August 1998

On the second day of the Intensive Observation Period (IOP) 2 of the Atmospheric Pollution Over the Paris Area (ESQUIF) campaign, a local photo-oxidant pollution event occurred (Menut, 2003), in which the local emissions play a major role (Menut et al., 2000a). An ozone plume developed in the Southwest of Paris and the difference between upstream and downstream ozone concentrations due to local emissions was at least 50 ppb. The event was simulated with the forecast version of CHIMERE (25 25 cells over 11 vertical levels, covering a 150 150 km area). The emission inventory was elaborated by AIRPARIF for the summer of 1998 (described in details in Vautard et al., 2003). It gives the hourly 6 6 km ﬂuxes of 16 anthropogenic emitted species among which NOx speciated in 10% of NO2 and 90% of NO. The studies led during the ESQUIF campaign have shown that ozone concentrations simulated in the afternoon plume were underestimated by around 8%. In parallel, NO concentrations simulated downtown during the morning were overestimated as compared to measurements, particularly between 6 and 10 am (Menut et al., 2000b).

3. Application to the real case 3.1. The inverted problem: spatial aggregation

Considering the possible link between ozone and NO miss-estimations, NOx emissions from 3 to 10 am are inverted, assuming that (i) the distribution inside the family remains unchanged (ii) all the other parameters and parameterizations are perfect or at least do not explain the discrepancies between simulation and measurements. To better satisfy the latter assumption, the BLH, that was found to be underestimated by the meteorological data used by the model as compared to measurements made during the ﬁeld campaign, has been corrected to match the more reliable data. Since the number of individual ﬂuxes is very high, it is necessary to reduce the size of the problem to match the available quantity of information. First, ﬂuxes less than 5 1011 molecules cm2 s1 located in the rural areas surrounding the domain are not inverted. Then, zones are deﬁned to aggregate the ﬂuxes to be inverted. In each zone, the same correction is applied to all the aggregated ﬂuxes. In order to satisfy this assumption, the zones are deﬁned by taking into account (i) the intensity of the emission ﬂuxes and (ii) the sensitivity of the concentrations to these

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Figure 2. Principle of spatial aggregation.

emissions. This leads to a dynamical spatial aggregation that makes use of the a priori information available in the emission space. A simple example of aggregation is shown on Fig. 2: the four big zones are broken into smaller ones acknowledging for the gradient of intensity in the emission ﬂuxes. 3.2. Results 3.2.1. Spatial distribution of corrections

The inversion time-window ranging from 3 to 10 am, the spatial distributions of the corrections at 5 and 8 am are displayed on Fig. 3. This shows that the pattern of corrections in not homogeneous, neither in time nor in space. At 5 am, after the optimization, the most intense ﬂuxes located downtown are decreased (multiplied by 0.5–0.75) whereas the intense ﬂuxes located outside the city upstream are increased (multiplied by 1.25–2.75). On the contrary, at 8 am, the most intense ﬂuxes are almost unchanged by the optimization with corrections less than 5% whereas less intense ﬂuxes located outside the city are decreased (multiplied by 0.75–0.95). Since the situation differs according to the location, the time evolution of the optimized ﬂuxes is studied for two locations representative of the results obtained downtown and in the suburbs. 3.2.2. Optimized emissions downtown

The time evolution of NO emissions in the city are displayed on Fig. 4 for the Southwest part of Paris that is representative of the whole town. The morning emission peak is reduced from 5 h long (from 5 to 9 am) to only 2 h long (from 8 to 9 am). Since (i) the time proﬁle of the optimized emissions does not show oscillations and (ii) the decrease in intensity is

Inverse Modeling of Local Surface Emissions

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Figure 3. Spatial distribution of corrections.

consistent with the fact that the ﬁrst-guess inventory has actually been built for July 1998 (trafﬁc is generally lighter in August in the Paris area), the inversion methodology seems to be reliable for real studies. 3.2.3. Optimized emissions in the suburbs

Four ﬂuxes located in the suburbs upstream Paris were hugely corrected (shown in orange on Fig. 3) for 1 or 2 h. The time evolution of NO emissions is displayed on Fig. 4 for Tremblay that is representative of these suburban ﬂuxes.

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Figure 4. Time evolution of ﬁrst-guess and optimized NO emissions.

If the peak in the optimized emissions is correctly timed as compared to the measured concentrations, its intensity (more than 4.75 1012 molecules cm2 s1) seems unrealistic. It was then assumed that the optimized peak corresponds to a real trafﬁc peak, that has been measured but not

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Figure 5. Difference (ppb) between optimized and ﬁrst-guess ozone concentrations.

simulated and was maybe due to departures on holiday, but the intensity of which is exaggerated probably because of a lack of representativity of the constraints. This problem, which arises for only four isolated ﬂuxes, may indicate that the realism of the corrections decreases with the density of the measurement network. 3.2.4. Impact on ozone concentrations

To validate optimized emissions, their impact on ozone concentrations is examined. Figure 5 presents ozone concentrations simulated in the afternoon plume at 3 pm, which are increased by less than 5% by NOx emissions optimized in the city during the morning. Nevertheless, since ﬁrst-guess ozone concentrations were underestimated by 8%, the optimization thus reduces the underestimation to only 3.5%. This shows that the inversion is able to lead to a better simulation of key events.

4. Conclusion

A new inversion methodology has been designed. It uses an iterative scheme calling on a kriging technique to generate the constraints.

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Focusing on the Paris area and NOx emission ﬂuxes, it is shown that the constraints provided by kriging make it possible to retrieve an emission inventory with a good accuracy, for a wide range of quality of measurements and ﬁrst-guess inventories. The new methodology is applied to the pollution event that occurred on the 7th of August 1998, second day of the IOP 2 of the ESQUIF ﬁeld campaign. A dynamic spatial aggregation technique has been specially elaborated to further reduce the size of the problem. The optimization of NOx emissions has particularly lead to a reduction of the duration of the morning peak and to a decrease of the underestimation of ozone concentrations in the plume. An operational limitation has nevertheless been encountered: the reliability of the results decreases with the density of the measurement network, which explains that ozone measurements cannot be used presently. The prospects of this work include (i) a quantiﬁcation of the uncertainty on optimized inventories, possibly by interacting with Monte-Carlo simulations, (ii) the application to other areas and (iii) the use of satellite data instead of/together with surface data.

Discussion

T. Odman:

L. Menut:

P. Builtjes:

I thought you were using this approach in forecasting. I must have misunderstood but I will ask the question anyway. Given that you are only associating errors with emissions, if you were using this approach for forecasting would you select a day from the past with meteorology similar to the forecasting day or just use the previous day’s corrected emissions? The best use of this methodology is in the framework of past events analysis. We are not using this approach in forecast mode but this is in the course: the goal is to inverse the emissions of the day before in order to rerun the model with ‘‘optimized emissions’’ and, thus, to have ‘‘best’’ modeled chemical concentrations ﬁelds to initiate the forecast model simulation. Is there a limit to the improvement of the errors in the emission by inverse modeling, and would that be in the order of 20%?

Inverse Modeling of Local Surface Emissions

L. Menut:

E. Genikhovich:

L. Menut:

B. Physick:

L. Menut:

29

It is very difﬁcult to answer this interesting question. This type of percentage depends on the source types, the studied domain, the studied time periods and thus their corresponding characteristics (extreme event, usual polluted situationy). We agree with P. Builtjes for our speciﬁc study over the Paris area but over several months the improvement of errors in the emissions is certainly more or less 20%. In your approach, all the discrepancies between model and measurement results are attributed to the errors in emissions. Such an approach could be applied to any model, even simple Gaussian ones with linear chemistry, for example. How can you be sure that your corrected emissions are realistic if they strongly depend on the error of the model etc.? For the ﬁrst part of the question: Yes, the discrepancies between model and measurements are all attributed to potential lacks in emissions but only after a systematic debiasing of the other input parameters such as boundary conditions, boundary layer height and temperature (on an hourly basis). The corrected emissions depend on the model error (this is the main hypothesis in inverse modeling whatever the studied problem and the model used). We think that our corrected emissions are realistic because: (i) the modeled concentrations ﬁelds of various species (and not only one) are better, (ii) this latter point is true for a long period whatever the meteorological situation, the location of the measurements station within the studied domain, i.e., the whole Paris area (including urban, suburban and rural areas). If you are primarily interested in ozone forecasting, you will have to adjust the VOC emission ﬂuxes as well as the NOx ﬂuxes. Have you done any work on this? Of course, there would be less routine measurements and the VOC ﬂux dependence on temperature is an additional complication. No, the work is not already done for VOCs. The reasons are mainly (i) our ﬁrst goal was to

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elaborate a new methodology, (ii) to achieve this, we select the most uncertain source i.e., the trafﬁc (and thus NOx) over Paris, (iii) our sensitivity tests about the minimum number of required measurements versus the model grid cell numbers to be inverted showed that not enough measurements are available for VOCs.

ACKNOWLEDGMENTS

This work was done under a TOTAL-CNRS grant and in the framework of the PRIMEQUAL2 project OPTEMI supported by the French Ministry of Environment. REFERENCES Blond, N., Bel, L., Vautard, R., 2003. Three-dimensional ozone data analysis with an air quality model over the Paris area. J. Geophy. Res. 108(D23), 4744. Gilbert, J.-C., Lemare´chal, C., 1989. Some numerical experiments with variable-storage quasi-Newton algorithms. Math. Prog. 45, 407–435. Menut, L., 2003. Adjoint modelling for atmospheric pollution processes sensitivity at regional scale during the ESQUIF IOP2. J. Geophy. Res. 108(D17), 8562. Menut, L., Vautard, R., Beekmann, M., Honore´, C., 2000a. Sensitivity of photochemical pollution using the adjoint of a simpliﬁed chemistry-transport model. J. Geophy. Res. 105(D12), 15379–15402. Menut, L., Vautard, R., Flamant, C., Abonnel, C., Beekmann, M., Chazette, P., Flamant, P.H., Gombert, D., Gue´dalia, D., Lefebvre, M.P., Lossec, B., Martin, D., Me´gie, G., Perros, P., Sicard, M., Toupance, G., 2000b. Measurements and modelling of atmospheric pollution over the Paris area: An overview of the ESQUIF Project. Ann. Geophys. 18(11), 1467–1481. Vautard, R., Beekmann, M., Roux, J., Gombert, D., 2001. Validation of a hybrid forecasting system for the ozone concentrations over the Paris area. Atmos. Env. 35(14), 2449–2461. Vautard, R., Martin, D., Beekmann, M., Drobinski, P., Friedrich, R., Jaubertie, A., Kley, D., Lattuati, M., Moral, P., Neininger, B., Theloke, J., 2003. Paris emission inventory diagnostics from ESQUIF airborne measurements and a chemistry transport model. J. Geophy. Res. 108(D17), 8564.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06014-7

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Chapter 1.4 Numerical simulations of microscale urban ﬂow using the RAMS model Tamir Reisin, Orit Altaratz Stollar and Silvia Trini Castelli Abstract The RAMS model, in its latest version (V6), is used to simulate the ﬂow around a set of buildings with a resolution of 1-2 meters in all directions. The k-e and k-e RNG turbulence parameterization were implemented in the RAMS model. The ﬁrst one is commonly used in urban environments, however it was found to have some deﬁciencies when applied to the simulation of ﬂow impingement and separation. The second parameterization is found to overcome these deﬁciencies. Results show that the ﬂow that develops around the buildings simulated in the present work is notably complex and was found to be sensitive both to the turbulence closure scheme and to the boundary conditions as speciﬁed at the buildings’ walls. 1. Introduction

There is renewed interest in urban dispersion modeling due to the need for tools that can be employed for responding to, planning for, and assessing the consequences of an airborne release of toxic materials potentially threatening to human life. Releases of hazardous gases and aerosols may result from on-site accidents as in the case of industrial chemical releases, occur during transport of hazardous chemicals as in tanker truck or railroad spills, or may be premeditated as in a chemical, biological, or radiological agent terrorist attack (Brown, 2004). Although, the dispersion models perform well in open terrain, additional work is required in order to predict dispersal in urban environments (Davis and Prosnitz, 2003), and in particular to make the operational models accurate enough that ﬁrst responders can make decisions on protective intervention with a reasonable degree of conﬁdence.

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Transport and dispersion in urban environments is extremely complicated and modeling atmospheric ﬂows and pollutant dispersion over urban areas is a problem of unique characteristics. Buildings alter the ﬂow ﬁeld, causing updrafts and downdrafts, channeling between buildings, areas of calm winds adjacent to strong winds, horizontally and vertically rotating eddies between buildings, at street corners, and other places within the urban canopy (Hosker, 1984). Accounting for the impacts of buildings on the transport and dispersion is crucial in estimating the travel direction, areal extent, toxicity levels of the contaminant plume, and the extent of population exposure (Brown, 2004). The airﬂow around buildings involves impingement and separation regions, building wakes with multiple vortices, interference from adjacent buildings and jetting effects in street canyons. Thus, accurate simulations of building-scale transport and dispersion require appropriate physics sub-models and also signiﬁcant computing resources, making its numerical modeling a challenging task (Chan et al., 2000). Urban emissions occur mainly within or shortly above the canopy layer, i.e., within a zone where the atmospheric ﬂow is heavily disturbed by buildings and other obstacles. In comparison to unobstructed terrain, building effects can change local concentrations by more than an order of magnitude. As a consequence, it is inappropriate to consider only buildings within a surface roughness parameterization, particularly on the scale of a few streets or city blocks (Schatzmann and Leitl, 2002). The urban scales span a large range, from the microscale characteristic of the physical processes in a street canyon (1–10 m) to the regional scale through the city scale (tens of km), so that buildings and complex terrain are to be considered simultaneously to characterize the city’s inhomogeneities. The interaction of the urban site with atmosphere complicates boundary layer variables, which require speciﬁc studies, since the urban small-scale ﬂuid dynamics superposes to the atmospheric mesoscale ﬂow and turbulence. Thus, in order to treat the urban modeling problem in a comprehensive way, a merging of the characteristics and performances of atmospheric models and complex geometry ﬂuid-dynamics models is necessary. This article presents the work performed, thus far, on the modeling of the microscale ﬂow, in the framework of a collaboration for the development of a modeling system for urban air pollution, which encompasses the simulation of all relevant scales: synoptic, mesoscale, and the urban microscales. The main purpose of the project at this initial stage was to adapt the regional atmospheric modeling system (RAMS) atmospheric model to simulate the ﬂow around buildings.

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2. The numerical model

The RAMS is a well-known and worldwide used atmospheric model, simulating atmospheric circulations ranging in scales from an entire hemisphere down to eddies in the planetary boundary layer. A comprehensive review of the RAMS model can be found in Pielke et al. (1992) and Cotton et al. (2003). In the latest version of RAMS (RAMS6.0), an alternative option to the traditionally employed terrain-following coordinates makes possible the use of a Cartesian grid extending from sea level to the model top. In this coordinate system, low elevation grid cells are completely or partially submerged beneath elevated topography. The new approach, called ADAP (or ADaptive APerture), allows for arbitrarily steep and even overhanging topography. This method is especially suitable for application such as ﬂow around buildings (Walko and Tremback, 2002). This version of RAMS enables simulation with very high resolution (several meters) of the ﬂow in an urban environment. The standard RAMS model includes turbulence parameterizations that are suitable to atmospheric ﬂow (e.g., Mellor and Yamada 2.5). In urban environments, turbulence models of the kind k–e have been commonly used. In recent years, a standard version of the k–e turbulence closure model was implemented and tested in RAMS (Trini Castelli et al., 1999, 2001, 2005; Ferrero et al., 2003). This scheme, even though widely used, has some deﬁciencies when applied to the simulation of ﬂow impingement and separation (Castro and Apsley, 1997). An improved model has been developed by Yakhot and Orszag (1986) that applied the renormalization group method to the Navier-Stokes equations and the equations of passive scalar to evaluate turbulence statistics. This is called the renormalization group (RNG) k–e turbulence model. The principal idea of the RNG method lies in the systematic removal of small-scales of turbulence by representing their effects in terms of larger scale motions and a modiﬁed viscosity (Yakhot et al., 1992). In the present work, we implemented in RAMS the formulation of the RNG k–e scheme as suggested by Kim and Baik (2004). The Reynolds stresses and turbulent ﬂuxes in the Navier-Stokes equations and in the transport equation for a passive scalar are parameterized in terms of grid resolvable variables on the basis of the K-theory as @U i @U j 2 dij k þ (1) ui uj ¼ K m 3 @xj @xi

Tamir Reisin et al.

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and cuj ¼ K c

@C @xj

(2)

where Ui is the ith components of the mean velocity, C is the mean concentration of any passive scalar (e.g., a pollutant), and ui and c the ﬂuctuations from their respective means. Km and Kc are the turbulent viscosities of momentum and pollutant concentration respectively, dij the Kroenecker delta, and k the turbulent kinetic energy (TKE). In the RNG k–e turbulence scheme, Km is given by !2 1=2 Cm k (3) Km ¼ n 1 þ n 1=2 where Cm is an empirical constant. This last expression for Km includes molecular kinematic viscosity as well. The value for Kc is calculated from the Schmidt number Sc ( ¼ Km/Kc) that is speciﬁed as 0.9. The RNG k–e turbulence scheme as presented by Yakhot et al. (1992) differs from the standard k–e turbulence scheme in that it includes an additional sink term in the turbulence dissipation equation to account for non-equilibrium strain rates, and employs different values for the model coefﬁcients. The prognostic equations of TKE and its dissipation rate can be written as @k @k @U i @ K m @k (4) ¼ ui uj þ þ Uj @xj @xj sk @xj @t @xj @ @ @U i @ K m @ 2 þ Uj C 2 R ¼ C 1 ui uj þ @xj @xj s @xj k @t @xj k

(5)

where sk, se, Ce1, and Ce2 are empirical constants. The last term in the last equation is an extra strain rate term given by C m Z3 ð1 Z=Z0 Þ2 ð1 þ b0 Z3 Þk

(6)

@U i @U j @U i 1=2 þ @xj @xi @xj

(7)

R¼ where k Z¼

The constants in the parameterization scheme were set as ðC m ; sk ; s ; C 1 ; C 2 ; b0 ; Z0 Þ ¼ ð0:0845; 0; 7179; 0:7179; 1:42; 1:68; 0:012; 4:377Þ Special consideration was given to the near-wall region ﬂow, to account for the inﬂuence of walls’ friction on the ﬂow and for the heat ﬂuxes from

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buildings’ walls. For this purpose, in this initial work we have chosen for a simpliﬁed approach to determine the boundary conditions at the building faces. An ‘‘inﬂuence region’’ was deﬁned around the buildings, in which the walls affect the values of the dynamic and thermo-dynamic atmospheric variables of wind and temperature. In the inﬂuence region, a logarithmic interpolation between the values at the building face and the values of the variable in the ambient atmosphere near the buildings was imposed. For the wind velocity components, the no-slip condition was used for the tangential component. For the temperature, the logarithmic interpolation is between the value of the temperature at the building faces, and the atmospheric values at the second grid points closest to the building faces. For the turbulence variables k and e, we imposed null values at the grid points identiﬁed as the building faces. Then, as analogously done for the ﬁrst level close to the ground, a boundary condition derived from the surface layer similarity theory is imposed at the ﬁrst atmospheric grid points proximate to the building faces as follows: u2n Cm

(8)

u3n KC m

(9)

kbc ¼ and bc ¼

where u* is the friction velocity and K is the von Karman constant. 3. Model and simulation setup

The RAMS model was used to simulate atmospheric ﬂow in the surroundings of Igud Arim Ashdod (IAA, Regional Environmental Authorities) building in the city of Ashdod, Israel. The location was chosen because it is a relatively isolated building in an industrial zone and is a candidate for a future ﬁeld campaign of tracer measurements. The buildings complex includes an upside down U-shape building 9 m height and a 15 m height building to its south (see Figs. 3 and 4 for a plan view). Two sets of test simulations were performed. In the ﬁrst, corresponding to a nighttime simulation on September 3, 2004, a qualitative comparison between the standard k–e and the RNG-k–e closure simulations was conducted. The meteorological situation was a ground inversion with southwesterly wind with a speed around 1 m s1 at the buildings’ height. The second set of simulations was devoted to an analysis of the effects of the buildings’ temperature on the ﬂow. The nighttime development of

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atmospheric conditions was simulated for December 3, 2004, corresponding to a synoptic situation of a high pressure over Israel with northeasterly winds and a stable atmosphere. During nighttime the effect of the buildings’ temperature on the wind ﬁeld is expected to be more signiﬁcant because the atmosphere is more stable and calmer than during the day. Two runs were conducted; in the ﬁrst the temperature of the buildings’ walls was set to 51C and in the second to 201C. According to the initial conditions, corresponding to the proﬁles provided by larger scale RAMS simulations at IAA grid point and used as a homogeneous base state environment, the surrounding temperature of the air at 1 m was 11.51C, and the ambient wind speed at the buildings heights was between 1 and 2 m s1. Some preliminary simulations were conducted in order to ﬁnd a spatial resolution that enables resolution of the main characteristics of the ﬂow. Since the narrowest ‘‘ﬂow channel’’ in the domain was around 6 m, a resolution of 1 m was found to be sufﬁcient to appropriately describe the local ﬂow (including the description of vortices development) while maintaining affordable computation times. Therefore, the simulations were conducted with a spatial resolution of 1 m in the horizontal directions, the vertical resolution was of 1 m up to a height of 20 m (5 m higher than the highest building), and then it was gradually increased up to 10 m between 130 m and 230 m height. The horizontal domain was 120 m in the x direction and 140 m in the y direction. The time step was automatically set by RAMS in order to maintain numerical stability (around 0.01 s).

4. Results

Figures 1 and 2 show XZ cuts for both wind and TKE ﬁelds for the ﬁrst set of calculations, following 3.5 min of model simulation. The cut is at the middle of the domain in the Y direction (see Figs. 3 and 4). The ﬁgures show relatively consistent differences when using the k–e or the RNG-k–e closure models. The main characteristics of the mean ﬂow and turbulence, like the horizontal and vertical vortices between the two buildings and the reverse ﬂow and recirculation over the roof of the western building, are similar, but a rather different energy distribution is produced. In fact, the k–e model transfers more energy from the mean ﬂow to turbulence, generating consistently higher values of TKE than with the RNG-k–e closure, which shows larger values for the wind speed. We notice that already at 1 m resolution, both closures are able to produce ﬂow separation at the front corner of the impact building and

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Figure 1. k–e closure scheme, wind vectors and turbulent kinetic energy after 3.5 min of simulation.

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Figure 2. RNG-k–e closure scheme, wind vectors and turbulent kinetic energy after 3.5 min of simulation.

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Figure 3. Horizontal and vertical wind vectors after 5 min of simulation with walls temperature of 51C.

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Figure 4. Horizontal and vertical wind vectors after 5 min of simulation with walls temperature of 201C.

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reverse ﬂow on the roof, while this was found to be occasionally a problem with standard k–e in Kim and Baik (2004). Figures 3 and 4 show wind vectors for the second set of simulations. Figure 3 corresponds to the case with walls’ temperature of 51C and Fig. 4 for the 201C case. The bold line in XY plane shows the location of the vertical XZ cut, and the horizontal cut is at 5 m height. In the following discussion the focus is on the ﬂow characteristics along the passage between the northern buildings and the southern one. The effect of the walls’ temperature on the surrounding ﬂow was very signiﬁcant, as not only the wind speed was different but the ﬂow direction was completely different in the region of interest. Because of the stable atmosphere at nighttime, the impact of the cool walls (51C) was more distinct. It was characterized by the relatively strong downdrafts that developed. The effect of the warm walls (201C) was dumped by the atmospheric stability. Regarding the 51C case, the northeasterly air ﬂowing along the roofs of the buildings cools down and sinks at the southern/western faces of the buildings. At the eastern buildings, the air sinks at the gap with the southern building and the cooling effect is increased due to the effect of the surrounding walls around the narrow passage and the downdrafts increase. These downdrafts diverge at the surface resulting in horizontal winds at different directions on both sides of the passage. On the western side the easterly winds along the passage are enhanced when they encounter the easterly component of the downdrafts coming from the western building’s roof. The maximum horizontal wind speed at 5 m height reached 1.2 m s1 at the western outlet of the passage and about 0.9 m s1 on the eastern outlet. The maximum downdrafts were of the order of 1 m s1. In the 201C case the air ﬂowing along the roofs is expected to warm up; therefore, downdrafts are not expected to develop. Updrafts along the walls are weak because of the nighttime stable atmosphere. The air that ﬂows along the eastern face of the eastern building enters the passage between the buildings producing an easterly wind within it. The wind slows down towards the western outlet and encounters a westerly component resulting in a vortex at the southwestern corner of the western building. The ﬂow in the passage is mainly horizontal at the eastern side while an updraft appears on the western side due to convergence with the ﬂow coming from that direction. The maximum horizontal wind speed at the passage was reached between the eastern building and the southern one and was of the order of 1 m s1; on the western side it was considerably lower being around 0.5 m s1. The updrafts that appear between the two buildings were lower than 0.5 m s1, while those on the western side of the buildings were larger than 1 m s1.

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5. Conclusions

The latest version of the RAMS model was found to be suitable to simulate the ﬂow around buildings. The ﬂow that develops around the buildings simulated in the present work is notably complex and was found to be sensitive both to the turbulence closure scheme and to the boundary conditions as speciﬁed at the buildings’ walls. Obviously, the impact of the changing ﬂow and turbulence characteristics on pollutants dispersion is expected to be signiﬁcant. Efforts in the next stages of this research will be devoted primarily to a more quantitative comparison between the two turbulent models and then to issues related to boundary conditions. A criterion to establish the length scales of the IR needs to be found, on the basis of possible studies of the effects of the building presence on the environment. The introduction of boundary conditions derived by the similarity theory and the correspondent variables’ proﬁles depending on the stability will be considered. In the present case, the main similarity parameters need to be redeﬁned in order to properly account for the inﬂuence of the building. Alternatively, the boundary conditions at the building walls could be deﬁned computing the momentum ﬂux on the basis of the shear and a drag coefﬁcient and the heat ﬂux on the basis of heat transfer laws.

REFERENCES Brown, M.J., 2004. Urban dispersion—Challenges for fast response modeling. AMS Conference on Applications of Air Pollution Meteorology. Castro, I.P., Apsley, D.D., 1997. Flow and dispersion over topography: A comparison between numerical and laboratory data for two-dimensional ﬂows. Atmos. Environ. 31, 839–850. Chan, S.T., Stevens, D.E., Lee, R.L., 2000. A model for ﬂow and dispersion around buildings and its validation using laboratory measurements, Proc. 3rd Symposium on the Urban Environment, 14–18 August, Davis, CA, pp. 56–57. Cotton, W.R., Pielke, R.A., Walko, R. Sr., Liston, G.E., Tremback, C.J., Jiang, H., McAnelly, R.L., Harrington, J.Y., Nicholls, M.E., 2003. RAMS 2001—Current status and future directions. Meteor. Atmos. Phys. 82, 5–29. Davis, J., Prosnitz, D., 2003. Technical and policy issues of counterterrorism—a primer for physicists. Phys. Today, April 2003, 39–44. Ferrero, E., Trini Castelli, S., Anfossi, D., 2003. Turbulence ﬁelds for atmospheric dispersion models in horizontally non-homogeneous conditions. Atmos. Environ. 37(17), 2305–2315. Hosker, R.P. Jr., 1984. Flow and diffusion near obstacles. In: Randerson, D. (Ed.), Atmospheric Science and Power Production, DOE/TIC-27601. U.S. Dept. of Energy, Washington, DC, pp. 241-326, Ch. 7.

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Kim, J.-J., Baik, J.-J., 2004. A numerical study of the effects of ambient wind direction on ﬂow and dispersion in urban street canyons using the RNG k–e turbulence model. Atmos. Environ. 38, 3039–3048. Pielke, R.A., Cotton, W.R., Walko, R.L., Tremback, C.J., Lyons, W.A., Grasso, L.D., Nicholls, M.E., Moran, M.D., Wesley, D.A., Lee, T.J., Copeland, J.H., 1992. A comprehensive meteorological modeling system—RAMS. Meteorol. Atmos. Phys. 49, 69–91. Schatzmann, M., Leitl, B., 2002. Validation and application of obstacle-resolving urban dispersion models. Atmos. Environ. 36, 4811–4821. Trini Castelli, S., Ferrero, E., Anfossi, D., 2001. Turbulence closures in neutral boundary layers over complex terrain. Bound. Layer Meteorol. 100, 405–419. Trini Castelli, S., Ferrero, E., Anfossi, D., Ohba, R., 2005. Turbulence closure models and their application in RAMS. Environ. Fluid Mech. 5, 169–192. Trini Castelli, S., Ferrero, E., Anfossi, D., Ying, R., 1999. Comparison of turbulence closure models over a schematic valley in a neutral boundary layer. Proceeding of the 13th Symposium on Boundary Layers and Turbulence—79th AMS Annual Meeting, pp. 601–604. Walko, R., Tremback, C., 2002. The Adaptive Aperture (ADAP) Coordinate. 5th RAMS Workshop and Related Applications, Santorini, Greece. Yakhot, V., Orszag, S.A., 1986. Renormalization group analysis of turbulence. J. Sci. Comput. 1, 3–51. Yakhot, V., Orszag, S.A., Thangam, S., Gatski, T.B., Speziale, C.G., 1992. Development of turbulence models for shear ﬂows by a double expansion technique. Phy. Fluids A 4, 1510–1520.

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Chapter 1.5 Assessment of dust forecast errors by using lidar measurements over Rome P. Kishcha, P. Alpert, A. Shtivelman, S.O. Krichak, J.H. Joseph, G. Kallos, P. Katsafados, C. Spyrou, G.P. Gobbi, F. Barnaba, S. Nickovic, C. Perez and J.M. Baldasano Abstract In this study, forecast errors in dust vertical distributions were analyzed. This was carried out by using quantitative comparisons between dust vertical proﬁles retrieved from lidar measurements over Rome, Italy, and those predicted by models. Three models were used: the four-particle-size Dust Regional Atmospheric Model (DREAM), the older one-particle-size version of the SKIRON model from the University of Athens (UOA), and the pre-2006 oneparticle-size Tel Aviv University (TAU) model. SKIRON and DREAM are initialized on a daily basis using the dust concentration from the previous forecast cycle, while the TAU model initialization is based on the Total Ozone Mapping Spectrometer aerosol index (TOMS AI). The quantitative comparison shows that (1) the use of four-particle-size bins in the dust modeling instead of only one-size bin improves dust forecasts, (2) cloud presence could contribute to additional dust forecast errors in SKIRON and DREAM, (3) as far as the TAU model is concerned, its forecast errors were mainly caused by technical problems with TOMS measurements from the Earth Probe satellite. As a result, dust forecast errors in the TAU model could be signiﬁcant even under cloudless conditions. 1. Introduction

In order to evaluate the model capabilities for providing reliable forecast of 3D-dust distributions in the atmosphere, we used dust forecasts of three different forecasting systems: the four-particle-size Dust Regional Atmospheric Model (DREAM) (Nickovic et al., 2001), the pre-2006 one-particle-size Tel Aviv University (TAU) dust prediction system

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(Alpert et al., 2002), and the older one-particle-size version of the SKIRON model of the University of Athens (UOA) (Kallos et al., 1997). All these three model versions have their origin in the same predecessors described by Nickovic and Dobricic (1996), Kallos et al. (1997), and Nickovic et al. (1997), with various components upgraded afterwards. The dust forecasts were compared against lidar remote soundings over Rome, Italy (41.81N, 12.61E) performed over the 3-year period 2001–2003 (e.g., Gobbi et al., 2004). The full description of this study has been previously submitted for publication to the Journal of Geophysical Research (Kishcha et al., 2007).

2. Dust prediction system

The older version of SKIRON forecasting system of the University of Athens, used in this study, includes a dust module with the one-particle-size representation of dust aerosol (Kallos et al., 1997). This SKIRON system has been in operational use since 1998 providing 72-h weather and dust forecasts for the Mediterranean region. Dust is driven by the hydrostatic NCEP/Eta model (Mesinger, 1997). The SKIRON system covers a domain including the Mediterranean Sea, Europe, North Africa, and Middle East. In the vertical, 32 levels are using stretching from the ground to the model top (15,800 m). In the horizontal, a grid increment of 0.241 is applied. The system includes packages for dust initialization, transport, and wet/dry deposition, developed within the framework of the Mediterranean Dust Experiment (MEDUSE) EU project (Janjic, 1994; Nickovic and Dobricic, 1996; Nickovic et al., 1997). The dust module is dynamically coupled with the atmospheric model; therefore, at each time step, the prognostic atmospheric and hydrological conditions are used to calculate the effective rates of the injected dust concentration based on the viscous/turbulent mixing, shear-free convection diffusion, and soil moisture. Special care was taken to deﬁne as accurately as possible the dust productive areas since soil properties (soil structure, soil wetness, vegetation cover) dictate the dust quantity that may be available when the turbulent state of the surface atmosphere triggers its injection into the atmosphere. The speciﬁcation of the model dust sources and the calculation of dust-related processes are obtained from high-resolution data sets of vegetation and soil texture types. For the geographical distribution of the land cover and the dust sources, Olson World Ecosystem Data of 10 min resolution and with 59 vegetation types are used. For the soil texture distribution, the UNEP/FAO data set is applied after its

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conversion from soil type to soil textural ZOBLER classes (Papadopoulos et al., 1997). The dust is considered as a passive substance, i.e., no dust feedback effects are included in the radiation transfer calculations. It should be mentioned that a new version of SKIRON has been developed at the University of Athens, which includes a dust module with the fourparticle-size representation of dust aerosol (Nickovic et al., 2001; Kallos et al., 2006). Being in operational use since January 2003, it has the same elements as DREAM described below, since their development was done in the framework of the SKIRON and MEDUSE and later the ADIOS projects. The one-particle-size SKIRON system, after modiﬁcation, was put into operation and has been used for short-term dust predictions at TAU since November 2000 until the end of 2005 (Alpert et al., 2002). Several modiﬁcations were made to the model including development of a new dust initialization procedure, determination of the dust sources employing Ginoux et al.’s (2001) method, and expansion of the forecast area to include the Atlantic Ocean. These improvements were undertaken in order to support the joint Israeli–American Mediterranean Dust Experiment (MEIDEX). The model domain is 0–501N, 501W–501E. The model has a horizontal resolution of 0.51 and 32 vertical levels. Dust forecasts are initialized with the aid of the Total Ozone Mapping Spectrometer aerosol index (TOMS AI) measurements (Alpert et al., 2002). The initial dust vertical distribution over each grid-point, within the model domain, is determined according to the value of TOMS indices among four categories of model-calculated averaged dust proﬁles over the Mediterranean and among four other proﬁles over North Africa. The dust component is based on a single-particle-size bin with radius of 2–2.5 microns. The four-particle-size DREAM model incorporates the state-of-the-art parameterizations of all the major phases of atmospheric dust life such as production, diffusion, advection, and removal (Nickovic et al., 2001). In DREAM, the emission parameterization combines the ﬂux scheme of Shao et al. (1993) and viscous sub-layer model of Janjic (1994). Its dust module includes effects of the particle-size distribution on aerosol dispersion. In particular, special attention was made in order to properly parameterize the dust production phase. Dust productive areas in the model are speciﬁed using the US Geological Survey (USGS) data of 30 s resolution on land cover. For each soil texture class the fractions of clay, small silt, large silt, and sand are estimated with four-particle-size radii of 0.7, 6.1, 18.0, and 38 microns, respectively. In DREAM, the dust cycle is described by a set of K-independent Euler-type concentration equations allowing no inter-particle interactions, where K ¼ 4 indicates the number of particle size class. The area covered by the model is 201W–451E and

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151N–501N. The model has a horizontal resolution of 0.31 and 24 vertical levels between the surface and 16,000 m. To compare the dust forecast with lidar-derived volume proﬁles, modeled mass concentration proﬁles over Rome were divided by dust density, assumed as 2.5 gcm3, in agreement with the majority of other dust models, e.g., Kinne et al. (2003, their Table 4). 3. Lidar data

Lidar measurements employed in this study were collected by a singlewavelength, polarization-sensitive lidar system (VELIS), operational since February 2001 in the ISAC laboratories (41.841N–12.641E, 130 m above sea level) at the outskirts of Rome. Measurements were carried out daily at non-synchronous times between 7 am and 9 pm (UTC). The lidar radiation source is a frequency-doubled Nd:YAG laser, emitting planepolarized pulses at 532 nm. The intensity and repetition rate of laser pulses are generally set as 30 mJ and 10 Hz, respectively. The system set up allows collecting the complete tropospheric backscatter proﬁle between 300 m and 14 km from the ground. Backscattered light is recorded on either the parallel (//) or the perpendicular (?) polarization planes with respect to the laser one. Lidar proﬁles are obtained as 10-min averages and their vertical resolution is 37.5 m. A thorough description of the lidar signal analysis is given by Gobbi et al. (2002). The Barnaba and Gobbi (2001) approach was used in the current study to derive heightresolved dust volumes from lidar measurements of backscatter. In particular, comparisons between lidar data and in situ measurements showed a slight (1%) lidar tendency to underestimate desert dust volume, and an average agreement within 720% (Gobbi et al., 2003). Since those comparisons were performed in the near range portion of the lidar trace (lidar levelso500 m), we expect an additional random error to affect the farther ranges: 8% at 4–6 km and 30% at 6–8 km. In this study, the dust forecasts were compared against lidar data for 34 days taken randomly during the 3-year period 2001–2003, for the high dust activity season from March to June. Further details about the selected days are in Kishcha et al. (2005). Since this study was aimed at checking the quality of dust forecasts available at 12 UTC, the lidar proﬁles closest to 12 UTC were selected for the analysis. 4. Quantitative inter-comparison

The correspondence between model data and lidar measurements over Rome is evaluated by means of scatter plots with lidar-derived versus

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model-simulated dust volumes (not shown). Examples of such scatter plots are in Kishcha et al. (2005). The bisector curves indicate ideally accurate forecasts, i.e., the points on or close to the bisector represent the best correspondence between the model-simulated data and the lidar ones.

4.1. The TAU model vs. lidar

The distribution of points in the scatter plot reveals that the model results vary between 0.04 1012 and 95.0 1012 cm3cm3, whereas the lidar data are between the lower detection limit of 3.62 1012 and 163.0 1012 cm3cm3. Overall, for the 34 days under consideration, the correlation (r ¼ 0.47) between lidar and model derived data was found, as displayed in Table 1. The correlation is statistically signiﬁcant within the 0.05 level. In a previous paper (Kishcha et al., 2005), attention was paid to the fact that inaccurate forecasts were associated with cloudiness over the area where the initial 3-D dust distribution had been obtained (with the aid of TOMS indices on the day previous to the forecast). Earth Probe Total Ozone Mapping Spectrometer reﬂectivity measurements (see http://daac.gsfc.nasa.gov) were used in order to identify cloudy conditions for all points in the above-discussed scatter plots. Averaged reﬂectivity of less than 20% over the area, where dust was initialized 24 h before the forecast time, was found to correspond mainly to acceptable forecast points. The area, where dust was initialized, is deﬁned by the rectangular area around the starting points of 24-h back trajectories. However, as found in the current study for the TAU model, the forecast errors could be signiﬁcant even in cloudless conditions. In particular, the correlation for 22 cases with reﬂectivity less than 20 over the area, where dust was initialized, was rather low (r ¼ 0.44) (Table 1). This is mainly caused by technical problems with TOMS measurements in accordance with NASA announcements (http://daac.gsfc.nasa.gov/data/ dataset/TOMS).

Table 1. Correlation between lidar data and 24-h model-predicted dust volumes, averaged within the dust layer (following Kishcha et al., 2007) Model Four-particle-size DREAM One-particle-size SKIRON One-particle-size TAU

All 34 days

22 days with low cloud presence

0.60 0.49 0.47

0.71 0.54 0.44

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4.2. SKIRON vs. lidar

The distribution of points in the scatter plot reveals that the model results vary between 0.42 1012 and 211.16 1012 cm3cm3. The SKIRON data correspond a little better to the lidar ones compared with the TAU model results; the correlation (r ¼ 0.49) was found for all 34 days under consideration (Table 1). However, it was surprising that for 22 days with low or without cloud presence over the area, where dust was initialized, the model-lidar correspondence is noticeably better, a higher correlation (r ¼ 0.54) was found. 4.3. DREAM vs. lidar

The distribution of points in the scatter plot indicates that the DREAM data better correspond to the lidar ones compared with other two models. As shown in Table 1, this is supported by higher correlation (r ¼ 0.60) for all 34 days under consideration. It is noticeable that for 22 days with low or without cloud presence over the area, where dust was initialized, the model-lidar correspondence is distinctly better, a higher correlation (r ¼ 0.71) was found. This suggests that non-included dust-radiation and dust–cloud interactions in the modeling system could result in the forecast of lower accuracy in the presence of clouds. In general, the correlation analysis between lidar data and all three models in question gave an indication that multi-particle-size-bin DREAM is always better than other two one-particle models both for all 34 days and also for cloudless conditions. As for the TAU model, its forecast could be less accurate both for all days and for cloudless conditions. Our analysis gave us proof that the accuracy of TOMS measurements is not satisfactory for dust forecasting because of problems connected with inaccurate spectrometer calibration, as announced by NASA. 4.4. Statistical histograms of forecast errors

An analysis of statistical distributions of forecast mean errors allows the clariﬁcation of the difference between the three dust prediction systems under investigation. To this end, statistical histograms of 24-h forecast errors for averaged dust volume within the dust layer over Rome were constructed, as shown in Fig. 1. The forecast mean error was deﬁned as the difference between the common logarithm of lidar-derived dust volume and the one simulated by the model. Consequently, positive errors mean underestimating of lidar data by the model, while negative ones

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Figure 1. Statistical distributions of 24-h forecast errors for model-simulated dust volume over Rome (averaged dust volume within the dust layer): (a) for all the 34 days under investigation, (b) for the 22 days with low cloudiness less than 20%, and (c) for the 10 days with cloudiness exceeding 20% over the area where the dust originated (after Kishcha et al., 2007).

mean overestimating. Different histograms are analyzed in Fig. 1: (1) for all the 34 days in question (Fig. 1a), (2) only for the 22 days with low or without cloudiness, and (3) for the 10 days with cloudiness exceeding 20%, as characterized by the averaged TOMS reﬂectivity (Fig. 1c). The histograms in Fig. 1a reveal the following characteristic features of forecast-error distributions: (a) Maxima of the histograms are close to zero, meaning that, on average, both SKIRON and DREAM produce acceptable forecasts. (b) However, the TAU model errors are spread over a wide range, indicating that the TAU model predictions tend to underestimate lidar data; the errors are mainly positive. (c) The error distribution for the SKIRON model is more symmetric; DREAM more overestimates lidar data.

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Figure 1b demonstrates that for days with low or without cloud presence, forecast errors are spread over a narrower range. This highlights the fact that the cases without clouds are mainly associated with dust forecasts of higher accuracy. As for the TAU model, forecast errors are spread over a wider range because of inaccurate TOMS data. In contrast, as shown in Fig. 1c for cloudy days, for all models errors are spread over a wider range, pointing to that these cases are frequently associated with dust forecasts of lower accuracy. The TAU model errors shift to positive values (underestimation), while those for DREAM/ SKIRON shift to negative values (overestimation).

5. Conclusions

Our comparative analysis of model capabilities for providing reliable forecasts of dust vertical distribution in the atmosphere highlights the following (Kishcha et al., 2007): The model vs. lidar comparison clearly shows the advantage of using multiple-particle-size representation in dust modeling. The use of fourparticle-size bins in the dust model DREAM (and evidently in the newer four-particle-size version of SKIRON), instead of the use of only one-size bins in the older one-particle-size version of SKIRON, improves dust forecasts. The correlation between model and lidar data for all 34 days under consideration is equal to 0.60 for DREAM against 0.49 for the one-particle-size SKIRON model. This is also supported by the correlation estimates for cloudless conditions. For cases with low or without cloud presence over the area where the dust originated, a higher correlation was found: 0.71 for DREAM and 0.54 for SKIRON. This highlights that cloud presence could contribute to additional dust forecast errors in SKIRON and DREAM. Two possible reasons are suggested: (1) Weather forecast errors in cloud position, amount, and structure could affect the radiation balance over the dust sources. This implies additional errors in dust emission because of its link with the sensible heat ﬂux over dust sources (Perez et al., 2006). In particular, a smaller outgoing sensible turbulent heat ﬂux reduces both dust emission and the turbulent momentum transfer from the atmosphere; (2) non-included dust-radiation and dust–cloud interactions in the modeling systems could result in the forecast of lower accuracy in the presence of clouds. Recently, Perez et al. (2006) introduced the dust-radiative effect into DREAM, outlining its critical inﬂuence on the weather and dust forecasts produced by the model.

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The present study, however, has the following limitations: (1) uncertainties in the lidar data, (2) only one lidar station (Rome) was used in the validation of the models, and (3) a limited number of dust episodes were analyzed. For these reasons, the hypotheses aforementioned should be explored in detail, using a larger set of episodes and measurements. As for the pre-2006 TAU model, its forecast errors were mainly caused by the TOMS measurement problems. The TOMS problems took place even in the absence of cloudiness. Therefore, for the TAU model, the forecast errors could be signiﬁcant even in cloudless conditions. The technical problems with TOMS measurements explain NASA’s decision to replace the calculation of TOMS indices based on the Earth Probe satellite measurements by OMI indices from the AURA Earth Observing System; this was put into practice from January 1, 2006.

Discussion

Ø. Seland: P. Kishcha:

I. Tegen:

P. Kishcha

Are there any changes in the vertical distribution due to precipitation not reaching the ground? A dry and wet dust deposition scheme with a constant washout parameter is included in all three models under discussion. Therefore, the precipitation effects on dust are taken into consideration. However, there is no parameterization considering the case with precipitation not reaching the ground. The speaker identiﬁed the difference in size bins as the largest difference between the DREAM and SKIRION models. To which extent could differences in the initialization contribute to differences in the results? Regarding the comparison of modeled dust loads and lidar retrievals of dust volumes, the main differences were found at low dust concentrations. Do the lidar results only show dust concentrations or could other aerosols ‘‘contaminate’’ the lidar retrievals? The discussed models (DREAM, the older one-particle version of SKIRON, and the pre-2006 TAU model) have much in common because of their origin in the same predecessors with various components upgraded afterwards. On the other hand, the main differences between the models are (a) multiple particle representation in DREAM in contrast to others, (b) their

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approach to dust initialization: DREAM and SKIRON use their previous forecast while the pre-2006 TAU model uses TOMS aerosol indices. The mode –vs. lidar comparison showed that (1) the use of four particle size classes instead of only one improves dust forecast, (2) using TOMS indices for dust initialization could result sometimes in signiﬁcant forecast errors because of the technical problems with TOMS measurements, as announced by NASA. In particular, at low dust concentrations the forecast errors caused by incorrect TOMS indices were frequently detected. Lidar-derived vertical proﬁles, used in the quantitative model vs. lidar comparison, related only to mineral dust volume in accordance with the special technique developed by G.P. Gobbi’s group (e.g., Barnaba and Gobbi, 2001). ACKNOWLEDGMENTS

This study was supported by the Israeli Ministry of Environment’s grant, by the Urban air pollution Italian–Israeli joint project, and also by the GLOWA-Jordan River BMBF-MOS project. The authors gratefully acknowledge Boris Starobinets for helpful comments and discussion. REFERENCES Alpert, P., Krichak, S.O., Tsidulko, M., Shaﬁr, H., Joseph, J.H., 2002. A dust prediction system with TOMS initialization. Mon. Weather Rev. 130(9), 2335–2345. Barnaba, F., Gobbi, G.P., 2001. Lidar estimation of tropospheric aerosol extinction, surface area and volume: Maritime and desert-dust cases. J. Geophys. Res. 106(D3), 3005–3018. (Correction: J. Geophys. Res., 10.1029/2002 JD002340, 2002). Ginoux, P., Chin, M., Tegen, I., Prospero, J.M., Holben, B., Dubovik, O., Lin, S.-J., 2001. Sources and distributions of dust aerosols simulated with the GOCART model. J. Geophys. Res. 106, 20255–20274. Gobbi, G.P., Barnaba, F., Ammannato, L., 2004. The vertical distribution of aerosols, Saharan dust and clouds at Rome (Italy) in the year 2001. Atmos. Chem. Phys. 3, 2161–2172. Gobbi, G.P., Barnaba, F., Blumthaler, M., Labow, G., Herman, J., 2002. Observed effects of particles non-sphericity on the retrieval of marine and desert-dust aerosol optical depth by lidar. Atmos. Res. 61, 1–14. Gobbi, G.P., Barnaba, F., Van Dingenen, R., Putaud, M., Mircea, M., Facchini, M.C., 2003. Lidar and in situ observations of continental and Saharan aerosol: Closure analysis of particles optical and physical properties. Atmos. Chem. Phys. 3, 2161–2172.

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Janjic, Z.I., 1994. The step-mountain Eta coordinate model: Further developments of the convection, viscous sublayer and turbulence closure schemes. Mon. Weather Rev. 122, 927–945. Kallos, G., Katsafados, P., Papadopoulos, A., Nickovic, S., 2006. Transatlantic Saharan dust transport: Model simulation and result. J. Geophys. Res. 111, D09204, doi:10.1029/2005JD006207. Kallos, G., Nickovic, S., Papadopoulos, A., Jovic, D., Kakaliagou, O., Misirlis, N., Boukas, L., Mimikou, N., Sakellaridis, G., Papageorgiou, J., Anadranistakis, E., Manousakis, M., 1997. The regional weather forecasting system SKIRON: An overview. Paper presented at the International Symposium on Regional Weather Prediction on Parallel Computer Environment, University of Athens, Athens, Greece, 15–17 October 1997. Kinne, S., Lohmann, U., Ghan, S., Easter, R., Chin, M., Ginoux, P., Takemura, T., Tegen, I., Koch, D., Herzog, M., Penner, J., Pitari, G., Holben, B., Eck, T., Smirnov, A., Dubovik, O., Slutsker, I., Tanre, D., Torres, O., Mishchenko, M., Geogdzhayev, I., 2003. Monthly averages of aerosol properties: A global comparison among models, satellite data and AERONET ground data. J. Geophys. Res. 108(D20), 4634, doi:10.1029/2001JD002011. Kishcha, P., Alpert, P., Shtivelman, A., Krichak, S.O., Joseph, J.H., Kallos, G., Katsafados, P., Spyrou, C., Gobbi, G.P., Barnaba, F., Nickovic, S., Perez, C., Baldasano, J.M., 2007. Forecast errors in dust vertical distributions over Rome (Italy): Multiple particle size representation and cloud contributions. J. Geophys. Res. in press. Kishcha, P., Barnaba, F., Gobbi, G.P., Alpert, P., Shtivelman, A., Krichak, S.O., Joseph, J.H., 2005. Vertical distribution of Saharan dust over Rome (Italy): Comparison between 3-year model predictions and lidar soundings. J. Geophys. Res. 110, D06208, doi:10.1029/2004JD005480. Mesinger, E., 1997. Dynamics of limited area models: Formulation and numerical methods. Meteor. Atmos. Phys. 63, 3–14. Nickovic, S., Dobricic, S., 1996. A model for long-range transport of desert dust. Mon. Weather Rev. 124, 2537–2544. Nickovic, S., Kallos, G., Kakaliagou, O., Jovic, D., 1997. Aerosol production/transport/ deposition processes in the Eta model: Desert cycle simulations. Preprints. Proc. Symp. on Regional Weather Prediction on Parallel Computer Environments, University of Athens, Athens, Greece, pp. 137–145. Nickovic, S., Kallos, G., Papadopoulos, A., Kakaliagou, O., 2001. A model for prediction of desert dust cycle in the atmosphere. J. Geophys. Res. 106, 18113–18129. Papadopoulos, A., Kallos, G., Nickovic, S., Jovic, D., Dacic, M., Katsafados, P., 1997. Sensitivity studies of the surface and radiation parameterization schemes of the SKIRON system, In: Kallos, G., Kotroni, V., Lagouvardos, K. (Eds.), Proceedings of Symposium on Regional Weather prediction on Parallel Computer Environments. The University of Athens, Greece, pp. 155–164. ISBN: 960-8468-22-1. Perez, C., Nickovic, S., Pajanovic, G., Baldasano, J.M., Ozsoy, E., 2006. Interactive dustradiation modeling: A step to improve weather forecasts. J. Geophys. Res. 111(D16206), doi:10.1029/2005JD006717. Shao, Y., Raupach, M.R., Findlater, P.A., 1993. Effects of saltation bombardment on the entrainment of dust by wind. J. Geophys. Res. 98, 12719–12726.

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Chapter 1.6 Modelling urban heat island in the context of a Mediterranean city F. Santese, S. Di Sabatino, E. Solazzo and R. Britter Abstract Urban heat island (UHI) is one of the most well known forms of localised anthropogenic climate modiﬁcation. It causes a local alteration of atmospheric stability. According to a top-down methodology, mesoscale meteorological modelling is a commonly used approach to study the impact of UHI on atmospheric stability at the urban scale. Our work is an effort to investigate UHI using a bottom-up approach by looking at the UHI through a computational ﬂuid dynamics (CFD) model applied to the street canyons of a neighbourhood area. The CFD code is set up to model the thermal response (structure surface temperature and ambient air temperature) of an urban system to the outside climate. The determination of the air temperature in an urban unit allows the calculation of the DTur factor representing the difference between the air temperature in the urban system (u) and the air temperature recorded at the closest meteorological station (r), generally situated in the countryside. This factor, introduced by Oke in Boundary Layer Climate, (1987), enables the analysis of the heat island generated by an urban system. The simulation results obtained from the CFD model allows the estimation of the DTur factor in relation to physical aspects and geometrical conﬁgurations. We apply this technique to study UHI of a Mediterranean city of which some urban temperature measurements and morphometry from a digital elevation model (DEM) are available. 1. Introduction

The urban heat island (UHI) effect is the result of many complex processes and although it has been well described in many experimental and

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numerical works, simple prediction methods are still required. In particular, it is of interest to estimate UHI intensity within an urban area as a function of time, weather conditions and building characteristics and for practical applications such as road climatology, phenology, energy conservation and weather forecasting. In the last 30 years, much research has been devoted to the study of UHI both to understand if and how it inﬂuences mesoscale circulation and how it impacts on human life. Following Oke’s original framework in which there is a distinction between UHI in the urban boundary layer (UBL) and UHI in the urban canopy layer (UCL), it has been clariﬁed that within an urban area, many UHIs exist, each with their own characteristics controlled by different combinations of energy exchange processes. However, both deﬁnitions have historically in common the fact that they have an air temperature excess over rural environs. In other words an urban heat island develops when rural cooling rates are greater than the urban ones (Oke and Maxwell, 1975). The major challenge still remains in the modelling of such phenomena. Many numerical studies based on mesoscale modelling are available (Murakami et al., 1997; Ichinose et al., 1999; Martilli et al., 2002). Conversely, at the street scale the so-called urban canyon models (Kondo and Liu, 1998) allow the estimation of the UHI from a local energy balance. Many studies deal with the inﬂuence of building geometry on the ﬂow, using ﬁeld and wind tunnel experiments, numerical and theoretical models (Sini et al., 1996; Bentham and Britter, 2003; Kastner-Klein and Rotach, 2004). There are few studies dealing with thermal aspects of the urban environment and the inﬂuence of the temperature distribution on the urban climatology is not well understood yet. The study of the transfer of scalars requires a separate study and analysis from what we know about the momentum exchange (Barlow et al., 2004). This work represents our ﬁrst step towards the characterisation of UHIs-type in a Mediterranean medium-size city. We use a bottom-up approach by analysing the inﬂuence of building conﬁgurations at the urban scale on both the ﬂow and temperature distributions through computational ﬂuid dynamics (CFD) simulations in a neighbourhood zone where temperature measurement campaigns are currently being ongoing. Before proceeding with the real simulation and using the CFD code as an instrument for investigating temperature patterns at the neighbourhood area, we have validated the CFD model against wind tunnel experiments (Uehara et al., 2000). In the experimental work, thermal stratiﬁcation is created by controlling wind temperature and wind tunnel ﬂoor temperature. We compare experimental measurements of wind speed and temperature proﬁle with our computational results.

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2. Fluent modelling set up for all simulations

The computational study was made using Fluent, a ﬁnite-volume based commercial code (Fluent, Inc., 2003). A standard steady case k–e turbulence model was used for all simulations. At the inlet the proﬁles for wind velocity U(z), turbulent kinetic energy k(z) and turbulent dissipation rate e(z) were provided by the following formulas: u zd u2 z u3 z 1 ; ðzÞ ¼ 1 (1) ; kðzÞ ¼ ð1=2Þ UðzÞ ¼ ln k kz z0 d d Cm where d is the height of the boundary layer, z0 is the roughness length, d is the zero-plane displacement length; u is the friction velocity, Cm( ¼ 0.09) and k( ¼ 0.40) are constants. Boussinesq approximation and the thermal expansion coefﬁcient were set out. The CFD code is set up to model the thermal response (building surface temperature and ambient air temperature) of an urban system to the outside climate using a similar set up as in Solazzo et al., 2005. 2.1. Wind tunnel simulation runs

For the validation of our simulations on an array of buildings, wind tunnel experiments from Uehara et al. (2000) were used. Those experiments were conducted using a model that represented city streets with simple shaped block forms, while varying atmospheric stability across seven stages from stable (bulk Richardson number—Rb ¼ 0.79) to unstable (Rb ¼ 0.21). The Richardson number that deﬁnes atmospheric stability (Snyder, 1972; U.S. Environmental Protection Agency, 1981; Cermak, 1984) is given by Rb ¼ gHðT H T 0 Þ=ðU 2H TÞ; where g is the acceleration due to gravity, H the building height, TH the temperature at the top of the street canyon, T0 the temperature of the ground, UH the mean wind speed at the top of the canyon and T the mean temperature. The wind speed range in the wind tunnel was between 0.2 and 10 m s1. For our computational runs, we used a tetrahedric unstructured mesh, with the smaller cell size equal to 15 mm and an expansion rate of 1.25. This resulted in a computational domain of about 714,000 computational cells. All parameters were set as suggested in Uehara et al. (2000) for the approaching ﬂow that is the boundary layer height d ¼ 0.7 m; the roughness length z0 ¼ 3.3 103 m, the displacement height d ¼ 35 103 m. UrefU700 ¼ 1.5 m s1, the friction velocity u is given by u/Uref.. Figure 1 shows the geometry conﬁguration reproducing blocks arrangement in the wind tunnel. The dimensions of the computational domain are

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Figure 1. Computational domain size and geometry conﬁguration of CFD simulations used for comparison with measurements from a wind tunnel experiment. Table 1. Temperature and Rb set-up values in the wind tunnel and in the CFD model Rb Ta (K) Tf (K)

0.79 351 294

0.43 331 294

0.11 311 294

0 293 293

0.12 292 313

0.19 292 332

0.21 293 352

6 m along X-axis, 1.5 m along Y-axis and 1 m along Z-axis. Each block is 100 mm by 100 mm by 100 mm; all elements are spaced 100 mm apart in X direction and 50 mm in Y direction giving an aspect ratio of 1:1. The distance between the inlet surface and the ﬁrst row of blocks was set to be equal to 5H, where H is the height of the element block, while the distance between the last row of elements and the output surface is set to be equal to 20H; the distance between the elements and the right-hand side symmetry is equal to 5H. Figure 1 also shows the measurement axis along which we calculated velocity and temperature proﬁles. In the wind tunnel it indicates the position where measurements were made that is between the ﬁfth and sixth rows of building, 1 m leeward from the leading edge of city blocks. We set symmetry condition at the top of the domain and at the two sides, an outﬂow condition at the downstream edge of the domain, and a no-slip condition at the side facets, street and roof of the buildings. The temperature distribution was set at the inlet zone (wind temperature, Ta) and at the bottom (Tf) of our domain, following the indication from the wind tunnel setting for thermal stratiﬁcation and ﬁxing temperature as thermal boundary condition. According to wind tunnel experimental readings, we set our computational runs with multiple sets of temperature for both the ﬂow and the ﬂoor. Table 1 reports temperatures with related Rb used in the wind tunnel and in our CFD simulations.

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2.2. Simulation runs on DEM

Geometry conﬁguration for the real case is available through a digital elevation model (DEM) (Chirizzi, 2004) of a neighbourhood zone of Lecce, a Mediterranean city in southern Italy (Fig. 2). City zone occupies an area of 210 m by 230 m and the chosen 3D computational domain has dimensions of 960 m by 310 m by 300 m. The black line in Fig. 2 indicates the only street canyon within the area where we imposed a temperature difference between the walls and the ﬂow. We chose to heat the building facets in the east direction (negative Y-axis), in order to simulate eastward solar radiation (particular of heated facets in Fig. 3). We created an unstructured mesh with over 1,700,000 cells with a minimum volume cell equal to 5.5 102 m3. Morphological parameters were calculated according to methodology developed by Ratti et al. (2002) which gave: z0 ¼ 0.1 m, u ¼ 0.446 m s1, d ¼ 10.1 m, d ¼ 200 m. 2.2.1. Boundary condition

North wind direction was chosen for our runs (along X-axis in Fig. 2). We set air temperature (in inlet) at 303 K (this value was derived by

Figure 2. Geometry of the neighbourhood city area used in the CFD modelling.

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Figure 3. Details of the city geometry showing the heated facets of buildings in the eastward side.

several measurements taken during summer 2005, in situ); temperature of heated facets was initially set to 305 K and 313 K in subsequent runs.

3. Results and discussion 3.1. Evaluation of computational results against wind tunnel data

In Fig. 4 wind proﬁles calculated with Fluent are compared against wind tunnel proﬁles. Both proﬁles are similar, qualitatively speaking but we observe that when zoH for all case that is the stable case (Rb ¼ 0.79), neutral case (Rb ¼ 0) and unstable case (Rb ¼ 0.21) simulated wind speeds are greater than the measured ones. However, above the buildings top, simulated wind speeds are lower than wind tunnel measurements for the ﬁrst two cases; conversely, for the unstable case (and for intermediate others), calculated values are slightly larger than the measured ones. In general, we can say that wind speed in and above the street canyon, U/ U700, diminishes as the stratiﬁcation strengthened (it drops almost to zero), but wind speed increases as instability increases (inside the street canyon the reverse ﬂow is strong because of the instability). With Fluent results the effect of reverse ﬂow is less evident. A possible explanation for this to happen is that the vortex that forms in the street canyon becomes weaker when the atmosphere is stable, and stronger when the atmosphere is unstable. On the other hand, we may expect that when atmosphere is unstable mixing processes in the street canyon cause the vertical temperature gradient to grow smaller (see Fig. 5), and thus the instability to decrease.

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Figure 4. Comparison of the experimental and modelled non-dimensional wind proﬁles (u/ Uref) as a function of the non-dimensional height for different Rb values.

Figure 5. Comparison of the experimental and modelled non-dimensional temperature proﬁles (TTf)/(TaTf) as a function of the non-dimensional height for different Rb values.

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Figure 6. Modelled temperature proﬁles at different distances from the heated wall.

3.2. Evaluation of DEM simulation

In Fig. 6 modelled temperature proﬁles for different distances from wall are shown. The ﬁrst plot refers to simulation with DT ¼ |TwallTair| ¼ 2 K, the second one to DT ¼ 10 K. It is evident that there is a temperature gradient only for positions near the heated facet (at 1 m from this one). At the centre of street canyon temperature proﬁle seems to be uniform, as a strong mixing is taking place. Calculated wind proﬁles appear to be unaffected by the imposition of different temperatures on the wall. 4. Conclusion

Results of the comparison of computational CFD simulations of urban street canyons with heated walls and wind tunnel data show a good agreement in terms of qualitative behaviour of temperature proﬁles. Results of CFD simulations of a real neighbourhood area where a street canyon was partially heated conﬁrm that vortex circulation and mixing tend to uniform temperature distribution, even in the case of asymmetric thermal boundary condition. Temperature patterns and their possible role in ﬂow modiﬁcation will be also investigated by addressing the study of the effect of UHI on urban stability by a top-down approach and by looking at the relationship between the heated surface and exchange processes at the top of street canyons. REFERENCES Barlow, J.F., Harman, I.N., Belcher, S.E., 2004. Scalar ﬂuxes from urban street canyon. Part I: Laboratory simulation. Bound. Layer Meteorol. 113, 369–385.

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Bentham, T., Britter, R., 2003. Spatially averaged ﬂow within obstacle arrays. Atmos. Environ. 37, 2037–2043. Cermak, J.E., 1984. Physical modelling of ﬂow and dispersion over complex terrain. Bound. Layer Meteorol. 30, 261–292. Chirizzi, C., 2004. Realizzazione ed analisi di Modelli Digitali di Elevazione (DEM), Tesi di Laurea, A.A.2003–2004, Universita` degli Studi di Lecce. Fluent Inc., 2003. FLUENT 6.2 user’s manual. http://www.ﬂuent.com Ichinose, T., Shimodozono, K., Hanaki, K., 1999. Measurement of thermal environment in Kyoto city and its prediction by CFD simulation. Atmos. Environ. 33, 3897–3909. Kastner-Klein, P., Rotach, M.W., 2004. Mean ﬂow and turbulence characteristics in an urban roughness sublayer. Bound. Layer Meteorol. 111, 55–84. Kondo, H., Liu, F., 1998. Study on the urban thermal environment obtained through onedimensional urban canopy model. J. Jpn. Soc. Atmos. Environ. 33(3), 179–192. Martilli, A., Clappier, A., Rotach, M.W., 2002. An urban surface exchange parameterisation for mesoscale models. Bound. Layer Meteorol. 104, 261–304. Murakami, S., Mochida, A., Kim, S., Ooka, R., 1997. Inﬂuence of land-use conditions on velocity and temperature ﬁelds over Kanto Plane. Trans. AIJ 491, 31–39. Oke, T.R., Maxwell, G.B., 1975. Urban heat island dynamics in Montreal and Vancouver. Atmos. Environ. 9, 191–200. Ratti, C., Di Sabatino, S., Caton, F., Britter, R., Brown, M., Burian, S., 2002. Analysis of 3-D urban databases with respect to pollution dispersion for a number of European and American cities. Water, Air and Soil Pollution: Focus 2, 459–469. Sini, J.-F., Anquetin, S., Mestayer, P.G., 1996. Pollutant dispersion and thermal effects in urban street canyons. Atmos. Environ. 30, 2659–2677. Snyder, W.H., 1972. Similarity criteria for the application of ﬂuid models to the study of air pollution meteorology. Bound. Layer Meteorol. 3, 113–134. Solazzo, E., Di Sabatino, S., Britter, R., 2005. Transfer processes in a simulated urban street canyon using morphological parameters from DEM analysis. Proceedings of the 5th International Conference on Urban Air Quality, Valencia 29–31 March 2005. Uehara, K., Murakami, S., Oikawa, S., Wakamatsu, S., 2000. Wind tunnel experiments on how thermal stratiﬁcation affects ﬂow in and above urban street canyons. Atmos. Environ. 34, 1553–1562. U.S. Environmental Protection Agency, 1981. Guidelines for ﬂuid modeling of atmospheric diffusion, EPA-600/8-81-009, EPA, Ofﬁce of Air Quality Planning and Standards, Research Triangle Park, North Carolina.

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Chapter 1.7 Evaluation of land surface scheme modiﬁcations on atmospheric transport and deposition patterns in Copenhagen metropolitan area Alexander Mahura, Alexander Baklanov, Steen Hoe, Jens H. Sorensen, Claus Petersen and Kai Sattler Abstract The spatial and temporal variability of the concentration and deposition ﬁelds simulated by the Local Scale Model Chain (LSMC) of ARGOS system (resulted from hypothetical accidental releases occurred in the metropolitan area of Copenhagen, Denmark) are evaluated. For that, the HIgh Resolution Limited Area Model (HIRLAM) is used to simulate meteorological ﬁelds taking into account urban-related modiﬁcations of the land surface scheme. In this study, several speciﬁc dates—typical, low, and high winds and high precipitation—are studied in details. 1. Introduction

The generated output of the Numerical Weather Prediction (NWP) models serves as an input in many applications, and especially in those related to atmospheric pollution tasks. In particular, the 3D meteorological ﬁelds are used to simulate atmospheric transport, dispersion, and deposition of short- and/or long-term releases of harmful matter. Although the resolution of current operational NWP models has increased up to almost 1 km resolution, the urban areas are still poorly resolved in models and especially in parameterizations for the surface and boundary layers. The EU-project entitled ‘‘Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure, FUMAPEX’’ (http:// fumapex.dmi.dk) suggested several approaches for urbanization of NWP models (Baklanov et al., 2005a, b). These (all having different computational requirements and time) included modiﬁcations of the effective roughness and urban heat ﬂuxes approach (Baklanov et al., 2005a, b), the

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building effect parameterization urban sub-layer module (Martilli et al., 2002), and the soil model for submeso scales urbanized version (Dupont et al., 2004; Otte et al., 2004). In this paper, following example of Baklanov et al. (2007), the ﬁrst mentioned inexpensive way of urbanization of NWP model, based on modiﬁcations of the ISBA scheme, was used. The diurnal variations of wind and temperature, ﬂuxes ﬁelds in the low surface layer produced by the DMI HIgh Resolution Limited Area Model (HIRLAM) model were evaluated taking into account modiﬁcations done in the Interaction Soil– Biosphere–Atmosphere (ISBA) scheme with respect to urban roughness and anthropogenic heat ﬂux (AHF). The variability in spatial distribution of concentration and deposition patterns produced by the Local Scale Chain Model of the ARGOS system was analyzed. Combined impacts were estimated for the Copenhagen (CPH) metropolitan area of Denmark and surroundings.

2. Methods 2.1. Meteorological modelling

The present DMI weather forecasting system (Sass et al., 2002) performs daily forecasts of meteorological ﬁelds employing the HIRLAM model (Unden et al., 2002) consisted of two nested models called DMIHIRLAM-S05 and -T15. Both models are identical, except for horizontal resolution (5 vs. 15 km) and boundaries of domains. The lateral boundary values for T15 (modelled every 6 h) are from the ECMWF model. The current operational DMI-model is semi-implicit, with semi-Lagrangian advection and leapfrog time stepping (with the semi-Lagrangian advection as optional). Physics such as short- and long-wave radiation, turbulence (except gravity wave drag), deep and shallow convection, cloud and precipitation generation, and air–sea/air–land interactions are parameterized and included. The operational model applies an implicit digital ﬁlter initialization technique in order to remove the large amplitude gravity wave oscillations in the ﬁrst few hours of forecast. There are also several experimental research DMI-HIRLAM models with a high resolution of 1.4 km. Currently, these are run for limited periods for selected territories mostly with a focus on the CPH metropolitan area. The main assumptions in these models are identical to the operational versions, and boundary conditions are taken from T15 and S05. Modiﬁcations for the urban effects, considered in the following section, were included into high-resolution runs.

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2.2. Land use classification and urban features

The land use classiﬁcation for the current version of DMI-HIRLAM is based on several datasets including high-resolution CORINE dataset. In HIRLAM some ﬁelds, such as roughness, albedo, vegetation type, orography, etc., are assumed to be constant in modelling domains during operational runs. These ﬁelds are once produced and stored in the climate generation ﬁles and are available for analyses and forecasts. The reclassiﬁcation of datasets into 20 major classes is performed following Sattler (2000). Then, it is reduced (based on the dominating and secondary class approach) into ﬁve major tiles of the ISBA land surface scheme (Navascue´s et al., 2003; Rodrı´ guez et al., 2003) in HIRLAM represented by water, ice, low vegetation, forest, and no vegetation. The characteristics (such as monthly leaf area index, albedo, roughness, etc.) of dominating types (from 20 classes) are used further in the ISBA calculation. In the scheme, the urban class was treated with characteristics of bare soils modiﬁed with accordance of urbanized features. 2.3. Pollution modelling

In our study, the ARGOS system (Hoe et al., 2002) with the Local Scale Model Chain (LSMC) (Mikkelsen et al., 1997) was employed to simulate atmospheric transport, dispersion, and deposition resulting from hypothetical accidental releases of radioactive matter from a selected location. It consists of the atmospheric dispersion model called RIso Mesoscale PUFF (RIMPUFF) model (Mikkelsen et al., 1984). It is consisted of plume rise, inversion, ground level reﬂection, and gamma dose formulations and algorithms. The model output includes the surface level air concentration, deposition, and gamma dose rates. As input, the 3D meteorological ﬁelds produced by the DMIHIRLAM-U01 model were used. For all selected dates, the release point is located in the CPH metropolitan area, the duration of release is equal to 12 h (i.e., starting at 03 UTC and ending at 15 UTC), the radionuclide considered is 137Cs, and the emission rate is equal to 1011 Bq s 1. 3. Results and discussion

Based on long-term runs of the DMI-HIRLAM high-resolution model during summer of 2005, several speciﬁc dates were selected using the wind velocity and direction from the surface and radiosounding observations as criteria. These dates have represented typical winds (18 Jun), low winds

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(19 Jun), high winds (4 Jun), and high precipitation (30 Jul) conditions for atmospheric transport over the Island of Zeeland, Denmark where CPH is situated. In these runs in the ISBA scheme, the roughness for cells (where the urban class was represented in the modelling domain) was increased up to 2 m; and second, the contribution of AHF up to 200 W m 2 was incorporated into the scheme. The generated meteorological output was further used to simulate atmospheric transport, dispersion, and deposition of pollution over the metropolitan area. In this paper, the diurnal cycle (i.e., 00 UTC+24-h forecast) variability of meteorological and pollution (concentration and deposition) output ﬁelds were analyzed for mentioned dates. The meteorological ﬁelds’ simulations for the urbanized areas were driven using boundary conditions of the DMI-HIRLAM-S05 model. These conditions were used as input for simulation of meteorological ﬁelds for the DMI-HIRLAM research version with resolution of 1.4 km, which includes the CPH and Malmo¨ metropolitan areas and surroundings. The diurnal cycle of meteorological variables such as wind velocity (at 10 m) and temperature (at 2 m) as well as concentration and deposition patterns were analyzed comparing outputs of the control run vs. runs with modiﬁed parameters for urban class. At each UTC term, the 2D (values in latitude vs. longitude gridded domain) difference ﬁelds for mentioned variables were produced/analyzed by subtracting outputs from the control run without any changes made vs. run with changes made for roughness and AHF. It was found, that for the low wind conditions (LWC) date, the modiﬁed run showed increase in temperature at 2 m over the urban areas. This increase was more than 11C during 18–06 UTC (with a maximum of 1.61C at 04–05 UTC), and it was less than 0.91C during 07–17 (with a minimum of 0.51C at 16–17 UTC). On average, decrease in wind velocity at 10 m was around 2 m s 1 during nighttime, and it was around 1.5 m s 1 during daytime, with a maximum of up to 5 m s 1. An example is shown in Fig. 1. For the typical wind conditions (TWC) date, the urbanized run also showed an increase in temperature at 2 m, but this increase was substantially lower than for LWC, i.e., on average, during 06–17 UTC it was less than 0.31C over most parts of the urbanized areas. During 18–05 UTC, it was within a range of 0.4–0.71C with a maximum of 1.11C at 20 UTC. On average, the decrease in wind velocity at 10 m was around 1.7 m s 1 during nighttime, and it varied 1–2 m s 1 during daytime, with a maximum of up to 4 m s 1. For the high wind conditions (HWC) date, during all terms the temperature increase over the urbanized areas was always less than 0.21C

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Figure 1. Difference plots (between outputs of the DMI-HIRLAM-U01 control vs. urbanized runs) for temperature at 2 m (top) and for wind velocity at 10 m (bottom) at 03 UTC forecast on 19 May 2005.

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(with a maximum of 0.81C at 04 UTC). On average, the decrease in wind velocity at 10 m was less than 0.5 m s 1 during evening hours and nighttime, and it became slightly larger after 10 UTC reaching a maximum of 1.6 m s 1 at 15 UTC. For the high precipitation conditions (HPC) date, the synoptic situation had been characterized by a relatively continuous precipitation pattern during daytime. The temperature differences over the urban areas varied 0.2–11C. A clear pattern on a diurnal cycle was not identiﬁed because it was also signiﬁcantly affected by the frontal passage. The same situation was characteristic for wind at 10 m (with an observed maximum of 1.5 m s 1). Only for the LWC date, the differences in concentration and deposition patterns were signiﬁcant as shown in Fig. 2. As seen it has been characterized by a wider spreading of the contaminated cloud over the urbanized area and surroundings, and hence, affecting a larger group of population. For the TWC and HWC dates, there are no signiﬁcant differences between the control vs. urban runs, due to smaller changes in meteorological variables’ values on a scale of diurnal cycle (as described above) as well as smaller sizes of urbanized areas affected by these changes. For the HPC date, the situation is more complex for analysis due to substantial removal of pollution from the contaminated cloud at the initial stages of emissions into the atmosphere.

4. Conclusion

In this study, we evaluated diurnal variability of meteorological and pollution patterns based on changes (as a function of roughness and AHF) in the ISBA land surface scheme. The four speciﬁc—low, high, and typical winds, and high precipitation—cases/dates during summer of 2005 were analyzed. It was found that changes in roughness and AHF modify the structure of the surface layer wind and temperature ﬁelds over urban areas. On average, the decrease in wind velocity is the highest (1.5 m s 1) for LWC, and it is the lowest (less than 0.5 m s 1) for HWC. Similarly, the average increase in temperature is the highest (more than 0.51C) for LWC, and it is the lowest (less than 0.31C) for HWC and HPC. The signiﬁcant differences in concentration and deposition patterns were observed for LWC. For TWC and HWC, there are no large differences in spatial and temporal structure of pollution-related patterns. For HPC, this impact was not observed due to substantial removal of pollution at the initial stages.

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Figure 2. Total deposition patterns of 137Cs at 17 UTC forecast on 19 May 2005 for the DMI-HIRLAM-U01 control (top) vs. urbanized (bottom) runs resulting from hypothetical accidental release.

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Discussion

E. Batchvarova: In the 1.4 km grid resolution of HIRLAM, do you assign the dominant class of land use to the grid cell or do you combine/average between several classes? A. Mahura: For this resolution, in the ISBA land surface scheme of HIRLAM model, the ﬁrst dominating class was assigned for each grid cell. In these urban grid cells situated over the Copenhagen metropolitan area, the urban class was treated with characteristics similar to the bare soils’ tail of the scheme. E. Genikhovich: In your talk you presented results from sensitivity studies rather than a validation of the model. Do you have results of a validation against ﬁeld measurement data on scales corresponding to the accidental release of atmospheric pollutants from a point source? A. Mahura: To simulate atmospheric transport, dispersion, and deposition of caesium resulted from hypothetical accidental releases in the Copenhagen metropolitan area, the dispersion RIMPUFF model was employed. It is a part of the ARGOS system. This model, at least, was veriﬁed vs. the Chernobyl, ETEX, and Algeciras data and it showed a relatively good performance of the model on local and mesoscales.

ACKNOWLEDGMENTS

Thanks to the DMI Computer Support and HIRLAM group for the collaboration. Financial support of this study came from EU FUMAPEX (EVK4-CT-2002-00097) and HIRLAM international projects. REFERENCES Baklanov, A., Ha¨nninen, O., Slørdal, L.H., Kukkonen, J., Bjergene, N., Fay, B., Finardi, S., Hoe, S.C., Jantunen, M., Karppinen, A., Rasmussen, A., Skouloudis, A., Sokhi, R.S., Sørensen, J.H., Ødegaard, V., 2007. Integrated systems for forecasting urban meteorology, air pollution and population exposure. Atmos. Chem. Phys. 7, 855–874. Baklanov, A., Mahura, A., Nielsen, N.W., Petersen, C., 2005a. Approaches for urbanization of DMI-HIRLAM NWP model. HIRLAM Newslett. 49, 61–75. Baklanov, A., Mestayer, P., Clappier, A., Zilitinkevich, S., Joffre, S., Mahura, A., Nielsen, N.W., 2005b. On the parameterisation of the urban atmospheric sublayer in meteorological models. Atmos. Chem. Phys. Discuss. 5, 12119–12176.

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Dupont, S., Otte, T.L., Ching, J., 2004. Simulation of meteorological ﬁelds within and above urban and rural canopies with a mesoscale model (MM5). Boundary-Layer Meteorol. 113, 111–158. Dupont, S., Mestayer, P., Guilloteau, E., Berthier, E., Andrieu, H., 2005. Parameterization of the urban water budget with the submesoscale soil model. J. Appl. Meteorol. and Climatol. 45(4), 624–648. Hoe, S.C., Muller, A., Gering, F., Thykier-Nielsen, S., Sorensen, J.H., 2002. ARGOS Decision Support System for Nuclear Emergencies. In: Proceeding of the Radiation Protection and Shielding Division Topical Meeting, 14–17 April 2002, Santa Fe, New Mexico, USA. Martilli, A., Clappier, A., Rotach, M., 2002. An urban surface exchange parameterisation for mesoscale models. Boundary Layer Meteorology 104, 261–304. Mikkelsen, T., Larsen, S., Thykier-Nielsen, S., 1984. Description of the Riso puff diffusion model. Nucl. Technol. 67, 56–65. Mikkelsen, T., Thykier-Nielsen, S., Astrup, P., Santabarbara, J., Sorensen, J., Rasmussen, A., Robertson, L., Ullerstig, A., Deme, S., Martens, R., Bartiz, J., Pasler-Sauer, J., 1997. MET-RODOS: A comprehensive atmospheric dispersion module. Radiat. Prot. Dosim. 73, 45–56. Navascue´s, B., Rodrı´ guez, E., Ayuso, J.J., Ja¨rvenoja, S., 2003. Analysis of surface variables and parameterization of surface processes in HIRLAM. Part II: Seasonal assimilation experiment, Norrko¨ping. HIRLAM Techn. Rep. 59, 38. Otte, T.L., Lacser, A., Dupont, S., Ching, J., 2004. Implementation of an urban canopy parameterization in a mesoscale meteorological model. J. Applied Meteorol. 43, 1648–1665. Rodrı´ guez, E., Navascue´s, B., Ayuso, J.J., Ja¨rvenoja, S., 2003. Analysis of surface variables and parameterization of surface processes in HIRLAM. Part I: Approach and veriﬁcation by parallel runs, Norrko¨ping. HIRLAM Tech. Rep. 58, 52. Sass, B., Nielsen, N.W., Jørgensen, J.U., Amstrup, B., Kmit, M., Mogensen, K.S., 2002. The operational DMI-HIRLAM system—2002-version. DMI Tech. Rep. 02-05, 60. Sattler, K., 2000. New high resolution physiographic data and climate generation in the HIRLAM forecasting system at DMI, an overview. HIRLAM Newslett. 33, 96–100. Unden, P., Rontu, L., Ja¨rvinen, H., Lynch, P., Calvo, J., Cats, G., Cuhart, J., Eerola, K., et al. 2002. HIRLAM-5 scientiﬁc documentation. December 2002, HIRLAM-5 Project Report, SMHI.

Regional and intercontinental modelling Chairpersons: Carlos Borrego Douw Steyn Sven-Erik Gryning Nadine Chaumerliac Adolf Ebel Rapporteurs: Joana Valente Bernd Heinold Oswald Knoth Maya Milliez Ralf Wolke

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Chapter 2.1 Modeling of secondary aerosols in Switzerland in summer 2003 S. Andreani-Aksoyoglu, J. Keller, A.S.H. Prevot, U. Baltensperger and J. Flemming Abstract The formation and transport of secondary aerosols during a 4-day period in summer 2003 were studied using the three-dimensional air quality model CAMx (Comprehensive Air quality Model with eXtensions) and meteorological model MM5 over an area covering Switzerland and part of the surrounding countries. The modeled components are particulate sulfate, nitrate, ammonium, and secondary organic aerosols (SOA) with a particle diameter smaller than 2.5 mm. Higher concentrations are predicted in southern Switzerland and northern Italy. Differences between the regions in the north and the south of the Alps are discussed with respect to the aerosol concentrations and to the sensitivity of aerosol formation. Sensitivity tests using reduced NH3 and NOx emissions suggest that in northern Switzerland secondary aerosol formation is unlikely to be limited by NH3 but rather by HNO3. On the other hand, aerosol formation around Milan seems to be similarly dependent on HNO3 and NH3 most of the time. However, there are times when limitation by NH3 is stronger. The contribution of biogenic sources to SOA was predicted to be rather high, about 70% in the north, matching the measurements whereas it was lower in southern Switzerland (40%). In northern Italy, anthropogenic sources contribute much more to SOA formation. 1. Introduction

Atmospheric aerosols play an important role in climate change by modifying the radiative balance of the atmosphere (IPCC, 2001) and they are also known to have adverse health effects. The legal threshold for the yearly average for PM10 (particles smaller than 10 mm in aerodynamic diameter d) is 20 mg m 3 in Switzerland. As a short-term threshold, the

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concentration averaged over 24 h may exceed 50 mg m 3 only once a year. PM10 concentrations in Switzerland frequently exceed the threshold values, especially in the south of the Alps. While the formation of gaseous pollutants such as ozone is well known, there is still a lack of knowledge about aerosol formation. Gehrig and Buchmann (2003) evaluated the long-term PM2.5 and PM10 measurements at various Swiss sites. The chemical composition of atmospheric PM was investigated by Hueglin et al. (2005). Understanding the partitioning behavior of semivolatile species between the gas and aerosol phases can help us predict how changes in anthropogenic and biogenic activity will inﬂuence the formation of aerosols in the atmosphere. In recent years, several air quality models have been upgraded to include aerosol dynamical processes (Schell et al., 2001; Hass et al., 2003; Held et al., 2004; Zhang et al., 2004). Recent experimental evidence for oligomerization reactions in organic aerosols indicated the need to readdress the current assumptions in models about the partitioning of oxidation products (Kalberer et al., 2004). Applications of aerosol models are partly limited due to the lack of speciated aerosol measurements at high temporal and spatial resolution. Most of the aerosol model applications have been performed in the United States and Canada (Held et al., 2004; Yin et al., 2004). In Europe, there are relatively few applications (Bessagnet et al., 2004; Cousin et al., 2005). In Switzerland, there is hardly any model study on aerosols yet (Andreani-Aksoyoglu et al., 2003, 2004). In view of the forthcoming European legislation on particles, air quality simulations including aerosol processes are urgently needed. This study on modeling of secondary aerosols will provide more information in this ﬁeld.

2. Modeling method

In this study, the three-dimensional photochemical model CAMx (Comprehensive Air Quality Model with Extensions, version 4.11s) was used with two nested domains (Environ, 2003). The coarse domain is 945 km 783 km with a resolution of 27 km 27 km. The ﬁne domain has a resolution of 9 km 9 km and covers all Switzerland and the greater Milan area. There are 10 s-layers in a terrain-following coordinate system (Lambert Conic Conformal). Simulations started on August 4, 2003 at 0000 UTC and ended on August 7, at 2400 UTC. Meteorological data were calculated by the MM5 meteorological model (PSU/NCAR, 2004). MM5 was initialized by data of the Alpine Model (aLMo) of MeteoSwiss. The emission inventory was prepared by compiling European and Swiss

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anthropogenic emissions from various data sources. Using land-use and meteorological data, biogenic emissions were calculated by means of temperature and irradiance dependent algorithms. Initial and boundary conditions were obtained from the regional European model REM-3/ CALGRID. Calculations of aerosols with do2.5 mm were performed with the ﬁne/coarse option of the aerosol module. Primary particle emissions were not considered due to the lack of a particle emission inventory. The CBM-IV mechanism with the extensions for aerosol modeling (mechanism 4) was used for the simulations of this study (Gery et al., 1989). Dry deposition of gases is based on the resistance model of Wesely (1989). The aerosol precursors are supplied to the aerosol chemistry module, which performs the following processes: aqueous sulfate and nitrate formation in cloud water using the RADM aqueous chemistry algorithm (Chang et al., 1987), partitioning of condensable organic gases to secondary organic aerosols to form a condensed organic solution phase using a semivolatile equilibrium scheme called SOAP (Strader et al., 1998), partitioning of inorganic aerosol constituents (sulfate, nitrate, ammonium, sodium, and chloride) between the gas and particle phases using the ISORROPIA thermodynamic module (Nenes et al., 1998).

3. Results and discussion 3.1. Secondary aerosols

The modeled secondary PM2.5 concentrations averaged over the entire period varied between 5 and 10 mg m 3. As seen in Fig. 1, the highest afternoon secondary aerosol mass concentrations were predicted in the polluted area of Milan. The relatively higher levels in the southern part of Switzerland are probably due to the vicinity of the Milan area with high emissions of gaseous precursors for secondary aerosols and the thermal winds towards the Alps advecting also high ozone concentrations. The diurnal variations of predicted individual particulate components in Fig. 2 show a good correlation between ammonium and nitrate concentrations at locations in the north such as Zurich whereas sulfate levels remain almost constant, around 3 mg m 3. These results suggest that in northern Switzerland, enough ammonia exists to neutralize sulfate and then to produce ammonium nitrate. On the other hand, at the southern site Lugano, nitrate levels are very low, and sulfate and ammonium are well correlated. Around Milan, aerosol concentrations are higher than in Switzerland. The average secondary PM2.5 during the studied period is about 15 mg m 3. Especially sulfate concentrations are higher than those

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in the north. At night and in the morning, the correlation between nitrate and ammonium at both Zurich and Milan indicates the formation of ammonium nitrate. The sulfate and ammonium peaks at southern sites in the afternoon show ammonium sulfate production. The model results are compared with all the available ambient aerosol measurements (Fisseha et al., 2005; Hueglin et al., 2005). Measurements of inorganic aerosols are available only for the years 1998 and 2002. The measured PM2.5 concentrations were higher in August 2003 than in previous years. Assuming that the relative increase in measured PM2.5 was the same also for the inorganic aerosol concentrations, the sum of sulfate, nitrate and ammonium for 2003 was estimated. These estimations are close to the model predictions. In order to make a better validation, more detailed measurements of aerosol species are needed. Calculations suggest that the contribution of biogenic SOA to total SOA is rather high, about 70% in the north. 14C measurements of different carbonaceous particle fractions from ambient aerosols showed that the water soluble organic compounds (WSOC) comprised 65–82% biogenic carbon at Zurich in summer 2002 (Szidat et al., 2004). The reported SOA concentration of 2.3 mg m 3 and the high biogenic fraction (65–82%) agree very well with the model prediction of 2.7 mg m 3 and

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65% biogenic contribution in Zurich. On the other hand, the biogenic contribution to SOA is substantially lower in southern Switzerland (about 40%) and around the polluted area in northern Italy (15–25%). In order to check for the effects of initial and boundary conditions (IC and BC, respectively) two more simulations using different IC and BC values for particles were carried out. The concentrations of gaseous species were the same in all cases. In the ﬁrst case (set 1), no particle concentration was given in the IC and BC ﬁles; therefore, the model uses the constant default values (1.10 9 mg m 3) for particles. In the second case (set 2), annual means of long-term measurements at a rural site were used (Hueglin et al., 2005). The last case (set 3) is the base case where IC and BC were taken from the REM3/CALGRID output and the data are variable in time and space. Results of these tests are shown in Fig. 3 for Zurich. The use of low data (set 1) leads to the lowest values during the ﬁrst two days. On the other hand, results of simulations using long-term measurements (set 2) are slightly higher than the base case (set 3). Differences between various cases are evident for the ﬁrst 36 h. Later on, the results of all three cases are similar.

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3.2. Sensitivity to NH3 and HNO3

The reaction of ammonium with sulfate leads to the formation of ammonium sulfate. After all available sulfate is neutralized, ammonia reacts with nitric acid leading to the formation of ammonium nitrate. The concentration of ammonia is, therefore, crucial for aerosol formation. At low ammonia levels, formation of ammonium nitrate is limited by the availability of ammonia. On the other hand, in regions with high ammonia concentrations, ammonium nitrate formation may depend on the availability of nitric acid. In order to investigate the sensitivity of aerosol formation to emissions, additional simulations were carried out with 50%, 70%, and 90% emissions of NH3 and NOx separately. The sum of the secondary aerosol concentrations was calculated for each case. The relative changes in the diurnal variation of total secondary aerosol mass concentrations due to various reductions of NH3 and NOx emissions give detailed information about the sensitivity of aerosol formation on emissions (Fig. 4). Under the conditions used in this study, aerosol formation in Switzerland is unlikely to be limited by ammonia. In the region of Milan on the other hand, aerosol formation seems to be more sensitive to

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ammonia levels. However, it has to be kept in mind that such sensitivity analyses strongly depend on NH3 emissions, which have high uncertainties. The time when the changes occur differs from one site to the other. In Zurich, in the north of the Alps, reduction in aerosol levels takes place mainly at night and during the morning hours and corresponds to the nitrate peak. In Lugano, south of the Alps, a relatively small decrease in the aerosol concentration due to reduced emissions corresponds to sulfate peaks in the afternoon. The reduction of NOx emissions is predicted to be

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more effective to reduce secondary aerosol mass concentrations in Zurich than in Lugano. In Milan, in northern Italy, the results look different than in the two locations in Switzerland. Both, the nitrate peak in the morning and the sulfate peak in the afternoon, decrease when ammonia emissions are reduced. On the other hand, the reduction of NOx emissions leads to a decrease in nitrate in the morning. The aerosol mass increases at noon due to increasing particulate sulfate concentrations. Ozone concentrations increase when NOx emissions are reduced as expected at urban areas with high emissions. The increase in ozone and OH concentrations is probably the cause of the increase in particulate sulfate levels. Aerosol formation seems to be similarly dependent on HNO3 and NH3 most of the time, although there are times when limitation is stronger by ammonia.

4. Conclusions

The modeled average secondary aerosol mass concentrations in Switzerland were predicted to vary between 5 and 10 mg m 3 depending on the location. Comparisons with a few available measurement data suggest that the CAMx model is able to reproduce the secondary aerosol formation and distribution reasonably well. As suggested also by the PM2.5 measurements, the modeled secondary particle concentrations are higher in southern Switzerland. This is most probably due to the vicinity of the polluted Milan area with high emissions of precursors and the thermal winds advecting the pollutants towards the Alps as well as to higher solar radiation increasing photochemical activity. Sensitivity tests indicate that the initial and boundary conditions could affect the modeled particle concentrations especially during the ﬁrst 36 h. Formation of inorganic aerosols in northern Switzerland is predicted to be limited by nitric acid and therefore by NOx emissions, while it is similarly dependent on nitric acid and ammonia around Milan. The model prediction of a high biogenic SOA fraction (about 70%) in northern Switzerland is supported by the 14C data. This high fraction can be attributed to the large amount of monoterpene emissions in the Swiss Plateau. In southern Switzerland the biogenic contribution to SOA formation drops to 40%. In northern Italy anthropogenic sources contribute much more to SOA formation, supporting our previous calculations. For further studies in this ﬁeld, a complete emission inventory including the primary particle emissions as well as more aerosol measurements are needed.

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Discussion

M. Kaasik: S. Andreani-Aksoyoglu:

How did you calculate the biogenic VOC emissions? Biogenic emissions were calculated using temperature and irradiance dependent algorithms as described in AndreaniAksoyoglu, Keller, 1995. J. Atmos. Chem. 20, 71–87; and Keller et al., 1995. Air Pollution III, WIT Publications. We have a detailed speciation of forest types, not only as deciduous and coniferous, which is very important for biogenic emissions.

ACKNOWLEDGMENTS

We are grateful to the following people for providing various data: F. Schubiger and C. Voisard (MeteoSwiss), R. Stern (FUB), A. Graff (UBA), M. van Loon (TNO), R. Zbinden, M. Keller and J. Heldstab (INFRAS), Th. Kuenzle and B. Rihm (METEOTEST), S. Szidat (University of Bern), C. Hueglin (EMPA), and R. Weber (BUWAL). This project was supported by BUWAL as well as the SBF in the framework of the Network of Excellence ACCENT.

REFERENCES Andreani-Aksoyoglu, S., Keller, J., Dommen, J., Pre´voˆt, A.S.H., 2003. Modelling of air quality with CAMx: A case study in Switzerland. Water, Air & Soil Pollution: Focus 3, 281–296. Andreani-Aksoyoglu, S., Pre´voˆt, A.S.H., Baltensperger, U., Keller, J., Dommen, J., 2004. Modeling of formation and distribution of secondary aerosols in the Milan area (Italy). J. Geophys. Res. 109, doi:10.1029/2003JD004231. Bessagnet, B., Hodzic, A., Vautard, R., Beekmann, M., Cheinet, S., Honore, C., Liousse, C., Rouil, L., 2004. Aerosol modeling with CHIMERE: Preliminary evaluation at the continental scale. Atmos. Environ. 38, 2803–2817. Chang, J.S., Brost, R.A., Isaksen, I.S.A., Madronich, S., Middleton, P., Stockwell, W.R., Walcek, C.J., 1987. A three-dimensional eulerian acid deposition model: Physical concepts and formulation. J. Geophys. Res. 92, 14681–614700. Cousin, F., Liousse, C., Cachier, H., Bessagnet, B., Guillaume, B., Rosset, R., 2005. Aerosol modeling and validation during ESCOMPTE 2001. Atmos. Environ. 39, 1539–1550. Environ., 2003. User’s Guide, Comprehensive Air Quality Model with Extensions (CAMx), Version 4.00. Environ. International Corporation, California.

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Fisseha, R., Dommen, J., Gutzwiller, L., Weingartner, E., Gysel, M., Emmenegger, C., Kalberer, M., Baltensperger, U., 2005. Seasonal and diurnal characteristics of water soluble inorganic compounds in the gas and aerosol phase in the Zurich area. Atmos. Chem. Phys. Discuss. 5, 5809–5839. Gehrig, R., Buchmann, B., 2003. Characterizing seasonal variations and spatial distribution of ambient PM10 and PM2.5 concentrations based on long-term Swiss monitoring data. Atmos. Environ. 37, 2571–2580. Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C., 1989. A photochemical kinetics mechanism for urban and regional scale computer modeling. J. Geophys. Res. 94, 925–956. Hass, H., Loon, M. v., Kessler, C., Stern, R., Matthijsen, J., Sauter, F., Zlatev, Z., Langner, J., Foltescu, V., Schaap, M., 2003. Aerosol modeling: Results and intercomparison from European regional-scale modeling systems. A contribution to EUROTRAC-2 subproject GLOREAM. Held, T., Ying, Q., Kaduwela, A., Kleeman, M., 2004. Modeling particulate matter in the San Joaquin Valley with a source-oriented externally mixed three-dimensional photochemical grid model. Atmos. Environ. 38, 3689–3711. Hueglin, C., Gehrig, R., Baltensperger, U., Gysel, M., Monn, C., Vonmont, H., 2005. Chemical characterization of PM2.5, PM10 and coarse particles at urban, near-city and rural sites in Switzerland. Atmos. Environ. 39, 637–651. IPCC, 2001. Climate change 2001: The scientiﬁc basis. Contribution of working group I to the third assessment report of the international panel on climate change. Cambridge University Press, Cambridge, UK, New York, USA. Kalberer, M., Paulsen, D., Sax, M., Steinbacher, M., Dommen, J., Pre´voˆt, A.S.H., Fisseha, R., Weingartner, E., Frankevic, V., Zenobi, R., Baltensperger, U., 2004. Identiﬁcation of polymers as major components of atmospheric organic aerosols. Science 303, 1659–1662. Nenes, A., Pandis, S.N., Pilinis, C., 1998. ISORROPIA: A new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosols. Aquat. Geoch. 4, 123–152. PSU/NCAR, 2004. MM5 Version 3 Tutorial Presentations. Schell, B., Ackermann, I.J., Hass, H., Binkowski, F.S., Ebel, A., 2001. Modeling the formation of secondary organic aerosol within a comprehensive air quality model system. J. Geophys. Res. 106, 28275–28293. Strader, R., Gurciullo, C., Pandis, S.N., Kumar, N., Lurmann, F.W., 1998. Development of gas-phase chemistry, secondary organic aerosol, and aqueous-phase chemistry modules for PM modeling. Sonoma Technology, Inc., Petaluma, CA, STI-97510-1822-FR. Szidat, S., Jenk, T.M., Gaggeler, H.W., Synal, H.-A., Fisseha, R., Baltensperger, U., Kalberer, M., Samburova, V., Reimann, S., Kasper-Giebl, A., Hajdas, I., 2004. Radiocarbon (14C)-deduced biogenic and anthropogenic contributions to organic carbon (OC) of urban aerosols from Zurich, Switzerland. Atmos. Environ. 38, 4035–4044. Wesely, M.L., 1989. Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models. Atmos. Environ. 23, 1293–1304. Yin, D., Jiang, W., Roth, H., Giroux, E., 2004. Improvement of biogenic emissions estimation in the Canadian Lower Fraser Valley and its impact on particulate matter modeling results. Atmos. Environ. 38, 507–521. Zhang, Y., Pun, B., Vijayaraghavan, K., Wu, S.-Y., Seigneur, C., Pandis, S.N., Jacobson, M.Z., Nenes, A., Seinfeld, J. H., Binkowski, F.S., 2004. Development and application of the model of aerosol dynamics, reaction, ionization and dissolution (MADRID). J. Geophys. Res. 109, doi:10.1029/2003JD003501.

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Chapter 2.2 Application of the CMAQ mercury model for U.S. EPA regulatory support$ O. Russell Bullock Jr. and Thomas Braverman Abstract On March 15, 2005, the U.S. Environmental Protection Agency (EPA) issued its Clean Air Mercury Rule (CAMR) to permanently cap and reduce mercury emissions from coal-ﬁred Electric Generating Units (EGUs). Part of the development of the CAMR involved simulations of atmospheric mercury emission, transport and deposition across a large part of North America using a special version of the Community Multi-scale Air Quality (CMAQ) model to assess the expected decrease in mercury deposition from various emission control options. CMAQ model simulations of a 2001 base case and a 2020 case with full implementation of the CAMR and the separate Clean Air Interstate Rule (CAIR) showed that mercury emissions from EGUs are expected to decrease by 48% by 2020. Emission reductions of 68% were shown for reactive gaseous mercury, the form of mercury most readily deposited from the atmosphere. Simulated wet deposition of atmospheric mercury for 2001 was compared to observations from the Mercury Deposition Network (MDN). The CMAQ modeling was able to resolve about 60% of the observed site-to-site variance. The modeling also showed that the reduction in mercury deposition expected by 2020 from the CAIR and CAMR is similar to that which would have been obtained by completely eliminating mercury emissions from EGUs in the U.S. in 2001. Most of the emission reductions from the CAMR are in the form of elemental mercury. The CMAQ modeling showed nearly all of these emissions were exported from the modeling domain. Thus, it is expected that the CAMR will result in a reduction of the transport of mercury to other parts of the world. $

This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reﬂect their policies or views.

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86 1. Introduction

This paper describes the air quality modeling performed by the U.S. Environmental Protection Agency (EPA) as part of a beneﬁts analysis for the Clean Air Mercury Rule (CAMR). EPA used the Community Multiscale Air Quality (CMAQ) model to simulate mercury (Hg) deposition for a 2001 base case, a 2001 test case with Hg emissions from Electric Generating Units (EGUs) removed from the simulation, and a 2020 case assuming implementation of both the Clean Air Interstate Rule (CAIR) and the CAMR. CMAQ is a three-dimensional Eulerian-type air quality model designed to estimate pollutant concentrations and depositions over a range of spatial scales from urban to continental. For this analysis, CMAQ was applied for an area covering all of the contiguous United States and most of southern Canada and northern Mexico. The boundary and initial pollutant concentrations for all cases were developed from a simulation of 2001 using the GEOS-Chem model, a three-dimensional global atmospheric chemistry and transport model (Yantosca, 2005). Estimates of future-year Hg emissions were obtained through application of EPA’s Integrated Planning Model (IPM) which is described at www.epa.gov/airmarkets/epa-ipm. 2. Emissions inventories and estimated emissions reductions

The CAMR Emissions Inventory Technical Support Document (TSD) provides detailed information on development of the 2001 and 2020 emissions inventories used in this air quality modeling study (U.S. EPA, 2005). These inventories are summarized in Table 1. Approximately 115 Table 1. Summary of mercury emissions by species: 2001 and 2020 Emissions source

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26.26 37.85 5.05 69.16

20.58 13.33 1.53 35.44

1.73 7.60 0.96 10.29

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2020 (with CAIR and CAMR) EGUs 17.65 Non-EGU point 28.03 Non-point 5.69 Total, all sources 51.37

6.57 10.37 1.30 18.24

0.83 6.61 0.77 8.21

25.05 45.01 7.76 77.82

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tons of Hg was emitted from all U.S. sources in 2001 based on the EPA inventory. EGUs emitted 48.6 tons, or 42.3% of Hg emissions from all sources during this base year. About 20.6 tons of reactive gaseous Hg (RGM) was emitted by these EGUs, comprising 42.4% of their total Hg emissions in all forms. The 2020 emissions inventory accounts for increases in economic activity and population growth between 2001 and 2020, as well as implementation of regulatory policies from Maximum Achievable Control Technology (MACT) standards (primarily on non-EGU sources) and CAIR and CAMR controls as applied to EGUs in the U.S. Total anthropogenic Hg emissions in 2020 are estimated at roughly 78 tons, reﬂecting a net reduction of about 37 tons (or 32%) from 2001 levels. The reduction in Hg emissions from EGUs is over 23 tons, a 48% reduction from 2001 levels. EGU emissions of RGM, the most rapidly deposited form of Hg, are reduced by 14 tons or 68% relative to 2001.

3. Model description

The CMAQ modeling system is a comprehensive three-dimensional gridbased Eulerian air quality model designed to operate on a range of domain sizes from urban to continental (Byun and Schere, 2006). CMAQ reﬂects the state-of-the-science in addressing the atmospheric processes critical for simulating oxidant precursors and non-linear chemistry associated with the transformation and deposition of Hg. CMAQ version 4.3 was used as a basis for Hg modeling for CAMR. Various updates to the base model were made to improve the underlying science and address comments from peer review. The updates in Hg chemistry used for CAMR from those described in the ﬁrst published version of the CMAQ Hg model (Bullock and Brehme, 2002) are as follows: (1) the gaseous elemental Hg (Hg0) reaction with H2O2 assumes the formation of RGM rather than particulate Hg (PHg); (2) the gaseous Hg0 reaction with ozone assumes the formation of 50% RGM and 50% PHg rather than 100% PHg; (3) the gaseous Hg0 reaction with OH assumes the formation of 50% RGM and 50% PHg rather than 100% PHg; and (4) the kinetic rate constant for the gaseous Hg0+OH reaction was lowered slightly to 7.7 10 14 cm3 molecules 1 s 1. CMAQ requires a variety of input ﬁles that contain information pertaining to the modeling domain and simulation period. These include hourly emissions estimates and meteorological data in every grid cell as well as a set of pollutant concentrations to initialize the model and to specify concentrations along the boundaries of the modeling domain.

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Key science options for the CMAQ as applied for the CAMR analysis include: Gas-phase chemical solver: Euler Backward Iterative (EBI) scheme Advection scheme (vertical and horizontal): Piecewise Parabolic Method (PPM) Vertical diffusion: K-theory eddy diffusivity with 1 m2 s 1 minimum Dry deposition: M3DRY module with Pleim-Xiu land surface model Aqueous chemistry: RADM bulk scheme Cloud scheme: RADM cloud scheme Vertical coordinate: Terrain-following sigma-pressure coordinate 3.1. CMAQ modeling domain and configuration

The CMAQ horizontal modeling domain employed for this study encompasses all of the lower 48 states and a large part of Canada and Mexico and consists of 16,576 grid cells with dimensions of 36 km by 36 km. The vertical modeling domain contains 14 layers extending from the surface to the 10 kPa (100 mbar) pressure level in a sigma-pressure terrain-following coordinate system. 3.2. Time period modeled for mercury deposition

CMAQ was run for a full calendar year using 2001 meteorology and boundary concentration data for each of the scenarios modeled. The overall wall-clock time for completing an annual simulation was reduced by dividing the year into two 6-month periods, which were simulated in parallel. One simulation was for January through June and the other was for July through December. Each 6-month simulation included a 10-day ‘‘spin-up’’ period designed to minimize the inﬂuence of the initial condition used at the start of the simulation. Model predictions from these spin-up periods were not used in analyses of the modeling results. The meteorological conditions, initial conditions and boundary conditions (BCs) were the same for each of the annual scenarios modeled. 3.3. Meteorological inputs to CMAQ

The CMAQ model requires a speciﬁc suite of meteorological input ﬁles in order to simulate the physical and chemical processes affecting Hg and the other pollutants. Meteorological input ﬁles were derived from a simulation of the Pennsylvania State University/National Center for Atmospheric Research Mesoscale Model (Grell et al., 1994) for the entire year

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of 2001. This limited-area, non-hydrostatic model is commonly referred to as MM5. For this analysis, version 3.6.1 of MM5 was used. The MM5 horizontal domain consisted of 36 36 km grid cells just like the CMAQ domain, except the MM5 domain was expanded by six grid cells on all four sides to avoid boundary effects from the meteorological model being incorporated into the CMAQ chemical modeling. The MM5 vertical domain was comprised of 34 layers interspersed within the 14 CMAQ layers using the same sigma-pressure coordinate system. Some of the key model physics options employed were as follows:

Cumulus parameterization: Kain-Fritsch Planetary boundary layer scheme: Pleim-Chang Explicit moisture scheme: Reisner 2 Radiation scheme: RRTM longwave scheme Land surface model: Pleim-Xiu Four-dimensional data assimilation (FDDA): analysis nudging only

The MM5 outputs were processed to create model-ready inputs for CMAQ using the Meteorology–Chemistry Interface Processor (MCIP) (U.S. EPA, 1999). MCIP version 2.2 gvm was used to convert the MM5 output to CMAQ meteorological input. This version contained two differences from the main MCIP version 2.2 in that (1) it allowed for treatment of the graupel associated with the Reisner 2 microphysics scheme and (2) it included a patch to compensate for a minor error in MM5 associated with vegetation fractions. 3.4. Initial and boundary condition inputs to CMAQ

Limited-area modeling as performed for this analysis requires the deﬁnition of BCs to account for the inﬂux of pollutants and precursors from upwind source areas outside the modeling domain. A number of recent studies show that long-range, intercontinental transport of pollutants is important for simulating seasonal/annual ozone, PM and Hg (Jacob et al., 1999; Fiore et al., 2003; Jaffe et al., 2003; Selin, 2005). A commonly employed approach to estimate incoming pollutant concentrations associated with intercontinental transport is to use a global chemistry model to provide dynamic BCs for the regional model simulations. For all CAMR Hg modeling cases, we used an annual simulation of calendar year 2001 obtained from a global three-dimensional chemistry model, the GEOS-Chem model (Yantosca, 2005), to provide the BCs and initial concentrations. The global GEOS-Chem model simulates atmospheric chemical and physical processes driven by assimilated meteorological observations from the NASA Goddard Earth Observing System (GEOS).

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This model was run for the entire year with a grid resolution of 2.01 2.51 (latitude–longitude) and 20 vertical layers. The results were used to provide one-way dynamic BCs at 3-h intervals and initial concentration ﬁeld for all CMAQ simulations. We used an interface utility tool developed at the University of Houston (Moon and Byun, 2004) to link the GEOSChem with CMAQ. 3.5. CMAQ model applications

For this analysis, the following modeling cases were studied: a 2001 base case, a 2001 test case with utility Hg emissions zeroed-out and a 2020 projection with CAIR, CAMR and all expected non-utility Hg controls incorporated. 4. CMAQ model performance evaluation

An operational model performance evaluation for Hg wet deposition for 2001 was performed to estimate the ability of the CMAQ modeling system to replicate base-year wet depositions of Hg. Comparisons of model predictions to the corresponding measurements from the Mercury Deposition Network (MDN) were made. Only sites where data was available for more than half of all four seasons were used for the annual performance evaluation. There were 52 MDN sites in 2001 that meet the data completeness requirements, of those sites 48 were located east of 1001W and 4 were located west of 1001W. A scatter plot of the observed versus predicted annual Hg wet deposition for all the sites is shown in Fig. 1. Although the CMAQ model tends to underestimate Hg wet deposition to a small degree, the majority of predictions are within 30% of observed values. Most of the remaining sites have predictions that are within 50% of observations. There is one MDN site in British Columbia where the model overestimates by greater than a factor of 2. However, the precipitation at this site was overestimated by the meteorological input model by 55%. With this observation removed from the analysis, CMAQ was able to explain 60% of the variance in the observed data. 5. Impacts of CAMR on mercury depositions

Total Hg deposition for the 2001 base case as simulated by the CMAQ model is displayed in Fig. 2. The simulated reduction in total Hg deposition resulting from elimination of all U.S. power plant Hg emissions in 2001 is presented in Fig. 3. The projected reduction in total Hg deposition

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Figure 1. Comparison of CMAQ-simulated total Hg wet deposition for 2001 to observations from the Mercury Deposition Network (MDN).

Figure 2. Total Hg deposition (mg m 2) from the base-case 2001 simulation of CMAQ.

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Figure 3. Percent reduction in mercury deposition from the 2001 base case when EGU mercury emissions are eliminated.

Figure 4. Percent reduction in mercury deposition from the 2001 base case when CAMR, CAIR and other non-utility emission controls are implemented in 2020.

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from the 2001 base case when CAMR, CAIR and other non-utility emission controls are implemented in 2020 is given in Fig. 4. The modeling results reveal that the reduction in Hg deposition stemming from the implementation of CAIR, CAMR and other minor non-utility Hg emission decreases by 2020 is similar to that obtained by completely eliminating EGU Hg emissions in the U.S. in 2001. CAIR eliminates over 60% of RGM emissions from EGUs through the implementation of scrubber control technology. However, most of the Hg emission reductions from CAMR are in the form of Hg0. This form of Hg is not readily deposited and nearly all of it is transported outside the modeling domain with no impact on the Hg deposition ﬂux in the modeling domain. Thus, it is expected that CAMR will result in a reduction of the transport of Hg to other parts of the world. Discussion

B. Fisher:

O.R. Bullock Jr.:

M. Soﬁev:

O.R. Bullock Jr.:

Could you explain what kind of mercury speciﬁc controls will be applied to U.S. generating units to achieve the 2020 mercury emission limit? Much of the mercury control expected in 2020 will be achieved as a co-beneﬁt from sulfur controls that are a part of the U.S. EPA’s Clean Air Interstate Rule. The Clean Air Mercury Rule speciﬁes emission reductions for mercury, but does not dictate the technology that is to be used. The U.S. EPA believes that carbon particle injection and recovery can achieve the necessary mercury emission reductions, but any other suitable technology that may be developed can be used. Hg is known to have a very inertial background contamination level (1.5 ng m 3). Have you seen any impact of the U.S. emission measures to this level? Is the territory of the U.S. a net importer or exporter of the Hg species? Assessment of the expected impact of U.S. mercury emission reductions on the global background level would require global-scale modeling that the U.S. EPA has not yet performed. However, we know that the U.S. electric utility emissions of mercury are currently a very small fraction of the total mercury emission ﬂux worldwide, so we would expect the impact of these future controls on the background level to be minimal. Our modeling shows total

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mercury deposition to the U.S. is larger than the current industrial emission ﬂux of mercury. In that sense, you could say that the U.S. is a net importer. However, this does not take into account emissions of mercury from natural processes that take place in the U.S. Those emissions are a combination of natural geologic mercury and mercury from past atmospheric deposition that is revolatilized. Therefore, the question of import versus export of mercury is very complicated and has no certain answer. ACKNOWLEDGMENTS

The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce’s National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW13921548. REFERENCES Bullock, O.R., Brehme, K.A., 2002. Atmospheric mercury simulation using the CMAQ model: Formulation, description, and analysis of wet deposition results. Atmos. Environ. 36, 2135–2146. Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and other components of the models-3 Community Multiscale Air Quality (CMAQ) modeling system. Appl. Mech. Rev. 59, 51–77. Fiore, A.M., Jacob, D.J., Liu, H., Yantosca, R.M., Fairlie, T.D., Li, Q., 2003. Variability in surface ozone background over the United States: Implications for air quality policy. J. Geophys. Res. 108, 4787–4803. Grell, G.J., Dudhia, J., Stauffer, D., 1994. A description of the ﬁfth-generation Penn State/ NCAR Mesoscale Model (MM5). Publication NCAR/TN-398+STR. National Center for Atmospheric Research, Boulder, Colorado, p. 138. Jacob, D.J., Logan, J.A., Murti, P.P., 1999. Effect of rising Asian emissions on surface ozone in the United States. Geophys. Res. Lett. 26, 2175–2178. Jaffe, D., McKendry, I., Anderson, T., Price, H., 2003. Six ‘new’ episodes of trans-Paciﬁc transport of air pollutants. Atmos. Environ. 37, 391–404. Moon, N.K., Byun, D.W., 2004. A simple user’s guide for GEOS2CMAQ code: Linking CMAQ with GEOS-Chem, version 1.0. Interim Report from Institute for Multidimensional Air Quality Studies (IMAQS), University of Houston, TX. Selin, N.E., 2005. Mercury rising: Is global action needed to protect human health and the environment? Environment. 47, 22–35. U.S. EPA, 1999. Science algorithms of the EPA models-3 Community Multi-scale Air Quality (CMAQ) modeling system. U.S. Environmental Protection Agency publication EPA/600/R-99/030.

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U.S. EPA, 2005. Clean air mercury rule emission inventory technical support document. U.S. Environmental Protection Agency web publication, http://www.epa.gov/ttn/atw/ utility/emiss_inv_oar-2002-0056-6129.pdf Yantosca, B., 2005. GEOS-Chem v7-03-06 User’s Guide. Atmospheric Chemistry Modeling Group, Harvard University, Cambridge, MA, posted 8 November 2005 at http:// www-as.harvard.edu/chemistry/trop/geos/doc/man/index.html

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Chapter 2.3 Multi-objective analysis to control ozone exposure Claudio Carnevale, Giovanna Finzi, Enrico Pisoni and Marialuisa Volta Abstract Regional authorities need suitable tools to develop air quality plans, in particular when dealing with secondary pollutants, such as ozone, characterized by complex and non–linear dynamics. This work presents a two objective problem considering both effectiveness and costs of alternative environmental policies. To solve the multiobjective problem, an integrated modeling system has been designed and implemented. It includes (1) pollutant-precursor models identiﬁed by processing the simulations of the GAMES modeling system, and (2) cost functions computing emission reduction costs. The methodology has been applied to a complex domain in Northern Italy, including Milan metropolitan area, a region characterized by high ozone levels in the summer season. 1. Introduction

In recent years, tropospheric secondary pollution (namely PM10 and ozone) episodes have become more and more critical all over Europe. A major task of regulatory agencies is to develop plans to mitigate such heavy pollution episodes and direct their effort towards a stable solution of this problem. These plans must be formulated in terms of precursor emission reduction, as nitrogen oxides (NOx) and volatile organic compounds (VOC). In principle, such reductions should be ﬁxed on the basis of techniques such as cost–beneﬁt analysis, cost-effectiveness analysis (Schleiniger, 1999; Streets et al., 2001) or multi-objective optimization (Finzi and Guariso, 1992). Cost–beneﬁt analysis selects best plans minimizing the sum of two or more objectives, i.e., pollution damages and emission abatement costs. Cost-effectiveness analysis also permits to treat not economically assessable objectives. The multi-objective problem (Finzi and Guariso, 1992; Shih et al., 1998; Scho¨pp et al., 1999; Friedrich

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and Reis, 2000; Guariso et al., 2004) allows to consider separately different objectives. In the multi-objective approach, the pollution–precursor relationship can be simulated by deterministic 3D modeling systems, describing transport and chemical phenomena. Such models have so high computational costs that they are not of practical use in a multi-objective mathematical program. So the identiﬁcation of simpliﬁed models synthesizing the relationship between the precursor emissions and secondary pollutant concentrations is required. To get round the problem, in literature the source–receptor relationship has been described using ozone isopleths (Flagen and Seinfeld, 1988; Loughlin, 1998) or with reduced-form models. The latter can be divided into (1) simpliﬁed photochemical models as in Venkatram et al. (1994) and (2) statistical models on the results of deterministic 3D transport-chemical models (long-term simulations in Friedrich and Reis (2000), short-term simulations in Barazzetta et al. (2002). In this paper, a two-objective analysis (air quality and costs) aimed to select effective AOT40 control plans is presented. The non-linear relations between emissions and ozone are identiﬁed for Lombardia region (Northern Italy) by means of neural network models, calibrated processing longterm simulations, performed in the frame of Citydelta II project by means of GAMES multi-phase modeling system (Volta and Finzi, 2006). Cost functions are calculated for emission reductions for each CORINAIR macrosector and then the two-objective problem is solved using as decision variables NOx and VOC reductions.

2. Problem formulation

Ground-level ozone control can be formulated as a two-objective mathematical programming problem including the effectiveness of emission reduction policies and their costs. As photochemical pollution is formed from emissions of nitrogen oxides and of VOC in the presence of sunlight, the daily cell exposure to ozone is function of meteorological parameters (that cannot be handled) and of precursor emissions (decision variables). A regional authority can impose a reduction to a certain emission sector, and so the daily cell emissions are expressed with respect to a reference situation and split in 11 sectors according to the CORINAIR classiﬁcation (EMEP/CORINAIR, 1999). The ﬁrst objective considered is the minimization of seasonal domain ozone exposure (OE), expressed in terms of seasonal accumulated ozone dose (AOT40) above the 40 ppb cut-off value for daylight hours over a

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grid domain. The objective can be expressed as minðOEÞ ¼ min W

D XX

N m V ½AOT40i;j ðN m i;j ðdÞ ð1 rm Þ; V i;j ðdÞ ð1 rm ÞÞ

8i;j d¼1

(1) where AOT40i,j is the daily value of AOT40 for cell i, j; D is the amount of m the summer days; N m i;j ðdÞ and V i;j ðdÞ are respectively the daily cell NOx V and VOC emissions in the reference case for sector m; WðrN m ; rm Þm¼1;...;11 is the decision variable set, namely the percentage of sector precursor emission reductions (respectively for NOx and VOC); m ¼ 1,y,11 is the CORINAIR emission sector. The second objective of the ozone planning is the minimization of precursor (NOx and VOC) emission reduction costs: minðCostsÞ ¼ min W

11 X N N m V V V ðN m rN m cm ðrm Þ þ V rm cm ðrm ÞÞ

(2)

m¼1

with the constraints: N 0 rN m Rm V 0 rV m Rm N V V where cN m ðrm Þ and cm ðrm Þ are functions giving the unit costs related respectively to NOx and VOC emission reduction for sector s; Nm and Vm are the seasonal domain NOx and VOC emissions in the reference case for V sector s; RN m and Rm are the maximum feasible reductions allowed by the available technologies for sector s.

3. Precursor-pollutant models

The precursor–ozone relationship should be given by the results of deterministic 3D modeling system simulations, but they require so high computing times that such models cannot be used in an optimization problem. In this section, neural network precursor–ozone simpliﬁed models, tuned processing deterministic 3D modeling system simulations, are formalized and implemented for a domain placed in Northern Italy (Fig. 1), a densely inhabited and industrialized area that is regularly affected by high ozone concentrations.

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3.1. GAMES long-term simulations

Ground-level ozone concentrations have been simulated by the GAMES modeling system consisting of (a) the multiphase eulerian 3D photochemical model TCAM (Carnevale et al., 2005) and (b) the meteorological pre-processor CALMET (Scire et al., 1990); the emission processor POEM-PM (Carnevale et al., 2006). The modeling system has been run over Northern Italy (Fig. 1), in the frame of CityDelta-CAFE project. The domain (300 300 km2) has been horizontally subdivided into 60 60 cells, with a resolution of 5 5 km2 each. Vertical domain extends up to 3900 m above sea level, subdivided into 11 layers of growing thickness. Simulations have been performed getting initial and boundary conditions by a nesting procedure from the results of the EMEP Uniﬁed Model working at European scale (Simpson et al., 2003). Assuming the actual meteorology, emission scenarios and boundary conditions of that period, the base case simulation has been performed, supplying pollutant hourly concentration ﬁelds.

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The run of such a simulation takes about 12 days of CPU time, and this explains why GAMES cannot be directly integrated in an optimization procedure that should process hundreds of model runs. Keeping the same meteorology in input and applying two different emission control strategies (current legislation (CLE) and most feasible reduction (MFR), http://www.iiasa.ac.at) reducing ozone precursor emissions, two alternative scenarios have been simulated in the frame of CityDelta-CAFE EU Project (http://aqm.jrc.it/citydelta/). Their outputs have been used for the calibration of the simpliﬁed source-receptor models described in the following section. 3.2. Neural network models

Simpliﬁed source-receptor models have been set up by means of neural networks. Several network structures have been considered. The chosen architecture for this work is the Elman Neural Network (Elman, 1990). One neural network has been identiﬁed for each group of 2 2 (10 10 km2) domain cells. The input data are the daily NOx and VOC emissions estimated for each cell group. The inputs are pre-processed by means of a normalization procedure. The target data are the AOT40 daily mean values computed by the GAMES system. The tuning and validation data series are selected processing the GAMES simulations performed on the three different emission scenarios. Each simulation covers a period from April to September. The validation set has been yielded extracting, from the simulation data set, the third week of each month. The identiﬁcation set includes the remaining patterns. The identiﬁed nets are characterized by the features shown in Table 1. The scatter plot presented in Fig. 2 shows that the neural network system ensures high capability to simulate the non-linear source-receptor relationship between AOT40 and the emission of ozone precursors. The scatter plot highlights that all the points are very close to the bisecting line, even if the identiﬁed neural networks slightly overestimate AOT40. Table 1. Neural network architecture NN features Nodes of the input layer Nodes of the output layer Nodes of the hidden layer Training set Validation set

Value 3 1 8 423 126

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Figure 2. Neural network versus GAMES ozone concentrations on each grid point of the domain.

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4. Cost functions

In this work, NOx and VOC reductions are considered as decision variables. These reductions are deﬁned according to the CORINAIR emission classiﬁcation, which considers emissions as grouped in 11 sectors. Not all the sector emission reductions can be considered during the optimization, due to the fact that for some CORINAIR sectors reduction costs are not available; so cost functions cannot be identiﬁed. More precisely, macrosectors 1 and 10 are not considered for VOC, while macrosectors 1, 5, 6, 9 and 10 are not considered for NOx reductions. Biogenic emission reductions (macrosector 11) also are not considered. The abatement cost curves used are estimated on the basis of a large data set collected for Italy by RAINS-IIASA database (http://www. iiasa.ac.at). The cost of a unit emission reduction, incurred by adopting several technological alternatives for each activity included in each sector, has been assessed. These data allow to estimate an emission abatement cost function for each sector, ﬁtting the costs and the efﬁciency of the available technologies (Fig. 3). This ﬁtting has been performed only within zero and the maximum feasible reduction, with the constraint of estimating a monotonically increasing and convex function. As an example, Fig. 3 shows the cost curves estimated for road trafﬁc (SNAP macrosector 7).

5. Results

The base emission scenario implies no costs and produces a reference air pollution index. Instead, if one adopts the best (and more expensive) reduction technologies in all the sectors considered, the cost is about 974,000 Keuro (see Fig. 4) with a decrease of the other objective of about 44% with respect to the base case value. This corresponds to emission reduction close to 40% for VOC and 30% for NOx. Figure 4 (left) depicts the set of non-dominated solutions of the two objective problems. At right in the same ﬁgure, the both axes have been rescaled to the maximum feasible variation. In Fig. 5, the values of the decision variables giving the optimal solutions are presented. The ﬁgure suggests the set of macrosector emission reductions that are more efﬁcient to reach optimal solutions, in other words, the reduction priorities to be implemented in AOT40 ablation. In terms of absolute values, Fig. 6 stresses the macrosector emissions corresponding to the optimal solution curve.

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Figure 3. Cost curves estimated for road trafﬁc (SNAP macrosector 7) for VOC and NOx.

A solution corresponding to 50% of the maximum air quality improvement (in Fig. 4 stressed with a vertical line) can be attained with only 13% of the maximum cost, while the marginal costs grow with the increase of emission reductions. As shown in Fig. 6, to obtain a reduction of 50% of the air quality objective a decision maker needs to focus on solvent use (n. 6), road transport (n. 7) and other mobile sources (n. 8) for VOC reduction, and production processes (n. 4) and road trafﬁc (n. 7) for NOx reduction. The other sectors become of interest only when very strong emission reduction is required.

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6. Conclusion

A procedure to formulate a multi-objective analysis to control ozone exposure has been presented. The procedure implements Elman neural networks tuned by the output of a deterministic 3D model and cost functions of different emission abatement strategies. The methodology has been applied over a Northern Italy domain, often affected by high ozone levels, using as data set the outputs of the GAMES modeling

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system for three different simulations (a base case and two alternative emission scenarios obtained varying the ozone precursors emission). Results of the optimization point out which are the emission sectors that the regional authorities should ﬁrst take into consideration to reduce ground-level ozone concentration with minimum abatement costs. Furthermore, it is shown that a consistent reduction of ozone peaks can be attained in Lombardy with a small fraction (about 13%) of the costs of adopting the best reduction technologies.

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AOT40 reduction (% max) Figure 6. VOC and NOx emission required to generate an optimal solution.

Discussion

F. Murena:

Does the neural network model still effectively evaluate O3 concentrations if emission rates are strongly reduced following the optimization procedure?

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C. Carnevale:

B. Fisher:

C. Carnevale:

E. Genikhovich:

C. Carnevale:

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During the identiﬁcation phase, one of the three emission scenarios used concerns the most feasible reduction available for each pollutant. The reductions assumed in this scenario are also used as bounds for the decision variable of the optimization problem; so the emission values in the optimization phase have to be inside the bounds of the identiﬁcation set. Can one generalize the neural network so that there are more input nodes representing different VOC species with different reactivities? In principle, this generalization is possible. Therefore, due to the increasing in the number of the neural network inputs, a very large number of scenarios have to be simulated by GAMES modeling system in order to create the identiﬁcation set. In order to avoid the high sensitivity of the optimization plan to the errors in the cost function, one should use a regularization procedure. Did you apply any in your work? In this ﬁrst analysis, no regularization procedure has been applied, but this is one of the topics we intend to investigate in the future.

ACKNOWLEDGMENTS

This work has been partially supported by the Italian Ministry of University and Research (MIUR). The work has been developed in the frame of CityDelta-CAFE project and ACCENT EU Excellence Network. The authors are grateful to Davide Fasoli (Universita` di Brescia) for his kind and valuable co-operation in data processing.

REFERENCES Barazzetta, S., Corani, G., Guariso, G., 2002. A neural emission-receptor model for ozone reduction planning. In: Proc. iEMSs 2002. Vol. 2. pp. 130–135. Carnevale, C., Finzi, G., Volta, M., 2005. Design and validation of a multiphase 3D model to simulate tropospheric pollution, 44th IEEE Conference on Decision and Control and European Control Conference 2005, Seville (E).

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Carnevale, C., Gabusi, V., Volta, M., 2006. POEM-PM: An emission model for secondary pollution control scenarios. Environ. Modell. Softw. 21, 320–329. Elman, J., 1990. Finding structure in time. Cogn. Sci. 14, 179–211. EMEP/CORINAIR, 1999. Atmospheric Emission Inventory Guidebook, second ed. Tech. rep., European Environment Agency. Finzi, G., Guariso, G., 1992. Optimal air pollution control strategies: A case study. Ecol. Model. 64, 221–239. Flagen, R.C., Seinfeld, J.H., 1988. Fundamentals of Air Pollution Engineering. PrenticeHall, Englewood, NJ. Friedrich, R., Reis, S., 2000. Tropospheric Ozone Abatement. Springer-Verlag, Berlin Heidelberg. Guariso, G., Pirovano, G., Volta, M., 2004. Multi-objective analysis of ground level ozone concentration control. J. Environ. Manage. 71, 25–33. Loughlin, D.H., 1998. Genetic algorithm-based optimization in the development of tropospheric ozone control strategies. PhD thesis. Graduate Faculty of North Carolina State University. Schleiniger, R., 1999. Comprehensive cost-effectiveness analysis of measures to reduce nitrogen emissions in Switzerland. Ecol. Econ. 30, 147–159. Scho¨pp, W., Amann, M., Cofala, J., Heyes, C., Klimont, Z., 1999. Integrated assessment of European air pollution emission control strategies. Environ. Modell. Softw. 14, 1–9. Scire, J.S., Insley, E.M., Yamartino, R.J., 1990. Model formulation and User’s Guide for the CALMET meteorological model. Tech. Rep. A025-1, California Air Resources Board, Sacramento, CA. Shih, J., McRae, G.J., Russell, A.G., 1998. An optimization model for photochemical air pollution control. Eur. J. Oper. Res. 106(1), 1–14. Simpson, D., Fagerli, H., Jonson, J., Tsyro, S., Wind, P., 2003. Transboundary acidiﬁcation, eutrophication and ground level ozone in Europe—Part I: Uniﬁed EMEP model description. Tech. Rep. 1/2003, EMEP MSC-W. Streets, D.G., Chang, Y.S., Tompkins, M., Ghim, Y.S., Carter, L.D., 2001. Efﬁcient regional ozone control strategies for eastern United States. J. Environ. Manage. 61, 345–365. Venkatram, A., Karamchandani, P., Pai, P., Goldstein, R., 1994. The development and application of a simpliﬁed ozone modelling system (SOMS). Atmos. Environ. 28, 3665–3678. Volta, M., Finzi, G., 2006. GAMES, a comprehensive Gas Aerosol Modelling Evaluation System. Environ. Modell. Softw. 21, 587–594.

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Chapter 2.4 Atmo-rhenA: a common air quality modelling system in the Upper Rhine Valley R. Deprost, J. Bernard, E. Riviere, N. Leclerc and C. Schillinger Abstract The ASPA is in charge of regional air quality assessment and forecast (measurements and modelling), emission inventory realisation and gridding, and population information. In the frame of the European INTERREG III program, it is working together with the networks of Baden Wu¨rttemberg (Landesanstalt fu¨r Umwelt, Messungen und Naturschutz Baden-Wu¨rttemberg, LUBW) and Basel (Lufthygieneamt beider Basel, LHA) towards a ‘‘common air quality assessment and information system in the Upper Rhine Valley’’. The current and last step of the project is the set up of a common regional air quality modelling platform (simulation and forecast), which completes the bilingual website presenting daily measurements of the networks. The Atmo-rhenA platform is based on the CHIMERE (Institut Pierre Simon Laplace, IPSL, http://euler.lmd.polytechnique.fr/ chimere) chemistry-transport model (CTM) and the MM5 meteorological model (National Centre for Atmospheric Research, NCAR). It is fed up with a detailed transboundary inventory update on the domain, integrating a speciation of non-methane volatile organic compounds (NMVOC) (Sambat et al., 2004) and particulate matter (PM) (Pregger et al., 2004) worked out by IER (Institut fu¨r Energiewirtschaft und Rationelle Energieanwendung) and CITEPA (Centre Interprofessionnel Technique d’Etudes de la Pollution Atmosphe´rique). Daily forecasts for the Upper Rhine Valley are operational since the beginning of 2005 and will be soon available on Internet. Indicators on the forecasting performances of the platform are calculated thanks to comparison with the routine measurements. Further, Atmo-rhenA allows the study of ozone production in the Upper Rhine Valley and the simulation of the impact of emission

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reduction scenarios on ozone peaks. For instance it has been used to assess the impact of a local short-term scenario (application of emergency measures) and of European long-term scenarios from the European Clean Air For Europe (CAFE) programme, both applied on the transboundary emission cadaster.

1. Introduction and context

During the ﬁrst INTERREG project between 1993 and 1995, the ASPA and the LUBW realised the atmosphere protection plan for the transboundary space Strasbourg/Ortenau. During the second INTERREG project between 1996 and 2000, the same working community realised a transboundary analysis of the air quality in the Upper Rhine Valley, with emission and immission cadasters of the Valley and geostatistical study of population exposition to NO2 and C6H6 thresholds exceedance. Within the French-German-Swiss Upper Rhine Conference, the Expert Group on Air quality of the Working Group Environment initiated this third INTERREG programme in the region and is in charge of the work validation, while the action programmes INTERREG PAMINA and Upper Rhine Centre South are responsible for the FEDER funds management. In the frame of this third INTERREG project named Atmo-rhenA, three regions are working together towards a ‘‘common assessment and information system on air quality in the Upper Rhine Valley’’: the BadenLand and the South-Palatinate in Germany, the two cantons of Basel in Switzerland and the Alsace region in France. The project started in 2001 and will be completed at the end of 2006. The ﬁnancial partners represent 3 Mh, with about the half of the funds coming from the three air quality networks, one-third being FEDER funds and complementary funds coming from the Alsace Region and from the French Government Agency for Environment and Energy Management (ADEME).

2. The third INTERREG programme on air quality in the Upper Rhine Valley 2.1. Common air quality measurements web page

The ﬁrst part of the programme was headed by the LUBW, and its aim was to set up a common and permanent information on air quality in the Upper Rhine Valley: daily and long-term cartographic information on air quality measurements, daily diffusion of common air quality indexes with trend forecast for the next day and special communications in case of

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particulate matter (PM) pollution episodes particularly in winter or ozone (O3) pollution episodes particularly in summer. To meet these objectives a bilingual website was designed and is available since June 2005 on http://www.atmo-rhinsuperieur.net (in French) or http://www.luft-am-oberrhein.net (in German). 2.2. Transboundary emission inventory and its gridding

In the frame of the second part of the programme, piloted by the ASPA, a transboundary communal and annual emission inventory was realised and gridded into a kilometre and hourly cadaster. Primary data on emissions activities and factors for the French part, and on emissions for the German and Swiss parts were gathered. Then the EMISS’AIR software tool was designed at the ASPA to compute the transboundary inventory by integrating emissions of the three parts into a common database: surfacic, linear and ponctual sources are considered, with 40 pollutants, 10 sectors, 50 sub-sectors and different fuels. MANAGAIR was created at the ASPA to grid the inventory using a geographic information system (GIS) and a detailed land use, to introduce a detailed PM (Pregger et al., 2004) and NMVOC (Sambat et al., 2004) speciation of IER and CITEPA, and to give an hourly distribution of emissions for each sector, under-sector and day type (week, Saturday, Sunday). The sides of the domain are completed with EMEP data (Fig. 1).

Figure 1. From transboundary primary data on emission to a gridded and hourly distributed emission cadastre as air quality models input.

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The cadaster is used as input in the CHIMERE CTM. This tool can be used for any domain or spatial resolution and allows the preparation of emission scenarios by cutting or reducing emissions of chosen activity sectors. 2.3. Transboundary air quality modelling platform

In the third and last part of this INTERREG programme, headed by the ASPA, a regional deterministic modelling platform has been set up and is used for daily air quality forecasts as well as for studies like determination of local and regional pollution inﬂuence, biogenic and anthropic emissions inﬂuence or simulation of emission reduction scenarios. 3. Description of the modelling system

For the study of a domain like the Upper Rhine Valley, 3-dimensions deterministic meteorological and CTMs are necessary. Indeed it combines most of the key factors in air pollution: urban areas, roads and motorways, strong industrial areas with reﬁneries, petrochemistry, power plants and a complex regional meteorology due to the topography with the Vosges Mountains in the West and the Black Forest in the East. There are also strong biogenic emissions over the extended areas of grass, forest and farming in the valley. Moreover, the CHIMERE CTM is now known for its good performances as predicting tool both for short-term forecasts and for the assessment of long-term emission abatement strategies. For the meteorology, the MM5 model is used with a 12 by 12 km resolution over the ‘‘Central Europe’’ mother domain and with a 4 by 4 km resolution over the ‘‘Upper Rhine Valley’’ domain nudged in the ﬁrst one. MM5 runs with 25 pressure sigma-levels from surface to 100 hPa, and is forced at the lateral boundaries by GFS 6-hourly analyses or forecasts (NCEP). Its output ﬁelds are provided to the CHIMERE CTM (Vautard et al., 2004). The CHIMERE CTM is used over a 4 by 4 km resolution grid, with 48 cells in longitude and 64 in latitude (Fig. 2). It is used with 8 vertical pressure layers (vertical coordinate being terrain-following), going from surface to 500 hPa, so it encloses the boundary layer. The CTM is nested within European PREVAIR (http://www.prevair.org) analyses and/ or forecasts according to the scenario or forecast mode of the simulation. 4. Assessment and validation of the modelling system

The regional modelling system reliability assessment was based on the comparison of simulations with the observations of the transboundary

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Figure 2. Grid of the CHIMERE chemistry-transport model (CTM) and topography of the Upper Rhine Valley.

measurement campaign that took place in the Upper Rhine Valley in 2003, from the 19th of May to the 16th of June. For this validation part a cooperation took place with the LPAS of the EPFL, which studied the ﬁeld campaign with the deterministic models METHPOMOD and FVM/ TAPOM, identiﬁed the ozone regimes of the valley and the parts of the

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domain where regional pollution phenomena are predominant, which means that local actions on emissions can have a real impact on pollution episodes.

4.1. Transboundary measurement campaign

Near the ﬁxed measurement networks (ASPA, LUBW, LHA and METEO FRANCE), there were 12 laboratory trucks of air quality networks participating in the campaign. There were 17 French, German, Swiss and Italian research teams working in 3 speciﬁc measurement sites. The measurement campaign began with an intercomparison of measurement devices. The two intensive observation periods (IOPs) were from the 3rd to the 5th and from the 10th to the 12th of June 2003. The measured chemical ﬁelds were O3, NO, NO2, NOx, NOy, SO2, CO, CO2, PM10, PM2.5, COV, NMVOC, HONO, HCHO, H2O2, HNO3, MHP, HMHP, etc. For the meteorology, only variables in direct interaction with photochemistry (wind ﬁeld, pressure, nebulosity, radiation, mixing height, humidity, temperature, precipitations y) have been measured. In 2004, the whole data were made available to all participants on a server, where they can extract data by FTP for their own studies (a workshop with presentation of the results took place in January 2006 in Strasbourg). NETCDF observations ﬁles have been built and have been used to compare measurements directly with simulated ﬁelds.

4.2. Validation of the modelling system

The main results are that MM5 reproduces well the temperature ﬁelds and accounts properly for heat storage in the urban canopy during summertime, especially in bigger cities. However, there is a small underestimation of temperatures during the night over the whole domain. The relative humidity is well reproduced. Horizontal wind ﬁelds are also very well reproduced (Fig. 3), and the model also produces diurnal mountain and valley breezes on the sides of the valley (positive vertical wind during the day and negative vertical wind during the night). CHIMERE accounts properly for the ozone concentrations in the ﬁrst and in other layers of the model (Fig. 4). The NOx are well reproduced by the model, showing the good quality of the temporal proﬁles of the emission cadaster.

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Figure 3. Horizontal wind direction in degrees during the ﬁrst IOP, measured (gray) and modelled (black).

5. Transboundary daily air quality forecasts

Daily air quality forecasts for the Upper Rhine Valley are operational since January 2005. The skill of the whole system in simulating and forecasting ozone and aerosols at the ground level is quantiﬁed with classical statistical parameters (mean bias and Root Mean Square Error, RMSE) using the routine measurements (Table 1). In June 2005, the performances of the model in forecasting the ozone maxima of the day were pretty good compared to other known modelling systems.

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Figure 4. Measured (gray) and modelled (black) ozone concentrations during the ﬁrst IOP in mg m 3 for three station types: urban (left), suburban (middle) and rural (right). Table 1. Statistical indicators on the forecasts skill of the modelling system for O3 in summer 2005 (daily maxima, only French stations) Station type/ term (mg m 3)

Urban Suburban Rural Trafﬁc

D 1

D

D+1

D+2

Mean Bias

RMSE

Mean Bias

RMSE

Mean Bias

RMSE

Mean Bias

RMSE

15 9 8 15

25 20 19 25

14 7 7 14

24 21 20 24

11 5 5 11

24 21 20 24

12 6 6 12

24 22 21 24

The regional modelling system provides daily boundary conditions to URBAN’AIR, an urban deterministic platform with a few hundred meters resolution used at the ASPA to get daily air quality forecasts over the city of Strasbourg (Fig. 5). 6. Origin of ozone plumes in the Upper Rhine Valley

For the last day of the ﬁrst IOP, the model gives ozone concentration maxima up to 160 mg m 3 in the plume of Strasbourg. Like anthropic

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Figure 5. PM10 forecasts for the 01/02/2006 by urban modelling system URBA’NAIR (www.atmo-alsace.net).

Figure 6. For the typical summer days studied the inﬂuence of boundary conditions (light gray) and of local biogenic (middle gray) and anthropic (dark gray) emissions was like represented on the left for ozone on the edges of the valley including Vosges and Black Forest (left), in the valley (middle) and in the plume (right).

emissions, the calculation of biogenic emissions by the modelling system has been updated and adapted to the tree and forest distribution of the Upper Rhine Valley. It is possible to suppress all local emissions in the model, which allows to assess the contribution of chemical boundary conditions to the local ozone plume. The same work can be done for local biogenic emissions alone (Fig. 6).

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7. Ozone regimes in the Upper Rhine Valley

Ozone isopleths show the non-linearity of ozone concentrations towards anthropic NMVOC and NOx emissions (Couach, 2003). Some examples of ozone isopleths are shown in Fig. 7: for the 11th of June 2003, Mannheim Centre (urban area) is clearly VOC limited, whereas Strasbourg Ouest (suburban area) is rather NOx limited and Schwarzwald Sud (rural area) is clearly NOx limited. Other methods have been investigated to determine ozone regimes in the Upper Rhine Valley. Maps of ozone concentrations differences obtained with same reductions of anthropic NMVOC and NOx have shown strong VOC limited regimes in urban areas and very lightly NOx limited regimes in suburban and rural areas (Clappier et al., 2005). The Sillman indicator H2O2/HNO3 has also been determined, it was generally between

Figure 7. Ozone isopleths in mg m 3 for the 11th of June 2003 for Mannheim Centre, Strasbourg Ouest and Schwarzwald Sud.

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0.1 and 0.2 and its cartography did not always give the same results as the emission reduction scenarios.

8. Emission reduction scenarios studies

Several short-term local emission reductions like emergency measures as well as long-term European emission scenarios have been tested. Longterm evolutions are taken from the CAFE propositions. The differences between the following emissions and the EMEP 2000 base case are

Figure 8. Decrease of the number of threshold exceedance hours (gray squares) with the application of the 2020 CLE scenario on the 12th of June 2003. Former and new geographic zone of threshold exceedance in gray and black lines.

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applied to the local transboundary emission inventory: 2020 Current Legislation Europe (CLE), 2020 Low, Medium and High Scenarios Ambitions, and 2020 MTFR scenario (CAFE). The ﬁgure shows one of the examples of the result: the application of the CLE scenario to boundary conditions and to local emissions on the 12th of June 2003. During this day an ozone threshold exceedance was observed and reproduced by the model in the base case. With the application of the scenario, an exceedance is still observed but the geographic area is restricted and the number of hours of threshold exceedance decreases for 60% of the population in the Upper Rhine Valley (Fig. 8). 9. Conclusion and perspectives

The MM5 and CHIMERE modelling system simulates the meteorological and chemical parameters with a sufﬁcient skill to allow its use for operational air quality forecasts, and for the studies performed in the frame of the INTERREG programme in the Upper Rhine Valley in 2006. For instance it allows to assess the efﬁciency of local short-term emission reduction like emergency measures or European long-term scenarios like those elaborated in the CAFE programme. Discussion

P. Builtjes:

R. Deprost:

In case of exceedances of limit values of NO2 and/or PM10/2.5, is there the effort to have similar abatement strategies across the borders of the three countries? The cross-border cooperation in the Upper Rhine Valley as regards air quality is essentially present within the Upper Rhine Conference. This intergovernmental institution gives the ‘‘environment’’ working group the mandate to launch projects that are then developed within the ‘‘air quality’’ experts group. The ‘‘air quality’’ experts group is linked with the ‘‘environment’’ working group. The mandate that led to achieve the INTERREG III project presented in Leipzig, was ﬁnanced by the European Union and included the common broadcast of information and the setting of a modelling. As regards the long-term policy, the setting of common cross-border strategies of pollutant emission reduction in order to reduce the continuous background pollution has

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been dealt within the frame of an INTERREG I project concerning the creation of a cross-border air protection plan for Strasbourg/Ortenau (approved on the 8th of December 1995). Taking into account the regulatory abilities of the Rhine’s both sides, this plan just resulted in combining two different catalogues of measures: one for the Communaute´ Urbaine de Strasbourg and the other for the Ortenau Kreise including three cross-border topics about (I) the proposal of a cross-border tariff union for public transports (including rail), (II) to check whether it is possible for the city of Kehl to join the public transport network of Strasbourg and (III) the creation of another common cycle track. As a consequence, the matter of the long-term common measures has only been orally mentioned during one or the other meeting, just as a possible topic of an INTERREG IV project in the Upper Rhine region for example. The experts know how necessary it is to conclude ﬁrst the different preliminary reviews. The organised exchange on the air quality protection plans represented a part of it. The INTERREG III conclusions are in progress (end 2006) and will represent another part, especially with the air quality simulation for 2020 from more or less controlled scenarios. Concerning the setting of short-term emergency measures when ozone peaks appear, the Upper Rhine Conference asked the question of the available means set up by the different authorities in the Upper Rhine region after the heat wave in 2003 in order to restrict pollution peaks and their effects. A progressive working programme was chosen: (I) to identify the short-term measures set up for each of the three countries, (II) to analyse the coordination possibilities of the measures and (III) to deﬁne the effectiveness of their setting. In order to deepen the ﬁrst topic mentioned the ‘‘air quality’’ experts group received another term of reference. A report on the ﬁrst topic is in progress at the moment. On the other side, the chairman of the Communaute´ Urbaine de Strasbourg also asked all the Upper Rhine communities after the heat wave of 2003 for a study to know whether it is possible to reduce the common emissions that are under their responsibilities. The

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working group dealing with this question applied to the ‘‘air quality’’ experts group to deﬁne the feasibility, which is done within the frame of the INTERREG III project. One of the topic difﬁculties consists in the difference between French (regulatory emergency measures that have to be set up within the frame of the Air Act and the European Ozone Directive) and German (emergency measures do not have a signiﬁcant effect for possible emission reduction) policies.

ACKNOWLEDGMENTS

We are thankful to NCAR, IPSL, NCEP and INERIS for the daily use of the MM5 and CHIMERE models, and of the GFS and PREVAIR systems outputs. REFERENCES Clappier, A., Couach, O., Kirchner, F., 2005. Simulations des Pe´riodes d’Observation Intensives et de diffe´rents sce´narios de re´duction d’e´missions, Rapport Final, LPAS – Laboratoire De Pollution Atmosphe´rique et Sol, EPFL Ecole Polytechnique Fe´de´rale de Lausanne. Couach, O., 2003. Eude et mode´lisation de la pollution photochimique dans l’espace du Rhin Supe´rieur-Analyse des re´gimes de production d’ozone-Elaboration de sce´narios de re´ductions des e´missions, Syste`me commun d’e´valuation et d’information sur la qualite´ de l’air dans l’espace du Rhin Supe´rieur, Projet INTERREG III, Theme3 (E23.1-8A). Pregger, T., Sambat, S., Friedrich, R., 2004. Study on particulate matter emissions: Particle size distribution and chemical composition, IER—Institut fu¨r Energiewirtschaft und Rationelle Energieanwendung, Universita¨t Stuttgart, CITEPA—Centre Interprofessionnel Technique d’Etudes de la Pollution Atmosphe´rique, November 2004. Sambat, S., Theloke, J., Friedrich, R., Allemand, N., 2004. Bibliographic study concerning the speciation of NMVOC, IER—Institut fu¨r Energiewirtschaft und Rationelle Energie-anwendung, Universita¨t Stuttgart, CITEPA—Centre Interprofessionnel Technique d’Etudes de la Pollution Atmosphe´rique, November 2004. Vautard, R., Beekmann, M., Bessagnet, B., Blond, N., Hodzic, A., Honore´, C., Malherbe, L., Menut, L., Rouil, L., Roux, J., 2004. The use of MM5 for operational ozone/ NOx/aerosols prediction in Europe: strengths and weaknesses of MM5, Institut Pierre-Simon Laplace, CNRS, Paris, France, INERIS, Verneuil en Halatte, France, Laboratoire interuniversitaire des Syste`mes Atmosphe´riques, CNRS, Cre´teil, France.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06025-1

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Chapter 2.5 Perturbational downscaling and its applications in air pollution and meteorological problems Eugene Genikhovich and Guy Schayes Abstract A widely used approach to reduction of the downscaling errors was suggested by Davis and based on the brute-force dumping of distortions of the numerical solution in a ‘‘strip’’ of the sub-domain adjacent to the boundary. It produces noticeable unphysical effects, especially in the near-boundary strip, so that the applicability of the whole numerical solution looks questionable. An alternative approach is based on the reformulation of the governing equations in the perturbation form. The governing equations derived for this perturbation are to be solved on the ﬁne grid mesh to generate, as a result, the downscaled ﬁelds, reducing considerably the interference between coarse and ﬁne grid solutions. In this paper, the efﬁciency of such an approach is demonstrated not only on model examples but also on mesometeorological applications using the non-hydrostatic Thermal Vorticity Mesoscale (TVM) model which solves the dynamic equations using the transformation into the vorticity. Also, by construction, this approach automatically satisﬁes the continuity equation that is of utmost importance when the computed wind ﬁelds are used to drive the models of the atmospheric transport and dispersion of tracers. 1. Introduction

Numerous applications require more detailed description of meteorological ﬁelds than allowed by ‘‘standard’’ Numerical Weather Prediction (NWP) models. These applications, which vary from regional and local weather forecasting and nowcasting to dispersion modelling and applied climatology, usually exploit the approach known as nesting. One-way nesting (referred also as downscaling) procedures are aimed to increase the resolution of the NWP model inside a certain sub-domain of the

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computational domain; in two-way nesting procedures, the detailed solution obtained in this sub-domain is used to correct meteorological ﬁelds in the rest of the domain under consideration. Other techniques are also used to increase the resolution of NWP models (e.g., multiscale grids). In this paper, however, our focus is only on downscaling (i.e., one-way nesting) based on solution of dynamic equations. In a usual downscaling procedure, the data from the coarse grid, being used to determine initial and boundary conditions in a sub-domain of interest, drive a solution in this sub-domain on a ﬁner grid mesh. As simple as this idea is, its practical implementation is complicated because these initial and boundary conditions are obtained by a kind of interpolation from the coarse grid to the ﬁne one. The main problem that arises here is that these interpolated initial and boundary conditions are, generally speaking, incompatible with the governing equations though the ﬁelds on the coarse grid were obtained as a solution of those equations. In order to demonstrate the nature of this problem, let us consider the simplest case of the linear equation @u ¼ Lu þ f ; @t

ujt¼t0 ¼ u0 ;

ujz¼zs ¼ g;

ujG ¼ uG

(1)

where z ¼ zs corresponds to the underlying surface, G denotes other boundaries of the computational domain D; u0, f and g are given functions. For the sake of simplicity, it is assumed here that the only goal of downscaling is to improve the resolution of the numerical solution in a sub-domain S of the computational domain. To justify the need for downscaling, let us assume that the part g of the underlying surface in S is more inhomogeneous than in the rest of the computational domain D. In order to visualize L, one can assume, for example, that L is the advectiondiffusion operator, i.e., L ¼ (@i(Kij@j)Ui@il), where @i means the partial derivative along the xi axis (x3 z corresponds to the vertical direction); Ui and Kij are components of the wind velocity and the eddy-diffusivity tensor; l is inversely proportional to the half-life time scale of the pollutant considered; and the Einstein rule of summation from 1 to 3 over repeated indexes is implied (in this case u is the concentration of the pollutant). As a result of the numerical solution of Eq. (1) in the domain D with characteristic resolution h, one can obtain a grid function that is deﬁned only at the grid points. Let us denote as uh the ‘‘smooth’’ function deﬁned at all points of the computation domain that is a ‘‘continuator’’ of this

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grid function. Then, uh should satisfy the following equation: @uh ¼ Luh þ f þ h ; uh jt¼t0 ¼ u0h ; uh jz¼zs ¼ gh ; uh jG ¼ uGh (2) @t where u0h and uGh denote continuators of uh at the initial time and at the boundary, respectively, and gh denotes the continuator of the grid function with resolution h, which corresponds to g. Actually, Eq. (2) could be considered as a deﬁnition of the term eh, which results from the errors of discretization, numerical solution, etc., and, if the numerical scheme is ‘‘efﬁcient’’, should, generally speaking, tend to zero when h tends to zero. Let us assume ﬁrst that one has obtained in the domain D the numerical solution corresponding to the resolution h ¼ h1, which is not sufﬁcient to resolve the small-scale features of g in S but reproduces this function fairly well at all other points of the underlying surface. Then downscaling means that, starting at the moment of time t ¼ t0, one should calculate in the sub-domain S the solution sought with the resolution h ¼ h2oh1. It means that, using uh1 ; one should reconstruct the values of uh2 jt¼t0 in S, and on its boundary, Gs, the next step being to solve the corresponding equation with reconstructed initial and boundary conditions. Let us denote these results of reconstruction as uh2 ðh1 Þjt¼t0 and uGh2 ðh1 Þ; similarly, the notation gh2 ðh1 Þ is used here but, in fact, for the sake of simplicity it is assumed further that gh2 ðh1 Þ ¼ gh2 : Thus, the ‘‘standard’’ downscaling procedure is based on solution of the following problem: @v ¼ Lv þ f þ h2 ; vjt¼t1 ¼ uh2 ðh1 Þjt¼t0 ; vjz¼zsjg ¼ gh2 ðh1 Þ; vjGs ¼ uGh2 ðh1 Þ (3) @t

where z ¼ zs|g indicates the points of the underlying surface located in S. In fact, however, the ‘‘exact’’ (global) numerical solution w with resolution h2, which is supposed to be reproduced with the downscaling procedure, should satisfy in S the following equation: @w ¼ Lw þ f þ h2 ; wjt¼t0 ¼ uh2 jt¼t1 ; wjz¼zsjg ¼ gh2 ; wjGs ¼ uGh2 (4) @t Subtracting Eq. (4) from Eq. (3), one could ﬁnd the equation describing the error d ¼ vw of the standard downscaling procedure @d ¼ Ld; djt¼t0 ¼ uh2 ðh1 Þ uh2 ; djz¼zsjg ¼ gh2 ðh1 Þ gh2 ; djGs ¼ uGh2 ðh1 Þ ujGh2 (5) @t

For the sake of simplicity, one can assume here that d|z ¼ zs ¼ 0 (actually, it is not always true, especially, if the conditions at the underlying surface include the ﬂuxes of the substances of interest). Now it is

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obvious that the standard downscaling procedure has an inherent drawback because its result is visibly inﬂuenced by the propagation of the errors of the coarse-grid solution, represented in the initial and boundary conditions of Eq. (5), into the downscaling domain. Because h1 is assumed to be larger than h2, one can expect that the errors of the coarsegrid (‘‘outer’’) problem should dominate in forming the error d. Usually Eq. (5) describes the attenuation of the inﬂuence of errors in initial and boundary conditions with time and distance from the boundaries. It seems to be not the best idea, however, to impose the ‘‘task of reduction’’ of the errors of the ‘‘outer’’ problem on the solution of the ﬁne grid (‘‘inner’’) problem, in particular, because of inﬂuence on these errors of ‘‘outer’’ initial and boundary conditions that are not reproduced directly in the downscaled sub-domain. One can see from Eq. (5) that the major source of the error in downscaling is due to the interpolation of the computed ﬁelds from the coarse grid into the ﬁner one. As a possible way to reduce its inﬂuence, Davies (1976) and Miyakoda and Rosati (1977) suggested the use of numerical procedures of damping of corresponding errors inside a strip of grid cells located near the boundaries of the downscaled sub-domain. Indeed, if certain mathematical restrictions are satisﬁed in the problem (Eq. (1)), one could expect that the initial perturbations due to the erroneous initial conditions should decrease in time and that the perturbations due to the erroneous boundary conditions should decrease with the distance from the boundary. The brute-force damping suggested by Davies and Miyakoda and Rosati should evidently speed up these processes. Still, such an approach does not look logical enough because one ﬁrst introduces the errors in the initial and boundary conditions and then tries to suppress these errors. Moreover, the dumping procedure actually results in decoupling of the inner and outer solutions and, when applied to NWP problems, in non-physical effects reproduced in the grid cells corresponding to the above mentioned strip. In this paper, we discuss a different approach to reduction of the errors of downscaling which is based on the ideas of the perturbation theory (e.g., Nayfeh, 1973).

2. General formalism of the perturbational downscaling

The general idea of the proposed approach is to eliminate the terms uh2 ðh1 Þjt¼t0 ; uGh2 ðh1 Þ and, possibly, gh2 ðh1 Þ ¼ gh2 from the initial and boundary conditions for the downscaling domain. The corresponding technique has been widely used in perturbation methods. It is based on decomposition of the problem, which is characterized by the presence of

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two signiﬁcantly different length scales. A typical example of such a problem (the Blasius problem) is dealing with the formation of the thin boundary layer above the ﬂat plate placed in the free large-scale viscous ﬂow. In this case, the perturbation method recommends decomposing the problem into two successive ones. The solution of the ﬁrst (‘‘outer’’) problem describes the large-scale outer ﬂow with no account for the viscosity effects near the surface of the plate; the second (‘‘inner’’) one describes the effects of friction in a smaller domain adjacent to the plate surface using the solution of the ﬁrst problem to derive the boundary conditions for the second one. One of the principal features of the perturbation method is that one should not correct the outer solution when solving the inner problem. This requirement could be satisﬁed, in particular, when the solution of the inner problem is represented as a sum of the solution of the outer problem and its ‘‘perturbation’’ localized inside the inner sub-domain (i.e., in the vicinity of the plate). It is supposed in some applications that this perturbation is ‘‘small’’ but such an assumption, generally speaking, is not an essential condition of the perturbation theory. For the sake of simplicity, the corresponding re-formulation of the downscaling problem will be presented here for the same linear problem represented by Eq. (1). So, let us consider ﬁrst the auxiliary problem @u0 ¼ Lu0 þ f ; @t

u0 jt¼t0 ¼ u0 ;

u0 jz¼zs ¼ g0 ;

u 0 jG ¼ u G

(6)

which differs from Eq. (1) only because of replacement of the function g with g0 . The last function coincides with g outside g and reproduces the ‘‘smooth component’’ of g on g so that Eq. (6) could be efﬁciently solved in domain D using the sparse grid with resolution h1. Let us also represent the solution u of Eq. (1) in the following form: u ¼ u0 þ u00 00

(7)

0

where u is perturbation of u due to the local inhomogeneity of the underlying surface g. The equation for u00 , which could be easily derived from Eqs. (1) and (6), looks as follows: @u00 ¼ Lu00 ; @t

u00 jt¼t0 ¼ 0;

u00 jz¼zs ¼ g00 ;

u00 jG ¼ 0

(8)

where g00 ¼ 0 outside g and g00 ¼ gg0 at points of g; thus, g is the support of g00 . The outer solution, u0 , could be found numerically from Eq. (2) with h ¼ h1 (actually, here g0 is just function g corresponding to the resolution h1). The perturbation downscaling procedure is based on the numerical solution of Eq. (8) in the sub-domain S where the solution of

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Eq. (8) differs signiﬁcantly from zero (as compared with u0 ). The same approach is completely applicable in the non-linear case too with the only difference that the operator L could depend on both, u0 and u00 ; more speciﬁcally, Lu00 in the right hand side of Eq. (8) could be replaced with L(u0 +u00 )L(u0 ). The results of application of this approach to the numerical solution in the model case of the non-linear Blasius problem (when the solution is known and could be found in numerous textbooks in hydrodynamics) were presented by Genikhovich et al. (2004). In this paper, it is applied to the downscaling with a realistic mesoscale model (TVM) which is non-linear and accounts for major meteorological effects. An additional generalization of the described approach was introduced here to account for the difference in differential operators used in solution of the outer and inner problems. The use of the downscaling technique is important in solution of many problems of atmospheric diffusion. As an example, one can mention here the problem of modelling of the dispersion of accidental releases discharged in the atmosphere as a result of an accident at the nuclear power station. The scale of contamination could reach here thousands of kilometres (as it happened after the Chernobyl accident). However, the main impact (including signiﬁcant deposition of the radioactive materials) was registered in the radius of several tens of kilometres around the power station. In order to account for the loss of material in the vicinity of the source, meso- and regional-scale dispersion models used for evaluation of dispersion in this case should effectively describe the processes in the near ﬁeld too. That is the problem which could be solved with the use of the perturbation downscaling. The increased resolution of the dispersion model near the source, however, is not sufﬁcient for solving this problem correctly. It might be of the similar importance (or, in some cases, even more important) to increase resolution of the meteorological driver which feeds governing meteorological ﬁeld into the dispersion model. The wind ﬁelds generated by this met driver should strictly satisfy to the continuity equation (otherwise artiﬁcial sources and sinks of the pollutant could appear in the computational domain). The approach used in development of the Thermal Vorticity Mesoscale (TVM) model has an advantage as compared with other approaches because it ensures the continuity equation by deﬁnition.

3. Implementation using the TVM model

The original TVM model is a three-dimensional non-hydrostatic anelastic mesoscale atmospheric model solving the dynamic equations in vorticity

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mode (for details see Schayes et al., 1996; Thunis and Clappier, 2000). This section explains the methodology followed by the present implementation of the perturbation formulation on the TVM model and presents a test case using wind observations in Belgium. Starting from the basic Reynolds averaged momentum and thermal equations, we separate the variables Ui, y, P, r in large scale (L) and meso-local scale (M) contributions (e.g., Ui ¼ UiL+UiM) and make use of the Boussinesq approximation. The LS ﬂow is assumed to be in equilibrium, non-turbulent and in hydrostatic equilibrium. So, we have for the LS horizontal ﬂow (j ¼ 1, 2) and for the vertical component, respectively @rL U iL @rL U iL U jL @PL @PL þ ¼ 2rL ijk Oj U kL ; 0 ¼ rL g @t @xj @xi @x3

(9)

The remaining perturbation form of the mesoscale equation becomes @rL U iM @rL U iM U jL @rL U iL U jM @rL U iM U jM þ þ þ @t @xj @xj @xj 0 0 @PM @ rL ui uj ¼ di3 rM g 2rL ijk Oj U kM @xj @xi

ð10Þ

On the left hand side, we have three advection terms Term 2 is the advection of the mesoscale wind by the large-scale wind. Term 3 is the advection of the large-scale wind by the mesoscale wind (mainly effective along the vertical). Term 4 is the advection of the mesoscale wind by the mesoscale wind. Terms 2 and 4 can be combined in @U j U iM =@xj where Uj is the total wind. Following the same development, we obtain a similar equation for the temperature yM. These equations are solved by TVM for the UiM and yM variables. For the dynamic part, TVM solves the equations using the vorticity z ¼ r (rLUiM). In the present formulation, the boundary conditions over any mesoscale perturbation variable FM ¼ Ui, y is speciﬁed as follows: - Upper BC: perturbation FM ¼ 0. - Lateral BC: @FM =@xi ¼ 0 - Bottom BC: for wind, we have UiM ¼ UiL, for temperature, an ordinary ground energy balance is performed on y and then yM ¼ yyL. The perturbation variable is also forced to decrease exponentially with a time constant of about 4–12 h, decreasing with height above ground.

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4. Input data

The external forcing LS data comes from global scale model. The needed data are extracted from the European Center for Medium range Weather Forecasts (ECMWF) reanalyses. It speciﬁes the proﬁles of wind UiL(z) and temperature yL(z) near the centre of the domain under study. For the present level of approximation, we need the proﬁles of temperature and wind speed at the centre of the mesodomain only and these proﬁles are supposed to be valid for the whole mesodomain. This approximation is supposed to be applicable if the domain size is not too large, say less than 50 km across, as is in the present case.

5. Examples of results

In the example shown below, the small-scale domain is 40 km across with a horizontal grid size of 1 km. As in many standard mesoscale models, this run uses 25 vertical levels on a stretched grid in the vertical (the resolution is highest close to the ground) and the lowest level is at 10 m. The topography data are extracted from the GTOPO database and the land use is obtained from the CORINE database. In this version, the model uses a standard Monin–Obukhov similarity theory formulation for the surface layer. Detailed wind data on an hourly basis were available for a few potential wind energy sites over Belgium. We present here some results for the site of Rumes (South-West of Belgium), which is surrounded with small hills without complex topography. Figure 1 shows the wind speed compared to observation for the period from 21 to 28 October 2003. The TVM results are shown with those from two conventional mesoscale models (MAR and ARPS)1 using the usual nesting technique. All three models are forced with the same LS data from ECMWF. As can be expected, some differences may be noticed. So for the TVM results, some wind minima or maxima do not appear simultaneously with observations or are not enough reproduced. Still, as shown in Table 1 (main statistical characteristics), the overall behaviour of TVM perturbation is very similar to the one of other standard models. Moreover, all results present the same kind of short-lived peak deviations from the observations. Quite noticeable in this case, TVM presents the smallest RMS deviation from the observations of all models but this is 1 MAR (Mode`le Atmosphe´rique Re´gional), Univ. of Louvain, with permission of P. Marbaix. ARPS (Advanced Regional Prediction System), in use at VITO, Mol, Belgium (with permission of K. De Ridder).

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Rumes Autumn 2003: Time Series Wind Speed Obs 27 m

ARPS 27 m

MAR 27 m

TVM 27 m

12

Wind speed [m/s]

10

8

6

4

2

0 21/10/03

22/10/03

23/10/03

24/10/03

25/10/03

26/10/03

27/10/03

28/10/03

Time Figure 1. Comparison of wind speed observed and simulated with the ‘‘perturbation’’ method and with two other mesoscale models (MAR and ARPS) using the conventional nesting technique.

Table 1. Statistical characteristics of the results of three mesoscale models for the Rumes data showing the mean wind speed (WS) and the deviations Model

Observed mean WS (ms1)

Calculated mean WS (ms1)

Deviation mean WS (%)

RMS error (ms1)

TVM MAR ARPS

4.40 4.40 4.40

4.53 4.65 4.71

3 6 7

1.34 1.36 1.67

not always the case. For the wind direction (not shown), the correspondence is also adequate with the exception of low wind speeds, but this is rather normal reaction. Simulations made for other sites show a similar behaviour.

6. Conclusions

The examples presented here show that, even with simple working hypothesis, the perturbation approach can reproduce a correct response on

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a local scale, at least comparable to the standard nesting technique. However, it is still very possible that the present data passing between the LS and mesoscale domain, as well as the BC applied are not the optimal choice. Finally, it must be noted that in this exercise, no intermediate (nesting) domain exists between the LS data (ECMWF) and the local scale TVM model. Using the ideas from the perturbation theory, the proposed approach can be generalized on the case of two-way nesting. In this case, the ﬂuxes at the outer boundaries of the downscaling sub-domain should be used to correct the corresponding boundary conditions in the outer problem. Its solution, in turn, could be used to reconstruct the second iteration of solution of the inner problem.

Discussion

D. Anfossi:

E. Genikhovich:

D.W. Byun:

E. Genikhovich:

Comparing classical models to your modiﬁed model, it appears that the agreement between model simulations and observations (wind) increases. It would be interesting to see the agreement or disagreement of the input larger scale model (ECMWF) simulation in order to better appreciate the improvement of your model. All three models compared here were used to downscale the same ECMWF meteorological ﬁelds, and their results were validated using data of local observations at three different meteorological stations located inside a comparatively small domain. We did not try to validate the larger scale model because of the limited time- and spatial span of the observational data sets in use. When I read the paper and the presentation of the theoretical part of the perturbational downscaling, it seems that the derivation is based on a linearized system. However, the application example, shown by the TVM, is applied to a non-linear momentum ﬁeld. Wouldn’t there be a type of closure problem associated with this approach? We do not use linearization or any assumptions justifying linearization. In the ﬁrst part of the paper, we applied the proposed approach to a linear system of equations only for the sake of

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simplicity of explanations. Exactly the same approach is applicable to non-linear systems, and the results shown with TVM could be considered as a proof of this statement.

ACKNOWLEDGMENT

This work was partly supported by the Science Policy Ofﬁce of Belgium under project CP/54 in network CP/H8/541. REFERENCES Davies, H.C., 1976. A lateral boundary formulation for multi-level prediction models. Q. J. R. Met. Soc. 102, 405–418. Genikhovich, E.L., Soﬁev, M., Gracheva, I.G., 2004. Interaction between meteorological and dispersion models at different scales. In: Borrego, C., Norman, A.-L. (Eds.), Air Pollution Modelling and Its Applications XVII. Springer (2007), pp. 158–166. ISBN10:0-387-28255-6. Miyakoda, K., Rosati, A., 1977. One-way nested grid models: The interface conditions and the numerical accuracy. Q. J. R. Met. Soc. 105, 1092–1107. Nayfeh, A.H., 1973. Perturbation Methods. Wiley, New York. Schayes, G., Thunis, P., Bornstein, R., 1996. Topographic vorticity-mode mesoscale-b (TVM) model. Part I: Formulation. J. Appl. Meteorol. 35, 1815–1823. Thunis, P., Clappier, A., 2000. Formulation of a nonhydrostatic mesoscale vorticity model (TVM). Mon. Weather Rev. 128, 3236–3251.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06026-3

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Chapter 2.6 Increase in nitrate deposition as a result of sulfur dioxide emission increase in Asia: Indirect acidiﬁcation Mizuo Kajino and Hiromasa Ueda Abstract It was previously reported that the abundant volcanic sulfate, emitted from the July 2000 eruption of Miyakejima volcano in Japan, resulted in the expulsion of non-volcanic nitrate in aerosols into the gas phase. As the deposition velocity of nitric acid gas is much larger than nitrate aerosols, the deposition of nitrate was accelerated. In the current study, this indirect acidiﬁcation effect was estimated quantitatively using a regional-scale Eulerian aerosol transport model. According to one of the emission scenarios, sulfur dioxide emission in China may grow by 2.42 times in 2030 compared to its level in 2000. Using this scenario, monthly mean nitrate deposition will increase up to 1.5 times in spite of the assumption that nitrogen oxides emission does not change. The indirect effect is signiﬁcant, but the precise extent of its effects is still uncertain and further studies are necessary to accurately estimate these effects.

1. Introduction

Kajino et al. (2005) have revealed that the abundant volcanic sulfate emitted from the July 2000 eruption of Miyakejima Volcano (located on the Northwest Paciﬁc Ocean, 180 km south of Tokyo, Japan) resulted in an expulsion of non-volcanic NO 3 in aerosols into a gas phase. The deposition of NO 3 was accelerated as its deposition velocity is much larger in a gaseous (HNO3) form than as a particulate form (NO 3 ) in aerosols. This effect was referred to as indirect acidiﬁcation, compared to the direct acidiﬁcation caused by the deposition of the volcanic SO2 4 ; and was for the ﬁrst time estimated quantitatively using a regional-scale Eulerian aerosol transport model. The indirect effect was generally less pronounced than direct effect, although it was 2.1 times greater over the

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North Paciﬁc Ocean in winter. The minimum contribution of indirect acidiﬁcation was found to be 7%, which is not negligible. The volcanic SO2 emission was 9 Tg over a 1-year period between September 2000 and August 2001. It amounts to a quarter of all the anthropogenic emissions from Asia in 2000 (34.3 Tg year1; Streets et al., 2003). Trends of Asian SO2 emissions are uncertain. Klimont et al. (2001) have estimated that SO2 emission in East Asia will grow by 46% from 1990 (24.5 Tg year1) until 2030 (35.7 Tg year1). Carmichael et al. (2002) found changing trends in Asian SO2 emissions, which may grow only to 40–45 Tg year1 by 2020, which is substantially lower, compared to other estimates in the early 1990s of 80–110 Tg year1. Fujino et al. (2002) estimated that the Asian SO2 emissions will change from 30.8 Tg year1 in 1998 to 21.9–85.1 Tg year1 in 2032, under four different socio-economic scenarios. The volcanic emission is comparable to these changes in the future Asian anthropogenic emissions estimates. Consequently, the indirect acidiﬁcation mechanism is supposed to occur in general air pollution, accelerating NO 3 deposition under sulfate-rich contaminated air masses.

2. Methodology

A chemical transport model, MSSP (Model System for Soluble Particles; Kajino et al., 2004) was employed to simulate regional-scale emission, tropospheric chemistry, transport and deposition in East Asia. The model consists of three parts: a meteorological model (MM5), a chemical transport model and a gas–aerosol equilibrium model (SCAPE). The model domain is 20–501N, 90–1451E, with a horizontal 0.51 0.51 grid in spherical coordinates and 12 vertical levels in a terrain following coordinates from the ground to the tropopause. The simulation results were compared with EANET (Acid Deposition Monitoring Network in East Asia) data and their consistency was veriﬁed (Kajino et al., 2005). As inputs to the MSSP model, the present and future emission inventories were derived by multiplying Asian emission inventory in the year 2000 with the resolution of 0.51 0.51 (Streets et al., 2003), and the increased ratios which were estimated using an AIM/Trend Model (Asian-Paciﬁc Integrated Model; Fujino et al., 2002). An AIM/Trend was developed in order to assess the future environmental loads based upon the past socio-economic trends. By using this model, the emission trends were estimated in 42 AsiaPaciﬁc countries (including 22 countries in Asia) until 2032, under four different scenarios. In this paper, the simulation results of the emission of 2030 under one of these scenarios, namely the Security First (SF) scenario,

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are compared with the emission in 2000, in order to assess the indirect acidiﬁcation mechanism. The SF scenario assumes a world of great disparities where inequality and conﬂict, brought about by socio-economic and environmental stresses, will prevail. As a result, the largest emission growth ratio is estimated under the SF scenario among all the scenarios employed in the AIM/Trend. Four types of simulations were performed to observe the effect of SO2 and NOx emissions on NO 3 deposition. The same meteorology was used as an input in all simulations. RUN-0 is a simulation using both the SO2 and NOx emissions in the year 2000. RUN-S is a simulation where SO2 emission increases by 2030 (2.69 times in Asia) and NOx emission does not change. RUN-N is a simulation where only NOx emission increases (2.45 times), and both SO2 and NOx emissions increase in RUN-B.

3. Results and discussion

We would ﬁrst like to brieﬂy explain the indirect acidiﬁcation processes under sulfur-rich contaminated air masses. Increase in SO2 emission results in an increase in SO4 aerosol. This increased SO4 aerosol expels particulate NO 3 into the gas phase. 2NH4 NO3 þ H2 SO4 ! ðNH4 Þ2 SO4 þ 2HNO3 ð"Þ

(1)

The deposition velocity of HNO3 gas is much larger than that of par2 ticulate NO 3 . The dry deposition velocity of particulate NO3 is 10 – 4 1 1 10 cm s , while that of gaseous HNO3 is approximately 4 cm s . The Henry’s law constant for HNO3 is 2.1 105 mol L1 atm1, which is much larger than those for SO2 and NOx, at 1.23 and 0.01 mol L1 atm1, respectively (Kajino et al., 2005). Therefore, the increased expelled HNO3 gas rapidly deposits and the total nitrate (HNO3 gas plus NO 3 aerosol) deposition is accelerated. Although total nitrate does not increase, nitrate deposition associated with an increase in the SO2 emission will increase. Hereinafter, the simulation results will show how much the environment can be acidiﬁed in future by this indirect effect. In this paper, March 2001 is selected as several studies have been conducted in this period (e.g., TRACE-P and MICS-Asia phase II). Figure 1 shows the past, present and future trends of SO2 emission from 1995 up to 2032, released from 22 Asian countries following the SF scenario, estimated by the AIM/Trend. The Chinese SO2 emission in 2030 is estimated to be 2.42 times than that in 2000, and the emission in the whole Asian region is expected to be 2.69 times for the same years. As the socio-economic scenarios in the AIM/Trend are on a country basis, we

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Figure 1. The past, present and future trends of SO2 emission released from 22 Asian countries following the Security First scenario, estimated by the AIM/Trend model.

Figure 2. Modeled monthly-accumulated precipitation (mm) and monthly mean wind vector (m s1) at the lowest model grid (50 m AGL) in March 2001.

were not able to consider any concentrations of population or expansions of urban or industrial areas in each identical country. Therefore, the same multipliers of growth ratio are applied to all grids within each country. Figure 2 shows monthly-accumulated precipitation (mm) and monthly mean wind vector (m s1) at the lowest model grid (50 m AGL: above ground level) in March 2001. Northwesterly and westerly continental outﬂows prevailed over the ocean in that period. Precipitation was seen

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Mizuo Kajino and Hiromasa Ueda

over the ocean and southern Chinese continent, where no speciﬁc wind direction prevailed and substances were less transported. Dry deposition depends on atmospheric concentration, surface air current and ground conditions, while wet deposition depends on atmospheric concentration and precipitation. As the wet deposition amount is most signiﬁcantly affected by the precipitation, distributions of transport and acidiﬁcation should substantially change not only by season but also through the years. Figure 3 shows monthly mean increase in gas phase fraction of nitrate (RUN-S minus RUN-0). The gas phase fractions in the source region (Northern China) and its downwind areas over the ocean increase by 5–15%. As the increase of gas phase fractions indicates decrease in particulate phase at the same level, SO2 emission increase resulted in changes in the gas–aerosol equilibrium of nitrate by 10–30%. Figure 4 shows changes in nitrate deposition due to an increase in SO2 emission (RUN-S minus RUN-0). Despite the fact that NOx emission does not change, NO 3 deposition increases due to the indirect effect. deposition increases (110–150%) are found over the Widespread NO 3 source region and the ocean. NO 3 deposition is much larger in the northeastern part of China (larger than 300% as maximum) than in northern China (200% as maximum). Thus, the indirect acidiﬁcation is

Figure 3. Modeled monthly mean increase in gas phase of nitrate (%).

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Figure 4. Changes in nitrate deposition due to an increase in SO2 emission (%).

more pronounced in downwind area than the source region. Although SO2 emission decreases scenario by 15% in Japan, NO 3 deposition increases over northern and southwestern parts of Japan by 20–40% due to the indirect effect. The indirect acidiﬁcation effect has another important aspect. Faster deposition implies less transport. Figure 5 shows changes in the total nitrate (gas plus aerosol) concentration on the surface due to an increase in the concentration of sulfate (RUN-S minus RUN-0). Surface nitrate concentration decreases to 70–90% over the area where gas-phase fraction and NO 3 deposition increases are found. Thus, the indirect effect can not only cause widespread acidiﬁcation by nitrate over downwind area from the SO2 emission source region but also lead to a reduction in the transport of nitrate and thus NO 3 deposition may decrease in an area farther than where NO 3 deposition increases. The indirect and the direct acidiﬁcation effects were next compared. The term ‘‘direct acidiﬁcation’’ is used here for an increase in NO 3 deposition as a result of NOx emission increase, whereas it was used for the deposition of volcanic SO 4 in a previous Miyakejima eruption study (Kajino et al., 2005). Figure 6 shows changes in NO 3 deposition in the case where only NOx emission increases (left; RUN-N; direct effect), and

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Mizuo Kajino and Hiromasa Ueda

Figure 5. Changes in the total nitrate (gas plus aerosol) concentration on the surface due to an increase in the concentration of sulfate (%).

in the case where both NOx and SO2 emissions increase (right; RUN-B: direct plus indirect effect). When only NOx emission increases following the SF scenario up to 2030 (2.45 times in Asia), there is a 2.5–4 times increase in NO 3 deposition in northwestern China and South East Asia, whereas the increase in this ratio is 1.5–2.5 times over central China and the ocean near the coast. In the presence of indirect effect, NO 3 deposition increases substantially over the downwind areas of northern Chinese and South East Asian emission source regions by a ratio of more than ﬁve times as maximum and is more widespread over the ocean. In the right panel of the ﬁgure, the area where the deposition decreases is slightly extended in the ocean near the east coast of Japan. It is probably due to the above-mentioned inverse effect, which reduces the transport distance. We would like to conclude by stating that the indirect acidiﬁcation process is important in general air pollution. Future SO2 emission reduction may beneﬁt not only from SO4 deposition decrease but also from NO 3 deposition decrease. This process is expected to occur in downwind regions worldwide with large SO2 emission sources. However, the effects of this process on the future emission trends in Asia are unclear. Further studies are necessary in order to accurately estimate the effects of this process.

Indirect Acidification Figure 6. (Left) changes in NO 3 deposition in the case where only NOx emission increases, and (right) in the case where both NOx and SO2 emissions increase.

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142

Changes in basic components such as NH+ 4 and other crustal materials (Ca2+, Mg2+, etc.) may also affect this effect substantially, but not considered in this study as the future trends are not provided by the AIM/ Trend model. Also, changes in VOC (volatile organic compounds) emissions and its speciation can possibly affect the process through NOx–O3 chemistry, but not considered in order to simplify the discussion. Discussion

M. Soﬁev:

M. Kajino:

Comment: An inverse problem was discussed during building the 2nd Sulphur Protocol in Europe: How will SOx reduction affect NOx/NHx deposition? At that time, we could not get any signiﬁcant signal from our model runs and it is very pleasant to see the strong evidence for such an elegant theory. Question: This secondary acidiﬁcation depends on SOx, NOx and NHx. How did you deal with NHx emissions? Thank you for your meaningful comment. It is still difﬁcult to model deposition processes precisely, so our results may contain uncertainties. Next step we should take is to detect this mechanism from several observation results and monitoring data sets to support the theory. As for the question, we did not treat the NHx emission change in the current stage. However, it will affect gas–aerosol partitioning of nitrate substantially; so I will take into account it as well for next.

ACKNOWLEDGMENT

The authors feel obliged to Dr. Junichi Fujino of National Institute for Environmental Studies, Japan for providing access to the AIM/Trend model.

REFERENCES Carmichael, G.R., Streets, D.G., Calori, G., Amann, M., Jacobson, M.Z., Hansen, J., Ueda, H., 2002. Changing trends in sulfur emissions in Asia: Implications for acid deposition, air pollution, and climate. Environ. Sci. Technol. 36(22), 4707.

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Fujino, J., Matsui, S., Matsuoka, Y., Kainuma, M., 2002. In: Kainuma, M., Matsuoka, Y., Morita, T. (Eds.), AIM/Trend: Policy Interface, Climate Policy Assessment. Springer, Tokyo, p. 217. Kajino, M., Ueda, H., Satsumabayashi, H., An, J., 2004. Impacts of the eruption of Miyakejima Volcano on air quality over far east Asia. J. Geophys. Res. 109, D21204, doi:10.2029/2004JD004762. Kajino, M., Ueda, H., Satsumabayashi, H., Han, Z., 2005. Increase in nitrate and chloride deposition in East Asia due to increased sulfate associated with the eruption of Miyakejima Volcano. J. Geophys. Res. 110, D18203, doi:10.1029/2005JD005879. Klimont, Z., Cofala, J., Scho¨pp, W., Amann, M., Streets, D.G., Ichikawa, Y., Fujita, S., 2001. Projections of SO2, NOx, NH3 and VOC emissions in East Asia up to 2030. Water, Air, Soil Pollut. 103, 193. Streets, D.G., Bond, T.C., Carmichael, G.R., Fernandes, S.D., Fu, Q., He, D., Klimont, Z., Nelson, S.M., Tsai, N.Y., Wang, M.Q., Woo, J.-H., Yarber, K.F., 2003. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000. J. Geophys. Res. 108, D21, 8809, doi:10.1029/2002JD003093.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06027-5

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Chapter 2.7 Predicted aerosol concentrations over East Asia and evaluation of relative contribution of various sources with global chemical transport model T. Kitada, Y. Shirakawa, K. Wagatani, G. Kurata and K. Yamamoto Abstract Global scale chemical transport model for aerosol particles (AGCTM) has been developed and applied for 40-days-simulation in February – March, 2001. Calculated results were compared with observations in various cities in China and Japan. They showed acceptably good agreements, and showed relative importance of emission sources of soil dust, anthropogenic fuel combustion, and biomass burning at the observation sites. 1. Introduction

To evaluate relative contribution of both anthropogenic and natural emission sources on aerosol concentration, AGCTM (Aerosol Global scale Chemical Transport Model) has been developed. The model can trace source-receptor relations on various chemical species forming aerosol particles. Numerical simulations of transport/chemistry/ deposition of aerosols and other chemical species were performed during February 20 –March 31, 2001. Performance of the AGCTM has been evaluated by comparing the calculation results with TSP (total suspended particulates) concentration at observation sites in China and with PM10 at Tokyo and Osaka in Japan; the observation sites in China are shown in Fig. 1.

2. Global scale chemical transport simulation 2.1. Model

The model is solved with the LOD-FEM (Kitada et al., 1983) using spherical coordinate; application of the model to a partial sphere of the

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Figure 1. Observation sites of TSP (total suspended particulates) in China: 1. Beijing, 6. Shenyang, 10. Shanghai, 16. Hefei, 18. Xiamen, 36. Kunming, and 37. Lhasa, where calculation will be compared with observation in Figs. 8 and 9. See circles for the sites.

earth was made in several occasions (Kitada and Nishizawa, 1998; Kitada et al., 2001). The model currently uses 2.5 2.5 degree grid with 23 vertical layers from the earth’s surface to 10 hPa. Thirty chemical species are advected, while steady-state assumption is applied for 13 species. A model of 97 chemical reactions, which is a simpliﬁed version of the chemistry system used in Kitada and Regmi (2003) is incorporated into the model. All the major processes such as advection, diffusion, dry/wet depositions, etc., are also included in the model. Hypothetical particle simulations were additionally performed to visualize movement of anthropogenic pollutant and soil dust. On the soil dust emission, a widely used model which assumes the ﬂux is proportional to the fourth power of friction velocity, u4 ; and a model including the 3.75 power of un were tested; the 3.75 power model was derived in this study as a best ﬁtted line for observed vertical ﬂux of soil dust plotted against friction velocity on a log–log graph referred in Shao (2000). The obtained equation is as follows: uth Flux ¼ 2:09 106 ðu Þ3:75 1:0 ðkg m2 s1 Þ u

(1)

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T. Kitada et al.

Figure 2. NOx emission by fuel combustion (kmol m2 s1, EDGAR).

where uth denotes a threshold friction velocity for soil particle inﬂation, and was tentatively assumed as 0.25 m s1. Soil dust was classiﬁed into four groups with its diameter: 0.2–2.0, 2.0–3.6, 3.6–6.0, and 6.0–12.0 (mm). 2.2. Emission sources 2.2.1. Anthropogenic

EDGAR (2005) was used as a base for anthropogenic emission sources; as an example, Fig. 2 shows annually averaged NOx emission by EDGAR. Fuel consumption may show large variability with climatic condition and also with economic situation. In this study, several sets of seasonal factor on fuel consumption were tested. The seasonal factor was tentatively determined by considering monthly averaged temperature and information on fuel consumption such as that shown in Fig. 3; for example, MF ¼ 1 for TZ151C, 1.2 for 15>TZ101C, 1.6 for 10>TZ51C, 2.5 for To51C. 2.2.2. Biomass fire

Biomass burning emissions were estimated every three days using a ﬁre spot distribution reported in Web Fire Mapper (2004). Global emission strength was based on EDGAR (2005) and GEIA (2002), and monthly

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Example of Monthly Factor for Consumption of Heavy Oil, Natural-Gas, and Kerosene in Tokyo, Japan (-) 30

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factor of the biomass ﬁre emission was taken from GEIA (2002) data in 1987. 2.2.3. Other emission sources

Volcanic SO2 emission of Mt. Miyakejima was included in the calculation. Emission of soil dust has been described above. 3. Results 3.1. High aerosol concentrations in Beijing and Shanghai in March 2001

During the simulated period from February 20 to March 31, 2001; the ‘‘Kosa’’ event, i.e., a heavy soil dust storm was reported three times around 1–5, 10, and 20–23 March. Figure 4 shows these three high concentration episodes in northern and northeastern cities such as Beijing (upper panel) and eastern cities of Shanghai, etc. (lower panel). Though air pollution at both Beijing and Shanghai areas show, in appearance, similar monthly variation in March as in Fig. 4, examination of the results of Lagrange particle simulations suggests different reason for high concentration at Beijing and Shanghai

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Figure 4. Observed monthly variations of air pollution index (API) from February to May in 2001: the upper panel for northern cities of Beijing etc., and the lower panel for eastern cities such as Shanghai (from TRACE-P web-site by Zhong Guo and Huanbaoju Wang).

during 20–22 March. The Lagrange simulation utilizes nonbuoyant hypothetical particles discharged from natural and anthropogenic emission source areas so that the polluted air mass originating from the areas can be kept tracked. In Shanghai area, synoptic scale pressure gradient was weak with high pressure in the south, and low pressure in the north during 18–21 March (see Fig. 5 for surface ﬂow during 20–23 March), and thus the pollutants discharged for the last 3 days were remained as indicated in Fig. 6 showing distribution of ‘‘anthropogenic’’ particles on 22 March. In contrast, Beijing and other northern cities were largely inﬂuenced by the dust storm generated due to the strong low-pressure system, which had its center at 45–501N, and the soil dust formed high TSP concentration. Particle distribution released from the assumed soil dust area (see Fig. 7) demonstrates this situation.

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Figure 5. Surface wind over Asian Continent, longitude: 501E–1801E and latitude: 101S– 701N, from 00GMT March 20 to 00GMT March 23, 2001 (clockwise; every 12 h).

Figure 8 compares calculated SPM with observed TSP for March 2001 at (a) Beijing and (b) Shanghai. Calculation also shows chemical components of sulfate, nitrate, organic, and black carbon (both originated from fossil fuel and biomass burning), and soil dust with ﬁne (do2 mm) and coarse sizes. With the results of particle simulations in Figs. 6 and 7 in mind, it is suggested: (1) timing of peak concentrations is relatively well simulated both in Beijing and Shanghai. (2) High TSP concentrations around 22–24 March, however, are rather underpredicted. In particular, calculated SPM in Beijing differs from observation also in terms of its phase. (3) Extremely high TSP events are mostly due to soil dust; soil dust (‘‘yellow’’ and ‘‘pink’’ in Fig. 8) can explain the peak values except for it around 22–24 March at Shanghai. 3.2. Comparison between observed TSP and calculated SPM

Figure 9 similarly presents observed TSP and calculated SPM at cities in China and Japan: Lhasa near the Himalayan Mountains, Kunming in the southern mountainous area, Hefei in the middle plain area and west of Shanghai, Xiamen at the coast close to Taiwan, Shenyang in the

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Figure 6. Four-day transport simulation of hypothetical particles representing anthropogenic pollutant emitted from the eastern China source area: ‘‘red’’, ‘‘blue’’, ‘‘green’’, and ‘‘black’’ show the particles released on 18, 19, 20, and 21 March, respectively; the particles are projected on (a) horizontal, and (b) west–east vertical planes at 00GMT March 22, 2001.

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Figure 7. Same as in Fig. 6 but for soil dust; the particles were released from a modeled ‘‘yellow sand’’ source area.

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(1) Beijing: Observed TSP VS. Calculated SPM in March, 2001 1400

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(b) Shanghai Figure 8. Comparison between observed TSP and calculated SPM for 1–31 March, 2001 at (a) Beijing and (b) Shanghai. Solid and dashed lines show TSP observations, while vertical columns with different colors represent calculated aerosol components from bottom to top: sulfate, nitrate, organic carbon (OCF), black carbon (BCF), OCB, BCB, biogenic carbon (OCV), soil (ﬁne), and soil (coarse). Unit is in mg m3.

Predicted Aerosol Concentrations Over East Asia 153 Figure 9. Same as Fig. 8 but for (a) Lhasa, (b) Kunming, (c) Hefei, (d) Xiamen, and (e) Shenyang in China; (f) Tokyo, and (g) Osaka in Japan.

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Figure 9. (Continued)

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northeastern China, and Tokyo and Osaka in Japan. Characteristics of TSP variation in Lhasa is almost captured in Fig. 9a. However, TSP shows background-like concentration about 100 mg m3 at Lhasa, and this is not reproduced by the model, suggesting either existence of small scale local dust source or emission ratio of long staying ﬁne dust particles. At Lhasa contribution of combustion-originated SO4, NO3, BC, OC, etc., seems negligible. At Kunming, Hefei, and Xiamen, the model reproduces TSP variations quite well, but in these areas the soil dust is part of the total aerosol concentration and is not necessarily dominant component as shown in Fig. 9b, c, d. In contrast to other cities SPM at Kunming (Fig. 9b) is largely affected by sources of biomass burning and vegetation; about 25–30% of SPM is suggested to come from these sources. At Shenyang (see No.6 for its location in Fig. 1) in the northeastern China, the model (Fig. 9e) underestimates the observed TSP, and predicts low soil dust contribution to the total aerosol concentration. Since other observational fact also suggests nondirect effect of ‘‘Kosa’’, strong local source not counted and strong stable stratiﬁcation due to very low surface temperature might be key factors for the underestimation. Calculated SPM in Tokyo (Fig. 9f) and Osaka (Fig. 9g) relatively well simulate the observed PM10. The ‘‘observation’’ in Tokyo and Osaka were derived by the average of the data at more than 100 sites in the corresponding 2.5 2.5 degree grid cells, and this may partly explain the model’s good performance for Tokyo and Osaka. 4. Summary of the AGCTM performance based on China result

A global chemical transport model for aerosol has been developed. The model performance can be summarized as follows: (1) the global model with current 2.5 2.5 degree resolution produced relatively good and acceptable aerosol concentration except for some locations such as Shenyang. (2) The model potentially provides detailed source-receptor relationship for various kinds of aerosol particles such as sulfate, nitrate, organic carbon, black carbon, soil dust, etc. (3) Further adjustment of soil dust parameters and appropriate inclusion of marine aerosols will improve the model’s precision. Discussion

O. Hellmuth:

In your introduction, you mentioned the possibility to run different dust ﬂux parameterizations, especially with

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T. Kitada:

respect to the power dependency of the dust ﬂux in terms of friction velocity (threshold friction velocity). With regard to your coarse grid, can you comment on the sensitivity of your predicted dust distributions to the choice of the dust ﬂux parameterization? What is your experience/recommendation? Dust ﬂux model usually includes friction velocity, threshold friction velocity, and so on. Apart from the nature of soil and its condition such as moisture content, even estimation of the friction velocity depends on roughness length and thus area itself, since larger (or smaller) area may include different conditions of roughness elements in type, size, and spatial density. This indicates that in a numerical simulation it may be necessary more or less for us to regard coefﬁcients in a ﬂux model as the parameters to satisfy agreement between calculation and observation for a given grid cell. This was my stance. Then, for determination of the parameters in our soil ﬂux expression we picked up a ﬁgure in which some of the previous observations were summarized. By using this expression, we were able to obtain better agreement. As can be inferred from the equation form, the ‘‘3.75 power’’ model, compared with the ‘‘forth power’’ model, gave larger (smaller) ﬂux for friction velocity smaller (larger) than 1 m s1.

ACKNOWLEDGMENTS

This work has been supported in part by Grant-in-Aid for Scientiﬁc Research on Priority Areas (A), No. 14048211 and for Scientiﬁc Research (B), No. 17360256 by the Ministry of Education, Culture, Sports, Science and Technology, Japan, and also by Global Environmental Research Fund, No. B-8 and C-061 (P.I., S. Hatakeyama) by the Ministry of Environment, Japan.

REFERENCES EDGAR (Emission Database for Global Atmospheric Research), 2005. http://arch.rivm.nl/ env/int/coredata/edgar/

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GEIA (Global Emission Inventory Activity), 2002. http://weather.engin.umich.edu/geia/ index.html Kitada, T., Carmichael, G.R., Peters, L.K., 1983. The locally-one-dimensional, ﬁnite element method (LOD-FEM) for atmospheric transport/chemistry calculations. Numerical Methods in Engineering, Pluralis, Paris, France, Vol. 1, 223–233. ISBN: 2-86216-006-7. Kitada, T., Nishizawa, M., 1998. Modeling study of the long range transport of acidic pollutants over East Asia and the west Paciﬁc Ocean: Sensitivity of acid deposition to scavenging model parameters and emission source distribution. J. Global Environ. Eng. 4, 1–29. Kitada, T., Nishizawa, M., Kurata, G., 2001. Numerical simulation of the transport of biomass burning emissions in Southeast Asia—September and October, 1994. J. Global Environ. Eng. 7, 79–99. Kitada, T., Regmi, R.P., 2003. Dynamics of air pollution transport in late winter time over Kathmandu valley, Nepal: As revealed with numerical simulation. J. Appl. Meteorol. 42, 1770–1798. Shao, Y., 2000. Physics and Modelling of Wind Erosion. Kluwer Academic Publishers, p. 393. Tokyo Metropolitan Ofﬁce, 2003. http://www2.kankyo.metro.tokyo.jp/kaizen/kisei/taiki/ baienH14/tukibetu14.htm Web Fire Mapper, 2004. http://maps.geog.umd.edu/Global_simp/launch.html

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Chapter 2.8 Long-term simulations of photo-oxidants and particulate matter over Europe with emphasis on North Rhine-Westphalia M. Memmesheimer, S. Wurzler, E. Friese, H.J. Jakobs, H. Feldmann, A. Ebel, C. Kessler, J. Geiger, U. Hartmann, A. Brandt, U. Pfeffer and H.P. Dorn Abstract The EU directive 1999/30 allow for air quality modelling as a tool to plan strategies for air pollution reduction in Europe. A comprehensive air quality model, including ozone, NO2 and atmospheric particle (PM10) is applied to estimate the future development of air quality in Europe. As a base year 2002 is selected, model calculations are performed for 2005 and 2010 for Europe as well as for North Rhine-Westphalia (NRW) as a strongly polluted core region in central Europe. The results show an improvement in air quality, in particular in western and central Europe. However, major problems to fulﬁl the limit values as given in the EU directives might occur with respect to PM10. 1. Introduction

In September 2002, the amendment of the 22nd German Federal Immission Control Ordinance transferred the EC air quality framework directive 96/62/EC as well as the 1st and 2nd EC air quality directives 1999/30/EC and 2000/69/EC into German national law. Similar law amendments were carried out in all other EC member states. Limit values to protect human health and ecological system have been established within the framework of the EU directives on air quality for SO2, NOx, NO2, CO, benzene, Pb, aerosol particles (total and PM10) and ozone (3rd air quality directive 2002/3/EC). In short, since 2002 the air quality has to be determined and monitored area wide in each member state using the combined efforts of observational networks and modelling. Threshold concentrations will have to be kept for e.g., PM10 in 2005 and for e.g., NO2 in 2010. Comprehensive air quality models (AQMs) have been

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developed during the last decades to simulate the transport, chemical transformation and deposition of air pollutants on the regional scale. In recent years, the formation of secondary particles and particle dynamics has been included into the models (Binkowski, 1999; Schaap, 2000; Andersson-Sko¨ld and Simpson, 2001; Schell et al., 2002; Riemer et al., 2003; Kerschbaumer et al., 2005; Stern, 2005) even on the global scale (Stier et al., 2005). The rapid development of modern information technology now allows the application of comprehensive AQMs on an annual time scale as well as for short-term prediction, even on the hemispheric scale (Jakobs et al., 2002; www.eurad.uni-koeln.de). The modelling systems can be applied as a tool to develop optimised air pollution abatement strategies (Amann et al., 2005). The model results allow the assessment of air quality in regions where observations are incomplete or missing.

2. Model description and results

In this paper, the results of long-term runs for the years 2002, 2005 and 2010 carried out with the European Air Pollution Dispersion Model (EURAD, Jakobs et al., 2002; Memmesheimer et al., 2004) are discussed with special emphasis on atmospheric particulate matter over Europe and especially over North Rhine-Westphalia (NRW), representing the area of Germany with the highest density of heavy industry. The aim of the study is to investigate the future changes in air quality with respect to the EC air quality framework directive in the highly industrialised area of NRW. Long-range transport across the boundaries of NRW is included by a nesting procedure. The EURAD model (EURAD) predicts the transport, chemical transformation and deposition of air pollutants. Meteorological ﬁelds are provided by the meteorological model MM5; transport is modelled by solving the 3-D advection and diffusion equation. Gasphase chemistry is handled with the RACM-MIM chemical mechanism (Geiger et al., 2003), which has been implemented and evaluated within the framework of the AFO 2000 project IDEC to account for an improvement of the treatment of isoprene oxidation (Dorn et al., 2005). Dry deposition is treated with a resistance model. The Modal Aerosol Dynamics Model including the formation of secondary organic particles (MADESORGAM, Schell et al., 2002) has been applied with extensions to account for the formation of secondary organic aerosols. MADE provides size resolved concentrations of secondary and primary aerosol species. The calculations are performed using a one-way nesting scheme. The horizontal grid resolution is 125 km on the European scale, 25 km for an

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intermediate scale covering central Europe and 5 km to simulate the region of NRW (see Fig. 1). A long-term run for the base year 2002 has been carried out, including nesting focussed to NRW. The impact of emission projections on air quality has been investigated for the years 2005 and 2010. Model results have been analysed with respect to the requirements of the EC air quality directive with emphasis on PM10, ozone, NO2 and SO2. Figure 2 shows, as an example, the results for the annual averages of NO2 and PM10 for the base run in 2002 for all model domains. The limit value for the annual average (40 mg m 3) is exceeded for PM10 in the Ruhr area (Duisburg, N2-domain), the region of Paris

Figure 1. Model domains for the numerical simulations with the EURAD system with nesting. The mother domain (N0) has a horizontal grid size of 125 km, the intermediate nest 1 (N1) 25 km. The innermost domain is focussed on North Rhine-Westphalia (NRW) with a grid size of 5 km. The model design for this simulation has 23 layers in the vertical, about 16 below 3000 m, the upper boundary is at 100 hPa (about 16 km) and the thickness of the lowest layer is in the order of 35–40 m.

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Figure 2. (Left) Annual average of NO2 concentration (mg m 3) for Europe (125 km grid size), central Europe (25 km grid size) and North-Rhine-Westphalia (nest 2, 5 km grid size); (right) same as for NO2, but for PM10. All ﬁgures are for the base year 2002.

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Figure 3. (a) Annual average of NO2 concentration (mg m 3) for Europe (125 km grid size) and for the scenario years 2005 and 2010 (left), same for PM10 (right). Concentrations, in general, are decreasing; however, there is less decrease in eastern Europe, and in the Moscow area even with the coarse resolution of 125 km the annual limit value for PM10 is still exceeded in 2010. (b) As (a), but for North-Rhine-Westphalia (N2-domain). The annual averaged concentrations decrease signiﬁcantly from 2005 to 2010, and are below the annual limit values even in the Rhine-Ruhr area, which is the most heavily polluted region in NRW.

and even in the mother domain with its coarse horizontal resolution in the Moscow area. The highest measured value is 46 mg m 3 in Duisburg– Bruckhausen. In the mountain areas in NRW, the annual average of PM10 is below 20 mg m 3 for the N2-simulation (not for the N1-simulation or the mother domain). This agrees quite well with the observations in that area. The three measurement sites in the mountain regions in NRW show annual averages of 15 and 18 mg m 3, respectively. Therefore, the difference between strongly polluted urban areas and rural regions

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Figure 3. (Continued)

seems to be represented best for the N2-domain with 5 km spatial resolution. For NO2, the limit value is only exceeded in the model simulation at one grid box in the Neuss/Du¨sseldorf area; however, observations show more than the limit value for NO2 (40 mg m 3) in street canyons in several cities, which cannot be resolved on a spatial scale of 5 km. Figures 3a, b and 4 show examples for the scenario runs with emission projections from 2005 and 2010, for the mother domain and the N2domain, respectively. The concentrations as calculated in the model decrease signiﬁcantly till 2010, in particular in Western Europe. However, problems still remain with respect to the limit values of PM10, ozone and NO2 in highly industrialised regions with high population and heavy trafﬁc. In particular, the number of exceedances for the daily average of PM10 (limit value is 50 mg m 3, 35 exceedances are allowed within a year)

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PM10, number of days with daily average > 50 µg/m3 2005 2005

2010 2010

Figure 4. (Left) PM10, number of days exceeding the 24 h limit value of 50 mg m 3 for the mother domain, Europe, (2005, 2010); (right) same as for the mother domain but for N2 with North-Rhine-Westphalia.

is hardly to be fulﬁlled in strongly polluted areas over the whole of Europe. Extensions of the modelling system to the hemispheric scale (intercontinental transport; Trickl, 2005) and to more local scales with a grid size of 1 km are discussed. Model results have been compared with observations, in particular within the region of NRW. The comparison shows a quite good agreement between observations and PM10 on the annual scale. However, more detailed comparisons with measured composition of atmospheric particles show an overestimation of the modelled secondary formed nitrate and ammonium, in particular in winter. The reasons for this

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behaviour of the model have to be investigated in further studies with the aim to investigate and improve the parameterisations of the dynamical and chemical processes used in the numerical model.

Discussion

D. Steyn:

M. Memmesheimer:

S. Andreani-Aksoyoglu:

M. Memmesheimer:

S. Andreani-Aksoyoglu:

M. Memmesheimer:

How much would your results have changed if you had used a different base-year meteorology? One interesting approach would be to run 2002 emissions with 2003 meteorology since 2003 was an extremely polluted year. Thank you very much for pointing out this interesting point. We currently prepare several model runs where we will try to analyze the impact of meteorology on the results. One approach is exactly your suggestion. As an extension we would like to use an appropriate ensemble of years or typical meteorological patterns. For your calculations for 2010, did you take the change in the background ozone into account? No, we did not do that in this study. Background concentrations are the same in all model runs. Currently a study in underway, where we will use the results of our hemispheric model version as boundary and background values for 2010 with global emission projections for 2010. For your calculations for 2010, did you use emissions according to the current legislation or the Go¨teborg Protocol? We have used the Current Legislative Emissions (CLE) as given by gridded EMEP/ IIASA for Europe. For North RhineWestphalia, we have used special information on local sources (if available) as given to us by the local environmental agency to perform the emission projects.

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ACKNOWLEDGEMENTS

This work was supported by the Environmental Agency of North RhineWestphalia (LUA) and the BMBF within the AFO2000 projects IDEC and ATMOFAST. The work has been supported by the RRZK/ZAIK, University of Cologne and the Research Centre Ju¨lich. Thanks also to EMEP, TNO, the EU and the UBA, Germany. Helpful discussions with R. Stern, E. Reimer, A. Kerschbaumer, P.J. Builtjes, H. Geiger, T. Trickl and A. Stohl are gratefully acknowledged.

REFERENCES Amann, M., Bertok, I., Cabala, R., Cofala, J., Heyes, C., Gyarfas, F., Klimont, Z., Scho¨pp, W., Wagner, F., 2005. A ﬁnal set of scenarios for the Clean Air For Europe (CAFE´) programme. CAFE´ Scenario Analysis Report Nr. 6, International Institute for Applied Systems Analysis (IIASA), Laxenburg, 106 Seiten, Juni 2005, http:// www.iiasa.ac.at/rains/CAFE_ﬁles/CAFE-D3.pdf Andersson-Sko¨ld, Y., Simpson, D., 2001. Secondary organic aerosol formation in Northern Europe: A model study. J. Geophys. Res. 106(D7), 7357–7374. Binkowski, F.S., 1999. Aerosols in MODELS-3 CMAQ, in Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modelling System, EPA 600/ R-99-030, EPA. Dorn, H.-P., Brauers, T., Ha¨seler, R., Johnen, F.J., Karl, M., Schlosser, E., Wahner, A., Memmesheimer, M., Jakobs, H.J., Friese, E., Feldmann, H., Kessler, C., Piekorz, G., Ebel, A., Kerschgens, M.J., Zimmermann, J., Klein, M., 2005. An integrated data archive of atmospheric chemical standard scenarios for the evaluation of chemistrytransport-models—IDEC. In: Winkler, R. (Ed.), BMBF 2005, Results of AFO 2000 (GSF Munich), pp. 44–47, Bonn-Berlin, www.afo2000.de Geiger, H., Barnes, I., Bejan, I., Benter, T., Spittler, M., 2003. The tropospheric degradation of isoprene: An updated module for the regional atmospheric chemistry mechanism. Atmos. Environ. 37, 1503–1519. Jakobs, H.J., Tilmes, S., Heidegger, A., Nester, K., Smiatek, G., 2002. Short-term ozone forecasting with a network model system during summer 1999. J. Atmos. Chem. 42, 23–40. Kerschbaumer, A., Stern, R., Reimer, E., 2005. Ausbreitungsrechnungen mit dem AerosolChemie-Transportmodell REM/CALGRID fu¨r die Region Berlin-Brandenburg. Abschlussbericht zum Vorhaben der Senatsverwaltung fu¨r Stadtentwicklung, Berlin: ‘‘Untersuchung des Potentials und der Umsetzbarkeit von MaXnahmen und der damit erzielbaren Minderungen der Feinstaub-(PM10) und Stickoxidemissionen in Berlin’’. Memmesheimer, M., Friese, E., Jakobs, H.J., Kessler, C., Feldmann, H., Piekorz, G., Ebel, A., 2004. Ausbreitungsrechnungen zur zuku¨nftigen Entwicklung der Luftqualita¨t in Nordrhein-Westfalen: Bewertung und MaXnahmenplanung zur Luftreinhaltung. AbschluXbericht zum FuE-Vorhaben AZUR, im Auftrag des Landesumweltamtes NRW, 413 Seiten, November 2004.

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Riemer, N., Vogel, H., Vogel, B., Fiedler, F., 2003. Modeling aerosols on the mesoscale-g: Treatment of soot aerosol and its radiative effects. J. Geophys. Res. 109, 4601, doi:10.1029/2003JD003448. Schaap, M., 2000. Aerosols in Lotos, TNO-Report 2000/405. Schell, B., Ackermann, I.J., Hass, H., Binkowski, F.S., Ebel, A., 2002. Modeling the formation of secondary organic aerosol within a comprehensive air quality modeling system. J. Geophys. Res. 106, 28275–28293. Stern, R., 2005. Der Beitrag des Ferntransports zu dem PM10- und NO2-Konzentrationen in Deutschland. Eine Modellstudie. KRdL-Experten-Forum ‘‘Partikel und Stickstoffdioxid,’’ VDI-KRdL Schriftenreihe 34, 2005. Stier, P., Feichter, J., Kinne, S., Kloster, S., Vinagati, E., Wilson, J., Ganzeveld, L., Tegen, L., Werner, M., Balkanski, Y., Schulz, M., Boucher, O., Minikin, A., Petzold, A., 2005. The aerosol-climate model ECHAM5-HAM. Atmos. Chem. Phys. 5, 1125–1156. Trickl, T., 2005. Atmospheric long-range transport and its impact on the trace-gas concentrations in the free troposphere over Central Europe (ATMOFAST). In: Winkler, R. (Ed.), BMBF 2005, Results of AFO 2000 (GSF Munich), Bonn-Berlin, pp. 145–149.

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Chapter 2.9 Developing and implementing an updated chlorine chemistry into the community multiscale air quality model$ Golam Sarwar, Deborah Luecken and Greg Yarwood Abstract An updated chlorine chemistry has been developed. The updated chlorine chemistry was combined with the Carbon Bond (CB-05) mechanism and incorporated into the Community Multiscale Air Quality (CMAQ) modeling system to evaluate the effects of chlorine emissions on O3 concentrations in the western United States. The study included anthropogenic molecular chlorine emissions, molecular chlorine released from sea-salt aerosols, and anthropogenic hypochlorous acid emissions. The anthropogenic molecular chlorine emissions are obtained from the 1999 National Emissions Inventory. The sea-salt emissions and chemistry module is linked with the gasphase chemistry module of the CMAQ modeling system using heterogeneous reactions that release molecular chlorine. Anthropogenic emissions from cooling towers and swimming pools were estimated using methods available in the literature and modeled in the form of hypochlorous acid. The results suggest that chlorine emissions only affect O3 concentrations in three areas of the western United States: Salt Lake area of Utah, southern California area, and Denver area of Colorado. 1. Introduction

Reactions of hydroxyl radical (OH) and volatile organic hydrocarbons (VOCs) play an important role in producing ozone (O3) in the atmosphere. Chlorine atoms can also react with VOCs at rates generally faster than OH and provide an alternate route to O3 production. However, $

This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reﬂect their policies or views.

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chlorine emissions and chemistry are usually not included in air quality models because of uncertainties in the sources of chlorine and gaps in our understanding of the associated chemical reactions. Because the ability of air quality models to reliably predict O3 concentrations is essential for understanding and controlling high O3 concentrations, it is important that we understand whether or not chlorine is an important pathway that needs to be included in air quality models. Recent studies provide evidence that chlorine chemistry might indeed enhance O3 formation in coastal and industrial areas of the United States (Chang et al., 2002; Knipping and Dabdub, 2003; Sarwar and Bhave, 2006). Chang et al. (2002) studied the effect of chlorine emissions and concluded that industrial chlorine emissions can increase O3 concentrations by up to 16 parts-per-billion (ppbv) in southeastern Texas. Chang and Allen (2006) reported that chlorine emissions can increase daily peak O3 concentrations by up to 10 ppbv in the Houston area. Knipping and Dabdub (2003) studied the impact of sea-salt derived-chlorine emissions on coastal urban O3 and reported that chlorine released via heterogeneous reactions can increase peak O3 concentrations by up to 4 ppbv in the South Coast Air Basin of California. Sarwar and Bhave (2006) studied the effects of chlorine emissions on O3 over the entire eastern half of the United States for July, 2001 and reported that chlorine emissions can increase O3 concentrations in the Houston and New York/New Jersey areas. The daily maximum 1-h and 8-h O3 concentrations in the Houston area increased by up to 12 ppbv and 8 ppbv, respectively. In the New York/New Jersey area, the daily maximum 1-h and 8-h O3 concentrations increased by up to 6 ppbv and 4 ppbv, respectively. While these studies suggest chlorine may affect O3 concentrations in speciﬁc geographic areas, little is known about the importance of the chlorine mechanism beyond these areas. In the current study, we present an updated chlorine mechanism and evaluate the effects of chlorine emissions on O3 concentrations in the western United States.

2. Methodology 2.1. Model description

All simulations for this study were performed by using the Community Multiscale Air Quality (CMAQ) modeling system (version 4.5) (Byun and Ching, 1999). The horizontal domain of the model consisted of 217 196 grid-cells for the western United States with a 12-km grid spacing. Initial

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and boundary conditions for this study were obtained from a larger model domain consisting of 148 112 grid-cells with a 36-km grid spacing for the continental United States. Detailed description of the model conﬁguration used in the study can be found elsewhere (Sarwar and Bhave, 2006). Two model simulations were performed for July 1–7, 2001 using (1) a base chemical mechanism without the chlorine chemistry and (2) an extended chemical mechanism that included the base mechanism and the updated chlorine mechanism described in the next section. The Carbon Bond (CB05) mechanism was used as the base mechanism (Yarwood et al., 2005; Sarwar et al., 2006). Details of the emissions used in this study can be found elsewhere (Sarwar and Bhave, 2006). 2.2. Gas-phase chlorine chemistry

Tanaka et al. (2003) developed a chlorine mechanism consisting of 13 chemical reactions. The chlorine mechanism of Tanaka et al. (2003) was updated, modiﬁed, and extended to be compatible with the CB05 mechanism. The chlorine mechanism used in this study and described in detail in Yarwood et al. (2005) is presented in Table 1 and consists of 20 gasphase reactions (1)–(20). Rate constants for these reactions were updated using the latest recommendations from the International Union of Pure and Applied Chemistry (IUPAC, 2005). The self-reaction of ClO radicals (4) is added to account for situations where intense sources of ClO (e.g., high Cl2 emissions) exceed the availability of NO or HO2 to act as sinks for ClO. The products of the ClO self-reaction are highly condensed to approximate the ﬁnal products under tropospheric conditions. To account for the loss of FMCl via atmospheric chemistry, the reaction of FMCl with Cl and its photolytic reaction are included (7–8). The reaction of Cl with CH4 is updated to include methylperoxy radical as a reaction product (9). The products of the reaction between Cl and PAR are updated to be compatible with the reaction between OH and PAR in the CB05 mechanism, which differentiates between acetaldehyde and higher aldehydes (10). The rate constant for reaction (10) is an average over the absolute rate constants of the alkanes listed in Tanaka et al. (2003). Since ETHA is an explicit species in the CB05 mechanism, its reaction with Cl is also included (11). The rate constant for the reaction between Cl and OLE (13) is an average over the absolute rate constants for the alkenes presented in Tanaka et al. (2003). Reaction products assume that reaction with the carbon–carbon double bond (CQC) proceeds by addition and that the Cl ends up in a carbonyl compound, represented by FMCl as a surrogate. The reaction of Cl and IOLE (14) is added to the mechanism. The rate constant for reaction 14 is estimated as

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Table 1. Reactions in the chlorine mechanism for use with the CB05 mechanism No.

Reactants

Products

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Cl2 HOCl Cl+O3 ClO+ClO ClO+NO ClO+HO2 OH+FMCl FMCl Cl+CH4 Cl+PAR

(11)

Cl+ETHA

(12) (13)

Cl+ETH Cl+OLE

(14)

Cl+IOLE

(15)

Cl+ISOP

(16) (17) (18) (19) (20) (21)

Cl+FORM Cl+ALD2 Cl+ALDX Cl+MEOH Cl+ETOH Cl+NO2

2Cl OH+Cl ClO 0.3Cl2+1.4Cl Cl+NO2 HOCl Cl+CO Cl+CO+HO2 HCl+MEO2 HCl+0.87XO2+0.13XO2N +0.11HO2+0.06ALD20.11PAR +0.76ROR+0.05ALDX HCl+0.991ALD2+0.991XO2 +0.009XO2N+HO2 FMCl+2.0XO2+HO2+FORM FMCl+0.33ALD2+0.67ALDX +2.0XO2+1.0HO21.0PAR 0.3HCl+0.7FMCl+0.45ALD2 +0.55ALDX+0.3OLE +0.3PAR+2.0XO2+1.0HO2 0.15HCl+1.0 XO2+1.0 HO2+0.85FMCl+1.0ISPD HCl+1.0 HO2+1.0CO HCl+C2O3 HCl+CXO3 HCl+1.0 HO2+1.0FORM HCl+1.0 HO2+1.0ALD2 ClNO2

(22) (23)

ClNO2 ClO+NO2

Cl+NO2 ClONO2

(24)

ClONO2

0.9Cl+0.9NO3+0.1ClO+0.1NO2

Rate expressiona Photolysis Photolysis 2.3 1011 e(200/T) 1.63 1014 6.4 1012 e(290/T) 2.7 1012 e(220/T) 5.00 1013 Photolysis 6.6 1012 e(1240/T) 5.00 1011

8.3 1011 e(100/T) 1.07 1010 2.5 1010 3.5 1010

4.3 1010 8.2 1011 e(34/T) 7.9 1011 1.3 1010 5.5 1011 8.2 1011 e(45/T) ko ¼ 1.8 1031 (T/300)2.0 KN ¼ 1.0 1010 (T/300)1.0 F ¼ 0.6 and N ¼ 1.0 Photolysis ko ¼ 1.8 1031 (T/300)3.4 KN ¼ 1.5 1011 (T/300)1.9 F ¼ 0.6 and N ¼ 1.0 Photolysis

Note: Cl2 ¼ molecular chlorine, Cl ¼ atomic chlorine, HOCl ¼ hypochlorous acid, OH ¼ hydroxyl radical, O3 ¼ ozone, ClO ¼ chlorine oxide, NO ¼ nitric oxide, NO2 ¼ nitrogen dioxide, HO2 ¼ hydroperoxy radical, FMCl ¼ formyl chloride, CO ¼ carbon monoxide, CH4 ¼ methane, ETHA ¼ ethane, MEO2 ¼ methylperoxy radical, HCl ¼ hydrochloric acid, PAR ¼ parafﬁn carbon bond, XO2 ¼ NO-to-NO2 operator, XO2N ¼ NO-to-nitrate operator, FORM ¼ formaldehyde, ALD2 ¼ acetaldehyde, ALDX ¼ propionaldehyde and higher aldehydes, OLE ¼ terminal oleﬁnic carbon bond, IOLE ¼ internal oleﬁnic carbon bond, ETHE ¼ ethene, ISOP ¼ isoprene, ISPD ¼ isoprene product, MEOH ¼ methanol, ETOH ¼ ethanol, C2O3 ¼ acetylperoxy radical, CXO3 ¼ higher acylperoxy radicals, NO3 ¼ Nitrate radical, ROR ¼ secondary organic oxy radical, ClNO2 ¼ nitryl chloride, and ClONO2 ¼ chlorine nitrate. a First order rate constants are in units of s1, second order rate constants are in units of cm3 molecule1 s1, third order rate constants are in units of cm6 molecule2 s1. Temperatures (T) are in Kelvin. Rate constants for reactions (21) and (23) are described by the falloff expression of the form k ¼ {ko[M]/(1+ko[M]/kN)} Fz, where Z ¼ {(1/N)+log10 [ko [M]/kN]2}1, where [M] is the total pressure in molecules/cm3, and ko, kN, F, and N are indicated in table.

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the rate constant for Cl reacting with OLE+2 PAR because IOLE is a 4-carbon species, whereas OLE is a 2-carbon species. The products assume that reaction proceeds 70% by Cl-addition and 30% by H-abstraction. The reaction between Cl and isoprene (15) is extensively revised from Tanaka et al. (2003). The goal of Tanaka et al. (2003) was to track a unique marker for the reaction between Cl and isoprene to permit comparison with ambient data. The goal for this mechanism is to track the fate of the Cl and the fate of the carbon in isoprene. The HCl and FMCl yields reﬂect the balance between H-abstraction and addition pathways of 15% and 85%. FMCl acts as a surrogate for the fate of all Cl-addition pathways. The lumped isoprene oxidation product (ISPD) from the CB05 mechanism accounts for the carbon-containing products. FMCl serves as a surrogate for all products where chlorine is incorporated into a chlorocarbonyl after an addition reaction. Reactions of Cl with ALD2, ALDX, MEOH, and ETOH (17)–(20) are also included. The heterogeneous reactions described in the next section use two chemical species that are not present in the updated chlorine mechanism. To facilitate the implementation of these heterogeneous reactions into the CMAQ model, four additional gas-phase reactions (21)–(24) involving ClNO2 and ClONO2 are added to the mechanism. 2.3. Heterogeneous reactions

Three heterogeneous chemical reactions have been developed by Knipping and Dabdub (2003) to model the release of chlorine emissions from sea-salt particles. These reactions have been incorporated into the CMAQ model: OHðgasÞ þ Cl ðaerosolÞ ! 0:5Cl2ðgasÞ þ OHðaerosolÞ

(25)

N2 O5ðgasÞ þ Cl ðaerosolÞ ! ClNO2ðgasÞ þ NO3ðaerosolÞ

(26)

ClONO2ðgasÞ þ Cl ðaerosolÞ ! Cl2ðgasÞ þ NO3ðaerosolÞ

(27)

Species N2O5 in reaction (26) is dinitrogen pentoxide. The pseudo-ﬁrst order rate constants for these reactions were calculated as gsoA/4, where gs is the reactive uptake coefﬁcient, o is the mean molecular speed, and A is the particle surface area. The reactive uptake coefﬁcient, gs, for reaction (25) was calculated using 0.04 [Cl], where [Cl] is the aerosol chloride ion concentration. The reactive uptake coefﬁcient, gs, for reactions (26) and (27) was taken as 0.02 [Cl] (Knipping and Dabdub, 2003).

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3. Results

Chlorine emissions affected O3 concentrations in many grid-cells in the western United States. Since the changes in the daily maximum 1-h and 8-h O3 concentrations for most locations did not exceed 1 ppbv, they are not included in the discussion. Chlorine emissions affected the daily maximum 1-h and 8-h O3 concentrations by more than 1 ppbv in three areas in the western United States: the Salt Lake area of Utah, the southern California area, and the Denver area of Colorado (Fig. 1). It should be noted that data in Fig. 1 represent the largest increases in daily maximum 1-h O3 concentrations for the study period, and decreases in O3 concentrations are not shown. When chlorine emissions were included in the model, they decreased O3 concentrations in many grid-cells in the Salt Lake area, but they also increased O3 concentrations in some grid-cells in this area. Generally the largest changes occurred during the morning hours. The largest decrease in the daily maximum 1-h O3 concentration was about 9 ppbv and the largest increase was 4 ppbv. The largest decrease in the daily maximum 8-h O3 concentration was 6 ppbv and the largest increase was 3 ppbv. The

Figure 1. The largest increases in daily maximum 1-h O3 concentrations due to chlorine emissions in the western United States. Decreases in O3 concentrations due to chlorine emissions occurred only in the Salt Lake area of Utah and are not shown.

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Salt Lake area contained the largest anthropogenic Cl2 emissions source in the United States and therefore the most signiﬁcant impact also occurred in this area. Since the concentrations of VOCs are low in this area, the production of O3 via reactions between Cl and VOCs was lower compared to the consumption of O3 by the reaction between Cl and O3. Consequently, chlorine emissions generally decreased O3 concentrations in this area. When chlorine emissions were included in the model, O3 concentrations in the southern California area increased primarily due to the sea-saltderived chlorine emissions. While the largest increase in O3 concentration ranged up to 4 ppbv during morning hours, the largest increases in the daily maximum 1-h and 8-h O3 concentrations in the southern California area were only about 2 ppbv. Chlorine emissions and chemistry decreased the concentrations of VOCs and increased the concentrations of O3 in this area as shown in Fig. 2. This is consistent with Cl production causing additional VOC oxidation and consequently enhancing the O3 formation. Southern California is an area where O3 concentrations are known to increase in response to more VOC reactions (Lawson, 2003). Chlorine emissions also increased O3 concentrations in the Denver area of Colorado primarily due to anthropogenic HOCl emissions. While the largest increases in O3 concentrations ranged up to 3 ppbv during morning hours, the largest increases in the daily maximum 1-h and 8-h O3 concentrations in this area were only about 1 ppbv. Similar to the southern

Figure 2. Increases in O3 concentrations (DO3) and decreases in VOC concentrations (DVOC) between the two cases at a representative grid-cell in southern California.

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California area, additional VOC oxidation by the chlorine chemistry enhanced the O3 concentrations in this area. 4. Summary

The results of this study suggest that chlorine emissions only affect O3 concentrations in three areas of the western United States: Salt Lake area of Utah, southern California area, and Denver area of Colorado. The largest impact occurred in the Salt Lake area where chlorine emissions decreased O3 concentrations in many grid-cells due to the presence of a large industrial Cl2 emissions source. But they also increased O3 concentrations in some grid-cells. The largest decreases in daily maximum 1-h and 8-h O3 concentrations ranged up to 9 ppbv and 6 ppbv, respectively. The largest increases in daily maximum 1-h and 8-h O3 concentrations ranged up to 4 ppbv and 3 ppbv, respectively. Chlorine emissions increased both daily maximum 1-h and 8-h O3 concentrations in southern California by up to about 2 ppbv primarily due to sea-salt-derived chlorine emissions. Chlorine emissions increased both daily maximum 1-h and 8-h O3 concentrations in Denver area of Colorado by up to 1 ppbv primarily due to anthropogenic HOCl emissions. Future studies should focus on evaluating the effects of chlorine emissions on O3 for a longer time period. Discussion

D.W. Byun:

G. Sarwar:

Chlorine and its products are not regularly measured. You mentioned that the uncertainty in the chlorine emission needs to be reduced. The ambient chlorine and its by-product concentrations are not known, though, so what is your strategy for the veriﬁcation of the newly proposed chlorine mechanism? The reaction between atomic chlorine and isoprene produces a unique marker species (1-chloro-3-methyl-3butne-2-one). This unique species was included in the previous version of the chlorine mechanism developed at the University of Texas at Austin. This unique species has been measured in Houston and used by the investigators to verify the previous chlorine mechanism. This species is not included in the newly developed chlorine mechanism, but can be easily added. The model results can be evaluated against observed data in Houston.

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ACKNOWLEDGMENT

The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and U.S. Department of Commerce’s National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW13921548. REFERENCES Byun, D.W., Ching, J.K.S., 1999. Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, U.S. Environmental Protection Agency, 1999, EPA/600/R-99/030. Chang, S., Allen, D.T., 2006. Environ. Sci. Technol. 40, 251–262. Chang, S., McDonald-Buller, E.C., Kimura, Y., Yarwood, G., Neece, J., Russel, M., Tanaka, P., Allen, D., 2002. Atmos. Environ. 36, 4991–5003. IUPAC, 2005. Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F., Hynes, R.G., Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J., Evaluated kinetic and photochemical data for atmospheric chemistry—IUPAC subcommittee on gas kinetic data evaluation for atmospheric chemistry. Available at http://www.iupac-kinetic.ch. cam.ac.uk/index.html Knipping, E.M., Dabdub, D., 2003. Environ. Sci. Technol. 37, 275–284. Lawson, D.R., 2003. Environmental Manager, July, 17–25. Sarwar, G., Bhave, P., 2006. Submitted to the J. Appl. Meteorol. Sarwar, G., Luecken, D., Yarwood, G., Whitten, G., Carter, W.P.L., 2006. Submitted to the J. Appl. Meteorol. Tanaka, P., Allen, D.T., McDonald-Buller, E.C., Chang, S., Kimura, Y., Mullins, C.B., Yarwood, G., Neece, J.D., 2003. J. Geophys. Res., 108, D4, 4145, doi:10.1029/ 2002JD002432, 6-1:13. Yarwood, G., Rao, S., Yocke, M., Whitten, G., 2005. Updates to the Carbon Bond Chemical Mechanism: CB05. Final Report to the US EPA, RT-0400675. Available at http:// www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06210-9

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Chapter 2.10 Modeling assessment of the impact of nitrogen oxides emission reductions on ozone air quality in the Eastern United States: Offsetting increases in energy use$ P. Steven Porter, Edith Ge´go, Alice Gilliland, Christian Hogrefe, James Godowitch and S. Trivikrama Rao Abstract A photochemical air quality model was used to evaluate the impact of a series of NOx control rules on ozone air quality in the eastern U.S. Thanks to the acid rain program and the NOx SIP Call, emission rates of NOx (mass NOx/energy content of fuel used) from industrial point sources have declined dramatically since 1997. Model simulations were performed with three emission scenarios: 2002 emissions, 2004 emissions, and a ‘no control’ scenario. The latter simulates conditions that would have existed in 2002 had new NOx emission controls not been imposed. All scenarios used 2002 meteorology. Controls lead to reductions of NOx emissions in 2002 and 2004 to roughly two thirds and one third of their 1997 levels, respectively. In response to these emission changes, the model predicted that maximun 8-h average ozone concentrations would have decreased from 3% to 8% between 2002 and 2004 given 2002 meteorolgy. The absence of control would have led to considerably higher ozone levels in most of the eastern U.S., with exceptions occurring in the vicinity of point sources, regions that would have experienced lower ozone levels thanks to a more intense titration process.

$

This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reﬂect their policies or views.

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1. Introduction

As in most industrialized countries, the daily maximum 8-h ozone concentrations continue to exceed their acceptable levels in the United States (U.S.). During the past three decades, the U.S. Environmental Protection Agency (EPA) has issued several rules limiting the emission of ozone precursors (nitrogen oxides (NOx) and volatile organic compounds (VOCs)). Among the large NOx emitters are the power industry (electricity generation units) and other industrial facilities, second only to mobile on-road and off-road sources. In 1995, for instance, EPA initiated the acid rain emission control program that, in its ﬁrst stage, imposed limits to SO2 and NOx emissions from speciﬁc types of coal-ﬁred boilers. Recognizing the work of the Ozone Transport Assessment Group (OTAG) on long-range transport of ozone and its precursors and the importance of regional management, EPA issued a regulation in 1998 requiring 21 states in the eastern U.S. and Washington D.C. to reduce their summertime NOx emissions. Figure 1 shows the group of states involved. For this new rule, known as the NOx SIP Call, the EPA established new statewide NOx emission budgets and asked the air pollution control agencies of each state involved to identify in their State Implementation

Figure 1. U.S. states affected by the NOx SIP Call.

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Plan (SIP) the emission control measures they would implement to meet these budgets. In contrast to the acid rain program, the NOx SIP Call did not target speciﬁc facilities or equipment and the states were given the ﬂexibility to develop their own control strategies, provided the new budgets were respected. These new budgets, established for each class of pollutant emitters, required signiﬁcant reductions for point sources, particularly those from the utility sector (electricity generation units (EGU)). The NOx SIP Call had to be fully implemented by May 31, 2004. Illustrating the magnitude of the changes point source emitters had to undergo, Fig. 2 displays the quantity of NOx released from sources in the 21 states of the NOx SIP Call region during the ozone seasons of 1997, 2002 and 2004 (panel a), as well as the corresponding heat input during these periods (panel b) (USEPA, 2005). While the heat input reported by the 21 states involved in the NOx SIP Call during the ozone season of 2002 was 15% more than in 1997, NOx emissions were only 70% of what they were in 1997. Heat input in 2004 was only 2% more than in 1997, probably because the generally moderate weather conditions of summer over the eastern U.S. allowed limited use of air conditioning. Yet, despite similar heat inputs, NOx emissions during the 2004 ozone season were about 37% of what they were in 1997. The objective of this study is to examine changes in ambient ozone concentrations estimated by a photochemical air quality model in response to the NOx emission reductions imposed on the utility sector. To accomplish this task, the air quality model simulations were performed

NOx emissions (103 kg)

a. 2.0E+06 1.5E+06 1.0E+06 5.0E+05 0.0E+00 1997

2002

2004

Heat input (mmBtu)

b. 8.0E+09 6.0E+09 4.0E+09 2.0E+09 0.0E+00 1997

2002

2004

Figure 2. NOx emissions (panel a) and heat input (panel b) by the continuous Emission Monitoring System reported for all states subject to the NOx SIP Call during the ozone season of 1997, 2002 and 2004.

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with three distinct sets of emission ﬁelds. The ﬁrst two scenarios simulate actual 2002 and 2004 emissions as best inferred from available emission data. These scenarios characterize emissions before and after implementation of the NOx SIP Call, respectively. The third scenario represents a hypothetical rendering of NOx emissions in 2002 had there been no emission controls imposed on the utility sector.

2. Description of the modeling system and its setting

The modeling system used for this study is the Community Multiscale Air Quality (CMAQ) model (version 4.5). See Byun and Schere (2006) for a review of CMAQ governing equations. The simulated domain encompasses most of the eastern U.S. and therefore includes all states subject to the NOx SIP Call. The horizontal grid size was set to 12 12 km. Fourteen layers were discerned vertically with the surface layer approximately 38 m thick. CMAQ was run with the Carbon-Bond 4 (CB4 version 4.2) gas-phase chemical mechanism module. The time-invariant lateral boundary conditions utilized correspond to ‘clean air’ assumptions. Meteorological ﬁelds were produced by MM5 (Penn State/NCAR Mesoscale Model—version 3.6.3—see Grell et al. (1994)) with a 12 12 km horizontal cell size and 34 vertical layers, reorganized and compacted into 14 layers by MCIP (Meteorology-Chemistry Interface Processor) for integration into CMAQ. The period simulated extends from June 1, 2002 to August 31, 2002 with 3 spin-up days. See Godowitch et al. (2006) for further information about the MM5 setup. Because ozone formation and accumulation depend on prevailing meteorological conditions, the effects of different meteorological conditions were eliminated from our assessment of changes linked to emission control by applying the same meteorology in conjunction with the three emissions scenarios. Emission ﬁelds were created with the SMOKE—version 2.2 (Sparse Matrix Operator Kernel Emission) program (Carolina Environmental Programs, 2003) by assembling mobile, biogenics, anthropogenic area and industrial point sources emissions. Gridded mobile emissions were calculated with the Mobile 6 program, based on the projected Vehicle Mile Transport (VMT) for 2002 and 2004 and appropriate ﬂeet factors. Natural biogenic emissions were calculated with the Biogenic Emission Inventory System algorithm (version 3.19), used in conjunction with the MM5-derived meteorological estimates. Area anthropogenic emissions (not tied to large industrial sources equipped with the Continuous Emissions Monitoring system) were calculated from the projected U.S. EPA National Emission Inventory 2001 (version 3). Industrial point sources

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emissions were assessed from the Chemical Emissions Monitoring databases of 2002 and 2004. As noted, the 2002 and 2004 emission scenarios were inferred from the available emission data. For the no-control case scenario, the domestic anthropogenic, mobile and biogenic emissions of 2002 were utilized. The point source emissions for this scenario, however, were obtained as the product of electricity demand (heat input) of 2002 and the NOx emission rate of 1997, i.e., an emission rate characterizing the pre-‘acid rain program’ and ‘NOx SIP Call’ periods. The no-control scenario simulated air quality that would have been encountered if no point source emission control program had been implemented. 3. Results and discussion

The daily maximum 8-h ozone concentrations at each grid cell and for the three emission scenarios were calculated from the model hourly estimates, and then ranked. The median and the fourth highest estimated values corresponding to each emission scenario and each model cell were mapped. The differences between ozone maps corresponding to the 2002 and 2004 actual emissions, as well as the 2002 actual emissions and 2002 no-control scenario, were then examined. 3.1. Comparison of 2002 and 2004 emission scenarios

Figure 3 shows the median daily maximum 8-h ozone concentrations estimated by CMAQ for the 2002 (panel a) and 2004 (panel b) emission scenarios, both driven by the 2002 meteorological ﬁelds, as well as the difference between these two maps (panel c), expressed as percentage of the 2002 values. It appears that CMAQ estimated the highest values in the southern part of the domain (northern Georgia, South Carolina and North Carolina) with median daily maximum 8-h ozone concentrations greater than 55 ppb, concentrations matched only by the eastern urban corridor from Washington D.C. to Connecticut. The global features (locations of high and low concentrations) of both maps appear to be similar. The 2002 and 2004 emission scenarios led to virtually identical median values along the western border of the simulated domain (Nebraska, Kansas) and in the northeast corner of the U.S. However, a detailed examination reveals that the implementation of the NOx SIP Call (changes between 2002 and 2004) led to reduced ozone concentrations in the heart of the NOx SIP Call territory, namely, along the southern border of Indiana, Ohio and Pennsylvania where the median

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Figure 3. Median daily maximum 8-h ozone concentrations simulated by CMAQ corresponding to the 2002 (panel a) and the 2004 (panel b) emission scenario, panel c shows the differences (%) between panel a and panel b.

daily maximum 8-h ozone concentrations decreased by up to 5% (2–3 ppb), and further south in Kentucky and West Virginia where concentrations decreased by about 2–3%. Analogous to Fig. 3, Fig. 4 depicts the fourth highest estimated daily maximum 8-h ozone concentrations corresponding to the 2002 and 2004 emission scenarios (both driven by the 2002 meteorological ﬁelds), and the difference between these two maps (panel c), expressed once again as percentages of the 2002 values. Simultaneous inspection of Figs. 3 and 4 shows that the differences between the fourth highest simulated values for the 2002 and 2004 emission scenarios are more pronounced than those depicting median concentrations. Sharp decreases between the two cases are visible in central Illinois, Indiana, Ohio and western Pennsylvania. Also noticeable is air quality improvement in Alabama and the western borders of South Carolina, North Carolina and Virginia. Further illustrating the previous statement, (panel c) of Fig. 4 shows that reduction in the fourth highest modeled concentrations greater than 5% are common throughout the simulated domain with the greatest changes (percentage-wise) along the border between Pennsylvania and Virginia, and in Tennessee and western Kentucky.

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Figure 4. Fourth highest daily maximum 8-h ozone concentrations simulated by CMAQ corresponding to the 2002 (panel a) and the 2004 (panel b) emission scenario, panel c shows the differences (%) between panel a and panel b.

Based on the modeling results presented, it appears that the emission changes resulting from implementation of the NOx SIP Call affected the extreme daily maximum 8-h concentrations (fourth highest simulated values) to a greater extent than the ‘background’ median values. 3.2. Comparison of 2002 and the no-control scenarios

As detailed in Section 2 of this paper, the no-control scenario is built using all emissions of the actual 2002 case but emissions from electricity generation facilities that are calculated as the product of the actual 2002 electricity demand (heat input) and the emission rate of 1997. Practically, NOx emissions from the utility sector in the no-control scenario are almost three times larger than what actually occurred. Moreover, electricity generation units on an average were responsible for only 21% of the total NOx emissions during the simulated 2002 period while their contribution without control would be 44% and total emissions would have been 40% higher than they actually were in 2002 (Fig. 5). In response to the no-control scenario, CMAQ estimates show that median daily maximum 8-h ozone concentrations would be higher in most of the simulated domain, especially in the southeastern states (Georgia, North Carolina) and the mid-Atlantic region, as illustrated in Fig. 6. A degradation of at least 6% (about 3–4 ppb) was estimated for

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184 Emissions from utility sector Total emissions

140 % of 2002 value 287 % of 2002 value

44 % of total emis. 21% of total emis.

Actual 2002 scenario

2002‘no-control’scenario

Figure 5. Fraction of total NOx emission due to electricity generation units in 2002 and in the no-control scenario; total NOx emission in 2002 and in the no-control case.

Figure 6. Median daily maximum 8-h ozone concentrations simulated by CMAQ for the no-control scenario (panel a), differences (%) between the no-control and the 2002 scenario shown in panel a of Fig. 3 (panel b).

most of the simulated domain; exceptions include the northern boundary (Minnesota, Wisconsin) and the New England area, for which the model did not predict major changes. Also interesting is the NOx control ‘disbeneﬁt’ occurring in the vicinity of large NOx emission areas (Ohio River Valley, New Jersey, Connecticut): ozone concentrations would be less than predicted for the 2002 emission scenario if no control was imposed on point sources, probably because NOx would be more readily available and the NO titration process would therefore be more important. The fourth highest modeled ozone concentrations (panel a) estimated by CMAQ in response to the no-control scenario and the changes between these values and the 2002 scenario, expressed in percent of the 2002 values, are displayed in Fig. 7. It appears that extreme concentrations would be at least 7 or 8% higher in most of the domain with some areas exposed to ozone levels 10% or even 20% higher. Even the New England area where median values were practically identical for both emission

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Figure 7. Fourth highest daily maximum ozone 8-h concentrations simulated by CMAQ for the no-control scenario (panel a), differences (%) between the no-control and the 2002 scenario shown in panel a of Fig. 4 (panel b).

scenarios would experience a greater degradation of 5–6% of the fourth highest concentrations. As previously noted, when comparing the 2002 and 2004 emission scenarios, CMAQ predicts more important changes, even in terms of percentage, for the extreme values (fourth highest concentration) than for the medians.

4. Summary

This paper describes the change in ozone concentration levels simulated by CMAQ in response to three emission scenarios designed to assess the impact of point source NOx emission control. CMAQ was utilized in conjunction with the MM5 model for the deﬁnition of meteorological ﬁelds and the SMOKE system for the deﬁnition of emission ﬁelds. Ozone concentrations in response to three emission scenarios were analyzed. The ﬁrst two emission scenarios reproduce emissions during the summers of 2002 and 2004. Comparison of ozone estimates obtained from these two scenarios allows measurement of the impact of the NOx SIP Call. The third set describes what emissions would have been in 2002 if no control had been imposed on point sources. All components of the no-control scenario are identical to those of the 2002 scenario, except the point source emissions that were obtained by multiplying the 2002 heat input (electricity demand) with the emission rates (kg NOx emitted/mmBTU utilized) in 1997. The modeling results show that the implementation of the NOx SIP Call led to a decline in the ozone concentrations in most of the eastern U.S. A decrease ranging from 3 to 5% was widely identifed between the median 8-h daily maximum concentrations modeled for 2004 and 2002. However, the fourth highest modeled values decreased by 5–8%. Without controls on the utility sector, total emissions in 2002 would have been

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40% higher than they actually were in 2002. However, while ozone concentrations would have been higher in most of the domain without the NOx SIP Call, local areas surrounding major sources would actually have had 8-h daily maximum concentrations 7–8% lower. For the two comparisons carried out, it appears that changes at the upper tail of the model estimates are greater than those measured at the median levels, suggesting that NOx emission reductions may have a greater effect on high ozone days than on background ozone levels. One should keep in mind that, in nature and in photochemical models, ozone formation and accumulation depend on the governing meteorological conditions. The results presented here were obtained as the response to the meteorological conditions of 2002 for all emission scenarios tested. Our assessment of the magnitude of ozone concentration changes from one emission scenario to another may have been different had the meteorological conditions of a different year (say, 2004) been used in CMAQ. Discussion

B. Fisher:

P.S. Porter:

T. Odman:

P. S. Porter:

Did you include the ‘plume in grid’ option within the Model 3 system? One would expect that it is important to include the treatment of dispersion from point sources, which may not be possible with a model resolution of 12 km. We didn’t use ‘plume in grid’ (PIG) because we were most interested in receptors far (much more than 12 km) from the major sources. Also, unpublished work performed by Jim Godowitch of EPA’s modeling division and Biswas et al (Presented at the Annual AWMA meeting, 2002) indicate that, on a regional basis at least, the PIG option has little impact overall on NOx emission control strategies (percent changes in ozone on a regional basis). On the other hand, PIG may inﬂuence estimates of human exposure near the major sources. The areas of O3 increase are small but they appear to be densely populated areas. Do you plan to evaluate the beneﬁts in terms of population exposure? Yes they do—that’s a good observations. We didn’t compute exposure for this particular study, but others in our group are analyzing this question.

Impact of Nitrogen Oxides Emission Reductions on Ozone Air Quality

P. Builtjes:

P. S. Porter:

C. Mensink: P.S. Porter:

S. Hanna:

P.S. Porter:

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How are the percentage reductions per state determined, and why is there not one percentage over all the states making NOx trading over all the states possible? EPA determined the NOx budget separately for each state and gave them ﬂexibility to achieve the NOx reductions. The states in the Northeast have reduced their NOx emissions as part of the region-wide control strategy endorsed by the Northeast Ozone Transport Commission, but other states in the eastern U.S. have not yet. By 2004 when the SIP Call was fully implemented, the NOx reductions in the eastern U.S. ranged from 55–70% of their 1997 levels. Did you consider any additional beneﬁts from NOx reduction (acidiﬁcation, particulate matter)? For this paper we did not, but others here are involved in ongoing study of the effects of NOx reductions on ﬁne particles, acid deposition and nutrient (i.e., nitrogen) enrichment of lakes and streams. Your results showed improvements in ozone concentrations of only a few percent. Did you calculate the statistical signiﬁcance of this change? For example, is the difference signiﬁcant at the 95% level? We did not attempt to compute model uncertainties for this particular set of model runs. However, we are dealing with differences between pairs of model outputs, which ought to reduce uncertainties that might occur were we to change model inputs or modeling systems. Also, I think it is difﬁcult to analyze deterministic model outputs in the context of classical statistical methods used to determine conﬁdence intervals and statistical signiﬁcance.

ACKNOWLEDGMENTS

The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency and the U.S. Department of Commerce’s National Oceanic and Atmospheric Administration and under the agreement number DW13921548.

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REFERENCES Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and other components of the models-3 Community Multiscale Air Quality (CMAQ) modeling system. Appl. Mech. Rev. 59, 51–77. Carolina Environmental Programs, 2003. Sparse Matrix Operator Kernel Emission (SMOKE) Modeling System, University of North Carolina, Carolina Environmental Programs, Research Triangle Park, NC. Godowitch, J., Gilliland, A., Gego, E., Draxler, R., Rao, S.T., 2006. Integrated observational and modeling approaches for evaluating the effectiveness of ozone control policies, this issue. Grell, G.A., Dudhia, J., Stauffer, D., 1994. A description of the 5th-generation Penn State/ NCAR Mesoscale Model (MM5). NCAR Technical Note, NCAR/TN-398+ STR. USEPA, 2005. Clean Air Markets Division, EGU NOx emissions, http://cfpub.epa.gov/ gdm/

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Chapter 2.11 Dispersion modelling of the concentrations of the ﬁne particulate matter in Europe Mikhail Sofiev, Erwan Jourden, Liisa Pirjola, Leena Kangas, Niko Karvosenoja, Ari Karppinen and Jaakko Kukkonen Abstract Numerical results are presented on the concentrations of PM2.5 and PM10 over Europe on a spatial resolution of 30 km, and in most of Fennoscandia (comprising Scandinavia and Finland) on a ﬁner resolution of 5 km. The results include daily, and partly also hourly, averages for the whole year of 2000 and in addition, a monthly period in 1999. The computations were performed using the SILAM dispersion modelling system based on a 3-D Lagrangian Monte-Carlo particle model, which can treat the aerosol size spectrum using either a bin or a modal approach. The emission data for Europe were obtained from the EMEP inventory for the year 2000, and a ﬁner scale (5 km) emission inventory was compiled for the Fennoscandia region. The meteorological input data were produced using the numerical weather prediction model HIRLAM (the Finnish variant, version 5.2.1). The results computed up to date include primary aerosols; consisting mainly of black and organic carbon, mineral dust, sea salt, and sulphates. We have evaluated the numerical predictions against observational data of the EMEP measurement network, and the data of more detailed ﬁeld campaigns that were conducted within the EU-BIOFOR project; these also include size-segregated aerosol measurements. The overall agreement of predictions and measurements is fairly good, although there are problems in predicting the secondary organic aerosol and natural dust. The Europe-wide results were also compared with the corresponding computations by the EMEP model, which has a resolution of 50 km.

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1. Introduction

Current paper presents the ﬁrst results of a research project ‘‘An integrated model for evaluating the emissions, atmospheric dispersion and risks caused by ambient air ﬁne particulate matter’’ (KOPRA) of Finnish Funding Agency for Technology and Innovation (TEKES). An overall goal of the project is to evaluate the whole cycle of aerosol air pollution in Finland including emission of particulate matter and its precursors, their dispersion, transformation and deposition, resulting contamination patterns, and their impact on public health, as well as possible ways to reduce the aerosol atmospheric concentrations. In this paper, we concentrate on dispersion simulations performed with the Finnish emergency and air quality model SILAM at Finnish Meteorological Institute. The main goal of these simulations was to assess a link between the Finnish and European emissions of various gaseous and particulate species and resulting aerosol contamination of northern Europe. A speciﬁc goal was to build as complete budget for the aerosol composition over Finland as possible and compare the resulting bulk values with the observations. Separate veriﬁcation was performed for some speciﬁc substances, such as aerosol precursors, primary PM, sea salt, etc.

2. Input data and modelling tools

Input data for the simulations were combined from several sources. European-wide anthropogenic emission of particulate matter (with a split to PM 2.5 and PM 2.5–10), as well as of sulphur oxides, was adopted from the WebDab database of European Monitoring and Evaluation Programme EMEP (http://www.emep.int). The information was available at annual level and with spatial resolution of 50 km. Temporal disaggregating to hourly ﬂuxes was made on a country-by-country basis using the results of EUROTRAC-GENEMIS project (Lenhart et al., 1997). Vertical distribution of emission followed the EMEP methodology based on 11 emission source categories and characteristic injection heights for each of them (Simpson et al., 2003). The European information was complemented with a high-resolution national emission inventory of the Finnish Regional Emission Scenario (FRES) model (Karvosenoja and Johansson, 2003). FRES includes detailed chemical and size-segregation splits: emission of primary particles was considered for ﬁve size classes (PM 0.1, PM 0.1–1, PM 1–2.5, PM 2.5–10, PM 10–TPM) and included separate estimates for the following

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compounds: black carbon, organic carbon, sulphates, and total PM. Emission of precursors included SO2, NOx, NH3, and anthropogenic NMVOC. The dataset included spatially distributed emission with a resolution of 1 km and over 250-point sources with physical stack characteristics. Temporal disaggregating was performed following the same GENEMIS methodology. Vertical injection height for area sources was assumed to be within the lowest 100 m, while the plumes from point sources were parameterised using actual stack heights though made independent from actual meteorological conditions to reduce the computation costs. Meteorological information and necessary geophysical and land cover maps were taken from the FMI-HIRLAM and ECMWF meteorological models. All input data covered the period of 2000–2002 and the simulations were also targeting this time interval. The main modelling tool used for regional- and mesoscale simulations was the Finnish Emergency and Air Quality Modelling System SILAM (Soﬁev et al., 2006). It is a lagrangian particle model with Monte-Carlo random-walk mechanism representing the vertical and horizontal turbulent diffusion. The system includes a sophisticated meteorological preprocessor for evaluation of basic features of boundary layer and free troposphere using the meteorological ﬁelds provided by NWP models. In implementation, SILAM assumes well-mixed boundary layer and ﬁxed turbulent diffusion coefﬁcients in free troposphere. Exchange between them is mainly driven by temporal variation of the top of boundary layer. A physico-chemical module of SILAM covers up to 496 radioactive nuclides, sulphur oxides, primary particles of various types as well as probability (used for evaluation of area of risk and for solution of inverse problems). The system accepts an arbitrary deﬁnition of the particle size spectrum described in the current study via a set of bins. Chemical transformations of SOx follow the scheme of DMAT model (Soﬁev, 2000). A local-scale model CAR-FMI (Kukkonen et al., 2001a, b) was used for evaluation of Helsinki city-scale pollution levels. Evaluation of the inﬂuence of aerosol dynamics is done using the aerosol dynamics model MONO32 (Pirjola and Kulmala, 2000; Pirjola et al., 2003). MONO32 is a box model covering gas-phase chemistry and aerosol dynamics. The model uses monodisperse representation for particle size distribution with an optional number of size modes. In this work, ﬁve modes are used: nucleation, Aitken, accumulation, and two coarse modes. All particles in a certain mode are characterised by the same size and composition, and they can consist of sulphuric acid, ammonium sulphate, ammonium nitrate, organic carbon, elemental carbon, sodium

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chloride, and mineral dust. Water content of aerosols is calculated separately. Particles can be emitted as primary particles or formed in the atmosphere by nucleation. Size and composition of particles in any class can change due to multicomponent condensation of sulphuric acid and organic vapours as well as due to coagulation between particles. A speciﬁc model, simulation setup, and input data were needed for evaluation of the desert dust pollution. Due to highly episodic character of this phenomenon as well as its strong inter-annual variability, computations over a single or a few years would be insufﬁcient to catch even an order of magnitude of its contribution to aerosol concentrations over Finland. Therefore, we utilised a simpliﬁed but computationally efﬁcient model DMAT (Soﬁev, 2000), which was forced by pre-processed NCAR 22-years long meteorological re-analysis over the Northern Hemisphere. More information on this study can be found in Hongisto and Soﬁev (2004).

3. Results and discussion

Following the strategy outlines above, four sets of simulations have been performed: at the European-scale—for primary PM 2.5, PM 2.5–10, sea salt, and SOx; at regional scale—for primary PM 0.1, PM 0.1–1, PM 1–2.5, PM 2.5–10, PM-coarse (over 10 mm size), and SOx; for Helsinki area—PM 2.5, PM 10, and NOx; ﬁnally, the wind-blown dust was computed for the Northern Hemisphere. European-scale resolution was 30 km with daily averaging, regional simulations provided 5 km daily output ﬁelds while hemispheric runs were made with 150 km grid and provided monthly mean values. The reference year was 2000; most of simulations were also performed for 2001 and 2002; hemispheric simulations were made for the period 1967–1988 in order to obtain a climatologically representative dataset. There were also a few case studies in the adjacent years (1999, 2003) made for the periods of observational campaigns. An example of the simulation results is presented in Fig. 1 for primary PM 2.5, whose characteristic level in 2000 was about 1 mg m 3 over large areas of Europe with several high-concentration areas. This is in a good agreement with the aerosol model simulations by EMEP Western Centre (Kanhert and Tarrason, 2003). However, nearly twice better spatial resolution of current simulations allowed for more detailed patterns over strongly polluted areas, such as Po Valley, Scandinavian capitals, etc. Improved resolution over Finland and, further, over Helsinki area also highlighted local-scale distributions—both as urban vs. regional

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µ

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Figure 1. An example of three-scale off-line nested simulations for primary PM 2.5.

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background (visible at 5-km map) and over speciﬁc parts of the city (local-scale map). It should be pointed out that the 5 km map in Fig. 1 is presented without the European background, which would bring the total level of primary PM 2.5 concentrations over southern Finland to the level of about 1–2 mg PM m 3. Direct comparison of primary particles with observations was not performed because in 2000 there were practically no representative observations resolving the chemical composition of aerosol and thus capable of separating the primary PM from secondary inorganic aerosol (sulphates, nitrates, and ammonia), sea salt, and mineral dust. The second part of aerosol budget constitutes from the secondary inorganic aerosol. Contributions of sulphates (representing its main part) to the European and regional pollution levels are shown in Fig. 2. Comparing Figs. 1 and 2 it should be kept in mind that values in Fig. 2 are given in S (following the standard for such compounds), which means that the numbers must be multiplied with a factor of 3 to get the PM mass. With this scaling, it is seen that the contribution of sulphates is about 2–3 mg PM m 3, i.e., exceeds that of primary PM 2.5 (and PM 10). However, the overall importance of primary aerosol is determined not by

An example of SO4 = multi-scale simulations for European and Finnish regional sources of secondary inorganic aerosol. Annual concentrations 2000, units = µg S m-3 for European and local maps, it ngS m-3, for Finnish regional one , Figure 2. Concentrations of sulphates over Europe and Finland in 2000.

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its absolute concentrations but rather by potential medical consequences and capabilities of soot particles to carry various toxic species attached to their surfaces. Toxicity of secondary inorganic aerosols is usually assumed to be lower. Comparison of sulphates and SO2 with airborne measurements of EMEP network as well as with other models is quite straightforward. According to that, the SILAM simulations have ‘‘standard’’ accuracy: the model tends to slightly underestimate sulphates being otherwise within a factor of two from the most of observations. Simulations for two more components of the atmospheric aerosol—sea salt and wind-blown dust—are shown in Figs. 3 and 4, respectively. Nearsurface concentrations of the wind-blown dust do not make much sense over Scandinavia because most of such aerosol ﬂies over thousands of kilometres before reaching the region, which implies a wide and often uneven distribution along the vertical of the arrived masses. Therefore, the only representative parameter for that component is the vertically integrated column burden. Observations of the sea salt are quite scarce and can be performed either via comparison of vertically integrated aerosol optical depth

Figure 3. Mean 2000 concentration of sea salt (all size classes). Unit mg PM m 3.

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Figure 4. Mean 1967–1988 vertically integrated wind-blown dust concentration in air column. Unit: mg PM m 2. Observe different scales!

observed by satellites or by comparing some chemical components speciﬁc for this type of aerosol, for example, Na+, which constitutes about 30% of the sea salt mass. Comparison with Na+ climatology at Mace Head showed that the model tends to underestimate the overall level of concentrations by a factor of 2–3, closely capturing the summer low-salt periods and being signiﬁcantly lower than measurements over winter periods when strong storms inﬂate the salt concentrations by 3–5 times. This seasonality in the model is less pronounced (possibly, due to speciﬁc-year meteorology). Additionally, the emission module is not yet fully tuned. Its current version is based on improved scheme of Monahan et al. (1986), which is known to underproduce the sub-micron particles, which have the longest transport distance. The other feature of the scheme—strong

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overproduction of particles smaller than 0.05 mm—has been corrected during implementation. Finally, SILAM emission module neglects the mechanism of production of coarse particles (over 20 mm) as they have no impact on regional and long-range transport. However, they can still signiﬁcantly affect observations at Mace Head. Veriﬁcation of wind-blown dust model can be found in Grigoryan and Erdman (1996). To assess the uncertainty due to omission of aerosol dynamics in SILAM, a few 24-h episodes were selected, and MONO32 computations were performed with the same input data as in SILAM computations, complemented with ﬁeld measurements. For the exercise, MONO32 has been combined with a chemistry module of dispersion model MATCH (Robertson et al., 1999).

4. Conclusions

Performed simulations reproduced the main components of the aerosol contamination over Europe and Finland with high spatial resolution. They showed that over the southern part of Finland, the contribution of these components is split to o2 mg PM m 3 of primary PM 10 (and 1.5 mg of PM 2.5), about 3 mg PM m 3 of sulphates, 2–5 mg PM m 3 of sea salt (with a pronounced west-to-east gradient) and highly variable in time contribution of wind-blown dust from Saharan and Caspian deserts. Separate simulations of Hongisto et al. (2003) estimated contribution of nitrates and ammonia to be within a level of 1–2 mg PM m 3. The model performance was good practically for all compounds evaluated to-date. However, explicit veriﬁcation of several of them (ﬁrst of all, primary PM) was not possible due to insufﬁcient observational information about the aerosol speciation. In several cases, the comparison hinted on limitations of the applied algorithms that need to be addressed in the future work.

Discussion

I. Tegen:

The discrepancy of PM10 from model and observations at Artern (Germany) was attributed to dust from agriculture. This should have a seasonal signal with high values in spring and fall, when wind speeds are high and the soils are bare. This would not explain the high PM10 observations in summer. Can you comment on this?

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S. AndreaniAksoyoglu:

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The seasonal signal is indeed very strong but with some peculiarities: strong wind is not enough, the soil has to be dry to generate dust. Therefore, mid- and lateautumn signal is weak. Concerning summer, I think that agriculture is not the only and possibly not the main source of PM for that period. Indeed, when crops grew up, the agriculture ﬁelds are no longer open bare land and dust ﬂux should decrease dramatically. Instead, PM matter may come from other sources, such as roads— and there are some estimates made in Scandinavia and other parts of Europe that this contribution can be substantial. Wind for such sources is of secondary importance because the road dust is also generated by car-induced turbulence. You showed the emission of particles as both, mass and number emissions. How do you deal with number concentrations in the model? At present, the number-emission is utilised in aerosol dynamics model MONO32, which has an off-line connection with dispersion model SILAM. SILAM itself does not deal with this parameter.

REFERENCES Grigoryan, S., Erdman, L., 1996. The preliminary modelling results of Saharan dust transport to the Mediterranean Sea and Europe. In: Guerzoni, S., Chester, R. (Eds.), The Impact of Desert Dust Across the Mediterranean. Kluwer Academic Publishers, The Netherlands, pp. 59–67. Hongisto, M., Soﬁev, M., 2004. Long-range transport of dust to the Baltic sea region. In: Goos, G., Hartmanis, J., van Leeuwen, J. (Eds.), Lecture Notes in Computer Science. Springer-Verlag, Heidelberg, Berlin, pp. 303–311. Also in Lirkov, I., Margenow, S., Wasniewski, J., Yalamov, P. (Eds.), Large Scale Scientiﬁc Computing. Proceedings of the 4th International Conference, LSSC 2003, Sozopol, Bulgaria. Hongisto, M., Soﬁev, M., Joffre, S., 2003. Hilatar, a limited area simulation model of acid contaminants: II. Model veriﬁcation and long-term simulation results. Atmos. Environ. 37, 1549–1560. Kanhert, M., Tarrason, L., 2003. Transboundary particulate matter in Europe. EMEP status report 4/2003, Olso, O-98134, p. 87. Karvosenoja, N., Johansson, M., 2003. The Finnish Regional Emission Scenario model—a base year calculation. Proceedings of Air Pollution XI Conference, Catania, Italy, pp. 315–324. Kukkonen, J., Ha¨rko¨nen, J., Karppinen, A., Pohjola, M., Pietarila, H., Koskentalo, T., 2001a. A semi-empirical model for urban PM10 concentrations, and its evaluation against data from an urban measurement network. Atmos. Environ. 35, 4433–4442.

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Kukkonen, J., Ha¨rko¨nen, J., Walden, J., Karppinen, A., Lusa, K., 2001b. Validation of the dispersion model CAR-FMI against measurements near a major road. Int. J. Environ. Pollut. 16(1–6), 137–147. Lenhart, L., Heck, T., Friedrich, R., 1997. The GENEMIS inventory, European emission data with high temporal and spatial resolution. In: Ebel, A., Friedrich, R., Rodhe, H. (Eds.), Transport and Chemical Transformation of Pollutants in Troposphere. Springer, Berlin, Vol. 7, pp. 217–222. Monahan, E.C., Spiel, D.E., Davidson, K.L., 1986. A model of marine aerosol generation via whitecaps and wave disruption. In: Monahan, E.C., MacNiochaill, G. (Eds.), Oceanic Whitecaps. D. Reidel, Norwell, MA, pp. 167–193. Pirjola, L., Kulmala, M., 2000. Aerosol dynamical model MULTIMONO. Boreal Environ. Res. 5, 361–374. Pirjola, L., Tsyro, S., Tarrason, L., Kulmala, M., 2003. A monodisperse aerosol dynamics module—a promising candidate for use in Eulerian long-range transport model. J. Geophys. Res. 108(D9), 4258, doi:10.1029/2002JD002867. Robertson, L., Langner, J., Engardt, M., 1999. An Eulerian limited-area atmospheric transport model. J. Appl. Meteorol. 38, 190–210. Simpson, D., Fagerli, H., Jonson, J.E., Tsyro, S., Wind, P., Tuovinen, J.-P., 2003. Transboundary Acidiﬁcation, Eutrophication and Ground Level Ozone in Europe. PART I. Uniﬁed EMEP Model Description. EMEP Report 1/2003. Norwegian Meteor. Institute, Oslo, ISSN 0806-4520, p. 104. Soﬁev, M., 2000. A model for the evaluation of long-term airborne pollution transport at regional and continental scales. Atmos. Environ. 34(15), 2481–2493. Soﬁev, M., Siljamo, P., Valkama, I., Ilvonen, M., Kukkonen, J., 2006. A dispersion modelling system SILAM and its evaluation against ETEX data. Atmos. Environ. 40, 674–685, doi:10.1016/j.atmosenv.2005.09.069.

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Chapter 2.12 The use of meteorological and dispersion models in stratiﬁed atmospheric boundary layers M.R. Soler, M. Bravo and S. Ortega Abstract Air pollution dispersion models depend critically on the pollutant emissions, on the atmospheric photochemical processes and especially on the meteorology. In this study we restrict the analysis to the last component, the meteorology, showing up the meteorological models ability to perform good air pollution forecast in very stable conditions during night time periods. We use the mesoscale model MM5 coupled to CMAQ model. Results indicate that in very stable conditions and in topographically complex areas, models usually tend to forecast lower winds and higher temperatures, thus noticeably modifying the dispersion patterns. 1. Introduction

Mesoscale air pollution dispersion phenomena, aside from pollutant emission and photochemical processes, are decisively inﬂuenced by atmospheric processes. During day time, under weak synoptic conditions, the dispersion is mainly inﬂuenced by thermal effects; buoyancy is the dominant mechanism driving turbulence, which is well described by existing similarity theories. In addition, mesoscale processes are governed by topography and inhomogeneities in the surface controls or strongly inﬂuenced dispersion, which are assumed to be correctly described by models. Both conditions lead to the result that model simulations represent quite well diurnal dispersion patterns. On the contrary, during night time dispersion processes simulated by mesoscale models present several problems. Recent observations and investigations (Cuxart et al., 2000; Poulos et al., 2002) reveal a wide variety of nocturnal boundary layer situations, with sporadic and intermittent turbulence, which several investigators have attempted to classify in different regimes (Mahrt, 1999; Mahrt and Vickers, 2002). However,

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results show that a uniform modelling approach is not available for conﬁdently simulating the diverse vertical structures producing mixing and pollutant dispersion, especially in very stable regimes. Among these different modelling difﬁculties caused by strong stability regime and therefore weak and intermittent turbulence, it is important to point out difﬁculties such as ‘‘run away’’ surface cooling in the model. To prevent these difﬁculties, models often include conditions on minimum allowable values of exchange coefﬁcients or turbulent kinetic energy (TKE). This is the case of the ETA planetary boundary layer scheme implemented in the Fifth Generation PennState/NCAR Mesoscale Model Version 3.7. In this scheme, the minimum value of TKE is set to 0.2 m2 s2, too high in weak turbulence conditions, because it could overestimate the mixing and make difﬁcult the formation of slope drainage winds, low-level jets and cool-air accumulation in low-lying regions. In addition, as MM5 model is very often being used in air quality modelling studies, these uncertainties could affect nocturnal pollutant transport and vertical mixing. To investigate this fact, a modiﬁcation of the MM5 ETA scheme allowing different TKE values from 0.2 to 0.0001 m2 s2 is applied in topographic complex areas located in the Northeast part of Spain. Simulation results give rise to maximum differences in wind speed up to 2 m s1 higher and temperatures 51C lower when the TKE values varies from 0.2 to 104 m2 s2. To investigate how these differences in the meteorological modelling results affect the predicted pollutant concentrations, the MM5 model is coupled to the CMAQ model. First simulation results obtained with the new TKE range considered show some differences in groundlevel pollutant concentrations downwind of the sources, which are analysed and evaluated.

2. Very stable boundary layer: Observational examples

In this section, we present some examples of very stable boundary layer based on data collected from a 100 m CIBA tower located in Spain. Figure 1 shows night time evolution of TKE with values much lower than 0.2 m2 s2, which is the minimum value prescribed by MM5 model. To check that very stable boundary layers are not single or sporadic situations, as they represent a signiﬁcant percentage of the nocturnal boundary layer situations, we have analysed data from CIBA tower from September 2002 to June 2003. The stability criterion used is that suggested by Mahrt et al. (1998) consisting in dividing the stable boundary layers into classes attending the different values of the adimensional

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TKE (m2s-2 )

0.08

0.06

0.04

0.02

0 18:00

22:00

2:00 Time (UTC)

6:00

Figure 1. Time evolution of TKE at two different levels 6 and 50 m, respectively (CIBA tower, Spain, 14–15 January 2003).

parameter z/L. Results point out that 29.3% of the data corresponds to z/ L values higher than 1 which corresponds, using the mentioned criterion, to a very stable boundary layer. We believe, therefore, that it is worth to improve boundary layer parameterizations in models in order to take into account these situations.

3. Mesoscale models utilized in this study

Mesoscale simulations are done coupling MM5 and CMAQ models. The PSU/NCAR mesoscale model, MM5 (Grell et al., 1994), version 3.7, was conﬁgured using four nested domains with two ways nesting interaction. We have deﬁned three ﬁxed outer domains and two different inner domains using 27, 9, 3 and 1 km resolutions, respectively. The dimensions of each domain are 45 69, 58 58 and 79 79 for the three outer domains and 67 85 and 76 49 grid points for the two inner domains, respectively (see Fig. 2). The biggest domain is centred at (41.421N, 1.41E). The initial and boundary conditions are updated every 6 h with information obtained from the European Centre for medium Range

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Figure 2. Map showing the different domains used in the model simulations at 27, 9, 3 and 1-km resolution.

Weather Forecast (ECMWF) model with a 0.51 0.51 resolution. For the three inner domains, we use a topography and land-use date base with 3000 resolution. For the two outer domains, the horizontal resolution is 50 . High vertical resolution is used in the ABL, with 53 levels, with higher resolution (10 m approximately) on the low levels. The boundary layer processes are calculated using the ETA scheme, based on a prognostic equation of TKE. In this study, a modiﬁcation of the ETA scheme is proposed, allowing lower TKE values than the minimum values of 0.2 m2 s2 prescribed by the model. Simulations with TKE values from 0.2 to 104 m2 s2 with intervals of 0.05 are performed. In all simulations, radiation is parameterized using a simple cooling scheme (Dudhia et al., 2000). The soil parameterizations used have differences with regard to the drag, heat and moisture coefﬁcients. Soil temperature at six different levels is predicted by means of the diffusion equation. The model surface properties (albedo, roughness length, moisture availability and heat capacity) are speciﬁed according to the 24 USGS land-use categories, which are then reduced to one of the 13 land-use MM5 categories. The chemical transport model used in this study is the U.S. EPA Models-3/CMAQ model (Byun and Ching, 1999). This model is supported by the U.S. Environmental Agency and is continuously under development, including a variety of the most advanced conﬁgurations and parameterizations. In this study, CMAQ model is coupled to MM5 model in order to simulate the dispersion of a nonreactive pollutant, used as a tracer, as the objective of this study is to assess the effect of the atmospheric stratiﬁcation on the behaviour of the dispersion pattern. The use of reactive pollutants would mask this effect.

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4. Characteristics of the areas

The areas we studied are La Plana and Tarragona, which correspond to domains 5 and 4, respectively (see Fig. 3). The ﬁrst area is a large basin— a plateau surrounded by mountains that are very often over 1000 m above sea level. La Plana is between 450 m and 600 m above sea level. This complex topography results in a particular thermal and dynamic regime in the area studied (Soler et al., 2004). In this section, we highlight only two phenomena that have a climatic signiﬁcance due to their frequency. The ﬁrst one is the stagnation that takes place at night, usually in anticyclonic situations, when the wind regime is calm (an average of 77% of the data analysed). The height of this stagnation is roughly 100 m, which causes stagnant cold air masses, the formation of strong thermal

0 -0.5 -1 -1.5 -2

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Figure 3. Spatial distribution of differences between wind velocity (a) and temperature (b) corresponding to simulations with prescribed TKE values of 0.2 and 104 m2 s2, respectively. Upper and lower panels show domains 5 and 4, respectively, which correspond to La Plana and Tarragona areas.

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inversions and fog. Especially in winter, the number of days with fog can be as high as 80 per year. The second phenomenon is the occurrence of a sea breeze. This starts in spring and ends in autumn and increases ozone concentrations and related primary and secondary pollutants. This area, therefore, has pollution problems caused mainly by the weak dispersive capacity of its air and the arrival of pollutants from industrial coastal areas when the wind regime is dominated by the sea breeze. These factors lead to maximum ozone during later spring and summer, and also maximum SO2 values during winter, exceeding the threshold value for a few days every year. High levels in the area require careful strategies for reducing the emission of primary pollutants and reaching the prescribed health and environmental targets. The second area studied, Tarragona’s area, is also characterized by a complex topography. The presence of the Mediterranean Sea, two mountain ranges, which rise steeply 1000 and 800 m, respectively, a coastal plain (height below 200 m) and a river valley, drastically inﬂuences the wind regime in the zone. During low-pressure gradients in the synoptic scale, the observed wind regime is dominated by sea breeze circulations during day time. During night time, the combination of local drainage winds creates complex circulation and dispersion patterns which mainly depend on the stability and the turbulence.

5. Meteorological results

The simulation was performed during 10–12 February 2001 corresponding to an anticyclonic situation with very weak synoptic forcing. This pattern allows the development of local wind circulations, such as downvalley winds, and strong temperature inversions. MM5 wind and temperature simulations show a tendency to increase wind speed and to decrease temperature as TKE values become lower, especially in areas where topography forces the formation of slope drainage winds and in low-lying areas. The difference can be of up to 4 m s1 for wind velocity and 51C for temperature. As an example, Fig. 3 shows, for domains 5 and 4, respectively, the spatial distribution of differences between wind velocity and temperature which corresponds to simulations with prescribed values of 0.2 and 104 m2 s2, respectively. In addition, the minimum TKE value of 0.2 prescribed by the MM5 model inhibits the development of nocturnal low-level jets measured in these areas. Figure 4 shows an example of this behaviour. The wind proﬁle measured in La Plana with a Doppler Sodar, together with the

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Height (m)

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m/s Figure 4. Wind velocity proﬁle measured by a Doppler Sodar and simulated with prescribed TKE values of 0.2 and 104 m2 s2.

MM5 simulations corresponding to a TKE value of 104 m2 s2, indicates a maximum of 3.5 m s1 at a height of 250 m, However, when the simulation is done with the prescribed value of 0.2 m2 s2, this maximum is practically inexistent.

6. Modelling dispersion results

To investigate how the differences in wind velocity and temperature, commented in the previous section, affect the dispersion patterns, we have considered the dispersion of a nonreactive pollutant, as carbon monoxide, coming from an imaginary point source which has been located at different points of the domains under study. To enhance the effect, the sources have been located in areas where the difference in wind velocity and temperature were noticeable. Comparison of models results using different TKE values shows quite similar dispersion patterns, but there are some differences. Figure 5 shows an example for La Plana domain, where surface concentration differences CTKE ¼ 0.2–CTKE ¼ 104 are calculated over this domain. Positive differences are located over the slopes as higher values of TKE inhibit the formation of drainage winds, pollutant dispersion decreases and concentration increases. Negative values are due to lower values of TKE favouring the generation of drainage winds increasing the pollutant dispersion, and therefore the concentration decreases. Their location over the domain points out that the plume is spread over different directions or is more widely dispersed.

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Figure 5. Spatial distribution of concentration differences (CTKE ¼ 0.2CTKE ¼ 104) over La Plana domain.

7. Conclusions

This study tries to investigate how the modiﬁcation of the MM5 ETA scheme by allowing different TKE minimum prescribed values, from 0.2 to 0.0001 m2 s2, can affect the predicted surface pollutant concentrations when MM5 model is coupled to the CMAQ photochemical model. To accomplish this objective, several MM5 simulations have been performed using different TKE values. Results show a tendency to increase wind speed and to decrease temperature accordingly as TKE values become lower, especially in areas where topography forces the formation of slope drainage winds and in low-lying areas. In addition, the minimum TKE value of 0.2 prescribed by the MM5 model inhibits the development of nocturnal low-level jets and underestimates the formation of drainage winds. All these meteorological results modify the dispersion patterns when MM5 is coupled to a dispersion model. Preliminary results show a tendency to predict lower surface concentration levels of a nonreactive pollutant as TKE values become smaller. The reason could be that in these conditions MM5 generates higher wind speed and forces higher mixing and dispersion. However, this conclusion is preliminary and more simulations must be performed to achieve ﬁnal and deﬁnitive conclusions. Discussion

D.W. Byun:

It is a very interesting study intending to understand the science issue using MM5/TKE under stable

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boundary conditions. Our group has practised the same option for the Houston/Gulf of Mexico application and noticed that the stable PBL is mostly limited to the lowest model layer. I am wondering if you have noticed similar problems in your study. A second question is, if the minimum TKE value is lowered, what TKE criterion was used to determine the height of PBL? Related to the ﬁrst question, we agree with you. In very stable boundary layers when the TKE is much lower than 0.2 m2 s2, which is the minimum value prescribed by MM5 model, we have usually a nontraditional boundary layer deﬁned as the layer where the buoyancy ﬂux decreases with height to small values and then remains relatively small at higher levels. In a nontraditional boundary layer, the turbulence could increase with height except in an extremely thin layer near the surface which could be less than 10 m deep (Smedman, 1988), perhaps as you say limited to the lowest level model. Related to the second question, the boundary layer depth is more easily deﬁned in terms of the buoyancy ﬂux because the small stratiﬁcation above the surface inversion layer often forces the buoyancy ﬂux to small values even when the turbulence energy and momentum ﬂux do not decrease with height. In this sense, the height dependence of the buoyancy ﬂux is more regular than that for the turbulence energy and momentum ﬂux (Mahrt and Vickers, 2003). The problem sometime is to deﬁne the boundary layer height because in very stable conditions, the buoyancy ﬂux increases in many cases due to near-collapsed turbulence close to the surface and there is signiﬁcant turbulence at higher levels due to the presence of shear generated by the nocturnal low-level jet (Ostdiek and Blumen, 1997), internal gravity waves (Chimonas, 2002), acceleration associated with decoupling (Derbyshire, 1999) or generation of turbulence associated with unstable waves and density currents (Terradellas et al., 2005). In this case, it is better to use a general formulation of the mixing length that includes cases where the boundary layer is not deﬁnable (Mahrt and Vickers, 2003).

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Chimonas, G., 2002. On internal gravity waves associated with the stable boundary layer. Bound. Layer Meteorol. 102, 139–155. Mahrt, L., Vickers, D., 2003. Formulation of turbulent ﬂuxes in the stable boundary layer. J. Atmos. Sci. 60, 2538–2548. Ostdiek, V., Blumen W., 1997. A dynamic trio: Inertial oscillation, deformation frontogenesis, and the Ekman–Taylor boundary layer. J. Atmos. Sci. 54, 1490–1502. Smedman, A.-S., 1988. Observations of a multi-level turbulence structure in a very stable atmospheric boundary layer. Bound.-Layer Meteorol. 44, 231–253. Terradellas, E., Soler, M.R., Ferreres E., 2005. Analysis of oscillations in the stable atmospheric boundary layer using wavelet methods. Bound.Layer Meteorol. 114, 489–518. REFERENCES Byun, D.W., Ching, J.K.S., (Ed.), 1999. Science algorithms of the EPA Models-3 Community Multi-scale Air Quality (CMAQ) modeling system, EPA Report, EPA/600/R-99/ 030, NERL, Research Triangle Park, NC. Cuxart, J., Yagu¨e, C., Morales, G., Terradellas, E., Orbe, J., Calvo, J., Ferna´ndez, A., Soler, M.R., Infante, C., Buenestado, P., Espinalt, A., Joergensen, H.E., Rees, J.M., Vila`, J., Redondo, J.M., Cantalapiedra, I.R., Conangla, L., 2000. Stable atmospheric boundary-layer experiment in Spain (SABLES 98): A report. Bound.-Layer Meteorol. 96, 337–370. Dudhia, J., Gill, D., Guo, Y.-R., Manning, K., Wang, W., Chiszar, J., 2000. PSU/NCAR mesoscale modeling system tutorial class notes and user’s guide: MM5 modeling system version 3. Grell, G.A., Dudhia, J., Stauffer, D.R., 1994. A description of the ﬁfth-generation Penn State/NCAR mesoscale model (MM5). NCAR Technical Note, NCAR/ TN-398+STR, p. 117. Mahrt, L., 1999. Stratiﬁed atmospheric boundary layers. Bound. Layer Meteorol. 90, 375–396. Mahrt, L., Sun, J., Blumen, W., Delany, T., Oncley, S., 1998. Nocturnal boundary-layer regimes. Bound. Layer Meteorol. 88, 255–278. Mahrt, L., Vickers, D., 2002. Contrasting vertical structures of Nocturnal Boundary Layers. Bound. Layer Meteorol. 105, 351–363. Poulos, G.S., Blumen, W., Fritts, D.C., Lundquist, J.L., Sun, J., Burns, S.P., Nappo, C., Banta, R., Newsom, R., Cuxart, J., Terradellas, E., Balsley, B., Jensen, M., 2002. CASES-99, A comprehensive investigation of the stable boundary layer. Bull. Am. Meteorol. Soc. 83, 555–581. Soler, M.R., Hinojosa, J., Bravo, M., Pino, D., Vila` Guerau de Arellano, J., 2004. Analyzing the basic features of different complex terrain ﬂows by means a Doppler Sodar and a numerical model: Some implications to air pollution problems. Meteorol. Atmos. Phys. 85, 141–154.

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Chapter 2.13 Modelling regional air quality over decades: Past and future trends in photochemical smog Robert Vautard, Sophie Szopa, Matthias Beekmann, Laurent Menut, Didier A. Hauglustaine and Laurence Rouil Abstract The question whether emissions have decreased, during the last ten years or so, as much as suggested by the current inventories is addressed. We perform an array of modelling experiments using a European-scale chemistry-transport model, CHIMERE, nested in the global-scale CTM LMDz-INCA, in order to evaluate the evolution of ozone during the period 1990-2004, during which large reductions in NOx and VOC emissions are expected. The results are put in perspective of future emission reductions in Europe and the evolution of air quality in the next 30 years is estimated, on the basis of the actual global emission scenarios. We show in particular that while ozone acute episodes will be less intense, global emissions should enhance the background levels. 1. Introduction

The history of regional air quality is highly non-linear. For several decades the pressure of man on the atmospheric environment became signiﬁcant and sometimes critical. In Europe, for most of the primary pollutants, an increase followed by a decrease of the emissions has been witnessed, due to the increase of consciousness of the impact of such pollution. At the European level policies enforcing the reduction of emissions are set up, with scientiﬁc assessment programs like the Clean Air for Europe (CAFE) programme. There are still air quality pollutants not having a clear tendency to decrease: ozone and particulate matter, probably due to their mixed regional and global spatial scale. Monitoring, but also modelling their behaviour on the long-term is essential in order to understand their evolution. Modelling pollutants like ozone over decades or performing

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experiments on sensitivity to the emissions is a challenge that is on the way to be achieved, due to the increased computer power and memory and the availability of long-term meteorological analyses. The modelling challenge is to establish a link between the past decadal changes in primary emissions with ambient concentrations, in order to gain conﬁdence in future scenario simulations. In this article, we focus on the behaviour of ozone at surface level over Europe. There, even though high surface O3 events are decreasing, the O3 baseline appears to have increased by up to few ppb per year at several surface sites during the last two decades (Monks et al., 2003; Naja et al., 2003; Carlsaw, 2005; Simmonds et al., 2005), with uncertainty on rates. Signiﬁcant trends in European surface O3 concentrations are difﬁcult to assess due to several antagonist processes, such as stratosphere– troposphere exchanges or long-range transport, stratospheric O3 depletion, boreal biomass burning. Among them, the drastic ozone precursor emission reduction in Europe, of 25–30% (Vestreng et al., 2004), tend to decrease O3 maxima and increase urban minima because of limitation of O3 titration by nitrogen monoxide (NO) (Lindskog et al., 2003; Monks et al., 2003; Jonson et al., 2005). Due to these multiple phenomena, the effective gain of the regulatory European effort to air quality improvement is hard to assess from the use of observations alone, and models are required. The evolution of ozone in the future decades is uncertain, also due to these multiple factors. The aim of this paper is to show that the regional air quality model, CHIMERE (Schmidt et al., 2001), that has been developed at Institut Pierre-Simon Laplace is able to simulate both daily and interannual variations of the surface ozone over Europe, to establish a link between decadal changes in emissions of ozone precursors and the behaviour of ambient concentrations in Europe, to evaluate the evolution of air quality in the next decades (in 2030) using scenarios of emissions. 2. The decadal simulation experiments

The ﬁrst series of experiments consists in simulating the ozone concentrations over Europe over the period 1990–2002, for which many observations are available. They are described in Vautard et al. (2006). The regional air quality simulations are carried out with the gas-phase version of the regional CHIMERE CTM (version V200501G), described in Schmidt et al. (2001). This model has been shown to give an accurate simulation of O3 daily maxima in summer, especially during acute episodes (Vautard et al., 2005).

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The chemistry-transport model is forced at the boundaries by a climatology of O3 and precursors issued from the global-scale LMDz-INCA model (Hauglustaine et al., 2004; Folberth et al., 2005), based on the monthly averages of a 5-year simulation using varying meteorology and biomass burning emissions from 1997 to 2001. CHIMERE uses hourly primary emissions derived from the EMEP inventory (Vestreng, 2003), which are available for the period under study on a yearly basis, except during the 1991–1994 period where a linear interpolation between 1990 and 1995 emissions is performed. A reference ‘‘control’’ simulation is performed using ﬁxed (with year) boundary conditions and year to year variations in emissions according to the EMEP inventory. In order to study the effect of assumed emission changes during the considered period, a simulation uses a ﬁxedyear emission set, all other parameters being equal. Year 2001 is arbitrarily selected for reference. Finally, in order to evaluate the inﬂuence of possible trends in boundary conditions, another simulation is carried out assuming a positive 0.4 ppb year 1 O3 trend added to the LMDz-INCA climatology, no additional ozone in 2001 being arbitrarily assumed. This value corresponds to an upper limit of summer trends in background O3 concentrations deduced from observations at Mace Head by Simmonds et al. (2005) and Carslaw (2005) who found respectively +0.3970.25 and +0.2570.06 ppb year 1. The 0.4 ppb year 1 trend is arbitrarily applied at all model boundaries (side and top), and regional emissions inside the domain are varied from year to year as in the control experiment.

3. Skill of simulations of ozone over a decadal period

In Fig. 1 we show the skill of the model in simulating the daily and interannual variability of summertime daily O3 maxima. Simulated ozone values are compared with the observations from the EMEP station network. Only 37 sites are taken into account, which have homogeneous observations along the time period. The simulated summer ozone averages faithfully follow the observed ones. Mean daily maxima correlations lie around 0.8 and their root mean square (RMS) errors range from 8 to 12 ppb. During the 13-year period there is a general decrease of the RMS, probably due to emission decrease, as photochemistry is more sensitive to meteorological variability (and thus to meteorological errors) as precursor emissions are higher. The ﬁxed-emissions and boundary-trended simulations, both based on year-varying emissions, have a comparable best skill. The simulation with ﬁxed emissions has a clear negative bias in the early 1990s, resulting in a

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Figure 1. Skill measures of the simulations of daily ozone maxima as a function of year, for the three simulations. Top panel: Yearly–daily maxima means (in mg m 3) evolution for each simulation and observations. Middle panel: Evolution of yearly correlation averaged over all stations. Bottom panel: Evolution of yearly root mean square error averaged over all stations.

larger RMS error, a ﬁrst sign of a real impact of emission reductions on O3 daily maxima. Overall these results demonstrate the ability of the model and the emissions taken together to simulate (i) the interannual variability and (ii) the decadal trend of ozone daily maxima. 4. Veriﬁcation of precursor emission reductions in Europe

The gain of European efforts to reduce precursor emissions is particularly clear when comparing simulated and observed ozone daily maxima 90th

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percentile, as it decreases by about 10 ppb over the period. A trend of similar amplitude is obtained in the simulations with yearly changing emissions, and no trend is found in the simulation with ﬁxed boundary conditions. The simulated 90th percentile difference evolutions (Fig. 2b) display a clear, statistically signiﬁcant positive tendency of 0.65 ppb year 1 (po0.01) for the simulation with ﬁxed emissions, while the slight negative tendencies (respectively 0.18 ppb year 1 and 0.07 ppb year 1) for the other simulations are not statistically signiﬁcant (p0.1). The evolution of observed lower percentiles (bottom curves of Fig. 2) does not exhibit any trend for simulations without trend in boundary conditions, and is not sensitive to emission changes since simulations are trend free and close to one another. The 10th percentile differences (Fig. 2b) do not show either a signiﬁcant tendency (respectively 0.03 ppb year 1 and 0.10 ppb year 1). However, the trend imposed in O3 boundary conditions leads to an equivalent tendency in differences of the 10th percentile (0.40 ppb year 1, po0.001). The model overestimates

Figure 2. Left: Time evolutions of the simulated and observed daily ozone maxima 90th and 10th percentiles (all stations taken together). Right panel: Differences (simulation— observations) of the percentiles, with regression lines.

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the O3 daily maxima 10th percentile by about 3 ppb, which is more difﬁcult to explain than high percentile bias, and could be due to model deﬁciencies on physical processes. From Fig. 2 we conclude that the highest daytime O3 values are sensitive to emissions and insensitive to increasing ozone at boundary conditions. Most likely, high O3 concentrations are obtained in episodic stagnant weather conditions where transport time is larger than deposition time. During these episodes, the O3 formation results from local or regional photochemical production. In cloudy and windy conditions the situation is reversed: O3 molecules largely come from outside Europe, which explains why the 10th percentile sensitivity to boundary conditions rather than to regional emissions. Of the three simulations the control simulation is the one that best ﬁts the observed 10th and 90th percentiles. One concludes that, most likely, (i) the assumed baseline O3 increase of 0.4 ppb year 1 at the whole domain boundaries is not consistent with observations, and (ii) the EMEP inventory emission changes, during the 13-year period, are consistent with the O3 observations over northwestern Europe, on an average. Thus, using a tendency analysis of the simulated-minus-observed differences in daily O3 maxima 90th percentiles we found that the decadal evolution of the emissions of the EMEP inventory is quantitatively consistent with the observations when all available European observations are taken together. This can be considered as a veriﬁcation of the ensemble of reported national emission estimates made in the framework of the international Convention on Long-Range Transboundary Air Pollution (CLRTAP). 4.1. Surface ozone in 2030

Even though global emission of ozone precursors is likely to increase, the emission of ozone precursors is expected to decline in the European Union (EU-25) until 2020 even under the assumption of accelerated economic growth (Amann et al., 2005). But air quality results from both regional and global contributions. The resulting global change of background ozone levels corresponds to a modiﬁcation of the baseline on which regional pollution events are added. We examine the impact of these future scenarios on Western Europe surface ozone concentrations (Szopa et al., 2006). These impacts are investigated using both a global and regional chemistry-transport models. The global model provides the global distribution of ozone and precursors responding to scenarios at low resolution. The regional model is used to study the higher-resolution, regional distributions of ozone, and takes

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the concentrations of the global model as boundary conditions. The relative contribution of long-range transport of ozone and its precursors with respect to the European emission control strategy is investigated. The global simulations are performed using the LMDz-INCA chemistry-climate model. The global emission scenarios of Cofala et al. (2004) and Dentener et al. (2005) are used in this study. For each scenario, the simulations are spun up for 3 months and performed over 1 year. The meteorological ﬁelds are relaxed toward the ﬁeld of 2001 (ECMWF ERA40 reanalysis), which is representative in terms of surface ozone variability and episodes over Europe. The daily average LMDz-INCA concentrations are taken as boundary conditions for the CHIMERE regional model in the same conﬁguration as above. Regional simulations for 2030 use the anthropogenic EMEP 2002 emissions (Vestreng, 2003), rescaled for carbon monoxide, non-methane hydrocarbons (NMHC) and nitrogen oxides, using, at each grid point the ratios between the 2030 scenarios and the present-day global inventory. For each of the three 2030 scenarios (CLE, MFR and SRES-A2), we applied LMDz-INCA and CHIMERE to investigate the response of European summer pollution to (1) both global and European changes in the anthropogenic ozone precursor emissions; (2) changes only in global anthropogenic emission (present-day emissions for Europe); (3) changes only in western European emissions (present-day emissions for global chemistry in LMDz-INCA). Figure 3 shows the surface ozone change calculated in July 2030, by comparison with July 2001, for the CLE scenario. The future emissions lead to an increase of surface ozone at the global-scale (Dentener et al., 2005). These changes are more contrasted over Europe. An increase of more than 4 ppbv is calculated over highly populated areas (e.g., in the vicinity of Paris, London, Manchester, Du¨sseldorf, Bruxelles, Milan, Cracovie). A general increase of 0.1–4 ppbv is predicted over northern Europe whereas a decrease of 0.1–3 ppbv is calculated over most of southern Europe. For this scenario, the average ozone daily maximum at European stations is almost unchanged whereas the number of days reaching 90 ppbv is decreased by a factor of 2.4. The MFR and the SRES-A2 scenarios provide far more homogeneous but extreme responses (Fig. 3). The MFR scenario induces a general reduction of surface ozone in 2030 larger than 2 ppbv over most of Europe. Nevertheless, the ozone levels are, as in the CLE scenario, increased by a few ppbv over Northern high emission spots. The general decrease is also accompanied by a 6.1 ppbv reduction of the averaged ozone daily maximum at European monitoring stations. In this optimistic case, the improvement of the average level of air quality is characterised by a decrease of the

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Figure 3. Monthly mean surface ozone changes in July 2030 (compared with July 2001) (a) at the global-scale, for the ‘‘current legislation’’ scenario; (b) over Europe, for the ‘‘current legislation’’ scenario; (c) over Europe, for the ‘‘maximum feasible reduction’’ scenario; (d) over Europe, for the SRES-A2 scenario.

dispersion of ozone values due to an important decrease of the number of high ozone concentration events. The number of days exceeding 90 ppbv over ground based stations is reduced by a factor of 31 while the percentile 90 reaches 57.9 ppbv against 70.4 ppbv in the present-day experiment. By contrast, the SRES-A2 scenario leads to an ozone increase, exceeding 5 ppbv in July over Europe. The average ozone daily maximum over European stations grows by 10.2 ppbv. The dispersion of the results is increased with a number of day exceeding 90 ppbv multiplied by a factor of 7.5 and a percentile 90 reaching 86.2 ppbv. The other sensitivity simulations show that emission changes expected in the future result in counterbalancing effects, of which amplitude remains quite uncertain due to the uncertainty in the future emissions themselves. The prediction of future air quality remains therefore an open topic, especially if climate itself changes, a factor that has not been accounted for in the present study.

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Discussion

S. AndreaniAksoyoglu:

R. Vautard:

A. Ebel: R. Vautard: C. Mensink:

R. Vautard:

Comment: There is a new study based on observations at Jungfraujoch, which gives an O3 background increase of 0.5 ppb year 1. This trend was based on measurements between 1992 and 2002. Authors also looked at the effect of special year 2003. The results we presented are not incompatible with an increase of background ozone at Jungrfraujoch. We found that there is no increase in mean surface daily maximum low percentiles, which may be driven by processes different from background ozone. What would be the role of the stratosphere for longterm trends in the troposphere? This is a very debatable point. There are studies showing changes in the frequency of tropopause foldings How would the year 2003 (with exceptional ozone concentrations) inﬂuence the results of the trend analysis 1990–2002? We do not know, as the experiment has not been carried out. Moreover, ERA40 data, which are used to force MM5 and CHIMERE, stopped in August 2002, which makes it difﬁcult.

REFERENCES Amann, M., Bertok, I., Heyes, F.G.C., Klimont, Z., Schopp, W., Winiwarter, W., 2005. Baseline Scenarios for the Clean Air for Europe (CAFE) Programme, Tech. Rep. BA-3040/2002/340248/MAR/C1, International Institute for Applied Systems Analysis. Carlsaw, D.C., 2005. On the changing seasonal cycles and trends of ozone 295 at Mace head, Ireland. Atmos. Chem. Phys. Discuss. 5, 5987–6011. Cofala, J., Amann, M., Klimont, Z., Scho¨pp, W., 2004. Scenarios of World Anthropogenic Emissions of SO2, NOx and CO up to 2030, Internal report of the transboundary air pollution programme, International Institute for Applied Systems Analysis, Laxenburg, Austria. Dentener, F., Stevenson, D., Cofala, J., Mechler, R., Amann, M., Bergamaschi, P., Raes, F., Derwent, R., 2005. The impact of air pollutant and methane emission controls on tropospheric ozone and radiative forcing: CTM calculations for the period 1990–2030. Atmos. Chem. Phys. 5, 1731–1755. Folberth, G., Hauglustaine, D.A., Lathie`re, J., Brocheton, F., 2005. Impact of biogenic hydrocarbons on tropospheric chemistry: results from a global chemistry-climate model. Atmos. Chem. Phys. Discuss. 5, 1680-7375/acpd/2005-5-10517. Hauglustaine, D.A., Hourdin, F., Jourdain, L., Filiberti, M.A., Walters, S., Lamarque, J.F., Holland, E.A., 2004. Interactive chemistry in the Laboratoire de Me´te´orologie

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Dynamique general circulation model: Description and background tropospheric chemistry evaluation. J. Geophys. Res. 109, D04314, doi:10.1029/2003JD003957. Jonson, J.E., Simpson, D., Fagerli, H., Solberg, S., 2005. Can we explain the trends in European ozone levels. Atmos. Chem. Phys. 6, 51–66. Lindskog, A., Beekmann, M., Monks, P., Roemer, M., Schuepbach, E., Solvberg, S., 2003. Tropospheric ozone research—TOR-2, Final Report, Eurotrac-2. http://eurotrac.ivl.se/ TOR2/FinalReport.htm Monks, P., Rickard, A.R., Dentener, F., Jonson, J.E., Lindskog, A., Roemer, M., Schuepbach, E., Friedli, T.K., Solberg, S., 2003. Tropospheric ozone and precursors, Trends, Budgets and Policy—EVK2-CT-1999-000043. Naja, M., Akimoto, H., Staehelin, J., 2003. Ozone in background and photochemically aged air over central Europe: Analysis of long-term ozonesonde data from Hohenpeissenberg and Payerne. J. Geophys. Res. 108(D2), 4063, doi:10.1029/2002JD002477. Schmidt, H., Derognat, C., Vautard, R., Beekmann, M., 2001. A comparison of simulated and observed ozone mixing ratios for the summer of 1998 in Western Europe. Atmos. Environ. 36, 6277–6297. Simmonds, P.G., Derwent, R.G., Manning, A.L., Spain, G., 2005. Signiﬁcant growth in surface ozone at Mace Head, Ireland, 1987–2003. Atmos. Environ. 38, 4769–4778. Szopa, S., Hauglustaine, D.A., Vautard, R., Menut, L., 2006. Future global tropospheric ozone changes and impact on European air quality. Geophys. Res. Lett. 33, L14805, doi:10.1029/2006GL025860. Vautard, R., Beekmann, M., Menut, L., Szopa, S., Rouil, L., Hauglustaine, D.A., Roemer, M., 2006. Are decadal anthropogenic emission changes in Europe consistent with surface ozone observations? Geophys. Res. Lett. 33, L13810, doi:10.1029/2006GL026080. Vautard, R., Honore´, C., Beekmann, M., Rouil, L., 2005. Simulation of ozone during the August 2003 heat wave and emission control scenarios. Atmos. Environ. 39, 2957–2967. Vestreng, V., 2003. Review and revision of Emission data reported to CLRTAP, EMEP Status report, July 2003. Vestreng, V., Adams, M., Goodwin, J., 2004. Inventory review 2004: Emission data reported to CLRTAP and under NEC directive, EMEP/MSC-W status report 1/04, The Norvegian Meteorological Institute, Oslo, Norway, 2004.

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Chapter 2.14 Forecasting ozone and PM2.5 in southeastern U.S. M. Talat Odman, Yongtao Hu, Michael E. Chang and Armistead G. Russell Abstract An air quality forecasting system was developed to aid the operational ozone and PM2.5 forecasting in Atlanta, Georgia. The system is based on three dimensional models for weather and air quality prediction and provides high resolution locally. A preliminary evaluation shows that the system has the potential of producing reliable forecasts. 1. Introduction

There is an increasing interest in day-to-day variation of air quality. As the public is becoming more health conscious, air pollution is being perceived as a serious problem. In response, local authorities are looking for short-term management strategies to avoid bad pollution episodes. The press and the media are beginning to carry air quality forecasts as routine extensions of weather forecasts. These air quality forecasts are produced using various techniques. Persistence, climatology, statistical regression, close neighbor, and decision tree models are among the most popular methods. More recently, three-dimensional (3-D) air quality models made their entrance into the forecasting world. Air quality forecasting in Atlanta, Georgia started with the 1996 Olympic Games and continues ever since (Cardelino et al., 2001). A panel of experts gets together every day and issues an ozone forecast for the next day. One of the outcomes of this forecast is ‘‘ozone alerts’’ that are displayed as electronic signs on the highways. These signs urge the drivers to telecommute or to refuel after sunset whenever an ‘‘ozone day’’ is imminent. 3-D modeling has been one of the methods used in Atlanta forecasts ever since the beginning (Chang and Cardelino, 2000). The Urban Airshed Model (UAM) is run daily using diagnostic meteorology. However,

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the emissions data used in this operation have not been updated in recent years, and the models and methods used do not reﬂect the current stateof-the-science. Among the other 3-D forecasting operations, the only one that covers the southeastern U.S. is the NOAA/EPA national forecast (Eder et al., 2006). The models used in this operation consist of the EtaCMAQ modeling system with 12-km resolution (Otte et al., 2005) over the eastern U.S. Last year, PM2.5 forecasting started in Atlanta in addition to ozone. Also, the forecasts are being expanded to other cities in Georgia (e.g., to Macon which is 135 km south-southeast of Atlanta). We have been asked to develop a state-of-the-science 3-D modeling system that can forecast ozone and PM2.5 over most of Georgia. This paper describes the initial version of the forecasting system we developed and gives an overview of our operation which started on May 1, 2006.

2. Forecasting system and its operation

Our goal is to provide accurate, ‘‘ﬁne-scale,’’ local air quality forecasts sufﬁciently in advance that the public and local authorities can take necessary actions. NOAA/EPA’s target is to issue nationwide 2-day forecasts with 2.5-km resolution in 10 years (Davidson et al., 2005). On a local scale, we want to get there, and go beyond, much faster. In particular, our objective is to forecast longer periods with ﬁner resolution (1 km). Also, in addition to air quality, we want to be able to forecast the effectiveness of local control strategies in order to avoid pollution episodes. We use the Weather Research and Forecasting (WRF) model for the forecasting of meteorology (http://wrf-model.org/). We initialize WRF with 84-h forecasts from the North American Mesoscale (NAM; formerly known as Eta) model (http://nomads.ncdc.noaa.gov/). We utilize the Sparse Matrix Operator Kernel Emissions (SMOKE) model for emissions (CEMPD, 2004). Finally, we use the Community Multiscale Air Quality (CMAQ) model for chemistry and transport (Byun and Ching, 1999). We are currently using the standard version 4.5 of CMAQ but to achieve our objectives we will soon incorporate the following model extensions we developed in recent years: (1) the time-saving variable step algorithm (Odman and Hu, 2004), (2) the direct decoupled method that allows calculation of emission sensitivities along with pollutant concentrations (Hakami et al., 2003), and (3) the adaptive grid algorithm that allows very high (100 m) resolution (Odman et al., 2002). The modeling domain is covered with three nested grids of different resolutions: (1) a 36-km grid (72 72) over the eastern U.S., (2) a 12-km

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grid (72 72) over most of the southeast, and (3) a 4- km grid (99 78) over Georgia and portions of neighboring states. The horizontal domains for WRF are slightly larger than those used in CMAQ. Also, while 34 vertical layers are used in WRF, there are only 13 unequally spaced vertical layers in CMAQ. In order to issue tomorrow’s forecast by 10 a.m. today, the operation must start 212 days in advance (e.g., Wednesday’s forecast by Sunday night). We ﬁrst simulate a 3-day period over the 36-km grid using 00Z NAM data, initial conditions from the previous cycle (i.e., warm start), and ‘‘clean’’ boundary conditions. Then we simulate 212 days over the 12-km grid using 12Z NAM data and initial/boundary conditions from the 36-km grid. Finally, we simulate 24 h over the 4-km grid using 12Z NAM data and initial/boundary conditions from the 12-km grid. The operation is mostly automated but it still requires about 1 h of human interaction per day. A total of 6 CPUs are employed. Emission inputs must be up-to-date for accurate forecasts. We projected the National Emissions Inventory (NEI) for the year 2002–2006 using growth and control factors. For example, we used the Economic Growth Analysis System (EGAS) model to project the major power plant emissions and applied controls from NOx State Implementation Plans. We use monthly averaged data for major point sources and wild-land ﬁres. We forecast mobile emissions by using emission factors based on forecasted daily average temperatures. Finally, we forecast biogenic emissions using summertime leaf indexes.

3. Forecasting products

The current products are the 24-h ozone and PM2.5 forecasts issued once per day. They are posted to a web site (http://www.ce.gatech.edu/ research/forecast/) as soon as they become available. The forecast for Atlanta is summarized in terms of the peak 1-h ozone and PM2.5 values, their location, and time of occurrence. For example, ‘‘Peak 1-h ozone tomorrow will be 65 ppb at Gwinnett at 2 p.m.’’ In addition to tomorrow’s forecast, today’s forecast remains posted until tomorrow. Finally, there is an evaluation for yesterday’s forecast. It compares the value, location, and time of the forecasted peak ozone and PM2.5 to the value, location, and time of the observed peaks. For example, ‘‘Peak 1-h ozone was predicted to be 72 ppb at Conyers at 4 p.m. The observed peak value was 66 ppb at Conyers at 4 p.m.’’ In this example, while the location and time of the peak was forecasted accurately the value was overestimated by 9%.

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Graphical products include charts showing time series of 1-h ozone and PM2.5 values at 11 monitoring locations in metropolitan Atlanta and several other cities in Georgia. These charts display the forecasts from the 4- and 12-km grids. For evaluation purposes, the observations are also plotted on the same charts as soon as they become available. Also, every day, the correlation between the predictions and observations is evaluated by means of scatter plots of all 1-h values at all sites. Finally, ozone maps are also available to compare our forecast on the 12-km grid visually to the NOAA/EPA forecast posted on the NOAA website (http:// www.nws.noaa.gov/aq/).

4. Operational evaluation

Atlanta’s ozone forecasting record from 2000 to 2004 is quite impressive. 577 days were forecasted correctly as non-events and 94 days as ozone days. There were 63 false alarms and 31 misses. Since our 3-D forecasting operation has a very short history (only 10 days at the time of this presentation) and no bad air quality days occurred up to this point, we will not attempt to calculate similar statistics. Instead, we will present more detailed evaluations. The forecasted 1-h average ozone and PM2.5 concentrations are compared with the observations published the next day by the Ambient Monitoring Program of the Georgia Department of Natural Resources (http://www.air.dnr.state.ga.us/amp/). Figure 1 shows such a comparison at all the monitoring locations in Metro Atlanta for all the hours on May 12, 2006. The bias in ozone is in the form of overestimations for ozone concentrations below 20 ppb. Most of these are nighttime values at some speciﬁc stations. These locations are probably under the inﬂuence of NOx titration that the model cannot simulate due to insufﬁcient resolution and/or uncertainties in land use and emissions data. PM2.5 concentrations are mostly overestimated below 5 mg m 3 and generally underestimated above that value. The forecasts are generally accurate but occasionally they fail to capture the temporal variation of pollution levels. For example, the forecasted ozone for Conyers on May 6, 2006 was in near perfect agreement with observations (Fig. 2). The fact that the 4-km forecast is more accurate than the 12-km forecast is encouraging for the pursuit of higher resolution. While May 6 had perfect conditions for ozone forecasting (clear and sunny), May 4 presented many challenges: there were scattered afternoon thundershowers throughout Atlanta. This led to the suppression of peaking afternoon ozone concentrations. Two such events can be

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224 O3 on May 12, 2006 0.080 y = 0.9113x R2 = 0.4841

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seen at Douglasville’s ozone observations in Fig. 2: one at 16 EDT and another at 18 EDT. Thundershowers are very difﬁcult to forecast and they were completely missed in this case. The forecasted ozone remained ﬂat due to cloud cover but no scavenging was predicted.

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O3 at CONYERS on 5/6/2006 0.240 4-km 12-km Obs.

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Figure 2. Good and poor ozone forecasts. May 6, 2006 was a sunny day and ozone was predicted almost perfectly at Conyers, with slightly better accuracy over the 4-km grid (top). Scattered thundershowers throughout the afternoon on May 4, 2006 were hard to predict leading to poor ozone predictions at Douglasville (bottom).

Figure 3 shows good agreement between the temporal variations of observed and predicted PM2.5 at South Dekalb on May 5. The peaks during morning rush hours, early afternoon, and late evening are all forecasted though their levels are slightly off. In this case, there is no clear indication that the 4-km grid is leading to a better forecast than the 12-km grid. However, predicting PM2.5 at Newnan on May 4 was very challenging for the models. There were very strong variations in PM2.5 throughout the day. Once again, the sudden drops in the afternoon are

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PM2.5 at SDEKALB on 5/5/2006 120.0 4-km 12-km Obs.

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Time (EDT) Figure 3. Good and poor PM2.5 forecasts. May 5, 2006 was a sunny day and PM2.5 was predicted almost perfectly at South Dekalb (top). Scattered thundershowers throughout the afternoon on May 4, 2006 as well as some local emission events led to a poor PM2.5 forecast at Newnan (bottom).

due to thundershowers that were not predicted. But the level of early afternoon and evening peaks are severely underestimated. This suggests that there might be some local emission events leading to these peaks but the models are unable to capture these events. 5. Conclusions and future work

A ‘‘ﬁne-scale’’ forecasting operation using 3-D models started in Georgia on May 1, 2006. Forecasts were issued on time every day since May 3;

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there were no bad air quality days so far (as of May 12, 2006). Ozone forecasts are generally accurate. The only bias seems to be the nighttime overestimations at some stations. The peak error is 10–20%. The correlation between predictions and observations is fairly good: R2 is around 0.6 but lower on some days. The diurnal variations of ozone are captured at many sites. PM2.5 is harder to forecast than ozone and it is generally underestimated. The peak error is 20–40%. The correlation between predictions and observations is not very strong: R2 is less than 0.4 on many days. The morning peaks are generally predicted at the right level and time but afternoon and evening peaks are generally underestimated and some are completely missed. We will continue the operation until September 30, 2006 and then conduct a thorough evaluation of the summertime forecasts. We will improve the modeling system based on identiﬁed weaknesses. Our goals for next year are to extend the domain of coverage, increase the resolution, elongate the forecasting period, issue daily updates, and improve the accuracy. Our longer-term goals are to link the forecast to health-effect studies such as investigating the impacts on asthmatic children (i.e., whether the forecasts improve the quality of their life) and conducting long-term exposure studies for which we are archiving our data. Another goal is to simultaneously forecast the impacts of predetermined short-term local control strategies in order to avoid imminent pollution episodes.

Discussion

E. Genikhovich:

M.T. Odman:

In Russia, we have a long-lasting practice of issuing the air pollution forecasts and applying them to short-term emission control programs. If such a program has started, the measured concentrations of atmospheric pollutants inﬂuenced by reduction of corresponding emissions are not used for evaluation of the forecast score. There are other ways to do it, in particular related to upper percentiles of annual PDF of concentrations. Our operation is fairly new and we can certainly beneﬁt from your experience. One of our goals is to forecast the sensitivities to emission reductions simultaneously with ozone and PM2.5 concentrations. The high-order direct decoupled

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method that we developed (Hakami et al., 2003) allows us to compute these sensitivities very accurately. If our sensitivity forecast is used by the local authorities and some local short-term control programs are activated on time to avoid pollution episodes, we would have achieved our objective (notwithstanding the implications of issuing a false alarm). Afterwards, when the forecast is being evaluated, we can use the forecasted sensitivity information along with the actual emission controls that took place to modify our original air quality forecast. This would be the best correction for the feedback, which alters the original forecast. In an ideal world, if the authorities inform us of their action plan on time, we can apply the necessary correction to our forecast before broadcasting it to the public (and hopefully avoid false alarms). How long is a meteorological forecast? Would you consider a 3-day meteorological forecast to be too long? The current length of the meteorological forecast is 3 days (plus 5 h, which is the local time difference from UTC). This, of course, is a fairly long forecast to be accurate under rapidly changing meteorological conditions. We are planning several new measures for next year, which will reduce the operation time such that a 2-day meteorological forecast can be used instead. This is expected to improve the accuracy of our air quality forecasts signiﬁcantly.

ACKNOWLEDGMENT

This work is supported by the Georgia Department of Natural Resources. REFERENCES Byun, D., Ching, J. (Eds.), 1999. Science algorithms of the EPA Models-3 community multiscale air quality system. U.S. Environmental Protection Agency.

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Cardelino, C., Chang, M., St John, J., Murphey, B., Cordle, J., Ballagas, R., Patterson, L., Powell, K., Stogner, J., Zimmer-Dauphinee, S., 2001. Ozone predictions in Atlanta, Georgia: Analysis of the 1999 ozone season. J. Air Waste Manage. Assoc. 51, 1227–1236. CEMPD: SMOKE V2.1 User’s Manual. [Available online from http://cf.unc.edu/cep/empd/ products/smoke/version2.1/manual.pdf]. Chang, M., Cardelino, C., 2000. Application of the urban airshed model to forecasting nextday peak ozone concentrations in Atlanta, Georgia. J. Air Waste Manage. Assoc. 50, 2010–2024. Davidson, P., Seaman, N., McQueen, J., Mathur, R., Wayland, R., 2005. National air quality forecasting capability: Initial operational capability. NOAA presentation. Eder, B., Kang, D., Mathur, R., Yu, S., Schere, K., 2006. An operational evaluation of the Eta-CMAQ air quality forecast model. Atmos. Environ. 40, 4894–4905. Hakami, A., Odman, M.T., Russell, A.G., 2003. High-order, direct sensitivity analysis of multidimensional air quality models. Environ. Sci. Technol. 37, 2442–2452. Odman, M.T., Hu, Y., 2004. ‘‘A variable time-step algorithm for air quality models,’’ 27th NATO/CCMS International Technical Meeting on Air Pollution Modelling and Its Application held in Banff, Canada, October 24–29, Available at ohttp:// www.ce.gatech.edu/todman/27itm.pdf4 Odman, M.T., Khan, M.N., Srivastava, R.K., McRae, D.S., 2002. Initial application of the adaptive grid air pollution model. In: Borrego, C., Schayes, G. (Eds.), Air Pollution Modeling and its Application XV: Proceedings of the 25th NATO/CCMS International Technical Meeting on Air Pollution Modelling and Its Application held in Louvain-la-Neuve, Belgium, October 15–19, 2001. Kluwer Academic/Plenum Publishers, New York, pp. 319–328. Otte, T., Pouliot, G., Pleim, J., Young, J., Schere, K., Wong, D., Lee, P., Tsidulko, M., McQueen, J., Davidson, P., Mathur, R., Chuang, H., DiMego, G., Seaman, N., 2005. Linking the Eta model with the community multiscale air quality (CMAQ) modeling system to build a national air quality forecasting system. Weather Forecast. 20, 367–384.

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Chapter 2.15 Integrated observational and modeling approaches for evaluating the effectiveness of ozone control policies$ J. Godowitch, A. Gilliland, E. Gego, R. Draxler and S. Trivikrama Rao Abstract Although there has been much progress in improving ambient air quality, the concentrations of ozone continue to exceed their acceptable levels in the United States and many parts of the world. Because of the regional nature of this pollutant, the U.S. Environmental Protection Agency (EPA) required the States to undertake large reductions in nitrogen oxides (NOx) emissions by 2004 to address long-range transport of ozone and its precursors as part of their State Implementation Plans (SIPs). As a result of this control program, referred to as the ‘‘NOx SIP Call’’, 21 states in the eastern United States have achieved substantial NOx reductions from the utility sector by 2004. This emission control strategy should greatly reduce ambient ground-level ozone in the eastern United States. A systematic tracking of air quality achievements is necessary to properly evaluate U.S. policy and program results in protecting public health and the environment. Consequently, EPA has undertaken a comprehensive assessment of the effectiveness of this control program to meet its objectives. In this paper, integrated observational and modeling methodologies are described in order to assess the impact of NOx emission reductions on maximum ozone concentrations. In particular, the emphasis in this paper is on selected results from the photochemical modeling effort. A set of modeling scenarios was performed that included point source NOx emission measurements from extended summer periods before and after implementation of the emission reductions. The model results revealed discernable decreases in daily maximum 8-h ozone in $

This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reﬂect their policies or views.

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downwind areas of a major point source emission region exhibiting substantial NOx emission reductions, and the impacts were found to be greater at higher ozone values, which is a desirable beneﬁt of the control program. 1. Introduction

Numerous emission control programs have been implemented by the U.S. Environmental Protection Agency (EPA, 2005) in an effort to reduce ground-level ozone concentrations by reducing emissions of VOCs or NOx, the key precursor species involved in the photochemical production of ozone. Although there has been a downward trend in ozone levels, daily maximum 8-h ozone concentrations continue to exceed acceptable limits in widespread areas of the eastern United States. The ﬁndings of an extensive data analyses and modeling effort performed under the auspices of the OTAG (Ozone Transport and Assessment Group) program revealed that regional-scale transport of ozone and its precursors contributed to elevated ozone concentrations in downwind areas within the eastern states. Consequently, the EPA responded with the development and implementation of an emission control policy known as the NOx State Implementation Plan (SIP) Call. The NOx SIP Call rule was designed to reduce the interstate transport of ozone and its precursor species by requiring substantial NOx emission reductions from major point sources in 22 eastern states with full implementation of controls before the summer 2004 ozone season. It is important to assess whether a major emission control program achieves its objectives and provides a discernable improvement in air quality. Consequently, a research effort has been underway using a combination of observation-based and model-based methods to investigate the effectiveness of this emission control program in decreasing peak ozone concentrations. The modeling approach applied in this effort involves photochemical simulations with the Community Multiscale Air Quality (CMAQ) model. Accurate point source NOx emission measurements provide a reliable emissions data set for the model simulations. CMAQ model simulations were performed for 3-month summer periods in 2002 and 2004, which corresponded to ozone seasons before and after implementation of the NOx emission controls, respectively. A set of modeling scenarios was designed to allow for an examination of the separate impacts of the emission changes and meteorological differences on ozone concentrations. The observation-based approach employs a statistical ﬁlter/regression technique in an attempt to analyze multi-year time series of ozone and meteorological parameter measurements at an

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extensive monitoring network. Attributing an ozone trend to particular emission changes is confounded by the strong inﬂuence of weather conditions on ozone levels and the year-to-year variability in the meteorological conditions, as was the case during these two summer seasons. However, after accounting for the inﬂuence of meteorological variables, differences in meteorologically adjusted maximum ozone concentrations from separate summer seasons can be attributed to the effect of a change in total emissions. The methods and procedures employed with these two approaches are described. Selected results from the photochemical modeling effort are emphasized herein to demonstrate the impact on ozone and NOx concentrations from the point source emission changes in an attempt to provide evidence of the beneﬁts of this emission reduction program. 2. Data sets and methods 2.1. Observational data sets

The ozone and meteorological measurements used in this study were collected at monitoring sites of the Clean Air Status and Trends Network (CASTNet; http://www.epa.gov/castnet), which are situated in primarily rural locations of the United States. For this study, data from nearly 50 monitoring sites in the eastern U.S. were analyzed to determine the daily maximum 8-h ozone concentrations from hourly measurements at each site. Major U.S. point sources, particularly electrical generating units at fossil-fuel power plants and large industrial sources, have been equipped with Continuous Emissions Monitoring systems (CEMS) in order to provide direct, hourly emission measurements of NOx and SO2. The hourly CEMS data for the summer seasons of 2002 and 2004 were available for use in this analysis and modeling effort. 2.2. Observation-based analysis approach

The statistical ﬁlter/regression methods described by Milanchus et al. (1998) were applied to account for the meteorological inﬂuence on maximum ozone concentrations at each CASTNet site over a measurement period spanning 1988–2004. Brieﬂy, the methodology involves the spectral decomposition of the time series of ozone and meteorological variables into ﬂuctuations occurring on various temporal scales before quantifying the relationship between two observed variables. By the

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judicious application of an iterative moving average KZ ﬁlter algorithm (Rao and Zurbenko, 1994), separation of the variations was achieved at frequencies related to the shorter term, synoptic-scale from the seasonal or baseline time scales. Subsequently, multiple regression analysis was performed among the baseline components of ozone, total daily solar radiation, and speciﬁc humidity to derive the portion of the ozone baseline ﬂuctuation explained by these meteorological variables. Likewise, the regression technique was applied to the synoptic-scale components of ozone along with the total daily solar radiation and dew point depression to identify the ozone variability governed by these meteorological variables. Finally, meteorologically-adjusted ozone values were derived as the sum of the baseline and synoptic-scale residuals, which represent the contribution of ozone ﬂuctuations not explained by the meteorological variables. Further details about the application of this statistical ﬁlter/ regression approach and results are provided in Gego et al. (2007). 2.3. Photochemical modeling approach

The CMAQ modeling system (version 4.5) was applied in this modeling study. CMAQ is a comprehensive Eulerian air quality grid model that is capable of simultaneously treating a wide variety of atmospheric pollutants (e.g., oxidants, aerosols, air toxics, and mercury species). For the photochemical simulations performed in this study, the Carbon-Bond 4 (CB4 version 4.2) gas-phase chemical mechanism was used in conjunction with the Euler backward interactive (EBI) chemistry solver. CMAQ contains state-of-science algorithms to solve the relevant atmospheric processes. The CMAQ model was conﬁgured with the standard science options to treat particular physical processes as described in Byun and Schere (2006). Meteorological ﬁelds were generated by the Penn State/NCAR Mesoscale Model (MM5 version 3.6.3). The MM5 model simulations included four-dimensional data assimilation (FDDA) of observed wind, temperature, and moisture data to provide more accurate 3-D modeled ﬁelds, and an improved land-surface scheme to improve model response to varying soil moisture and vegetation conditions over the summer season. The CMAQ Meteorology-Chemistry Interface Processor (MCIP v3.1) program was exercised to extract/reformat MM5 output into data sets containing the hourly 2-D and 3-D meteorological ﬁelds required by CMAQ. The 3-D emission data sets were generated by the comprehensive Sparse Matrix Operator Kernel Emissions (SMOKE version 2.2; http:// www.smoke-model.org) processing system. Anthropogenic emissions

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from the EPA 2001 National Emissions Inventory (NEI version 3) were used to generate surface and elevated non-CEMS point source emissions. Natural surface emissions of NOx, isoprene and other biogenic VOC species were computed by the Biogenic Emissions Inventory System (BEIS version 3.13). The MOBILE6 model was applied to use projections of vehicle-miles-traveled (VMT) and ﬂeet factors to develop gridded motor vehicle emissions for the 2002 and 2004 periods. The SMOKE system also computed plume rise using stack parameters and meteorological ﬁelds in order to allocate all point source emissions into the proper vertical layers. 2.3.1. Model setup

The CMAQ modeling domain encompassed the eastern half of the United States and southeastern Canada with 205 199 horizontal grid cells with a 12-km grid dimension. The vertical structure consisted of 14 layers extending from the surface to over 15 km on a sigma-pressure, terrain-following coordinate system. The initial conditions and lateral boundary concentrations for each modeling scenario were deﬁned to be the same set of time-invariant tropospheric background values. The modeling period was from May 28 through August 31 for both summer seasons with the ﬁrst four days considered as a model spin-up period. Consequently, the model results for 92 days starting on June 1 of each summer were used in the analyses. 2.3.2. Modeling scenarios

Table 1 lists the model scenarios designed to allow for investigation of the impacts of emission change and different meteorological conditions on ozone concentration levels. Of particular interest, comparisons of base case results (M02E02, M04E04) with those from the M02E04 and M04E02 modeling scenarios permit an assessment of emission effects on ozone levels during the summer 2002 and 2004 periods, respectively. Additionally, comparative analysis of M02E02 and M04E04 results Table 1. CMAQ model simulation scenarios Summer meteorology

Emissions 2002 2004

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2004

M02E02 M02E04

M04E02 M04E04

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provides information on the combined effects of meteorology and emissions on ozone levels between the two summer periods. Since weather conditions during the summer of 2002 were more favorable for ozone formation than the cool and wet summer of 2004, results from the model scenarios highlighted in Table 1, which applied the 2002 meteorology using emissions reﬂecting pre-control and post-control CEM data, are the focus of this paper. 2.4. Trajectory modeling

The trajectory method employed in this effort was the Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT version 4.7, http://www.arl.noaa.gov/ready/hysplit4.html). The HYSPLIT model was applied to generate back trajectories originating from each CASTNet site location for each day to identify cases when the airﬂow at each monitoring site had previously passed through the Ohio River Valley (ORV) region, since this area contained numerous point sources exhibiting large NOx emission reductions. Forward trajectories in time were also generated starting from the speciﬁc location and altitude of selected point source plume emissions. Hourly trajectory coordinates were converted into CMAQ grid cell indices in order to extract the pollutant concentrations from the CMAQ 3-D concentration ﬁles. For this trajectory analysis, a new interface utility program (MCIP2ARL) was developed and applied to retrieve the MCIP hourly meteorological parameter ﬁelds, the same meteorology applied in the CMAQ simulations, to generate input date sets compatible for use in the HYSPLIT trajectory model calculations. 3. Results and discussion 3.1. Point source NOx emission changes

The locations of major point sources and the percentage change in their NOx emissions due to the NOx SIP Call program are depicted in Fig. 1. Substantial reductions in NOx emissions of greater than 80% are evident at numerous individual sources, particularly in the largest NOx-emitting point sources within the Ohio River Valley (ORV) region. Figure 2 shows that most major point source emissions are injected into model layers at heights from 200 m to over 600 m above ground over the diurnal period, which allows for considerable transport of elevated plumes during the nocturnal period. A signiﬁcant decrease is evident in NOx emissions

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Figure 1. Locations of major NOx point sources in the CMAQ modeling domain and percentage emission reduction between 2002 and 2004.

between the base and the post-control cases indicating less NOx is available for transport due to the control program. Results of analysis of the hourly CEMS measurements also indicated a substantial decrease in NOx emissions at all hours, even during the daytime peak-demand period. There was a 36% drop in NOx emissions from the CEMS point sources from 2002 to 2004; however, this contributed to a rather modest 8% decrease in total domain-wide NOx emissions between the M02E02 and M02E04 modeling scenarios. 3.2. Results of photochemical modeling

A variety of weather conditions and ﬂow patterns were simulated during the summer periods being modeled. Of particular interest are the cases when the synoptic pattern exhibits a high-pressure area in the eastern or southeastern U.S., which sets up a generally southwesterly wind ﬂow across the ORV emission region. Elevated ozone levels in the eastern U.S. are associated with this ﬂow pattern as horizontal pollutant transport is more pronounced toward the northeastern states. During the summer 2002 period, 19 cases were identiﬁed when a predominately southwestern ﬂow occurred across the ORV area. The impact on total nitrogen species (NOy) is depicted in Fig. 3, which displays the average percent difference between the M02E02 and M02E04 results in layer 5 (400 m AGL) at a

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Figure 2. Vertical distribution of daily NOx emissions from CEMS sources for 2002 base and post-control cases.

late afternoon hour (2100 UTC) from these cases. Although the largest decreases in NOy (NOy ¼ NOx+secondary nitrogen species) are clearly evident close to the point source locations due to much lower NOx levels from the emission reductions, the notable decreases in NOy found at further downwind distances from the ORV in north-central Pennsylvania, central New York state, southern Canada, and even off the east coast are due to decreases in secondary species (i.e., HNO3). Figure 4 reveals a broad area of lower ozone at the surface. However, a downwind area with a more pronounced decrease in maximum 8-h ozone exhibits a southwest/ northeast orientation due to the alignment of the many point sources in the ORV with the wind ﬂow pattern in these cases. Trajectory analysis also provided valuable information about the impact of the NOx point source reductions from these model simulations. Species concentrations generated from these modeling scenarios along a forward trajectory downwind from selected point sources in individual cases were examined to investigate differences between the modeling scenario results, since average results for the cases shown above tends to dampen the impact with distance because of ﬂow variations among the

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Figure 3. Reduction (%) in NOy aloft at 2100 UTC between M02E02 and emission reduction (M02E04) scenarios for SW ﬂows.

cases. Figure 5 provides a typical example of the substantial difference in NOx concentrations with time/distance over an 18-h period along a trajectory path starting from the location and height of a point source emission within the ORV. The trajectory, initiated at 0200 UTC (2200 eastern time), demonstrates considerable overnight transport and additional travel into the afternoon of the next day. This trajectory traveled over 300 km in 10 h until the next morning when the surface ozone responded primarily to downwind mixing of higher ozone levels aloft. Figure 5 also indicates lower ozone concentrations existed both at the surface (layer 1) and aloft in the M02E04 scenario. Thus, lower ozone values in the residual layer aloft are available for downward mixing and less photochemical ozone production also occurred presumably due to the lower NOx levels. Consequently, the ozone concentration in the M02E04 post-control results became increasingly less than the base scenario along the trajectory during the daytime period with surface ozone as much as 10 ppb lower at the end of the trajectory as it reached the eastern U.S. coastline after 18 h and 640 km downwind in this case. Back-trajectory analyses were performed to assess the impact of NOx reductions on maximum 8-h ozone concentrations for cases when each CASTNet site was downwind of the ORV. Results in Fig. 6 indicate that

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Figure 4. Reduction (%) in maximum 8-h ozone between the base M02E02 and M02E04 scenarios for SW ﬂows.

greater reductions in peak ozone values tended to occur at higher ozone levels, a desirable beneﬁt of the control program. Although the standard deviation bars reveal variability in results among the network sites, a similar trend was found at individual sites with differences in the slope apparent across the monitoring network. 4. Summary

Selected modeling results revealed discernable decreases in daily maximum 8-h ozone and in NOy concentrations downwind of a major point source region, which experienced substantial NOx emission reductions after implementation of the NOx SIP Call program. Results from CMAQ modeling scenarios from the summer 2002 period coupled with trajectory analysis are highlighted to show that point source NOx emission reductions produced notable decreases in NOx concentrations at considerable distances downwind from an elevated plume release at night. It is evident that a broad region of the eastern U.S. and southeastern Canada beneﬁted by lower maximum 8-h ozone values due to the NOx control program. Additional model simulations currently being pursued will

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Figure 5. Modeled concentrations from the M02E02 and M02E04 scenarios for NOx (left) and ozone aloft and at the surface (right) along a trajectory initiated from an elevated point source emission release at 0200 UTC on 11 June 2002. Note the different scales for the X-axes are time (left) and distance (right).

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Figure 6. CMAQ modeled reduction in maximum 8-h ozone between the M02E02 and M02E04 scenarios results compared to observed maximum 8-h ozone concentrations from all CASTNet sites during downwind cases from the ORV.

include emission changes in mobile sources in addition to the NOx point source reductions employed in the M02E04 scenario, as well as boundary conditions from a recent CMAQ model run on a continental domain, which would provide a better estimate of the full impact from a total emission change over the two-year period. ACKNOWLEDGMENT

The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce’s National Oceanic and Atmospheric Administration (NOAA) and under the agreement number DW13921548. REFERENCES Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and other components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system. Appl. Mech. Rev. 59, 51–77.

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Environmental Protection Agency, 2005. Evaluating ozone control programs in the eastern United States: Focus on the NOx budget trading program, EPA454-K-05-001, http:// www.epa.gov/airtrends/2005 Gego, E., Porter, P.S., Gilliland, A., Rao, S.T., 2007. Observation-based assessment of the effectiveness of nitrogen-oxide emission reductions on ozone air quality over the eastern United States. In press —J. Appl. Meteorol. Climatol. Milanchus, M., Rao, S.T., Zurbenko, I.G., 1998. Evaluating the effectiveness of ozone management efforts in the presence of meteorological variability. J. Air Waste Manage. Assoc. 48, 201–215. Rao, S.T., Zurbenko, I.G., 1994. Detecting and tracking changes in ozone air quality. J. of Air Waste Manage. Assoc. 44, 1089–1092.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06216-X

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Chapter 2.16 Medium-range puff growth Torben Mikkelsen, Søren Thykier-Nielsen and Steen Hoe Abstract For decision support to emergency management organizations dealing with accidental or intentional releases of hazardous materials, being nuclear, chemical, or biological of nature, puff models with parameterized horizontal and vertical dispersion coefﬁcients, sxy and sz, respectively, are advantageously used to provide fast real-time predictions of the potential downwind hazards. On the local scale (deﬁned as the range 0–20 km downwind from the source point) the dispersing puff’s sizes are typically smaller than, or of the order of 1 km, and ‘‘standard’’ similarity scaling of the puff dispersion is well established (Mikkelsen, T., Thykier-Nielsen, S., Astrup, P., Santaba´rbara, J.M., Sørensen, J.H., Rasmussen, A., Robertson, L., Ullerstig, A., Deme, S., Martens, R., Bartzis, J.G., Pa¨sler-Sauer, J., 1997. MET-RODOS: A comprehensive atmospheric dispersion module. Radiat. Prot. Dosim. 73, 45–56; Thykier et al., 2004). Beyond the initial stage, however, starting say at travel distances beyond the 10–20 km range from the source point, the puffs vertical extent will often be limited by the mixing height, while their horizontal growth will be inﬂuenced by the synoptic scale turbulence. For dispersion at travel times t larger than the Lagrangian time scale tL, Mikkelsen and Pecseli (1987) originally suggested, on dimensional grounds: sxy ðtÞ ¼ c1=2 ðtL tÞ3=4 Here, e (0.0005 m2 s–3) is a bulk tropospheric dissipation rate estimated from the spectral observations of Nastrom and Gage (1985) and tL a single tropospheric Lagrangian time scale estimated from the Coriolis parameter f 1104 (s). In this paper, the ‘‘medium-range puff formula’’ is re-derived more rigorously from a spectral model of turbulent puff diffusion. The parameterization has subsequently been implemented in the realtime puff dispersion model RIMPUFF (Thykier-Nielsen et al., 2004)

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for practical use with real-time emergency management of several real-time decision support systems of today, e.g., RODOS (Mikkelsen et al., 1997) and ARGOS (Schou-Jensen, L., Pehrsson, J., 2007. Argos Whitepaper www.pdc.dk/argos. Prolog Development Center A/S H.J. Holst Vej 3C-5C, DK 2605 Brøndby, Denmark.). The proposed sigma-formula comes into play for mid- and long-range dispersion calculations, that is, for calculation of puff growth when the horizontal puff size is bound within the range:1 kmosxyo400 km. 1. Introduction

The dispersion of puffs in the atmosphere, whether containing chemical, biological or radio-ecological releases, is for emergency management and decision support parameterized in terms of the puffs standard deviations as function of diffusion time, for real time application in connection with a puff model. On local scales (0–10 km from the source), puff sizes are usually smaller than 1 km, and Monin–Obukhov similarity scaling of boundary-layer dispersion is well established. Beyond the local scale (after about 1 h of diffusion, or 10–20 km downwind range from the source point), puffs vertical extent become limited by the mixing-layer height, at the same time as its horizontal extent becomes inﬂuenced by larger mesoscale and synoptic scale turbulence characteristics of the troposphere. For practical real-time puff diffusion at ‘‘medium range’’ (i.e., diffusion times in the range 1 h to 1 day), we investigate here a ‘‘medium-range puff growth’’ prediction formula, originally proposed by Mikkelsen et al. (1987): sxy ðtÞ ¼ c1=2 ðtL tÞ3=4

(1) 2 3

Here, e is the bulk stratospheric dissipation rate 5 (cm s ) estimated from the spectral observations of Nastrom and Gage (1985), t the diffusion or travel time, and tL a Lagrangian integral time scale that turns out to depend on the subrange, in which the puff, according to its size, is embedded. The constant of proportionality, cI0.4, has been obtained by comparison to numerical results of Lagrangian particle tracking in the troposphere. The formula is implemented in the real-time puff diffusion model RIMPUFF (Thykier-Nielsen et al., 2004) where it serves as a ‘‘medium-range puff growth prediction formula’’ for parameterization of horizontal puff dispersion in connection with decision support and emergency management for puff sizes ranging between 3 kmosxyo60 km, corresponding to travel times between 1 hott1 day.

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2. Puff diffusion theory

Following Csanady (1973), the diffusivity (i.e., growth rate) for relative puff diffusion 1=2ðds2p =dtÞ can be expressed as the product of the mean square velocity variance v2r ðtÞ of the puff particles motion relative to their common centre-of-mass, and a corresponding relative diffusion time scale: 1 ds2p ¼ v2r ðtÞ tr ðtÞ 2 dt

(2)

Based on the theory of Mikkelsen et al. (1987), Mikkelsen et al. (1988) proposed a simple parameterization of the relative velocity variance: Z 1 v2r ðtÞ ¼ F 11 ðk1 Þdk1 (3) 1=sp

where F11(k1) is the horizontal energy spectrum parameterized as function of the horizontal wave number k1, and a relative diffusion time scale tr(t) given by: Z t Z t t t dt ¼ tL 1 exp (4) rL ðtÞdt ¼ exp tr ðtÞ ¼ tL tL 0 0 In this equation, tL, the puff’s integral time scale, represents the correlation time scale in the puff’s moving frame turbulence, i.e., the Lagrangian correlation time of the particle’s velocity relative to the puff’s centre-of-mass: Z tL 0

Z

1

rL ðtÞdt ¼

0

1

vr ðtÞvr ðt tÞ dt v2r ðtÞ

(5)

in conjunction with an assumed exponential relative diffusion autocorrelation function rL. Compared with the case of single particle (G.I. Taylor) diffusion theory, the puff variance, v2r ðtÞ and its corresponding Lagrangian integral time scale, tL, will here not in general be (constant) properties of the surrounding turbulence, but will, via the ﬁltering process deﬁning the puffs centre-of-mass reference system, be shown to depend on the puffs actual size, sp(t). Atmospheric turbulence exhibits distinct spectral subranges: inertial, enstrophy cascade, shear production, etc. Suppose each spectral subrange has a speciﬁc outer characteristic (constant) Lagrangian time scale, then, the small and large time limit of the time scale function tr correspondingly

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becomes:

( tr ¼

t

for t tL

tL

for t tL

(6)

Assuming a power law representation for the horizontal energy spectrum within a subrange: F11(k1)dkp; (po1), the puff model, Eqs. (2)–(6), can predict asymptotic scaling laws, where for instance p ¼ 5/3 represents the inertial subrange and p ¼ 3 the enstrophy cascade subrange in the troposphere: for p ¼ 5=3; t tL ðaÞ sp ðtÞ 1=2 t3=2 ðbÞ sp ðtÞ 1=2 ðtL tÞ3=4 ðcÞ

sp ðtÞ s0 e

t=T c

for p ¼ 5=3;

t tL

for p ¼ 3;

t tL

(7)

Here, e ¼ d3/2 represents the dissipation rate of kinetic energy in the e2/3k5/3 inertial subrange and Tc ¼ Z1/3 ¼ d1/2 the constant outer time scale in the enstrophy cascade subrange ð1=T 2c Þk3 (Z being the enstrophy cascade rate, cf. Nastrom and Gage, 1985). The predictions (a) and (c) are already well-known inertial and enstrophy cascade predictions, cf. Batchelor (1952) and Lin (1972), respectively, whereas the prediction (b) is the proposed candidate for evaluation using simulated tropospheric puff diffusion data (Maryon and Buckland, 1995). 2.1. A spectral model for the puffs Lagrangian time scale tL

For the diffusion of a puff on the tropospheric scales, Gifford (1984), with his extensive work with the ‘‘Random Force Model’’ diffusion theory, proposed a single characteristic time scale related to the Coriolis parameter, f: (8) tL f 1 104 ½s 3 h Here, however, the relative diffusion integral time scale, tL, deﬁned in Eq. (5) will be related to the puff-ﬁltered turbulence as seen from the puffs moving frame of reference, and, as such, will be related to the turn-over time of the biggest eddies as seen from the puffs moving frame of reference. By deﬁnition, the Lagrangian velocity spectrum is (Mikkelsen et al., 1987): Z 1 1 SL ðnÞ ¼ rL ðtÞei2pnt dt (9) p 0 Following Pasquill and Smith (1983), the Lagrangian and Eulerian spectral forms are similar in shape and differ only by a scale factor, given by the ratio between their respective time scales btL/tE. The Lagrangian

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ﬁxed-frame spectra can now be estimated from the corresponding observable Eulerian spectra Su(n) (m2 s1), viz.: u0 2 SL ðnÞ b Su ðbnÞ

(10)

where, u0 2 is the ﬁxed frame horizontal variance, being an identical quantity in both spectra. An outer ﬁxed-frame (single particle) Lagrangian time scale for the turbulence is usually estimated from the lowest spectral energy pSu ðn ! 0Þ tL ¼ b (11) u0 2 By Taylor’s frozen turbulence hypothesis, this Eulerian frequency spectrum can be related to its corresponding wave number spectra (Ho¨gstro¨m et al., 2002) k1 1 (12) Su ðnÞ ¼ F 11 ðk1 Þ ¼ F 11 ðk1 Þ n U where U denotes the mean wind speed. A relative diffusion Lagrangian integral time scale can now be estimated from the spectral energy and eddy size of the biggest turbulence structures in the puff-frame high-pass ﬁltered turbulence. This wave number corresponds to the biggest movingframe wavelength k1E1/sp, and the corresponding turbulent kinetic energy is F11(k11/sp). By combination of Eq. (12) and Eq. (11), an expression for the puffs Lagrangian integral time scale, as function of the actual puff size, sp(t), results pbF 11 ð1=sp Þ tL ðsp Þ ¼ (13) Uu0 2 A closed set of equations is now established for the puff’s diffusivity, 1=2ðds2p =dtÞ (Eqs. (2)–(4) and (13)) that are solvable for sp(t) given an estimate of the energy spectrum, F11(k1), as function of wave number k1. 2.2. The horizontal energy velocity spectrum F11(k1)

The horizontal wind spectra exhibit distinct spectral subranges. By denoting spectra by Su(n) (m2 s1) in frequency presentation and F11(k1) (m3 s2) in wave number representation, where k1 is the horizontal wave number and n is frequency (Hz), we distinguish, according to Ho¨gstro¨m et al. (2002), a number of distinct subranges, according to scale and height z: (i) For k1z1: Kolmogoroff inertial subrange, with spectral slope e2/3k5/3.

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(ii) Near the surface, where horizontal eddies are bigger than z, a ‘‘surface eddy range’’ F 11 ðk1 Þ u2 k1 exists, Lsok1oz1, where Ls is a length scale associated with the boundary layer height h and where u* denotes the friction velocity. (iii) For the lower wave numbers k1oz1 above the eddy surface layer, Perry et al. (1986) and Hunt and Morrison (2000) predict a ﬂat energy spectrum k1 F 11 ðk1 Þ nS u ðnÞ Ls n (iv) (14) ¼ ¼ u2 u2 U down to scales given by the spectral gap (i.e., to periods 1/21 h), or down to corresponding horizontal scales k12p/10 h, where h is the depth of the boundary layer. (v) Below the ‘‘spectral gap’’, on horizontal scales ranging from 3 to 400 km, aircrafts measurements by Nastrom and Gage (1985) revealed a second e2/3k5/3 inertial subrange representing tropospheric turbulence. (vi) On the largest two-dimensional horizontal scales (400–4000 km), aircraft data shows an enstrophy cascade subrange exists, with a spectral range k3.

3. Puff scaling laws according to subrange

The puff model Eqs. (2)–(4) and (13) can been solved for each of the spectral subranges (i)–(v). Results for (i), (iii), and (iv) are shown next: [i] This represents the classic Kolmogoroff subrange where puffs grow according to Richardson’s (1926) and Batchelor’s (1952) classical scaling law sp ðtÞ 1=2 t3=2 ;

t tL½i

(15)

According to Eq. (13), tL(t)pt5/2 and the condition ttL is fulﬁlled. [iii] This represents a ‘‘ﬂat’’ part of the boundary-layer energy spectrum, which extends decades in scale, from the spectral gap to z1:Lsok1oz1. Inserting Eq. (14) in Eq. (13) shows that tL[iii] does not depend on puff size in this subrange tL½iii ðsp Þ ¼

pbLs u2

(16) Uu0 2 The length scale Ls is, according to Ho¨gstro¨m et al. (2002), about three times the boundary layer depth, h0.2u*/fc, i.e., LsEAu*/f;

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A0.6 whereby tL½iii ¼

pbLs u2 Uu0 2

4 0:6pu u2 Uf u0 2

1 Oð104 Þ 3 h f

approximating u2 =u0 2 Oð1Þ; and 4 0:6pu =¯u Oð1Þ: We ﬁnd, as Gifford (1984), an integral time scale proportional to the Coriolis parameter tL[iii]Ef1. However, where Gifford proposed this time scale to apply for both regional and large tropospheric scale diffusion, this prediction is restricted to subrange (iii) only. [iv] Within this tropospheric inertial subrange, puffs growth scale as in subrange (i), if subrange (iii), with its characteristic time scale tLf1 had not separated the two. But due to the puffs non-negligible ‘‘initial size’’ (sp[iv](t ¼ t0[iv])3–5 km) at its entrance to subrange (iv), its initial virtual diffusion time, t0[iv], compares to, or is even bigger than, the initial Lagrangian time scale in this subrange (tL[iv]0.5f1 estimated from Eq. (13)). Consequently, diffusion starts in the beginning of this subrange at least, in the limit (tZtL[iii]), and must consequently obey the scaling Eq. (7b): sp ðtÞ 1=2 ðtL½iii tÞ3=4

for p ¼ 5=3;

t tL½iii

(17)

After some time, however, depending on the puffs initial size, and with tL[iv] growing proportional to ðpb5=6 =Uu0 2 Þf 5=4 t5=4 ; tL[iv] will again, using F11(k1)e2/3k5/3, with eE0.5 (cm2 s3), after approximately 40 h of travel, overtake t , so that totL[iv] again, and the classical scaling law for such an inertial subrange will reappear: sp ðtÞ 1=2 t3=2

for p ¼ 5 =3;

t tL

(18)

Two asymptotic scaling laws are therefore predicted to apply to tropospheric inertial subrange, Eq. (17) for tt2 days, and Eq. (18) for t2 days.

4. As an initial small puff grow up through the subranges

While an initially small puff is released into the boundary layer, its size remains within subrange (i), and its Lagrangian time scale is (much) bigger that the puffs diffusion time tL[t]t. When its size reaches the scales of subrange (iii), however, tL[iii] will stop growing and remain constant f1. As subrange (iii) is extensive, the puff’s diffusion time t will eventually become comparable to or bigger than tL[iii]f1, viz.: (tZtL[iii]). As the

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puff reaches the medium-range regional scale (sp3 km), its size has grown past the spectral gap and entered into the tropospheric inertial subrange (iv). Here, however, because the diffusion time t is now bigger than the integral time scale tL[iv]0.5f1, the growth follows Eq. (17) while ttL[iv], until eventually (after about 2 days of diffusion) its size within subrange (iv) has grown enough that again tL[iv]t, where after usual inertial subrange scaling Eq. (18) applies, i.e., given ttL[iv]. In conclusion, a ‘‘medium-range puff scaling law’’ is proposed sp ðtÞ 0:41=2 ðf 1 tÞ3=4 ;

1 hoto 2 days

(19)

for the instantaneous relative puff diffusion at medium-range scales in the boundary layer. With given parameters for the dissipation rate and a midlatitude Coriolis parameter value, puff sizes ranges between 3 and 50 km. The prediction compare with simulations from a Lagrangian global multi-particle model using wind ﬁelds obtained with the Met U.K.

Figure 1. Mean value of s2p plotted against time on logarithmic axes. (Adapted from Maryon and Buckland, 1995.) The thick straight line indicates the proposed medium-range puff-growth formula sp(t)0.4e1/2(tLt)3/4. The thin straight lines indicate slopes corresponding to sp(t)t1 and sp(t)e1/2t3/2.

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Ofﬁce’s operational uniﬁed numerical weather prediction (NWP) model (Maryon and Buckland, 1995) (cf. Fig. 1). Discussion

E. Genikhovich:

It is well known that there is an expression for the eddy diffusivity Ky, that is as follows: K y ¼ 1=2ðds2y =dtÞ

S. Thykier-Nielsen:

M. Soﬁev: S. Thykier-Nielsen:

So if syt3/4 then Kyt1/2. Because the aforementioned expression for sy is supposed to be valid for 50 nmoxo400 nm, it results in a change of Ky of approximately three times. Does that mean that models based on the solution of the advection-diffusion equation cannot be applied to real-world problems, if the dependency of Ky on the travel time is not taken into account? Yes, that is a good point to bring up. Modelling puff diffusion with an advection-diffusion equation based model system with constant diffusivity will only apply correctly in the hypothetical far-time limit, where syt1/2. To our opinion therefore, numerical advection-diffusion equation-based modelling systems are therefore not suited for modelling the dispersion of an instantaneous puff releases and that’s actually why we prefer to work with puff models in such cases. Could you please compare the RIMPUFF and DERMA algorithms of plume growing? DERMA’s horizontal puff diffusion algorithm is powered by F. Gifford’s Random-Force Model equation, which he based on the Langevin equations solution for conditional particle motion, Gifford et al. (1988, his Eqs 9): s2xy ¼ s20 þ 2K H tL fT ð1 eT Þ 1=2ð1 eT Þ2 g where T ¼ t/tL and KH the horizontal diffusivity (1.1 104 m2 s1).

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Gifford’s formula is not directly comparable with the proposed sxy ðtÞ ¼ c1=2 ðtL tÞ3=4 model, but plotted over the range 1 h to 1 day, it is only for diffusion times less than 3–4 h (corresponding to distances less than 50–100 km) that there is any notable difference between the two. (DERMA seems to under predict the initial dispersion by 20% compared with our proposed puff diffusion formula.) REFERENCES Batchelor, G.K., 1952. Diffusion in a ﬁeld of homogeneous turbulence II. The relative motion of particles. Proc. Camb. Philos. Soc. 48, 245–362. Csanady, G.T., 1973. Turbulent diffusion in the environment. Geophysics and Astrophysics Monographs. D. Reidel Publishing Company, Dordrecht-Holland/Boston. Gifford, F.A., 1984. The random force theory—application to meso-scale and large-scale atmospheric diffusion. Bound.-Layer Meteorol. 30(1–4), 159–175. Gifford, F.R., Barr, S., Malone, R.C., More, E.J., 1988. Tropospheric relative diffusion to hemispheric scales. Atmos. Environ. 22(9), 1871–1879. Ho¨gstro¨m, U., Hunt, J.R.C., Smedman, A., 2002. Theory and measurements for turbulence spectra and variances in the atmospheric neutral surface layer. Bound.-Layer Meteorol. 103, 101–124. Hunt, J.C.R., Morrison, J.F., 2000. Eddy Structure in the turbulent boundary layers. Eur. J. Mech. B—Fluids 19, 673–694. Lin, J.T., 1972. Relative dispersion in the enstrophy cascade inertial range of homogeneous two-dimensional turbulence. J. Atmos. Sci. 29, 394–395. Maryon, R.H., Buckland, A.T., 1995. Tropospheric dispersion: The ten ﬁrst days after a puff release. Q. J. R. Meteorol. Soc. 121, 1799–1833. Mikkelsen, T., Larsen, S.E., Pe´cseli, H.L., 1987. Diffusion of Gaussian puffs. Q. J. R. Meteorol. Soc. 113, 81–105. Mikkelsen, T., Larsen, S.E., Pe´cseli, H.L., 1988. Spectral parameterization of large-scale atmospheric diffusion. In: Dop, H. van (Ed.), Air Pollution Modeling and Its Application VI. Proceedings of the 16 NATO/CCMS International Technical Meeting. Lindau, 6–10 April 1987 (NATO Challenges of Modern Society, 11). Plenum Press, New York, pp. 579–591. Nastrom, D., Gage, K.S., 1985. Climatology of atmospheric wave number spectra of wind and temperature observed by commercial aircraft. J. Atmos. Sci. 42, p. 950. Pasquill, F., Smith, F.B., 1983. Atmospheric Diffusion. Third ed. Wiley, New York. Perry, A.E., Henbest, S., Chong, M.S., 1986. A theoretical and experimental study of wall turbulence. J. Fluid Mech. 165, 163–199. Richardson, F.L., 1926. Atmospheric diffusion shown on a distance-Neighbour Graph. Proc. Roy. Soc. A 97, 354. Thykier-Nielsen, S., Deme, S., Mikkelsen, T., 2004. Rimpuff: R I M P U F F atmospheric dispersion module. In: Thykier-Nielsen, S., Deme, S., Mikkelsen, T. (Eds.), Version: RIMDOS8, Module Description. Risø National Laboratory, Denmark.

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Chapter 2.17 Operational evaluation of the Eastern Texas air quality (ETAQ) forecasting system based on MM5/SMOKE/CMAQ Daewon W. Byun, Meong-Do Jang, Chang-Keun Song, Soontae Kim, Fang-Yi Cheng, Ryan Perna and Hyun-CHeol Kim Abstract An air quality forecasting system for Eastern Texas has been developed utilizing the customized MM5/SMOKE/CMAQ modeling system. We performed 2-day air quality forecasting simulations for the 12 km Eastern Texas regional domain, and the 4 km HoustonGalveston area (HGA) domain. Dynamic boundary conditions were provided by the 36 km resolution conterminous US (CONUS) domain CMAQ simulations. Initial meteorological conditions were provided by the daily Eta forecast results. A set of complex operational scripts was used to allow automatic operation of the data download, sequencing processors, performing simulations and graphical analyses, building database archives, and presenting on the web. The public access web-based user interface has been developed to link the air quality forecasting results with other GIS databases including population and health effects databases. Three different streams of air quality forecasting with slightly different meteorological model options were performed during the summer and fall 2005. The ozone forecasting results were evaluated daily with meteorological and air quality measurements from the surface continuous air monitoring sites in HGA.

1. Introduction

The main objective of the Second Texas Air Quality Study (TexAQS-II) for 2005 and 2006 is to understand emissions and processes associated with the formation and transport of ozone and regional haze in Texas. The target research area is the more populated eastern half of the state, roughly from Interstate 35 eastward. Accurate meteorological and

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photochemical modeling efforts are essential to support this study and further enhance modeling efforts for establishing the State Implementation Plan (SIP) by Texas Commission on Environmental Quality (TCEQ). An air quality forecasting (AQF) system for Eastern Texas has been developed at the Institute for Multidimensional Air Quality Studies (IMAQS), University of Houston (UH) to provide daily meteorological and air quality data for the experiment and to further facilitate retrospective simulations for improving understanding of ozone episodes and emissions in the Eastern Texas region. In the present paper, we describe a 2-day air quality forecasting system for the Eastern Texas and Houston-Galveston area (HGA) and evaluate the results of 2005 summer and fall forecasting simulations.

2. Description of the East Texas air quality forecasting (AQF) system

MM5-SMOKE-CMAQ system (Byun and Schere, 2006) has been implemented for the AQF operation. The vertical structure used in the Mesoscale Model version 5 (MM5) simulation extended from the surface to 5000 Pa, with 43 sigma levels to better resolve frontal circulations. The initial and boundary conditions (IC/BC) for the MM5 simulation, including sea surface temperatures, were obtained from the Eta forecasting data, available from the National Center for Atmospheric Research (NCAR). The MM5 meteorological output is processed with the MeteorologyChemistry Interface Processor (MCIP) to provide meteorological inputs for air quality modeling. Some key planetary boundary layer (PBL) parameters, such as surface momentum and heat ﬂuxes, and PBL heights (except for nighttime PBL heights) are speciﬁed directly from the MM5 simulations. Community Multiscale Air Quality (CMAQ) model simulates urban/ regional scale transport, gas-phase and aqueous chemistry, deposition, and aerosol processes. The model conﬁguration and science options used in CMAQ are a piecewise parabolic method (PPM) for horizontal advection and the CB-4 (Carbon Bond version 4) chemical mechanism. The horizontal and vertical diffusivities and vertical advection algorithms used for the calculation of transport processes are quite different between the two models. CMAQ is conﬁgured with scale-dependent diffusivity for horizontal diffusion, and PBL similarity-based eddy diffusivity for vertical diffusion. CMAQ is set up with 23 vertical layers, which follows the MM5 and 43-layer vertical structure exactly from the ground to 1 km. The lowest model level has a thickness of about 34 m. Above 1 km, two or three MM5 layers at a time are collapsed to reduce the computational cost of CMAQ. To provide 2-day air quality forecasting for Houston

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metropolitan area at 4 km resolution, IC/BC of pollutants are obtained from the 36 and 12 km resolution regional domain CMAQ simulations. To establish a set of optimal model conﬁgurations and inputs for better forecasting, we established three streams of AQF, basically by employing slightly different MM5 physics options. Table 1 presents the input and science option differences of the three AQF streams. The primary air quality forecasting system (AQF-1) utilizes a comprehensive Noah land surface model option in MM5 with detailed land use and land cover (LULC) data (Cheng et al., 2003). The secondary forecasting (AQF-2) relies on MM5 simulations with default USGS (US Geological Survey) LULC data with slab land-surface model. The tertiary forecasting (AQF-3) utilizes the MM5 simulations from Texas A&M University (TAMU), which uses similar physics options as AQF-2 but for expanded 12 km and 4 km modeling domains with two-way interactions. 3. Operation of AQF systems

We interviewed target clients of forecasting information such as City of Houston, TCEQ, TexAQS-II scientists, and other air quality stake holders to establish a forecasting schedule consistent with computer power, modeling requirements, and forecaster needs. As present, the meteorological model run starts daily around 15:30 CST (Central Standard Time) as soon as high-resolution Eta forecasted boundary conditions are available for the 12:00 CST analysis from the National Centers for Environmental Prediction (NCEP). Simulations of three different streams of air quality forecasting are performed using three independent Beowulf parallel CPU machines. The operation schedules of the three systems are similar. Forecasts from the AQF-1 and AQF-2 systems are available before 7:00 CST. Total 54 h simulations are performed to provide 2-day forecasting. Air quality simulation for AQF-3 is initiated after the MM5 output is transferred from TAMU to UH High Performance Center. The data transfer ends around 6:00 CST and the forecasting of the AQF-3 system ﬁnishes around 12:00 CST. The present scripts for the photochemical model simulations are set up for each day and each domain separately due to the data dependency as shown in Fig. 1. 4. Human forecasting

UH provides ‘‘human’’ forecasts at 8:00 CST in support of daytime activities. When appropriate, the forecasts would be provided at the operations center for TexAQS-II. On days in which intensive operations

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Table 1. Summary of science options for three independent air quality forecasting systems operated Primary Air Quality Forecasting (AQF-1) Transferred via FTP

MM5 version

V 3.6.1 with modiﬁed Noah landsurface model (LSM), and MRF PBL scheme, one-way at 4 km, TFS LULC data V 2.3, with UH vertical interpolation algorithm V 2.1 (except layer fraction w/ v1.4), imputed cb4_ae3_aq V 4.4 Monthly averaged BC Cloud Attenuation V 2.3 (Korn shell) V 6.1 Geosciences AQM Server 1, Server 2

MCIP SMOKE CCTM PAVE IDL Web Server

Tertiary Air Quality Forecasting (AQF-3)

Transferred via FTP, download time 00UTC V 3.6.1 5-layer slab model, one-way nesting (standard MM5 set up), one-way at 4 km, original USGS LULC V 2.3

Transferred via FTP (or LDM)

V 2.1 (except layer fraction w/ v1.4), imputed cb4_ae3_aq V 4.4 Proﬁle BC Cloud Attenuation

V 2.1 (except layer fraction w/ v1.4), regular cb4_ae3_aq V 4.4 Monthly averaged BC Cloud Attenuation V 2.1.1 (C shell) V 6.1 HPC AQM web server

V 2.1.1 (C shell) V 6.1 Geosciences AQM Server 1, Server 2

V 3.6.1 5-layer slab model, one-way, to be replaced with two-way, downloaded from Texas A&M V 2.3

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ETA forecasting data

Secondary Air Quality Forecasting (AQF-2)

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Job flows of UH AQF system MM5 simulations on the 24 CPUs ETA initial data download Begins at 15:30 CST

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Figure 1. Job ﬂows and run schedule of the MM5-SMOKE-CMAQ forecasting system implemented on a 48-CPU Beowulf Linux parallel computing system utilizing three simulation domains; the conterminous USA at 36 km, East Texas and adjacent states at 12 km and the Houston-Galveston-Beaumont-Port Arthur area at 4 km resolution. Daily operation begins at 15:30 CST, ﬁrst downloading the Eta initialization data.

are not planned but situation-speciﬁc activities are taking place, a scheduled conference call will be established for regular daily forecast brieﬁngs. A summary of the day’s forecast and previous day’s air pollution meteorology will be posted on the modeling websites. When no observational activities are planned, only a brief weather summary will be posted on the modeling websites. The forecast was completed Monday–Friday in the early morning hours and submitted into the IMAQS internal website. The daily forecast began by viewing the forecast maps provided by the National Weather Service. This gave an overall view of what synoptic features (if any) would be affecting Houston’s weather. The goal of the initial time period was to get a grasp on the synoptic picture and how it will affect Houston. Satellite, radar, and water vapor imagery were also used to determine the current synoptic conditions. Once the current conditions were understood both at the surface and in the upper-levels, models were then used to help ascertain what the forecast conditions would be in the future.

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After the general meteorological conditions were assessed, available air quality forecasts and models were viewed. This included the TCEQ air quality forecast discussion. The models that were predominately relied upon were the AQF-1 and AQF-2 CMAQ simulations at UH. This allowed a more informed air quality decision using the wind speeds and trajectories as well as the ozone forecast peaks and regions. The AQF-1 model was most often used for the peak ozone forecast. Once all of the meteorological conditions/forecasts were viewed along with the air quality information, the forecast was entered into graphic form at the top of the web page. Pictures were used to mimic what the sky should look like during the forecast period. Forecast parameters were ozone category, high/low temperature, average wind speed/direction, and cloud cover. A general overview of the weather forecast was presented below the graphics that were designed for public viewing and understanding.

5. Performance evaluation 5.1. Meteorological forecasting

The default MM5 meteorological modeling system utilizes USGS 25-category LULC data, which is roughly at 1 km resolution data elements with the reference year 1990. In the USGS LULC dataset, a large area of the Houston metropolitan area is classiﬁed as the contiguous urban land type without resolving urban vegetations. Recently, Texas Forest Service (TFS) generated an updated and more accurate LULC dataset for Houston and its surrounding eight county areas using the 30 m resolution LANDSAT satellite imagery and ancillary datasets for the reference year 2000 (hereafter, called TFS-LULC). MM5 input utilized for AQF-2 is based on the improved MM5 simulations with the default USGS LULC by introducing urban canopy water (Cheng et al., 2003). On the other hand, AQF-1 utilizes MM5 adapted to use TFS-LULC (Byun et al., 2004). Here, we compared performance of surface temperature and wind speed predictions between the two forecasts for June and October 2005. We replaced the long-wave radiation scheme in the MM5 simulations since September to improve the minimum surface temperature predictions. Figure 2 demonstrates that the modiﬁed MM5 used for AQF-1 is slightly better than that of AQF-2. It is difﬁcult to determine if one meteorological model performs better than the other using existing surface observations. Cheng et al. (2003) evaluated the MM5 simulation results and concluded that AQF-1 performed better than AQF-2 for the TexAQS August 2000 episode.

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R-04k-temp 1.0

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Figure 2. Comparison of correlation coefﬁcient (R) of predicted and measured daily surface air temperature between AQF-1 and AQF-2. R for AQF-1 is 0.67 and the same for AQF-2 is 0.65. MM5 long-wave radiation scheme was changed since September.

5.2. Peak ozone predictions

Figure 3 shows trend analysis of predicted ozone concentrations from three different forecasts. In general, model predicted trends are in good agreement with observations. Predictions usually matched with observed high ozone days and the locations of predicted and observed high ozone peak areas (not shown here). Among the three forecasts, AQF-1 performed best overall. Due to shut down of the UH Computing Center during Hurricane Rita, no forecasts were made during August 22–26, 2005. Reanalysis of a few high ozone episodes, in particular affected by Hurricane Katrina (last week of August) and Rita, are underway. Table 2 presents number of days in each month a model predicted better peak ozone values than the other between the pairs of forecasts. As shown in the table, AQF-1 predicted observed ozone peak days better than the other two. The same conclusion is drawn from the error estimates (sum of absolute ozone concentration difference between observed and predicted peaks in ppb). Except for July, for which AQF-3 showed the least difference from observation, AQF-1 provided better forecasts than the others.

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Table 2. Monthly number of better peak ozone forecasting days between each pair of forecasts Hourly Ozone June July August September

AQF-1

AQF-2

AQF-1

AQF-3

16 17 20 15

12 12 7 8

x 12 17 x

x 18 10 x

6. Conclusions

An AQF system for Eastern Texas has been developed to provide these data and to further facilitate retrospective simulations to allow for model improvement and increased understanding of ozone episodes and emissions. A 54-h air quality simulations for the Eastern Texas using meteorological and air quality models for each day was provided for June– September 2005. The ozone forecasting results were evaluated daily with meteorological and air quality measurements from the surface continuous air monitoring sites in the Houston-Galveston area.

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ACKNOWLEDGMENTS

The research described in this article has been funded in part by the Houston Advanced Research Center (HARC) H45: Modeling Strategy in Support in Support of TexAQS-II and 8-h Ozone Assessment and the United States Environmental Protection Agency through Grant R-83037701 to the University of Houston. However, it has not been subjected to the Agency’s required peer and policy review and therefore does not necessarily reﬂect the views of the Agency and no ofﬁcial endorsement should be inferred. REFERENCES Byun, D.W., Kim, S.-T., Czader, B., Cheng, F.-Y., Stetson, S., Nowak, D., Bornstein, R., Estes, M., 2004. Modeling effects of land use/land cover modiﬁcations on the urban heat island phenomenon and air quality in Houston, Texas. HARC Project H17 Final Report, IMAQS, University of Houston, Houston, Texas. Byun, D.W., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and other components of the models-3 community multiscale air quality (CMAQ) modeling system. Appl. Mech. Rev. 59(2), 51–77. Cheng, F.-Y., Byun, D.W., Kim, S.-B., 2003. Sensitivity study of the effects of land surface characteristics on meteorological simulations during the TexAQS 2000 period in the Houston-Galveston area, Extended Abstract. The Thirteenth Penn State/NCAR MM5 User’s Workshop, June, 2003, Boulder, Co.

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Data assimilation and air quality forecasting Chairpersons: Adolf Ebel Peter Builtjes Rapporteurs: Ralf Wolke Martyn Schaap

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Chapter 3.1 Improvement in particles (PM10) urban air quality mapping interpolation using remote sensing data Nuno Grosso, Francisco Ferreira and Sandra Mesquita Abstract The integration of remote sensing instruments in air quality monitoring systems at an urban and regional scale is a valid methodology with signiﬁcant improvements regarding spatial distribution. The spatial and temporal consistency in terms of data acquisition makes satellite data a privileged source of information on the concentration of atmospheric pollutants and on their distribution patterns at different scales. This information can be used to complement the information given by ground measurements and modeling. The paper will focus on the integration of aerosol optical thickness (AOT) satellite retrievals and particle concentration (PM10) measured by ground monitoring stations in the Lisbon region to improve PM10 concentrations mapping. The work outlines a methodology to improve the mapping of this pollutant by using geostatistical methods to interpolate PM10 data obtained by the linear relation between the AOT satellite retrieved data and the values from the ground monitoring stations. By choosing the AOT points with the highest degree of conﬁdence and using the assessed linear function to determine PM10 concentrations for several locations in the Lisbon region it was possible to increase the number of ground sampling points used in the interpolation. The satellite data refers to results from two MODIS images from the year 2001 obtained using a contrast reduction method suitable for urban AOT retrieval. The work includes the characterization of PM10 maps in terms of dispersion factors such as wind, topography, emission inventories, and past air quality campaigns to check for some inconsistencies in the particulate matter dispersion patterns. Overall results of the new methodology seem to present a consistent representation of the spatial distribution of PM10 pollution.

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The results obtained by this method have shown some high concentration areas not identiﬁed by the emission inventories or the ground measurements. The reliability of these maps will be analyzed in further studies. 1. Introduction

Aerosols are one of the major air pollutants responsible for human health problems related with the respiratory system and are responsible for several climate change inducing mechanisms. Their suspension and transport potential as its relationship with SO2 and NO2 makes aerosols a good indicator of air pollution at urban and regional scales thus emphasizing the importance of developments in satellite aerosol retrieval. The study of aerosols or any other atmospheric pollutant dispersion patterns relies on spatial and temporal data series obtained from air quality monitoring networks or measurement campaigns that entail a high implementation and maintenance costs and are limited in terms of spatial coverage. Such limitations can be minimized through integration of statistical and physical modeling and introduction of a wide range of satellite sensors that can complement ground data. Satellite imagery can be used to better assess the spatial structure of air pollution and interactions on global, regional, or local dispersion patterns.

2. Methodology

The number of ground stations present in urban air quality monitoring networks is usually not sufﬁcient to produce reliable maps of particulate distribution over a considerable area. The work presented in this paper outlines a methodology to improve the mapping of this pollutant in the Lisbon region by using geostatistical methods to interpolate PM10 data obtained by the linear relation between the AOT remote sensed data and the values from the ground monitoring stations. The spatial resolution of the remote sensed data will increase substantially the number of sampling points to use in the interpolation and therefore allow a more realistic representation of particulate matter concentrations across the study area. The most common aerosol parameter retrieved from satellite images is the aerosol optical thickness (AOT). It is a dimensionless parameter that quantiﬁes the degree to which aerosols prevent the transmission of light and it is directly correlated with the aerosol loading in the total atmospheric vertical column. The DTA algorithm was developed to calculate AOT based on the contrast reduction effect that aerosols have on a

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satellite image, particularly in the visible bands. This loss of contrast is caused by the contribution of the background to the reﬂectance measured in a speciﬁc pixel due to aerosol scattering. This effect increases with AOT (Sifakis and Deschamps, 1992) and translates in an overall loss of sharpness or contrast of the surface features for areas where its reﬂectance is spatially heterogeneous as is in the case of urban areas. Work done ﬁrst by Tanre´ et al. (1988) and Tanre´ and Legrand (1991) and later by Sifakis and Deschamps (1992) and Sifakis et al. (1998) determined a relation between the AOT of two images acquired in two different moments in time and the standard deviation (s), used as a measure of contrast of the reﬂectance values of those images inside a determined window size: s1 t1 t2 (1) þ ¼ exp s2 cosðy1 Þ cosðy2 Þ For cases where sensor viewing angle y is equal in both images (y1 ¼ y2 ¼ y) and considering that one of the images was acquired in a day of low pollution (reference image, t1E0), the AOT of the second image could be deduced as s1 (2) t2 ¼ cosðyÞ ln s2 By comparing two images acquired at distinct times and assuming surface contribution of the measured reﬂectance as being time invariant, this will allow surface signal removal from the equation and the variations in apparent reﬂectance will be mainly due to changes in aerosol content. The time invariance assumption is veriﬁed for most urban areas with low biomass content (Sifakis and Deschamps, 1992), not susceptible to seasonal or short-term variations in the surface reﬂectance. To determine an optimal window size for which the standard deviation will be calculated, an analysis of the variation of the standard deviation for different window sizes should be performed and then analyzed. 3. Results

The data presented in this section refer to preliminary results from the calibration and validation process of the DTA algorithm for MODIS images. The three images used were Band 1 MODIS level 1B images. Each image was treated to screen out water and cloud pixels and submitted to the Lisbon region. The ﬁrst band of MODIS is well suited for aerosol related products since it measures in the 620–670 nm visible bandwidth, sensible to aerosol scattering derived reﬂectance, and has a

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Figure 1. PM10 stations for the Lisbon Metropolitan Area.

spatial resolution of 250 m. The reference image is from 05/08/2001 and the two polluted images are from 12/08/2001 and 13/09/2001, a Sunday and Thursday, respectively. They were chosen based on the analysis of the PM10 ground measurements from monitoring stations of the Lisbon Metropolitan Area air quality network maintained by the Comissa˜o de Coordenac- a˜o e Desenvolvimento Regional da Regia˜o de Lisboa e Vale do Tejo (Fig. 1). The satellite hour of passage for all three images was 11:45 a.m. (UTC) and their geometry of visualization is similar corresponding to a near nadir satellite view. After running the algorithm, the most appropriate window size is chosen based on an analysis of the variation of the standard deviation (structure function) for several window sizes. The analysis of the structure function for the different PM10 stations and previous work done using the DTA algorithm in MODIS images (Grosso, 2005) has shown that a window size of 3000 m (13-by-13 pixels) is ideal for MODIS AOT retrieval, especially for urban areas. Following the AOT calculations for the two images, results for the PM10 stations coordinates were plotted against their respective PM10 values (Fig. 2). Because the reference day still presents some degree of PM10 pollution that can produce a bias in the analysis, the values used were not the absolute values from the polluted days but their difference with the PM10 for the reference day. Also to prevent a bias caused by large hourly variations in PM10 ground measurements and considering that the hour of passage was around 11:45 a.m. (UTC), the average PM10 difference value for 11 and 12 h was taken as reference for correlation with the AOT values.

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The scatter plot showed a strong linear correlation between AOT values and PM10 differences for the different stations in the two considered days (r2 ¼ 0.77, a ¼ 0.05). From that correlation a linear regression model was retrieved to derive PM10 values from AOT (equation shown in Fig. 2). In these calculations only pixels that used more than 60% of possible reﬂectance values to determine the AOT were used in order to increase the degree of conﬁdence in the results. Based on this model, PM10 maps for the two days were generated as presented in Fig. 3a and c. In order to interpolate the results and attenuate possible inconsistencies in terms of spatial distribution of PM10 values caused by errors in the AOT retrieval (i.e., errors in cloud masking) the geostatistical method Ordinary Kriging was applied resulting in the maps shown in Fig. 3b and d. The software used to perform the interpolation was the extension Geostatistical Analyst from ArcGIS 9.0. The unusual high PM10 values near the Tagus and Sado Estuaries for the 13/09/2001 image (Fig. 3c) seems to be due to variations in vegetation content between the reference and the polluted images. These variations can introduce an error in the AOT calculations since they will compromise the time invariance of the surface targets reﬂectance between the reference and polluted images. In consequence, when applying the DTA algorithm, the differences in surface signal from the two images will produce a bias, in this case an overestimation, in the retrieved AOT. The variations can be explained by the existence of rice and other irrigated ﬁelds (Fig. 4a) in these areas since the polluted image is taken in September when, for instance, the rice is usually cropped. This will cause signiﬁcant differences in the surface reﬂectance of the two images. This

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Figure 3. Main results for the Lisbon region: (a) PM10 satellite based estimates (12/08/ 2001); (b) PM10 mapping after kriging interpolation (12/08/2001); (c) PM10 satellite based estimates (13/09/2001); (d) PM10 mapping after kriging interpolation (13/09/2001).

explanation was conﬁrmed by analyzing the NDVI 16-day MODIS product for the time periods that include the two image dates. They have shown a signiﬁcant decrease in NDVI near those estuarine areas from the date of acquisition of the reference image to the polluted one. These points were therefore removed (points marked in white in Fig. 3c) and are not present in the kriging process. To analyze the PM10 maps of the Lisbon Metropolitan Area, based on satellite AOT retrievals, several sources of information were compiled namely national emission inventories for 2001 (Fig. 4c), NO2 2001 campaign using diffusive sampling for the Lisbon Metropolitan Area (Fig. 4d), topography (Fig. 4b), and wind information (shown in Figs. 3a–d). The national emissions inventory desegregation was based on the location of the major point sources, as power plants and major roads, and the population density as an indicator of the area sources. The

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Figure 4. Auxiliary data: (a) Corine Land Cover 2000 for the Lisbon region (rice and irrigated ﬁelds marked in black); (b) the Lisbon region topography; (c) 2001 National Air Emissions Inventory; (d) 2001 NO2 Lisbon Region Summer campaign with diffusive sampling.

2001, NO2 Lisbon Region Summer campaign with diffusive sampling refers to a 1-week campaign between July 3 and 10, 2001. The ﬁrst conclusion to draw from the PM10 satellite based estimates maps is that the overall levels of particulate pollution in the 12/08/2001 are lower than the 13/09/2001. This is consistent with the ground stations measurements and with what was expected since the ﬁrst polluted day refers to a Sunday and the second to a weekday (Thursday). A similar distribution pattern is observed for the two days, although more widespread, on the second day. For both days the urban area that deﬁnes the Lisbon Metropolitan Area, namely the municipalities of Lisbon, Oeiras, Sintra, Amadora, and Loures to the north and Almada, Seixal, and Barreiro to the south are more polluted than the remaining areas. Those are the most densely populated regions and where most of main autoroutes

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and industries of the region are located. Therefore they present a higher degree of potential point, area and mobile anthropogenic particulate emissions sources, as reﬂected in the National Emissions Inventory and in the 2001 NO2 Summer campaign. Also in both images a high PM10 loading can be observed along the west coast probably mainly due to the presence of maritime aerosols brought by northwest winds. This effect diminishes with the distance from the shore stopping as it approaches topographic barriers to the west and north of Lisbon. Another interesting observed feature is the high PM10 values of an area to the north of the Tagus estuary in 13/09/2001. That area corresponds to an electric power plant that is a signiﬁcant PM10 point emission source as can be seen in Fig. 4c. Nevertheless, the possibility of result contamination from differences in surface reﬂectance due to NDVI variability is not to be excluded. The kriging interpolation removed part of the variability observed in the original PM10 satellite based values allowing a more coherent display of the distribution pattern of background PM10 levels. Nevertheless, a question remains if some of that variability is not real and therefore should not be smoothed. Comparing the results from the PM10 satellite based estimates before and after the kriging interpolation with the PM differences for the ground stations for the most polluted day (13/09/2001) it is possible to observe that, especially for the kriged map, the background stations (Loures, Olivais, and Lavradio), present a similar value to the estimations where as the trafﬁc stations like Liberdade and Entrecampos show higher values. This reveals a tendency for this methodology to map background values more effectively than peak values due to spatial resolution limitations of the remote sensed data (3000 m) and the characteristics of the interpolation method.

4. Conclusions

Overall results of the new methodology seems to present a consistent representation of the spatial distribution of PM10 pollution, especially for background pollution levels. Higher concentrations found in the Lisbon Metropolitan Area were in agreement with emission inventory and pollutant measurements campaign data. The results obtained by this method also deﬁned some high concentration areas not identiﬁed by the emission inventories or ground measurements like maritime aerosols intrusion along the west coast. The reliability of these conclusions will be further analyzed in studies where the methodology will be applied to an extended dataset of images

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to include a broader range of PM10 dispersion conditions. The application of the DTA algorithm to MODIS images still requires calibration and validation before it is considered to be a stable methodology for AOT determination in urban areas using regional sensors of medium resolution. Factors like differences in surface reﬂectance characteristics due mainly to variations in vegetation content in the two images and differences in geometry of visualization (not explored in this paper since all images had similar geometries) can result in signiﬁcant errors. The validation of these results with high resolution satellite data (Landsat) results and with the already validated MODIS aerosol product will improve the performance of the algorithm and the conﬁdence in its results. As for the retrieval of AOT based PM10 estimates it can be further improved in order to integrate information on the vertical structure of aerosol loading in the mixing layer and humidity from lidar and ground meteorological stations. This will allow the determination of PM10 and PM2.5 estimates at dry atmospheric conditions and ground height and therefore more comparable with ground truth.

Discussion

U. Schlink:

N. Grosso:

S. AndreaniAksoyoglu: N. Grosso:

C. Mensink:

N. Grosso:

The AOT represents the total amount of the particulate matter. Did you somehow include the thickness of the mixing layer into your approach? At this moment no information on the mixing layer height is included in the AOT product. Nevertheless, I hope to include as soon as possible as I’ve seen in other works done with this method it greatly improves the quality of the correlation between the AOT and the PM10 since it approximates better the ground truth. How did you deal with clouds and water surfaces? I use a water and cloud mask that result from other MODIS products namely the MOD09GST or the MOD04_L2. Is the method sensitive to the type of aerosol, i.e., can it make a distinction between, e.g., primary PM, secondary PM and soot based on reﬂectance? No. Nevertheless, there is the possibility to distinguish between coarse and ﬁne particulate matter using the

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Angstrom coefﬁcient that is a ratio between the AOT taken at two different wavelengths. The smaller the Angstrom coefﬁcient is the coarser the particles are.

ACKNOWLEDGMENTS

This work was supported by the Foundation for Science and Technology (FCT) and the European Union under Operational Program ‘‘Science, Technology, and Innovation’’ (POCTI), PhD grants ref. SFRH/BD/6516/ 2001, inserted in the IIIrd Community Support Framework (2000–2006). REFERENCES Grosso, N., 2005. Retrieving aerosol optical thickness from a MODIS dataset for the Lisbon Metropolitan Area using a contrast reduction method, in 25th EARSeL Symposium—Global Developments in Environmental Earth Observation from Space Proceedings, June 6–11, Porto, Portugal. Sifakis, N., Deschamps, P.Y., 1992. Mapping of air pollution using SPOT satellite data. Photo. Eng. Rem. Sens. 58, 1433–1437. Sifakis, N., Soulakellis, N., Paronis, D., 1998. Quantitative mapping of air pollution density using Earth observations: A new processing method and application on an urban area. Int. J. Rem. Sens. 19, 3289–3300. Tanre´, D., Deschamps, P.Y., Devaux, C., Herman, M., 1988. Estimation of Saharan aerosol optical thickness from blurring effects in Thematic Mapper data. J. Geophys. Res. 93, 15955–15964. Tanre´, D., Legrand, M., 1991. On the satellite retrieval of Saharan dust optical thickness over land: Two different approaches. J. Geophys. Res. 96, 5221–5227.

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Chapter 3.2 The use of ensemble weather forecast for dispersion uncertainty modelling Lennart Robertson, Andrew Jones, Francois Bonnardot and Stefano Galmarini Abstract Ensemble weather forecasts have been used to demonstrate their applicability on regional dispersion models. The conclusion made so far is that ensemble weather predictions beyond forecast day 3 should be used with care as the randomness in dispersion patterns may be of less use for decision making. The lagged forecast approach, or the poor-man ensemble technique, enables use of high resolution forecasts. The drawback is the shorter forecast ranges possible and that lagged forecasts mainly demonstrated the consistency between forecasts. 1. Background

This work is part of the EU project PREVIEW (PREvention, Information and Early Warning) that was launched in July 2005. The project objective is to provide pre-operational services for management of risk for Europe in the following areas: (a) atmospheric risks, (b) geophysical risks and (c) man-made risks. The atmospheric risks cover wind-storms, ﬂoods and forest ﬁres and the geophysical risks earthquakes, landslides and volcanoes, while the man-made risks cover industrial accidents. One of the general themes is uncertainty modelling and to provide probabilities of severe events on the basis of ensemble weather predictions (EPS), speciﬁcally addressed in the work packages wind-storms, ﬂooding and man-made risks. Uncertainty estimates related to air pollution modelling have earlier been addressed in the ENSEMBLE project (Galmarini et al., 2004a, b) based on the so-called multi-model approach, where several dispersion models from a number of participating institutes are combined to probabilistic output for risk analysis. In the PREVIEW project, EPS will be further explored with the similar objective as in the

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ENSEMBLE project. An issue of importance is the relevant probability metrics to be used that make sense to the end-users. This paper presents some of the early ﬁndings at this stage of the PREVIEW project. 2. Risk management

The products we at the best are able to provide with the ensemble technique are probabilities or likelihoods for different dispersion events. This should not be mixed up with the notation of risk that is the product of the probability and the vulnerability of an event. This means that the products have to be incorporated into a risk management process where vulnerability estimates are included. At the end, the end-user has to be part of the product deﬁnition so that the vulnerability levels of speciﬁc concern could be directly incorporated into the ﬁnal products. 3. Ensemble weather predictions

Ensemble weather forecasts have been around for more than a decade (see e.g., Palmer et al., 1990; Toth and Kalnay, 1993; Buizza et al., 2004), but very few attempts have been made to use this information for uncertainty modelling in dispersion applications. One possible reason is that state-of-the-art chemistry-transport models (CTM) demand full three-dimensional input, and only limited amount of EPS outputs are archived (due to the enormous amount of data produced), insufﬁcient to meet the requirements of the CTM models. A second reason is the rather coarse resolution in EPS data, E100 km, while air pollution modelling is very much devoted to high resolutions. Complete sets of weather information may, however, be available. At e.g., ECMWF full EPS data on all model layers are available within a 24-h window for every forecast release. This in turn demands running the CTM at ECMWF computer facilities, and consequently less suited for operational purposes. The ECMWF EPS system provides 51 ensemble members and procedures to reduce the number of members in order to limit the processing time is also called for. Even though ensemble weather forecasts are in the front-end of research, other approaches have to be considered for operational reasons. Moreover, on the meso-scale there are no ensemble predictions yet available. The ‘‘poor-man’’ ensemble technique, or lagged forecasts, is an attractive alternative where the ‘‘ensemble members’’ are forecasts with different leading time.

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4. Demonstration of ensemble dispersion forecasts

We have tentatively investigated the use of ECMWF EPS data for dispersion applications. The ECMWF EPS system provides 51 ensemble members including the so-called control run (an unperturbed run at the same resolution as the ensembles). We should notice that the resolution is roughly 100 km and thus less attractive for meso-scale dispersion modelling. The ECMWF EPS system is also tuned to give a reasonable spread after day 2 of the forecast. We have therefore used EPS forecasts from day 1 and onwards, in this demonstration, in order to assure some spread from the start of the dispersion run. Figure 1 shows some statistical output from a demonstration run after 2 and 4 days of integration, respectively. The demonstration run uses a hypothetical constant release over the entire period of calculation. The dispersion calculations are made on a 5-km grid, which is a bit to stretch beyond the limits of 100 km resolution in EPS data. The large spread shown at day 4 is rather striking. This feature has appeared repeatedly in the test runs we have made. This almost random distribution does apparently not provide any useful information to an end-user. The randomness may have some physical explanations as wind directions will become more random distributed for long enough integration periods. At this stage, we may conclude that ensemble weather predictions should not be used for dispersion applications beyond forecast day 3 of the ensemble predictions. 5. Poor-man ensemble: A meso-scale approach

The poor-man ensemble technique is a far more simple approach than ensemble weather predictions. The technique is based on lagged forecasts, or forecasts with different leading time but valid at the same time period. In our case, we have used the HIRLAM forecast model on 22 km resolution. HIRLAM forecasts are available every 6 h and each forecast provides a 48-h forecast. In this demonstration we have made a 12-h dispersion simulation that then is covered by seven HIRLAM forecasts with different leading time. A hypothetical 6 h release scenario is used. Figure 2 presents similar statistics as in Fig. 1, but in addition the latest forecast (and normally the most trustworthy) is also shown. The drawback with the poor-man approach is that the statistics rather describe consistency of consecutive forecasts than probabilities. Another aspect is that the statistics most likely has to include different weights on the various forecasts, as the older forecasts may not be as much trusted as the latest one. The means to deﬁne such weighting is still unclear.

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Figure 1. A demonstration of dispersion runs using 51 weather ensembles members compiled to the mean, median, upper quartile and max, after 2 days of integration (left) and 4 days (right). A hypothetical constant release has been used.

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Figure 2. A demonstration dispersion runs based on seven lagged forecasts compiled to median, max, upper quartile and the latest forecast after 12 h of integration of a hypothetical 6-h release with constant release rate.

An apparent limitation of the poor-man approach is the length of the simulation period that has to rather short in order to have enough forecasts to cover the period of interest. By contrast, too old forecasts may introduce undesired uncertainties. The best setup for the poor-man approach will be evaluated among others with full EPS data. 6. Reduction of ensemble members

In order to limit the computational efforts, a reduction mechanism that brings down the number of weather ensemble members is called for. The reduction should be relevant for the phenomena that is of importance for dispersion. Our approach to arrive at such a reductions mechanism is based on Warner et al. (2004) that proposed a rather handy method for model

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evaluation. The basis is a two-dimensional measure described by estimates of false positive and false negative predicted areas, from which the Figure of Merit in Space (FMS) could be derived. By checking all EPS members against each other, using the FMS as the measure, we may construct an cross-evaluation matrix, that will be symmetric and positive deﬁnite. The eigenvector associated with the largest eigenvalue will then be a condensed measure of the similarity and differences between ensembles. Components in the eigenvector of equals sign and similar amplitude indicate similarity. A rather pragmatic mechanism to reduce the number of ensemble members, labelled the ‘‘Gardener’’ approach, is proposed. The approach mimics the way a gardener single out of carrots in the grocery garden by iteratively removing eigenvector components, and thus associated ensemble members, where the components are most dense.

Figure 3. Demonstration of the ﬁrst eigenvector components, of a cross-evaluation matrix of 51 ensemble members of 12 h precipitation, plotted on the 1:1 line. The upper panel shows the full set and the lower panel a reduced set using the ‘‘Gardener’’ approach.

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The upper panel in Fig. 3 demonstrates the eigenvector, derived from 51 ensemble members of 12 h integrated precipitation, by plotting the eigenvector components on the 1:1 line. It becomes evident that some clustering appears in this example. The lower panel of Fig. 3 shows a reduced set of ensemble members where the ‘‘Gardener’’ reduction mechanism has been used. Still more work remains to ﬁnd the meteorological variable that produce the ‘‘best’’ reduced set of ensemble members for dispersion applications. 7. Conclusion

This is an early phase of the PREVIEW project where we are evaluating various approaches to introduce uncertainty modelling into dispersion applications. The basis has been ensemble weather predictions (EPS) but such products available at the ECMWF suffers from the coarse resolution and it is a bit ambiguous which forecast hours that should be used. A pragmatic approach to reduce the number of EPS members to a number that is operationally feasible is presented. The poor-man approach with lagged forecasts are promising in the sense that high resolution forecasts could be utilized. The drawback is that the dispersion products rather reﬂects the consistency between consequently weather forecasts than probabilities as such. In this presentation, we have focused on the uncertainty in dispersion modelling that is induced by the forcing weather data. Work is initiated to also include the uncertainty in the release scenarios that could be of more prominent uncertainty. The success to include uncertainty measures into decision making is very much dependent on the end-user acceptance of this sorts of information. Training and education of end-users will therefore be a central issue during the course of the PREVIEW project. Discussion

Silvia Trini Castelli:

What is the time lag between the leading times of the different forecasts used in the ‘‘poor-man approach’’? Does this mean that you are using different ECMWF initial and boundary conditions in HIRLAM for the different lagged forecasts? I expect this could affect the HIRLAM simulations,

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did you notice any sensitivity to varying ECMWF input forecasts? We have not elaborated on different boundary conditions in this work. What is true is that a sequence of HIRLAM forecasts would use boundaries from the same ECMWF forecast, as HIRLAM is produced every 6 h while ECMWF provides boundaries twice a day. We have though noticed that the oldest forecast, used to build a poor-man ensemble, could be signiﬁcantly different from the latest ones, and that a weighting may have to be included that diminish the impact from the older forecasts.

REFERENCES Buizza, R., Houtekamer, P.L., Toth, Z., Pellerin, G., Wei, M., Zhu, Y., 2004. A comparison of the ECMWF, MSC and NCEP global ensemble prediction systems. Mon. Weather Rev. 133, 1076–1097. Galmarini, S., Bianconi, R., Klug, W., Mikkelsen, T., Addis, R., Andronopoulos, S., Astrup, P., Baklanov, A., Bartniki, J., Bartzis, J.C., Bellasio, R., Bompay, F., Buckley, R., Bouzom, M., Champion, H., D’Amours, D., Davakis, R., Eleveld, H., Geertsema, G.T., Glaab, H., Kollax, M., Ilvonen, M., Manning, A., Pechinger, U., Persson, C., Polreich, E., Potemski, S., Prodanova, M., Saltbones, J., Slaper, H., Soﬁev, M.A., Syrakov, D., Srensen, J.H., Van der Auwera, L., Valkama, I., Zelazny, R., 2004. Ensemble dispersion forecasting—Part I: Concept, approach and indicators. Atmos. Environ. 38(28), 4607–4617. Galmarini, S., Bianconi, R., Addis, R., Andronopoulos, S., Astrup, P., Bartzis, J.C., Bellasio, R., Buckley, R., Champion, H., Chino, M., D’Amours, D., Davakis, R., Eleveld, H., Glaab, H., Manning, A., Mikkelsen, T., Pechinger, U., Polreich, E., Prodanova, M., Slaper, H., Syrakov, D., Terada, H., Van der Auwera, L., 2004b. Part II: Application and evaluation. Atmos. Environ. 38(28), 4619–4632. Palmer, T.N., Brankovic, C., Molteni, F., Tibaldi, S., Ferranti, L., Hollingsworth, A., Cubasch, U., Klinker, E., 1990. The European Centre for Medium-Range Weather Forecasts (ECMWF) program on extended-range prediction. Bull. Am. Meteorol. Soc. 71(9), 1317–1330. Toth, Z., Kalnay, E., 1993. Ensemble forecasting at NMC: The generation of perturbations. Bull. Am. Meteorol. Soc. 74(12), 2317–2330.

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Chapter 3.3 Forward and inverse modelling of radioactive pollutants dispersion after Chernobyl accident Mikhail Sofiev, Ilkka Valkama, Carl Fortelius and Pilvi Siljamo Abstract The paper re-analyses the consequences of Chernobyl catastrophe for the radionuclide contamination of the European region. In the re-analysis, we tried to use the best available information and establish the ground for the source-apportionment studies, similar to those conducted by the team for ETEX experiment. The modelling tool used in the simulations was the Finnish Emergency and Air Quality modelling system SILAM v.3.8, which is based on a Lagrangian Monte-Carlo random-walk. The system was run through the 1-month-long period with the source-term information for 22 nuclides, which altogether comprise >99% of the total inventory of the release, and varying vertical emission proﬁle, which reﬂected the different stages of the accident. This information and detailed meteorological data from the state-of-the-art NWP model HIRLAM, which was re-run for the considered period, resulted in accurate reproduction of the contamination pattern and its motions over Europe. Comparison of the simulated deposition map with the results of the radioactive deposition atlas of Europe showed very good agreement between the patterns. However, source-apportionment simulations showed insufﬁcient both temporal resolution and spatial coverage of the data, and thus are not discussed in details.

1. Introduction

Numerous national and international efforts during past 20 years collected a vast amount of information about Chernobyl catastrophe, described the source characteristics and deposition patterns for all main radionuclides. This information is gradually becoming available and currently constitutes one of the most comprehensive datasets on dispersion of multi-component,

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chemically and radiologically active species released into the atmosphere due to an accident. The available data were used for a set of simulations with the Finnish emergency modelling system SILAM (a 3D Lagrangian random-walk model). The ﬁrst run was a ‘‘standard’’ forward run with known source characteristics aiming at concentration and deposition patterns and their development during and shortly after the release. The second set of runs (still under analysis, not discussed in the current pre-print) constituted an inverse problem study by means of data assimilation (the methods used are variations of the 4D-VAR approach).

2. Simulation setup

The accident at Chernobyl power plant (511170 N, 301150 E) started on 25.04.1986 at 21:23 and lasted for at least 10 days. Despite a vast amount of studies of the disaster, even after 20 years, the actual emission data of the Chernobyl release are not known precisely: uncertainty, according to several sources, can be up to 750%. The release estimates are based on the core inventory at the time of accident, on the estimates of radionuclides released as a fraction of core inventory and on ﬁeld measurements made during the years. Due to violent nature of the initial explosion, and its highly variable strength, the emission heights are also difﬁcult to estimate. The total values used in various simulations were ﬁrst based on the initial USSR report to the International Atomic Energy Agency (USSR, 1986) and derived from a summation of the material deposited within the countries of the former USSR. This report was also the basis of the often-quoted ATMES source term (Klug et al., 1992). These data have been found in several studies to be too low—by at least a factor of 2. The more recent estimates were published by Devell (1995), Waight et al. (1995) and De Cort et al. (1998). The total activity of radioactive materials in the release is estimated in this study to be 1.17E+19 Bq. The data are based on Devell (1995), De Cort et al. (1998) and Izrael (2002). Volatile ﬁssion products were found in small particles (0.5–1 mm). The daily fractions of the total release are based on those given in Waight et al. (1995) and De Cort et al. (1998). The release was the strongest during the ﬁrst days of the accident (25–26 April 1986) and then decreased gradually until day 6 (1 May 1986). On day 7 (2 May 1986), it started to increase again, reaching a secondary maximum on day 10 (5 May 1986), due to a build-up of high temperatures in the core debris, and ﬁnally dropping sharply on day 11 (6 May 1986).

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This emission information has been submitted to the Finnish Emergency Modelling System SILAM, which has simulated a month-long distribution of the radioactive materials around Europe. SILAM is a Lagrangian random-walk model (Soﬁev et al., 2006), capable of both forward and inverse simulations. The model radiological database includes 496 nuclides with their radioactive decay chains and gas–aerosol partitioning information and deposition features. For the Chernobyl source term, we selected 22 most important nuclides (Table 1). Together with their decay-chain daughters, this resulted in 31 nuclides whose concentration and deposition patterns have been evaluated. The vertical distribution of emission (Table 2) was taken after Persson et al. (1986). Similar distribution was also used by Hass et al. (1990) and Brandt et al. (2002). According to Po¨lla¨nen et al. (1997), these height estimates are conservative and the ﬁrst-day release might have been even higher.

Table 1. Estimates of the radionuclide releases during the Chernobyl accident Radionuclide

85

Kr Xe 131 I 134 Cs 136 Cs 137 Cs 132 Te 89 Sr 90 Sr 140 Ba 95 Zr 99 Mo 103 Ru 106 Ru 141 Ce 144 Ce 238 Pu 239 Pu 240 Pu 241 Pu 239 Np 242 Cm 133

Total release (Bq)

3.30E+16 6.30E+18 1.76E+18 6.84E+16 4.18E+16 1.06E+17 1.15E+18 1.15E+17 1.10E+16 2.40E+17 1.96E+17 1.68E+17 1.68E+17 7.35E+16 1.96E+17 1.16E+17 3.50E+13 2.98E+13 4.2E+13 5.95E+15 9.45E+17 1.51E+15

Release fractions (% of core inventory) USSR (1986)

Devell (1995)

This study

100 100 20 10 – 13 15 4 4 5.6 3.2 2.3 2.9 2.9 2.3 2.8 3 3 3 3 3 3

100 100 50–60 33+10 – 33+10 25–60 4 4 4 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5

100 100 55 33 33 33 43 5 5 5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5

Total releases (Bq) used in this study and the release fractions proportional to the core inventory from two sources and this study.

286 Table 2. Relative distribution of radioactive releases in time and in height during the Chernobyl accident 1986 (based on Persson et al., 1986) Height (m)

25/26.4

27.4.

28.4.

29.4

30.4.

1.5.

2.5.

3.5.

4.6.

5.5.

6.5.

0–350 350–550 550–800 800–1300 1300–1900 1900–2500 Daily total EBq (1017)

0 0 0 50% 40% 10% 28.92

0 10% 50% 40% 0 0 10.1

50% 50% 0 0 0 0 8.14

50% 50% 0 0 0 0 5.72

50% 50% 0 0 0 0 4.64

50% 50% 0 0 0 0 4.64

50% 50% 0 0 0 0 9.49

50% 50% 0 0 0 0 11.5

50% 50% 0 0 0 0 15.9

50% 50% 0 0 0 0 17.8

50% 50% 0 0 0 0 10.2

Total radioactivity release is 1.17E+19 Bq.

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Meteorological input data for the simulations were generated by the HIRLAM (Unde´n, 2002) limited-area weather prediction model. We used the currently operational version, similar to so-called Regular-Cycle-Runs HIRLAM at FMI (Kangas, 2004; Kangas and Sokka, 2005). The model has been re-run for 1986 covering the Chernobyl accident period (from 20.4.1986 to 20.5.1986). Temporal resolution was 1-h with the forecast length from 3 to 8 h. Horizontal resolution was 0.21 (about 20 km) with 40 vertical levels.

3. Results and discussion

Development of the case in time is presented in the sequence of maps in Fig. 1 highlighting four entirely different periods of the dispersion. Comparison of Fig. 1 (panel d) and data of De Cort et al. (1998) shows that SILAM reproduced the overall pattern very well, including the tiny details, such as limited contaminated region in the immediate vicinity of the station, strong depositions onto European mountains in Norway, Austria and Italy, strong and narrow peak near Gotland, etc. Absolute levels of the deposition are also very close to the observations, providing the uncertainties in the emission. Therefore, the simulations conﬁrmed the reliability of SILAM and also highlighted the quality of HIRLAM meteorological ﬁelds, which managed very well with timing and places of precipitation ﬁelds. For the current simulations, we accepted comparatively small size of particles—between 0.1 and 1 mm for the bulk of the release. An exception was made for heavy non-volatile nuclides, whose diameter was up to 10 mm. However, total activity released with so coarse aerosol is several orders of magnitude lower than that of the ﬁne-size particles. These assumptions seem to be in qualitative agreement with (admittedly, few) observations and other modelling studies, which usually refer to airsamples and fallout measurements made in May 1986 (Garger et al., 1998). The volatile (131I, 134Cs, 137Cs, 132Te) or semi-volatile elements (90Sr, 103Ru, 106Ru) were found to be attached to small particles with activity median aerodynamic diameters (AMAD) ranging from 0.3 to 1.5 mm (Kauppinen et al., 1986; Tschiersch and Georgi, 1987; Bennett et al., 2000). Most of iodine was released as a gas (75–80% of total iodine; Makhon´ko et al., 1996). Organic iodine (20–25% of the total) associated with smaller particles than Cs or Te (Jost et al., 1986; Baltensperger et al., 1987). Non-volatile radionuclides, such as 95Zr, 95Nb, 140La, 141Ce, 144Ce, and transuranium nuclides (fuel particles) were found in larger particles with AMAD of 10 mm and larger (Bennett et al., 2000; Po¨lla¨nen, 2002).

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b) eastern turn - to Finland and eastern Russia

c) South-western turn

d) Final deposition pattern

CS-137 concentrations and depositions for the main dispersion events.

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Figure 1.

a) initial plume towards Sweden

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Figure 2. A probability distribution for location of the source of the observed contamination from Chernobyl accident. Relative units (scaled).

Inverse simulations appear to be much more complicated due to the absence of the information for the ﬁrst days of the plume dispersion. As shown in Fig. 2, the probability distribution for the source location ﬁts the true station position very accurately. However, this result is largely dependent on just few stations, which appear to be under the original plume from the reactor. Most of European observations measured the polluted masses of a couple of days old, which strongly reduced their information content due to complicated and intensive meteorological mixing.

4. Conclusions

Preliminary analysis of the SILAM simulations showed that modern dispersion models forced by the up-to-date meteorological input re-run for the past-time episodes can provide qualitatively and quantitatively correct picture and dynamics of large-scale contamination after industrial

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catastrophes. However, the success of the model application strongly depends on quality of the emission term. In particular, the presented simulations were based on vast number of studies analysing the Chernobyl accident and quantifying its release parameters. Inverse problem solution, being successful for the speciﬁc case, appeared to be strongly dependent on the abundance of the observational information and, in particular, on the number of stations that observed the pollution during ﬁrst 1–2 days of the accident. Discussion

I. Tegen: M. Soﬁev:

M. Kaasik:

M. Soﬁev:

M. Jantunen:

M. Soﬁev:

Were the data for Cs-137 deposition corrected for ‘‘bomb’’ Cs originating from atmospheric tests? The model computations certainly did not include these data, while observations did. All what we could do was to assume some background values originated from nuclear tests and subtract it from the maps. However, these values are small enough to be even neglected, especially for the concentrations data. Where was the single station being responsible for almost the entire information about the location of the source? That was the Ukranian site located a few tens of kilometres southeast of Chernobyl. Its name was Varishinka. Comment: At the time of Chernobyl, we did not only depend on numerical atmospheric simulations. Analyses of collected samples identiﬁed the source and reactor type, of which there are only four in use. Backtrajectories identiﬁed, which of them was ﬁnally responsible for the contamination. Critical accidents in a similar RBMK reactor had been identiﬁed as the most likely nuclear accident affecting Finland. The consequences of such an accident had been simulated at the time of the accident. On Tuesday evening after the accident, its type and location were already known. Thanks. However, the accident happened on 25th of April, which was Friday of the previous week. This very delay of four days during which the plume had spread over most of Europe and for which we have

Modelling of Radioactive Pollutants Dispersion

A. Ebel:

M. Soﬁev:

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little information even today was critical and this was the issue I tried to stress. How was the varying emission height treated in the forward case, and what is its effect for inverse modelling? For forward modelling, we took a ‘‘generallyaccepted’’ injection height, which exceeded 2 km for the initial phase and was mostly conﬁned within the boundary layer during the second and third phases. In inverse modelling, we have not seen much of that signal, mainly because there was no information about the very ﬁrst plume, which went to Scandinavia at high altitudes. Nearly all observations available were reporting the low-level releases during the second and third phases.

REFERENCES Baltensperger, U., Ga¨ggeler, H.W., Jost, D.T., 1987. Chernobyl radioactivity in sizefractioned aerosol. J. Aerosol. Sci. 18, 685–688. Bennett, B, Bouville, A., Hall, P., Savkin, M., Storm, H., 2000. Chernobyl accident: exposures and effects. Proceedings of the 10th International Congress of the International Radiation Protection Association (IRPA-10). Paper T-12-1. Hiroshima, Japan; May 14–19; 2000 Brandt, J., Christensen, J.H., Frohn, L.M., 2002. Modelling transport and deposition of caesium and iodine from the Chernobyl accident using the DREAM model. Atmos. Chem. Phys. 2, 397–417. De Cort, M., Dubois, G., Fridman, Sh.D., Germenchuk, M.G., Izrael, Yu.A., Janssens, A., Jones, A.R., Kelly, G.N., Kvasnikova, E.V., Matveenko, I.I., Nazarov, I.M., Pokumeiko, Yu.M., Sitak, V.A., Stukin, E.D., Tabachny, L.Ya., Tsaturov, Yu.S., zAvdyushin, S.I., 1998. Atlas of caesium deposition on Europe after the Chernobyl accident, Luxembourg, Ofﬁce for Ofﬁcial Publications of the European Communities 1998. ISBN 92-828-3140-X, Catalogue number CG-NA-16-733-29-C. EUR 16733, p. 176. Devell, L., 1995. The Chernobyl reactor accident source term: Development of a consensus view. CSNI Report, OECD/NEA, Paris, 1995. Garger, E.K., Kashpur, V., Paretzke, H.G., Tschiersch, J., 1998. Measurements of resuspended aerosol in the Chernobyl area. Part III; Size distribution of radioactive particles. Radat. Environ. Biophys. 36, 275–283. Hass, H., Memmesheimer, M., Geiss, H., Jacobs, H.J., Laube, M., Ebel, A., 1990. Simulation of the Chernobyl radioactive cloud over Europe using the EURAD model. Atmos. Environ. 24A(3), 673–692. Izrael, Yu.A., 2002. Radioactive Fallout After Nuclear Explosions and Accidents. Elsevier, Amsterdam, p. 281.

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Jost, D.T., Gaggler, H.W., Baltensperger, U., Zinder, B., Haller, P., 1986. Chernobyl fallout in size-fractioned aerosol. Nature 321, 22. Kangas, M. (2004). The operational HIRLAM at FMI. Hirlam Newsletter 45, 15–22 http:// www.hirlam.knmi.nl Kangas, M., Sokka, N. (2005). Operational and RCR HIRLAM at FMI. Hirlam Newsletter 48, 14–20 http://www.hirlam.knmi.nl Kauppinen, E., Hillamo, R., Jokiniemi, J., Aaltonen, H., Sinkki, K., 1986. Radioactive size distributions of ambient aerosols in Helsinki, Finland, during May 1986 after the Chernobyl accident: Preliminary report. Environ. Sci. Technol. 20(12), 1257–1259. Klug, W., Graziani, G., Grippa, G., Pierce, D., Tassone, C., 1992. Evaluation of long range atmospheric transport models using environmental radioactivity data from the Chernobyl accident, The ATMES Report, Elsevier Applied Science, London and New York, p. 366. Makhon´ko, K.P., Kozlova, E.G., Volokitin, A.A., 1996. Radio-iodine accumulation on soil and reconstruction of doses from iodine exposure on the territory contaminated after the Chernobyl accident. Radiation & Risk (7), 90–142. Persson, C., Rodhe, H., De Geer, L.-E., 1986. The Chernobyl accident––A meteorological analysis of how radionucleides reached Sweden, SMHI/RMK Report No. 55. Po¨lla¨nen, R., 2002. Nuclear fuel particles in the environment—Characteristics, atmospheric transport and skin doses. STUK-A188, Radiation and Nuclear Safety Authority, Helsinki, Finland, p. 64. (with appendix). Po¨lla¨nen, R., Valkama, I., Toivonen, H., 1997. Transport of radioactive particles from the Chernobyl accident. Atmos. Environ. 31(21), 3575–3590. Soﬁev, M., Siljamo, P., Valkama, I., Ilvonen, M., Kukkonen, J., 2006. A dispersion modelling system SILAM and its evaluation against ETEX data. Atmos. Environ. 40, 674–685, doi:10.1016/j.atmosenv.2005.09.069 Tschiersch, J., Georgi, B., 1987. Chernobyl fallout size distribution in urban areas. J. Aerosol. Sci. 18, 689–692. Unde´n, P., (Ed.), 2002. HIRLAM-5 Scientiﬁc Documentation, HIRLAM-5 Project, Norrko¨ping, Sweden, 2002, http://hirlam.knmi.nl/open/publications/SciDoc_Dec2002.pdf USSR, 1986. The accident at the Chernobyl Atomic Power Plant and its Consequences. USSR State Committee on the Utilization of Atomic Energy. IAEA translation, Vienna, Austria, August 1986. Waight, P., Me´tivier, H., Jacob, P., Souchkevitch, G., Viktorsson, C., Bennett, B., Hance, R., Kumazawa, S., Kusumi, S., Bouville, A., Sinnaeve, J., Ilari, O., Lazo, E., 1995. Chernobyl Ten Years On. Radiological and Health Impact. An Assessment by the NEA Committee on Radiation Protection and Public Health, November 1995, OECD Nuclear Agency, p. 71.

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Chapter 3.4 PREV’AIR: A platform for air quality monitoring and forecasting C. Honore´, L. Menut, B. Bessagnet, F. Meleux, L. Rouı¨l, R. Vautard, N. Poisson and V.H. Peuch Abstract We present the operational air quality forecasting and monitoring system PREV’AIR and its evaluation. It became operational in 2003, as a result of a cooperative effort between several public organizations. The system is designed to forecast and analyze air quality throughout Europe, with a zoom over France. The ability of the PREV’AIR system to forecast the evolution of photochemical and particle pollution on the domains considered is demonstrated: for instance daily ozone maxima forecasts correlate with observations with 0.75–0.85 mean coefﬁcients. The skill of PREV’AIR real-time ozone analyses is also evaluated: it is shown that these analyses provide an accurate and comprehensive description of surface ozone ﬁelds over France. 1. Introduction

Since 2003, the PREV’AIR system has been delivering information about air quality, dedicated to any people or organisation interested by the day-by-day evolution of air quality as well as by its long-term trends, in relation with trends in emission levels. Forecasts and observation maps of air pollutants ozone, nitrogen dioxide and particles are published on a daily basis on the Internet (http://www.prevair.org). The forecasts are delivered up to 3 days in advance, at various spatial scales (global, Europe or France) depending on the pollutant. The air quality maps published everyday are the result of numerical simulations performed using chemistry transport models that allow the calculation of the evolution of air pollutant concentrations in the lower layer of the atmosphere. Several public organisations are involved in PREV’AIR: INERIS (National Institute for Industrial Environment and Risks),

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under the supervision of the French Ministry in charge of ecology, hosts and develops the PREV’AIR system; delivers daily air quality information; provides and archives the data. Pierre Simon Laplace Institute (IPSL) and National Centre for Meteorological Researches (CNRM) are research institutes, under the supervision of the National Centre for Scientiﬁc Research (CNRS) for IPSL and the French meteorological ofﬁce (Me´te´o France) for CNRM. They develop the chemistry transport models (CHIMERE and MOCAGE, respectively) used within PREV’AIR. Finally, Agency for Environment and Energy Management (ADEME), under the supervision of the Ministries in charge of research, ecology and energy, ensures the gathering, archiving and transmission of real-time air quality data collected locally by the French qualiﬁed air quality monitoring associations, thus building up the BASTER near real-time database used by PREV’AIR. Two important ideas underlie the PREV’AIR project: ﬁrst, the information delivered is based on both modelling tools and observation data. So far, only ground in situ data have been used in the system, but there are some studies running at the moment, aiming at taking advantage also of 3D data, such as satellite and LIDAR data. Second, there is the will to build up a system in close cooperation with its ﬁnal users, i.e., the air quality monitoring organisations. Thus, numerical forecast data are available on request enabling the air quality monitoring organisations to run their own forecast or diagnostic tools over their own domain at a ﬁner resolution.

2. Architecture of PREV’AIR

PREV’AIR relies on a chain of numerical tools, such as deterministic air quality simulation (CHIMERE and MOCAGE) models and meteorological (MM5 and ARPEGE/ALADIN) models (Fig. 1). Some modules ensure the provision of input data to the system (emissions, meteorology, observations), as well as post-treatment of the numerical data computed by the system: drawing of maps, extraction of numerical data, building up of ‘‘analyses’’, statistical comparison against observations, statistical adaptation of the forecasts. Observed air quality data, such as French near real-time air quality data from BASTER, are used by the PREV’AIR system, thus enabling (i) to correct the model raw simulations (in order to build ‘‘analyses’’), (ii) to produce statistically adapted forecasts and (iii) to evaluate the ability of the system to forecast air pollution episodes.

PREV’AIR: A Platform for Air Quality Monitoring and Forecasting Input data: Landuse, emissions etc.

295

Meteorological forecast: AVN, Arpege, ECMWF, MM5

Chemical forecast with models: CHIMERE MOCAGE

4D concentrations fields (several horizontal and vertical resolution, 1h time step output) Optimal interpolation analysis realistic maps of events

Real−time surface observations (on−line BASTER network)

PREV’AIR

Scores: Models forecast quality

Figure 1. Architecture of the PREV’AIR system.

3. Kriging methods: Ozone analyses

Observations of ozone concentrations are considered within the simulation chain, and allow to subsequently draw up so-called analysed maps on a daily basis, which, in turn, are based on concentrations simulated by the available models and data. These maps are generated in the late afternoon, once enough observations have been made to provide representative information. The mathematical method used to carry out the analyses is a geo-statistical process, called kriging of innovations (Blond, 2002). It allows to adjust the raw simulations produced by the modelling process in order to reduce the difference between calculations and measurements taken at the observation points. The adjustment depends directly on the errors made by the model at the points where pollutant measurements are available. The analysis for 15 July 2005 is given in Fig. 2, which displays the model output together with the observations collected on that day. The result of the analysis process is displayed in Fig. 3, where the difference between the simulated and analysed ozone ﬁelds is displayed on the bottom right hand side. Red areas are associated with model over-estimations of the ozone peak; blue areas with model underestimations. Only areas where observations have been conducted are analysed, since the adjustments are considered to be unreliable where only few measurements are available. The same method has been applied recently to PM10 daily mean concentrations, showing promising results as displayed in Fig. 4.

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Figure 2. Surface ozone peak over France for the 15 July 2005: The direct simulation compared to observations.

4. Performances of PREV’AIR: Statistical evaluation against observations

Throughout each forecast period (i.e., from 1 April to 30 September for the ‘‘summer’’ period; from 1 October to 31 March for the ‘‘winter’’ period), standard statistical scores are computed with the aim of comparing expected pollutant concentrations with the available measurements and thus to assess the performance of the air quality forecast models used in the PREV’AIR system. The concentrations forecast by the models are spatially interpolated to the measurement points for the various compounds modelled. All the measurements available for a given

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Figure 3. Surface ozone peak over France for the 15 July 2005: Same than previous ﬁgure but with the kriged map.

forecast are used. A distinction is made between rural stations, on the one hand, and stations in suburban areas, on the other hand. The agreement between the amounts simulated by the models and the observations for each forecast period (from the previous day to the day after the following day) are assessed using cumulative statistical indicators, updated on a daily basis. Tables 1 and 2 show the statistical indicators computed for ozone (modelling over France with model CFM) and PM10 (European model AWM) for D 1 and the ﬁrst day of forecast D+0, between the 1 April 2005 and 30 September 2005.

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Figure 4. Ozone peak correction: Analysis minus forecast.

The comparison to near real-time observations enables one to identify some weaknesses of the model and the impact of various parameterisations. For example, the CHIMERE chemistry transport model was lately improved to account for the inﬂuence of natural sources like erosion dust entrainment, re-suspension and long range transport (desert dust) (Vautard et al., 2005). Moreover, the introduction of background Saharan dust boundary conditions in the model simulation led to a greatly improved model performance over southern Europe, and to a smaller extent also over northern Europe.

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Table 1. Scores estimated for the period 1 April 2005 to 30 September 2005 O3 CFM Lag D 1 D+0 D 1 D+0 D 1 D+0 D 1 D+0 D 1 D+0

Stat. indicator Mean (obs) Mean (model) Normalised bias (%) NMSE (%) Corr.

Rural

Suburban

Urban

103.0 103.0 105.4 104.9 5.5 5.2 20.3 21.0 0.81 0.79

99.6 99.6 104.8 104.3 8.7 8.4 22.8 23.7 0.83 0.82

96.4 96.4 103.7 103.3 11.0 10.7 26.0 26.4 0.80 0.79

Number of observations used: 8650 for rural sites, 18356 for suburban sites and 31838 for urban sites. Table 2. Scores estimated for the period 1 April 2005 to 30 September 2005 PM10 AWM Lag D 1 D+0 D 1 D+0 D 1 D+0 D 1 D+0 D 1 D+0

Stat. indicator Mean (obs) Mean (model) Normalised bias (%) NMSE (%) Corr.

Rural

Suburban

Urban

16.9 16.9 16.7 16.7 3.4 3.4 43.1 43.8 0.51 0.50

17.7 17.7 16.5 16.8 3.8 2.4 37.7 38.8 0.57 0.56

19.3 19.3 16.0 16.1 12.8 12.0 37.8 38.7 0.58 0.55

Number of observations used: 518 for rural sites, 4515 for suburban sites and 19547 for urban sites.

Discussion

P. Builtjes:

L. Menut:

You use kriging for assimilation. Would it be better to use 4DVAR or Ensemble Kalman Filtering including noise for emissions? We could certainly use more advanced data assimilation methods. The purpose here is not, like in meteorology, to obtain the most accurate initial conditions, but to have a surface ﬁeld of ozone or

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PM10 that is the closest to reality. Kriging of the innovations is based on the same principles, as 3DVAR, and it should yield equivalent results. It has the advantage of being robust and free of time statistical assumptions. Ensemble Kalman ﬁltering with noise for emissions is a very interesting technique to be developed in the future. I understood that you use MM5 as a meso-scale driver. Why don’t you use French meso-scale models? MM5 was used because it was easy to work on PCs in order to perform validation over extended periods of time.

REFERENCES Blond, N., 2002. Assimilation de donne´es photochimiques et pre´vision de la pollution troposphe´rique. Ph.D. dissertation. Vautard, R., Bessagnet, B., Chin, M., Menut, L., 2005. On the contribution of natural Aeolian sources to small particle concentrations in Europe: Testing hypotheses with a modelling approach. Atmos. Environ. 39, 3291–3303.

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Chapter 3.5 Estimation of sulphur emissions using ensemble smoothers Alina L. Barbu, Remus G. Hanea, Arnold W. Heemink, Martijn Schaap and Pierre Girardeau Abstract Fine particulate matter (PM) is relevant for human health, and aerosol components are associated with climate effects. Large uncertainties in emissions, formation routes and sinks of these particles cause model performances for PM and its components to be relatively poor. We are developing a system to assimilate observed particulate mass concentrations within a regional chemistry transport model. At the moment, we focus on the component for which a lot of information is available: sulphate. In this study, data assimilation schemes based on ensemble ﬁltering and ensemble smoothing techniques are used to combine the results of the simpliﬁed chemistry transport model with the measurement information to estimate emission parameters. For this purpose, we applied three smoother algorithms: the ensemble Kalman smoother introduced by Evensen and van Leeuwen in 2000, a ﬁxed-lag ensemble smoother and the smoothing implementation proposed recently by Ravela and McLaughlin. The performance of the smoother algorithms to estimate sulphur emissions is studied in a twin experiment. Two processes were considered: transport and chemistry. The quality of the smoothed estimates is studied in relation to different sources of uncertainties. We present the different smoother implementations and their performance in this non-linear data assimilation application. The efﬁciency of the smoother algorithms is of main interest for us. 1. Introduction

The atmospheric particulate matter (PM) is a complex mixture of anthropogenic and natural airborne particles. The PM in ambient air has been associated consistently with excess mortality and morbidity in human

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population (Brunekreef, 1997; Hoek et al., 2002). Composition of PM10 tends to divide into two principal groups: coarse particles, mostly larger than 1 mm in aerodynamic diameter, and ﬁne particles, mostly smaller than 1 mm in aerodynamic diameter. The ﬁne particles contain secondary aerosols (sulphate, nitrate and ammonium), combustion particles (OC and EC) and condensed organic and metal vapours. The larger particles usually contain sea salt, earth crust materials and fugitive dust from roads and industries. Various components of ﬁne particulate matter (PM2.5) in the atmosphere also have climate-forcing impacts, either contributing to or offsetting the warming effects of greenhouse gases (Hansen and Sato, 2001). Furthermore, secondary inorganic aerosol formation and transport has been studied for decades as they contribute to acidiﬁcation of soils. During the last decades, models were developed to describe the fate of (particulate) pollutants over Europe. However, large uncertainties in emissions, formation routes and sinks of particles cause model performances for PM and its components to be relatively poor (van Loon et al., 2004). Data assimilation schemes can be used to combine the results of a numerical atmospheric chemistry model with the measurement information available to obtain an optimal reconstruction of the dynamic behaviour of the aerosol concentrations. We are developing such a data assimilation system to be used to, in the end, estimate parameters such as conversion rates and emission strengths. Successful application of data assimilation techniques requires a relatively large set of measurements. Hence, we focus on the component for which a most information is available: sulphate. In our study, the ensemble smoother approach has been analyzed to estimate the emissions and the concentrations of SO2. The general framework for the ensemble ﬁltering is presented in the second section of the paper. The ensemble smoother techniques are discussed in Section 3, as well as the implementation of three algorithms, followed in Section 4 by the description of the twin experiment. The results of data assimilation calculations, i.e., the general behaviour of a smoother assimilation, the performances of the algorithms and their efﬁciency are presented and discussed in Section 5. The conclusions are given in the last section.

2. Filter 2.1. Ensemble Kalman filter

The ensemble Kalman ﬁlter introduced by Evensen has been successfully used in many applications. This Monte Carlo approach is based on the

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representation of the probability density of the state estimate by a ﬁnite number of N randomly generated system states. In the ﬁrst step of this algorithm, an ensemble of N states xa(0) is generated to represent the uncertainty in x(0). The true state vector is propagated in the second step by the stochastic model, in the forecast step: x fj ðkÞ ¼ Mðxaj ðk 1ÞÞ þ wj ðk 1Þ x f ðkÞ ¼

N 1X x f ðkÞ N j¼1 j

Lf ðkÞ ¼ ½x f1 ðkÞ x f ðkÞ; x f2 ðkÞ x f ðkÞ; . . . ; x fN ðkÞ x f ðkÞ

ð1Þ

When the measurements become available, the mean and the covariance are replaced with equivalent ones in the analysis step. The advantages of this algorithm are: the covariance matrix is positive deﬁnite, and the linear tangent model is not required anymore because the ensembles are propagated through the model using the original operator. Also, in the ﬁnal implementation of the algorithm, the covariance matrices need not to be computed (Evensen, 2003). As the result, the computational effort required for the EnKF is approximately N model simulations. The errors in the state are of a statistical nature and decrease slowly with the number of ensembles. 2.2. The implementation

We deﬁne the matrix holding the ensemble members xi 2 Rn ; A ¼ ðx1 ; x2 ; . . . ; xN Þ 2 RnN ; N being the number of the ensemble members and n is the size of the state vector. The ensemble perturbation matrix is A0 ¼ A A ¼ AðI 1N Þ; where the ensemble mean is stored in each column of A ¼ A1N : The ensemble covariance matrix Pens is deﬁned by T Pens ¼ A0 A0 =ðN 1Þ: The EnKF uses an ensemble of forecasts to estimate background-error covariances. Houtekamer and Mitchell (1998) showed that in order to maintain sufﬁcient spread in the ensemble and prevent ﬁlter divergence, the observations should be treated as random variables. They introduced the concept of using perturbed sets of observations to update each ensemble member. The perturbed observations consist of the actual observations and random noise. We consider the vector of measurements d 2 Rm ; where m being the number of the measurements and deﬁne N vectors of perturbed observations as d j ¼ d þ j for every j ¼ 1; . . . ; N which can be stored in the columns of a matrix: D ¼ ðd 1 ; d 2 ; . . . ; d N Þ 2 RmN ; while the ensemble of

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perturbations are stored in the matrix U ¼ ð1 ; 2 ; . . . ; N Þ 2 RmN : Now we are able to construct the ensemble representation of the observations error covariance matrix Rens ¼ UUT =ðN 1Þ: The ﬁlter analysis at current time l in terms of the ensemble covariances matrices Pens becomes Aa ðlÞ ¼ A f ðlÞ þ Pens ðl ÞH T ðHPens ðl ÞH T þ Rens Þ1 ðD HAÞðl Þ T

Aa ðl Þ ¼ A f ðl Þ þ A0 ðl ÞAðlÞ0 H T ðHA0 ðl ÞA0 ðl ÞT H T þUUT Þ1 ðDHAÞðl Þ ð2Þ It is assumed that the ensemble perturbations and observation errors are uncorrelated, i.e., HA0 U 0: Then we have the following decompoT sition: HA0 A0 H T þ UUT ¼ ðHA0 þ UÞðHA0 þ UÞT ; and we can produce the following compact update formulation using some new matrices: T

Aa ¼ Af þ A0 A0 H T ððHA0 þ UÞðHA0 þ UÞT Þ1 ðD HAÞ T

Aa ¼ Af þ A0 ðHA0 Þ X 3 Aa ¼ Af þ A0 X 4 Aa ¼ Af þ Af ðI 1N ÞX 4 Aa ¼ Af ðI þ X 4 Þ Aa ¼ Af X 5 where we have used 1N X 4 0: This ﬁnal form of the analysis ensemble is obtained by transforming the predicted ensemble with a matrix X5 in a non-linear way. 3. Smoother

Given a stochastic model for dynamics and observations, the Kalman ﬁlter is able to compute the optimal estimate of the current state when all data from the past are given, but future measurements are not taken into account. For ofﬂine applications such as parameter estimations, not considering data after the analysis time is the disadvantage of the ﬁlter. The general idea in a smoother problem is to augment the state vector with the parameter values in the past. 3.1. Ensemble fixed-lag smoother

For the usual state vector, we determine recursive equations for the estimate for all k and some ﬁxed-lag N x^ kN=k ¼ E½xkN jy0 ð0Þ; y0 ð1Þ; . . . ; y0 ðkÞ

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and the associated error covariance. We consider the model and the observation operator, respectively 2

MðkÞ 0 6 I 0 6 6 0 I Y ðk þ 1Þ ¼ 6 6 .. 6 .. 4 . . 0 0

... ...

0 0

... .. . ...

0 .. . I

3 2 3 0 GðkÞ 6 0 7 07 7 6 7 7 6 7 0 7Y ðkÞ þ 6 0 7wðkÞ 7 6 7 .. 7 6 .. 7 4 . 5 .5 0 0 2

y0 ðkÞ ¼ HðkÞ 0

...

0

xð0Þ ðkÞ

3

6 ð1Þ 7 6 x ðkÞ 7 7 0 6 6 .. 7 þ vðkÞ 4 . 5 xðNÞ

As can be seen from the equations, xð0Þ ðkÞ ¼ xðkÞ; xð1Þ ðkÞ ¼ xðk 1Þ; . . . ; xðNÞ ðkÞ ¼ xðk NÞ: In other words, the one-step prediction estimate of the entire state of the augmented state vector contains smoothed estimates of the state for lag length up to N. The ensemble Kalman ﬁlter results applied to this augmented model lead to the equations involving the following state estimates, the augmented Kalman gain matrix and the augmented covariance matrix, respectively. We adopt the following notations (Anderson and Moore, 1979): 2 3 2 3 x^ ð0Þ ðk=kÞ K ð0Þ ðkÞ 6 7 6 6 x^ ð1Þ ðk=kÞ 7 6 K ð1Þ ðkÞ 7 7 6 7 6 7 6 7 6 7, ; 6 7 . 6 7 . 6 7 6 .. .. 7 6 7 4 5 4 5 K ðNÞ ðkÞ ^xðNÞ ðk=kÞ 2 ð0;0Þ 3 P ðk=k 1Þ Pð0;1Þ ðk=k 1Þ . . . Pð0;NÞ ðk=k 1Þ 6 ð1;0Þ 7 6 P ðk=k 1Þ Pð1;1Þ ðk=k 1Þ . . . Pð1;NÞ ðk=k 1Þ 7 6 7 6 7 .. .. .. .. 6 7 6 7 . . . . 4 5 PðN;0Þ ðk=k 1Þ PðN;1Þ ðk=k 1Þ . . . PðN;NÞ ðk=k 1Þ For convenience, we use the notiﬁcations: Pð0;1Þ ðk=k 1Þ ¼ Pð1;0Þ ðk=k 1Þ> ; Pð0;0Þ ð0= 1Þ ¼ Pð0Þ and P ði; jÞ ð0= 1Þ ¼ 0 for i, j not both zero.

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The following equations are extracted directly from the augmented Kalman ﬁlter equations: h i x^ ðiÞ ðk=kÞ ¼ x^ ðiÞ ðk=k 1Þ þ K ðiÞ ðkÞ y0 ðkÞ HðkÞx^ ð0Þ ðk=k 1Þ . For 0 i N; the gain matrices are given by K ðiÞ ðkÞ ¼ Pði;0Þ ðk=k 1ÞHðkÞ> ½HðkÞPð0;0Þ ðk 1jkÞHðkÞ> þ RðkÞ1 , where Pði;0Þ ðkjkÞ ¼ Pði;0Þ ðkjk 1Þ K ðiÞ ðkÞHðkÞPð0;0Þ ðkjk 1Þ and the other covariance submatrices of interest are Pði;iÞ ðk=kÞ ¼ Pði;iÞ ðk=k 1Þ K ðiÞ ðkÞHðkÞPð0;iÞ ðkjk 1Þ. 3.2. Ensemble Kalman smoother

The EnKS, described in Evensen and van Leeuwen (2000), updates the ensemble at prior times every time when new measurements are available. The updates exploit the space–time correlations between the model forecast at measurement locations and the model state at a prior time. Thus, every time a new set of measurements becomes available, the ensemble at the current and all prior times can be updated. For simplicity, we consider only one new available observation from the future data. The EnKS analysis for the prior time k and l4k can be produced analogously to the analysis given by Eq. (2), T

T

Aa ðkÞ ¼ Af ðkÞ þ A0 ðkÞA0 ðlÞH T ðHA0 ðl ÞA0 ðlÞH T þ UUT Þ1 ðD HAÞðlÞ. Considering the deﬁnition of X5, this matrix of coefﬁcients at time l is used on the updated ensemble at the prior time k. Therefore, we compute the smoothed estimate at time k in the past using data from the future. In the ﬁxed-lag smoothing approach, the state at time k is updated with observations in a ﬁxed time window ðk; k þ w; where w is the lag length. The equation is simpliﬁed to produce the following compact rule as in the EnKF analysis: Aa ðkÞ ¼ Af ðkÞ

kY þw j¼kþ1

X 5 ð jÞ.

(3)

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3.3. FIFO-lag algorithm

The faster ﬁxed-lag algorithm developed by Ravela and McLaughlin is computational improvement to previous ensemble smoothing technique. This algorithm is called FIFO because the smoother is implemented via the ﬁrst-in-ﬁrst-out queue. New information is added to the front of the queue and old information is removed from the back of the queue. Introducing a new matrix X 6 ¼ Pkþw j¼kþ1 X 5 ð jÞ; Eq. (3) becomes, Aa ðkÞ ¼ Af ðkÞX 6 ðkÞ. In the ﬁxed lag smoother, X6 (k) and Aa do not need to be computed separately. To deﬁne a forward recursion, X5 is initialized with the identity at all unobserved model steps. The matrix X6 is initialized at k ¼ 0 as in the following formula: X 6 ð0Þ ¼

w Y

X 5 ð jÞ.

j¼1

The following recursion deﬁnes ﬁxed-lag smoothing and is done on a single forward pass: X 6 ðkÞ ¼

kY þw

X 5 ð jÞ ¼ X 1 5 ðkÞX 6 ðk 1ÞX 5 ðk þ wÞ.

j¼kþ1

4. Assimilation with simulated data

This section describes the setup and the results of the smoother experiments with simulated data. Before using the algorithms for real-life data assimilation problems, ﬁrst we deﬁne a twin experiment. 4.1. The model

We consider the 2D advection diffusion equation @c @c @c @2 c @2 c þu þv ¼n 2þn 2þS @t @x @y @x @y with the square domain ½0; D ½0; D and zero initial conditions. Here, c is the concentration, [u,v] is the velocity ﬁeld, n represents the dispersion coefﬁcient and S is the source term. A backward Lagrangian scheme is used to discretize the equation on the 30 30 grid. In addition, we consider the simple chemistry which describes the conversion of SO2 to SO4

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using the following chemical reactions: k1

SO2 þ OH ! SO4 k2

SO2 ! SO4 where the rates k1 ¼ 1:5 ðmin1 Þ and k2 ¼ 8:3e 5 ðmin1 Þ are considered to be constant. The second reaction is used to represent cloud chemistry and oxidation pathways. A loss term k3 ¼ 0:1 ðh1 Þ was considered which represents a constant deposition rate for SO4 component. The change in concentration ﬁelds can be described as follows: @SO2 ¼ ðk1 OH þ k2 ÞSO4 ; @t

@SO4 ¼ ðk1 OH þ k2 ÞSO4 k3 @t

Having two processes: transport and chemistry, both in the same time step, we use the splitting operator to separate the processes and to solve them separately. In order to compare the algorithms, some experiments were carried out. A reference solution was generated by inserting constant SO2 emissions at ﬁve grid cells. The ﬁnal product is SO4. Therefore, the observations were generated from simulated true concentrations of SO4, which were computed by adding ﬂuctuations to the mean emissions according to w~ s ðk þ 1Þ ¼ as w~ s ðkÞ þ ws ðkÞ where ws is the independent Gaussian white noise process with E½ws ðkÞ ¼ 0 and var½ws ðkÞ ¼ 1: The index s refers to some source locations. Negative emissions are truncated and white observational noise (with variance 0.1) is added to the true concentrations. The state vector x ¼ ½cSO2 ; cSO4 ; eSO2 consists of 1805 compounds: 30 30 concentrations of SO2, 30 30 concentrations of SO4 and 5 points of pollutant emissions. Figure 1 shows the true and computed concentration ﬁelds. 4.2. Uncertainties in emissions

In our model, the emissions of the SO2 have been modelled to be uncertain. The stochastic emissions are modelled according to the formula eSO2 ðk þ 1Þ ¼ maxð0; eSO2 ðkÞÞ þ wðkÞ where eSO2 is the deterministic value of the SO2 emission in a grid cell. We treat the emissions as the model input and deﬁne our model in the

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True concentrations

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following way: "

cðk þ 1Þ eðk þ 1Þ

#

" ¼

AðkÞ BðkÞ 0

#"

I

cðkÞ eðkÞ

#

" þ

0 GðeÞ

# wðkÞ

xðk þ 1Þ ¼ MðkÞxðkÞ þ W ðkÞ y0 ðkÞ ¼ HðkÞxðkÞ þ vðkÞ The noise in the model and measurements are ﬁxed for the simulated data. We use the ﬁltering and smoothing procedures for several noise statistics in order to study the smoother behaviour. We know that SO4 is already well estimated using EnKF, since it is the pollutant that we have measured. Therefore, we predict that the best improvement by the smoother algorithms will be on the estimates of SO2 and on the emissions.

5. Results

A number of the simulations have been performed using the smoothing techniques. The assimilation results depend on the model and parameters involved in the processes, e.g., the number of observations, the accuracy of the algorithms and the noise speciﬁcation. In this section, several aspects of the smoother assimilation are discussed to describe the behaviour of the retrospective analysis in combination with the small atmospheric model and the sulphate observations. The focus in the experiments is on the improvement of the accuracy of the simulated sulphur concentrations and emissions using the smoothing procedure and on the improvement of the computational complexity. In the ﬁrst experiments, the simulated reality was considered to be deterministic, i.e., the emissions are treated as perfect. In order to estimate the constant value of one of the emission points, we consider our ﬁlter to be stochastic adding noise to the emissions part of the state vector. The experiments were performed with the same initial conditions, number of ensembles and lag length. In order to illustrate the performances obtained using the smoother procedure, the time series plots are studied. As seen in Fig. 2, the ﬁlter is not able to estimate the constant value of one of the emissions accurately. Studying the innovations, we need to tackle the divergence problem of the ﬁlter. In order to avoid this problem, the noise in the ﬁlter has been increased and the quality of the estimates has been improved as seen in Fig. 3.

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Third emission point 14 12

truth std std filter smoother

10 8 6 4 2 0 2 4

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The innovations 1.5 Innovations Minus theoretical standard error Theoretical standard error

Innov

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-0.5

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Figure 2. Time evolution of one of the emission components and the innovations; the line represents the true constant emission.

In the next experiments, the reference solution and the model were considered to be stochastic using white noise and the time series evolutions are depicted in Fig. 4. A parameter that is important for the assimilation in all ensemblebased algorithms is the number of the ensembles used in the assimilation. Simulations with different number of ensembles were performed to study the sensitivity of the model to this parameter. The accuracy of the estimates is expected to increase with the number of modes, but the

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14 12

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10 8 6 4 2 0 2 4

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8

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Figure 3. Time evolution of the SO2 and SO4 concentrations for deterministic case.

computational time also increases. The errors are calculated for the 50 last steps of the run, so that the effect of the initial conditions should not be taken into account in the calculation of the errors. For example, one may note that the improvement is larger between the ﬁlter and the 10-lag smoother than between the 10-lag smoother and the 80-lag smoother. Increasing the lag length, the state vector is augmented. Consequently, a sufﬁcient number of ensembles has to be used. Figure 5 depicts the

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Emission reconstruction 30 truth EnKF FIFO

one emission

25 20 15 10 5 0

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3.5 3 2.5 2 1.5 1 0.5 0 -0.5 0

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Figure 4. Time evolution of the emission and SO2 concentration for stochastic case using the FIFO with 30-lag length.

errors provided by the ﬁlter and the smoother for several lag lengths and ensembles. This experiment was done for 9 measurement points. We notice that the 20-lag smoother with 30 ensemble members provides better estimates than the ﬁlter with 150 ensemble members. The lag length, chosen to stabilize the error, depends on the number of observations considered. A number of the simulations have been performed using different set of measurements. Figure 5 illustrates the effect

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Figure 5. RMS error of the assimilation using the ensemble ﬁxed-lag smoother for several lag lengths, numbers of ensembles and measurement points.

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of observations by depicting the RMS error of the ﬁxed-lag smoother using three different sets of the measurement points: 5, 9 and 20, respectively. As one would expect, the errors decrease with the number of the measurements. Some numerical results are presented in the following table for two different initial conditions. Comparison of the RMS error using the EnKF and ﬁxed-lag smoother for concentrations and emissions RMS error for SO4

RMS error for SO2

RMS error for emissions

0.16 0.08 0.38 0.35

0.66 0.32 1.41 0.23

0.58 0.31 0.96 0.84

Filter Fixed-lag smoother Filter Fixed-lag smoother

In the next table, computational time is compared. The experiments were made with the same initial conditions, the ensemble of 100 random replicates and the same data. The FIFO-lag algorithm is more efﬁcient for the large windows. For example, comparing the smoothers with 80-lag length, the computational time for the FIFO-lag algorithm is twice smaller than for the EnKS. Comparison of the computational aspect between the EnKS and FIFO for several lag lengths Filter 10 EnKS 30 EnKS 50 EnKS 80 EnKS Time (min)

2.89

3.33

5.52

6.98

Filter 10 FIFO 30 FIFO 50 FIFO Time (min)

2.89

2.96

3.71

4.04

100 EnKS

10.15

24.72

80 FIFO

100 FIFO

5.39

10.95

6. Conclusions

In this paper, the algorithms for the parameter estimation using the ensemble smoother have been presented, three types of smoothers have been compared.

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In the twin experiment used for this study, the analysis proved the feasibility of the ensemble smoothing schemes to reconstruct the pollutant emissions. The smoother techniques are able to improve the ﬁlter estimates providing more accurate results in terms of the RMS error. Although the techniques presented are found to be efﬁcient, an application to real life still imposes large difﬁculties in the computations. In this study, it is shown that the most efﬁcient algorithm is the Ravela and McLaughlin’s implementation (Ravela and McLaughlin, 2005) which produce the same estimates with less computational effort. The FIFO-lag algorithm is faster than the smoother proposed by Evensen (2003), especially for large lag lengths that helps to make the ensemble smoothing a practical option for the retrospective analysis of large air pollution data sets. Discussion

M. Kahnert:

A.L. Barbu:

P. Builtjes: A.L. Barbu:

When you generate an ensemble of system states, how large must the ensemble typically be to get a statistically meaningful representation of the probability density of the state? EnKF is based on a representation of the probability density of the state estimate by a ﬁnite number of ensemble members. When the size N of the ensemble increases, the errors in the solution will go to zero at a rate proportional to N1/2. The ensemble size is model dependent and inﬂuenced by the non-linearity of the system. Is your method also capable of treating non-linear chemistry? The EnKF and ensemble-based methods are designed to solve the problem related to the use of the eKF with non-linear models. The ensembles are propagated through the model using the original (non-linear) chemistry and dynamics.

REFERENCES Anderson, B.D.O., Moore, J.B., 1979. Optimal Filtering. Prentice-Hall, Englewood Cliffs. Brunekreef, B. 1997. Air pollution and life expectancy: Is there a relation? Occup. Environ. Med. 54 781–784. Evensen, G., 2003. The ensemble Kalman ﬁlter: Theoretical formulation and practical implementation. Ocean Dyn. 53, 343–367.

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Evensen, G., van Leeuwen, P.J., 2000. An ensemble Kalman smoother for nonlinear dynamics. Mon. Weather Rev. 128, 1852–1867. Hansen, J.E., Sato, M., 2001. Trends of measured climate forcing agents. Proc. Natl. Acad. Sci. USA 98(26), 14778–14783. Hoek, G., Brunekreef, B., Goldbohm, S., Fischer, P., van den Brandt, P.A., 2002. Association between mortality and indicators of trafﬁc-related air pollution in the Netherlands: A cohort study. Lancet 360, 1203–1209. Houtekamer, P., Mitchell, H.L., 1998. Data assimilation using an ensemble Kalman ﬁlter technique. Mon. Weather Rev. 126, 796–811. van Loon, M., Roemer, M., Builtjes, P.J.H., 2004. Model intercomparison in the framework of the review of the Uniﬁed EMEP model. TNO-Rep. R, 2004/282. Ravela, S., McLaughlin, D., 2005. Fast ensemble smoothing. Unpublished manuscript.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06036-6

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Chapter 3.6 Application of four-dimensional variational (4DVAR) data assimilation for optimal estimation of mineral dust and CO emissions in eastern Asia Keiya Yumimoto and Itsushi Uno Abstract A four-dimensional variational (4DVAR) data assimilation system was developed for a regional chemical transport model (CTM). In this study, we applied it to inverse modeling of CO emissions and mineral dust emission ﬂux over East Asia, and demonstrated the feasibility of our assimilation system. In CO inverse modeling, three ground-based observations were used for estimating CO emission over East Asia. Assimilated results showed better agreement with observations; the RMS differences were reduced by 16–27%. CO emission over industrialized east central China between Shanghai and Beijing has increased markedly, and the results show that the annual anthropogenic (fossil and biofuel combustion) CO emission over China are 147 Tg. In dust inverse modeling, NIES LIDAR observations were used. The assimilated results better reﬂects the presence of the elevated dust layer and improved the under-prediction of dust concentrations. We obtained an 18% increase in calculated dust emissions through data assimilations, especially over the Mongolian region, indicating that the observed high-dense dust layer might originate in that region. These data assimilation results indicate that the 4DVAR method is very powerful for uniﬁcation of observation and numerical modeling by CTM. 1. Introduction

An accurate emission inventory of chemical species has an important role in chemical transport models (CTMs) because quantitative estimates of emissions are essential input parameters for CTMs. In addition, their uncertainties largely determine the accuracy of the model outputs. However, quantiﬁcation of emissions is difﬁcult because such estimates depend

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on numerous parameters (e.g., socioeconomic, energy, and environmental data for CO, and wind speed, soil moisture, and land use for mineral dust). Consequently, these emission estimates are prone to error, requiring further improvement and reﬁnement using information provided by observations and numerical models. Four-dimensional variational (4DVAR) data assimilation method, which is based on the adjoint model, provides insight into various underlying inputs of numerical models (e.g., initial conditions and emissions). Recently, 4DVAR method has come to be applied to CTMs for inverse modeling. Various observations are used in conjunction with CTMs to evaluate emissions of several chemical species and to optimize model parameters (e.g., Hakami et al., 2005). However, applications of 4DVAR method for CTMs remain quite limited; further research is required. This study developed a new 4DVAR data assimilation system based on the regional chemical transport model (RAMS/CFORS-4DVAR), which is applied to assimilating CO and mineral dust concentrations and optimization of their emissions over eastern Asia. Ground-based direct observation of CO concentrations and LIDAR observation of dust extinction coefﬁcient are used for assimilations. We demonstrate the feasibility of the system in the assimilation process.

2. RAMS/CFORS-4DVAR

A schematic diagram of RAMS/CFORS-4DVAR (Yumimoto and Uno, 2006) is depicted in Fig. 1. The forward CTM and the corresponding adjoint model are built on RAMS/CFORS (Uno et al., 2003), which is designed as a multi-tracer system built within the Regional Atmospheric Modeling System (RAMS; Pielke et al., 1992). In this study, the cost (objective) function is deﬁned as 1 1X JðxÞ ¼ ðx xb ÞT B1 ðx xb Þ þ ðH i ðxÞ yi ÞT R1 ðH i ðxÞ yi Þ (1) 2 2 i¼1 p

where x represents a control parameter (e.g., initial condition and emission) and xb denotes a priori (basic) control parameter. In the data assimilation procedure, the control parameter (x) is optimized to minimize the cost function through constraints of observations (y). In addition, B and R respectively represent the background error and observation error matrix. The observation operator H represents the forward model and the transformation from x into observation y.

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Figure 1. Schematic of the RAMS/CFORS-4DVAR data assimilation system.

3. CO emissions over eastern Asia 3.1. Model setup and observations

RAMS/CFORS-4DVAR is applied for assimilation of CO outﬂows. Its monthly emissions over eastern Asia during April 2001 are chosen as the control parameter (x) in Eq. (1). The modeling domain for this study encompasses East Asia with 80-km resolution. The basic (priori) gridded CO emission inventory is based on Streets et al. (2003), which extends throughout Asia (emissions over Russia were not included) with 11 resolution. The model domain and the priori emission distribution are shown in Fig. 2. Three ground-based observation data were used to assimilate CO concentrations and optimize its emissions. Observation sites are also shown in Fig. 2. The assimilation window is from 29 March to 30 April 2005 and observations in April 2001 are used.

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Figure 2. Modeling domain and spatial distribution of the basic (priori) emission of CO (left), and the difference between the priori emission and the posteriori values at each grid (right). Triangles show observation sites.

3.2. Results and discussion

Figure 3 shows a time series of measured and simulated CO concentrations at three observation sites. In general, the CO concentrations without assimilation capture the overall behavior and variation of the observations. However, some differences between the simulated and observed concentrations are considerable. Furthermore, almost all are under-predicted. The posteriori (optimized) emissions compensate the under-predictions and

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Figure 3. Comparison between simulated (without data assimilation and assimilated) and observed CO mixing ratios (ppbv) at Rishiri, Ryori, and Yonaguni, Japan.

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bring modeled concentration values closer to observed ones. Large discrepancies at high-CO observation values are reduced greatly by the assimilations. Assimilation reduces the RMS difference by 16–27%. A validation of the assimilation is performed using independent (not used in the assimilation) observations that were made on board the R/V Ronald H. Brown (Bates et al., 2004). Assimilation improves the underpredictions of the simulation results and reduces the RMS difference by 14% (not shown here; Yumimoto and Uno, 2006). Figure 2b shows the difference between priori and posteriori emission values at each grid point. The posteriori emissions in the Korean Peninsula are reduced slightly, although those in eastern and northeastern China and Japan are increased. Especially, CO emissions for highly industrialized regions between Shanghai and Beijing have increased considerably. Our data assimilation evaluates the total optimized CO sources in China in April as about 147% of the priori bottom-up emission. Multiplying the priori annual emission (Streets et al., 2003; 100 Tg) by this ratio, we estimate annual Chinese anthropogenic (fossil and biofuel) CO emissions as 147 Tg. Our estimates of anthropogenic CO emissions over China are consistent with other estimates (Yumimoto and Uno, 2006).

4. Asian dust emission and transport 4.1. Model setup and observations

Mineral aerosols in CFORS are injected into the atmosphere by highvelocity surface winds. In this study, one-bin dust particles (assuming 2-mm diameter) are modeled. The total dust uplift ﬂux is calculated using a fourth power-law of surface friction velocity un as F dust ¼ xu3 ðu u;th Þ

(2)

where x is the daily emission constant and control parameter (function of surface condition, which is optimized through data assimilation), and un ,th is the threshold friction velocity. Natural dust emission areas are deﬁned as desert and semi-desert areas by the U.S. Geological Survey. The numerical model domain is identical to that of the CO assimilation shown in Fig. 1. We restrict our ﬁrst application to the extreme dust episode of 30 April 2005 in Sendai Japan, as reported by Sugimoto et al. (2005). We use NIES LIDAR network observation data for data assimilation. The LIDARs measure time-height backscattering and the depolarization

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Figure 4. Increments of the dust emission ﬂux that occur as a result of assimilation. Circles indicate the locations of LIDAR observation sites.

ratio with 30-m vertical resolution. The LIDAR signals are converted to dust extinction intensity (Shimizu et al., 2004). The RAMS/CFORS4DVAR can use dust extinction coefﬁcient directly, but in this feasibility study, we decided to use the dust concentration (mixing ratio) using the mass/extinction conversion factor described in Sugimoto et al. (2003). However, this conversion factor is dependent upon particle size and is probably much smaller for transported dust of smaller particles. Of the 12 NIES LIDAR network points over East Asia, we used only four sites (Sendai, Sapporo, Toyama, and Tsukuba, as shown in Fig. 4). The LIDAR data were interpolated vertically to model vertical resolution. Then 1-h averaged LIDAR data were used for the data assimilation with a 3-h interval. The assimilation window was 26 April–1 May 2005 and LIDAR data observed on 30 April were used for assimilation. 4.2. Results and discussion

Figure 5 shows a time-height cross-section of observed and simulated dust concentrations at Sendai. Figure 5a shows concentrations observed by LIDAR. A high dust concentration is clearly visible at 3–5 km on 30 April. The maximum extinction coefﬁcient reaches 1 km1. Similar dust layers were observed at Sapporo, Tsukuba, and Toyama during that period (not shown). In general, dust concentrations without assimilation (Fig. 5b) capture the overall behavior and variation of the observations (despite a missing observation). However, differences between the simulation results and observations are considerable. The assimilated result better reﬂects the presence of the elevated dust layer (both height and concentration) on 30 April. However, the values are still smaller than the

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Figure 5. Time–height cross-section of dust concentration at Sendai: (a) LIDAR observation, (b) modeled without data assimilation, and (c) modeled with data assimilation.

observations; they cannot reproduce the thin dust layer structure perfectly. The horizontal resolution of 80 km and thickness of the vertical layer might be insufﬁcient to reproduce this observed thin dust layer. Further improvements (e.g., introduction of a multi-particle dust bin, a high resolution, and assimilation of extinction observation without mass/ extinction conversion factor) are necessary. Figure 4 also shows increments between conditions before the assimilation and after assimilation. Assimilation markedly increases dust emissions over Mongolia, particularly western Mongolia. For that reason, under-predictions might result from insufﬁcient dust emissions from Mongolia. Our data assimilation indicates that the total optimized dust emission during this dust episode must be 18% larger than the original CFORS estimates.

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5. Conclusions

We applied a new 4DVAR data assimilation system for optimization of CO and dust emissions in East Asia. For CO assimilation, we optimized monthly CO emissions in April 2001 over East Asia and estimated annual Chinese anthropogenic CO emission using ground-based observations. For dust assimilation, we applied data assimilation to the dust episodes that occurred on 30 April 2005 using NIES LIDAR network observations. The ﬁrst feasibility study of RAMS/CFORS-4DVAR was performed. We found the following: 1. Optimized CO emission brought assimilated CO concentrations into better agreement with ground-based observations. Especially, substantial discrepancies of high-CO observation values were reduced considerably. The assimilation increased CO emissions in east and northeast China and Japan. Especially, CO emission intensity for the highly industrialized area between Shanghai and Beijing increased remarkably. We obtained annual anthropogenic CO emissions over China of 147 Tg (147% of the priori estimate). Our estimate is consistent with other recent estimates. 2. Assimilated dust emissions improved the under-prediction of dust concentrations, but the agreement is not yet perfect. Results showing dust assimilation indicated increased dust emissions in Mongolia. We obtained an 18% increase of dust emissions using data assimilation. In this study, we used four LIDAR observations in Japan, which are all very distant from the dust source region. The assimilation results agree with the TOMS AI distribution (not shown) and indicate that the 4DVAR method is very powerful for uniﬁcation of observations and modeling. For this study, we restricted our ﬁrst application to the one-dust particle model and only four LIDAR observation nodes. Further improvements (e.g., introduction of other surface and satellite observation, a multi-dust-particle bin, and high resolution) are necessary and will be the next step of 4DVAR application. Discussion

D. Steyn:

K. Yumimoto:

How does your 160-Tg emission estimate compare with estimates using more conventional inventory methods? Streets et al. (2003) estimated the Chinese anthropogenic CO source in 2000 to be 100 Tg year1 using conventional method. Carmichael et al. (2003)

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showed under-predictions of CO outﬂows in eastern Asia using CO emission inventory by Streets et al. (2003) based on the regional CTM (STEM) during the TRACE-P observation period. They suggested the increase from the Chinese domestic sector would be necessary. Our current assimilation results estimate CO source as 147 Tg increasing by 47% from Streets et al. (2003) and are consistent with their suggestion. More recently, Streets et al. (2006) updated the CO source to be 142 Tg, which is comparing with our estimate by 4DVAR. Furthermore, our estimate agrees well with other estimates by inversion modelings (e.g., Pe´tron et al., 2004 and Wang et al., 2004).

ACKNOWLEDGMENTS

This work was partly supported by the Global Environmental Research Fund, Ministry of Environment, Japan and a grant-in-aid for scientiﬁc research under Grant No. 17360259 from the Ministry of Education, Culture, Sports, Science and Technology, Japan. The CO observation data at Rishiri were provided by Dr. H. Tanimoto of the National Institute of Environmental Studies (NIES); data for Ryori and Yonaguni were provided by the World Data Centre for Greenhouse Gases (WDCGG, http://gaw.kishou.go.jp/wdcgg.html): CO data from R/V Ronald H. Brown were provided by ACE-ASIA (http://www.joss.ucar.edu/ace-asia/dm/ data_access_frame.html). LIDAR observation data were provided by Dr. N. Sugimoto and Dr. A. Shimizu of the National Institute of Environmental Studies (NIES).

REFERENCES Bates, T.S., Quinn, P.K., Coffman, D.J., Covert, D.S., Miller, T.L., Johnson, J.E., Carmichael, G.R., Uno, I., Guazzotti, S.A., Sodeman, D.A., Prather, K.A., Rivera, M., Russell, L.M., Merrill, J.T., 2004. Marine boundary layer dust and pollutant transport associated with the passage of a frontal system over eastern Asia. J. Geophys. Res. 109(D19S19), doi:10.1029/2003JD004094. Carmichael, G.R., Tang, Y., Kurata, G., Uno, I., Streets, D.G., Thongboonchoo, N., Woo, J.-H., Guttikunda, S., White, A., Wang, T., Blake, D.R., Atlas, E., Fried, A., Potter, B., Avery, M.A., Sachse, G.W., Sandholm, S.T., Kondo, Y., Talbot, R.W., Bandy, A., Thorton, D., Clarke, A.D., 2003. Evaluating regional emission estimates using the TRACE-P observations. J. Geophys. Res. 108(D21), 8810.

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Hakami, A., Henze, D.K., Seinfeld, J.H., Chai, T., Tang, Y., Carmichael, G.R., Sandu, A., 2005. Adjoint inverse modeling of black carbon during the Asian Paciﬁc regional aerosol characterization experiment. J. Geophys. Res. 110(D14301), doi:10.1029/ 2004JD005671. Pe´tron, G., Granier, C., Khattotov, B., Yudin, V., Lamarque, J.-F., Emmons, L., Gille, G., Edwards, D.P., 2004. Monthly CO surface sources inventory based on the 2000–2001 MOPITT satellite data. Geophys. Res. Lett. 31, L21107. Pielke, R.A., Cotton, W.R., Walko, R.L., Tremback, C.J., Lyons, W.A., Grasso, L.D., Nicholls, M.E., Moran, M.D., Wesly, D.A., Lee, T.J., Copeland, J.H., 1992. A comprehensive meteorological modeling system: RAMS. Meteorol. Atmos. Phys. 49, 69–91. Shimizu, A., Sugimoto, N., Matsui, I., Arao, K., Uno, I., Murayama, T., Kagawa, N., Aoki, K., Uchiyama, A., Yamazaki, A., 2004. Continuous observations of Asian dust and other aerosols by polarization lidars in China and Japan during ACE-Asia. J. Geophys. Res., 109(D19S17), doi:10.1029/2002JD003253. Streets, D.G., Bond, T.C., Carmichael, G.R., Fernandes, S.D., Fu, Q., He, D., Klimont, Z., Nelson, S.M., Tsai, N.Y., Wand, M.Q., Woo, J.-H., Yarber, K.F., 2003. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000. J. Geophys. Res. 108(D21), 8809. Sugimoto, N., Okamoto, H., Satake, S., Matsui, I., Shimizu, A., Uno, I., Fujiyoshi, Y., Toriyama, S., Dong, X., 2005. Asian dust phenomena of April 30, 2005 in Sendai observed by Lidar. Tenki 52, 11 in Japanese. Sugimoto, N., Uno, I., Nishikawa, M., Shimizu, A., Matsui, I., Dong, X., Chen, Y., Quan, H., 2003. Record heavy Asian dust in Beijing in 2002: Observations and model analysis of recent events. Geophys. Res. Lett. 30, 1640, doi:10.1029/2002GL016349. Uno, I., Carmichael, G.R., Streets, D.G., Tang, Y., Yienger, J.J., Satake, S., Wang, Z., Woo, J.-H., Guttikunda, S., Uematsu, M., Matsumoto, K., Tanimoto, H., Yoshioka, K., Iida, T., 2003. Regional chemical weather forecasting system CFORS: Model descriptions and analysis of surface observations at Japanese island stations during the ACE-Asia experiment. J. Geophys. Res. 108(D23), 8668. Wang, Y.X., McElroy, M.B., Wang, T., Palmer, P.I., 2004. Asian emissions of CO and NOx: constraints from aircraft and Chinese station data. J. Geophys. Res. 109(D24304). Yumimoto, K., Uno, I., 2006. Adjoint inverse modeling of CO emissions over the East Asian region using four dimensional variational data assimilation. Atmos. Environ. 40, 6836–6845.

Model assessment and veriﬁcation Chairpersons: Selahattin Incecik Ann-Lise Norman George Kallos Jose M. Baldasano Rapporteurs: Alina Lavinia Barbu Christian Hogrefe Maria Alexandra Monteiro Raphaelle Deprost

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Chapter 4.0 A review of uncertainty and sensitivity analyses of atmospheric transport and dispersion models Steven R. Hanna Abstract A comprehensive review is given of uncertainty and sensitivity methods as they are applied to atmospheric transport and dispersion models, including an example of a recent Monte Carlo (MC) uncertainty study involving simulations of air toxics by the dispersion models AERMOD and ISC. The four components of uncertainty are described and set of deﬁnitions given for a variety of the more widely used uncertainty and sensitivity options. The special needs of atmospheric models are distinguished from the procedures followed for general environmental or health risk models. Examples of the use of ensembles to estimate the uncertainty of linked meteorological and dispersion models are given.

1. Introduction and objectives

Many scientists and engineers are carrying out evaluations of a wide variety of models and model systems and analyzing their overall uncertainty and their sensitivity to variations in inputs. There are several text books describing procedures for conducting uncertainty and sensitivity analysis (e.g., Iman and Helton 1988; IAEA, 1989; Morgan and Henrion, 1990; NCRP, 1996; Helton, 1997; Cullen and Frey, 1999; Saltelli et al., 2004). Yet many of these texts are focused on a speciﬁc area of study, for which some approaches work better than others, and for which there are speciﬁc deﬁnitions that are not universal. This paper is primarily concerned with atmospheric transport and dispersion models. The major objective is to provide a consistent general framework of deﬁnitions and methodologies for uncertainty analysis of atmospheric models used to estimate transport and dispersion, and to tie this methodology to what is used in other environmental and risk

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assessment disciplines. A second objective is to provide readers with a list of references on this topic. Some early reviews and descriptions of methods to estimate the uncertainties of transport and dispersion models were published by Fox (1982), Lamb and Hati (1987), Venkatram (1988), Lewellen and Sykes (1989), and Weil et al. (1992). Pielke (1998) points out the need to assess the uncertainties of linked meteorological and dispersion models. Rao (2005) recently published a general review of the topic. A seminal paper by Oreskes et al. (1994) focuses on veriﬁcation, validation, and conﬁrmation of numerical models in the earth sciences, and concludes that such models can never be truly validated because of the uncertainties in the environmental systems. For example, even if a model is shown to be satisfactory for three ﬁeld experiments, it may have poor performance on the next ﬁeld experiment. It is said that anyone can do an uncertainty analysis, since all that is needed is to run the model a few times with variable inputs. The resulting spread in the model outputs could be claimed to be the uncertainty. Or, common rules of thumb exist such as (a) the uncertainty in boundary layer wind predictions is about 1 or 2 m s 1, or (b) the uncertainty in predictions of concentrations is about plus and minus a factor of 2. Thus some persons might claim that there is no need to do a detailed uncertainty analysis since these rules of thumb are as good as we can do. In addition to the simple rules of thumb, there exist several detailed well-tested approaches to estimating uncertainty and sensitivity that are computer-intensive and/or involve complex mathematics. For example, the Monte Carlo (MC) approach is straightforward but is computer intensive. The Response-Surface method involves ﬁtting orthogonal functions to model outputs. These methods (and others) are brieﬂy reviewed in Section 2. 2. Overview of uncertainty and sensitivity methodologies and deﬁnitions of terms

This section touches on a number of approaches to uncertainty and sensitivity analysis. 2.1. General concepts concerning uncertainty and sensitivity

Assume that the total ‘‘error’’ or ‘‘uncertainty’’ in a set of model predictions, P, compared to the observations, O, is the square root of the mean of (Pi–Oi)2, where i is an individual realization in the set. Venkatram (1988), Hanna (1993), and Irwin and Hanna (2005) provide

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the rationale for the assumption that the total ‘‘error’’ is made up of four components: (1) errors and nonrepresentativeness in O (2) errors and nonrepresentativeness in observed inputs to the model (3) Model errors (i.e., faulty physics assumptions, omissions, uncertainties in parameters) (4) Stochastic (random) ﬂuctuations in O The term ‘‘nonrepresentativeness’’ means that the observation is not entirely representative of the area where the instrument is located. For example, an observed wind speed at a speciﬁc location is not exactly the same as the observed wind speed 500 m away (Hanna and Chang, 1992). Or, a point observation of a pollutant concentration may not be representative of the grid-volume average that is simulated by some regional models. The many references at the end of this paper each cover a subset of the four uncertainty components, but seldom does any one paper deal with all of them. For example, the MC method addresses primarily components 2 and 3 (input errors and model parameter errors). The SCIPUFF model discussed by Sykes et al. (1984) addresses primarily component 4 (stochastic ﬂuctuations). Uncertainty/sensitivity analysis methods are widely used, and descriptions are usually included as chapters under the general topic of statistical analysis techniques described in texts on nonparametric statistics. Statistical procedures applied to environmental and risk assessment modeling studies are reviewed by Beck (1987), IAEA (1989), and Cullen and Frey (1999). Beck (1987) focuses on water quality models and on models that are best ﬁt to observations. Thus, there is a large emphasis on the uncertainty in the observations that are used to derive the best-ﬁt formulas. Some of the discussions are less useful for atmospheric models, which tend to be based on fundamental physics equations such as the equations of motion and continuity. The International Atomic Energy Agency (IAEA, 1989) document emphasizes MC uncertainty studies of large complex modeling systems. Its lead author, O.F. Hoffman, has published extensively in the area of propagation of uncertainty in risk assessment. A more recent overview of uncertainty analysis, including some chapters on air quality, is given in the book by Cullen and Frey (1999). The author has been studying uncertainty for many years from several viewpoints, and recently encountered problems with a MC uncertainty study in deﬁning the uncertainties of the model inputs such as wind speed and emissions. Some experts who were consultants to the project come from the general ﬁelds of risk assessment and advised us that we should

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break down the input uncertainties into ‘‘variability’’ and ‘‘uncertainty’’. However, this is impossible to do in a clear way for meteorological variables such as wind speed. In another study involving collaborations with National Center for Environmental Prediction (NCEP) scientists who develop and run the large weather forecast models, it was found that the forecast community is using the ‘‘ensemble’’ methodology to estimate uncertainty (e.g., Hamill et al., 2000). It is assumed that the best forecast is the median or mean of the forecasts of an ensemble of about 10–20 or 30 model runs with varying inputs, physics assumptions, boundary conditions, and so on. The range or ‘‘spread’’ of the ensemble of runs is a measure of the uncertainty. The following subsections attempt to deﬁne the various terms and approaches used in uncertainty and sensitivity analysis, and give examples of some speciﬁc studies. 2.2. Ensemble approach to estimating uncertainty

The term ‘‘ensemble’’ has wide use in the general probabilistic modeling community but has taken a speciﬁc meaning in the weather forecasting community, which uses an ‘‘ensemble’’ of about 10–30 or more different model predictions to deﬁne a mean or median forecast, and to deﬁne the ‘‘spread’’ or ‘‘uncertainty’’ in the forecast. The ensemble of different model predictions can be comprised of different models, different initial and boundary conditions, and/or different model physics modules. It is an ‘‘art’’ to choose the ensemble members (see Hamill et al., 2000; Toth, 2001; Du et al., 2004). The ensemble methodology has been used in a few dispersion model exercises, too, as discussed below. 2.2.1. Ensemble mean

Prior to the development of the ensemble approach to estimating uncertainty, the air quality modeling community had long recognized that the predictions of most of their models are meant to represent ‘‘ensemble means’’. The model gives a single deterministic unique answer, thought to represent the mean of a given set or ensemble of very similar scenarios, perhaps as deﬁned by input variables (e.g., an ensemble might consist of 100 similar scenarios with wind speeds of about 5 m s 1 from the northwest and neutral conditions and ﬂat rural terrain). It is assumed that an observation during a single ﬁeld experiment is simply a member of a population or ensemble of possible observations that would occur if

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hundreds of very similar experiments would take place with very nearly the same inputs. 2.2.2. Spread of ensemble

Describes the range of the predictions in an ensemble model forecast system. The median of the ensemble model weather forecasts has been shown to be a better forecast than any individual model forecast. The spread or range is a measure of the uncertainty. Typically, the modelers check the spread against the range of observations. 2.2.3. Ensemble modeling of air pollution

This is a rapidly growing ﬁeld, primarily because increases in computer speed and storage now allow multiple model runs to be quickly made for the same scenario. Some modeling systems focus only on dispersion models, and others involve linked meteorological and dispersion models. The investigators carry out several runs with alternate assumptions for model physics options and inputs, or even for entirely different models, and assume that the spread in the results is a measure of the uncertainty. Uncertainties estimated by ensembles of linked meteorological and dispersion models are discussed by Straume et al. (1998), DOD (2000), Leach and Chin (2001), Straume (2001), Warner et al. (2002), and Galmarini et al. (2004a, b). Straume (2001) uses multiple meteorological models as input to a dispersion model. The DOD (2002) study is concerned with calculating concentrations in Iraq due to possible chemical agent release to the atmosphere in 1991. Three meteorological models and three dispersion models are used. The Warner et al. (2002) study focuses on the MM5 meteorological model and the HPAC dispersion model. The project reported by Galmarini et al. (2004a, b) involves an operational real-time forecast system in Europe where several countries provide the concentration predictions of their linked meteorological and dispersion models and a central group produces an ensemble of the various countries’ contributions. This capability was developed after problems arose in obtaining real-time forecasts during the Chernobyl accident. Uncertainties in ensembles of dispersion model simulations are described by Dabberdt and Miller (2000), Delle Monache and Stull (2003), and Draxler (2003). For example, Draxler (2003) runs the HYSPLIT dispersion model multiple times using slight perturbations in meteorological input ﬁelds. In all of these ensemble simulations, most of the spread in the forecasts of air quality is found to be caused by variations in wind directions.

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2.3. Contributions to stochastic or random concentration variations 2.3.1. Meandering

When a movie of a plume is shown, the instantaneous plume can be seen to wave or ‘‘meander’’ back and forth. The meandering is due to turbulent eddies with sizes that are larger than the plume width. At a speciﬁc point, the time series of concentration therefore may have many zeroes when the plume has meandered away from that location. Meandering contributes to intermittency, and results in the time-averaged plume being broader than the instantaneous plume. 2.3.2. In-plume concentration fluctuations

There are always stochastic or random concentration ﬂuctuations inside of a plume. These ﬂuctuations are due to turbulent eddies with sizes smaller than the plume width. Observations of in-plume concentration ﬂuctuations made during several ﬁeld experiments are summarized by Hanna (1984), who suggests a simple formula for the distribution of the ﬂuctuations. The SCIPUFF dispersion model directly predicts this component (Sykes et al., 1984; Lewellen and Sykes, 1989; Weil et al. 1992). Stein and Wyngaard (2001) discuss this component as it relates to ﬂuid modeling. 2.3.3. Intermittency

Instantaneous plumes of contaminants tend to often have zero concentrations (or perhaps background values) outside of the main body of the plume. Sometimes concentrations are observed to be zero even inside the plume. Consequently, the time series of concentration at a given location is ‘‘intermittent’’. 2.3.4. Averaging time

The time over which an observation or a model prediction is averaged. The most frequently used averaging time in air pollution meteorology applications is 1 h. Usually, the averaging time is tied to a speciﬁc health or environmental effect. 2.3.5. Averaging volume

A three-dimensional time-dependent model uses meteorological variables or concentrations averaged over a grid volume. If the model is a

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Computational Fluid Dynamic (CFD) model, the averaging volume could be about 1 m3. If the model is a mesoscale meteorological model or a regional photochemical model, the averaging volume could be about 10 km by 10 km horizontally and 100 m vertically, or about 1010 m3. 2.3.6. Spectrum

Dispersion of contaminants is determined by the turbulent velocity spectrum. Generally, spectra are calculated for the three components (x, y, and z). The rate of plume spread in a given direction is proportional to the turbulent velocity, and the integral time or length scale of the spectrum determines where the rate of spread changes from a linear power law to a square-root dependency. The turbulent eddies with sizes less than the plume size tend to cause internal plume dispersion, while the turbulent eddies with sizes larger than the plume size tend to cause meandering of the plume. A time series of concentration observations can be used to calculate a spectrum for the concentration ﬂuctuations and to determine their integral time or length scale. This is sometimes complicated because of the frequent ‘‘zeros’’ when the plume is not located over a given receptor. 2.4. Monte Carlo methodology

In uncertainty analysis, the term MC refers to an approach where a given model is run many times using random simultaneous variations in a set of the more important model inputs and model parameters. There is usually a ‘‘base run’’ with well-deﬁned model inputs and parameters that are assumed to represent the medians of the variables being perturbed. The results are analyzed to determine (a) total uncertainty in important outputs, and (b) inputs and parameters whose variations have the most effect on variations in the outputs. If the process is completely random, then standard nonparametric statistical tests can be applied. Unlike the sensitivity analysis method, the MC method is intended to investigate the behavior of the model outputs when all of the major model inputs and parameters are simultaneously and randomly varied. The MC method can handle much complexity and nonlinearity in the model system. 2.4.1. Uncertainty

In meteorology, uncertainty represents all types of variations in the system, due to many causes such as instrument errors, model errors, and stochastic processes. In the broad risk assessment ﬁeld and in generalized

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MC studies, uncertainty refers to the subjective or controllable part of the total ﬂuctuations, such as model physics errors and instrument errors. In the risk assessment ﬁeld, subjective uncertainty is synonymous with ‘‘epistemic’’, ‘‘type B’’, ‘‘reducible’’, and ‘‘subjective’’. 2.4.2. Variability

In meteorology, the term ‘‘variability’’ is often synonymous with uncertainty. In the broad risk assessment ﬁeld and in generalized MC studies, variability refers to the random or stochastic part of the total ﬂuctuations. In the atmosphere, these ﬂuctuations are due to turbulent eddies. In the risk assessment ﬁeld, variability is synonymous with ‘‘aleatory’’, ‘‘type A’’, and ‘‘irreducible’’. 2.4.3. Probability distribution function

Model input and output variables have a range or distribution known as a probability distribution function (PDF), which can usually be described by a mean (or median) and a standard deviation. The most common assumptions for PDFs for environmental variables are the normal (or Gaussian) PDF and the log-normal PDF. In a MC study, the PDFs for all input variables and model parameters must be prescribed, and the model outputs are analyzed by ﬁtting a PDF to the distribution. Sometimes correlations between PDFs can be prescribed, too (for example, cloudy skies are correlated with neutral stabilities). However, it has been found that correlations should be greater than about 0.7 to make a major difference in the outputs of the MC model. The integral of the PDF is the Cumulative Distribution Function (CDF). 2.4.4. Model input

Model input refers to a value that is externally ‘‘input’’ to the model. That is, the model is looking for a ﬁle of inputs to read. For example, the surface moisture may be input to a mesoscale meteorological model. 2.4.5. Model parameter

In MC uncertainty studies, model parameter refers to an internal model ‘‘constant’’ that is hard-wired in the source code. For example, the model may internally contain an expression that a distance scale is a constant, A, times the height above ground. The constant, A, is a model parameter.

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2.4.6. Bayesian

The Bayesian MC method uses comparisons with observations to give a higher weight to MC runs using inputs which produce better agreement with the observations. 2.4.7. Latin hypercube sampling

This sampling method is used so as to produce a distribution of samples that most closely agrees with the distribution that is being resampled. For example, the PDF is broken into 10 equally probable groups. Then if N MC samples are to be drawn, the system makes sure that N/10 of the samples are from each of the 10 groups. However, because the sampling is not entirely random, there is less justiﬁcation for applying standard statistical tests. 2.4.8. Simple random sampling

This term describes the most straightforward sampling method, which is amenable to nonparametric statistical testing. If there are N MC runs to be made and M inputs with assumed PDFs, then each MC run involves simultaneous random sampling from each of the M PDFs. 2.4.9. Expert elicitation

This term is used in MC uncertainty studies to refer to a process where experts are asked to estimate the uncertainties of speciﬁc input variables or model parameters. Then, the value of the uncertainty used in the MC study is assumed to be, say, the median of the values suggested by the experts. Expert elicitation is a substitute for having sufﬁcient observations for direct calculation of the uncertainties. For example, when asked what is the uncertainty (95% range) in estimates of plume dispersion, sy, most experts answer ‘‘about 7 a factor of 2’’. There are many examples of the MC uncertainty method applied to atmospheric transport and dispersion models. The ﬁrst were by Freeman et al. (1986) and Irwin et al. (1987), who estimate the effects of uncertainties in inputs and in model parameters on the outputs of simple Gaussian plume models. Bergin and Milford (2000) study a Lagrangian photochemical model using Bayesian MC methods, where the probability of an individual MC run is adjusted based on comparisons with observations. Hanna et al. (1998, 2001, 2005, 2007) present examples of MC methods with Simple Random Sampling to regional ozone models and to

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the short-term ISC and AERMOD models. In all examples, uncertainties in emissions are accounted for in addition to uncertainties in meteorological inputs and in model parameters. 2.5. Response surface methods

There are several types of analytical uncertainty methods. This method refers to estimating the sensitivity or uncertainty using an equation or a set of equations. Because a full MC uncertainty study can be so expensive and time consuming, some mathematicians have proposed to develop response surfaces that ‘‘ﬁt’’ the available MC inputs and outputs. This amounts to a simpliﬁed model of the full MC modeling exercise. Future studies can make use of the response surface rather than going through the detailed MC study (see Downing et al., 1985; Isukapalli et al., 1998). 2.6. Fuzzy methods

Another way to account for uncertainties in environmental data and models is the ‘‘fuzzy method’’, such as described by Fisher (2003). The basic concepts are similar to what are discussed above in other contexts. The method is used in order to provide an alternative to the ‘‘single number’’ decisions in use in many environmental agencies. For example, air pollutant standards are usually written as single numbers, not to be exceeded. As Fisher (2003) describes, the observations and model predictions, as well the air pollutant standards, are better considered to be distributions, and the width of the distribution is an indication of the ‘‘fuzziness’’ of the number. However, the use of fuzzy methods (uncertainty) in air quality decisions, such as whether a new coal-ﬁred power plant should be built, is likely to lead to different interpretations depending on the point of view of each individual. Environmental groups will be likely to support the use of the conservative side of the fuzzy distribution, while the industrial group who wants to build the power plant is likely to support the nonconservative side of the distribution. 2.7. Sensitivity analysis

In general, sensitivity analysis is used to estimate the variations in a model output caused by slight variations in a model input. For example, the variations in a dispersion model’s prediction of SO2 concentration at a speciﬁc location and time could be estimated for two slightly different inputs of wind speed: 2 and 2.1 m s 1. It is seen that this deﬁnition is equivalent to the deﬁnition of a partial derivative. It is most useful for

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modeling systems that are linear (that is, the relative variations in the output are not very sensitive to the exact value of the input) and that do not have complicated intercorrelations between various inputs. Saltelli et al. (2004) describe the general principles of sensitivity analysis of environmental models. Menut (2003) presents an adjoint method. 2.7.1. One-at-a-time sensitivity studies

The sensitivity analysis can be carried out for a single model input, in which case the analysis is referred to as ‘‘one at a time’’ (OAT). Seefeld and Stockwell (1999) describe an application to PAN and ozone models. A problem with this approach occurs for nonlinear modeling systems, where the sensitivity varies in magnitude and even in sign with the value of the input variable. 2.7.2. Automatic differentiation as used in sensitivity studies

Software has been available for many years where a model code can be automatically differentiated to produce the sensitivity coefﬁcients. Carmichael et al. (1997) show the utility of the method for atmospheric chemistry models. 2.7.3. Process analysis

This is a form of sensitivity analysis carried out with complex modeling systems such as photochemical grid models where a speciﬁc chemical or physical process is studied rather than a speciﬁc input variable or model parameter. For example, all equations related to the formation of a speciﬁc group of organic compounds might make up a process. 2.8. Predictability

The term ‘‘predictability’’ was coined by the weather forecast community and refers to the fact that, at longer and longer forecast times, the atmosphere becomes less and less predictable. It is assumed that the initial conditions can be estimated but that the forecast is made without the use of updated observations (data assimilation) as time goes on. Lorenz (1969) attributes the decrease in predictability to the growth of small random variations in the system (the ‘‘butterﬂy effect’’). Note that predictability and uncertainty are not the same, since the system is uncertain even at the initial time.

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3. Methodology for a quantitative Monte Carlo uncertainty analysis

The probabilistic MC approach can treat three types of uncertainties: (1) model algorithms (e.g., emissions components, plume rise, sy) or can even handle alternate models such as categorizing mobile source emissions as area or line sources, or ISCST3 versus AERMOD; (2) model parameters (e.g., leading constants in formulas for emissions terms); and (3) model inputs that are part of required input ﬁles (e.g., wind speeds, background or boundary concentrations, trafﬁc count, population in county). For uncertainties concerned with alternate emissions or transport and dispersion algorithms, a probability can be assigned for the correctness of each algorithm or model, and the MC sample is drawn randomly from those probabilities. These probabilities are determined by discussions with experts and review of the literature. For uncertainties related to model parameters, it is obviously necessary to have access to the source code so that the model parameter can be changed by each MC resample. The following steps for quantitative uncertainty analysis are recommended in Safety Series 100 of the International Atomic Energy Agency (IAEA, 1989): 1. Deﬁne the question being asked and the assessment endpoint or output to be studied. As with any research study, it is important to clearly state the question being asked, such as ‘‘What is the range of uncertainties in the maximum predicted annual benzene concentration in Houston in 1996 due to uncertainties in ISC model inputs?’’ The assessment endpoint or output may be an environmental concentration, a Lifetime Cancer Risk to a member of a particular population subgroup, or any number of other possibilities. It is clearly important to speciﬁcally state the output of interest. 2. List all potentially important uncertain inputs or parameters, such as required inputs to the model, internal parameters or constants in the model code, or alternate modules. 3. Specify the maximum conceivable range of possibly applicable values for each uncertain input with respect to the endpoint or end objective of the assessment. This is to prevent unphysical values being selected by the random sampling method. 4. Within the above range, specify a probability distribution that quantitatively expresses the state of knowledge about alternative values for each input. The distribution may be continuous or discrete. If the uncertainty ranges over more than 7 a factor of 2, the distribution usually should be log-normal.

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5. Identify any variables that are dependent on each other and account for these dependencies either in the model structure, or through the use of correlation coefﬁcients. 6. Propagate the uncertainty in model parameters using a MC procedure to randomly sample from probability distributions speciﬁed for each uncertain input to produce a random value for the assessment endpoint (the output). Usually, MC simulation involves hundreds to thousands of simulations. This can be reduced to a few dozen using statistical tolerance limits (IAEA, 1989; NCRP, 1996; Cullen and Frey, 1999). 7. Derive a 90 or 95% conﬁdence interval and a central estimate from the probability distribution of model output produced using the MC procedure in Step 6 above. 8. Identify the uncertain inputs that are most important in terms of their contribution to the overall uncertainty in model outputs, using scatter plots, correlation coefﬁcients, or statistical regression analysis of inputs versus outputs. 9. Interpret and present the results of the analysis.

4. Example of Monte Carlo uncertainty study with AERMOD and ISC

If more space were available, it would be possible to present examples of the various kinds of sensitivity and uncertainty approaches mentioned above. However, because of space limitations, only one example is given— a MC uncertainty study of the AERMOD and ISC models. A detailed presentation of the study is given by Hanna et al. (2007). The uncertainties of simulations of concentrations of air toxics in the Houston Ship Channel industrial region are analyzed, using census-tract population centroids for hypothetical receptors. A test example for the Houston area was described by the EPA (2002), using a Gaussian plume model, the Industrial Source Complex Short Term Version 3 (ISCST3) model (EPA, 1995). A MC probabilistic uncertainty approach has been used to assess the model uncertainties because it allows the combined inﬂuences of the uncertainties in many model inputs and parameters to be assessed. The resulting total uncertainty in the model outputs can be determined as well as correlations between uncertainties in inputs and outputs. The study focuses on a 15-km by 15-km Houston domain covering the area around the Houston ship channel, and includes many oil reﬁneries and chemical processing plants, as well as numerous major highways. The domain is just east of downtown Houston and also contains several residential and commercial areas. The modeling addresses two air toxics, benzene and

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1,3-butadiene, whose emissions are distributed among mobile sources, industrial sources, and area sources such as service stations. The concentrations have been calculated using emissions information from sources on a 30-km by 30-km domain encompassing the smaller receptor square. Annual-averaged concentrations at 46 receptor locations (43 at census tract population centroids and three at monitoring sites) have been calculated on the 15-km by 15-km receptor domain. The annual averaging time is chosen because that is the basis for health effect estimates. Two alternate Gaussian plume dispersion models have been run: ISCST3 (EPA, 1995) and AERMOD (Cimorelli et al., 2005). The emissions ﬁles were provided by the EPA for 1996. In this MC exercise, the models are run 100 times for random choices from the distributions characterizing the input parameters. To investigate the effects of assumptions of rural vs. urban terrain in ISCST3, that model was run in two ways (all sources in urban terrain, and sources split into mixed rural/ urban terrain grids). The assumed uncertainties in emissions, meteorological inputs, and dispersion model parameters are listed below. The uncertainties are expressed as 95% ranges, which encompass 7two standard deviations from the mean. The benzene and 1,3 butadiene emissions sources are divided into 24 and 13 basic groups, respectively, and uncertainties estimated based on data and on expert surveys. It is concluded that the 95% uncertainty range in emissions is 7 a factor of 3 for each emissions group. Wind speed is assumed to have a 770% uncertainty (covering the 95% range). According to National Weather Service procedures, it is assumed that any wind speed less than 2.5 knots is listed as ‘‘calm’’. A procedure was devised so that the fraction of calms before and after the MC perturbations are applied would remain the same, on average. Wind direction, in degrees (1), is assumed to have a 7601 uncertainty (covering the 95% range). Selected wind direction values greater than 3601 and less than 01 are corrected to be in the range of 01 to 3601 (e.g., 151 is corrected to 3451). Cloud cover, ranging from 0 (clear) to 1.0 (overcast), is assumed to have an uncertainty of 70.2 (covering the 95% range). If the randomly selected cloud cover is greater than 1.0, it is reset to 1.0. If it is less than 0.0, it is reset to 0.0. Mixing height is assumed to have a 740% uncertainty. Surface roughness z0 (AERMOD only) is assumed to have a 95% range of 7 a factor of 3. Note that, since ISC does not directly input z0, and instead assumes either urban or rural surface conditions, those

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uncertainties are being handled in the current study by running ISC in two modes—(1) assuming all sources are surrounded by urban terrain, and (2) assuming sources are surrounded by a grid with either rural or urban terrain, following EPA (1995) criteria. Bowen ratio (AERMOD only) is the ratio of the sensible heat ﬂux to the latent heat ﬂux at the ground surface. Assume a 95% range of 7 a factor of 2. dT/dz (ISC and AERMOD) is assumed to have a 95% range of 7 a factor of 2. Note that variations in dT/dz are expected to primarily affect the plume rise calculations. The perturbations to dT/dz are applied inside the code after the temperature gradients have been calculated by the internal modules. sy and sz perturbations are applied inside the code after the dispersion parameters have been calculated by the internal modules. Assume a 95% range of 7 a factor of 2. No correlation is assumed between sy and sz (i.e., they are varied randomly and independently). The ISC and AERMOD model outputs that have been analyzed are the average-annual maximum concentration at any single census tract or monitor as well as the concentration averaged over the 43 census tracts. The results shown in Fig. 1 suggest that the 95% range (deﬁned by the

Figure 1. Signiﬁcant points on the cumulative distribution function (CDF) based on 100 Monte Carlo (MC) runs for predicted annual-averaged benzene concentrations (mg/m3) in the Houston Ship Channel Area for the spatial-average over all 43 census tract population centroids. The 95% range is deﬁned by the 2.5th and 97.5th percentiles.

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2.5th and the 97.5th percentiles on the CDF) in MC-predicted annualaveraged concentrations is about 7 a factor of 2–3 for the air toxics, with little variation by model. The data show that there is little variation in the uncertainty between benzene and 1,3-butadiene. The input variables whose variations have the strongest effect on the predicted concentrations are light-duty mobile sources (i.e., cars and small trucks) and some industrial sources (dependent on chemical), as well as wind speed, surface roughness, and vertical dispersion parameter sz. In most scenarios, the uncertainties of the emissions input group contribute more to the total uncertainty than the uncertainties of the meteorological/dispersion input group. Discussion

M.A. Monteiro:

S.R. Hanna:

S.T. Rao:

In the actual European directive, the model acceptance uncertainty criterion is the maximum relative error below 50%. In fact, this is still in revision. Do you have any suggestion, or any data quality objective, to substitute or improve this model uncertainty criterion? The decision concerning the acceptance criteria should depend on the pollutant, the averaging time, the scenario, the evaluation history, and other factors. For example, a relative error of less than 50% is possible only for some pollutants (e.g., ozone) and conditions. A much larger uncertainty would be expected, for example, for a time-varying toxic chemical release in a mountainous area far from the nearest meteorological station. Part 1—You indicated that if different models are run, the spread in the predicted concentrations can be viewed as a measure of uncertainty in model prediction. I wonder if this really represents the range of uncertainty since each model used in the ensemble needs to be run with different initializations. Do you know of any studies that include not only individual models, but also different initializations in individual models of the ensemble set? Part 2—Furthermore, our models are deterministic in nature and stochastic is not explicitly included in these models. Therefore, would it make sense to talk

Atmospheric Transport and Dispersion Models

S.R. Hanna:

E. Genikhovich:

S.R. Hanna:

D. Steyn:

S.R. Hanna:

347

of a lower bound for model uncertainty since the true uncertainty in the models cannot be fully determined? Part 1—Most of the weather forecast model ensembles run in the U.S. use only one or two models but different initializations or physical module options. There are plans to include sufﬁcient numbers of different models and different initializations in future operational ensemble approaches. Part 2—Dr. Rao is correct that if a model could estimate the uncertainty, then we could more easily compare the model uncertainties with the variations in the observations and/or the model performance. Nevertheless, my main conclusion would still be valid—the naturally occurring stochastic uncertainty in the atmosphere will not allow a perfect model to be developed. It follows from your talk that large uncertainties are mainly due to the use of models for predicting the ‘‘individual’’ values of variables at given locations and time. Wouldn’t it be more efﬁcient to use the models for predicting some stable statistics, like PDFs, upper percentiles, etc., rather than individual values? Yes, it would help if models would predict the statistics such as PDFs. In fact some models, such as SCIPUFF, already do this. Any second order closure model is capable of predicting variances as well as ensemble means. Given an assumption for the PDF shape (clipped normal in SCIPUFF), the percentiles, etc., can be determined. Uncertainties of models are often tied up with model calibration. What is your sense of importance of this technique? Calibration is used mainly to remove mean biases. This is a good thing, since the models can then more conﬁdently be used to make regulatory decisions. In addition, there may be some outliers due to special combinations of inputs and these can also be ‘‘calibrated’’ out. After the bias and the outliers are removed, random uncertainties will remain, as well

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R. Vautard: S.R. Hanna:

as uncorrected or unaccounted for scientiﬁc issues in the model. Are all combinations of parameters physical? How can one eliminate the unphysical ones? It is of course true that not all combinations or parameters in an uncertainty study are physical. For example, high wind speeds and very stable conditions cannot coexist near the surface. The ensemble approach can take pains to not allow such unphysical combinations. Other statistical approaches such as the Monte Carlo uncertainty methodology can have conditions added to prevent the occurrence of unphysical combinations.

ACKNOWLEDGMENT

This study was supported by the U.S. Defense Threat Reduction Agency, with CDR Stephanie Hamilton as project manager.

REFERENCES Beck, M.B., 1987. Water quality modeling: A review of the analysis of uncertainty. Water Resour. Res. 23, 1393–1442. Bergin, M.S., Milford, J.B., 2000. Application of Bayesian Monte Carlo analysis to a Lagrangian photochemical air quality model. Atmos. Environ. 34, 781–792. Carmichael, G.R., Sandu, A., Potra, F.A., 1997. Sensitivity analysis for atmospheric chemistry models via automatic differentiation. Atmos. Environ. 31, 475–489. Cimorelli, A.J., Perry, S.G., Venkatram, A., Weil, J.C., Paine, R.J., Wilson, R.B., Lee, R.F., Peters, W.D., Brode, R.W., 2005. AERMOD: A dispersion model for industrial source applications. Part I: General model formulation and boundary layer characterization. J. Appl. Meteorol. 44, 682–693. Cullen, A.C., Frey, H.C., 1999. Probabilistic Techniques in Exposure Assessment. A Handbook for Dealing with Variability and Uncertainty in Models and Inputs. Plenum Press, New York, 335 pages. Dabberdt, W.F., Miller, E., 2000. Uncertainty, ensembles, and air quality dispersion modeling: Applications and challenges. Atmos. Environ. 34, 4667–4673. Delle Monache, L., Stull, R.B., 2003. An ensemble air-quality forecast over western Europe during and ozone episode. Atmos. Environ. 37, 3469–3474. DoD (U.S. Department of Defense), 2000. Technical Report—Modeling and Risk Characterization of US Demolition Operations at the Khamisiyah Pit. Special assistant to the Under Secretary of Defense (Personnel and Readiness) for Gulf War Illnesses, Medical Readiness, and Military Deployments, U.S. Department of Defense, p.193, Available from http://www.gulﬂink.osd.mil/khamisiyah_tech/

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Downing, D.J., Gardner, R.H., Hoffman, F.O., 1985. Response surface methodologies for uncertainty analysis in assessment models. Technometrics 27, 151–163. Draxler, R.R., 2003. Evaluation of an ensemble dispersion calculation. J. Appl. Meteorol. 42, 308–317. Du, J., McQueen, J.T., DiMego, G., Black, T., Juang, H., Rogers, E., Ferrier, B., Zhou, B., Toth, Z., Tracton, M.S., 2004. The NOAA/NWS/NCEP Short Range Ensemble Forecast (SREF) system: Evaluation of an initial condition vs. multiple model physics ensemble approach. Proceedings, 16th Conference on Numerical Weather Prediction. American Meteorological Society, Paper 21.3, Seattle, WA, p. 10. EPA, 1995. User’s Guide for the Industrial Source Complex (ISC3) Dispersion Model (revised): Vol. II—Description of Model Algorithms. EPA-454/b-95-0036, USEPA, RTP, NC 27711. EPA, 2002. Example Application of Modeling Toxic Air Pollutants in Urban Areas. EPA-454/R-02-003, OAQPS/EPA, RTP, NC 27711, p. 91, http://www.epa.gov/ scram001/tt25htm#toxics Fisher, B., 2003. Fuzzy environmental decision-making: Applications to air pollution. Atmos. Environ. 37, 1865–1877. Fox, D.G., 1982. Uncertainty in air quality modeling. Bull. Am. Meteorol. Soc. 65, 27–35. Freeman, D.L., Egami, R.T., Robinson, N.F., Watson, J.G., 1986. A method for propagating measurement uncertainty through dispersion models. J. Air Poll. Control Assoc. 36, 246–253. Galmarini, S., Bianconi, R., Klug, W., Mikkelsen, T., Addis, R., Andonopoulos, S., et al., 2004a. Ensemble dispersion forecasting – Part I: concept, approach and indicators. Atmos. Environ. 38, 4607–4617. Galmarini, S., Bianconi, R., Addis, R., Andonopoulos, S., Astrup, P., Bartzis, J., et al., 2004b. Ensemble dispersion forecasting – Part II: application and evaluation. Atmos. Environ. 38, 4619–4632. Hamill, T.M., Mullen, S.L., Snyder, C., Toth, Z., Baumhefner, D.P., 2000. Ensemble forecasting in the short to medium range: Report from a workshop. Bull. Am. Meteorol. Soc. 81, 2653–2664. Hanna, S.R., 1984. Concentration ﬂuctuations in a smoke plume. Atmos. Environ. 18, 1091–1106. Hanna, S.R., 1993. Uncertainties in air quality model predictions. Bound. Lay. Meteorol. 62, 3–20. Hanna, S.R., Chang, J.C., 1992. Representativeness of wind measurements on a mesoscale grid with station separations of 312 m to 10000 m. Bound. Lay. Meteorol. 60, 309–324. Hanna, S.R., Chang, J.C., Fernau, M.E., 1998. Monte Carlo estimates of uncertainties in predictions by a photochemical grid model due to uncertainties in input variables. Atmos. Environ. 32, 3619–3628. Hanna, S.R., Lu, Z., Frey, H.C., Wheeler, N., Vukovich, J., Arumachalam, S., Fernau, M., 2001. Uncertainties in predicted ozone concentration due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain. Atmos. Environ. 35, 891–903. Hanna, S.R., Paine, R., Heinold, D., Kintigh, E., Baker, D., 2007. Uncertainties in air toxics calculated by the dispersion models AERMOD and ISC in the Houston Ship Channel area. J. Appl. Meteorol. (accepted for publication). Hanna, S.R., Wilkinson, J., Russell, A.G., Vukovich, J., Hansen, D.A., 2005. Monte Carlo estimation of uncertainties in BEIS3 emission outputs and their effects on

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uncertainties in Chemical Transport Model predictions. J. Geophys. Res. 110, D01302, doi:10.1029/2004JD004986. Helton, J.C., 1997. Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty. J. Stat. Comput. Simul. 57, 3–76. IAEA, 1989. Evaluating the reliability of predictions made using environmental transfer models. IAEA Safety Series No. 100, International Atomic Energy Agency, Vienna, Austria. Iman, R.L., Helton, J.C., 1988. An investigation of uncertainty and sensitivity analysis techniques for computer models. Risk Anal. 8, 71–90. Irwin, J.S., Hanna, S.R., 2005. Characterizing uncertainty in plume dispersion models. Int. J. Environ. Poll. 25, 16–24. Irwin, J.S., Rao, S.T., Petersen, W.B., Turner, D.B., 1987. Relating error bounds for maximum concentration estimates to diffusion meteorology uncertainty. Atmos. Environ. 21, 1927–1937. Isukapalli, S.S., Roy, A., Georgopoulos, P.G., 1998. Stochastic response surface methods (SRSMs) for uncertainty characterization and propagation: Application to environmental and biological systems. Risk Anal. 18, 351–363. Lamb, R.G., Hati, S.K., 1987. The representation of atmospheric motion in models of regional-scale air pollution. J. Appl. Meteorol. 26, 837–846. Leach, M.J., Chin, H.-N., 2001. Uncertainty in dispersion forecasting using meteorological ensembles. In: Gryning, S.-E., Schiermeier, F. (Eds.), Air Pollution Modeling and Its Application XIV. Kluwer Academic/Plenum Publishers, New York, pp. 659–663. Lewellen, W.S., Sykes, R.I., 1989. Meteorological data needs for modeling air quality uncertainties. J. Atmos. Oceanic Tech. 6, 759–768. Lorenz, E.N., 1969. The predictability of a ﬂow which possesses many scales of motion. Tellus 21, 289–307. Menut, L., 2003. Adjoint modeling for atmospheric pollution process sensitivity at regional scale. J. Geophys. Res. 108(D17), 8562. Morgan, M.G., Henrion, M., 1990. Uncertainty: A guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, New York. NCRP, 1996. In: Hoffman F.O. (Ed.), A Guide for Uncertainty Analysis in Dose and Risk Assessments Related to Environmental Contamination. NRCP Commentary Number 14, Nat. Council on Radiation Protection and Measurement, 7910 Woodmont Ave., Bethesda, MD. Oreskes, N., Shrader-Frechette, K., Beitz, K., 1994. Veriﬁcation, validation, and conﬁrmation of numerical models in the earth sciences. Science 263, 641–646. Pielke, R.A. Sr., 1998. The need to assess uncertainty in air quality evaluations. Atmos. Environ. 32, 1467–1468. Rao, K.S., 2005. Uncertainty analysis in atmospheric dispersion modeling. Int. J. Pure Appl. Geophys. 162, 1893–1917. Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M., 2004. Sensitivity Analysis in Practice. A Guide to Assessing Scientiﬁc Models. Wiley, New Jersy. Seefeld, S., Stockwell, W.R., 1999. First-order sensitivity analysis of models with timedependent parameters: An application to PAN and ozone. Atmos. Environ. 33, 2941–2953. Stein, A.F., Wyngaard, J.C., 2001. Fluid modeling and the evaluation of inherent uncertainty. J. Appl. Meteorol. 40, 1769–1774. Straume, A.G., 2001. A more extensive investigation of the use of ensemble forecasts for dispersion model evaluation. J. Appl. Meteorol. 40, 425–445.

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Straume, A.G., Kofﬁ, E., Nodop, K., 1998. Dispersion modeling using ensemble forecasts compared to ETEX measurements. J. Appl. Meteorol. 37, 1444–1456. Sykes, R.I., Lewellen, W.S., Parker, S.F., 1984. A turbulent transport model for concentration ﬂuctuations and ﬂuxes. J. Fluid Mech. 139, 193–218. Toth, Z., 2001. Meeting summary: Ensemble forecasting in WRF. Bull. Am. Meteorol. Soc. 82, 695–697. Venkatram, A., 1988. Inherent uncertainty in air quality modeling. Atmos. Environ. 22, 1221–1227. Warner, T.T., Sheu, R.-S., Bowers, J.F., Sykes, R.I., Dodd, G.C., Henn, D.S., 2002. Ensemble simulations with coupled atmospheric dynamic and dispersion models: Illustrating uncertainties in dosage simulations. J. Appl. Meteorol. 41, 488–504. Weil, J.C., Sykes, R.I., Venkatram, A., 1992. Evaluating air quality models: Review and outlook. J. Appl. Meteorol. 31, 1121–1145.

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Chapter 4.1 Lagrangian particle model simulation of tracer dispersion in stable low wind speed conditions D. Anfossi, S. Alessandrini, S. Trini Castelli, E. Ferrero, D. Oettl and G. Degrazia Abstract Recently we have been studying the turbulence and dispersion characteristics during meandering (large horizontal oscillations of the atmosphere) associated to low wind speed conditions. It was found that the autocorrelation functions (AE) of the horizontal wind components, computed for the low wind cases, show an oscillating behaviour with the presence of large negative lobes and that these AE can be very well ﬁtted by an analytical relationship that contains two parameters, one associated to the classical integral time scale and the second to the meandering characteristics. Then, a new system of two coupled Langevin equations for the horizontal wind components, able to simulate the dispersion in low wind speed conditions, was derived. In this paper we brieﬂy review these topics and, in particular, we show the results, obtained with these new coupled Langevin equations, of the simulation of the INEL and Graz low wind tracer experiments. The comparison showed very good results and, in particular, put in evidence the advantage of using this new low wind simulation tool with respect to previous classical models. 1. Introduction

The simulation of the dispersion in low wind speed conditions (LW) is a particularly difﬁcult task since even when the atmospheric stability reduces the vertical dispersion, meandering disperses the plume over rather wide angular sectors. Thus, the resulting ground-level concentration is generally much lower than that predicted by standard Gaussian plume models. Recently, Anfossi et al. (2005a, b) and Oettl et al. (2005) studied the turbulence and dispersion characteristics during meandering (large horizontal oscillations of the atmosphere) associated to low wind speed

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conditions. It was found that (i) meandering seems to exist under all meteorological conditions regardless the stability or wind speed; (ii) the Eulerian autocorrelation functions (AE) of the horizontal wind components, computed for the low-wind cases, show an oscillating behaviour with the presence of large negative lobes; (iii) these AE can be very well ﬁtted by the following relationship, originally proposed by Frenkiel (1953) and Murgatroyd (1969) in different contexts: mt 2 RðtÞ ¼ eðt=ðm þ1ÞT 3 Þ cos 2 (1a) ðm þ 1ÞT 3 or RðtÞ ¼ ept cosðqtÞ

(1b)

where p¼

1 ðm2 þ 1ÞT 3

and

q¼

m ðm2 þ 1ÞT 3

(1c)

They contain two parameters (T, m or p, q), one related to the classical integral time scale and the other to the meandering period. Then, a new hypothesis was proposed for the cause of meandering, where this was last explained as an inherent property of atmospheric ﬂows in low wind speed conditions which, generally, does not require any particular trigger mechanism to be initiated. Furthermore, a new system of two coupled Langevin equations for the horizontal wind components was derived for simulating the dispersion in LW turbulence homogeneous conditions, whose expression is the following: pﬃﬃﬃﬃﬃﬃﬃﬃﬃ (2a) du ¼ ðpu þ qvÞdt þ su 2pdtwu dv ¼ ðqu þ pvÞdt þ sv

pﬃﬃﬃﬃﬃﬃﬃﬃﬃ 2pdtwv

(2b)

It is worth noting that Eqs. (2a) and (2b) in the original Oettl et al. (2005) paper (where they appeared as Eq. (36)) were wrongly written due to a misprint. Finally, a version of Eqs. (2a) and (2b) for the more general case of inhomogeneous turbulence and for the total velocity was proposed in Anfossi et al., 2005b). This version was based on the ‘‘Thomson simplest solution’’ (Thomson, 1987). In this presentation, we will show the preliminary results obtained with these new coupled Langevin equations for the simulation of two LW tracer experiments: the INEL low-wind tracer experiments (Sagendorf and Dickson, 1974) and the Graz tracer experiments (performed in 2003 by the Graz University of Technology and CNR-Torino). The complete analysis can be found in Anfossi et al. (2006). Due to the limited range of

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these experiments (50–400 m) and the availability of meteorological information at one point only, in both simulation experiments, a simpliﬁed version of these equations, namely pﬃﬃﬃﬃﬃﬃﬃﬃﬃ du ¼ fpðu u¯ Þ qðv v¯ Þgdt þ 2pdt su wu (3a) dv ¼ fqðu u¯ Þ pðv v¯ Þgdt þ

pﬃﬃﬃﬃﬃﬃﬃﬃﬃ 2pdt sv wv

(3b)

was used. They were inserted in our Lagrangian stochastic particle models LAMBDA (Brusasca et al., 1992; Ferrero et al., 1995) and GRAL (Oettl et al., 2001; Oettl et al., 2003). 2. A brief outline of experiments and simulations

Main speciﬁcations of the two tracer experiments are resumed in Table 1. We add that in both cases SF6 was used as tracer. In the INEL experiments, SF6 concentrations were also detected at 2 m, 4.5 m, 6 m and 9 m on a few towers located on the second arc in some experiments. Meteorological information was as follows: In the INEL experiments, wind speed and direction and standard deviation of wind direction were measured on a meteorological tower at sixth levels (2 m, 4 m, 8 m, 16 m, 32 m and 61 m) with traditional anemometers, whereas in the Graz experiments, wind data were collected by sonic anemometers at 1.5 m and 6.0 m. In both experiments, raw wind data were available and this allowed us to compute the input parameters of Eqs. (3a) and (3b), i.e., p and q, with a bestﬁt procedure of the wind time series (at each measuring height) to the autocorrelation functions (Eqs. (1a), (1b) and (1c)). A second simulation of the INEL experiments, named WY, was carried out with LAMBDA in the traditional version (i.e., not including Eqs. (3a) and (3b), but keeping all the other options equal); this aims at estimating how much the new LW scheme (Eqs. (3a) and (3b)) improves the classical simulation. Following Sagendorf and Dickson (1974), observed and predicted concentrations for the INEL experiments are given as normalised concentrations (expressed in m2), C n ¼ Cu4 =Q , where C is the dimensional Table 1. Tracer experiments speciﬁcation Exp.

Exp. number

Duration (h)

Release height (m)

Sampler arcs

Distance (m)

Samplers per arc

INEL Graz

10 5

1 0.5

1.5 1.4

3 1

100, 200, 400 50

60 30

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concentration (g m3), u4 the hourly mean wind speed at 4 m and Q the tracer emission rate. 3. Results and discussion

A ﬁrst result regards the estimation of the p and q parameters (Eqs. (3a) and (3b)). Figure 1 shows, as an example, p and q as a function of height obtained for the INEL experiments 13 and 9. We recall that they are estimated by ﬁtting Eq. (1b) to the AE derived from the different 1-h wind time series recorded at the different heights. The general result is that for both INEL and Graz experiments, while most of the q proﬁles tend to be constant with the height (this being probably related to the fact that q is related to the mean wind gradients at mesoscale), p proﬁles were found more variable with the height (probably because besides depending on mean wind gradients at mesoscale, they are also affected by the local turbulence conditions). In Fig. 2, which refers to experiment 14, ﬁrst arc illustrates some typical INEL simulation results obtained with LAMBDA. It can be clearly seen that the WY option predicts a plume much smaller than the observed plume, while the LW option is able to spread the tracer over the entire arc. Then, while the maximum concentration is almost correctly captured by LW, WY leads to a signiﬁcant overestimation. Figures 3 and 4 show an example of the comparison between the two LAMBDA options (LW and WY) for the samplers located at 2 m and 6 m, respectively, along some towers during experiment 11. It again appears that LW option is able to capture the shape of the plume, while the WY option fails. This is an interesting result since it veriﬁes that the new LW option is able to yield a complete 3-D description of the tracer plume. Figure 5 depicts the observed and computed CFDs for the Graz experiments applying the new LW option in GRAL. Even though the experiments were taken in rather inhomogeneous terrain, the calculated CFD agrees very well with the observed one. In conclusion, the examination of these two tracer datasets collected in low wind speed condition (and in different terrain characteristics) suggests that it is veriﬁed that the LW option, taking explicitly into account the wind meandering, allows a correct and reliable simulation of the tracer dispersion in low wind speed conditions. The comparison showed very good results and, in particular, put in evidence the advantage of using this new low-wind simulation tool with respect to previous classical models since it produces a plume wider and less peaked than that obtained with the same model but without any speciﬁc treatment of meandering.

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Figure 1. Computation of p and q parameters versus height for experiments 13 (left panel) and 9 (right panel). In both graphs, p(z) is on the left and q(z) on the right.

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357

Figure 2. Normalised g.l.c. (m2) for experiment 14 at 100 m versus sampler angles. Open circles refer to observed concentrations and crosses to simulated concentrations. Left and right panels show LW and WY simulations, respectively.

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Figure 3. INEL experiment 11. Normalised g.l.c. (m2) versus sampler angles; samplers are located at 2 m on some towers in the second arc; observed concentrations are indicated as open circles, whereas crosses and continuous line refer to predicted concentrations. Left panel corresponds to the new LW scheme and right panel to LW to the standard LAMBDA simulations.

Tracer Dispersion in Stable Low Wind Speed Conditions

Figure 4. As in Fig. 3, but for samplers at 6 m.

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360 100% 90% 80% obs. GRAL-A

70% CFD

60% 50% 40% 30% 20% 10% 0% 0

2000

4000 6000 conc. [µg/m³]

8000

10000

Figure 5. CFD calculated with GRAL including the new LW option and observed CFD for the Graz experiments.

Discussion

B. Physick: D. Anfossi:

Is the low wind speed version also applicable to higher wind? Yes. The code is organised in such a way to switch from the low wind to the windy option when the wind speed overcomes a threshold value (1.5 m s1). In this case, parameter p becomes 1/TL and q is 0.

REFERENCES Anfossi, D., Oettl, D., Degrazia, G., Goulart, A., 2005a. An analysis of sonic anemometer observations in low wind speed conditions. Bound.-Layer Meteorol. 114, 179–220. Anfossi D., Tinarelli, G., Trini Castelli, S., Ferrero, E., Oettl, D., Degrazia, G., 2005b. Well mixed condition veriﬁcation in windy and low wind speed conditions, Proceedings of Harmo10, Crete, 17–20 October 2005. Anfossi, D., Alessandrini, S., Trini Castelli, S., Ferrero, E., Oettl, D., Degrazia, G., 2006. Tracer dispersion simulation in low wind speed conditions with a new 2-D Langevin equation system. Atmos. Environ. 40, 7234–7245. Brusasca, G., Tinarelli, G., Anfossi, D., 1992. Particle model simulation of diffusion in low windspeed stable conditions. Atmos. Environ. 26A, 707–723. Ferrero, E., Anfossi, D., Brusasca, G., Tinarelli, G., 1995. Lagrangian particle model: Evaluation against tracer data. Int. J. Environ. Pollut. 5(4–6), 360–374.

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Frenkiel, F.N., 1953. Turbulent diffusion: mean concentration distribution in a ﬂow ﬁeld of homogeneous turbulence. Adv. Appl. Mech. 3, 61–107. Murgatroyd, R.J., 1969. Estimations from geostrophic trajectories of horizontal diffusivity in the mid-latitude troposphere and lower stratosphere. Quart. J. R. Met. Soc. 95, 40–62. Oettl, D., Almbauer, R.A., Sturm, P.J., 2001. A new method to estimate diffusion in stable, low wind conditions. J. Appl. Meteorol. 40, 259–268. Oettl, D., Almbauer, R.A., Sturm, P.J., Pretterhofer, G., 2003. Dispersion modelling of air pollution caused by road trafﬁc using a Markov Chain—Monte Carlo model. Stoc. Environ. Res. Risk Assess. 17(1–2), 58–75. Oettl, D., Goulart, A., Degrazia, G., Anfossi, D., 2005. A new hypothesis on meandering atmospheric ﬂows in low wind speed conditions. Atmos. Environ. 39, 1739–1748. Sagendorf, J.F, Dickson, C.R., 1974. Diffusion under low windspeed, inversion conditions. NOAA Technical Memorandum ERL ARL-52, p. 89. Thomson, D.J., 1987. Criteria for the selection of stochastic models of particle trajectories in turbulent ﬂows. J. Fluid Mech. 180, 529–556.

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Chapter 4.2 Application and sensitivity analysis of CAMx and CHIMERE air quality models in a coastal area Isabelle Coll, Guido Pirovano, Fanny Lasry, Stefano Alessandrini, Marco Bedogni, Matteo Costa, Veronica Gabusi, Laurent Menut and Robert Vautard Abstract This work is dedicated to the evaluation of the inﬂuence of input data on the ability of chemical transport models to reconstruct a given pollution episode. Such a question remains critical in the frame of air pollution forecasting and management. The study relies on the large European ESCOMPTE campaign that took place in the Marseilles area (South-East of France) in summer 2001, and that was dedicated to the constitution of a detailed 3D chemical and meteorological database for a CTM intercomparison exercise. The huge amount of measurement data indeed allowed us to investigate the behaviour of CTMs in a complex environment. In fact, the domain is characterised by the presence of large urban and industrial poles along the coastline served by a dense road network. Submitted to land-sea breeze phenomena, it shows an increasing orographic altimetry which constrains the transport of polluted plumes inland. Simulations, carried out with CAMx and CHIMERE, were focused on two intense ozone episodes: 21–23 June 2001 (moderate synoptic wind) and 24–26 June 2001 (local sea–land breeze circulation). The speciﬁcity of this work is to compare the models outputs both in their original conﬁgurations and gradually changing the input conﬁguration (meteorological data, boundary conditions) in order to quantify the related effect on the results. The model results were compared with the very rich set of dynamical parameters and chemical data of the campaign, obtained from ground stations, Lidar, aircrafts, boats, balloons, radiosoundings. Statistical indices and performance indicators were also set up and showed that the models

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could correctly reproduce photochemical smog phenomena over the Marseille area.

1. Introduction

While the role of chemical transport models (CTMs) is getting more and more important in air quality management, the degree of uncertainties brought into the model outputs, by the choice of input data on one hand, and by the model internal parameterisations on the other hand, remain mainly unknown. In particular, in air pollution forecasting, the question of the error bars to be associated with a given predicted photochemical situation is becoming of primary importance, so as to correctly interpret the simulated concentration ﬁelds and predicted diurnal maximum values. The work proposed here aims to describe and to quantify the sensitivity of a model answer to the choice of the input modules and data, and to estimate if this answer appears to be model-dependent. The study is based on the simulation of a 6-day regional episode observed in the South of France with two different models, CAMx (Environ, 2003) and CHIMERE (Vautard et al., 2005), having their own initial conﬁgurations, and then switching their meteorological ﬁelds and chemical boundary conditions. A statistical comparison of the model outputs was conducted over the whole set of model conﬁgurations. The results of all the model conﬁgurations have been examined in order to determine how much the changes in dynamical and chemical input data affect the models outputs, trying to discriminate the inﬂuence of internal and external conﬁguration choices.

2. Presentation of the simulations 2.1. Domain of the study

The ESCOMPTE programme has been set up in order to closely evaluate the ability of regional CTMs to produce consistent concentration ﬁelds over a complex terrain. This project was based on a fully descriptive measurement campaign that took place in the South of France, in the region of Fos-Berre-Marseille, in June 2001. The domain of interest (see Fig. 1) is characterised by its speciﬁc geographical position on the Mediterranean coast favouring the onset of breeze cells and its complex topography with a very heterogeneous relief constraining the transport of air masses. Moreover, a rich mixture of urban, road-trafﬁc, industrial and

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biogenic emissions, leads to frequent exceedances of the pollutant concentration thresholds in summer. 2.2. Set up of the simulations

The two models have been run on a domain of about 140 140 km2, with a horizontal resolution of 3 3 km2 for CAMx and 4 4 km2 for CHIMERE. The speciﬁc inventory developed for the purposes of the ESCOMPTE programme has been used for all simulations. The photochemical episode simulated here is the Intensive Observation Period no. 2 of the ESCOMPTE campaign, which can be divided into two parts: 3 days with moderate synoptic winds blowing from the north-west and dominating the transport of moderately polluted plumes to the southeastern part of the coast around Toulon, and 3 days characterised by low wind speed, high temperatures, the formation of local land–sea breeze circulation and elevated ozone maxima inland, downwind the city of Marseille and downwind the industrial areas of the Berre pond. A larger description of this IOP and the associated collected data can be found in Cros et al. (2004). The models were run as a base case (hereafter called CAMx_base and CHIMERE_base), with MM5 meteorological ﬁelds and chemical boundary conditions provided by the

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continental version of CHIMERE. Sensitivity simulations were then conducted, ﬁrst by changing the meteorological ﬁelds with RAMS ﬁelds (so-called CAMx_RAMS and CHIMERE_RAMS simulations), and then by replacing the CHIMERE boundary conditions in the base case by MOCAGE boundary conditions (CAMx_MOCAGE and CHIMERE_ MOCAGE simulations). In both cases, the meteorological calculations were forced at the boundaries by ECMWF ﬁelds. 3. Results and discussion 3.1. Meteorological simulations

As for meteorological ﬁelds, the models are skilful in reproducing the different features of the atmospheric circulation of both IOP2a and 2b. During IOP2a, both models reproduce well the Mistral synoptic conditions, characterised by a quite homogenous wind blowing from the northwest. A more interesting result have been achieved during IOP2b, as models have shown to correctly describe the diurnal evolution of the sea–land breeze, both along the coast and in the inland part of the domain. Indeed, the observed and modelled situations, as illustrated in Fig. 2 for 25/6, show similar mean features as the changing sea breeze direction and intensity between the western and the eastern part of the domain, and the very local wind variations that result from the inhomogeneity of the domain relief (in particular at the east and north-east of Marseille around Plan d’Aups). Despite some variations in the description of the breeze situation by the two models (exact time of breeze reverse, local directions of the windy), signiﬁcant and systematic discrepancies at ground level have been noticed between the two simulations, mainly during IOP2b, as MM5 underestimated wind speed, while RAMS overestimated it (see also Fig. 2). Such a difference should lead to a quite different restitution of the chemical situation. More details on the evaluation of the meteorological simulations can be found in Pirovano et al. (2006). 3.2. Photochemical simulations

The whole set of photochemical simulations conducted with CAMx and CHIMERE also provided a good understanding of the regional situation. As observed, the simulations showed moderate ozone maxima around 85 ppbv located out at sea/off the Toulon coast during IOP2a under the inﬂuence of synoptic north-westerly winds. During IOP2b, ozone peaks

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exceeding 100 ppbv are built-up in a plume transported by the sea breeze from the coast to the North of the domain. Time series conﬁrm the consistency of the model outputs, as presented here for the base cases in Aix, Cadarache and Mourre Negre (Fig. 3). Both models correctly restitute the switch from a synoptic situation bringing background ozone from the North during IOP2a, and signiﬁcant ozone production inland during IOP2b. Both models provide the right order of magnitude for ozone concentrations, all along the day (night time values are closely dependent on very local scale processes and difﬁcult to reproduce), whereas we can already detect a slight underestimation of ozone production by CAMx_base. Maps of ozone at ground level reveal quite large differences between the two models and between the model conﬁgurations, especially during the days of intense ozone production. In their original conﬁguration,

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CAMx and CHIMERE both show on the 25/6, from 13:00 to 16:00 CET (Fig. 4), the development of an ozone plume inland, parallel to the coastline, extending from Toulon to Avignon. The CAMx plume appears to be much more diffuse and less intense than the CHIMERE one. It extends out at sea showing peaks of the same order of magnitude as inland, whereas CHIMERE simulates on that day a well-deﬁned ozone plume inland, and a contrasted situation between the land and the sea, where maxima are 25 ppbv lower than inland. As previously observed with CHIMERE sensitivity tests (Lasry et al., 2005), the inﬂuence of boundary conditions on the ozone plume is huge over the sea but it is almost null inland in that region (see Fig. 5 for CAMx_MOCAGE). The CAMx_MOCAGE simulation outputs indeed look alike the CAMx_base results inland, but they show a pronounced and homogeneous decrease of ozone concentrations over the sea, of approximately 10–15 ppbv. Such a feature reveals the determining role of strong local emissions that perturbs the content of air masses arriving from the sea and drive alone the intensity of the photochemistry downwind emitting areas. On the reverse, the change in dynamical inputs strongly modiﬁes the shape of the oxidant plume. For both models, the strong RAMS wind

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ﬁelds transport much faster the emitted compounds to the North, and at the same time contribute to a greater dilution of primary pollutants, the ozone maxima thus being signiﬁcantly reduced in RAMS conﬁgurations compared to the base cases (see Fig. 6). Statistic calculations (Table 1) based on the observed and simulated hourly daily maxima of ozone in each measurement site have been computed separately for IOP2a and IOP2b. The results conﬁrm a global difference of 6 ppbv between CAMx_base and CHIMERE_base for all IOP2b at ground level, the difference mainly arising from the sites located directly at the north-east of the Berre pond and along the Durance Valley (zones 3 and 4). There, the increase in ozone between western coastal sites and the inland central part of the domain is underestimated by CAMx_base (+4 ppbv) compared to the observed (+15 ppbv) and the CHIMERE_base ones (+11 ppbv). In CAMx_MOCAGE, this gradient is better reproduced (+9 ppbv), but the model still underestimates the absolute peak value inland, revealing lower ozone production whatever the conﬁguration in CAMx compared to CHIMERE. The most interesting feature of the analysis is the major

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impact of the wind strength overestimation in RAMS calculations that totally erases the geographical structure of the plumes during IOP2b in both models outputs and induces homogeneous mean ozone maxima all over the domain. Models performance evaluations have also been conducted along the vertical proﬁles: Fig. 7 shows the comparison between observed and computed vertical proﬁle at the ALTO LIDAR site on 25/6. As for the other days of the IOP, both models have been able to capture the

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change in vertical gradient corresponding to the planetary boundary layer height that takes place at 1800 m agl on 25/6. However, inside the PBL, both models depict a rather constant ozone proﬁle due to strong vertical mixing, and are not able to capture the high ozone layer observed around 1000 m agl, which has been attributed to a vertical intrusion of ozone started on June 24 (Kalthoff et al., 2005). In the base case, CAMx estimations are in good agreement with the observations, while CHIMERE overestimates ozone concentrations below 1000 m. Models show the same sensitivity to meteorological input with a relevant decrease of 10–15 ppb inside the PBL when switching from MM5 to RAMS. It is worth noting that CAMx estimation of the PBL height is sensitive to meteorological input while it is not the case for CHIMERE. Computed vertical proﬁles are logically sensitive to boundary conditions. Switching from CHIMERE to MOCAGE continental ﬁelds induces a noticeable variation of the ozone concentration all along the vertical proﬁle and not only inside the PBL. Remarkable variations are observed also at ground level, thus conﬁrming the relevance of long-range transport of ozone in the development of photochemical pollution at local scale.

4. Conclusions

The sensitivity tests conducted with the CAMx and CHIMERE models for the IOP2 of the ESCOMPTE campaign allowed us to put in evidence major differences between the outputs of two CTMs running with an identical conﬁguration (chemical mechanism, emissions, input chemical and dynamical ﬁelds) at a similar resolution (3 and 4 km). A ‘‘model signature’’ is thus visible on the output data. Although both runs satisfactorily ﬁt with ground stations, signiﬁcant differences have been observed in oxidant production downwind the main anthropogenic sources, in polluted air masses over the sea, and thus in the shape and extent of the ozone plume. The parameterisation of vertical mixing, but also the deposition of ozone over the sea, as well as the intensity of photolysis rates are many parameters that can account for such a difference in the ozone behaviour. In particular, over coastal areas such as the Marseille region, the boundary layer development and extent over the land may represent a critical parameter for ozone production and primary pollutant accumulation in land–sea breeze situations. Such a parameter should be investigated in more details.

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The analysis carried out brought us to the following conclusions: There are strong similarities between the answers of the two models to the implementation of a speciﬁc input module. This effect may overpass the inﬂuence of the internal conﬁguration of the model. Boundary conditions do have a great importance on the extent of the episode, but this inﬂuence is very weak downwind strong emissions. The main differences between RAMS and MM5 take place only at ground level, while differences in ozone are relevant along all the PBL: ozone inside the whole PBL appears to be mainly driven by ground level effects in poorly windy conditions. Simulated ozone plumes are strongly inﬂuenced by the model representation of the wind circulation, even when using similar dynamical modules based on the same type of parameterisations. Indeed, although ozone production rates along the day are mostly emission-dependent, the structure of the ozone plume over the domain is completely driven by wind ﬁelds. The meteorological module represents a critical choice in modelling air quality in coastal areas. Discussion

D.W. Byun:

M. Bedogni:

P. Builtjes:

M. Bedogni:

In the HNO3 time series, nitric acid concentrations from CAMx are much higher than the ones from CHIMERE. Will you explain the peak O3 difference between the two models in relation to the HNO3 concentration difference? We think that HNO3 differences could be related to both deposition velocities and also to chemistry. As for the latter, higher HNO3 concentrations could indicate a stronger radical termination in CAMx than in CHIMERE, hence explaining differences in Ozone peaks. The plots show differences in O3 concentrations between CAMx and CHIMERE, especially over the sea. Is there a difference in the treatment of dry deposition in the models? In fact we think that different treatment of deposition phenomena maybe inﬂuence the ozone performances of the two models over the sea, currently further studies are trying to conﬁrm this feeling.

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Both model set ups signiﬁcantly overestimated O3 during night time, in particular in the morning hours, far higher than CAMx versus CHIMERE. Do you have an explanation for this? Generally, ozone night time values are closely dependent on very local scale processes and are difﬁcult to reproduce. The differences between the two models could be related to the different dispersion schemes, particularly along the vertical side. Moreover several monitoring stations even placed in rural areas, show an ‘‘urban’’ shape, probably due to very local sources that can’t be captured by models.

ACKNOWLEDGMENTS

CESI contribution of this paper has been supported by the MICA in the frame of Energy Research Program for the Italian Electric System (Decree of February 28, 2003). Technical contribution of the Mobility and Environment Agency has been sustained by the Municipality of Milan. REFERENCES Cros, B., Durand, P., Cachier, H., Drobinski, Ph., Fre´jafon, E., Kottmeier, C., Perros, P.E., Ponche, J.L., Robin, D., Saı¨ d, F., Toupance, G., Wortham, H., 2004. The ESCOMPTE program: an overview. Atmos. Res. 69, 241–279. ENVIRON., 2003. CAMx (Comprehensive Air Quality Model with extensions)—User’s Guide Version 4.00, Internal Report, Environ Int. Corp. Kalthoff, N., Kottmeier, C., Thurauf, J., Corsmeier, U., Said, F., Frejafon, E., Perros, P.E., 2005. Mesoscale circulation systems and ozone concentrations during ESCOMPTE: a case study from IOP 2b. Atmos. Res. 74, 355–380. Lasry, F., Coll, I., Fayet, S., Samaali, M., Causera, G., Lesponne, C., Francois, S., Ponche, J.L., 2005. Ozone sensitivity study—evaluation of the efﬁciency of the legislations for the year 2010, Thirteenth International Conference on Modelling, Monitoring & Management of Air Pollution, WIT, Cordoba, Spain, 16–18 May 2005. Pirovano, G., Coll, I., Bedogni, M., Alessandrini, S., Costa, M.P., Gabusi, V., Lasry, F., Menut, L., Vautard, R., 2006. On the inﬂuence of meteorological input on photochemical modelling of a severe episode over a coastal area. Atmos. Environ. doi:10.1016/j.atmosenv.2007.04.011. Vautard, R., Bessagnet, B., Chin, M., Menut, L., 2005. On the contribution of natural aeolian sources to particulate matter concentrations in Europe: Testing hypotheses with a modelling approach. Atmos. Environ. 39, 3291–3303.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06043-3

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Chapter 4.3 A comparison between CHIMERE, CAMx and CMAQ air quality modelling systems to predict ozone maxima during the 2003 episode in Europe: Spain case study R. San Jose´, J.L. Pe´rez and R.M. Gonza´lez Abstract One of the most important advantages of the modern and stateof-the-art air quality modelling systems is the capability to produce air pollution forecasts and particularly for ozone. In this contribution, we have applied the CMAQ (EPA), CMAx (Environ, Co.) and CHIMERE (IPSL, INERIS, LISA, CNRS) air quality mesoscale modelling system to produce ozone air quality forecasts and compare the results for the 2003 August episode. Modern air quality modelling systems are composed by a complete, modern and robust mesoscale meteorological model and a chemical transport module. The mesoscale meteorological model used in this application is the well-known MM5 model developed by PSU/NCAR (USA). The MM5-chemical transport model requires emission data sets according to the grid spatial and temporal resolution. In this application we have considered a model domain of more than 5000 5000 km with 27 spatial resolution centred over Madrid city (Spain) and covering a substantial part of the north of Africa and most of the western European region. We have used the 5–10 August 2003 period where maximum ozone values around 300 mgm 3 were observed in several air quality monitoring stations in Europe. This contribution analyzes the results of the three different models and discuss the results according to available statistical tools.

1. Introduction

Air quality models have experimented considerable advances during the last decades. The models nowadays are complex modular structures, which are capable to run over fast and powerful computer platforms.

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By contrast, the capability to reproduce the measuring data is usually reaching correlation coefﬁcients between 0.6 and 0.7 but when high levels of pollution during special episodes like that occurred during the summer 2003 (August), the photochemical production of ozone should perform with the maximum capability since values of 300 mg m 3 can be reached and even higher values. The performance of state-of-the-art models (third generation of Eulerian air quality models) is tested in this contribution by comparing the modelling results with observed results. In this experiment, only monitoring data from stations located in Madrid (Spain) area have been used but European maps will be shown for the three tested models. The third generation of air quality modelling systems such as MM5 (PSU/NCAR)—as a reliable meteorological driver—and CMAQ (EPA, USA), CAMx (Environ, Co.) and CHIMERE (IPSL, France) form a reliable software tool to simulate the air quality concentrations. The advances in computer capabilities in the recent years have been also considerable and the capability to use a set of PCs (clusters) to run complex models such as MM5, CMAQ, CAMx and CHIMERE is nowadays a real issue. The Eulerian non-hydrostatic mesoscale meteorological models and dispersion models including chemistry with clouds and aerosols are today reliable software tools which can be used in real-time and forecasting mode. During the second generation of atmospheric air pollution models (McRae and Seinfeld, 1983; Venkatram et al., 1988; Carmichael et al., 1991; San Jose´ et al., 1994), a considerable attention was given to the 3D Eulerian simulations and the operational basis. A clear limitation of these models was found in several areas: the limited information provided by the boundary conditions so that long-range transport was almost not present. Simpliﬁed aqueous and cloud processes were present and poor attention was given to biogenic emissions and the impact on the chemical reactions and subsequent products. This contribution focuses on results from simulations over short–medium range by covering all the spatial domains affected by the temporal simulation. The so-called third generation of air pollution models (Peters et al., 1995) is presented by using the MM5-CMAQ modelling system and comparing the results at high-resolution level (almost street level) with the OPANA model (San Jose´ et al., 1999)—representative of the second generation of air pollution models but with full on-line linkage between meteorology and chemistry. The three photochemical Eulerian models used in this contribution are full representatives of the third generation of photochemical Eulerian models and are under continuous improvement and research. These models have been run for the same heat wave period in Europe with observed monitoring ozone concentrations higher than 250–300 mg m 3.

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2. Experimental set up

In our experiment, we have selected the period 1–15 August 2003. During this period of time, several stations reached values between 250 and 300 mg m 3. The selected period has a continuous increase in ozone concentrations. We have implemented the CMAQ, CAMx and CHIMERE models which receive meteorological wind ﬁelds from the MM5 (PSU/ NCAR) mesoscale meteorological model. The MM5 model receives boundary conditions from the GSM global model in NCEP (USA). The three models have been run over Europe with 50 km spatial resolution by using the EMEP emission inventory adapted to time resolution by the EMIMO model (San Jose´ et al., 1999). Since the CMAQ and CAMx are using the same projection system (Lambert Conformal Conic, LCC), these two models are ﬁtted perfectly to the European model domain. These models are run with 88 83 horizontal grid cells. The CHIMERE model uses geographical projection so that the model domain is approximated to the other two model domains. CHIMERE model is run with a spatial resolution of 0.51 with 67 46 horizontal grid cells. For this particular experiment, 12 vertical layers have been used for the three models. The CHIMERE model domain is 42% of the model domain used for CMAQ and CAMx. The 0.51 spatial resolution can be compared with the 50-km spatial resolution for CMAQ and CAMx. Figure 1 shows a scheme of the implementation of the three models and the different

Figure 1. Schema of the architecture used for this contribution to run CHIMERE, CAMx and CMAQ by using MM5 meteorological model to provide proper wind ﬁelds for the three photochemical models.

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structure for generating the emission inventory. CHIMERE has been run with the EMEP to CHIMERE interface provided by CHIMERE developers and CMAQ and CAMx have been run with the EMIMO V2 model. The emission inventory used as reference is the EMEP. In Fig. 2, we see the EMEP emission map for total NOx in 2003 and in Fig. 3 we observe the EMEP-EMIMO V2 emission map in Lambert Conformal

Figure 2. EMEP total emissions for NOx during 2003.

Figure 3. EMEP-EMIMO v2 Lambert Conformal Conical projection for CAMx and CMAQ.

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Figure 4. VOC distribution by source according to the SNAP emission types.

Figure 5. Hourly emission distribution by sources according to SNAP categories.

Conical projection for 2003. The 2912 point source emissions registered in EPER for the European domain have been included. In Fig. 4, we observe the VOC distribution by source as produced by EMEP-EMIMO in our model domain. In Fig. 5, we present the hourly distribution pattern used by EMIMO for this experiment. 3. Results

The three models generate the maximum ozone concentrations over Europe on 5 August 2003 for this simulation period. In Figs. 6–9, we see the results for the three models.

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Figure 6. CMAQ (left) and CAMx (right) maxima of the day for 5 August 2003 over Europe.

Figure 7. CHIMERE maxima of the day for 5 August 2005 over Europe.

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Figure 8. Comparison between observed and modelled ozone concentrations in station 5 (Fuenlabrada, Madrid, Spain) by using the CMAQ model (left) and in station 6 (Mo´stoles, Madrid, Spain) by using CAMx model (right).

The results for the maxima of the day show that CAMx model produces higher values than CMAQ and CHIMERE and over a larger domain centred in the north part of Europe (France, Belgium, Holland, U.K. and Germany) and also some parts in the north of Italy and Moscow. CMAQ compared with CAMx produces maxima values for 5 August 2003 similar to CAMx but over a reduced extension domain. CHIMERE produces less ozone concentrations than CMAQ and CAMx although the maxima is in the same range but the extension domain where these maxima values are produced is reduced to the northern parts of France and U.K. CAMx seems to be more sensitive than CMAQ and CHIMERE since in the Scandinavian countries lower values than CMAQ are shown (out of model domain in the case of CHIMERE). When we compare the monitoring data in Madrid area with the modelling results for the 120 h period, we observe in Table 1 the correlation coefﬁcients for different stations and also for the average value for all stations in Madrid for the three models.

4. Conclusions

We have implemented and simulated the CMAQ, CAMx and CHIMERE models over Europe with similar spatial resolutions although CHIMERE has been simulated over a domain of about 40% of the domain where the

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Figure 9. Comparison between observed and modelled ozone concentrations in station 6 (Mo´stoles, Madrid, Spain) by using CHIMERE model. Table 1. Correlation coefﬁcients between observed and modelled data for the three tested models over the Madrid area Correlation coefﬁcients: monitoring data/modelling data of ozone Station Average correlation coefﬁcient over all stations Best correlation coefﬁcient

CMAQ

CAMx

CHIMERE

0.832

0.880

0.764

0.832 (average) 0.822 (station 5)

0.880 (average) 0.854 (station 6)

0.857 (station 6)

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CMAQ and CAMx models have been run. CAMx model obtained the best correlation coefﬁcients although CMAQ and CHIMERE also obtained good values. CHIMERE, however, showed signiﬁcant differences with the maximum values measured by different stations. CPU time used by CAMx was the highest of the three models. Further experiments should be done by using the nesting capabilities of the three models. Also, analysis of CPU time by using double core processors (shared memory) should also be performed. Actual modern Eulerian models seem to be prepared to forecast ozone maxima of the day with high degree of accuracy. Detailed and local emission inventory is an essential module to improve the performance at local level, particularly over high pollutant areas such as urban environs. ACKNOWLEDGMENTS

We would like to thank EPA, Environ Co. and IPSL (France) for providing the codes of the photochemical models CMAQ, CAMx and CHIMERE, respectively. Also, we would like to thank PSU/NCAR for providing the code of MM5 mesoscale meteorological model. REFERENCES Carmichael, G.R., Peters, L.K., Saylor, R.D., 1991. The STEM-II regional scale acid deposition and photochemical oxidant model—1. An overview of model development and applications. Atmos. Environ. 25A, 2077–2090. McRae, G.J., Seinfeld, J.H., 1983. Development of a second generation mathematical model for urban air pollution. II. Evaluation of model performance. Atmos. Environ. 17, 501–522. Peters, L.K., Berkowitz, C.M., Carmichael, G.R., Easter, R.C., Fairweather, G., Ghan, S.J., Hales, J.M., Leung, L.R., Pennell, W.R., Potra, F.A., Saylor, R.D., Tsang, T.T., 1995. The current state and future direction of Eulerian models in simulating the tropospheric chemistry and transport of chemical species: A review. Atmos. Environ. 29(2), 189–222. San Jose´, R., Rodrı´ guez, L., Moreno, J., 1994. An application of the ‘‘Big Leaf’’ deposition approach to the mesoscale meteorological transport and chemical modelling in a three dimensional context. In: Borrel, P.M., Borrell, P., Cvitas, T., Seiler, W. (Eds.), Proceedings of the EUROTRAC Symposium. p. 620. San Jose´, R., Rodrı´ guez, M.A., Pelechano, A., Gonza´lez, R.M., 1999. Sensitivity studies of dry deposition ﬂuxes. In: San Jose´, R. (Ed.), Measuring and Modelling Investigation of Environmental Processes. WIT Press Computational Mechanics Publications, pp. 205–246. ISBN: 1-853125660. Venkatram, A., Karamchandani, P.K., Misra, P.K., 1988. Testing a comprehensive acid deposition model. Atmos. Environ. 22, 737–747.

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Chapter 4.4 Final results of the model inter-comparison of very high-resolution simulations with numerical weather prediction models for eight urban air pollution episodes in four European cities B. Fay, L. Neunha¨userer, A. Baklanov, G. Bonafe´, S. Jongen, J. Kukkonen, V. Ødegaard, J.L. Palau, G. Perez-Landa, M. Rantama¨ki, A. Rasmussen, R.S. Sokhi and Y. Yu Abstract Meteorological conditions during eight air pollution episodes were simulated in the target cities Helsinki, Oslo, Bologna and Valencia with varying participation of three HIRLAM versions, Lokalmodell/ LAMI, MM5 and RAMS. The models were used with their operational parameterisations but tested for increased resolution of up to 1 km by multiple nesting. The meteorologically extreme winter episodes in Helsinki, Oslo and Bologna are dominated by groundbased inversions in very stable stratiﬁcation which are poorly simulated, and the causes investigated. For the less meteorologically extreme summer photochemical episodes in Valencia and Bologna, the models perform better and succeed in simulating the dominating mesoscale circulations. In Bologna, the variable border between an Apennine mountain-valley breeze and a larger Po valley circulation reduces predictability. 1. Introduction

Very high-resolution meteorological input data (1 km resolution and below) are requested for air quality and dispersion modelling in urban information and emergency preparedness systems. With a growing part of the world population living in conurbations and stricter air pollution abatement legislation, NWP data are increasingly demanded and applied for the urban environment as well (Baklanov et al., 2002; Neunha¨userer et al., 2006).

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Simulations with high-resolution NWP models were evaluated in previous studies mainly for rural areas (see Mass et al., 2002). Only few studies had been performed for urban areas, often with the Mesoscale Model MEMO (e.g., (Moussiopoulos, 1995) or the MM5 (e.g., Kotroni and Lagouvardos, 2004). In the EU FP5 project FUMAPEX (Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure), these simulations serve to evaluate the performance of operational numerical weather prediction (NWP) and mesoscale models in providing suitable meteorological input data to urban air quality models. The models were used with their operational parameterisations but tested for increased resolution of up to 1 km by multiple nesting during air pollution episodes.

2. Episode description, model evaluation and inter-comparison

In FUMAPEX, simulations were performed for eight different pollution episodes in four target cities (Helsinki, Oslo, Bologna and Valencia) with varying operational NWP models and European partners, mainly weather services. The episodes are characteristic for the regions: six winter inversion-induced particle episodes in Helsinki, Oslo and Bologna; two summer photochemical (ozone) episodes in Bologna and Valencia. In northern Europe, ground-based inversions, stable atmospheric stratiﬁcation, wind speed and topography are the key meteorological episode factors (Kukkonen et al., 2005). Wintertime episodes prevail which involve particle formation or suspension and often involve meteorologically extreme situations. In southern Europe, photochemical summertime episodes are typical and may persist for many weeks in areas like Valencia, interrupted by short destruction phases only (Milla´n et al., 1996). These episodes may be frequent or even chronic and are not characterised by meteorological extremes. The key episode factors are mesoscale circulations driving pollutant dispersion. The following partners participated in the simulations with their distinct model chains: CEAM (Valencia) with RAMS (40, 13, 4.5 and 1.5 km), DMI (Copenhagen) with DMI HIRLAM (5 and 1.4 km), DNMI (Oslo) with DNMI HIRLAM (10 km) and MM5 (9, 3 and 1 km), DWD (Offenbach) with Lokalmodell (LM, 7, 2.8 and 1.1 km), ARPA-SMI (Bologna) with Lokalmodell version LAMI (7, 2.8 and 1.1 km), FMI (Helsinki) with FMI HIRLAM (33 and 22 km), and the University of Hertfordshire (UH) with MM5 (27, 9, 3 and 1 km). Forty-eight-hour forecasts, starting at 00UTC for each day of the episode, were simulated in order to supply the requested forecasts for the

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urban information systems. The model performance in forecasting the main meteorological factors characterising the episodes was evaluated using horizontal ﬁelds, vertical cross-sections and vertical proﬁles for single parameters at station locations every six forecast hours, 48-h forecast time series of single parameters and of vertical proﬁles at station locations. 3. Results and discussion

The major results for the episodes are summarised below. A full description of single model performance by episode-relevant meteorological parameters and model resolution, and a model inter-comparison (of the highest model resolution results) for each episode, summaries for each model and parameter, a longer term evaluation, description of model deﬁciencies and recommendations are presented in Fay et al., 2004, 2005; Neunha¨userer et al., 2004; Fay and Neunha¨userer, 2005. 3.1. Increasing model resolution

First, the inﬂuence of increasing resolution (without adapting any parameterisations) was analysed for each episode and model separately. In comparison with station observations, increasing model resolution to about 1 km in the non-hydrostatic model scale below 10 km usually shows little effect but is largest for coastal stations (Helsinki/Valencia): Improvements are mainly due to a better land/sea description of the coastline and the associated soil type distribution which affects the surface ﬂuxes via its speciﬁc thermal and hydrological characteristics (Fig. 1). The substantial inﬂuence of varying surface properties and physiographic parameters with increased resolution near the coast is stated for FMI HIRLAM (Rantama¨ki et al., 2003), DMI HIRLAM and DWD LM (Fay and Neunha¨userer, 2005). for mountainous areas (Oslo/Valencia/Bologna): Large impact and often some improvement (e.g., for T2 m and v10 m) at station locations are mainly due to the more detailed orography leading to improved topography effects like blocking, shading, ﬂow around mountains, increased channelling in valleys, more clearly deﬁned convergence/divergence lines, improved foehn simulations and mesoscale circulations. The changes/improvements are very distinct when looking at horizontal model ﬁelds of temperature, wind speed and direction (Fig. 2). These results conﬁrm the reports of Mass et al. (2002)

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Figure 1. Inﬂuence of increased resolution at coastal stations, 48-h forecasts. Left: Helsinki, Kaisaniemi, 48-h forecast of 2-m temperature with DWD LM 7 km (hollow circle), 2.8 km (grey), 1.1 km (rhombus) and observations (black dots), 23 March 1998, 00UTC. Right: Oslo, Hovin, 10 m wind speed for DMI HIRLAM 15.0 km (rhombus), 5.0 km (light grey), 1.4 km (dark grey) and observations (grey dots), 18 November 2001, 00UTC.

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Figure 2. Increased complexity in mountains for higher resolution. Top: Bologna, horizontal ﬁelds of 10 m wind direction for ARPA LAMI, 7.0 km (left) vs. 1.1 km (right), observation stations (black dots), 12 June 2002, 00UTC+36 h. Bottom: Oslo episode, 17 November 2001, 00UTC+18 h, vertical cross-section along Hallingdal valley NW of Oslo, DWD LM forecasts of vertical velocity (shaded contours) and horizontal wind ﬁeld (arrows) with 7 km vs. 1.1 km resolution (showing foehn waves in vertical velocity ﬁeld).

about largely improved mesoscale structures but possible degradation of forecasts evaluated at station locations for increasing horizontal resolution below 10 km. 3.2. Model performance and inter-comparison 3.2.1. Winter episodes

The temporal evolution of temperature inversions, wind speed and atmospheric stability are the most suitable meteorological predictors for winter episodes. The episodes Helsinki December 1995, March 1998 and April 2002 (both spring dust episodes), Oslo November 2001 and January 2003 and Bologna January 2002 are dominated by inversions. Comparison of model episode performance with one-year statistical scores showed unusually poor model performance and apparently extreme

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meteorological conditions for the Helsinki December 1995, the Oslo January 2003 and the Bologna January 2002 episodes. The 1995 Helsinki episode is caused by an exceptionally persistent, strong and shallow ground-based inversion reaching 181C in the lowest 125 m with radiative cooling of the snow-covered ground under a clear sky (Rantama¨ki et al., 2005). The largest model deﬁciencies are found for this episode. They involve large overprediction of surface temperatures and underprediction of inversion strength for most models (Fig. 3, left). Boundary layer winds are also often overpredicted and calms not simulated, pointing to excessive vertical exchange and insufﬁcient stability. This applies to a lesser and varying extent to the other extreme episodes as well (Fig. 3, right). These results indicate various insufﬁciencies in turbulence parameterisation especially in very stable conditions, deﬁciencies in soil parameters, snow and sea ice treatment and data assimilation in the NWP models. Large regional temperature gradients between cold winter land masses and the ice-free sea (Helsinki, Oslo) or between high mountains and valleys (Oslo, Bologna) together with false wind speed and direction forecasts may lead to erroneous temperature advection deteriorating the inversion forecasts. A detailed analysis of vertical proﬁles of temperature, wind speed and direction evaluated against radiosoundings, for example, for the Oslo January 2003 episode shows that the quality of the inversion forecast may depend on the skill of the combined forecast of wind speed and direction, i.e., of vertical wind shear and temperature advection, both near the surface and aloft (Fay and Neunha¨userer, 2005). 3.2.2. Summer episodes

The investigated summer episodes are photochemical episodes in Valencia September 1999 and Bologna June 2002. The key to episode prediction is the simulation of the often-complex mesoscale circulations that determine pollutant dispersion and concentration. The diurnal 2-m temperature cycles of the Valencia September 1999 episode with their gradually increasing maximum values are well described, catching the character of the typical recharging period (Fig. 4). The diurnal breeze circulation of higher daytime SSE sea breeze values and lower nocturnal NW drainage winds important for the transport of ozone precursors and air pollutants is generally captured well. The simulated wind ﬁelds are closely inter-linked to the orography with channelling effects at high resolutions. Thus, both participating models succeed well in forecasting the observed key meteorological factor in the

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Figure 3. Helsinki: Inter-comparison of CEAM RAMS, DMI HIRLAM, DNMI MM5, DWD LM and UH MM5 at highest resolution with measurements. Left: vertical proﬁle of temperature for Kivenlahti, 28 December 1995, 00UTC+06 h. Right: 48-h forecast of 2-m temperature for Kaisaniemi from 23 March 1998, 00UTC (observations: small hollow circles).

Valencia episodes, i.e., the mesoscale circulations like the complex combined sea breeze and upslope wind circulation (Fig. 5), night-time drainage winds, convergence and divergence lines that are described in Milla´n (2002) and dominate episode development. In Bologna, the models reproduce winter and nocturnal drainage winds and summer valley-mountain breezes in the Apennine mountains under

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Figure 4. Valencia: Coastal station Onda, 2-m temperature, forecast series over six days starting 26 September 1999, 00UTC+0 h. Black: observations, grey: overlapping 48-h forecasts. Left: CEAM RAMS, right: DWD LM.

high-pressure conditions and also the complex wind ﬁeld pattern of a superposed large east-west sea breeze circulation regime in the Po valley in summer. The highly variable border between this larger Po valley circulation and the Apennine mountain-valley breeze (visible in Fig. 2, top) reduces predictability especially of wind ﬁelds. 3.3. Longer term model evaluation

A longer term statistical evaluation (usually one year) was performed in order to analyse a more representative data sample and place the episode

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Figure 5. Valencia: Developed combined circulation. Identical vertical cross-sections (perpendicular to coast north of Valencia) for vertical velocity (shaded contours) and horizontal wind vector (arrows), 28 September 1999, 00UTC+35 h. Left: CEAM RAMS (1.4 km), right: DWD LM (1.1 km). Orographic updraft chimney for pollutant injection on mountaintop.

results in the longer term context. Especially, the results for the 50 stations for LM/LAMI were also investigated in grouped categories: urban, suburban, rural, Po valley and Apennine mountains. Different models perform well or poorly depending on chosen station or group of stations, meteorological parameter and partly the time of the

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day (forecast hour), chosen statistical score (bias or rmse) and partly also on the season for the same parameter. Models perform differently for the different episodes mainly depending on their ability to forecast the speciﬁc meteorological episode features in sometimes complex locations and even for extreme meteorological conditions, and also on station representativeness and observation quality. The dependence of model performance on the location being urban, suburban or rural is much smaller in comparison. 3.4. Conclusions and outlook

Comparing the results of the different models for the different episodes in terms of their skill in forecasting air pollution episodes, the models apparently perform better in predicting the summer ozone episodes than the winter/spring inversion episodes. In some regions like Valencia, summer episode conditions are very frequent and possibly require less unusual or extreme meteorological conditions than rarer episodes in most other areas. This picture is also conﬁrmed in the evaluation of the episode performance against the background of longer term statistical scores. Additionally, the key episode predictor of mesoscale circulation simulation generally improves with increasing model resolution (Mass et al., 2002), while more local effects like strong inversions and atmospheric stability are harder to forecast even in rural areas and performance scores may even deteriorate for point evaluation with increasing model resolution. In summary, model performance at station locations was poorest for extreme inversions episodes and stable winter or nocturnal stratiﬁcation, improved for less extreme spring inversion episodes and showed better skill in the photochemical episodes dominated by mesoscale circulations especially in the Valencia area. These results clearly show the scope, and also the limitations of even highly resolved mesoscale NWP models, especially for the sometimes extreme episode conditions. The difﬁculties encountered also highlight questions of model predictability, representativeness of city measurement stations and the necessity of performing concentrated city measurement campaigns including vertical soundings and of developing methodologies for the assimilation of these urban observations into the NWP models. NWP models do have the potential to provide data almost continuously in time and space and with higher physical consistency than many meteorological pre-processors. Model improvements, however, should start with improving the performance for some well-known general NWP model forecast deﬁciencies (e.g., strong inversions, stable atmospheric

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conditions, low wind speeds and accompanying wind directions) that also play an important role in air pollution episodes and occur in rural and urban areas alike. They need to be addressed with higher model resolution, scale-adapted soil, surface and boundary layer parameterisations and extended and improved data assimilation. Only if the overall forecast performance in the greater urban area or even region is high, then the introduction of urbanised physiographic parameters and surface and soil parameterisations will be able to achieve the aspired local urban improvements. Urbanisation measures of various complexity were recently performed for several mesoscale NWP models like MM5 (Dupont et al., 2004), RAMS, Lokalmodell LM (Neunha¨userer et al., 2006) and its Swiss version aLMo, DMI-HIRLAM and others, partly achieved in the FUMAPEX project (Baklanov, 2005). ACKNOWLEDGMENT

This study is part of the EU FP5 project FUMAPEX (Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure), 2002–2005, WP3, funded by the EU under contract no. EVK4-CT-2002-00097. REFERENCES Baklanov, A., Rasmussen, A., Fay, B., Berge, E., Finardi, S., 2002. Potential and shortcomings of numerical weather prediction models in providing meteorological data for urban air pollution forecasting. Water Air Soil Pollut. Focus 2(5–6), 43–60. Baklanov, A. (Ed.), 2005. FUMAPEX ﬁnal scientiﬁc report. 3 Vol., DMI Copenhagen, December 2005, in press. Dupont, S., Otte, T.L., Ching, J.K.S., 2004. Simulation of meteorological ﬁelds within and above urban and rural canopies with a mesoscale model (MM5). Bound.-Layer Meteorol. 113, 111–158. Fay, B., Neunha¨userer, L., 2005. Evaluation of very high-resolution simulations with the non-hydrostatic numerical weather prediction model Lokalmodell for urban air pollution episodes in Helsinki, Oslo and Valencia. Atmos. Chem. Phys. Discuss. 5, 8233–8284 www.atmos-chem-phys.org/acpd/5/8233/ Fay, B., Neunha¨userer, L., Palau, J.L., Dieguez, J.J., Ødegaard, V., Bjergene, N., Soﬁev, M., Rantama¨ki, M., Valkama, I., Kukkonen, J., Rasmussen, A., Baklanov, A., 2004. Model simulations and preliminary analysis for three air pollution episodes in Helsinki. FUMAPEX Report D3.3, DWD, Offenbach, Germany, p. 60. Fay, B., Neunha¨userer, L., Palau, J.L., Perez-Landa, G., Dieguez, J.J., Ødegaard, V., Bonafe, G., Jongen, S., Rasmussen, A., Amstrup, B., Baklanov, A., Damrath, U., 2005. Evaluation and inter-comparison of operational mesoscale models for FUMAPEX target cities. FUMAPEX Report D3.4, DWD, Offenbach, Germany, p. 110.

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Kotroni, V., Lagouvardos, K., 2004. Evaluation of MM5 High-resolution real-time forecasts over the urban area of Athens. Greece. J. Appl. Meteorol. 43, 1666–1678. Kukkonen, J., Pohjola, M., Sokhi, S., Luhana, L., Kitwiroon, N., Fragkou, L., Rantama¨ki, M., Berge, E., Ødegaard, V., Slørdal, L.H., Denby, B., Finardi, S., 2005. Analysis and evaluation of selected local-scale PM10 air pollution episodes in four European cities: Helsinki, London, Milan and Oslo. Atmos. Environ. 39(15), 2759–2773. Mass, C.F., Owens, D., Westrick, K., Colle, B.A., 2002. Does increasing horizontal resolution produce more skillful forecasts? Bull. Am. Meteorol. Soc. 83, 407–430. Milla´n, M.M. (Ed.), 2002. Ozone dynamics in the Mediterranean basin. Air Pollution Research Report 78. A collection of scientiﬁc papers resulting from the MECAPIP, RECAPMA and SECAP Projects. European Commission and CEAM, Valencia, Spain, p. 287. Milla´n, M.M., Salvador, R., Mantilla, E., Artin˜ano, B., 1996. Meteorology and photochemical air pollution in southern Europe: Experimental results from EC research projects. Atmos. Environ. 30(12), 1909–1924. Moussiopoulos, N., 1995. The EUMAC Zooming Model, a tool for local-to-regional air quality studies. Meteorol. Atmos. Phy. 57, 115–133. Neunha¨userer, L., Fay, B., Palau, J.L., Pe´rez-Landa, G., Rasmussen, A., Baklanov, A., Ødegaard, V., Bjergene, N., Rantama¨ki, M., Valkama, I., Kukkonen, J., 2004. Evaluation and comparison of operational NWP and mesoscale meteorological models for forecasting urban air pollution episodes—Helsinki case study. In: Suppan, P. (Ed.), Proceedings of the 9th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, 1–4 June 2004, Garmisch-Partenkirchen, Germany, Vol. 2, pp. 245–249. Neunha¨userer, L., Fay, B., Raschendorfer, M., 2006. Towards urbanisation of the nonhydrostatic numerical weather prediction model Lokalmodell (LM). Bound.-Layer Meteorol., January 2006, submitted for print. Rantama¨ki, M., Pohjola, M.A., Kukkonen, J., Karppinen, A., 2003. Evaluation of the HIRLAM model against meteorological data during an air pollution episode in southern Finland 27–29 December 1995. In: Sokhi, R.S., Brechler, J. (Eds.), Proceedings of the 4th Urban Air Quality Conference., Prague, March 2003. University of Hertfordshire, UK, pp. 420–423. Rantama¨ki, M., Pohjola, M.A., Tisler, P., Bremer, P., Kukkonen, J., Karppinen, A., 2005. Evaluation of two versions of the HIRLAM numerical weather prediction model during an air pollution episode in southern Finland. Atmos. Environ. 39/15, 2775–2786.

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Chapter 4.5 Uncertainty in air pollution models used for regulatory and risk assessment purposes$ Bernard Fisher and Robert Willows Abstract An approach to assessing uncertainty in air quality models is outlined based on Monte Carlo simulation, but this alone is not sufﬁcient for decision making. Instead the problem should be turned into one involving the optimisation of an objective function, which in air quality management should be a suitable integral of weighted exceedences over the whole region of interest.

1. Introduction

Some measure of uncertainty is essential when applying the results of air pollution models and a Monte Carlo simulation is commonly applied. Two of the most common examples of routine air quality assessment are associated with models used to estimate critical loads and concentrations for air quality management purposes. 2. Critical load assessments

A critical load assessment is based on the idea that the acid deposition to a grid square should not exceed the critical load of that grid square. The critical loads are estimated for various areas of the U.K. based on the dominant ecosystem. The critical load model for sulphur Clmax(S) is given by an algebraic equation in which each term has an uncertainty associated

$

The views expressed in this paper are those of the authors and are not necessarily those of the Environment Agency.

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with it:

Bcdep þ Bcw Bcu Clmax ðSÞ ¼ BCdep Cldep þ BCw BCu þ 1:5 ðBc=AlÞcrit 1=3 Bcdep þ Bcw Bcu þ Q2=3 1:5 ð1Þ ðBc=AlÞcrit K gibb In Eq. (1) * refers to the non-marine contribution. The sufﬁxes represent: dep, deposition; w, weathering (release of base cations from soil or rock minerals); u, uptake by plants into perennial tissues. BC is the ﬂux of base cations (Na++K++Ca2++Mg2+). Bc is the ﬂux of base cations other than Na. (Bc/Al)crit is the critical Bc/Al ratio deﬁned by the user. This is the fundamental criterion set equal to 1. Q is effective the rainfall/runoff. Kgibb is the Gibbsite equilibrium constant, deﬁning the relationship between H+ and Al3+ concentrations in the soil solution. The probability distribution function of the exceedence, the amount E by which the deposition exceeds the critical load, SdepClmax(S), at a position x, clearly depends on a number of parameters. Assumptions need to be made about the probability distribution of the parameters in the model. Examples of the cumulative distribution function at three forest locations (Skefﬁngton et al., 2007), for which measured data is available for some of the parameters, are given in Fig. 1 below. 100

Cumulative percentile

80

60

Liphook Aber Thetford

40

20

0 -2,000 -1,500 -1,000

-500

0

500

1,000 1,500 2,000 2,500 3,000

Exceedance (eq.ha-1.yr -1) Figure 1. Cumulative distribution function for the exceedence E at three speciﬁc coniferous forest sites, at which local information is known. E ¼ Sdep–Clmax(S).

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There is some degree of exceedence (E is greater than zero) at all three sites (about 95% at Aber, 68% at Liphook, and 2% at Thetford). For these three forested sites there is some site-speciﬁc information, but expert judgement is required to select the type and characteristics of the probability distributions of each parameter. A few Monte Carlo runs cause exceedence at Thetford, though the limestone geology produces a very large deterministic critical load. The long tails on these graphs imply that it would be expensive or difﬁcult to achieve very high probabilities of protection, just taking into account parameter uncertainty. The inclination is to choose some level of compliance, e.g., the 90 percentile, for which the sites Liphook and Aber would exceed, but the question remains as to how this level should be selected. Examples of how the uncertainty in critical loads may be presented are given in Heywood et al. (2006a, b). The Monte Carlo simulation of the parameters in the model used to derive the probability distribution may not capture the full range of uncertainty. For example, there is no time dependence in the algebraic equation deﬁning the critical load. It turns out (Abbott et al., 2003) that often the uncertainty using Monte Carlo simulation is somewhat less than one would expect. The critical load is rather like the sum of a number of random variables, for which the standard deviation is the sum of the standard deviation of individual terms divided by the square root of the number of terms, which for a large number of random variables attenuates the uncertainty.

3. Air quality management example

The change in concentrations of NO2 and PM10 as the result of building houses in the south-east of England has been assessed using a dispersion model (Fisher, 2006). The model used has also been applied to London and Bangkok (Fisher and Sokhi, 2000; Mutchimwong, 2005). The model is again based on rather simple algebraic equations representing the roadside and urban background concentrations of the form: CðdÞ ¼ qf ðdÞ þ C urban

(2)

where f is the dilution which depends on the distance d to the nearby major road, represented by a line source with an emission strength q g km1, and Curban is the urban background concentration in the locality. For NO2 and PM10 there are further factors, which modify the dispersion formula but do not fundamentally change the structure of the equations.

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NO2 conc ug/m3

A range of values is assumed for some of the model input parameters. In all there are some 30 parameters in the model describing emissions, meteorology, dispersion and chemical reactions, so that there is considerable scope for ﬁtting concentration ﬁelds to measurements. The choice of parameter ranges is subject to expert judgement. Figure 2 below shows the incremental increase in the annual average concentration of NO2 near the centre of the most densely populated area of the proposed south-east England development. The ﬁgure illustrates the effect of considering a number of options involving the use of cars. These range from a high car usage option (1) with the annual mileage of 20,000 km year1 with the average journey length being 15 km, (2) 15,000 km year1 annual mileage and average journey length of 15 km, (3) 10,000 km year1 with average journey length of 15 km, (4) 7500 km year1 with average journey length of 15 km, the current situation in the south-east, (5) 5000 km year1 with an average journey length of 10 km, and (6) 2500 km year1 with an average journey length of 10 km, a low car use option. The increase in NO2 concentration is dependent on the trafﬁc option, and is always positive, since we are considering an addition to a baseline. However, the magnitude of the increase is a small fraction of the annual average objective. On the other hand, it represents an increase of concentration, which would make the attainment of air quality objectives more difﬁcult to achieve. The error bars denote uncertainty for each option, estimated by considering concentrations subject to different assumptions, such as alternative transport emission factors, trafﬁc ﬂows, fraction of heavy duty vehicles, etc., within a range chosen to reﬂect expert judgement. The ranges of uncertainty are considerable but do not change the conclusion drawn from comparing different car use options. A sustainable community with infrastructure, which reduces the need to travel by road, leads to the smallest increases in concentration. The increase in annual average NO2 in urban centre

0.8 0.6 0.4 0.2 0 1

2

3 4 Traffic options

5

6

Figure 2. The predicted increase in NO2 concentration according to 6 trafﬁc options, in the centre of a sustainable community in south east England at a location near the centre, but not adjacent to a major road.

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concentration of PM10 is similar. The model, with uncertainty bounds included, provides a range of results, but it is for the policy maker to decide whether the range lies within acceptable bounds. The calculation need not be complex to demonstrate the consequences of various possible options.

4. Interpretation of uncertainty

In these examples the air pollution model does not give a single deterministic prediction. The results of the models are probability distributions (Borrego et al., 2006). Ranges occur if ﬁxed bounds are put on the choice of parameter values. The spread of the results gives some insight into the actual uncertainty, but the interpretation is subjective depending on the approach of the policy maker using the model. The advantage of showing uncertainty is that a decision can be made robust to factors that are not known. The disadvantage is that generally the policy maker wishes to have simple information, such as a spatial map showing concentrations, and regions where the concentration is greater than the objective. This means there is a policy choice to be made, such as presenting exceedence or concentration values at say the 95 percentile in the cumulative probability distribution. It is argued in this paper that the interpretation of the model results from this stage on is a matter for the policy maker, or is subject to other considerations or constraints, which are not dependent on the model. This can be illustrated by considering the general shape of the cumulative probability distribution and focusing on exceedence. The cumulative probability distribution of the difference between the deposition and the critical load, where each has been assumed to have a rectangular distribution and the deposition is generally greater than the critical load, is illustrated in Fig. 3. The horizontal axis is the exceedence E equal to the deposition minus the critical load. There is a sharp cut off at the upper and lower bounds because of the choice of rectangular distributions for the deposition and critical load probability density distributions. The question is how to interpret a probability distribution in terms of a pass or a fail. The policy maker will seek an answer as to whether an exceedence occurs. One could look for exceedence at the 5% level (a risk taker’s view) or at the 95% level (a precautionary view) or at the 50% level (trading between uncertainties). If the exceedence at the speciﬁed level is positive, one would conclude that the critical load is exceeded. (Alternatively, one could consider the probability when the exceedence is zero, and express the answer as a probability of exceedence. However, then one has to decide what degree of probability is sufﬁcient to

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Figure 3. Examples of exceedence functions E. The solid curved line shows the cumulative probability distribution of E, a straight-line approximation to this curve and a sigmoid function.

demonstrate an exceedence). None of these possible decisions depends entirely on the Monte Carlo analysis. They rely on extra information or opinion, which the policy maker brings to the problem. This may involve adjusting the probability distribution in a suitable way. For example, in Fig. 3 a straight line has been drawn approximating the cumulative probability distribution over most of its range, specifying exact upper and lower bounds in simple ways, which are not as extreme as the limits of the original curve. The dotted line is a simple sigmoid (S shaped) function used to approximate the cumulative distribution function. The merit of a sigmoid function is that it can be easily adjusted to describe the shape of the cumulative distribution function, depending on the coefﬁcient of variation ( ¼ standard deviation/mean) in a Monte Carlo simulation. In air quality management one is interested in deﬁning Air Quality Management Areas, and the outcome of an air quality assessment should be a cumulative probability distribution as a function of exceedence ( ¼ concentration minus the air quality objective). Figure 3 could be modiﬁed to show examples of different cumulative distribution functions for this type of application. There are a number of reasons for adapting the result of the Monte Carlo simulation. The way in which the air quality objective had been chosen might be considered inherently precautionary. On the other hand, in real situations there may be measurements, which could reduce the

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estimated errors. Epistemic uncertainty (lack of knowledge) cannot been included in the Monte Carlo simulation, so further precaution might be appropriate. The policy maker is unlikely to wish to see the cumulative probability distribution at each receptor point in an urban area. It is much more likely that a broad-brush approach would be taken determined by the kind of remedial action, which it would be possible to introduce.

5. Application to air quality assessment of an urban area

In this example the same urban dispersion model as in Section 3 has been considered, but it is applied to a small region of London to investigate the variation of uncertainty near to roads. The estimated coefﬁcient of variation of the annual average NO2 and PM10 concentrations (not shown), as a result of a series of runs with parameter values chosen at random within speciﬁed ranges, has small-scale variability with somewhat higher values near to road junctions. Though the spatial pattern of the coefﬁcient of variation may approximate the actual local uncertainty, it is not very useful from the view of making a decision. It would be computationally expensive to do the same calculation for the whole of London with tens of thousands of road links. An alternative approach is to choose an objective function describing a weighting on exceedences, with certain desirable properties. One approach is to assume that weighting on the exceedence of the air quality objective takes the following sigmoid form everywhere: S¼

1 1 þ ebE

(3)

where E is the exceedence ¼ concentrationobjective, using the ‘best’ choice of parameter values and b represents the degree of uncertainty. The value of b ﬁxes the degree of uncertainty. Nothing is implied about the probability of exceeding the objective by this choice, but S can be thought of as a relative measure of the weighting on the exceedence, taking high values near to 1, when E is high, and values near zero when E is highly negative. For high uncertainty (b low), the difference between high and low exceedences is suppressed. In effect the concentration has been transformed using the cumulative probability distribution arising from the Monte Carlo distribution. We can consider the degree of exceedence at every receptor in terms of a single transformed variable S and do not need to consider the probability distribution. (In other cases one might consider alternative weightings,

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Figure 4. Estimated weighting based on Eq. (3). Darker shading represents a higher weighting on exceedence of NO2.

such as those, which would emphasise extreme exceedences only). The transformed variables describing the degree of exceedence for NO2 is shown in Fig. 4 with results for PM10 similar. The policy maker is likely to be mainly interested in the opportunity for decisions inﬂuencing the degree of exceedence. Hence instead of detailed spatial mapping the interest is in the degree of change brought about by policy actions. The starting point is to look at ways of optimising the aggregated degree of exceedence, in this case integrated over the whole area of interest. ZZ 1 d2 x (4) Z¼ 1 þ expðbEðxÞÞ Applying Monte Carlo sampling methods using efﬁcient algorithms one can determine the optimum strategy to reduce this objective function Z. Computational effort is better spent searching for ways to minimise this objective, interpreted in terms of realistic measures, rather than reﬁning the uncertainty in the spatial pattern.

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Z is mainly sensitive to changes in a few parameters around their mean values estimated from the scaled derivative (dZ/dV)/(Z/sV), where V is a parameter, sV is its standard deviation based on the estimated uncertainty of parameter V. For PM10, Z is mainly sensitive to changes to some factors relating to dispersion, the parameter b, the wind rose, non-mobile sources, the mean emission density, and the PM10 air quality objective. For most of these parameters it is not possible to take actions to change Z, apart from changing the emission density. One can consider changes to the emission density within the range of possible remedial actions and decide where the maximum improvement may be possible.

6. Conclusions

From the air quality examples discussed above, a number of conclusions may be drawn. One can express a measure of uncertainty using Monte Carlo simulation and this is preferable to purely deterministic predictions even though some subjective judgement is involved. The result is a cumulative probability distribution. The approach includes subjectivity because of the decisions made regarding input parameter values. In addition it does not provide the policy maker with a result on which to base a decision. One needs to consider an easier way of deﬁning an objective on which to base decisions, which can be recalculated many times. It is therefore recommended that the cumulative concentration distribution, suitably adjusted to take account of wider considerations, is used to formulate an objective function from which the relative beneﬁts of taking remedial action can be based. In our example this objective function was chosen to be the spatial aggregate of exceedence, obtained by integrating the weighting on exceedences of the relevant air quality objective over the whole region of interest. The decision for the policy maker then becomes an optimisation to minimise aggregated exceedence, within the bounds of plausible variations of the input parameters, from which different air quality action plans can be evaluated. The optimisation will involve recalculating the air quality model over a range of different parameter values. This could be done in a very detailed way, but this is likely to be extremely computationally demanding. It is preferable to expend computational effort doing this in a broad way, concentrating on those parameters that can be inﬂuenced by measures, rather than to make extremely detailed estimates of the uncertainty on a local scale.

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Discussion

M. Jantunen:

B. Fisher:

There is a systematic discrepancy in policy options evaluation where 1 Million Euro are considered as a huge amount of money to be spent, and where orders of magnitude higher costs are paid, often without any serious effort to assess the accountability of the policy— that is, does the policy deliver the beneﬁts which were expected from it? There appear to be few attempts to evaluate whether a decision concerning a policy option has been effective, especially in circumstances when the decision involves large expenditure and has widespread implications. There are some examples at this conference where this evaluation is starting to happen. However, it is important to remember that although the decision may be a choice between discrete policy options, the prior and post decision evaluations will have uncertainties associated with them and may be described by probability density distributions rather than single numbers. One may not be able to state with complete certainty that one policy option is preferred. The decision on a policy option will depend on the decision maker’s attitude to risk.

REFERENCES Abbott, J., Hayman, G., Vincent, K., Metcalfe, S., Dore, T., Skefﬁngton, R., Whitehead, P., Whyatt, D., Passant, N., Woodﬁeld, M., 2003. Uncertainty in acid deposition modelling and critical load assessments. R&D Technical Report P4-083(5)/1. Environment Agency. Borrego, C., Miranda, A.I., Costa, A.M., Monteiro, A., Ferreira, J., Martins, H., Tchepel, O., Carvalho, A.C., 2006. Uncertainties of models and monitoring. Air4EU Project Report M.2 http://www.air4eu.nl/reports_products.html Fisher, B.E.A., 2006. Impacts of proposed housing growth in South and East England: Air quality. Environment Agency report SC040047/SR1 http://www.publications. environment-agency.gov.uk Fisher, B.E.A., Sokhi, R.S., 2000. Investigation of roadside concentrations in busy streets using the model GRAM: Conditions leading to high short-term concentrations. Environ. Pollut. 14, 488–495. Heywood, E., Hall, J., Reynolds, B., 2006. A review of uncertainties in the inputs to critical loads of acidity and nutrient nitrogen for woodland habitats. Environ. Sci. Pollut. 9, 78–88.

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Heywood, E., Whyatt, J.D., Hall, J., Wadsworth, R., Page, T., 2006. Presentation of the inﬂuence of deposition uncertainties on acidity critical load exceedance across Wales. Environ. Sci. Pollut. 9, 32–45. Mutchimwong, A., 2005. A methodology for the assessment of air quality in London and Bangkok. PhD thesis, University of Hertfordshire, U.K. Skefﬁngton, R., Whitehead, P., Heywood, E., Hall, J., Reynolds, B., Wadsworth, R., 2007. Uncertainty in critical load assessment models. Environment Agency Report SC030172/ SR http://publications.environment-agency.gov.uk/pdf/SCHO0307 BMDC-e-e.pdf

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06046-9

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Chapter 4.6 Aerosol mass budget analysis over Berlin city area by means of the CTM REM_Calgrid Andreas Kerschbaumer, Matthias Beekmann and Eberhard Reimer Abstract A climatological Mass Budget Analysis for primary and secondary aerosol components has been performed on a yearly basis for the urban conglomerate of Berlin by means of the Aerosol Chemistry Transport Model REM_Calgrid. The inﬂuence of single simulated processes on accumulation and dispersion of PM constituents has been determined on different time-scales in order to give advice to local authorities in reducing effectively PM10 concentrations. REM_Calgrid is able to simulate in a feasible manner hourly aerosol concentrations for a whole year, and its modularity makes process-oriented mass budget analyses possible. Inorganic as well as organic PM-components are calculated in a bulk-approach. Height-dependent mass exchange rates have shown a predominance of advective processes in dispersing mainly primary aerosols over the whole year from the city toward the surrounding areas, while secondarily built organic and inorganic aerosols exhibit seasonal characteristics. Advective versus local emission and chemical production determines the local contribution to urban PM10concentrations. Accumulation due to inﬂow of sulphate and of organic aerosol components depends on wind direction and on season. Acidity of aerosol mass in Berlin has been determined analysing the contribution to secondary inorganic aerosol mass accumulation by aerosol building processes. Primary PM10, including EC and OC, are produced in the city and dispersed via advection toward the surrounding region. Local production to long-term transport ratio is between 3 and 8. Sulphate and secondary organic carbons accumulation in Berlin is due to advection from the South East (ca. 40%), while all other components show a preferred inﬂow from the west. The overall inorganic ions

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budget is in equilibrium giving neutral aerosols except of summer, when high ammonium production is responsible for alkaline PM in Berlin. 1. Introduction

Understanding the atmospheric aerosol growth and its interactions with advective accumulation and deposition processes is of paramount importance in taking adequate reduction strategies. In the last years, much effort has been taken to understand single processes such as the building of secondary inorganic and organic aerosol components and its simulation in smog chambers as well as in chemical transport models (Grosjean and Seinfeld, 1989 and references therein; Odum et al., 1996; Nenes et al., 1998; Schell et al., 2000). Also, long-term processes of primary coarse particles such as Saharan Dust (Vautard et al., 2005) or the lifetime of ﬁne and ultra ﬁne particles such as elemental and organic carbon has been studied extensively (Bukowiecki et al., 2003). Aerosol Chemistry Transport Models are built in order to use in a feasible way all the knowledge of intensive observation measurements in nature and in smog chambers and give the unique opportunity to analyse the contributions of single processes to the total aerosol mass. Mass budget considerations have become a frequently used tool to estimate the contribution of the single processes to the total concentration ﬁelds in many different air quality models (Jeffries and Tonnesen, 1994; Jang et al., 1995a, b). This mass budget study has been conducted within the German Atmospheric Research Programme AFO2000 in the sub-project HoVerT (Horizontal and Vertical Transport of Ozone and Aerosol). A one-year measurement campaign has been carried out sampling and analysing aerosol components on a daily basis in and around the Berlin Area (John et al., 2004). The Aerosol Chemistry Transport Model REM_Calgrid (RCG) has been used to simulate the same period on two different scales—one Europewide with a resolution of 30 km and a nested domain around Berlin with a resolution of 4 km and an extension of ca. 300 300 km2. Observational results have been used to evaluate and to improve the model (Beekmann et al., 2007). This paper is intended to present a mass budget experiment in a climatologic way for the whole HoVerT campaign period from September 2001 until September 2002. The RCG model has been used to analyse the single simulated processes within a control volume over Berlin. The considered area comprises the urbanised area of Berlin with an extension of 1600 km2. One of the most prominent characteristics of the chosen area is the net difference between the emissionintensive urbanised Berlin area and the mostly rural surrounding domain

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with very few industries and households and only few trafﬁc lines. Besides secondary inorganic and organic carbon aerosol components, RCG is able to simulate elemental carbon as well as other primary carbons and primary particles. This makes it possible to analyse the accumulation and loss terms for a variety of particulate matter constituents over a long-term period.

2. Method

RCG is a multi-scale Eulerian 3D grid transport and chemistry model, which simulates mean hourly concentrations (Ci) of atmospheric pollutants solving the following differential equation for every single simulated species: ¯i @C ^ C¯ i Þ þ P ¯i ¯ i Þ þ rðkr ¯ i L¯ i þ Q ¼ rð~ uC @t The ﬁrst term on the left hand side indicates the temporal change of the concentration of the i-th species, the ﬁrst and the second terms on the right hand side indicate advective and turbulent transports including deposition, respectively, Pi and Li indicate chemical production and loss, respectively and Qi indicates direct emission. The lower boundary condition is given by the deposition ﬂux equation: ^ C ¯ i Þ~ F ¼ vD;i C¯ r;i ¼ ðkr nh where ~ nh is the unit vector perpendicular to the ground, vD;i is the dry ¯ r;i is the mean concentration deposition velocity of the i-th species and C on the reference height r. The modelling system is conﬁgured to include detailed treatment of horizontal and vertical advection, turbulent diffusion based on K-theory (Yamartino, 2003), gas-phase chemical transformations using a modiﬁed version of the CBM-IV chemical mechanism (Gery et al., 1989; Stern et al., 2003), bulk solid-phase aerosol building mechanism for inorganic and organic aerosols (Nenes et al., 1998; Schell et al., 2000), anthropogenic and natural emissions comprising wind blown dust, dry and wet depositions, and attenuation of photolysis rates due to the presence of clouds. Emission data have been described extensively in Beekmann et al. (2007). Every individual budget process is calculated explicitly and thus mass budget analysis can be done considering the following relationship and

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using the Gauss’ theorem: Z ¯ @M @ C¯ i dV ¼ @t @t I I ^ C ¯ i Þd f~ þ ðkr ¯ i Þdf ¼ ð~ uC F

F

Z

¯i L ¯ i ÞdV þ ðP

þ V

Z

¯ dV Q

V

The ﬁrst and second term on the right hand side of the equation describe the advective and the turbulent ﬂux through the integration volume including deposition processes, whereas the third term depicts the net contribution of the chemical reactions, while the forth term is the contribution of emissions to the temporal change of the total mass M. By contrast, the net ﬂux through the volume’s surfaces is equal to the non-transport speciﬁc mass accumulation and removal processes inside the chosen volume. The control volume has been chosen within the nested 4-km horizontal resolution RCG application. The domain extensions are about 42 km (West–East direction) 36 km (South–North direction) around Berlin area (13.1251E–13.751E, 52.343751N–52.656251N) on a horizontal plane and 1600 m height. The whole volume is thus 2.35 1012 m3. In RCG, each of the physical and chemical processes is cast into modules following the operator-time-splitting approach. Each process module operates on a common concentration ﬁeld, making it possible to analyse budgets of modelled species by examining the contribution from each modelled process. On an hourly basis, contributions to the total concentration ﬁelds of the processes have been taken into account considering the mass difference in the control volume at the end and at the beginning of every hour. We summarised the results on different temporal intervals, considering the yearly mass changes and seasonal differences. By contrast, mass ﬂuxes through the lateral boundaries of the control volume around Berlin have been memorised on an hourly basis in order to be able to give indications on preferred directions of pollutants transport. We distinguished primary coarse and primary ﬁne particles considering them non-participant at any further physical or chemical transformation, originating mainly from direct injection into the atmosphere by anthropogenic emissions and by dust dispersion due to wind. Primary elemental carbon and primary organic carbon constitute separate classes, not coinciding with the class of ‘‘primary ﬁne particles’’. The class of ‘‘Secondary inorganic aerosols’’ comprises sulphate, nitrate, ammonium, chloride and sodium ions, while secondary organic aerosols comprise build-up species containing organic carbons. We distinguished

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the net and the gross contribution to the mass budget in Berlin. Net contribution indicates the sum of positive and negative parts giving the total mass budget, while gross contributions differentiate positive (accumulation) parts from negative (loss) parts. Percentages sum up to 100% distinctively for accumulating and removing processes.

3. Results

The mass transport of modelled primary aerosols through the lateral boundaries around Berlin gives a gross 58.4 kT year1 positive contribution from the surrounding area towards the city of Berlin and a gross 71.0 kT year1 contribution of the city of Berlin towards the outskirts. Primary ﬁne particles form the biggest part of the transported material. 43.5% of the imported and 38.5% of exported mass is ﬁne mode particles. The total mass change due to emission of aerosols accounts for 15.92 kT year1, having its main part in the emission of coarse mode aerosols (almost 50%). This aerosol mode is also the preferred deposited PM component. The wet part in removing primary aerosols from the atmosphere is for coarse particles ca. 70% and only 30% is due to dry deposition. Almost 100% of the deposition of ﬁne particles, elemental carbon and organic primary carbons is removed due to scavenging effects. Primary particulate matter is accumulated in the city of Berlin due to transport processes for almost 80% and due to local emissions for 20%. The coarse fraction shows a greater contribution from emission (almost 40%), while the ﬁne mode particles exhibit a more consistent part from advective processes. In fact, almost 90% of ﬁne mode primary aerosol accumulation is due to transport processes. Elemental carbon and organic primary carbon are accumulated in Berlin due to local production for about 30%. Wind-blown dust is the reason for the high contribution of primary ﬁne particles coming from the surrounding areas to the city of Berlin. The fact that the control volume does not make any difference between Berlin Centre and Berlin suburbs makes the rather low city contribution of primary aerosol components explicable. The most prominent process in removing primarily emitted mass from the city of Berlin in advection which accounts for more than 90% of the loss terms in the removal budget. Considering the fact that 58.4 kT year1 of primary aerosols are imported to Berlin and that 71 kT year1 leave the urban area, Berlin exports ca. 12.6 kT year1 of aerosols to the surrounding areas, which are due to Berlin speciﬁc emissions cleared by deposited mass. 125.8 kT year 1of secondary inorganic and organic

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aerosol components are transported towards Berlin, while 125.63 kT year 1leave the domain of Berlin. Considering a gross direct contribution of 0.56 kT year1 sulphate emissions and a 6.31 kT year1 chemical production on the accumulation side, and a gross 3.72 kT year1 deposition and 0.92 kT year1 of chemical loss on the removal side, there is an overall 2.39 kT year 1of secondary aerosol accumulation in Berlin due to non-local phenomena. This is an annual increment of ca. 1%. The transported, incoming as well as outgoing, mass in Berlin is with almost 95% the most important process in the local mass budget. The chemical transformation rate within the urban boundaries is about 5% in accumulating mass and only about 1% in removing mass. Reducing secondary aerosols from Berlin is due to wet deposition only by ca. 3%. This strongly indicates a fundamental pass-through of secondary aerosol components. Considering the components of the secondary aerosols, sulphate is with more than 40% of the species which undergoes the strongest mass exchange via advection. Nitrate with 30% and Ammonium with 20% contribute to the incoming as well as outgoing mass transports. The remaining 8% are due to secondary organic carbon transported towards and from Berlin, where the biogenic part is 70% and the anthropogenic part only about 30% of the advective contribution to the mass budget in Berlin. Considering the chemical transformation, nitrate is with 45% in the production process and with more than 85% in the destruction process, the species which contributes most to mass changes in the secondary aerosol budget. Sulphate and ammonium contribute both ca. 23% to the accumulating mass budget, while in the destruction process, sulphate plays almost no role and ammonium contributes about 7% to the mass budget loss. In the overall secondary aerosol chemistry budget, the organic carbon accumulation as well as loss contributes with about 7.5% to the mass budget. Within this process, the biogenic secondary organic aerosol production rate is more than double than the anthropogenic part, while in the chemical removal process the anthropogenic part is about 90%. The city of Berlin is responsible for the anthropogenic aerosol building processes, while the fundamentally rural surrounding areas of Berlin contribute to a biogenic aerosol accumulation in Berlin. Analysing separately the wind direction-dependent inﬂow and outﬂow characteristics of Berlin PM mass budget, primary aerosol components enter the volume predominantly from the West and leave the Berlin volume through the East side. This is also the predominant wind direction in Berlin. Sulphate ions’ favourite entering side is the East. This holds also for secondary organic aerosols. Both PM components leave the control volume most favourably towards the North. Nitrate and ammonium components, again, are brought to Berlin advectively from the West

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and exported towards the East, as all primary aerosol components. This interesting different behaviour of sulphate and secondary organic aerosol components gives some hints about the precursors’ origin which lays in the South-East of Berlin. The same conclusions have been found also by Lenschow et al. (2001). Seasonal analyses show for primary aerosol components a predominant inﬂow direction from the West during all seasons except for primary organic carbons during autumn when a considerable amount arrives also from the South and for primary coarse fraction aerosols during summer when the preferred inﬂow direction is from the South. This indicates a more frequent wind direction from the South during summer. Secondary inorganic nitrate components arrive to Berlin during all seasons from the West, while sulphate ions arrive always from the South. Ammonium arrives in the cold season from the West and during the warm season from the South. This different behaviour of different secondary inorganic aerosol components again shows the high dependence of the precursors’ origin for the accumulation in Berlin contradicting the only dependency from wind direction. Biogenic secondary organic carbons arrive to Berlin from the South during the cold season and from the East during the warm season, while the anthropogenic secondary organic carbons arrive to Berlin to equal parts from the West and from the South during all seasons. Acidiﬁcation of the Berlin aerosol has been evaluated considering the mol budget from the Berlin domain by summing positive and negative ions produced during the aerosol chemistry processes of the secondary inorganic aerosol components for the whole simulation time period and for the single seasons. Sulphate ions are counted twice or once depending on the preferred neutralisation process with ammonium ions and accounting for the re-building processes of ammonium–sulphate. ISSOROPIA incorporates also chloride ions which are included in the mol-budget analysis. Throughout the whole year, the inorganic aerosol production in Berlin is in equilibrium between positive and negative ions when sulphate components are counted 1.6 times. 2 Concentrations of NH+ 4 and SO4 are combined in the molecular ratio 3/2 to form two molecules of (NH4) 1.6SO4 until all ammonia and sulphate is bound. This is in agreement with the equilibrium formulation, differs, whoever, considerably during different seasons. Winter is characterised by predominance of ammonium ions giving an overall negative molar ion production rate term. Ammonium bisulphate is the favourite state during winter which holds also for autumn in Berlin. More acidic aerosol is the favourite state during the cold season. Summer is in an almost equilibrium state, while spring exhibits a preference for ammonium sulphate production. Chemical inorganic aerosol production is dominated by nitrate ions throughout the whole year which contribute

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more than 60% of the total molar mass. Most evidently, winter shows a nitrate contribution of more than 70%, while sulphate ions contribute only 8% to the total positive inorganic ions molar mass production. Summer exhibits an opposite sign: sulphate ions production is about 60%, while nitrate molar mass production is about 33%. Autumn is simulated in a rather similar manner like winter, while spring is characterised by a gross 36% of positive inorganic aerosol ions coming from sulphate and ca. 50% coming from nitrate. ISORROPIA incorporates also chloride ions in the inorganic aerosol production. The net contribution of these positive ions to the particle production is rather high, ranging from ca. 8% during summer to 20% during winter.

4. Summary and outlooks

A comprehensive aerosol mass budget analysis has been performed on a yearly basis for a domain with strong emission gradients between the urban agglomerate of Berlin and the mostly rural surrounding area. Mass exchange rate analyses have shown a predominance of advective processes in dispersing mainly primary aerosols over the whole year from the city towards the surrounding areas, while secondarily built organic and inorganic aerosols exhibit seasonal characteristics. Accumulation due to inﬂow of sulphate and of organic aerosol components depends on wind direction and on season. Acidity of aerosol mass in Berlin has been determined analysing the contribution to secondary inorganic aerosol mass accumulation by aerosol building processes. Primary PM10, including EC and OC, are produced in the city and dispersed via advection towards the surrounding region. The local production to net transport ratio is 1.3 for primary aerosol components. Secondary PM constituents are net accumulated in Berlin via advection as well as via chemical production with high nitrate-related contributions which account for 45%. Sulphate and secondary organic carbon accumulation in Berlin is due to advection from the South-East (ca. 40%), while all other components show a preferred inﬂow from the West. The overall inorganic ions budget is in equilibrium giving neutral aerosols except of summer, when high ammonium production is responsible for alkaline PM in Berlin. The comprehensive mass budget analysis can be used as a tool in validating the RCG model showing clearly shortcomings in the parameterisation of local emissions such as wind-blown dust. Also, the contribution of chloride ions in the secondary inorganic particle production seems to be overestimated.

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Discussion

J.P. Shi:

A. Kerschbaumer:

C. Mensink: A. Kerschbaumer:

How did your mass budget consider particle resuspension, especially at construction sites and some industrial landﬁll sites? Emission data provided by the local authorities contained already mineral dust particles coming from construction sites. Moreover, REM_Calgrid parameterises wind-blown dust as a function of the friction velocity and the landuse-class, mainly. Did you consider any vertical transport? If so, how important is it? Vertical transport is considered through any vertical layer. On the overall budget analysis, vertical transport is considered explicitly through the top boundary. With respect to the horizontal transport, the transport into and from the ‘‘free troposphere’’ is small, though not negligible, especially for secondary inorganic aerosol components.

ACKNOWLEDGMENT

This work has been funded by the BMBF-Germany within the atmospheric research activity programme AFO2000.

REFERENCES Beekmann, M., Kerschbaumer, A., Reimer, E., Stern, R., Mo¨ller, D., 2007. PM measurement campaign HOVERT in the Greater Berlin area: Model evaluation with chemically speciﬁed particulate matter observations for a one year period. Atmos. Chem. Phys. 7, 55–68. Bukowiecki, N., Dommen, J., Prevot, A.S.H., Weingartner, E., Baltensperger, U., 2003. Fine and ultraﬁne particles in the Zu¨rich (Switzerland) area measured with a mobile laboratory. An assessment of the seasonal and regional variation throughout a year. Atmos. Chem. Phys. Discuss. 3, 2739–2782. Gery, M., Witten, G., Killus, J., 1989. A photochemical kinetics mechanism for urban scale and regional scale computer modelling. J. Geophys. Res. 100(D5), 8873–8892. Grosjean, D., Seinfeld, J., 1989. Parameterization of the formation potential of secondary organic aerosols. Atmos. Environ. 23, 1733–1747.

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Jang, J.C., Jeffries, H.E., Byun, D., Pleim, J.E., 1995a. Sensitivity of ozone to model grid resolution—I. Application of high-resolution regional acid deposition model. Atmos. Environ. 29(21), 3085–3100. Jang, J.C., Jeffries, H.E., Tonnesen, S., 1995b. Sensitivity of ozone to model grid resolution—II. Detailed Process Analysis for Ozone Chemistry. Atmos. Environ. 29(21), 3101–3114. Jeffries, H.E., Tonnesen, S., 1994. A comparison of two photochemical reaction mechanisms using mass balance and process analysis. Atmos. Environ. 28(18), 2991–3003. John, A., Kuhlbusch, T., Lutz, M., 2004. Quellenzuordnung anhand aktueller Immissions und Emissionsdaten in Berlin. In: Kuhlbusch, T., John, A., Top, S. (Eds.), Bericht zum Workshop: PMx-Quellenidentiﬁzierung: Ergebnisse als Grundlage fu¨r MaXnahmenpla¨ne (IUTA e.V. Duisburg) Hrsg. Umweltbundesamt, Berlin, pp. 111–121 (in German). Lenschow, P., Abraham, H.-J., Kutzner, K., Lutz, M., PreuX, J.-D., Reichenba¨cher, W., 2001. Some ideas about the sources of PM10. Atmos. Environ. 35(Suppl. 1), 23–33. Nenes, A., Plinis, C., Pandis, S.N., 1998. ISORROPIA: A new thermodynamic model for multiphase multicomponent inorganic aerosol. Aquat. Geochem. 4, 123–152. Odum, J.R., Hoffmann, T., Bowman, F., Collins, D., Flagan, R.C., Seinfeld, J.H., 1996. Gas/particle partitioning and secondary organic aerosol yields. Environ. Sci. Technol. 30, 2580–2585. Schell, B., Ackermann, I.J., Hass, H., Binkowski, F., Ebel, A., 2000. Modelling the formation of secondary organic aerosol within a comprehensive air quality model system. J. Geophys. Res. 106(D22), 28275–28293. Stern, R., Yarmatino, R., Graff, A., 2003. Dispersion modeling within the European community’s air quality directives: Long term modeling of O3, PM10 and NO2, in proceedings of 26th ITM on Air Pollution Modelling and Its Application. May 26–30, 2003, Istanbul, Turkey. Vautard, R., Bessagnet, B., Chin, M., Menut, L., 2005. On the contribution of natural Aeolian sources to particulate matter concentrations in Europe: Testing hypotheses with a modelling approach. Atmos. Environ. 39(18), 3291–3303. Yamartino, R., 2003. Reﬁned 3-D Transport and Horizontal Diffusion for the REM/ CALGRID Air Quality Model. Abschlussbericht zum Forschungs und Entwicklungsvorhaben 298 41 252 des Umweltbundesamts ‘‘Modellierung und Pru¨fung von Strategien zur Verminderung der Belastung durch Ozon’’.

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Chapter 4.7 The use of CFD and mesoscale air quality modelling systems for urban applications: Madrid case study R. San Jose´, J.L. Pe´rez and R.M. Gonza´lez Abstract The air quality in urban areas is of great concern due to the increasing number of vehicles in modern cities and also because the city population is constantly increasing. The need of having reliable, robust and efﬁcient tools to evaluate the impact of different trafﬁc policies in air quality concentrations has attracted the attention of the scientiﬁc community in the last decade. In this contribution we show the results of different scenarios produced by different models which constitute a complete urban air quality management tool. We have used a cellular automata model CAMO (UPM, Spain) to produce trafﬁc ﬂow with high spatial and temporal resolution. The model has been applied over a 1 km 1 km spatial grid cell in Madrid (Spain) city. CAMO model produces trafﬁc ﬂow which is used to generate emission data as input for a CFD model, MIMO (Ehrhard et al., 2000). MIMO also receives boundary and initial conditions from the MM5 mesoscale meteorological model (NCAR/PSU, USA) and the CMAQ air quality modelling system (EPA, USA). The CAMO-MIMO-MM5 model is validated by comparing the results with the values measured in an in-site air quality monitoring station. We have carried out several experiments with different scenarios based on the CORINE mobile emission factors and the different mobile subclasses. The system allows to identify ‘hot spots’ in the urban canopy with a very high spatial and temporal resolution. We run the model with 5 m spatial resolution and 10 vertical layers up to 50 m. The model simulates the nonlinear effects on street level air quality concentrations which helps air quality city authorities to take actions according to the expected results. Reduction in 30% of the total cars and increase of up to 15% in the public buses (EURO–IV) show important improvements in air quality concentrations.

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1. Introduction

Air Quality Models: The advances in the capability of computational ﬂuid dynamics models and air quality modelling systems during the last decade have been quite substantial. The increase in computer capabilities and in the knowledge of turbulence parameterization and numerical schemes has also been very important during the last 10 years. On the other hand, there is a considerable public interest on information related to the ‘‘real’’ pollution people are exposed to when they are walking in the street, going to work, driving a car from/to work or performing other daily activities. At street level, the differences in the concentration values at both sides of a street can be important, particularly, for instance, in relation to photochemical production during summer time in the Mediterranean regions. In this contribution, we have used the CFD model MIMO (University of Karlsruhe, Germany) and the mesoscale air quality modelling system MM5–CMAQ–EMIMO (NCEP/EPA/Technical University of Madrid) to simulate the impact of different emission reduction scenarios in the downtown area of Madrid City. These complex systems could evaluate the impact of several urban strategic emission reduction measures such as reduction of private trafﬁc, increase of public transportation, impact on introduction of new fuel cell vehicles, etc. Also, they could be used for the analysis of pollution concentrations at different heights (buildings) and in different areas of urban neighbourhoods. Air dispersion in urban areas is affected by atmospheric ﬂow changes produced by building–street geometry and aerodynamic effects. The trafﬁc ﬂow, emissions and meteorology are playing also an important role. Microscale air pollution simulations are a complex task since the time scales are compared to the spatial scales (micro) for such a type of simulations. Boundary and initial conditions for such a simulations are also critical and essential quantities to inﬂuence fundamentally the air dispersion results. Microscale computational ﬂuid dynamical models (CFDM) are playing an increasing role in air quality impact studies for local applications such as new road and building constructions, emergency toxic dispersion of gases at urban and local scale, etc. Microscale air dispersion simulations are applied to predict airﬂow and pollution dispersion in urban areas (Pullen et al., 2005). Different combinations and applications appear in the literature such as by integrating a Lagrangian model and a trafﬁc dynamical model into a commercial CFD code, Star-CD, to simulate the trafﬁc-induced ﬂow ﬁeld and turbulence. In this contribution, we have applied the microscale dispersion model MIMO (Ehrhard et al., 2000) to simulate different emission reduction scenarios in Madrid (Spain) related to the vehicle trafﬁc conditions. The MIMO CFD code has been adapted to and incorporated into a mesoscale

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air quality modelling system (MM5–CMAQ–EMIMO) to ﬁt into the oneway nesting structure. MM5 is a meteorological mesoscale model developed by Pennsylvania State University (USA) and NCAR (National Center for Atmospheric Research, USA) (Grell et al., 1994). The CMAQ model is the ommunity multiscale air quality modelling system developed by EPA (USA) (Byun et al., 1998) and EMIMO is the emission model developed by San Jose´ et al. (2003). MM5 is a well recognized nonhydrostatic mesoscale meteorological models which uses global meteorological data produced by global models such as GFS model (NCEP, USA) to produce high-resolution, detailed three-dimensional ﬁelds of wind, temperature and humidity which are used in our case as input for the photochemical dispersion model CMAQ (San Jose´ et al., 1997). In addition to the MM5 output data, EMIMO model produces for the speciﬁc required spatial resolution the hourly emission data for different inorganic pollutants such as particulate matter, sulphur dioxide, nitrogen oxides, carbon monoxide and total volatile organic compounds (VOCs). The VOCs are split according to SMOKE (Sparse Matrix Operator Kernel Emissions; Coats, 1995; Williams et al., 2001). The CFD and mesoscale models solve the Navier–Stokes equations by using different numerical techniques to obtain ﬂuxes and concentrations at different scales. Mesoscale air quality models cover a wide range of spatial scales from several thousands of kilometres to 1 km or so. In this contribution, we have applied the MM5–CMAQ–EMIMO models over Madrid domain to obtain detailed and accurate results of the pollutant concentrations and the MIMO CFD model over a 1 km 1 km domain with several spatial resolutions (2–10 m) and different vertical resolutions. MM5–CMAQ–EMIMO data serve as initial and boundary conditions for MIMO modelling run. In Fig. 1, we observe the spatial architecture for the application of the MM5–CMAQ–EMIMO mesoscale air quality modelling system. In Fig. 2, we show a detailed diagram of the EMIMO modelling system. EMIMO is currently operating with the so-called version 2, which includes the CLCL2000 with 44 different land use types with 100-m spatial resolution. EMIMO 2.0 also uses the CIESIN 3000 (CIESIN, 2004) population database and the Digital Chart of the World 1-km land use database to produce adequate emission data per 1-km grid cell per hour and per pollutant. In order to apply the EMIMO CFD model, we need detailed information related to the building structure in the 1-km grid cell. This information is shown in Fig. 3 for the total of the Madrid community (Spain). The height of the buildings is not included in this ﬁle, and it has been estimated directly for this experiment. A cellular automata trafﬁc model (CAMO) has been developed. CAMO—which has been included

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Figure 1. MM5–CMAQ–EMIMO architecture for this application.

Figure 2. EMIMO model basic architecture.

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Figure 3. E00 vector ﬁle for Madrid community.

into the EMIMO modelling system—is based on transitional functions deﬁned in a discrete interval t as follows: sðt þ 1Þ ¼ pðsðtÞ; aðtÞÞ uðtÞ ¼ vðsðtÞÞ where s(t+1) and s(t) are deﬁned states, a(t) an input symbol and u(t) an output symbol. We have used the Moore neighbourhood with eight different surrounding cells where each cell—representative of a vehicle—can move on. The whole system focusing on the 1 km 1 km urban area in Madrid downtown is called MICROSYS system. We have selected a subdomain of 300 m 300 m with 5-m spatial resolution and 15 vertical layers for this particular experiment The ﬁrst 10 layers are equally spaced with 5-m spatial resolution up to 50 m in height and the last ﬁve layers are located at 55, 61.55, 68.20, 75.52 and 83.58 m in height. In Fig. 4, we see a subdomain of the Madrid area where the CFD model has been implemented. 2. Results

We have carried out several tests. A preliminary test for July 8, 2002 at 14:00 is shown in Fig. 5 for NO2 data. In Fig. 6, we show the results for

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Figure 4. Subdomain view for this experiment in Madrid City downtown area.

Figure 5. NO2 concentrations at 14:00, July 8, 2002 produced by MIMO.

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Figure 6. NO2 concentrations at 10:00:15, May 20, 2003 produced by MIMO.

May 20, 2003 at 10:00. Completely different wind patterns are shown in the ﬁgures. Northern winds are presented early morning on that day and Southern winds are shown at 14:00 on the same day. The complexity of the wind pattern structure is clear from both pictures. In this experiment, the prognostic mode has been used for producing both Figs. 5 and 6. In a further application, the system was applied to produce test results for a synthetic experiment related to the impact of different emission reduction scenarios and shown on the Internet into the OSCAR EU project (EVK4CT2002-00083 OSCAR). In this particular application, the system was run for different domains and scenarios in Helsinki (Finland), London (the United Kingdom) and Madrid (Spain). In the case of Madrid, two different emission scenarios have been run: (a) Normal trafﬁc conditions and (b) decrease of 30% in the total number of private cars and increase of 15% in the total number of public buses. Figure 7 shows the NO2 percentages obtained as differences between scenario (b) and scenario (a) divided by the concentrations obtained in scenario (a). We observe on the lower left corner a detail of a street angle located in the southern area of the square. In the whole domain, the impact of reducing the total number of private cars by

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Figure 7. On the left side, MIMO CFD results for NO2 at 08:00:55, May 20, 2003 for the differences between scenario (b) and scenario (a) in percentage with respect to scenario (a). Differences between +40% and 30% are found. Most of the highest differences are found in the corners of the streets as expected due to the complexity of the turbulence on those areas. On the right side, zoom in over two subareas in model domain for NO2 percentage impact when implementing scenario (b) compared with scenario (a). Lower left image shows that in the square, most of the impacts are on the negative side with reductions up to 3.2% on NO2 concentrations. In the upper right image, we observe that two grid cells are ‘‘hot spots’’ with the highest increases and decreases in percentage (+40% and 30%).

30% and increasing the total number of public buses by 15% results in an increase in air pollution by 40% in very speciﬁc areas (normally in the areas located near the buildings in the square or in speciﬁc hot spots in the streets. Most of the values for the ‘‘open’’ areas in the square and streets are on the negative side so that we have a reduction on NO2 concentrations when implementing the emission reduction strategy for this experiment described in scenario (b). In Fig. 8, we also see details by zooming-in into the central and north areas of the domain. We observe that in the square itself, most of the data are on the negative side with reductions up to 3.2%, but in the north area, we observe that in the street on the left, there are two hot spots with the maximum increase and decrease—+40% and 30%—which gives us a good conﬁdence on model behaviour.

3. Conclusions

The MM5–CMAQ–EMIMO modelling system has been used to provide detailed initial and boundary conditions for a system called MICROSYS,

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which is composed by the MIMO CFD microscale dispersion model and CAMO, which is a cellular automata trafﬁc model. The results show that the air quality modelling system offers realistic results, although no comparison with eddy-correlation measurement system has been performed in the area. The tool can be used for many air quality impact studies but in particular for trafﬁc emission reduction strategies.

ACKNOWLEDGMENT

We would like to thank Professor N. Moussiopoulos (AUTH, Greece) for providing the MIMO model. Also to EPA/PSU/NCAR for providing the MM5–CMAQ modelling system and OSCAR EU project.

REFERENCES Byun, D.W., Young, J., Gipson, G., Godowitch, J., Binkowsky, F., Roselle, S., Benjey, B., Pleim, J., Ching, J.K.S., Novak, J., Coats, C., Odman, T., Hanna, A., Alapaty, K., Mathur, R., McHenry, J., Shankar, U., Fine, S., Xiu, A., Lang, C., 1998. Description of the Models-3 Community Multiscale Air Quality (CMAQ) model. Proceedings of the American Meteorological Society 78th Annual Meeting, Phoenix, AZ, January 11–16, 1998. pp. 264–268. Center for International Earth Science Information Network (CIESIN), 2004. Global RuralUrban Mapping Project (GRUMP): Urban/Rural population grids. CIESIN, Columbia University, Palisades, NY, http://sedac.ciesin.columbia.edu/gpw/ Coats, C.J. Jr., 1995. High Performance Algorithms in the Sparse Matrix Operator Kernel Emissions (SMOKE) Modelling System. Microelectronics Center of North Carolina, Environmental Systems Division, Research Triangle Park, NC, p. 6. Grell, G., Dudhia, J., Stauffer, D., 1994. A description of the Fifth Generation Penn State/ NCAR Mesoscale Model (MM5). NCAR Technical Note, TN-398+STR, p. 117. Pullen, J., Boris, J., Patnaik, G., Young, T., Holt, T., 2005. Linked mesoscale-LES contaminant prediction for Manhattan. 9th GMU Conference on Atmospheric Transport and Dispersion Modeling. Fairfax, VA, July 18-20, 2005. San Jose´, R., Pen˜a, J.I., Pe´rez, J.L., Gonza´lez, R.M., 2003. EMIMO: An emission model. Springer-Verlag, pp. 292–298, ISBN: 3-540-00840-3. San Jose´, R., Prieto, J.F., Castellanos, N., Arranz, J.M., 1997. Sensitivity study of dry deposition ﬂuxes in ANA air quality model over Madrid mesoscale area. In: San Jose´, R., Brebbia, C.A. (Eds.), Measurements and Modelling in Environmental Pollution. Computational Mechanics Publications, Great Britain by Anthony Rowe, Chippenham, Wiltshire, pp. 119–130, ISBN: 1-85312-461-3. Williams, A., Caughey, M., Huang, H.-C., Liang, X.-Z., Kunkel, K., Tao, Z., Larson, S., Wuebbles, D., 2001. Comparison of emissions processing by EM-S95 and SMOKE over the Midwestern U.S. Preprint of International Emission Inventory Conference: One Atmosphere, One Inventory, Many Challenges. Denver, Colorado, May 1–3, pp. 1–13.

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Chapter 4.8 Modelling the dynamics of air pollutants over the Iberian Peninsula under typical meteorological situations P. Jime´nez, O. Jorba and J.M. Baldasano Abstract The Iberian Peninsula (IP) and the Western Mediterranean Basin (WMB) are characterized by its complex topography and a particular location between two continents and a major ocean and a smallwarm sea. The combination of mesoscale wind ﬂows, emissions and complex local orography is crucial in the production and transport of photochemical pollutants within the domain. These particularities and the global circulation at middle latitudes induce several different meteorological situations affecting the IP, and a particular behavior of the dynamics of air pollutants related to this atmospheric evolution. To quantify the variability of the synoptic meteorology of the region, a cluster analysis of the different synoptic situations of 4 years of meteorological data was applied. In order to assure a full coverage of the different air quality scenarios, seven meteorological situations were selected to simulate and discuss the dynamics of air pollutants over the IP. Simulation covers a domain of 1392 1104 km2 centered in the IP, that uses EMEP-based emissions corresponding to year 2000. Model resolution is 24-km horizontally, and 16 layers of variable thickness in altitude. Dynamical meteorological ﬁelds were derived from MM5 model. Photochemistry is represented by Models-3/CMAQ model with CBM-IV chemical mechanism including heterogeneous chemistry. Model simulations were evaluated by comparing data from meteorological and air quality stations with simulation outputs. The results revealed that summer situations are characterized by high daily-average levels of photochemical pollutants over the whole IP and the WMB. Furthermore, high ozone levels during the central part of the day exceed the regulatory thresholds established in European Directive 2002/3/EC. The combination of sea breezes, upslope winds and important valley canalizations transports

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pollutants from coastal areas inland, causing high levels of photochemical pollution. In addition, the anticyclonic situation modelled also shows high ozone levels at noon due to the important subsidence and poor wind development across the region within the whole troposphere. The advective situations present low ozone levels related to the intense ﬂows that do not contribute to the conditions for the photochemical production of ozone. The particular behavior of the dynamics of the region connected to the emission location contributes to the development of episodes of air pollution in several regions of the IP. Also, the maritime trafﬁc around the IP plays an important role on the ozone levels over the Alboran sea (southwestern Mediterranean coast). These results help understanding the contribution of the different processes to the atmospheric pollution episodes in a complex region as the IP. 1. Introduction

The Iberian Peninsula (IP) and the Western Mediterranean Basin (WMB) are characterised by its complex topography and a particular location between two continents, a major ocean and a small-warm sea. These particularities and the global circulation at middle latitudes induce several different meteorological situations affecting the IP, and a particular behaviour of the dynamics of air pollutants related to this atmospheric evolution. While the IP becomes isolated from the travelling lows and their frontal systems in summer with high occurrence of stagnant situations, in winter the weather is characterised by an increase of extratropical cyclone activity, becoming the wet season with strongest winds. The complex topography of the IP induces an extremely complicated structure of the ﬂow because of the development of large and local mesoscale phenomena that interact with synoptic ﬂows. The characteristics of the breezes have important effects in the dispersion of pollutants emitted. In addition, the ﬂow can be even more complex because of the nonhomogeneity of the terrain, the land-use and the types of vegetation. In these situations, the structure of the ﬂow is extremely complicated because of the overlap of circulations of different scale. To quantify the variability of the synoptic meteorology of the region, Jorba et al. (2004) applied a statistical classiﬁcation of the different synoptic situations of 5 years of meteorological data. In this contribution, six meteorological situations were selected from these results to simulate the dynamics of air pollutants over the IP. A numerical approach was adopted in order to study the dynamics of air pollutants over the IP with a third-generation air quality model (MM5-EMEP-CMAQ) (Fig. 1).

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Figure 1. Air quality modelling system MM5/Emissions/CMAQ.

2. Models

The following models were implemented in the supercomputational framework provided by MareNostrum (held in the Barcelona Supercomputing Center). In order to have a detailed picture of the processes that contribute to air pollution in the IP, six meteorological situations were run with the PSU/NCAR Mesoscale Model 5 (Dudhia, 1993). Two nested domains were selected, which essentially covered Europe (Domain 1, D1) and the IP (Domain 2, D2). A one-way nesting approach was used. The vertical resolution was of 29 s-layers for all domains, the lowest one situated approximately at 10 m AGL and 10 of them below 1 km AGL. The upper boundary was ﬁxed at 100 hPa. Initialisation and boundary conditions were introduced with analysis data of the ECMWF and AVN global model. Data at 11 resolution were available (100 km approximately at the working latitude) at the standard pressure levels every 6 h. The simulations were initialised with a cold start at 1800 UTC of the previous day, and ran for 30 h. The emission model applied was based on the EMEP emission inventory (www.emep.int). A spatial and temporal desegregation was performed to

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convert the initial 50 km emission inventory resolution to the 24 km model grid for the domain of the IP. A consistent mass conservation check was performed between the model and the inventory grid, with high level of agreement. The emission model includes the emissions from vegetation, on-road trafﬁc, industries and emissions by fossil fuel consumption and domestic–commercial solvent use. The chemical transport model used to compute the concentrations of photochemical pollutants was Models-3/ CMAQ (Byun and Ching, 1999). The initial and boundary conditions were derived from a climatology database. A 48-h spin-up was performed for every simulation in order to minimise the effects of initial conditions. The chemical mechanism selected for simulations was CBM-IV (Gery et al., 1989), including heterogeneous chemistry. The horizontal resolution considered was 24 km (time resolution of 1 h), and 16 vertical layers. 3. Selected scenarios: Typical meteorological situations affecting the Iberian Peninsula

Jorba et al. (2004) describe the tropospheric ﬂow patterns arriving at IP by means of a cluster technique and a 5-year database (July 1997–June 2002) of atmospheric back trajectories. Cluster analysis was used to group trajectories according to wind speed and direction in order to describe the main ﬂows arriving at the IP, and to identify the origin of the air masses affecting the region. The cluster results have shown a marked zone component in the average long-range transport patterns to the IP with distinctive westerly groups representing the 48% at 5500 m and 23% at 1500 m of the total analysed situations. However, an elevated occurrence of stagnant situations (regional re-circulations) is also observed especially at low levels representing the 45% at 1500 m. An important decoupling of the lower from the middle troposphere is observed, especially during summertime, as a distinctive characteristic of the region in comparison with more northern areas. From these results, six meteorological situations were selected in order to analyse the dynamics of air pollutants over the Iberian Peninsula. Figure 2 shows the sea-level pressure and 500 hPa geopotential height analysis for the six meteorological scenarios. 4. Results 4.1. Azores anti-cyclone influence: 12 May 2003

This situation is characterised by an important inﬂuence of the Azores anticyclone wedge over the IP. It is noticeable the development of the Iberian

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Figure 2. Meteorological scenarios: (a) Azores anti-cyclone inﬂuence 12 May 2003, (b) northern advection 31 January 2003, (c) summer stagnant situation 12 August 2003, (d) south-western advection 28 April 2003, (e) western advection 25 December 2002 and (f) north-western advection 9 September 2003 at 1200 UTC.

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thermal low (ITL) in the southeast of the Peninsula, pointing out the heating of the land surface during the day (Milla´n et al., 1997). Surface winds are weak during the central part of the day, with the development of sea breezes along the coast, especially relevant in the eastern coast. The insolation is sufﬁcient to promote the development of prevailing mesoscale ﬂows associated with the local orography. The development of the PBL is important, but limited by the subsidence attributed to the anti-cyclonic wedge affecting the region. Some low clouds develop during the day, limiting the increase of surface air temperature. Nocturnal tropospheric ozone levels are relatively high, with concentrations of 70 mg m 3, as a consequence of the permanence of the anti-cyclonic wedge over the IP. Due to the moderate insolation and surface air temperatures, the O3 formation is not promoted during the hours of photochemical activity. The maximum values are observed over the WMB with 115 mg m 3. Figure 3a shows the surface O3 concentration at 1200 UTC. Although, this episode is not characterised by important surface O3 concentrations, it shows an important mixing of medium concentrations across the whole domain within the PBL. The subsidence is one of the main features that provoke this O3 behaviour.

4.2. Northern advection: 31 January 2003

The synoptic situation dominates the ﬂows at near surface levels, with some important local wind developments (e.g., Mistral, Tramuntana and Cierzo). The middle troposphere is characterised by northern winds over the IP that drive surface winds. Important turbulent kinetic energy levels are observed during the whole day attributed to the advective forcings within the PBL. Mechanical mixing dominates the mixing layer and the nocturnal boundary layer. Surface temperatures remain cold during the day, with low insolation over the eastern part of the IP due to the development of a cyclogenesis over the WMB. A cold front advances from north to south associated with the low of the WMB. The low moves eastwards allowing the penetration of the anti-cyclonic wedge over the western part of the IP. Surface winds are strong during all the day, with important mountain wave developments. Figure 3b shows the surface ozone concentration over the IP at 1200 UTC. The ozone concentration remains below 100 mg m 3 during the photochemical activity period. Important mixing is observed across the domain associated to the mechanical mixing of the advective situation. The higher concentrations are modelled within regions with high mixing and strong winds, as seen within the northern jet ﬂow affecting the southeast of IP at 1200 UTC. It is important to remark that the strong winds do not contribute to the accumulation of ozone precursors over the domain.

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Figure 3. Surface wind ﬁeld vectors and ozone concentrations (mg m 3) over the Iberian Peninsula on (a) 12 May 2003, (b) 31 January 2003, (c) 12 August 2003, (d) 28 April 2003, (e) 25 December 2002 and (f) 9 September 2003 at 1200 UTC.

4.3. Summer stagnant situation: 31 August 2003

This episode corresponds to a typical summertime low pressure gradient situation with high levels of photochemical pollutants over the Iberian Peninsula (Barros et al., 2003). This situation is related to a decrease in air quality. The day was characterised by a weak synoptic forcing, so

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that mesoscale phenomena, induced by the particular geography of the region would be dominant. This situation is associated with weak winds in the lower troposphere and high maximum temperatures. Under this weak synoptic forcing, strong insolation promotes the development of prevailing mesoscale ﬂows associated with the local orography, while the difference of temperature between the sea and the land enhances the development of sea–land breezes. The ozone concentrations in the gulf of Lyon and Marseille exceed the threshold set in the Directive 2002/3/EC (180 mg m 3). The photochemistry is clearly enhanced in this episode due to the summer stagnant situation, which allows the accumulation of ozone and its precursors (Fig. 3c). The high temperatures, the weak mesoscale winds combined with the emissions of NOx and VOCs from the Spanish Mediterranean coast (city of Barcelona) and the industrial area near the Rhone (southern France) favour the formation of ozone and other photochemical pollutants. Both the Atlantic and Mediterranean coast of the Iberian Peninsula present concentrations that exceed 150 mg m 3 during the hours of photochemical activity. 4.4. South-western advection: 28 April 2003

The western part of the IP is dominated by south-westerly ﬂows, while the eastern part presents an important inﬂuence of the anti-cyclone located over southeast Italy. The higher pressures over the east IP contribute to an accumulation and formation of photochemical pollutants. The southern ﬂows over the western IP do not contribute to a production of ozone, with higher rates of dispersion and mixing over the region. This situation presents low concentrations of ozone over most part of the region. The advection of maritime tropical air favours an increase of surface air temperatures. The maximum surface ozone concentrations obtained are around 140 mg m 3 (Fig. 3d). The complex topography of the south-eastern IP contributes to an accumulation of air masses with higher concentrations of surface ozone, which is formed from the photochemistry of precursors emitted in the industrial and port area of the strait of Gibraltar. It is also remarkably the air mass downwind the industrial zone of Bilbao (northern IP) with ozone concentrations of 80 mg m 3. 4.5. Western advection: 25 December 2002

This is a synoptic-dominated situation. The westerly winds are predominant over the region. The pass of a cold front produces a decrease on the air temperatures with some associated non-convective precipitation.

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The meteorological situation leads to a poor insolation over the IP during the central part of the day. Surface winds are strong, veering from southwest to west component. The PBL presents a neutral stratiﬁcation with some mechanical mixing within the mixing layer. The surface ozone concentration at 1200 UTC is shown in Fig. 3e. The whole IP presents low ozone concentrations, with low photochemical formation during the central part of the day (low concentrations of precursors). There are only some signiﬁcant ozone concentrations over the northeast of the IP at noon. This region presents values over 110 mg m 3, while the rest of the IP concentrations are below 60 mg m 3. 4.6. North-western advection: 9 September 2003

The IP is affected by north-western ﬂows during the morning, veering to west. The development of Cierzo (a north-westerly wind developed in the north-eastern IP) is well modelled with strong intensity. The southwest IP is dominated by weak winds, remaining protected of the northern strong ﬂows by the complex topography. Surface air temperatures stay moderate. During the central part of the day, the land surface heating is noticeable in the south-western IP, with a signiﬁcant increase of air temperatures. Figure 3f shows the surface ozone concentration at 1200 UTC over the IP. The higher concentrations are simulated over the Gibraltar Sea (south IP), reaching values slightly over 120 mg m 3. The southern IP presents higher ozone levels, and the concentrations are lower in northern regions, more affected by north-western advection. 5. Model evaluation

The air quality modelling system has been evaluated against data from EMEP stations in the Iberian Peninsula. Table 1 indicates that the model has a slight tendency to the underestimation of air pollutants (bias of 4.87% for ozone, 3.48% for nitrogen oxides and –2.03% for sulphur dioxide). The error is higher for primary pollutants than for tropospheric O3 (10.44% for O3, 29.81% for NOx and 24.99% for SO2), meanwhile the model slightly underestimates the maximum concentrations of NOx and SO2 (8.90% and 16.54%, respectively) and the ozone peak ( 5.47%). If these values are compared with the reference values set by the US EPA (2005), the results indicate that the model meets these standards. Figure 4 shows the behaviour of the model for hourly values. The hourly errors of O3, NOx and SO2 remain below the 50% of uncertainty set by the

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Table 1. Summary of the evaluation of the air quality modelling system: Normalised bias (MNBE), mean normalised gross error (MNGE) and unpaired peak accuracy (UPA) Reference US EPA Ozone (O3) Nitrogen oxides (NOx) Sulphur dioxide (SO2)

MNBE (%) 75–715% 4.87 3.48 2.03

MNGE (%) +30–+35%

UPA (%) 715–720%

10.44 29.81 24.99

5.47 8.90 16.54

Figure 4. Hourly results of the evaluation of the air quality modelling system for the six episodes analysed: Bias (MNBE) and error (MNGE).

European Directives 1999/30/EC and 2002/3/EC for every hour in the periods of study.

6. Conclusions

Six simulations with MM5-EMEP-CMAQ air quality modelling system (implemented in the supercomputer MareNostrum of the Barcelona Supercomputer Center) were carried out in order to assess the dynamics of air pollutants over the IP for typical meteorological situations affecting the region. The results revealed that the combination of mesoscale wind ﬂows, emissions and complex local orography is crucial in the production and transport of photochemical pollutants within the domain.

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Summer situations are characterised by high ozone levels during the central part of the day above regulatory thresholds (180 mg m 3), and high daily mean levels over the whole IP and the WMB. The combination of sea breezes, upslope winds and important valley canalisations transports pollutants from coastal areas to the inland, causing high levels of photochemical pollution. The anti-cyclonic situation modelled also shows important ozone levels at noon due to the important subsidence and poor wind development across the region within the whole troposphere. Advective situations present low ozone levels related to the intense ﬂows that do not contribute to the conditions for a proper production of ozone. The particular behaviour of the dynamics of the region connected to the emission location contributes to the development of important episodes of air pollution in several regions of the IP. These results help understanding the contribution of the different factors of the pollution episodes in a complex region.

Discussion

A. Ebel:

J.M. Baldasano:

Doing process studies with the EURAD model it was found that under anti-cyclonic conditions accumulation of ozone in the ABL through subsidence (vertical advection) could be as strong as chemical production in Central Europe. What is your experience when applying your model to high-pressure (summer stagnant) situations over the Iberian Peninsula? Right. During the summer period, the formation and accumulation of ozone takes place, especially in the area of Western Mediterranean Area. In fact, this topic was published as an article in the Journal of Geophysical Research (Jime´nez, P., Lelieveld, J., Baldasano, J.M., 2006. Multiscale modeling of air pollutants dynamics in the northwestern Mediterranean basin during a typical summertime episode. Journal of Geophysical Research 111, D18306, doi:10.1029/2005JD006516), where this phenomenon is analysed in depth.

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D.W. Byun:

J.M. Baldasano:

The model performance you showed has a more wonderful scalability than any other CMAQ application I have ever seen, including our own system. Would you tell us what parallel implementation or additional development you have done to have such a good performance? This performace is achieved because in BSC we work in collaboration with computer scientists specialised in increasing the scalability of the codes and programs, and it is feasible to obtain a good synergy between the different groups of research.

ACKNOWLEDGMENTS

This work was developed under the research contract REN2003-09753C02 of the Spanish Ministry of Science and Technology. The authors gratefully acknowledge E. Lo´pez and R. Parra for the implementation of EMEP emissions into GIS. REFERENCES Barros, N., Toll, I., Soriano, C., Jime´nez, P., Borrego, C., Baldasano, J.M., 2003. Urban photochemical pollution in the Iberian Peninsula: The Lisbon and Barcelona airsheds. J. Air Waste Manage. Assoc. 53, 347–359. Byun, D.W., Ching, J.K.S. (Eds.), 1999. Science algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. EPA Report N. EPA-600/ R-99/030, Ofﬁce of Research and Development. U.S. Environmental Protection Agency, Washington, DC. Dudhia, J., 1993. A nonhydrostatic version of the Penn State/NCAR mesoscale model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Weath. Rev. 121, 1493–1513. Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C., 1989. A photochemical kinetics mechanism for urban and regional scale computer modeling. J. Geophys. Res. 94(D10), 12925–12956. Jorba, O., Pe´rez, C., Rocadenbosch, F., Baldasano, J.M., 2004. Cluster analysis of 4-day back trajectories arriving in the Barcelona Area (Spain) from 1997 to 2002. J. Appl. Meteorol. 43(6), 887–901. Milla´n, M.M., Salvador, R., Mantilla, E., 1997. Photooxidant dynamics in the Mediterranean Basin in summer: Results from European research projects. J. Geophys. Res. 102(D7), 8811–8823. US EPA, 2005. Guidance on the use of models and other analyses in attainment demonstrations for the 8-hour ozone NAAQS. US EPA Report No. EPA-454/R-05-002. Ofﬁce of Air Quality Planning and Standards. Research Triangle Park, North Carolina, US, October 2005, p. 128.

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Chapter 4.9 Modelling of the July 10 STERAO storm with the RAMS model: Chemical species redistribution including gas phase and aqueous phase chemistry Maud Leriche, Sylvie Cautenet, Mary Barth and Nadine Chaumerliac Abstract The meso-scale RAMS model has been applied to the July 10, 1996, STERAO storm observed in Colorado using an idealized horizontally homogeneous sounding with warm bubble initiation. This simulation was done in the framework of the WMO cloud modeling workshop intercomparison on chemistry transport in deep convection led by Mary Barth. The RAMS model coupled with gas and aqueous chemistry simulates CO and O3 mixing ratios similar to observations and other models. The anvil area, mass ﬂux, CO ﬂux, and NOx ﬂux simulated by the RAMS-chemistry model are found to be within 35% of the values deduced from observations. We further examine the simple parameterization of NO production from lightning used in the RAMS simulations, which lead to a good agreement between computed and observed NOx ﬂuxes. Moreover, because the RAMS model allows using either single or double moment microphysical schemes, the impact of the microphysical scheme is examined in terms of chemical species redistribution by the storm. Finally, the effect of gas phase versus aqueous phase chemistry on chemical species redistribution by the storm is also studied.

1. Introduction

Deep convection is a very efﬁcient process to transport chemical species from the planetary boundary layer to the free troposphere (Wang and Prinn, 2000) where they can be efﬁciently transported at the regional scale. It is well known that deep convection affects tracer concentrations in the upper troposphere and lower stratosphere (UTLS) by direct ascents of poor or rich boundary layer air and by replenishment of tracer

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precursors. Observations as well as modelling studies have shown that ozone concentrations as well as the HOx budget are inﬂuenced by deep convection (Jaegle´ et al., 2001; Mari et al., 2003). However, some uncertainties remain as, for instance, on the redistribution of soluble chemical species in convective clouds (Barth et al., 2001; Yin et al., 2001, 2002) and on the production of NOx by lightning. The redistribution of soluble species in deep convective clouds is dependent on the vertical distribution of hydrometeors in the cloud, which relies on the representation of the cloud microphysics in numerical models and on the chemical reactivity in gaseous and aqueous phases. The lightning, which is the most important source of NOx in the upper troposphere, is also linked to the microphysics of the deep convective cloud (Barthe et al., 2005); and both parameterizations of microphysics and lightning NOx production in models impact the quantiﬁcation of NOx in the upper troposphere by numerical studies. This study aims at assessing the simulation of deep convection by the non-hydrostatic meso-scale three-dimensional RAMS (Regional Atmospheric Modelling System) model (Cotton et al., 2003) by looking at the microphysics scheme, at the parameterization of NOx production by lightning and at the redistribution of chemical species including soluble ones by convective clouds. In order to fulﬁl this objective, the July 10, 1996 STERAO (Stratospheric-Tropospheric Experiment: Radiation, Aerosol and Ozone) storm (Dye et al., 2000) has been used because it is a well-documented case with observations to evaluate tracer transport and NOx production from lightning. Moreover, this work was done in the framework of the WMO cloud modelling workshop, which took place in Hamburg in July 2004. The RAMS conﬁguration and the main conditions for the simulations are ﬁrstly described. Then, the structure of the simulated cloud for the two microphysical schemes is presented as well as the redistribution of chemical species, in particular for soluble ones and for NOx including production by lightning.

2. Conditions of the July 10 STERAO storm simulation with the RAMS model

The observed July 10, 1996 STERAO convective system took place near the southern border of Wyoming and Nebraska (see Dye et al., 2000 for an overview of the experiment). The storm developed during the late afternoon, the main cells propagated south–southeastward into northeastern Colorado before dissipating in the evening. The simulation of this storm performed with the RAMS model is based upon the one described by Skamarock et al. (2000) with some modiﬁcations. The convection is

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initiated with three warm bubbles (+11C) placed in the wind direction leading to a simulated convective system similar to the observed one, in particular, the transition from a multicellular line to a single super-cell is reproduced. The vertical resolution is the same as that in Skamarock et al. (2000) with 50 non-equidistant levels. Two nesting grids are used, the large one of 240 240 20 km with a horizontal resolution of 3 km and the small one of 120 120 20 km with a horizontal resolution of 1 km. The small grid moves into the large one with a constant velocity of 1.5 m s1 in the west–east direction and of 5.5 m s1 in the north–south direction. The time step is 5 s and the simulation lasts two and a half hours. The terrain height is 1.5 km. The model environment is initialized with a horizontally homogeneous thermodynamic sounding. The initial vertical proﬁles for chemical species are given in Barth (2004). The source of NOx due to production by lightning is considered following the simple parameterization of Pickering et al. (1998). The parameterization consists of four parts: ﬂash rate, ﬂash type, ﬂash location and NO production rate. The ﬂash rate is computed from the maximum vertical velocity using a power law. The fractions of intracloud (IC) and cloud to ground (CG) ﬂash are computed by estimating the depth of the layer from the freezing level (the 01C isotherm in the cloud) to the cloud top. The CG ﬂashes are placed within the 20 dBZ region from the surface to the model calculated 151C isotherm and the IC ﬂashes in the region of the cloud above the 151C isotherm. The NO production rate is then calculated for each CG and IC ﬂash using different rate values for CG and IC ﬂashes. First, two simulations are performed considering chemical species as inert tracers, one using the single-moment microphysics scheme (Walko et al., 1995) and another one using the two-moment microphysics scheme (Meyers et al., 1997); both schemes are available in RAMS version 4.3. These two microphysics parameterizations use gamma distributions for representing the hydrometeor size distributions. For the single-moment scheme, hydrometeor mixing ratios are prognostic and number concentrations are diagnosed assuming a ﬁxed diameter, except for cloud particles, for which the number concentration is ﬁxed and for pristine ice for which mixing ratio and number concentration are predicted. For the twomoment scheme, hydrometeors mixing ratio and number concentrations are predicted, except for cloud water, for which the number concentration is ﬁxed. The hydrometeor categories considered are the same as those in Barth et al. (2001): cloud water, rain, pristine ice, snow and hail. An additional simulation using the two-moment microphysics scheme and including chemical reactivity in gaseous and aqueous phases is performed. For gas phase, the mechanism describes the reactivity of ozone,

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NOy and VOC including isoprene chemistry. For aqueous phase, the mechanism includes the HOx chemistry and the formation of nitrate, sulphate and formic acid. 3. Results

As part of the intercomparison of tracer transport in deep convection, ﬂuxes across the anvil were calculated for air mass, CO and NOx. The ﬂux calculations from observations (Skamarock et al., 2003) were obtained from 1 h of aircraft data approximately 50 km southeast of the convective core. Likewise, ﬂux calculations from the model results were performed for the t ¼ 1 h–2 h time period of the model simulation at 10- min intervals. They are calculated as integrated ﬂuxes over the anvil through a cross section corresponding to the ﬂight plans divided by the crosssectional area of the anvil and are averaged over the time period sampled. Table 1 lists the anvil area and ﬂuxes from the observations, our two simulations, and those from other participants in the model intercomparison. In general, the results from the RAMS model compare well with the observations and with the results from other models participating in the intercomparison exercise organized by WMO. Participants are: Mary Barth and Si-Wan Kim from NCAR (WRF-AqChem model), Chien Wang from MIT, Ann Fridlind and Andrew Ackerman from NASA/ Ames (DHARMA model), Jean-Pierre Pinty and Ce´line Mari from LA/CNRS/UPS (Meso-NH model) and Kenneth Pickering, Lesley Ott and Georgiy Stenchikov from University of Maryland (GCE model). The ﬂuxes listed in Table 1 show that the mass ﬂux and the CO ﬂux are overpredicted by most models in comparison with values obtained from measurements. The NOx ﬂux predicted by the RAMS simulations is Table 1. Anvil area, mass ﬂux, CO ﬂux and NOx ﬂux across the anvil calculated from measurements and from participating models Model

Observations RAMS_single RAMS_two WRF-AqChem C. Wang DHARMA Meso-NH UMd/GCE a

Anvil area (km2)

Mass ﬂux (kg m2 s1)

CO ﬂux (105 mol m2 s1)

NOx ﬂux (108 mol m2 s1)

315 332 328 188 442 532 n.a. 274

5.9 7.68 7.83 6.75 6.72 7.69 5.41 9.06

1.9 2.29 2.32 1.94 1.95 2.39 1.59 2.54

5.8 6.5a 5.15a 7.23a 5.97a n.a. 2.84a 8.45a

Including production by lightning.

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similar to that obtained from measurements. The UMd/GCE model and the WRF-AqChem model both use the improved parameterization from Pickering et al. (1998) (DeCaria et al., 2000). These models tend to overestimate the ﬂux. The NOx ﬂux predicted by C. Wang’s model, which has an explicit electrical scheme (Wang and Prinn, 2000), is similar to the observations, while the Meso-NH model also using an explicit electrical scheme and a NOx production rate proportional to ﬂash length (Barthe et al., 2005) underestimates the ﬂux. Because the redistribution of NOx and soluble tracers depend on the amount and distribution of the cloud hydrometeor ﬁelds, we ﬁrst examine the cloud water, rain, ice, snow and hail mixing ratios. Figure 1 shows the vertical distribution of hydrometeor mixing ratio at 02:30 h of integration during the super-cell stage of the cloud for the two microphysical schemes. The cross section is done along the propagation line of the convective system. The comparison of the two microphysics schemes shows large differences for the hail vertical distribution. The comparison of the two cases shows large differences for the hail vertical distribution. For the single-moment scheme, hail vertical extension is limited to 5.5 km. For the two-moment scheme, it is up to 15 km. There are also differences between the two schemes for rain, showing more rain production for the two-moment scheme. The analysis of radar data available during the July 10 STERAO storm shows a vertical extension for hail up to about 10 km (W. Deierling, personal communication) indicating a more realistic representation of hail vertical distribution for the two-moment microphysics scheme by the RAMS model.

Single-moment scheme rain cloud hail

11.0

5.5

0.0 60

Two-moment scheme 16.5

75

rain cloud

ice snow

Altitude in km

Altitude in km

16.5

90 105 120 135 150 Distance in km

hail ice

snow

11.0

5.5

0.0 60

75

90 105 120 135 150 Distance in km

Figure 1. Vertical distribution of hydrometeor mixing ratio at 02:30 h of integration across the convective system. The 0.1 g kg1 isocontours are indicated, at left, for the singlemoment microphysics scheme, at right for the two-moment one.

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20 80 140 200 260 320 380 440 500 560 620

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75

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Two-moment scheme 135

150

0.0 60

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Figure 2. Vertical distribution of NOx mixing ratio at 02:30 h of integration across the super-cell, at left, for the single-moment microphysics scheme, at right for the two-moment one.

Figure 2 shows the vertical distribution of NOx including production by lightning at 02:30 h of integration. The comparison of the two microphysics schemes for NOx vertical distribution demonstrates a more efﬁcient production of NOx by lightning for the two-moment scheme, with production over 600 pptv at the top of the three cells. This is related to the results of the hail vertical distribution previously discussed. The feedback of microphysics on thermodynamics and dynamics modify the zone of IC and CG ﬂashes and the associated ﬂash rate. For soluble tracers, only results for the most realistic simulation (twomoment scheme) are shown. In the RAMS model, the scavenging of soluble tracers is considered kinetically, following mass transfer kinetics theory (Schwartz, 1986). Figure 3 presents vertical distribution of gas phase mixing ratio for a soluble species, formaldehyde and of a more soluble one, hydrogen peroxide. The scavenging of the most soluble tracer, the hydrogen peroxide, is efﬁcient and is located in cloud water and rainwater zone of the super-cell. The scavenging by rain is less efﬁcient in front of the system in relation with the location of rainbands as shown in Fig. 1. However, even if the scavenging is efﬁcient for hydrogen peroxide, signiﬁcant amounts are found in the upper troposphere due to vertical transport by the convective system. This is because the initial maximum concentration of H2O2 is located around 5 km similar to results from Wang and Prinn (2000). For formaldehyde, the scavenging is quite low and does not stop its vertical transport from the boundary layer, where its initial maximum concentration is located, to the upper troposphere.

Modelling of the July 10 STERAO Storm with the RAMS Model ppbv

ppbv

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

0.2 0.5 0.8 1.1 1.4 1.7 2.0 2.2 2.5 2.8 3.1

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Figure 3. Vertical distribution of formaldehyde and hydrogen peroxide mixing ratio at 02:30 h of integration across the super-cell (two-moment scheme).

pptv

ppbv 0

0.2 0.5 0.8 1.1 1.4 1.7 2.0 2.2 2.5 2.8 3.1

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Altitude in km

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90 105 120 Distance in km

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Figure 4. Vertical distribution of NOx mixing ratio at left and of hydrogen peroxide at right at 02:30 h of integration across the super-cell for the simulation including chemical reactivity (two-moment scheme).

Figure 4 illustrates the role of chemistry on chemical species redistribution by the super-cell storm. For NOx, which is an insoluble species, in comparison with simulation without chemistry (Fig. 2), the production by lightning is located at a lower altitude and covers a smaller area due to gas phase chemistry. As July 10 STERAO storm occurred at the evening, the photochemistry is very low and the consumption of NOx occurs mainly via the formation of nitrate radical and of nitrogen pentoxide, which is converted in nitric acid in aqueous phase. For hydrogen

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peroxide, which is a soluble species as seen before, in comparison with simulation without chemistry (Fig. 3), its scavenging is more efﬁcient and globally, its amount is smaller than before in the storm area. This has to be related to the aqueous phase chemistry: hydrogen peroxide is destroyed in aqueous phase through its oxidation of sulphur dioxide leading to sulphuric acid formation. As the scavenging is kinetically considered, this destruction leads to a more efﬁcient scavenging and consequently to a global deﬁcit in gas phase since the maximum mixing ratio for hydrogen peroxide is located at the altitude where the cloud water occurs.

4. Conclusion

The simulated ﬂuxes and anvil surface area show a good agreement with other models participating in the WMO Cloud Modeling Workshop intercomparison of tracer transport in deep convection and with values from airborne measurements. Signiﬁcant differences between the two microphysical schemes appear for NOx ﬂuxes, which are higher for the two-moment scheme. The detailed analysis of the structure of the cloud system and of chemical tracer redistribution show: an earlier development of the cloud system for the simulation using the two-moment scheme for cloud microphysics, a more efﬁcient vertical transport in the multicellular stage of the cloud for the two-moment scheme, no signiﬁcant differences in tracer redistribution during the super-cell stage of the storm between the two microphysical schemes and a more efﬁcient production of NOx from lightning with the two-moment scheme due to a more realistic vertical distribution of hail in the storm. Moreover, even if the scavenging of hydrogen peroxide by cloud and rain is efﬁcient, signiﬁcant amount is found in the upper part of the cloud. For formaldehyde, scavenging is low and its vertical transport is efﬁcient. Considering chemical reactivity, the NOx production by lightning is located at a lower altitude and covers on a smaller area and the hydrogen peroxide amount is smaller in the storm area due to the combined effect of kinetic scavenging and aqueous phase chemistry. Discussion

D. Steyn:

Can you explain the strong discontinuities in many variables at 5.5 km? I note that this discontinuity is also evident at roughly 3.0 and 7.5 km and therefore worry that this is a grid effect and not related to atmospheric physics.

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The discontinuities at 5.5 km are observed for the cloud mixing ratio and for soluble chemical species (hydrogen peroxide and formaldehyde). The vertical cross section of relative humidity in the convective system shows that the area encountering supersaturation is between 3 and 5.5 km. Moreover, the formation of cloud water is parameterized in the model such that the excess of water vapour is condensed into cloud water in case of supersaturation. That the reasons why discontinuities are observed at 3 and 5.5 km for cloud mixing ratio and for soluble species which are scavenged by cloud droplets.

ACKNOWLEDGEMENTS

This work was supported by the ‘‘Programme National de Chimie Atmosphe´rique’’ (PNCA) of the INSU (Institut National des Sciences de l’Univers). Computer resources were provided by the CINES (Centre Informatique National de l’Enseignement Supe´rieur project amp2055). We gratefully acknowledge all the participants of the intercomparison exercise. REFERENCES Barth, M.C., 2004. Chemistry Transport in Deep Convection Intercomparison as Part of the WMO Cloud Modeling Workshop, http://box.mmm.ucar.edu/people/barth/ﬁles/ Chem_Convec_Intercomparison/tracertransportdeepconvection.html Barth, M.C., Stuart, A.L., Skamarock, W.C., 2001. Numerical simulations of the July 10, 1996, Stratospheric-Tropospheric Experiment: Radiations, Aerosols, and Ozone (STERAO)-deep convection experiment storm: Redistribution of soluble tracers. J. Geophys. Res. 106, 12381. Barthe, C., Molinie´, G., Pinty, J.-P., 2005. Description and ﬁrst results of an explicit electrical scheme in a 3D cloud resolving model. Atmos. Res. 76, 95. Cotton, W.R., Pielke, R.A., Walko, R.L., Liston, G.E., Tremback, C.J., Jiang, H., McAnelly, R.L., Harrington, J.Y., Nicholls, M.E., Carrio, G.G., McFadden, J.P., RAMS 2001, 2003. Current status and future directions. Meteorol. Atmos. Phys. 82, 5. DeCaria, A.J., Pickering, K.E., Stenchikov, G.L., Scala, L.R., Stith, J.L., Dye, J.E., Ridley, B.A., Laroche, P., 2000. A cloud-scale model study of lightning-generated NOx in an individual thunderstorm during STERAO-A. J. Geophys. Res. 105, 11601. Dye, J.E., Ridley, B.A., Skamarock, W., Barth, M., Venticinque, M., Defer, E., Blanchet, P., Thery, C., Laroche, P., Baumann, K., Hubler, G., Parrish, D.D., Ryerson, T., Trainer, M., Frost, G., Holloway, J.S., Matejka, T., Bartels, D., Fehsenfeld, F.C., Tuck, A., Rutledge, S.A., Lang, T., Stith, J., Zerr, R., 2000. An overview of the Stratospheric-Tropospheric Experiment: Radiation, Aerosols, and Ozone (STERAO)-Deep Convection experiment with results for the July 10, 1996 storm. J. Geophys. Res. 105, 10023.

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Jaegle´, L., Jacob, D.J., Brune, W.H., Wennberg, P.O., 2001. Chemistry of HOx radicals in the upper troposphere. Atmos. Environ. 35, 469. Mari, C., Sau¨t, C., Jacob, D.J., Staudt, A., Avery, M.A., Brune, W.H., Faloona, I., Heikes, B.G., Sachse, G.W., Sandholm, S.T., Singh, H.B., Tan, D., 2003. On the relative role of convection, chemistry, and transport over the South Paciﬁc Convergence Zone during PEM-Tropics B: A case study. J. Geophys. Res. 108, 8232 doi:10.1029/ 2001JD001466. Meyers, M.P., Walko, R.L., Harrington, J.Y., Cotton, W.R., 1997. New RAMS cloud microphysics parameterization. Part II: The two-moment scheme. Atmos. Res. 45, 3. Pickering, K.E., Wang, Y., Tao, W.-K., Price, C., Mu¨ller, J-F., 1998. Vertical distributions of lightning NOx for use in regional and global chemical transport models. J. Geophys. Res. 103, 31203. Schwartz, S.E., 1986. Mass-transport considerations pertinent to aqueous phase reactions of gases in liquid water clouds. In: Jaeschke, W. (Ed.), Chemistry of Multiphase Atmospheric Systems, NATO ASI Series, G6. Spinger-Verlag, p. 415. Skamarock, W.C., Dye, J.E., Defer, E., Barth, M., Stith, J., Ridley, B.A., 2003. Observational- and modeling-based budget of lightning-produced NOx in a continental thunderstorm. J. Geophys. Res. 108, 4305 doi:10.1029/2002JD002163. Skamarock, W.C., Powers, J., Barth, M.C., Dye, J.E., Matejka, T., Bartels, D., Baumann, K., Stith, J., Parrish, D.D., Hubler, G., 2000. Numerical simulations of the 10 July STERAO/Deep Convection Experiment Convective System: Kinematics and transport. J. Geophys. Res. 105, 19973. Walko, R.L., Cotton, W.R., Meyers, M.P., Harrington, J.Y., 1995. New RAMS cloud microphysics parameterization. Part I: The single-moment scheme. Atmos. Res. 38, 29. Wang, C., Prinn, R.G., 2000. On the roles of deep convective clouds in the tropospheric chemistry. J. Geophys. Res. 105, 22269. Yin, Y., Carslaw, K.S., Parker, D.J., 2002. Redistribution of trace gases by convective clouds—Mixed phase processes. Atmos. Chem. Phys. 2, 293. Yin, Y., Parker, D.J., Carslaw, K.S., 2001. Redistribution of trace gases by convective clouds—Liquid phase processes. Atmos. Chem. Phys. 1, 19.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06410-8

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Chapter 4.10 A study of process contributions to ozone formation during the 2004 ICARTT period using the Eta-CMAQ forecast model over the northeastern U.S.$ Shaocai Yu, Rohit Mathur, Kenneth Schere, Daiwen Kang, Jonathan Pleim, Jeffrey Young and Tanya Otte Abstract Ozone, a secondary pollutant, is created in part by pollution from anthropogenic and biogenic sources. First, this study evaluates the Eta-CMAQ forecast model performances for O3, and related chemical species with the observational data from the aircraft (NOAA P-3 and NASA DC-8) ﬂights, Lidar and ozonesonde during the 2004 International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) ﬁeld experiments. The spatial and temporal performance of the model for surface O3 over the northeastern U.S. during this period is also examined through comparison with observations from the U.S. EPA Air Quality System (AQS) network. Second, the contributions of various physical and chemical processes governing the distribution of O3 during this period are investigated through detailed analysis of model process budgets using the integrated process rate analysis (IPR) with the model. 1. Introduction

Ozone (O3) pollution is a major concern in the U.S. since it can adversely affect human health and ecosystem. Tropospheric O3 is generated in the presence of solar ultraviolet radiation through a complex series of photochemical reactions involving many volatile organic compounds (VOC) and nitrogen oxides (NOx), originating from anthropogenic

$

This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reﬂect their policies or views.

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sources (e.g., industry and vehicle emissions) and biogenic sources (e.g., forest and soil). Harmful O3 levels are typically observed during high-pressure, hot, dry, and stagnant atmospheric conditions at the locations with substantial VOC and NOx concentrations. According to the revised 8-h National Ambient Air Quality Standard (NAAQS) for O3 (0.08 ppm) promulgated by the U.S. Environmental Protection Agency (EPA) in 1997, EPA (2004) estimated that about 160 million Americans are exposed annually to 8-h O3 concentrations that exceed this new NAAQS. It is desirable for local air quality agencies to accurately forecast ozone concentrations to alert the public of the onset, severity, and duration of unhealthy air and to encourage people to reduce emissionsproducing activities (such as reducing automobile usage and limiting outdoor activities). This study evaluates the Eta-CMAQ forecast model performances for O3 and related chemical species with the observational data from the aircraft (NOAA P-3 and NASA DC-8) ﬂights, Lidar, and ozonesonde during the 2004 International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) ﬁeld experiments. The spatial and temporal performance of the model for surface O3 over the northeastern U.S during this period is also examined through comparison with observations from the U.S. EPA Air Quality System (AQS) network. The contributions of various physical and chemical processes governing the distribution of O3 during this period are investigated through detailed analysis of model process budgets using the integrated process rate analysis (IPR) with the model.

2. Description of the Eta-CMAQ forecast model suite and observational database

The Eta-CMAQ air quality forecasting system (Otte et al., 2005), created by linking the NOAA Eta model (Rogers et al., 1996) and the U.S. EPA’s CMAQ modeling system (Byun and Ching, 1999) has been deployed over a domain covering the eastern U.S. during summer 2004. The domain has horizontal grid cell sizes of 12 km. Twenty-two layers of variable thickness set on a sigma-type coordinate are used to resolve the vertical extent from the surface to 100 hPa. The lateral boundary conditions are set using a horizontally constant and typically continental ‘‘clean’’ O3 and other trace gas proﬁles with some vertical variations based on climatology. The primary Eta-CMAQ model forecast for the next day’s surface-layer O3 is based on the current day’s 12 UTC Eta cycle. The target forecast period is local midnight through local midnight (0400 to 0300 UTC). The emissions are projected to 2004 from the 2001 U.S. EPA national emission inventory (Pouliot, 2005). The Carbon Bond chemical mechanism

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(CB-IV, version 4.2) is used to represent reaction pathways in the Eta-CMAQ system. Hourly O3 data at 614 sites in the eastern U.S. are available from the U.S. EPA’s AQS network. Four Atmospheric Investigation, Regional Modeling, Analysis, and Prediction (AIRMAP) sites provided continuous measurements of O3 and related photochemical species as well as meteorological parameters during the study; the sites include Castle Springs (CS), Isles of Shoals (IS), Mount Washington Observatory (MWO), and Thompson Farm (TF). From July 1 to August 15, 2004, measurements of vertical proﬁles of O3, its related chemical species (CO, NO, NO2, H2O2, HCHO, HNO3, SO2, PAN, isoprene, and toluene), and meteorological parameters (liquid water content, water vapor, temperature, wind speed and direction, and pressure) were carried out by instrumented aircraft (NOAA P-3 and NASA DC-8) and Lidar deployed as part of the 2004 ICARTT ﬁeld experiment. The model performance during July 1–August 15, 2004, is examined in this study based on the 12 UTC run for the target forecast period.

3. Results and discussion 3.1. Spatial and temporal evaluation over the eastern U.S. domain at the AIRNow sites

Figure 1a displays that the model reproduced the majority (73.1%) of the observed daily maximum 8-h O3 concentrations within a factor of 1.5. The scatter plot shows that the model generally over-predicted the observations in the low O3 concentration ranges, in part, due to the prescribed high background O3 levels speciﬁed in these simulations. Spatially, the model performed better over the western part with NMB (normalized mean bias) of 725% than eastern coastal part of the domain with NMB25%. The largest over-prediction of the observed daily maximum 8-h O3 concentrations existed across the northeast. The model captured the day-to-day variation in the observed maximum 8-h O3 concentrations very well (Fig. 1b). The domain mean values of NMB and NME (normalized mean error) during the ICARTT period for maximum 8-h O3 have been found to be 22.6% and 28.8%, respectively. 3.2. Evaluation of vertical profiles for O3 and meteorological parameters

Comparisons of modeled and aircraft- and Lidar-based observed vertical proﬁles provide an assessment of the ability of the model to simulate the

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Figure 1. Comparison of the modeled and observed peak 8-h O3 concentrations at the AIRNow monitoring sites (a) scatter plot (ppbv) (the 1:1, 1.5:1, and 1:1.5 lines are shown for reference), (b) daily variation of mean, NME, NMB, and correlation (r) during July 1 and August 15, 2004.

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vertical structure of air pollutants and meteorological ﬁelds. Following Mathur et al. (2005), modeled results were extracted by ‘‘ﬂying’’ the aircraft through the 3-D modeling domain for each ﬂight; the spatial locations of the aircraft were mapped to the model grid, whereas hourly resolved model outputs were linearly interpolated to the corresponding observational times. The tracks of aircraft show that measurements onboard the P-3 cover a regional area over the northeast around New York and Boston, whereas the DC-8 aircraft covers a broader regional area over the eastern U.S. All DC-8 measurements were conducted in the daytime (7:00 to 19:00 EST), and P-3 also conducted most of measurements at the daytime except on 7/11, 7/31, 8/3, 8/7, and 8/9, during which the P-3 measurements were conducted in the nighttime (20:00 to 6:00 EST). In order to compare the modeled and observed vertical proﬁles, the observed and modeled data were grouped according to the model layer for each day and each ﬂight, and then the layer mean values for each parameter were calculated. Thus, these vertical proﬁles may be regarded as representing the mean conditions along the ﬂight track for each day. An example of modeled and observed vertical proﬁles for O3 from the P-3, and DC-8 measurements presented in Fig. 2 reveals that while the model generally reproduced the observed O3 vertical structure most of time, it tended to over-predict at higher elevations, in part, possibly reﬂecting the assumed high background O3 levels speciﬁed in these simulations. The model’s ability to simulate the vertical proﬁles for CO and HNO3 measured by the P-3 and DC-8 aircrafts for some days is illustrated in Fig. 2. In general, the model captured the patterns of the vertical variation in the observed values for both species, with some exceptions. Noticeable among these are the consistent under-predictions for CO vertical proﬁles most of days. One of the reasons for this under-prediction is attributed to the inadequate representation of the transport of pollution associated with biomass burning from outside the domain (McKeen et al., 2002; Mathur et al., 2005). The signiﬁcant under-predictions of CO during July 20 and July 22, 2005, further support this explanation as the aerosol index images from the TOMS satellite observations (http:// toms.gsfc.nasa.gov/) clearly show that the eastern U.S. was signiﬁcantly inﬂuenced by large forest ﬁres in Alaska during this period. On 8/11, the P-3 encountered New York City (NYC) plume at 700 m during 02:30– 10:30 UTC with very high CO (>180 ppb). The model underestimated both CO and HNO3 in the NYC plume at this altitude as shown in Fig. 2. The model reproduced the vertical proﬁles of water vapor and wind speed very well most of the time with slight over-predictions of water vapor at low altitudes relative to P-3 observations as shown in Fig. 3 for some

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452 10 10 10 10

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Figure 2. Comparison of vertical O3, CO, and HNO3 (ppbv) proﬁles for the models and observations from the aircrafts P-3 (a) and DC-8 (b) during the ICARTT period.

10

10 10 Height (m)

10 7/9

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23 46 23 Wind speed (m/s)

46

10 10 10

7/15 0

8/7

8 16 8 16 Water vapor (g kg-1)

Figure 3. Comparison of vertical wind speed and water vapor proﬁles for the models and observations from the aircrafts P-3 (a) and DC-8 (b) during the ICARTT period.

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days. The model also tracked the vertical variations of temperatures, pressures, and wind directions very well most of the time (not shown). 3.3. Time-series comparison at the AIRMAP sites

The statistical summary of Table 1 shows that the model captured the hourly variations and broad synoptic changes seen in the observations of different gas species (O3, CO, and NOy; correlation coefﬁcient 40.49) except NO and SO2 at each site, although there were occasional major excursions. The model under-predicted CO by 20–50% consistently at each site, like those comparisons for the vertical proﬁles. Relatively large discrepancies between modeled and measured concentrations are noted for primary species such as NO and SO2. These are likely related to the discrepancies between modeled and observed wind speed and direction, which cause modeled plumes to be displaced leading to relatively larger error for primary species when the modeled and measured values are paired in space and time. The model reproduced the observed temperatures with 75% errors and relative humidity (RH) with 710% at each site but over-predicted wind speed. For the photolysis rates of NO2 (J NO2 ), we focus our analysis on daytime data by excluding data where J NO2 o5 105 s1. Table 1 indicates that the model reproduced 49.6%, 43.1%, and 53.8% of observed J NO2 values within a factor of 1.5 at the CS, MWO, and TF sites, respectively. DeMore et al. (1997) suggest that a 720% uncertainty can be associated with uncertainty in the cross-section and quantum yield data used in the calculation of J NO2 values. 3.4. Process analysis of O3 formation

The IPR in the CMAQ model enables an in-depth understanding of the contributions of various physical and chemical processes governing the O3 formation. As an example, the process analysis results at the Castle Springs site on July 22, 2004 are presented because there was an O3 episode over the northeastern domain on this day. Figure 4a illustrates the results of the mean O3 change rates due to seven processes [gas-phase chemistry (CHEM), vertical advection (ZADV), vertical diffusion (VDIF), dry deposition (DDEP), horizontal advection (HADV), horizontal diffusion (HDIF), and aqueous process (CLDS)] for each layer at the Castle Springs (CS) site from 10:00 to 16:00 EST on July 22, 2004. Horizontal advection and vertical diffusion play a dominant role in transporting O3 from adjacent and aloft cells into the CS site surface

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Table 1. Statistical summaries of the comparisons of the model results with the observations at the AIRMAP sites during the 2004 ICARTT [C]a Parameters

Observations

Castle Springs (N ¼ 1047) 35.17 O3 NO 0.14 CO 188.84 2.27 NOy SO2 1.16 3.18E03 J NO2 (s1) Temperature 19.65 (1C) RH (%) 78.69 Wind direction 182 Wind speed 1.04 (m s1) Isles of Shoals (N ¼ 1078) O3 36.68 CO 171.70 NO 0.76 Mount Washington (N ¼ 1076) 45.87 O3 NO 3.64 CO 152.43 4.04 NOy 0.74 SO2 J NO2 (s1) 3.59E03 Thompson Farm (N ¼ 1067) O3 28.80 NO 0.33 CO 173.07 3.93 NOy SO2 1.22 J NO2 (s1) 3.19E03 Temperature 20.33 (1C) RH (%) 80.97 Wind direction 198 0.96 Wind speed (m s1) a

r

% within a factor of 1.5b

% within a factor of 2b

Model

43.63 0.05 108.78 3.14 0.87 4.07E03 19.78

0.493 0.222 0.706 0.587 0.388 0.820 0.867

66.6 12.1 19.3 43.6 29.6 49.6 100.0

90.1 22.5 74.7 67.7 45.8 63.4 100.0

71.64 198 3.25

0.781 0.455 0.426

97.7 63.0 11.7

100.0 78.6 24.3

52.31 121.15 0.18

0.541 0.610 0.448

56.9 60.9 0.8

80.2 90.3 3.53

45.85 0.01 95.19 2.23 0.30 4.43E03

0.554 0.054 0.301 0.060 0.001 0.768

87.7 8.9 46.7 20.6 19.0 43.1

98.8 13.8 84.3 38.4 32.6 61.93

41.68 0.29 154.66 7.26 1.63 3.90E03 20.44

0.751 0.436 0.593 0.321 0.084 0.865 0.887

48.1 31.3 77.7 28.8 14.3 53.8 99.9

73.8 51.3 98.5 51.6 25.3 68.1 100.0

75.18 177 3.29

0.829 0.530 0.462

98.5 73.2 8.0

100.0 83.7 16.8

[C] is the mean concentration (ppb). Percentages (%) are the percentages of the comparison points at which model results are within a factor of 1.5 and 2 of the observations. N is number of samples. b

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Figure 4. (a) The mean O3 change rate due to different processes for each layer from 10:00 to 16:00 EST, (b) The hourly O3 change rate due to different processes at the surface layer, (c) O3 concentrations at each layer at 10:00 and 16:00 EST. All results are at the Castle Springs (CS) site on July 22, 2004.

with minor contribution from chemistry, whereas dry deposition and vertical advection are the most important paths to remove O3 at the CS site surface. The effects of horizontal diffusion and aqueous processes on O3 formation are negligible in this case. It is of interest to note that from layer 2 (74 m) to layer 8 (1000 m), chemistry and horizontal advection are still major sources, whereas the vertical diffusion and advection contributions become negative. These contribute to the enhancement of O3 vertical proﬁles from 10:00 to 16:00 EST as shown in Fig. 4c. The hourly O3 change rates of each process at the surface layer in Fig. 4b indicate that during the daytime (7:00–19:00), the O3 concentrations in the surface layer are enhanced dominantly by the vertical diffusion of O3-rich air from aloft, followed by horizontal advection and chemistry production, whereas dry deposition and vertical advection mainly deplete O3 at the surface layer. The vertical proﬁles of O3 at 16:00 EST in Fig. 4c show that O3 concentration increased slightly with height below layer 9 and had a maximum value of 91 ppbv at the layer 7. This analysis reveals that the high surface O3 concentrations at the CS site on this day were

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contributed mainly by mixing down to the surface of O3-rich polluted air from aloft that originates due to long-range transport to the CS area. ACKNOWLEDGMENT

The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce’s National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW13921548. REFERENCES Byun, D.W., Ching, J.K.S., (Eds.), 1999. Science algorithms of the EPA Models-3 Community Multi-scale Air Quality (CMAQ) modeling system, EPA/600/R-99/030. Ofﬁce of Research and Development, U.S. Environmental Protection Agency, RTP, North Carolina, USA. DeMore, W.B., Sander, S.P., Howard, C.J., Ravishankara, A.R., Golden, D.M., Kolb, C.E., Hampson, R.F., Kurylo, M.J., Molina, M.J., 1997. Chemical kinetics and photochemical data for use in stratospheric modeling, Evaluation 12. NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena. Mathur, R., Shankar, U., Hanna, A.F., Talat Odman, M., McHenry, J.N., Coats Jr., C.J., Alapaty, K., Xiu, A., Arunachalam, S., Olerud Jr., D.T., Byun, D.W., Schere, K.L., Binkowski, F.S., Ching, J.K.S., Dennis, R.L., Pierce, T.E., Pleim, J.E., Roselle, S.J., and Young, J.O. (2005). Multiscale air quality simulation platform (MAQSIP): Initial applications and performance for tropospheric ozone and particulate matter. J. Geophys. Res. 110, D13308, doi:10.1029/2004JD004918. McKeen S. A., Wotawa, G., Parrish, D.D., Holloway, J.S., Buhr, M.P., Hu¨bler, G., Fehsenfeld, F. C., and Meagher, J. F. (2002). Ozone production from Canadian wildﬁres during June and July of 1995, J. Geophys. Res. 107(D14), 4192, doi:10.1029/ 2001JD000697. Otte, T.L., Pouliot, G., Pleim, J.E., Young, J.O., Schere, K.L., Wong, D.C., Lee, P.C.S., Tsidulko, M., McQueen, J.T., Davidson, P., Mathur, R., Chuang, H.-Y., DiMego, G., Seaman, N.L., 2005. Linking the Eta model with the community multiscale air quality (CMAQ) modeling system to build a national air quality forecasting system. Weather and Forecasting 20, 367–384. Pouliot, G.A., 2005. The emissions processing system for the Eta/CMAQ air quality forecast system, Proceedings of the 7th Conference on Atmospheric Chemistry, The 85th AMS Annual Meeting, Paper 4.5, American Meteorological Society, San Diego, CA. Rogers, E., Black, T., Deaven, D., DiMego, G., Zhao, Q., Baldwin, M., Junker, N., Lin, Y., 1996. Changes to the operational ‘‘early’’ Eta Analysis/Forecast System at the National Centers for Environmental Prediction. Weather Forecasting 11, 391–413. U.S. EPA, 2004. The ozone report: Measuring progress through 2003. EPA-454-K-04-001. Ofﬁce of Air Quality Planning and Standards, the U.S. Environmental Protection Agency, Research Triangle Park, NC.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06411-X

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Chapter 4.11 Validation of the integrated RAMS-Hg modelling system Antigoni Voudouri and George Kallos Abstract RAMS-Hg and CMAQ-Hg are two state-of-the-science integrated modelling systems developed to study the complex chemical transformation, transport and deposition of atmospheric mercury. In the present study model results using RAMS-Hg are both compared with observations from the Hg deposition network (MDN) and wet deposition of Hg results using CMAQ-Hg. A preliminary validation of the RAMS-Hg model as well as quantitative estimation of the advantages of the proposed approach on coupling mercury processes to an atmospheric modelling system is presented. Model validation indicated that the comprehensive model simulated reasonably well the wet deposition measurements of Hg at the MDN sites. 1. Introduction

RAMS-Hg and CMAQ-Hg are the two state-of-the-science integrated modelling systems developed to study the complex chemical transformation, transport and deposition of atmospheric mercury. CMAQ-Hg is an expanded version of the US EPA’s Community Multiscale Air Quality, CMAQ model (Byun and Ching, 1999) modiﬁed accordingly by Bullock and Brehme (2002) to simulate the atmospheric cycle of mercury. RAMSHg is based on the well-known Regional Atmospheric Modelling System (RAMS) version 4.3 (Cotton et al., 2003) modiﬁed with embedded modules able to represent the atmospheric mercury cycle (Voudouri et al., 2005). Both models deal with elemental Hg (Hg0), divalent gaseous Hg (Hg2) and particulate Hg (HgP). Detailed calculations of the air-surface exchange for Hg were adapted to RAMS-Hg to describe Hg re-emissions and dry deposition from and to natural surfaces. Wet deposition mechanisms used to describe the removal of Hg2 and HgP are merged with the detailed cloud microphysical scheme in order to provide better representation of the wet deposition processes.

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The dependence on accurate precipitation calculations was pointed out by Bullock and Brehme (2002) using input meteorological data from MM5 (Grell et al., 1994), as well as the advantages of model coupling are examined in this study, for two experimental periods, namely 4 April– 5 May 1995 and 20 June–18 July 1995. Researchers using statistical measures, such as BIAS, percentiles, the Pearson correlation coefﬁcient of modelling results to actual observations of wet deposition and precipitation examined whether the performance of the model on calculating wet deposition of mercury is strongly dependent on the correlation between observed and modelled precipitation. In the present study, model results are both compared with observations from the Hg deposition network (MDN) and wet deposition of Hg results using CMAQ-Hg from the previous work of Bullock and Brehme (2002). The modelling system was applied to a domain covering most of eastern North America. A preliminary validation of the RAMS-Hg model as well as quantitative estimation of the advantages of the proposed approach on coupling mercury processes to an atmospheric modelling system, are presented.

2. Model runs, analysis of results

The RAMS-Hg model has been applied for two periods, the spring (4 April 1995–2 May 1995) and the summer (20 June–18 July 1995) simulation, also discussed in the study of Bullock and Brehme, 2002. Model results extracted using RAMS-Hg model for the spring and summer period have been compared with wet deposition and precipitation observations available for both simulation periods from MDN. Observed precipitation was derived from wet deposition (ng m3) and sample concentration (ng l1) data. Precipitation is the key parameter for wet deposited Hg, therefore observations have been also compared with total precipitation amount calculated using RAMS-Hg. Model results of wet deposited Hg and total precipitation versus observations for the spring season period are shown in Figs. 1 and 2 for precipitation and wet deposition respectively. Comparison of precipitation observations to model results in Fig. 1 is encouraging for most stations during the spring simulation period. Figure 2 illustrates weekly observed wet deposited Hg versus model results where the dependence of wet deposition mechanism on precipitation is conﬁrmed. Best agreement between observed and modelled wet deposited Hg is evident when model predicted precipitation amount accurately. On the contrary, during the second and third week of the spring simulation period (11–25 April), the atmospheric model did

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60 40 20 0 D E0 2 M N 16 N Y9 7 W I0 8* IL 11 M N 16 N Y9 7 D E0 2 KY 99 M N 18 SC 19 D E0 2 M N 16 N Y9 7

Precipitation (mm)

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RAMS-Hg

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D E0 2 M N 16 N Y9 7 W I0 8* IL 11 M N 16 N Y9 7 D E0 2 KY 99 * M N 18 SC 19 D E0 2 M N 16 N Y9 7

Wet Depos. Hg (ng/m2)

Figure 1. Weekly precipitation (mm) during 4 April–2 May 1995.

Station MDN 4-2/05/95

RAMS-Hg

Figure 2. Weekly wet deposited Hg (ng m2) during 4 April–2 May 1995.

not represent accurately total precipitation for stations KY99 and IL11, resulting to the poor agreement presented between wet deposition predictions and observations. These speciﬁc stations located at Kentucky and Illinois states respectively, were within the warm sector of a depression formed on 11 April and affected by a warm front on 15 April and successive depressions passage over the area from 16 to 18 April. RAMSHg predicted these depressions, however the exact speed and location of the depression centre on 11 April was inaccurately calculated. In other cases, where differences between the modelled and observed precipitation are less than 1 mm (e.g., MN18 from 4 to 11 April 1995, and NY97 during 25 April–2 May 1995), predicted differences on the calculated and observed wet deposited Hg can be attributed to inaccuracies on the estimated concentrations of Hg2 and HgP.

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Spring 4Apr-2May 1995 70 60 50 40 30 20 10 0 0

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Figure 3. Scatter plot of total precipitation (mm) during spring experimental period.

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Observed Wet Depos. Hg (ng/m2) Figure 4. Scatter plot of wet deposited Hg (ng m2) during spring experimental period.

The correlation between calculated and observed precipitation and wet deposited Hg has been evaluated through scatter plots for the entire simulation period, presented in Figs. 3 and 4. For the spring period the Pearson correlation factor is 0.76 for the precipitation and 0.7013 for the wet deposition of Hg. These correlation coefﬁcients indicate relatively good agreement between observations and model calculations with a slight tend to underestimate. This underestimation is evident in cases of heavy precipitation, while in cases where precipitation and wet deposited Hg are less than 10 mm and 200 ng m2 respectively, the model results are fairly well. Scatter plot of modelled versus measured wet deposition of Hg for the summer simulation period is illustrated in Fig. 5. RAMS-Hg underestimated wet deposited Hg average value equal to 209.2 ng m2 while the

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461

Modelled depos. Hg (ng/m2)

Summer 20 June-18 July 1995 1400 1200 1000 800 600 400 200 0 0

200

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800

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1400

Observed depos. Hg (ng/m2) Figure 5. Scatter plot of wet deposited Hg (in ng m2) for summer simulation period.

Modelled Precip. (mm)

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20

30 40 50 60 Observed Precip. (mm)

70

80

90

Figure 6. Scatter plot of precipitation (mm) during the summer simulation period.

average observed wet deposited Hg for the entire simulation period was 434.4 ng m2. Inspection of the scatter plot shows that in cases where total wet deposited Hg was less than 300 ng m2 model results are in good agreement with observations. However, the model did not predict cases where total amount of wet deposited Hg was greater than 500 ng m2. Thus the Pearson correlation coefﬁcient for this period was only 0.3963, suggesting poor correlation between observations and model results. Total precipitation is the controlling factor for wet deposition, therefore in order to investigate the poor agreement for the summer simulation period, model results have also been compared with the observed precipitation. The Pearson correlation between modelled and observed precipitation is 0.1615, indicating low correlation between modelled and observed values. A much wider scatter is evident in Fig. 6 during this

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simulation period indicating the importance of accurate meteorological simulation. The advantages of the proposed approach as well as differences between the two models were examined through a preliminary evaluation conducted by a comparison of statistics. Model predictions of weekly precipitation and wet deposited Hg using both CMAQ-Hg and RAMSHg are compared with weekly measurements made by MDN. Summary statistics of the comparative evaluation between observed and model using RAMS-Hg as well as published data from Bullock and Brehme (2002) are shown in Tables 1 and 2 for precipitation and wet deposited Hg respectively.

Table 1. Summary statistics for precipitation in mm Period

N

Source

Average

S

Min

Percentiles 25th

Spring

28

Summer

35

Spring and Summer

63

MDN CMAQ-Hg RAMS-Hg MDN CMAQ-Hg RAMS-Hg MDN CMAQ-Hg RAMS-Hg

16.14 10.88 18.91 26.5 34.4 33.8 21.88 23.97 26.34

18.9 13.37 15.58 22.01 35.78 20.99 21.17 30.32 20.31

0 4.1 0 1.9 0 4.8 0 7.975 0 3.57 2.39 18.32 0 6.53 0 3.57 0 9.62

Max

50th

75th

6.7 5.7 13.4 20.64 16.81 34.91 16.04 16.81 20.18

22.4 17.1 21.5 39.3 36.31 47.39 31.32 36.31 41.65

62.3 51.5 55.2 74.31 162.90 79.39 74.31 162.90 79.39

Table 2. Summary statistics for wet deposition of Hg in ng m2 Period

N

Source

Average

s

Min

Percentiles 25th

Spring

28

MDN CMAQ-Hg RAMS-Hg

150.17 189.1 168.92

222.3 232.0 145.0

0 0 0

Summer

35

MDN CMAQ-Hg RAMS-Hg Lin & Tao

389.3 623.7 187.6 409.6

327.3 621.1 124.9 318.9

0 0 6.85 0

Spring and Summer

63

MDN CMAQ-Hg RAMS-Hg

283.0 430.5 179.3

307.6 531.4 132.37

0 0 0

31 21.6 73.7 171.5 202.1 100.9 192.2

50th

Max 75th

70 157 103.1 268.6 139.82 199.87 347 482.5 184.19 388.2

905 843.5 539.51

561 1293 759.7 2598.5 270.96 503.96 583.5 1143.9

65.59 182 404 1293 82.50 247.10 576.30 2598.50 88.96 141.64 258.52 539.51

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An improvement is evident for both simulation periods using RAMSHg mainly on precipitation and wet deposition of Hg during spring. Modelled wet deposited Hg mean value is 168.92 ng m2 slightly over predicted compared to observed 150.17 ng m2 following over predicted modelled precipitation. The statistics for the summer period indicate that RAMS-Hg underestimated wet deposition of Hg, reﬂected in all four quartiles while precipitation is slightly overestimated. However, the weak relationship between observed and modelled precipitation as well as wet deposited Hg inhibits from any conclusion on model reliability for the summer simulation period. Total accumulated wet deposited Hg for summer experimental period has also been calculated by Lin and Tao (2003) as shown in Table 2. Lin and Tao (2003) have used CMAQ-Hg with meteorological data generated from MM5 simulations. Although these results indicate a slight improvement against previous work of Bullock and Brehme (2002) they are not considered in the RAMS-Hg evaluation. The speciﬁc intercomparison is not conducted due to inadequate published data on MM5 runs. The spatial resolution, duration of simulation for producing meteorological input data used is a critical issue, as meteorological parameters strongly affect the wet deposition of Hg. More comparisons of statistics are summarised in Table 3. The calculated correlation coefﬁcient for spring is 0.701, indicating a slight improvement over the correlation coefﬁcient 0.657 by Bullock and Brehme (2002). BIAS of modelled wet deposited Hg using RAMS-Hg is equal to 18.75 ng m2 compared to 38.93 ng m2 BIAS for CMAQ-Hg for spring simulation period. During summer simulation period CMAQ-Hg strongly overestimated measurements as indicated by the positive BIAS 234.4 ng m2 while the RAMS-Hg BIAS is negative 201.7 ng m2 suggesting an equally strong underestimation. However, the present study improved compared to the calculated by Bullock and Brehme (2002) mean values of wet deposited Hg by 51.8% and 13.9% for spring and summer respectively. This improvement is mainly attributed in the modelled precipitation, as the wet deposition simulation is directly affected by the input meteorological data.

Table 3. BIAS (in ng m2) and Pearson correlation coefﬁcient for wet deposition of Hg Measure

BIAS Pearson

Spring RAMS-Hg

CMAQ-Hg

18.75 0.701

38.93 0.657

Summer RAMS-Hg 201.7 0.396

CMAQ-Hg 234.4 0.329

Spring and Summer RAMS-Hg 103.7 0.484

CMAQ-Hg 147.5 0.474

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Combining both simulation periods’ results in order to increase the sample to 63 it is evident that RAMS-Hg underestimates (BIAS ¼ 103.7 ng m2) while CMAQ-Hg overestimates (BIAS ¼ 147.5 ng m2) observations of wet deposited Hg. This BIAS improvement stands for 29.6% for the entire sample (63 measurements for both spring and summer). The implementation of the developed mercury modules on the atmospheric model in addition to the new developments on the RAMS physics (Cotton et al., 2003) seem to have improved the performance of RAMS-Hg against CMAQ-Hg. This is also pronounced by other statistic measures not available for CMAQ-Hg results, such as the ABSBIAS and RMSE of RAMS-Hg equal to 197.2 ng m2 is 286.9 ng m2 respectively. 3. Conclusions

In the present study, an application of the developed RAMS-Hg model for two 4-week periods in April/May and June/July 1995 is performed. Model validation indicated that the comprehensive model simulated reasonably well the wet deposition measurements of Hg at the MDN sites. Results from both simulations revealed that RAMS-Hg can accurately calculate wet deposited Hg when regional scale meteorological systems prevail. The results of the spring simulation period indicated that the model improved the accuracy on calculated wet deposition of Hg, following the well-deﬁned precipitation pattern. The proposed approach on implementing mechanisms that describe mercury processes into the atmospheric model derogated limitations or uncertainties derived from meteorology prediction and reﬂected mainly on wet and dry deposition treatment. Considering the rather small sample size, model results are encouraging. Although this comprehensive regional modelling study was performed by including the up to date physiochemical transformation mechanism of Hg, treatment of Hg re-emission and dry deposition further development and model validation is planned based on the results of this study. Discussion

R. Bullock:

The high bias on Hg wet deposition shown for the Bullock and Brehme (2002) CMAQ-Hg results are in error, i.e., if the statistics shown are supposed to be for simulated wet deposition. Are the biases shown actually for ‘‘adjusted’’ results taking into account the low bias in precipitation depth?

Validation of the Integrated RAMS-Hg Modelling System

G. Kallos:

P. Kishcha:

G. Kallos:

E. Genikhovich:

G. Kallos:

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The biases shown have not been adjusted for any model. Actually, this is not feasible and cannot be done on a scientiﬁcally sound way. While comparing two models (RAMS versus CMAQ) with respect to wet deposition, the major difference was found to be in the position of precipitation. Which of the two models gives more realistic results? This is partially true but if someone takes in to the account that the simulation period is several days the error due to the position of precipitation cannot be considered as higher for one of the two models. According to the literature, RAMS has a better performance than MM5 concerning precipitation. Based on that and on the fact that RAMS-Hg gave better statistical ﬁgures than CMAQ we can say that our approach is worth the effort. I think something is wrong with the ﬁrst three rows of the table with the precipitation statistics. The maximum there is always less than the average value. The comment is correct as there was a typing error in the presentation. The decimal point was misplaced in the ﬁrst three rows. The table has been corrected in the text.

ACKNOWLEDGMENT

The authors wish to acknowledge the DG-Research of EU for funding within the framework of MERCYMS—‘An Integrated Approach to Assess the Mercury Cycling in the Mediterranean Basin’ (EVK3-200200070) project. REFERENCES Bullock, O.R. Jr., Brehme, K.A., 2002. Atmospheric mercury simulation using the CMAQ model: formulation description and analysis of wet deposition results. Atmos. Environ. 36, 2135–2146. Byun, D.W., Ching, J.K.S., 1999. Science algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) modeling system, EPA/600/R-99/030. Environmental Protection Agency, Ofﬁce of Research and Development, Washington, DC.

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Cotton, W.R., Pielke, R.A., Walko, R.L., Liston, G.E., Tremback, C.J., Jiang, H., McAnelly, R.L., Harrington, J.Y., Nicholls, M.E., Carrio, G.G., McFadden, J.P., 2003. RAMS 2001: Current status and future directions. Meteorol. Atmos. Phys. 82, 5–29. Grell, G.A., Dudhia, J., Stauffer, D.R., 1994. A description of the ﬁfth-generation Penn State/NCAR mesoscale model (MM5). NCAR technical note, NCAR/TN398+STR. Lin, X., Tao, Y., 2003. A numerical study on regional mercury budget for eastern North America. Atmos. Chem. Phys. 3, 535–548. Voudouri, A., Pytharoulis, I., Kallos, G., 2005. Mercury budget estimates for the State of New York. Environ. Fluid Mech. 5, 87–107.

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Chapter 4.12 Analyzing the response of a chemical transport model to emissions reductions utilizing various grid resolutions Rainer Stern, Robert J. Yamartino and Arno Graff Abstract Within the framework of a project ﬁnanced by the German EPA (Umweltbundesamt), photochemical and aerosol modelling simulations over Europe, Germany, the Federal State Brandenburg and the (agglomeration) metropolitan area of Berlin for the whole year 2002 were performed using the REM-CALGRID (RCG) model. The aim of the study was to analyze the relationships between regional and urban air quality levels and the modelled effectiveness of control measures in an urban area as a function of varying, horizontal grid resolution. The study should help to answer the question of how reliably regional scale model calculations can be used to compute air quality in urban areas. The RCG model was run at four different grid resolutions to assess the air quality in Berlin: 1. A European scale grid resolution of 0.251 Latitude by 0.51 Longitude (i.e., nominally about 30-km); 2. A national scale (Germany) grid having twice the resolution of grid #1 (i.e., approx. 15-km); 3. A Federal State (Brandenburg) grid having 8 times the resolution of grid #1 (i.e., approx. 4-km) and, 4. An urban (Berlin) grid having 32-times the resolution of grid #1 (i.e., approx. 1-km). High-resolution emissions data generated by the "bottom-up" approach were available for Berlin and Brandenburg. These emissions were used for grids #3 and #4. The European and German scale calculations on grids #1 and #2, respectively, were carried out employing the "top-down" TNO emissions inventory for Europe for base-year 2000 at the 0.1251 lat. by 0.251 longitude resolution of

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grid #2. Because the emissions estimates of the two inventories differ substantially for the greater Berlin area, the local bottom-up emissions were integrated into the large scale TNO data base, thus, allowing the usage of harmonized emissions at all scales. A base run for each scale was carried out and compared with PM10 and NO2 observations taken within the area of Berlin. It turned out that model performance was best for the run with the highest grid resolution. As might be expected, the urban increment was strongly underestimated by the regional (grid #1) model resolution. The results of two emissions scenarios, a 50% reduction of all emissions and the CLE-scenario (Implementation of current legislation until 2010), clearly showed that the effects of a measure will be underestimated in urban areas if they are based on a regional scale model application. 1. Introduction

Many efforts are underway in Europe to control the emission sources that are responsible for harmful air pollution effects. The ambient air quality framework directive 96/62/EC (FWD) of the European Commission provides an EU-wide framework for national, regional, and local measures to assess, manage, and protect European air quality. The Clean Air For Europe (CAFE´) programme of the European Commission (http://europa.eu.int/comm/environment/air/cafe/index.htm) encompasses technical analysis and policy development and focuses on the development of long-term, strategic, and integrated policy advice for the improvement of Europe’s air quality. The likely evolution of air quality in Europe is assessed taking into account the effects of current and planned emission control legislation and future economic development. The assessment is based on a Europe-wide evaluation of the cost-effectiveness of emissioncontrol strategies utilizing the results of the EMEP regional-scale, Eulerian chemical transport model (Amann et al., 2005). This model has been used to calculate source–receptor relationships that reﬂect the response of air quality to changes in emissions at a spatial resolution of 50 50 km2. Such an integrated assessment modelling resolution is sufﬁcient to cover all the aspects of long-range transport across Europe, but is too coarse to resolve the inhomogeneities in urban emissions patterns or the resulting high-concentration pollution patterns in areas where a large fraction of the European population lives. Thus, a question arises whether concentration changes (or deltas), resulting from emissions deltas and calculated using a coarse-resolution regional model, can be transferred from the regional scale to the urban scale. This

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question was the motivation for the City-Delta Project (Cuvelier et al., 2007). Based on an extended inter-comparison of 17 urban and regional atmospheric transport models applied to six different European cities, this project developed functional relationships that quantify the increments in concentrations that occur within cities compared with those modelled for the regional environment. These relationships describe the difference between the concentrations averaged over 5 5 km2 in an urban core and the average concentrations calculated over a 50 50 km grid cell covering the whole urban area and its surroundings. This so-called urban increment shall be used later in a modiﬁed version of the RAINS cost-effectiveness, optimization model (Amann et al., 2005). In the City-Delta exercise, each model was applied in its own standard conﬁguration without attempting to harmonize input data except for emissions. The urban increment was derived from the differences of model predictions utilizing the two model resolutions combined with the ‘‘ensemble modelling’’ concept (Cuvelier et al., 2007, see also http:// aqm.jrc.it/citydelta/), where it is assumed that the average of the model responses, or ‘‘ensemble response’’, gives the most reliable prediction of the emission reduction impact. The aim of the present study was to supplement City-Delta by using a single model to analyze the relationships between regional and urban air quality levels and the modelled effectiveness of control measures in an urban area as a function of horizontal grid resolution over the range from 30 km down to 1 km. This study should help answer the questions of (a) how reliably regional-scale model calculations can be used to compute air quality in urban areas and (b) what grid size seems to be adequate to describe air quality within a city. In this German EPA (Umweltbundesamt) funded project, photochemical and aerosol modelling simulations over Europe, Germany, the Federal State Brandenburg, and the agglomeration/metropolitan area of Berlin for all of 2002 were performed using the REM-CALGRID (RCG) chemical transport model.

2. The RCG model and four domains of this study

The RCG model is an Eulerian grid model of medium complexity that can be used on the regional through urban scales for short- and long-term simulations of oxidant and aerosol formation. RCG model evaluation was performed mainly within the framework of several European model inter-comparison studies (Hass et al., 2003; Stern et al., 2003; Van Loon et al., 2004).

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For this study, the RCG model was run in a latitude/longitude (Lat./Lon.) coordinate system at four different horizontal grid resolutions to assess the air quality in Berlin: A European-scale grid, covering North Europe with resolution of 0.251 (Lat.) by 0.51 (Lon.) (i.e., nominally about 30 km); Nest 1: a national-scale grid, covering Germany and having twice the resolution of grid #1 (i.e., 15 km); Nest 2: A German Federal State grid, covering Brandenburg and Berlin, and having eight times the resolution of grid #1 (i.e., 4 km); and, Nest 3: An urban-scale grid, covering Berlin, and having 32 times the resolution of grid #1 (i.e., 1 km). Figure 1 shows the four different modelling areas. For all domains, RCG was applied using the CBM-photochemical mechanism, the ISORROPIA module for the inorganic aerosol formation, and the SORGAM module for the organic aerosol formation. The model was run with ﬁve vertical layers: a 20-m thick surface layer, two equal-thickness

Figure 1. RCG modelling domains. Upper left: European-scale grid with resolution of 0.251 Lat., 0.51 Lon. Upper right: Nest 1, national-scale (Germany) grid with resolution of 0.1251 Lat., 0.251 Lon. Lower left: Nest 2, the Federal State (Brandenburg) grid with resolution of 0.031251 Lat., 0.06251 Lon., embedded in Nest 1. Lower right: Nest 3, urban grid Berlin with resolution of 0.00781251 Lat., 0.0156251 Lon., embedded in Nest 2 (and Nest 1) and also showing the major ‘‘ring’’ motorway around Berlin.

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layers below the mixing height, and two above the mixing height and extending to the domain top at 3000 m. Grid-dependent meteorological input data were produced employing a diagnostic meteorological analysis system based on an optimum interpolation procedure on isentropic surfaces. The system utilizes all available synoptic surface and upper air data as well as topographical and land use information. The boundary conditions for the nested applications (Nests 1–3) were taken from the next larger grid. The European-scale run uses monthly varying lateral and top boundary conditions for ozone taken from climatological background data. Boundary data for all other species are ﬁxed and chosen as typical background values. For the year 2002, RCG was applied on the four grids for the following three cases: base case simulation 2002; the CLE scenario (implementation of current legislation until 2010); and, a 50% emissions reduction of all anthropogenic species in each domain.

3. Treatment of emissions

A major problem for nested grid applications is the necessity of establishing consistency between the top-down emission data, typically used on the continental and regional scales, and the bottom-up emission estimates, typically used in urban scale modelling. This study utilized two different sets of emissions data: the regional TNO emissions inventory (Visscherdijk and van der Gon, 2005), which covers all of Europe at a horizontal resolution of 0.1251 Lat. and 0.251 Lon.; and, the local inventories of the States of Brandenburg and Berlin, available at a resolution of 1 1 km2. The sectoral totals of the TNO emissions data set conform to the country submissions to EMEP for the year 2000, i.e., the national totals agree with the ofﬁcially reported national emissions to the Convention on Long Range Transboundary Air Pollution (CLTRAP; see EMEP, 2003). The local emissions are also for the reference year 2000, and both emission inventories have a future emissions projection for 2010 based on the assumption of the so-called CLE scenario (Current Legislation scenario; see Amann et al., 2005). However, as could be expected, the two inventories differ substantially for the Brandenburg and greater Berlin areas covered by Nests 2 and 3, respectively. Because the differences between the TNO emissions and the urban emissions were too large to be neglected for the focus of this study, the TNO emissions within the Nest 2 and Nest 3 area were substituted by the ﬁne-scale local

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emissions. By contrast, the expected relative emission changes from 2000 to 2010 for the local inventory were set identical to those of the regional TNO data set for Germany on a sector-by-sector basis. This procedure guaranteed the use of harmonized emissions at all scales and assures that differences in the calculated concentration data were solely due to the differing grid resolution and not due to differing emissions. The modiﬁed regional-scale TNO inventory was used for the European and Nest 1 model runs, the local inventory for the Nest 2 and Nest 3 runs. The emission totals in the European grid cells covering the greater Berlin area are identical at all scales, but the emission density distribution is different, depending on the resolution of the grid.

4. Base case simulations

The base case simulation results at all scales were compared with available PM10 and NO2 measurements in the greater Berlin area to quantify how regional model predictions differ from those obtained with ﬁner resolution modelling. Examination of concentration isopleths plots (not shown) for NO2 and PM10 indicates that the European- and nationalscale grids fail to capture any of the inner-urban concentration variability. The city core of Berlin starts to be resolved by the 4 4 km2 grid of Nest 2, but really becomes clear only in the 1 1 km2 grid of Nest 3. The calculated NO2 concentration ranges in the greater Berlin area are 10–22 mg m 3 for the European scale, 10–26 mg m 3 for Nest 1, 10–30 mg m 3 for Nest 2, and 10–35 mg m 3 for Nest 3. A similar picture emerges for PM10 (not shown). The respective PM10 concentration ranges are 15–22 mg m 3 for the European scale, 15–28 mg m 3 for Nest 1, 15–28 mg m 3 for Nest 2, and 15–33 mg m 3 for Nest 3. Figure 2 shows the comparison of the calculated NO2 and PM10 annual mean values with the measurements at all stations which are not directly inﬂuenced by nearby trafﬁc emissions. In particular, for NO2, the agreement between predictions and observations steadily improves from the European scale to the Nest 3 scale. The European-scale model run and also the Nest 1 model run completely fail to capture the high NO2 observations. The Nest 2 model runs does a much better job, but the best simulation of the peak values is achieved in the Nest 3 model run. The model performance for PM10 is clearly worse than that for NO2 at all scales, but it is also obvious that a PM10 model calculation with a regional grid resolution is not able to reproduce the observed pattern in urban areas. For PM10, the Nest 1 run exhibits a little better correlation than the Nest 2 run, while Nest 2 yields a nearly perfect regression slope of 0.98 and

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Figure 2. Scatter diagram of observed and calculated NO2 and PM10 annual means in the greater Berlin area at four scales, including regression lines and correlation coefﬁcients. Dashed lines indicate the range of +/ 50% of the observations. For further explanations see text.

suggests an under-prediction by about 5.5 mg m 3. However, in view of the small number of stations, one should be careful not to over-interpret these statistical results. Again, most of the observations are underestimated, which is a well-known feature of the current PM10 simulations

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(van Loon et al., 2004). This underestimation can primarily be attributed to uninventoried particle sources, known to exist but difﬁcult to quantify (e.g., biogenic sources, wind-blown dust from agricultural sources and natural surfaces, and re-suspension of road dust). Also, particle-bound water which contributes to gravimetrically measured PM mass can be responsible for some of the underestimation, because the models usually only consider dry aerosol mass (Tsyro, 2005). In Nest 1 and Nest 2, the underestimation is approximately the same (i.e., about 5–6 mg m 3) in all observed concentration ranges. In Nest 3, the lower observed PM10 concentrations at the border of the Nest 3 modelling area are underestimated to the same degree as in Nest 1 and Nest 2, but the peak observations in the urban core are captured better. If the uniform underestimation can be attributed to too low a background level, this would lead to Nest 3 overestimation of some of the observed, urban core PM10 concentrations. This situation might indicate possible errors in the 1 1 km2 emission inventory, which are then diffused in the data sets with a higher spatial aggregation. Presently however, there are too few stations to draw ﬁnal conclusions. Figure 3 shows the concentration difference between the European-scale run and the nested runs at the same measurements stations as in Fig. 2. These differences can be interpreted as the urban increments that cannot be resolved by regional-scale model runs having grid resolutions of 30 km or larger. The stations are further characterized as city, suburban, and rural/suburban stations. At the city stations, the urban increments for NO2 and PM10 are as large as 16 and 10 mg m 3, respectively; however, away from the urban core, the increments decrease, and even turn negative, for stations in the suburbs and rural outskirts.

5. Scenario runs

Both scenarios, the 50% reduction of all anthropogenic emissions and the implementation of the CLE scenario, were performed at the four grid resolutions. For Germany, the CLE 2010 scenario implies an average reduction of the NOx emissions of 29%. The respective reductions for NMVOC, SO2, and PM10 are 30, 31, and 14%, respectively, relative to the 2000 emissions ﬁgures. The predicted NO2 concentration reductions based on the CLE scenario assumptions indicate that for the greater Berlin area calculated NO2 decreases are estimated at 3–5 mg m 3 in the European-scale run, 2–6 mg m 3 for the Nest 1 and Nest 2 runs, and 2–7 mg m 3 for the Nest 3 run. The highest resolution Nest 3 run shows the most inhomogeneous concentration delta ﬁeld over the Berlin area,

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Figure 3. NO2 and PM10 annual mean urban increments at the measurement locations shown in Fig. 2, characterized as city, suburban, and rural/suburban type stations. For further explanations see text.

with larger deltas in the city core and smaller deltas at the outskirts of the city, and these inhomogeneities are progressively diffused as one moves to the coarser resolution runs. Such smearing at coarser resolutions distorts the efﬁcacy of the CLE strategy in that it suggests greater reductions in the urban outskirts and smaller reductions in the urban core. A similar picture emerged for PM10 predictions. The respective PM10 concentration decreases in the greater Berlin area due to the CLE 2010 emission decreases are: 2–3 mg m 3 for the European-scale run, 2–4 mg m 3 for

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Nest 1 and Nest 2, and 2–5 mg m 3 for Nest 3. Figure 4 shows the scaledependent concentration deltas at 25 NO2 and 15 PM10 measurement sites in the greater Berlin area. The deltas predicted in Nests 1 through 3 are expressed relative to (or normalized by) the deltas calculated in the European-scale run. At city stations, the concentration decreases due to the CLE scenario and predicted by the high-resolution model run can be up to 50% larger than those estimated by regional models. By contrast, the regional run tends to predict somewhat larger decreases than the higher resolution modelling runs at the suburban outskirts of the city. In the second scenario, involving the uniform 50% reduction of all

Figure 4. NO2 and PM10 concentration deltas at 25 NO2 and 15 PM10 measurement sites characterized as city, suburban, and rural/suburban type stations. The deltas for Nests 1–3 are expressed relative to (i.e., divided by) the normalized deltas calculated for the Europeanscale run. For further explanations see text.

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anthropogenic emissions, the relative and absolute differences of the concentration deltas calculated in the four scales are larger than for the CLE-scenario runs. 6. Summary and conclusions

Applying the RCG chemical transport model to the greater Berlin area at four different grid resolutions, ranging from 30 km down to 1 km, demonstrates that model performance improves with increasing grid resolution. Similarly, the modelled effectiveness of emission control measures (e.g., the CLE2010 scenario and a scenario assuming a 50% reduction in all anthropogenic emissions) is ‘‘diffused’’ at coarser resolutions for an urban area: with underestimation of the strategy’s effectiveness occurring in the urban core and slight overestimation occurring in the suburbs or urban outskirts. This implies that the effects of measures will be underestimated in urban areas if they are based on a regional-scale model application. In urban areas with a highly inhomogeneous emission pattern even a resolution in the range of 5 km, such as used in the City-Delta exercise, can be too coarse for an adequate capture of the urban signal.

ACKNOWLEDGMENT

This work has been funded by the German Federal Environmental Agency (Umweltbundesamt) within the R&D-project 202 43 270.

REFERENCES Amann, M., Bertok, I., Cofala, J., Gyarfas, F., Heyes, C., Klimon, Z., 2005. Baseline scenarios for the clean air for Europe (CAFE) programme. CAFE Scenario Analysis Report Nr. 1. http://europa.eu.int/comm/environment/air/cafe/index.htm Cuvelier, C., Thunis, P., Vautard, R., Amann, M., Bessagnet, B., Bedogni, M., Berkowicz, R., Brandt, J., Brocheton, F., Builtjes, P., Coppalle, A., Denby, B., Douros, G., Graf, A., Hellmuth, O., Honore´, C., Hodzic, A., Jonson, J., Kerschbaumer, A., de Leeuw, F., Minguzzi, E., Moussiopoulos, N., Pertot, C., Pirovano, G., Rouil, L., Schaap, M., Stern, R., Tarrason, L., Vignati, E., Volta, M., White, L., Wind, P., Zuber, A., 2007. CityDelta: A model intercomparison study to explore the impact of emission reductions in European cities in 2010. Atmospheric Environment 41, 189–207. EMEP, 2003. Review and revision. Emission data reported to CLTRAP. MSC-W Status Report 2003. EMEP Status Report 2003. ISSN 0804-2446. Hass, H., van Loon, M., Kessler, C., Matthijsen, J., Sauter, F., Stern, R., Zlatev, R., Langner, J., Fortescu, V., Schaap, M., 2003. Aerosol modeling: Results and

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intercomparison from European regional-scale modeling systems. A contribution to the EUROTRAC-2 subproject GLOREAM. EUROTRAC report 2003. Stern, R., Yamartino, R., Graff, A., 2003. Dispersion modelling within the European Community’s air quality directives: Long term modelling of O3, PM10 and NO2. 26th ITM on Air Pollution Modelling and Its Application. May 26–30, 2003, Istanbul, Turkey. Tsyro, S.G., 2005. To what extend can aerosol water explain the discrepancy between model calculated and gravimetric PM10 and PM2.5? Atmos. Chem. Phys. 5, 515–532. Van Loon, M., Roemer, M., Builtjes, P., Bessagnet, B., Rouil, L., Christensen, J., Brandt, J., Fagerli, H., Tarrason, L., Rodgers, I., Teasdale, I., Stern, R., Bergstro¨m, R., Langner, J., Foltescu, V. 2004. Model intercomparison in the framework of the review of the Uniﬁed EMEP model. TNO-Report R 2004/282. Visscherdijk, A., Van der Gon, D., 2005. Gridded European anthropogenic emission data for NOx, SO2, NMVOC, NH3, CO, PM10, PM2.5 and CH4 for the year 2000. TNOreport B&O-A R 2005/106.

Aerosols in the atmosphere Chairpersons: Jose M. Baldasano S.T. Rao Dimiter Syrakov Bernard Fisher Rapporteurs: Raphaelle Deprost Golam Sarwar Sandro Baldi Keiya Yumimoto

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Chapter 5.1 Modelling of pollen dispersion with a weather forecast modelsystem H. Vogel, B. Vogel and Ch. Kottmeier Abstract The comprehensive model system LM-ART was extended by a parameterisation which describes the emission ﬂux of pollen grains. With this extension it is possible to simulate the dispersion of these airborne particles on the regional scale. LM is the operational forecast model of the German Weather Service. Therefore, it possible to run the whole model system in an operational mode. The implemented emission ﬂux depends on plant speciﬁc parameters and on meteorological parameters such as friction velocity and temperature. The model was applied to an alder pollen episode which occurred in Germany in February 2004. To overcome the problem that the exact spatial distribution of alder is unknown, a map of the potential alder vegetation was used. The results of the simulation were compared with measured daily mean values of the stations of the Polleninformationsdienst. The comparison shows a good agreement with respect to the order of magnitude but larger differences for the spatial distributions. That is an implication of the lack of exact input data.

1. Introduction

The transport and turbulent diffusion of pollen is of great interest mainly in two ﬁelds. First, pollen causes allergenic reactions for human beings, which is well-known as hay fever. Local pollen concentration varies extremely in time and space. To minimise the symptoms, allergy patients therefore have to know when and where there will be high pollen concentrations. Second, increasing genetic manipulation of plants leads to the problem of cross-pollination. To ensure purity of plants in the vicinity of genetically modiﬁed plants, exact knowledge of the distances that can be travelled by pollen grains is required for different plant species.

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Pollen grains are of irregular shape and diameter. They can hydrate and dehydrate. They may build up aggregates if they are entomophilies pollen grains, due to a pollen kit on their surface. They have different densities and settling velocities. Pollen grains have limited and mostly unknown viabilities, depending on the species. The concentration thresholds that cause symptoms for the patients also depend on the species. Present pollen modelling can be divided into two groups. The ﬁrst group deals with forecasting the start, the end, and duration of pollen seasons. The second group deals with the numerical simulation of the temporal and spatial distributions of pollen grains. 2. Method

In our study we will use the comprehensive model system LM-ART (ART, aerosols and reactive trace gases; LM, Lokalmodell) to simulate the pollen dispersion. The meteorological model LM of the German Weather Service (DWD) is a non-hydrostatic mesoscale model and is part of the forecast system of the DWD. The standard model domain covers whole Europe. Due to its operational use it is appropriate to be the meteorological driver for a pollen-forecast system. In order to calculate the temporal development of the pollen grain number density it is necessary to describe the emission ﬂux, the transport by the mean wind, the diffusion by atmospheric turbulence, the coagulation, the sedimentation and deposition, the re-suspension and the washout. Therefore LM-ART has been extended by parameterisations, which describe these processes to determine the temporal and spatial distribution of pollen grains from the local to the regional scale. To take this into account, the parameterisation of Helbig et al. (2004) was chosen and will be shortly described in the following. The basic idea of our parameterisation is that the vertical ﬂux of pollen grains Fe at the top of the vegetation is proportional to the product of a characteristic concentration and a characteristic velocity: F e ¼ ce K e

qp u LAI h

(1)

Friction velocity u* is used as the characteristic velocity. qp is given in pollen grains per square metre, describing the number of pollen grains produced in one season. It therefore reﬂects the maximum number of pollen grains, which can be emitted. Of course, qp is reduced by the already emitted amount of pollen grains. LAI is denoting the leaf area index and h is the typical canopy height of the corresponding plant

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species. ce is considered a plant-speciﬁc factor that describes the likelihood to bloom. This factor takes into account that not all ﬂowers are blooming at the same time, although the meteorological conditions are ideal for pollen release. Instead, the number of ﬂowers increases with time until a maximum is reached. Afterwards, the number decreases again until the end of the pollen season. It is obvious that the available pollen grains cannot be emitted into the atmosphere, if the meteorological conditions are unfavourable. This behaviour is taken into account by the meteorological adjustment factor Ke. Ke is parameterised in the following way: ute Ke ¼ 1 u 4ute u (2) Ke ¼ 0 u ute The so-called threshold friction velocity u*t is parameterised after Shao and Lu (2000) according to dust modelling. Since the pollen grains are not simply lying on the soil, but are located in the ﬂowers which require certain meteorological conditions to open, a modiﬁed threshold friction velocity u*te was introduced: ute ¼ ut a

(3)

The meteorological coefﬁcient a can be understood as a kind of resistance against the pollen release by the meteorological conditions. It is composed of three meteorological resistances controlling pollen release: a¼

3 vj=j~ vjte Þ c1 expð0:1ðT T te ÞÞ þ c2 ðU te =UÞ þ c3 ðj~

(4)

vjte are thresholds for temperature, humidity and wind Tte, Ute and j~ speed. Threshold values for temperature were found for different plant species by Wachter (1982), Fritz and Gressel (1983) and Puls (1985). In vjte could not be found. contrast to this, threshold values for Ute, and j~ They had to be assumed for this study. c1, c2 and c3 are plant-speciﬁc constants to weigh the inﬂuence of the corresponding meteorological resistance and will be set equal to 1 in this study. The settling velocity vs is calculated by v2s ¼

4rp d e g 3rcd

(5)

rp is the pollen grain density that depends on the water content (Aylor, 2002). cd is the drag coefﬁcient parameterised according to an approach of

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Fuchs (1964) and Friedlander (1977). de is the volume-equivalent diameter and r is the air density. Details can be found in Helbig et al. (2004).

3. Model simulations

The new parameterisations described in the previous section were implemented in the LM-ART model system. Figure 1 gives a survey of the individual model components of LMART and the necessary input data. The advantage of LM-ART with respect to other models is that identical numerical schemes and parameterisations are used for identical physical processes as advection and turbulent diffusion. This avoids truncation errors and model inconsistencies. LM is veriﬁed operationally by DWD, the model system can be embedded by one way nesting into individual global scale models as the GME model or the ECMWF model. All components of the model system are coupled on line with time steps on the order of tenth of seconds. Nesting of LM-ART within LM-ART is possible. The horizontal grid size for the operational forecast is 7 km but it can be also run with smaller grid sizes. In this study we use this model system to simulate an episode of alder pollen that lasts from 12 to 20 February 2004.

Figure 1. The modelsystem LM-ART (ART, aerosols and reactive trace gases).

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Figure 2. Potential alder vegetation in Germany and parts of the surrounding countries (Source: Bundesamt fu¨r Naturschutz).

In order to determine the ﬂux of pollen grains into the atmosphere, it is necessary to have information on the percentage contribution of alder trees for each grid point of the model domain. Unfortunately, this information was not available. Therefore, we decided to proceed in the following way. Information was available on the potential alder vegetation, but only for Germany and parts of the surrounding countries, which is shown in Fig. 2. It is furthermore not clear, if there are really alder and how many trees are in such an area. The pollen-speciﬁc parameters used for alder pollen grains are summarised in Tables 1 and 2. Pohl (1937b) determined major and minor principal radii for the elliptical alder pollen grains. From these, a volumeequivalent diameter of 25.65 mm was calculated. Figures 3 and 4 show the horizontal distribution of the daily averaged simulated pollen concentration close to the surface for 4 days of this

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486 Table 1. Input variables Alder Property 2

qp (pollen grains m ) S (days) LAI h (m) Emission height (m) dp (mm) dd (mm) rp (mg m3)

9

2.1 10 30 2 22 20 29a 26a 752

Reference Pohl (1937a)

Pohl (1937b)

a

The mean diameter dp of alder pollen grains was calculated from water contents measured by Pohl (1937b) and dd.

Table 2. Meteorological threshold values and other parameters Alder Tte (1C) Ute (%) j~ vjte (m s1) Utr (%) j~ vjtr (m s1) cr c1, c2, c3, c4, c5 a

8a 60 2.9 85 0.9 7 105 1

Puls (1985).

episode. For comparison with measurements there are only data from German stations available. The network of measurements was established by the Polleninformationsdienst and the stations are often located near hospitals. They are at the moment not equipped with meteorological stations. Figures 5–8 show the measured and simulated pollen concentration for the German stations. The biggest circle represents concentrations above 500 pollen grains m3 The measurements show ﬁrst high pollen concentration only at few stations at the beginning of the episode, then an increase at all station and at the end of the episode a rather homogeneous distribution for most stations but lower concentration. In comparison to the measurements, the simulated concentrations are in most cases higher than the measured ones and also the spatial variation of the concentrations is lower. At the end of the episode the

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Figure 3. Simulated daily averages of the pollen concentration, 13.02.2004 (left) and 15.02.2004 (right).

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Figure 4. Simulated daily averaged of the pollen concentration, 17.02.2004 (left) and 19.02.2004 (right).

concentrations are very low. The absolute values of the simulated and the measured concentrations are in the same order of magnitude. This is a rather positive result, because a lot of input data for the parameterisation of the emission process had to be assumed as well as the exact distribution of alder stands.

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Figure 5. Measured (left) and simulated (right) daily averages of the pollen concentration.

Figure 6. Measured (left) and simulated (right) daily averages of the pollen concentration.

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Figure 7. Measured (left) and simulated (right) daily averages of the pollen concentration.

Figure 8. tration.

Measured (left) and simulated (right) daily averaged simulated pollen concen-

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4. Summary

The parameterisation of Helbig et al. (2004) for pollen emission was implemented in the LM model system of the DWD. As a ﬁrst application a pollen episode of alder in February 2004 was simulated. The results were compared with measurements of the network of the German Polleninformationsdienst. Due to the fact that for many variables of the parameterisation assumptions have been made and also no data for the exact location of the alder stands was available the difference between measurements and simulations are quite large. But the simulated pollen concentrations are of the same order of magnitude like the measured one. Therefore, a larger improvement can be expected with increasing accuracy of the input data. REFERENCES Aylor, D.E., 2002. Settling speed of corn (Zea mays) pollen. J. Aerosol Sci. 33, 1601–1607. Friedlander, S.K., 1977. Smoke, Dust and Haze. Wiley, New York. Fritz, A., Gressel, W., 1983. Zur Wetter-, insbesondere zur Temperaturabha¨ngigkeit des Pollenﬂuges der Hasel, Birke und Gra¨ser in Ka¨rnten. med-met. Zeitschrift fu¨r Medizin und Meteorologie 2, 14–17. Fuchs, N.A., 1964. The Mechanics of Aerosols. Pergamon Press, Oxford. Helbig, N., Vogel, B., Vogel, H., Fiedler, F., 2004. Numerical modelling of pollen dispersion on the regional scale. Aerobiologia 20, 3–19. Pohl, F., 1937a. Die Pollenerzeugung der Windblu¨tler. Eine vergleichende Untersuchung mit Ausblicken auf den Besta¨ubungshaushalt tierblu¨tiger Gewa¨chse und die pollenanalytische Waldgeschichtsforschung. (Untersuchungen zur Morphologie und Biologie des Pollens VI.)—Botanisches Centralblatt. Beihefte des botanischen Centralblattes. BBC. Abteilung A, Morphologie und Physiologie der Pﬂanzen. Dresden, 56. Pohl, F., 1937b. Die Pollenkorngewichte einiger windblu¨tiger Pﬂanzen und ihre o¨kologische Bedeutung (Beitra¨ge zur Morphologie und Biologie des Pollens VII.)—Botanisches Centralblatt. Beihefte des botanischen Centralblattes. BBC. Botanisches Centralblatt. Beihefte des botanischen Centralblattes. BBC. Abteilung A, Morphologie und Physiologie der Pﬂanzen. Dresden, 57. Puls, K.E., 1985. Scheitert die Pollenﬂugvorhersage an der Wetteprognose? Allergologie, 8(1), 21–25. Shao, Y., Lu, H., 2000. A simple expression for wind erosion threshold friction velocity. J. Geophys. Res. 105(D17), 22437–22443. Wachter, R., 1982. Pollen- und Sporenug uber der Bundesrepublik Deutschland. Nr. 14. Allergopharma Joachim Ganzer KG.

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Chapter 5.2 Aerosol forecast over the Great Lakes for a February 2005 episode$ Pius Lee, Jeffery McQueen, Marina Tsidulko, Mary Hart, Shobha Kondragunta, Daiwen Kang, Geoff DiMego and Paula Davidson Abstract Many air pollution agencies in the Upper Midwest and the Great Lakes regions in the U.S. had issued air advisories between January 31 and February 4, 2005. Air Quality Index (AQI) issued on the EPA web site for Minnesota peaked at 155 on January 31. In the Chicago area, AQI measured between 110 and 140 for most of this ﬁrst week of February. The deterioration of the air quality over these regions for a rather prolonged duration had been attributed to the slow passing of broad high pressure systems centered over the Great Lakes during the period. The pressure systems were accompanied by extensive cloudiness and snow coverage over the same regions. This combination of meteorological conditions resulted in reduced atmospheric mixing, and high rates of atmospheric particle formation and growth due to high RH in the lower levels. In this study, the National Weather Service’s (NWS) Eta-CMAQ Air Quality Forecast System (AQFS) has been used in a research mode to predict the aerosol concentration and speciation of this poor air episode. The model result has been veriﬁed in a crude manner by comparing its Aerosol Optical Depth (AOD) prediction with that observed by the Geostationary Operational Environmental Satellites (GOES), and surface level aerosol concentration prediction with that compiled by the Aerometric Information Retrieval Now (AIRNOW) observation network. Qualitatively speaking, the predicted results $

The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce’s National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW13921548. Although it has been reviewed by NOAA and approved for publication, it does not necessarily reﬂect its policies or views.

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are comparable to these aforementioned observed ﬁelds. Further analysis of the model results suggested a largely anthropogenic nature of the particulate matter in the lower atmosphere over the regions of high AQI in the period. 1. Introduction

Many local health and air quality agencies in the Upper Midwest and the Great Lakes regions had issued air advisories between January 31 and February 4, 2005. Air Quality Index (AQI) values issued on the EPA web site for Minnesota peaked at 155 on January 31. In the Chicago area, the AQI measured between 110 and 140 for most of the ﬁrst week of February. The deterioration of air quality over these regions for a rather prolonged period has been attributed to the slow passing of broad high pressure systems centered over the Great Lakes. The pressure systems were accompanied by extensive cloudiness and snow cover over the same regions. This combination of meteorological conditions resulted in reduced atmospheric mixing; and high rates of atmospheric particle formation and growth due to high RH in the lower levels. In this study, the National Weather Service (NWS) Eta meteorological model (Rogers et al., 1996) coupled with EPA Community Multiscale Air Quality (CMAQ) model (Byun and Ching, 1999; Byun and Schere, 2006) in the Air Quality Forecast Capability (AQFC) (Davidson et al., 2004; Otte et al., 2005) was used in a research mode to predict aerosol concentration and speciation of this poor air episode. The model result has been veriﬁed in a qualitative manner by comparing its Aerosol Optical Depth (AOD) prediction with that observed by the Geostationary Operational Environmental Satellites (GOES) (NOAA, 2005a, b), and the surface level aerosol concentration prediction with that compiled by the Aerometric Information Retrieval Now (AIRNOW) (EPA, 2005) observation network.

2. Deriving PM2.5 and AOD

The aerosol module in CMAQ adopts a modal approach to represent the particles suspended in air (Binkowski and Roselle, 2003; Mebust et al., 2003). It uses the superposition of 3 log-normal sub-distributions to represent the size distribution of these particles. Fine particles with a diameter less than 2.5 mm (PM2.5) are represented by two of these subdistributions called the Aitken (i mode) for particles with diameters up to 0.1 mm, and the accumulation (j mode) for particles with diameters

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Table 1. A partial list of speciation and variable names used in the aerosol module Species

Aitken mode

Accumulation mode

Sulfate Ammonium Nitrate Anthropogenic secondary organic Primary organic mass Secondary biogenic organic mass Elemental carbon mass Unspeciﬁed anthropogenic mass Water mass

ASO4I ANH4I ANO3I AORGAI AORGPAI AORGBI ACEI A25I AH2OI

ASO4J ANH4J ANO3J AORGAJ AORGPAJ AORGBJ ACEJ A25J AH2OJ

between 0.1 and 2.5 mm. Table 1 shows a partial listing of the speciation of the particles in the i and j modes. In this version of the model, coarse mode (diameter 2.5 mm and greater) simulations are not included due to the large uncertainty in the determination of coarse particle emissions. Furthermore, in terms of health hazard considerations, the effects caused by the two ﬁner modes are of the most concern. By the same token, the model does not include coarse mode particles in its visual range calculations in terms of AOD. In the model, PM2.5 concentration in the surface level is derived by summing up all the concentrations pertaining to the species listed in Table 1. AOD, a dimensionless quantiﬁcation of visibility impairment, is deﬁned in the following equation: Z ModelTop Bsp dz (1) AOD ¼ 0

where Bsp is the aerosol extinction coefﬁcient in km1 and z is altitude in km. CMAQ calculates Bsp using Qext, the extinction efﬁciency, a measure of light scattering efﬁciency which in turn is estimated using approximations to the Mie theory (Binkowski, 1999). Z 3p 1 Qext dV d ln a (2) Bsp ¼ 2l 1 a d ln a where a ¼ (pD/l), D is the particle diameter, V is the volume of the particle, and l is the wavelength of the incident light. 3. Meteorology of January 31 and February 1, 2005

On both January 31 and February 1, 2005, moderate to weak high pressure systems dominated much of the continental US as shown in

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Fig. 1a and b. There were essentially three large high pressure systems that together covered much of the middle to northern parts of the continental US. The high pressure system in the middle, located over the Midwest and Great Lakes, was the weakest and the fastest moving of the systems. In contrast with the other stronger systems on either side, which

Figure 1. Surface weather map: (a) for January 31 and (b) for February 1, 2005.

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happened to be more stationary, this middle system experienced more cloudiness as it passed southward. The weak pressure gradients and generally fair weather conditions there rendered the air mass calm and stagnant. Satellite images on those days veriﬁed the cloudiness over the Midwest. Over the Upper Midwest and Great Lakes, snow cover was prevalent and the surface level air temperature in those areas varied between 5 and 51C during the period. These temperatures and the abundance of water vapor resulting from the melting and sublimating snow provided a favorable condition for fog and hydroscopic aerosol growth. Consequently, low clouds and fog further inhibited mixing activities in the lower atmosphere. This compound condition of stagnant air, low cloud, and rather warm temperatures around the freezing point gave rise to a heavy suspension of fog and aerosol particles in the lower atmosphere in the area for a prolonged period of time.

4. Veriﬁcation of AOD and PM2.5

Figures 2b and 3b show the model predicted AOD and surface level PM2.5 concentrations. The shaded ﬁelds, using the side color bar color code, depict the dimensionless AOD values. They were obtained by evaluating Eqs. (2) and (1) through the use of predicted instantaneous aerosol concentrations. The colored line contours depict PM2.5 concentrations in mg m3. To evaluate the model predicted AOD against observations, we used AOD values retrieved from GOES satellites imagery. The time resolution of the satellite data retrieval is 30 min. Cloudiness can, however, deprive us of the opportunity for AOD data retrieval. Figures 2b and 3b show high predicted values of AOD in the upper Midwest for the afternoons of both January 31 and February 1, 2005, respectively. Figure 2b shows high predicted values of AOD around southeastern Louisiana on January 31. Nonetheless, all these aforementioned areas were under clouds for most of time, preventing retrieval of AOD observation data from the GOES satellites. For the clear sky areas shown on the satellite imagery in Fig. 2c for January 31, such as areas along the U.S. Eastern Seaboard, the observed AOD ranged between 0.2 and 0.3, while there were a few sporadic high values above 1.0. They agreed rather well with those model predicted values shown in the corresponding areas in Fig. 2b. However, agreement in the high observed AOD values offshore of the Florida Panhandle is not good, as is shown in Fig. 2b and c. On February 1, this agreement offshore of the Florida

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Figure 2. Predicted and observed column total AOD and surface level PM2.5 values valid at approximately 19 UTC January 31, 2005: (a) observed PM2.5 compiled by the AIRNOW network where green, yellow and orange data points represent concentration between 10 and 20; 20 and 30; and 30 and 40 mg m3, respectively, (b) predicted column total AOD, color shaded in accordance with the side color bar; and PM2.5, colored contour lines with labels: light green for 15, dark green for 20, blue for 25, red for 30, and purple for 35 mg m3, respectively, and (c) GOES imagery showing clouds, along with retrieved AOD values.

Panhandle improved as shown in Fig. 3b and c. There was a belt of clear sky extending from the middle of Missouri to Northern Virginia which looped around to northeastern Georgia. The observed and predicted AOD agreed quite well within this sunny stretch on the afternoon of February 1 as is shown in Fig. 2b and c. Predicted surface PM2.5 aerosol concentrations have been evaluated with the AIRNOW compiled observation data for the selected days as shown in Figs. 2a and 3a. Figure 2b shows a cluster of high predicted surface PM2.5 concentrations equal to or larger than 35 mg m3 for most of Ohio and Indiana on January 31. It also shows two contour ‘tongues’ of values between 30 and 35 mg m3 extending from these states into southern Michigan and eastern Minnesota, respectively. The concentration levels of 15–20 mg m3 are shown along the U.S. Eastern Seaboard in

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Figure 3. Equivalent to Fig. 2, but for February 1, 2005.

Fig. 2b. These features were roughly apparent in the AIRNOW observations as shown in Fig. 2a. For the afternoon of February 1, the model predicted a cluster of high surface PM2.5 concentrations equal to or larger than 35 mg m3 expanding from the Upper Midwest southwards, reaching northern Oklahoma as shown in Fig. 3b. The ﬁgure also shows that the concentration level in the Boston–Philadelphia corridor increased to 30–35 mg m3. These two features are evident in the AIRNOW observations as shown in Fig. 3a.

5. Speciation and vertical distributions

Knowledge about the speciation and spatial distribution of aerosols are key to understanding how well the PM concentrations are predicted. However, observations of these properties are not commonly available. Intense ﬁeld campaigns help to provide data with dense spatial and temporal coverage during the campaign; in addition, there are a limited number of surface monitoring stations providing archival information on speciation. The U.S. EPA is purposefully working to expand the capability of the AIRNOW network to provide vertical concentration proﬁles

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of major gaseous and particulate pollutants within the planetary boundary of the Continental US. In this study, the surface PM2.5 concentration compiled by AIRNOW have been the primary veriﬁcation data used, as shown in Figs. 2a and 3a. A brief examination of predicted concentration proﬁles of component aerosol species at two locations (St. Paul and Chicago) serves to suggest some possible explanation of differences in surface level concentration of total PM2.5. The model predicted that SO2 4 and NO3 were the dominant species at the surface in regions of high PM2.5 concentration during the test period. Predicted high surface level concentrations of anthropogenic mass,

Figure 4. Predicted vertical concentration proﬁles at St. Paul, MN, for the following species: O3, NH3 (magniﬁed 100 times), NOx, and SO2, all in ppb; and for aerosol species in + mg m3: SO2 4 , NH4 , NO3 , organic, anthropogenic, and water content at (a) 19 UTC January 31, (b) 5 UTC February 1, (c) 13z February 1, and (d) 19 UTC February 1, 2005.

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primarily resulted from non-organic unspeciated emissions, shown in Figs. 4 and 5 further suggest that anthropogenic sources played a major role in the episode. Predicted vertical proﬁles of NOx and NO 3 follow one another closely as shown in Figs. 4 and 5. Similar precursor and product relationships are also reﬂected in SO2 and SO2 4 concentrations. The proﬁles of NH3 and NH+ are, however, rather different, possibly reﬂecting the short life-time 4 of the former relative to the latter. Predicted vertical concentration proﬁles of O3 and NOx almost always supplemented one another at all sites. This characteristic of the two species’ proﬁles was especially noticeable at night and in the early morning when O3 is titrated by NOx. Predictions for the O’Hare site reﬂect high emissions of NOx as shown in Fig. 5. This NOx-rich air mass was probably due to heavier emissions from the transportation sector in the Chicago vicinity relative to St. Paul.

Figure 5. Equivalent to Fig. 4, but for O’Hare Airport, Chicago, IL.

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Discussion

T. Odman:

P. Lee:

In the 2002 CMAQ evaluation that you have mentioned in the beginning (VISTAS work) there were two major modiﬁcations, ﬁrst to the NH3 emissions, second to the secondary organic aerosol formation. Did you adopt any of these modiﬁcations in your application or are you using standard NH3 emissions and standard SOA formation in CMAQ? Upon the application of the CMAQ model for the real time forecast of this episode, the standard NH3 emission and SOA mechanism have been used. I think the CMAQ release 4.5 last September has included improvement in the SOA mechanism based on the adsorptive partitioning theory of Pankow (1994). This source code upgrade will soon be incorporated into the forecast version of CMAQ at NCEP; however, emission inventory upgrades are usually lagged behind a bit further.

Pankow, J.F., 1994. An adsorptive model of the gas/aerosol partitioning involved in the formation of secondary organic aerosol. Atmos. Environ. 28, 189–193.

ACKNOWLEDGMENTS

The authors appreciate numerous valuable discussions with Drs. Rohit Mathur, Jon Pleim, Tanya Otte, George Pouliot, Jeff Young, and Ken Schere of the Atmospheric Sciences Modeling Division of the Air Resources Laboratory at the National Oceanic and Atmospheric Administration ofﬁce at Research Triangle Park, North Carolina. (They are currently on assignment to the National Exposure Research Laboratory, U.S. Environmental Protection Agency.) The EPA AIRNOW program staff provided the observations necessary for quantitative model evaluation.

REFERENCES Binkowski, F.S., 1999. The aerosol portion of Models-3 CMAQ. In: Byun, D.W., Ching, J.K.S. (Eds.), Science Algorithm of the EPA Models-3 CMAQ Modeling System. Rep. EPA-600/R-99/030, pp. 1–23.

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Binkowski, F.S., Roselle, S.J., 2003. Models-3 Community Mutliscale Air Quality (CMAQ) model aerosol component: 1: Model description. J. Geophys. Res. 108(D6), 4183, doi:10.1029/2001JD001409. Byun, D.W., Ching, J.K.S. (Eds.), 1999. Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. EPA-600/R-99/030, Ofﬁce of Research and Development, U.S. Environmental Protection Agency, Washington, DC. [Available from U.S. EPA, ORD, Washington, DC 20460]. Byun, D.W., Schere, K.L., 2006. Description of the Models-3 Community Multiscale Air Quality (CMAQ) Model: System overview, governing equations, and science algorithms. Appl. Mech. Rev. 59, 51–77. Davidson, P.M., Seaman, N., Schere, K., Wayland, R.A., Hayes, J.L., Carey, K.F., 2004. National air quality forecasting capability: First steps toward implementation. Sixth Conf. on Atmos. Chem., Am. Met. Soc., Seattle, WA, 12–16 Jan 2004. Preprints. EPA, 2005. AIRNOW Network [available at http://www.epa.gov/airnow]. Mebust, M.R., Eder, B.K., Binkowski, F.S., Roselle, S.J., 2003. Models-3 Community Mutliscale Air Quality (CMAQ) model aerosol component: 2: Model evaluation. J. Geophys. Res. 108(D6), 4184, doi:10.1029/2001JD001410. NOAA, 2005a. National Environmental Satellite, GOES aerosol and SMOKE Product. [Available at http://lwf.ncdc.noaa.gov/oa/climate/research/2005/ﬁre05.html]. NOAA, 2005b. Satellite Service Division and Fire Detection Program. [Available at http:// www.ssd.noaa.gov/PS/FIRE/hms.html]. Otte, T.L., Pouliot, G., Pleim, J.E., Young, J.O., Schere, K.L., Wong, D.C., Lee, P.C.S., Tsidulko, M., McQueen, J.T., Davidson, P., Mathur, R., Chuang, H.Y., DiMego, G., Seaman, N.L., 2005. Linking the Eta model with the Community Multiscale Air Quality (CMAQ) modeling system to build a national air quality forecasting system. Weather Forecast. 20(3), 367–384. Rogers, E., Black, T., Deaven, D., DiMego, G., Zhao, Q., Baldwin, M., Junker, N., Lin, Y., 1996. Changes to the operational ‘‘early’’ eta analysis/forecast system at the National Centers for Environmental Prediction. Weather Forecast. 11, 391–413.

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Chapter 5.3 On the contribution of the heterogeneous chemistry to nitrate concentrations over Europe based on modeling results and longterm and campaign measurements Alma Hodzic, Laurent Menut, Bertrand Bessagnet and Robert Vautard Abstract The signiﬁcance of the heterogeneous formation of particulate nitrate, rarely included in chemistry-transport models (CTMs), is investigated. According to several ﬁeld campaigns, particulate nitrate in a coarse particle (2–10 mm) can be obtained by reaction of nitric acid with calcium and magnesium carbonates. This process signiﬁcantly contributes to the total nitrate mass concentration over land particularly in summer. We introduce this heterogeneous reaction in the CHIMERE CTM and analyze its impact on total nitrate, nitric acid and aerosol particles, by statistical comparison of one-year-long simulations with EMEP measurements over Europe. Several values of the nitric acid uptake coefﬁcient are tested in order to investigate the sensitivity to this parameter, and we propose an optimal value for our study. The model performances in simulating nitrates and PM10 signiﬁcantly improve when the heterogeneous nitrate formation is included. This process also greatly improves the simulation of coarse nitrate in comparison with ﬁeld campaign measurements near Paris city.

1. Introduction

Nitrate is one of the major compounds of the suspended particulate matter in the atmosphere. It represents between 5% and 15% of the total aerosol mass and plays an important role both in the radiative forcing of the climate and in the atmospheric chemistry (Usher et al., 2003). Most of the nitrate mass is found in the aerosol ﬁne mode (smaller than 2.5 m m, PM2.5) in the form of ammonium nitrate (Putaud et al., 2004). Although the sub-micron fraction of nitrate is prevalent during winter, a coarse-mode

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fraction has been clearly identiﬁed in several studies (i.e., Wu and Okada, 1994) associated with sea salts and crustal elements: in sodium-rich marine conditions, nitric acid produces sodium nitrate (Kerminen et al., 1997), while over continental areas, nitric acid reacts with calcium and magnesium carbonates found on crustal materials from local soil erosion or desert dust and forms calcium and magnesium nitrates (i.e., Mamane and Gottlieb, 1992). The presence of nitrate in the coarse fraction associated with soil matter in Asia and Europe was conﬁrmed by several experiment studies (i.e., Parmar et al., 2001). This heterogeneous uptake of nitric acid is rarely considered in air quality models as well as its impact on PM10 and total nitrate. Recent model evaluation studies (i.e., Bessagnet et al., 2004; Van Loon, 2004) showed that the simulated total nitrate mass is underestimated even though the mechanisms of the ﬁne-mode nitrate formation are well understood. In order to improve 3D chemical transport models and to quantify the contribution of heterogeneous chemistry to the nitrate formation, several parameterizations (Goodman et al, 2000; Hanisch and Crowley, 2001) are tested over an entire year in Europe using the CHIMERE chemistry-transport model. The modeling results are assessed over Europe by comparison with ground-based nitrate measurements from EMEP stations and from the ESQUIF ﬁeld campaign. A detailed description of this study results are reported in Hodzic et al. (2006a).

2. Model description and simulations 2.1. Base model configuration

CHIMERE is a 3D chemistry-transport model that calculates concentrations of both inorganic and organic aerosols of primary and secondary origins, including primary particulate matter (PPM), mineral dust, sulfate, nitrate, ammonium, secondary organic species (SOA) and water. The aerosol population is represented using a sectional approach, considering six-size bins geometrically spaced from 10 to 40 m m diameter and particles internally mixed in each size section. In this study, the model is used with two types of conﬁgurations: (i) the ‘‘European’’ conﬁguration has a model domain, covering Western Europe, a 0.51 horizontal resolution and eight vertical sigma-pressure levels extending up to 500 h Pa; (ii) the ‘‘Ile-de-France’’ version has a domain similar to that of Hodzic et al. (2006b), with a resolution of 6 km and covering a square domain of 180-km size around the Paris city. The city-scale model is nested in the

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European model. A detailed description of the model conﬁguration and inputs used in this study is presented in Hodzic et al. (2006a). It should be noticed that the emissions of the mineral dust by wind erosion of soil particles are accounted for in CHIMERE. Since a large fraction of mineral dust mass originates from the Saharan regions not included in the model domain, dust boundary conditions coming from GOCART monthly means are considered for this long-range transport process. Moreover, in order to account for local erosion in Europe a simpliﬁed mineral dust emission formulation has recently been implemented in the model depending on wind speed and soil moisture (Vautard et al., 2005). 2.2. Gaseous and heterogeneous formation of nitrate

In the base model version, nitrate is present in the ﬁne aerosol mode as ammonium nitrate (NH4NO3), a semi-volatile compound. The equilibrium between ammonium nitrate and its gaseous precursors (nitric acid and ammonia) is calculated using the ISORROPIA equilibrium model (Nenes et al., 1998). The gas/aerosol partitioning of nitrate depends on the availability of its precursor gases (nitric acid) and on the ambient conditions. Nitric acid is produced mainly in the gas phase by NOx oxidation, and also by heterogeneous reaction of N2O5 on the aerosol surface, while ammonia is directly issued from primary emissions and converted into aerosol phase in ammonium nitrate and ammonium sulfate by neutralization with nitric acid and sulfuric acid. In this study, the heterogeneous reactions with carbonates, such as calcite (CaCO3) and dolomite (MgCa(CO3)2), which are the most reactive dust components (Usher et al., 2003; Krueger et al., 2004), have been added in the model. For calcite, the reaction could be written as 2HNOg3 þ CaCOs3 ! CaðNO3 Þs2 þ H2 O þ COg2

(1)

This heterogeneous formation of nitrate is treated with a kinetic approach assuming a total reaction in the forward direction. The uptake of gases onto aerosol particles is deﬁned by a ﬁrst-order rate constant that describes the mass transfer of gas-phase species to the aerosol surface. It depends on the particle diameter, the gas-phase molecular diffusion coefﬁcient and the uptake coefﬁcient of reactive species (g). In order to account for the large uncertainties in the determination of the uptake coefﬁcients, two uptake coefﬁcient values: (i) g ¼ 0.10 (Hanisch and Crowley, 2001) and (ii) g ¼ 2.5 103 (Goodman et al., 2000) are tested here and the sensitivity to this parameter is evaluated.

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2.3. Model simulations and data set

The model is run for the whole year of 2001 over the European domain, and simulations are evaluated using daily mean observations of total nitrate concentrations obtained from EMEP stations. Several model runs are performed: a reference (REF) simulation, which does not account for the heterogeneous pathway, and two other sensitivity simulations, which include the heterogeneous coarse nitrate formation with the nitric acid uptake coefﬁcients g ¼ 2.5 103 (G2001) and g ¼ 0.1 (HC2001), respectively. Comparisons are also made with size-resolved nitrate measurements from the ESQUIF ﬁeld campaign in the southwest of Paris from 19 to 26 July 2000 (Hodzic et al., 2006b).

3. Evaluation of the model skill in simulating nitrate

Figure 1 shows the spatial distribution of the surface seasonal average of nitrate concentrations simulated by CHIMERE REF model over Europe. In this run, nitrate is present only as ammonium nitrate and is maximal over land areas where ammonia is emitted. Highest nitrate concentrations (5 mg m3 in winter and 2 mg m3 in summer seasons) are indeed found over the Netherlands, Belgium, Northern Germany and Northern Italy. In winter, high concentrations are also found over Central and Western Europe (>2 mg m3), while in summer they hardly exceed 0.5 mg m3 over these regions. Simulated nitrate concentrations are weaker in summer than in winter due to higher temperatures and dry atmospheric conditions favoring the evaporation of the ammonium nitrate. Thus, very low nitrate concentrations (o0.5 mg m3) are simulated in summer over Spain, Southern Italy and the Balkans area. The skill of the base model to simulate nitrate is statistically evaluated against ground measurements over one-year-period and the results are reported in Hodzic et al. (2006a). The comparison results indicate that the model underestimates the average nitrate concentrations by more than 50% in summer. The nitrate underprediction is very pronounced at IT01 station (>80%) located in Central Italy and a large negative bias is found at NL09 and PL04 coastal stations (>55%) in Northern Europe. The former is likely to be inﬂuenced respectively by high dust concentration and the latter two by sea salt. Nitrate concentrations are less biased in winter with the mean bias close to 20%. Again, skill is lower at coastal and boundary stations. The larger nitrate biases obtained during the summer period suggest that the model is missing a typical summertime aerosol formation process, most probably the coarse nitrate formation.

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Figure 1. Seasonal average concentrations (mg m3) of total nitrates over Europe as simulated by CHIMERE model (REF run) for summer (a) and winter (b) periods. The location of EMEP stations is also indicated.

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Figure 2. Daytime(D) and nighttime(N) mean coarse-mode (diameter>2 mm) nitrate concentrations (mg m3) observed during ESQUIF campaign over Paris region from 19 to 26 July (white) and simulated by CHIMERE model REF (gray) and HC2001 (black) runs.

The evidence of missing coarse nitrate in model simulations was also conﬁrmed by a detailed model validation study conducted over the Paris region in the summer of 2000 (Hodzic et al., 2006b) in the frame of the ESQUIF ﬁeld campaign. Size-resolved impactor measurements have shown that nitrates are mainly present in the coarse aerosol fraction most likely attached to dust particles as conﬁrmed by a large amount of carbonate. During this period, the observed nitrate concentrations in the size bin of 2–10 m m in diameter (Fig. 2) range from 0.5 to 1.5 mg m3 and the coarse nitrate fraction represents up to 60% of the total nitrate mass during the night and 80% during the day. The corresponding modeling results reported in Hodzic et al. (2006b) during this summer period reveal a strong model underestimation of the coarse nitrate fraction. These discrepancies between modeled and observed coarse nitrate concentrations are expected since the current model version does not account for the coarse nitrate heterogeneous formation.

4. Impact of the heterogeneous formation of nitrate

We now evaluate the impact of heterogeneous coarse nitrate formation on mineral dust by comparing the concentrations of the two sensitivity experiments with EMEP nitrate measurements. A very different behavior is obtained for the two values of the uptake coefﬁcient. Relative to the reference run, there is no signiﬁcant change in simulations using Goodman’s low uptake coefﬁcient value while a clear increase in mean nitrate concentrations is obtained using Hanisch and Crowley’s higher value. In the

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Figure 3. Seasonal variation of monthly mean nitrate concentrations (mg m3) as observed and simulated (REF and HC2001 runs) at all EMEP stations considered in this study. The simulated coarse nitrate and dust concentrations are also presented for Hanisch and Crowley’s parameterization (HC2001).

latter, the model bias is considerably reduced at almost all sites: the bias reduction is about 35% in summer and 10% in winter. The bias reduction is highest in southernmost parts of the domain. At the IT01 site, it is reduced by more than 65%, while it is decreased by less than 20% in Northern Europe. The introduction of heterogeneous nitrate formation also has a strong impact on nitric acid concentrations at IT01 station, strongly inﬂuenced by dust emissions, where its bias is reduced by about 100%, while the impact is lower at HU01 station (20%). However, the fact that correlation coefﬁcients do not increase signiﬁcantly in the HC2001 experiment relative to the REF experiment may result from a bad simulated day-to-day variability of dust concentration. The large uncertainty in local erosion, together with the assumed monthly constant dust boundary conditions, hinders the accurate simulation of the dust concentration variability. These factors place a strong limitation to this work. The heterogeneous formation of nitrate exhibits an important seasonal variation as shown in Fig. 3. The highest concentrations of coarse nitrate are simulated from May to August with monthly mean values greater than 1 mg m3, although total nitrate exhibits a minimum during this period. Summer corresponds also to the highest REF model underestimation of total nitrate concentrations, in the range 50–70%. The nitrate production is strongly linked to the amount of boundary-imported dust particles. The strong seasonal variation of the mineral dust concentrations, with maximum emissions during summer and autumn and lower emissions during

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winter, has a signiﬁcant impact on the distribution of nitrates in the atmosphere and its seasonal variability. For example, the same amount of dust is simulated in May and October and leads to similar amount of coarse nitrates. The introduction of the heterogeneous formation of coarse nitrate is now tested by comparison of simulated coarse nitrate with corresponding measurements from the ESQUIF experiment described in Hodzic et al. (2006b). Figure 2 compares simulations without and with the heterogeneous nitrate formation. Coarse nitrate is deﬁned as 2–10 mm in diameter in both simulation and measurement. In the HC2001 experiment, simulated nitrate concentrations reach 0.5–2 mg m3 during the considered period, while for the REF run, they are much smaller. A fairly good agreement is obtained between the observed and simulated coarse nitrate concentrations during both day and night, except on 19 and 20 July when too high nitrate concentrations are predicted. During this pollution episode, stable atmospheric conditions led to a general model overprediction of most gaseous and aerosol components (Hodzic et al., 2006b). High coarse nitrate concentrations simulated during this episode are associated to high nitric acid and dust levels. This additional test conﬁrms the relevance of the proposed coarse nitrate parameterization. 5. Conclusion

In this study, the signiﬁcance of the heterogeneous formation of coarse nitrate and its inﬂuence on aerosol and gas-phase chemistry has been addressed. This process, resulting from the uptake of the nitric acid at the surface of mineral dust, has been introduced in the CHIMERE aerosols model and tested using available parameterizations proposed in literature. Model simulations with and without this process have been evaluated and compared with both coarse nitrate measurements taken during the ESQUIF ﬁeld experiment near Paris and ground-based nitrate measurements of the EMEP network taken at several European sites over an entire year. The results of this model validation study show several interesting features: (1) The introduction of the heterogeneous formation of nitrate leads to a signiﬁcant reduction of model biases for mean nitrate and nitric acid concentrations in summer. (2) The efﬁciency of the nitrate heterogeneous chemistry is strongly dependent on the assumptions made on the value of the nitric acid uptake onto dust particles. The best agreement between observed and modeled nitrate concentrations is obtained with the parameterization proposed by Hanisch and Crowley that assumes g close to 0.10.

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(3) The introduction of this new process in the model increases coarse nitrate concentrations to 0.5–3 mg m3 over Europe in summer with a marked gradient between the south and the north of Europe, due to the availability of Saharan dust particles. The improvement of simulation is also found in PM10 total concentrations, while surface ozone remains almost unchanged. (4) The main limitation to the present study is the use of monthly mean dust concentrations at model boundaries that does not account for the large time variability of dust emissions and their transport from the Saharan regions.

Discussion

P. Builtjes:

L. Menut:

A. Dore:

L. Menut:

How accurately do you know the percentage of Ca and Mg in mineral dust and can you validate the Ca and Mg concentrations with measurements? In the model, we assume a ﬁxed chemical composition of the dust, with 17% of calcite (CaCO3) and dolomite (MgCa(CO3)2) based on values proposed in literature (Avila et al., 1997; Blanco et al., 2003; Krueger et al., 2004, 2005). This is a typical value obtained for Saharan dust particles that are regularly transported over Europe. However, we could not evaluate the accuracy of this estimation since we do not dispose of measurements of Ca and Mg. Why does the model show that the formation of coarsemode nitrate aerosol is relatively more important than the formation of ammonium nitrate in the summer months? Nitrate can be formed both in the ﬁne mode (ammonium nitrate) through the thermodynamical equilibrium with nitric acid and in the coarse mode through heterogeneous reactions on dust particles. The efﬁciency of the production of nitrate depends on the availability of its precursors (gases: nitric acid, and particles: mineral dust) and on the ambient conditions. In winter, the sub-micron formation of nitrate (ammonium nitrate) prevails, while in summer a signiﬁcant amount of coarse nitrate can be found during dust episodes. Simulated nitrate concentrations are weaker in summer than in winter due to higher temperatures and dry atmospheric conditions favoring the evaporation of ammonium nitrate. However,

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in presence of desert dust particles, nitric acid reacts with calcium and magnesium carbonates and forms calcium and magnesium nitrates, which stay in particulate phase even at high temperatures. Avila, A., QueraltMitjans, I., Alarcon, M., 1997. Mineralogical composition of African dust delivered by red rains over northeastern Spain. J. Geophys. Res. 102(D18), 21977–21996. Blanco, A., De Tomasi, F., Filippo, E., Manno, D., Perrone, M.R., Serra, A., Tafuro, A.M., Tepore, A., 2003. Characterization of African dust over southern Italy. Atmos. Chem. Phys. 2147–2159. Krueger, B.J., Grassian, V.H., Cowin J.P., Laskin, A., 2004. Heterogeneous chemistry of individual mineral dust particles from different dust source regions: The importance of particle mineralogy. Atmos. Environ. 38, 6253–6261. Krueger, B.J., Grassian, V.H., Cowin, J.P., Laskin, A., 2005. Erratum to ‘‘Heterogeneous chemistry of individual mineral dust particles from different dust source regions: The importance of particle mineralogy’’ (Vol. 36, pp. 6253, 2004). Atmos. Environ. 39(2), 395.

REFERENCES Bessagnet, B., Hodzic, A., Vautard, R., Beekmann, M., Cheinet, S., Honore, C., Liousse, C., Rouil, L., 2004. Aerosol modeling with CHIMERE—Preliminary evaluation at the continental scale. Atmos. Environ. 38(18), 2803–2817. Goodman, A.L., Underwood, G.M., Grassian, V.H., 2000. A laboratory study of the heterogeneous reaction of nitric acid on calcium carbonate particles. J. Geophys. Res. 105(D23), 29053–29064. Hanisch, F., Crowley, J.N., 2001. The heterogeneous reactivity of gaseous nitric acid on authentic mineral dust samples, and on individual mineral and clay mineral components. Phys. Chem. Chem. Phys. 3, 2474–2482. Hodzic, A., Bessagnet, B., Vautard, R., 2006a. A model evaluation of coarse-mode nitrate heterogeneous formation on dust particles. Atmos. Environ. 40(22), 4158–4171. Hodzic, A., Vautard, R., Chazette, P., Menut, L., Bessagnet, B., 2006b. Aerosol chemical and optical properties over the Paris area within ESQUIF project. Atmos. Chem. Phys. 6, 3257–3280. Kerminen, V.M., Pakkanen, T.A., Hillamo, R.E., 1997. Interactions between inorganic trace gases and supermicrometer particles at a coastal site. Atmos. Environ. 31, 2753–2765. Krueger, B.J., Grassian, V.H., Cowin, J.P., Laskin, A., 2004. Heterogeneous chemistry of individual mineral dust particles from different dust source regions: The importance of particle mineralogy. Atmos. Environ. 38, 6253–6261. Mamane, Y., Gottlieb, J., 1992. Nitrate formation on sea salts and mineral particles— A single particle approach. Atmos. Environ. 26A, 1763–1769.

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Nenes, A., Pilinis, C., Pandis, S., 1998. ISORROPIA: A new thermodynamic model for inorganic multicomponent atmospheric aerosols. Aquat. Geochem. 4, 123–152. Parmar, R.S., Satsangi, G.S., Kumari, M., Lakhani, A., Srivastava, S.S., Prakash, S., 2001. Study of size distribution of atmospheric aerosol at Agra. Atmos. Environ. 35, 693–702. Putaud, J.-P., Raes, F., Van Dingenen, R., Bru¨ggemann, E., et al., 2004. A European aerosol phenomenology-2: Chemical characteristics of particulate matter at kerbside, urban, rural and background sites in Europe. Atmos. Environ. 38, 2579–2595. Usher, C.R., Michel, A.E., Grassian, V.H., 2003. Reactions on mineral dust. Chem. Rev. 103(12), 4883–4940. Van Loon, M., 2004. Model intercomparison in the framework of the review of the uniﬁed EMEP model. Technical report TNO-MEP R2004/282, Apeldoorn, the Netherlands. Vautard, R., Bessagnet, B., Chin, M., Menut, L., 2005. On the contribution of natural Aeolian sources to particulate matter concentrations in Europe: Testing hypotheses with a modelling approach. Atmos. Environ. 39(18), 3291–3303. Wu, P.-M., Okada, K., 1994. Nature of coarse nitrate particles in the atmosphere—A single particle approach. Atmos. Environ. 28, 2053–2060.

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Chapter 5.4 Modelling seasonal changes of aerosol compositions over Belgium and Europe Felix Deutsch, Filip Lefebre, Liliane Janssen, Jean Vankerkom and Clemens Mensink Abstract The EUROpean Smog model (EUROS) was extended with two special modular algorithms for atmospheric particles. The ﬁrst module is the Caltech Atmospheric Chemistry Mechanism (CACM) which describes in a mechanistic way the formation of precursors of secondary organic aerosols. The second module is the Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (MADRID 2), which describes the formation of secondary aerosols by means of an equilibrium between the gas phase and the aerosol phase. It includes inorganic as well as hydrophilic and hydrophobic organic compounds. Through this extension, the EUROS model was able to model mass and chemical composition of aerosols in two size fractions (PM2.5 and PM10-2.5). The model was validated for 3 seasonal episodes in 2002 and 2003. A comparison between modelled and observed aerosol concentrations showed that the trends in PM10 concentrations are well captured. A strong seasonal dependency was found in the chemical composition of the aerosols. Large contributions of secondary inorganic components were found for summer episodes with high aerosol concentrations, whereas during autumn/ winter episodes the concentrations of secondary aerosol were less abundant.

1. Introduction

During the last decade, Belgium and many other European countries are faced with episodes of high concentrations of particulate matter. The particles are associated with strong adverse health effects (Dockery et al., 1993; Pope et al., 1995). In 2003, several PM10 monitoring stations in

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Belgium measured more than 10 episodes of high particle concentrations (PM10100 mg m 3). In order to model these events and explain why and how these episodes occur, we extended the operational Eulerian air quality model EUROS with two special modular algorithms for atmospheric particles. The ﬁrst module is the Caltech Atmospheric Chemistry Mechanism (CACM, Grifﬁn et al., 2002), being the ﬁrst mechanism in describing the formation of precursors of secondary organic aerosols in the atmosphere in a mechanistic way. The second module is the Model of Aerosol Dynamics, Reaction, Ionization and Dissolution 2 (MADRID 2, Zhang et al., 2004), which treats the formation of secondary aerosols via equilibrium calculations between the gas phase and the aerosol phase for inorganic (ISORROPIA) and hydrophilic and hydrophobic organic compounds (AEC-SOA-module). The main characteristics of these modular algorithms and the further extensions of the PM version of the EUROS model are discussed in Section 2. Currently, EUROS is able to model mass and chemical composition of aerosols in two size fractions (PM2.5 and PM10 2.5). The chemical composition is expressed in terms of seven components: ammonium, nitrate, sulphate, primary inorganic compounds, elementary carbon, primary organic compounds and secondary organic compounds (SOA). A validation of the model was performed for summer and autumn/winter episodes in 2002 and 2003. The obtained aerosol concentrations were compared to observed concentrations at monitoring stations of the Flemish Environment Agency. The results of the comparisons are presented and discussed in Section 3.

2. Methodology

A ﬁrst step in the extension of EUROS towards aerosol modelling was the implementation of the CACM. It comprises 361 reactions among 122 components. CACM describes the formation of precursors of secondary organic aerosols. Apart from the ozone chemistry it also contains the reactions of various generations of organic compounds. These reactions can generate semi-volatile reaction products, which can equilibrate into the solid phase. Forty-two of these condensable products are treated in CACM and 31 products originate from anthropogenic NMVOCemissions and 11 products originate from biogenic NMVOC-emissions, e.g., monoterpenes. Various routines of EUROS (e.g., NMVOC-split, background concentrations) were adjusted to CACM. In MADRID 2, thermodynamic equilibrium calculations are carried out via ISORROPIA (Nenes et al., 1998) for inorganic compounds and via the

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new developed AEC-SOA-module (Pun et al., 2001, 2002) for organic compounds. Mass transfer between gas phase and solid phase can be taken into account via different approaches, e.g., the CMU Hybrid Model (Capaldo et al., 2000), supposing instantaneous equilibrium for small particles and calculating mass transfer for larger particles. In this work, the CIT hybrid approach (Meng et al., 1998) was used in which a full equilibrium is assumed but the condensing mass is distributed according to a growth law depending on particle size. As only two size fractions were simulated (PM2.5 and PM10 2.5), both coagulation and condensational growth of particles were omitted because they lead only to little exchange between the two size fractions in comparison to the gas/particle mass transfer. Nucleation of new particles was treated by calculating the relative rates of new particle formation and condensation onto existing particles. Deposition of particles was calculated following a resistance approach. Secondary organic aerosols are calculated after lumping the 42 condensable compounds from CACM into 5 hydrophilic and 5 hydrophobic surrogate compounds in MADRID 2. The hydrophilic compounds are equilibrated between the gas phase and the ‘‘liquid’’ phase of the aerosol particles according to Henry’s law. The necessary activity coefﬁcients are calculated by UNIFAC, which is based on group-contribution theory (Fredenslund et al., 1975; Saxena et al., 1995). The water content of the aerosols is calculated by the ZSR (Zdanovskii-Stokes-Robinson) algorithm (Stokes and Robinson, 1966). Hydrophobic organic reaction products from CACM are equilibrated between the gas phase and an absorbing organic aerosol phase, consisting of primarily emitted organic aerosols and of other secondary hydrophobic organic compounds. Because both the inorganic and the organic hydrophilic aerosol composition determine the amount of water connected to the aerosol particles and the acidity of the aqueous phase, the inorganic and the organic hydrophilic equilibrium module are called iteratively until constant liquid water content and acidity is reached. Only then the module for the organic hydrophobic compounds is called. For the further implementation of the aerosol version of EUROS, it was provided with additional emission data of point and surface sources for ammonia and two size fractions of particles (o2.5 mm; 2.5–10 mm). These data were obtained from the EMEP-database for Europe (Vestreng et al., 2004) and from a recent emission inventory for Flanders (VMM, 2004a). 3. Results and discussion

For several periods with high PM10- and PM2.5-concentrations in 2002 and 2003, the aerosol version of EUROS was applied. The base grid of

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EUROS covers nearly whole Europe with a resolution of 60 60 km2. A sub-grid of approximately 700 500 km2 with a resolution of 15 15 km2 was used for simulations over Belgium and surrounding regions. EUROS uses European Center for Medium range Weather Forecasting (ECMWF) meteorological data and emission data from EMEP/CORINAIR for the base grid and additionally more detailed emission data from a Flemish Emission Inventory for the sub-grid in and around Belgium. The vertical structure of the atmosphere is represented in EUROS by four layers: ground layer, mixing layer, reservoir layer and top layer. The obtained PM10-concentrations were compared to concentrations observed at the monitoring stations of the air quality monitoring network of the Flemish Environment Agency (VMM, 2003, 2004b). In Fig. 1 the hourly mean and daily mean PM10-concentrations measured between 1 January and 28 February 2002 at a monitoring station near the city centre of Antwerp (Borgerhout) are compared with the modelled hourly mean and daily mean PM10-concentrations for the 15 15 km2 grid cell in which this monitoring station is located. PM10concentrations have been measured by TEOM and subsequently corrected to account for losses of volatile material. Figure 2 shows a similar comparison for the measured and modelled PM10-concentrations for the time period between 1 October and 30 November 2002. Several shorter episodes with higher PM10-concentrations were observed during October

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Figures 1–3 show that the modelled PM10-concentrations generally follow the trend of the measured PM10-concentrations very well. Especially during the periods of January/February 2002 and October/ November 2002, the model reproduces the various measured short PMepisodes. Also, modelled daily mean values are generally consistent with measured daily mean concentrations. Only on a limited number of days the model underestimates the measurements. However, during the second part of the episode in July/August 2003, the model signiﬁcantly underestimates measured PM10-concentrations on some days. For the time period between 9 and 21 November 2002 additional measurements of daily mean concentrations of ammonium (NH+ 4 ), nitrate (NO3 ) and sulphate (SO24 ) were available for a monitoring station in Mechelen, 20 km south of Antwerp. Figure 4 compares the modelled daily mean concentrations for ammonium, nitrate and sulphate with those measured in the PM2.5 fraction at this location. In Fig. 2 we observe an increase in PM10-concentrations between 17 and 20 November 2002. This episode is probably caused by an increase in the contributions of secondary aerosols (especially ammonium and

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Table 1. Averaged chemical composition of the size fraction PM2.5 in Antwerp during the time periods 1 October–30 November 2002 and 1 July–31 August 2003 Component

October–November 2002 (%)

July–August 2003 (%)

Elementary carbon Primary inorganic Primary organic Ammonium Nitrate Sulphate Secondary organic

8 16 19 10 22 20 5

4 8 7 17 21 36 7

Total primary components Total secondary components

43 57

19 81

nitrate), as can be observed in Fig. 4. These increased contributions are relatively well captured by the model. Table 1 shows the modelled chemical composition of PM2.5 and the relative contribution of the various components for the period 1 October– 30 November 2002 and for the summer period 1 July–31 August 2003. From Table 1 it can be observed that ammonium, nitrate and sulphate dominate the composition in the summer period, with a total contribution of the secondary components of 81%. In the autumn/winter period the contribution of the secondary components is less, but with 57% it is still substantial. 4. Conclusions

The operational Eulerian air quality model EUROS has been extended with two special modular algorithms for modelling atmospheric particles. The chemical mechanism CACM describes the formation of precursors of secondary organic aerosols in the atmosphere. The advanced aerosol model MADRID 2 simulates the formation of secondary aerosols via equilibrium calculations between the gas phase and the aerosol phase for inorganic and hydrophilic and hydrophobic organic compounds. Both CACM and MADRID 2 were successfully implemented into the EUROS model. As a result EUROS is able to model mass and chemical composition of aerosols in two size fractions (PM2.5 and PM10 2.5). The chemical composition is expressed in terms of seven components: ammonium, nitrate, sulphate, primary inorganic compounds, elementary carbon, primary organic compounds and SOA.

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Modelled PM10-concentrations were compared with measurements in Antwerp for three seasonal periods in 2002 and 2003. The modelled concentrations and trends are generally consistent with observed PM10concentrations. The chemical composition of the aerosol showed a strong dependence on the season. High aerosol concentrations during the summer were mainly due to high concentrations of the secondary components nitrate, ammonium and sulphate in the size fraction PM2.5. During episodes in autumn and winter, secondary components were much less abundant in this size fraction, although ammonium, nitrate and sulphate still contributed signiﬁcantly to the total aerosol mass, as conﬁrmed by the measurements.

REFERENCES Capaldo, K.P., Pilinis, C., Pandis, S.N., 2000. A computationally efﬁcient hybrid approach for dynamic gas/aerosol transfer in air quality models. Atmos. Environ. 34, 3617–3627. Dockery, D.W., Pope, C.A. III, Xiping, X., Spengler, J.D., Ware, J.H., Fay, M.A., Ferries, B.G. Jr., Speizer, F.E., 1993. An association between air pollution and mortality in six US cities. N. Engl. J. Med. 329(24), 1753–1759. Fredenslund, A., Jones, R.L., Prausnitz, J.M., 1975. Group-contribution estimation of activity coefﬁcients in nonideal liquid mixtures. AIChE J. 21(6), 1086–1099. Grifﬁn, R.J., Dabdub, D., Seinfeld, J.H., 2002. Secondary organic aerosol 1. Atmospheric chemical mechanism for production of molecular constituents, J. Geophys. Res. 107(D17), 4332, doi:10.1029/2001JD000541 Meng, Z., Dabdub, D., Seinfeld, J.H., 1998. Size-resolved and chemically resolved model of atmospheric aerosol dynamics. J. Geophys. Res. 103, 3419–3435. Nenes, A., Pandis, S.N., Pilinis, C., 1998. ISORROPIA: A new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosols. Aqua. Geochem. 4, 123–152. Pope, C.A. III, Thun, M.J., Namboodiri, M.M., Dockery, D.W., Evans, J.S., Speizer, F.E., Heath, C.W. Jr., 1995. Particulate air pollution as predictor of mortality in a prospective study of US adults. Am. J. Resp. Crit. Care Med. 151, 669–674. Pun, B.K., Grifﬁn, R.J., Seigneur, C., Seinfeld, J.H., 2002. Secondary organic aerosol 2. Thermodynamic model for gas/particle partitioning of molecular constituents. J. Geophys. Res. 107(D17), 4333 AAC 4-1–4-15. Pun, B.K., Wu, S.-Y., Seigneur, C., 2001. Contribution of biogenic emissions to the formation of ozone and particulate matter: Modeling studies in the Nashville, Tennessee and Northeast domains, Phase 2 Report for CRC Project A-23, Document Number CP051-01-1, Coordinating Research Council Inc., Alpharetta, GA, USA. Saxena, P., Hildemann, L.M., McMurry, P.H., Seinfeld, J.H., 1995. Organics alter hygroscopic behavior of atmospheric particles. J. Geophys. Res. 100, 18755–18770. Stokes, R.H., Robinson, R.A., 1966. Interactions in nonelectrolyte solutions. I. Solutesolvent equilibria. J. Phys. Chem. 70, 2126–2130. Vestreng, V., et al., 2004. Inventory review 2004, Emission Data reported to CLRTAP and under the NEC Directive, EMEP/EEA Joint Review Report, EMEP/MSC-W Note 1/2004, ISSN 0804-2446.

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VMM, 2003. Luchtkwaliteit in het Vlaamse Gewest—2002, Vlaamse Milieumaatschappij, Aalst, Belgium, p. 266 and Appendix (in Dutch). VMM, 2004a. Lozingen in de lucht 1990-2003, Vlaamse Milieumaatschappij, Aalst, Belgium, p. 185 and Appendix (in Dutch). VMM, 2004b. Luchtkwaliteit in het Vlaamse Gewest—2003, Vlaamse Milieumaatschappij, Aalst, Belgium, p. 270 and Appendix (in Dutch). Zhang, Y., Pun, B., Vijayaraghavan, K., Wu, S.-Y., Seigneur, C., Pandis, S.N., Jacobson, M.Z., Nenes, A., Seinfeld, J.H., 2004. Development and application of the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID). J. Geophys. Res. 109, D01202, doi:10.1029/2003JD003501.

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Chapter 5.5 Modeling of Saharan dust events within SAMUM: Implications for regional radiation balance and mesoscale circulation Ju¨rgen Helmert, Bernd Heinold, Ina Tegen, Olaf Hellmuth and Ralf Wolke Abstract Using a new regional dust model system the sensitivity of radiative forcing to dust aerosol properties and the impact on atmospheric dynamics were investigated. The climatological based distribution of desert-type aerosol in the radiation scheme of the regional model LM was replaced by dust optical properties from spectral refractive indices derived from in-situ measurements, remote sensing, bulk measurements, and laboratory experiments, employing Mie theory. A model study of a Saharan dust outbreak in October 2001 was carried out, when large amounts of Saharan dust were transported to Europe. For October, 11, 2001 simulated dust radiative impact and feedback mechanisms on 2 m-temperature and 10 m-wind speed were investigated.

1. Introduction

Soil-derived dust, mainly produced by aeolian erosion in arid and semiarid areas and transported in the atmosphere over large distances represents one of the major components of the atmospheric aerosol. Due to the high population density in northern hemisphere many people are potentially affected by dust outbreaks, which decrease air quality and can lead to unhealthy conditions by increased PM10 concentrations as well possible reduction of trafﬁc activities by decreased visibility. Therefore, the forecast of dust events is also an issue for weather forecast models, where the inﬂuence of mineral dust in numerical weather prediction is mainly due to its impact on radiative ﬂuxes. Although various studies have shown the inﬂuence of aerosol particles on Earth’s climate through direct and indirect effects, the contribution of mineral dust contribution

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to aerosol-induced radiative forcing is still uncertain. The radiative impact of mineral dust depends primarily on its optical parameters and abundance in the atmosphere. Due to high uncertainties of these factors, they are a subject of interest in the Saharan Mineral Dust Experiment (SAMUM), which includes measurements in the Saharan desert (Morocco) as one of the largest aeolian dust sources accompanied by mesoscale simulations of the Saharan dust cycle using the MUltiscale Chemistry Aerosol Transport Model (MUSCAT) online-coupled with the mesoscale limited area model (LM) (Doms and Scha¨ttler, 1999) and a dust emission scheme (Tegen et al., 2002). The complex refractive index is considered to be the most important parameter determining dust optical properties and therefore the scattering and absorption behavior in the visible and thermal wavelengths.

2. Dust optical properties

In this study, spectral refractive index data of mineral dust from sunphotometer retrievals and remote sensing (Dubovik et al., 2002; Sinyuk et al., 2003), from laboratory measurements of bulk dust samples (Volz, 1973), and of mineral dust with known mineralogical composition (Sokolik and Toon, 1999) were used. In the latter case, an internal mixing with major components kaolinite (98%) with hematite (2%) was assumed and the Bruggeman approximation was employed to determine the spectral refractive index of this mixture. Dust optical properties that are the radiative parameters extinction efﬁciency, single scattering albedo, and asymmetry factor are calculated employing Mie theory based on an algorithm provided by Mishchenko et al. (2002). Although Mie theory requires spherical particles, an assumption that is not reasonable for most dust particles, errors are small in the hemispherical integration when compared to computations with spheroids (Lacis and Mishchenko, 1995). In dependence on the particle size of two effective radii (0.17 and 4.6 mm), the spectral distribution of single scattering albedo w0 on different dust refractive index data is shown in Fig. 1, covering of the most interesting regions of solar and thermal radiation. Obviously, there is a larger dependence of radiative parameters on particle size than on variations in refractive index data. Comparing the dust optical properties for the different particle sizes, it is notable that larger-size particles show stronger absorption (small values of w0) in the visible spectral range, and strong increase in extinction in the thermal range. While the ﬁrst effect can be explained by an increase of the light path through the optical active medium, the second effect is due to

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Figure 1. Spectral distribution of single scattering albedo of mineral dust using data from Sokolik and Toon (1999)—SOK99, Sinyuk et al. (2003)—SIN03, Dubovik et al. (2002)— DUB02, Volz (1973)—VLZ73, and Todd et al. (2005)—TDD05 for effective radii of 0.17 mm (small circles) and 4.6 mm (large circles).

nonlinear dependence of radiative parameters on particle size (Lacis and Mishchenko, 1995). Furthermore, in the thermal range, wavelengths begin to be larger than the particle size inducing the onset of a small particle state. Consequently, the smaller-size particles are at thermal wavelengths

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in the Rayleigh regime that induces higher absorption. However, comparing dust optical properties within a speciﬁc particle size, different refractive index data can induce signiﬁcant changes in radiative parameters. For larger wavelengths, large differences in extinction between Sokolik and Toon (1999) data and Volz (1973) data occur for smaller-size particles near the boundary between solar and thermal range at about 4.6 mm, while the deviations for the larger-size particles are smaller. However, in this spectral range, Sokolik and Toon (1999) data show much stronger absorption for the smaller-size particles of w0 ¼ 0.009 at 5 mm compared with w0 ¼ 0.152 from Volz (1973). Larger-size particles show similar deviations but smaller in magnitude. For visible wavelengths, Fig. 1 shows the higher absorption properties of hematite in the samples of Sokolik and Toon (1999) resulting in small values of w0 ¼ 0.57 at 440 nm for larger-size dust compared with w0 ¼ 0.79 for retrievals of Dubovik et al. (2002) and 0.82 for measurements in the Bodele depression of Todd et al. (2005). Compared with 4.6 mm particles, smaller-size particles are much reﬂective. To adopt the dust optical properties in the LM radiation scheme for all dust size bins, spectral integration of radiative parameters was performed following Liao and Seinfeld (1998). The simulation scenario is based on data of a Saharan dust outbreak in October 2001 where mineral dust with origin in the Sahara was observed over large parts of western and central Europe. Using a chemistrytransport model, the simulations take into account the mineral dust cycle of production, transport, and deposition. In order to quantify the impact of mineral dust and its uncertainties in refractive index on meteorological parameters, LM-MUSCAT simulations with different levels of dust feedback on LM radiative transfer parameterization were performed. These include simulations without dust feedback from MUSCAT to LM, that are the control run simulation without any dust impact in LM (hereafter denoted as CTRL) and a simulation with ﬁxed climatological mean distribution of desert dust (Tanre et al., 1984) in the LM radiation scheme (hereafter denoted as CLM). Simulations with dust feedback from MUSCAT are performed using a spectral composite of dust optical properties from sunphotometer retrievals and remote sensing (Dubovik et al., 2002; Sinyuk et al., 2003) in the visible and from bulk dust samples in the IR (Volz, 1973) which represents less absorbing and more reﬂecting dust (hereafter denoted as REFL). A further simulation represents more absorbing and less reﬂecting dust (hereafter denoted as ABS) using spectral dust optical properties from laboratory measurements (Sokolik and Toon, 1999). The model-predicted dust is transported as dynamic tracer in ﬁve independent size classes with radius limits at 0.1, 0.3, 0.9, 2.6, 8, and 24 mm.

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Dust is removed from the atmosphere by dry and wet deposition processes. Dust optical thickness is computed from the simulated dust concentrations, particle size distribution, and extinction efﬁciencies and read into the radiation scheme of the LM. 3. Results

Figure 2 shows the model results of columnar dust load at October 11, 2001, where due to a low pressure area west of Morocco as well as high pressure over northern Africa and the Mediterranean Sea, a plume of Saharan dust passed over the Iberian Peninsula. The results indicate that major dust emission occurred in northern Mauritania, Mali, northeastern and southern Algeria, Tunisia, and in Chad (Bodele depression). The simulated dust distribution at October 11, 12:00 UTC agrees well with SeaWiFS remote sensing (not shown). Based on the columnar dust load,

Figure 2. Saharan dust outbreak at October 11, 2001 over North Africa and Iberian Peninsula as modeled columnar dust load (left) at 12:00 UTC, and modeled and AERONET retrieved dust optical thickness (right) at 500 nm (12:00 UTC).

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dust optical thickness was determined following Lacis and Mishchenko (1995). Using simulation data with more absorbing dust of Sokolik and Toon (1999), Fig. 2 shows the resulting horizontal distribution of dust optical thickness for 500 nm wavelength at October 11, 12:00 UTC together with AERONET sunphotometer retrievals (Holben et al., 1998) at Thala, Oristano, Tarbes, Avignon, and Bordeaux. With the AERONET retrieved Angstrom exponent, the optical thickness at 500 nm was determined from the sunphotometer data at 440 nm. Corresponding to the horizontal distribution of the columnar dust load (Fig. 2), the dust optical thickness exceeds values of 5 in the Bodele depression and values of 2 in the northwestern Sahara. In Europe, dust optical thickness is below 1.5 with a local maximum in central and southern Spain. The dust radiative forcing on solar radiation budget at surface and model top from the regional model simulations using different dust optical properties is shown in Fig. 3. Radiative forcing is here deﬁned as changes in radiation budget compared with the control run (Fig. 3a and e). It was shown by Liao and Seinfeld (1998) that dust radiative forcing depends on a number of parameters. Primarily surface albedo, dust layer altitude, dust mass median diameter, dust optical thickness, occurrence, and location of clouds relative to the dust layer are impact factors. Therefore, the dust radiative forcing shown in Fig. 3 results from the complex interrelation of these radiative variables. This means that here, in contrast to other studies of the radiative effect of aerosols, the change in solar radiation includes not only the direct radiative forcing of mineral dust, but also atmospheric feedback processes (e.g., change in cloud coverage, water vapor content). In cloud-free desert regions with high values of dust optical thickness, the dust radiative forcing is very strong for simulations with interactive dust feedback on radiation. For example, in the Bodele, a decrease in solar radiation budget at surface occurs of about 650 W m2. In contrast, for the case with climatological dust feedback the decrease in solar radiation budget at surface (Fig. 3f) is much smaller (60 W m2). This is due to the ﬁxed optical depth of about 0.3 in the desert aerosol for the CLM case. This case indicates a smooth transition of solar radiation decrease to higher latitudes, corresponding to the ﬁxed desert aerosol distribution, while in cases with interactive dust feedback (REFL, ABS), the dust radiative forcing is limited to regions with positive dust load. Dust-induced radiative forcing at model top is smaller in total magnitude than at surface (Liao and Seinfeld, 1998) but also negative with values in the southern Sahara below 20 W m2 for CLM and lower than 330 W m2 for REFL. This means that in the latter case the upward

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Figure 3. Simulated solar radiation budget on October 11, 2001 12:00 UTC at model top (a–d) and surface (e–h) of the control run (a, e) and differences relative to the control run of CLM (b, f), REFL (c, g), and ABS run (d, h).

directed solar radiation has the largest values showing the enhanced cooling properties of the more reﬂective dust compared with the more absorbing dust with radiative forcing of about 250 W m2. Changes in 2 m-temperatures for October 11, 12:00 UTC (Fig. 4) between the different model experiments including various parameterizations of dust radiative parameters can be attributed to changes in dust radiative forcing and semidirect feedback processes of mineral dust on cloud coverage and moisture ﬁelds. Comparing the temperature differences in the model to the dust optical thickness distribution on this date (Fig. 2) we ﬁnd, as expected, the strongest temperature decrease occurring where the dust optical thickness is highest, in the southern Sahara. Here, the presence of dust causes a surface temperature decrease larger than 6 K. In the northern Sahara, the radiative-induced temperature decrease

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Figure 4. Simulated 2m-temperature at October 11, 2001 12:00 UTC of the control run (a) and differences relative to the control run of CLM (b), REFL (c), and ABS run (d).

is smaller in magnitude. At the Iberian Peninsula, a temperature reduction of the order of 1–2 K for reﬂective and absorbing dust occurs that can be attributed to the direct radiative effect of the dust plume with optical thickness larger than 0.2 in this area. Due to the climatological mask of dust optical thickness, the CLM case cannot account for the mesoscale variability in the dust load. Therefore, these simulation results show a reduction in 2 m-temperature also for dust-free regions in central Europe that is not the case for simulations with interactive dust feedback. High dust loads in the southern Sahara give also feedback in 10 m wind speed (not shown), where compared with the control run the wind decreases by about 4 m s1 for cases with reﬂecting and absorbing dust. Since dust emission ﬂux depends on the velocity of near surface wind, this decrease in wind speed limits the dust emission ﬂux. For comparison, the change in 10 m wind speed for the CLM case in the Bodele depression is much lower in magnitude (below 0.5 m s1) and has a more arbitrary behavior.

4. Summary

A new regional model system consisting of the regional model LM, a dust emission model (DES) and the transport scheme MUSCAT for simulation of dust emission, transport, deposition, and radiation effects within the framework of the SAMUM was developed. In order to test the model

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performance a far-ﬁeld study of a Saharan dust outbreak in October 2001, when large amounts of Saharan dust were transported to Europe was carried out. It could be shown that the model is capable of describing Saharan dust events in the mesoscale. Both the location of dust sources and the transport patterns are in agreement with observations. A good qualitative agreement has been found comparing the model-derived dust optical thickness with the observed aerosol optical thickness at selected AERONET stations. While as expected there is strong impact of the dust radiative forcing near the source region in the southern Sahara, the model results show the potential of dust to inﬂuence the atmospheric dynamics, even though the potential inﬂuence of dust particles on cloud microphysical processes is not taken into consideration in these model studies. During the dust outbreak, the aerosol in the southern Sahara causes a daytime temperature reduction that could exceed 6 K. Saharan dust show a large variability in radiative properties due to different mixture of clay aggregates. Since, this could have an impact on atmospheric chemistry and cloud formation, the properties of Saharan dust should be further clariﬁed in upcoming ﬁeld experiments. In summary, the model-system LM-MUSCAT-DES has been shown to be capable for regional modeling of Saharan aerosol. It will be used to accompany the analysis of the results gained during the SAMUM ﬁeld campaign in 2006. Results from the measurements during SAMUM will be used to improve the parameterization of the modeled dust processes and to specify the optical properties of Saharan dust.

Discussion

R. San Jose´:

J. Helmert:

What type of parameterization do you use in your model for emissions? Is it dependent on friction velocity? The implemented dust source scheme was developed by Tegen et al. (2002). It considers surface properties such as surface roughness, soil size distribution, vegetation cover, and soil moisture content as well as the location of preferential dust sources in order to calculate the erosion threshold velocity and the time- and sizeresolved horizontal and vertical dust ﬂuxes. The vertical dust ﬂuxes serve as dust emission sources in the transport code MUSCAT. Aeolian soil erosion mainly depends on the wind shear stress on the ground that is a function of the friction

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velocity. The mobilization of dust particles occurs above a certain threshold friction velocity, which is a function of the particle diameter and consequently depends on the soil size distribution. How do you distribute the uptaken dust mass to your dust size bins? How many bins do you use? The model predicted dust is transported in ﬁve independent size classes with radius limits at 0.1, 0.3, 0.9, 2.6, 8, and 24 mm assuming log-normal size distribution. You used theoretical data for validation. Could you correct for cirrus clouds? In order to determine the direct radiative forcing of mineral dust, a cloud screening was performed on the model results, where only grid points with total cloud coverage r1% were taken into account.

REFERENCES Doms, G., Scha¨ttler, U., 1999. The nonhydrostatic limited-area model LM (Lokal-Modell) of DWD. Tech. Rep. Part I: Scientiﬁc Documentation, Deutscher Wetterdienst, Gescha¨ftsbereich Forschung und Entwicklung, available at http://www.cosmo-model. org/ Dubovik, O., Holben, B., Eck, T.F., Smirnov, A., Kaufman, Y.J., King, M.D., Tanre, D., Slutsker, I., 2002. Variability of absorption and optical properties of key aerosol types observed in worldwide locations. J. Atmos. Sci. 59, 590–608. Holben, B., Eck, T.F., Slutsker, I., Tanre, D., Buis, J.P., Setzer, A., Vermote, E., Reagan, J.A., Kaufman, Y., Nakajima, T., Lavenu, F., Jankowiak, I., Smirnov, A., 1998. AERONET—A federated instrument network and data archive for aerosol characterization. Rem. Sens. Environ. 66, 1–16. Lacis, A.A., Mishchenko, M.I., 1995. Climate forcing, climate sensitivity, and climate response: A radiative modeling perspective on atmospheric aerosols. In: Aerosol Forcing of Climate: Report of the Dahlem Workshop on Aerosol Forcing of Climate. pp. 11–42. Liao, H., Seinfeld, J.H., 1998. Radiative forcing by mineral dust aerosols: Sensitivity to key variables. J. Geophys. Res. 103, 31637–31645. Mishchenko, M.I., Travis, L.D., Lacis, A.A., 2002. Scattering, absorption, and emission of light by small particles. Cambridge University Press, Cambridge, UK. Sinyuk, A., Torres, O., Dubovik, O. 2003. Combined use of satellite and surface observations to infer the imaginary part of refractive index of Saharan dust. Geophys. Res. Lett. 30. Sokolik, I.N., Toon, O.B., 1999. Incorporation of mineralogical composition into models of the radiative properties of mineral aerosol from UV to IR wavelengths. J. Geophys. Res. 104, 9423–9444.

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Tanre, D., Geleyn, J.-F., Slingo, J.M., 1984. First results of the introduction of an advanced aerosol-radiation interaction in the ECMWF low resolution global model. In: Gerber, H.E., Deepak, A. (Eds.), Aerosols and Their Climatic Effects. A. Deepak Publ., Hampton, Virginia, pp. 133–177. Tegen, I., Harrison, S.P., Kohfeld, K., Prentice, I.C., Coe, M., Heimann, M. 2002. Impact of vegetation and preferential source areas on global dust aerosol: Results from a model study, J. Geophys. Res. 107. Todd, M.C., Vanderlei, M., Washington, R., Lizcano, G., Bainael, S.M., Engelstaedter, S., 2005. Optical properties of mineral dust from the Bodele depression, northern chad during bodex 2005. Submitted for publication. Volz, F.E., 1973. Infrared optical-constants of ammonium sulfate, Sahara dust, volcanic pumice, and ﬂyash. Appl. Optics 12, 564–568.

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Chapter 5.6 Long-term aerosol simulation for Portugal using the CHIMERE model C. Borrego, A. Monteiro, J. Ferreira, A.I. Miranda, R. Vautard and A.T. Perez Abstract Air pollution is a major environmental health problem causing approximately 3 million deaths per year in the world, as a result of exposure to particulate matter (PM). Portugal, as a European Union Member, should follow the main objectives for management and quality of ambient air, namely those related to particulate matter. An increase of scientiﬁc studies during the last years have conﬁrmed that long-term exposure to particulate matter pollution leads to adverse health effects. These studies generally use air pollution measurements from stationary air monitoring sites to determine population exposure levels. However, because of the large local variations in pollution concentrations, the estimates are often associated with high uncertainties. Besides that, aerosols are comprised not only of primary particles emitted directly to the atmosphere, but also of products from gas-to-particle conversion (sulphur oxides, nitrogen oxides, volatile organic compounds and semi-volatile organic compounds) in clear air and clouds. Thus, considerable research is needed to better understand the complex processes of aerosols formation, transport and deposition. For an improvement on these processes knowledge and more accurate exposure estimation, air quality models can be used as important tools with the ability to provide detailed information on pollutants concentration ﬁelds. The main purpose of this study is to perform a ﬁrst long-term air quality assessment for Portugal, regarding aerosols and particulate matter pollution. The CHIMERE chemistry-transport model, forced by the MM5 meteorological ﬁelds, was applied over the Continental region of Portugal for the 2001 year period, with 10 km horizontal resolution, using an emission inventory obtained from a spatial topdown disaggregation of the 2001 EMEP national database.

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In order to verify the ability of the model system to simulate particulate matter, an evaluation model exercise is performed based on statistical comparison between PM10 and PM2.5 concentration data observed at 20 air monitoring ground stations and the correspondent simulated values. Besides that, PM components (nitrate, sulphate, ammonium, etc.) are evaluated at two speciﬁc sampling (experimental) sites near two urban waste incinerators. There is a model trend to overestimate particulate pollution episodes (peaks) at urban sites, especially in winter season. This could be due to an underprediction of the winter model vertical mixing or an incorrect spatial and temporal disaggregation and consequent overestimation of local PM emissions. Time series spectral decomposition was also used to evaluate the model performance (accuracy) at different time scales. Nevertheless, as a ﬁrst approach, and despite the complex topography and coastal location of Portugal affected by sea salt natural aerosols emissions, the results obtained show a modelling system able to reproduce the particulate matter levels temporal evolution and spatial patterns. The concentration maps reveal speciﬁc areas with critical particulate matter values that are not yet covered by the air quality monitoring network. In this way, this longterm simulation study could be used as a useful tool for air quality management, as well as for protecting human health. 1. Introduction

The particulate matter suspended in the troposphere is strongly linked to numerous air pollution problems. Heterogeneous processes within as well as on the surface of particles have the potential to modify the concentration levels and the spatial distribution of most acid and photochemical air pollutants found in the atmosphere (Ackermann et al., 1998). Suspended particles have recently received much interest because of increasing epidemiological and experimental evidence of their health impact. The chemical composition of PM10 is complex and varies from day to day. Different PM10 sources can be identiﬁed and listed, like trafﬁc, industry, energy generation and agriculture activities and also natural sources such as sea salt and resuspended dust. According to recent health studies (e.g., Moshammer and Neuberger, 2003), the PM10 standard concentration measurement seems to be an inadequate indicator. Indeed, characteristics on masses, numbers and even surfaces of ﬁne particles have been shown to correlate with acute health effects and measurable functional changes in the cardiovascular and respiratory systems. Scientists

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have developed modelling tools to better understand physical–chemical processes involving gaseous and particulate species and improve the prediction of pollution episodes (Seigneur, 2001). In Portugal, especially in urban areas, PM10 air concentrations are exceeding the correspondent limit value imposed by the EC Air Quality Framework Directive. Atmospheric models are then important tools for the evaluation and assessment of PM episodes that have been more frequent during the last decade. 2. Model formulation and application

CHIMERE is a three-dimensional CTM that simulates gas-phase chemistry (Schmidt et al., 2001), aerosol formation, transport and deposition (Bessagnet et al., 2004) at European and urban scales. In the present application, the model is run at a regional scale over a domain covering the whole Continental Portugal, with a 10-km grid size resolution (see Fig. 1), from 1 January to 31 December 2001. The vertical resolution consists of eight vertical layers of various thicknesses extending from ground to 500 hPa. Boundary conditions are provided by a prior large-scale simulation, covering Western Europe with a 0.51 resolution, using the Vautard et al. (2005) version of the model. Boundary conditions for the regional simulation are taken from monthly means of the GOCART model, as in Hodzic et al. (2005). The model simulates the concentration of 44 gaseous species and six aerosol chemical compounds. The gas-phase chemistry scheme has been extended to include sulphur aqueous chemistry, secondary organic chemistry and heterogeneous chemistry of HONO and nitrate (Bessagnet PORTO region

LISBON region

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Figure 1. Domains of simulation (a) and location of the PM10 monitoring stations (b).

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et al., 2004). The population of aerosol particles is represented by a sectional formulation, assuming discrete aerosol size sections (six diameter bins ranging between 10 nm and 40 nm, with a geometric increase of bin bounds, are used) and considering the particles of a given section to be internally mixed. The several meteorological ﬁelds required by CHIMERE for both mesoscale and regional CHIMERE domains were generated using the NCAR mesoscale modelling system MM5 (Dudhia, 1993). The model requires hourly spatially resolved emissions for the main anthropogenic gas and aerosol species. For the large-scale simulations, the anthropogenic emissions for NOx, CO, SO2, NMVOC and NH3 gas-phase species and for PM2.5 and PM10 are provided by EMEP (Vestreng, 2003) with a spatial resolution of 50 km. Over the Portuguese domain, the 2001 national inventory report (NIR) is used. The inventory takes into account annual emissions from line sources (streets and highways), area sources (industrial and residential combustion, solvents and others) and large point sources (IA, 2005). These annual emission data for each pollutant activity were spatially disaggregated in order to obtain the resolution required for the Portuguese domain simulation. The disaggregation is made in two steps. First, emissions are estimated at municipality level using adequate statistical indicators for each pollutant activity (types of fuel consumption) and then distributed according to the population density (Borrego et al., 2005). For large point sources, emissions were obtained directly from the available monitoring data of each industrial plant. Emissions for road transport are based on real ﬂux and average speed measured and on COPERT III emission factors, following a ‘‘bottom-up approach’’ (IA, 2005). The analysis of the national inventory (Table 1) shows that more than 60% of the PM emissions come from combustion activities (industrial and residential). Road transportation (14%) and production processes (10–30%) are the other important sources of PM. Besides the different activity source contribution between the two inventories (national and EMEP), the total PM emissions over Portugal are quite consistent between both inventories: e.g., the PM10 annual primary emission mass over the Portuguese domain is close to 63.3 kT year–1 in the EMEP database and 66.5 kT year–1 in the national inventory. This consistency shows, nevertheless, a slight overestimation of the national emission inventory, probably resulting from the methodology applied to estimate industrial emissions, which considered the worst scenario when there is no knowledge of the speciﬁc process activity. The uncertainties in the emission estimates are an important issue in the aerosol modelling. In fact, the uncertainty in annual total anthropogenic emissions is likely to be around 20% (Hass et al., 2003). This induces errors in the calculated

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Table 1. Contribution of SNAP activities to the total emissions of PM2.5 and PM10, concerning the national inventory (NIR) and the expert EMEP database PM2.5 annual emissions (%)

NIR EMEP

PM10 annual emissions (%)

Residential combustion

Industrial combustion

Production processes

Road transport

Residential combustion

Industrial combustion

Production processes

Road transport

45 40

28 14

10 5

14 20

32 33

20 15

27 10

13 19

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concentrations, particularly on short time scales (hours to days) and especially for primary-emitted components.

3. Model evaluation for PM10 concentrations

Results and observations of PM10 were compared for all the available PM10 monitoring sites (Fig. 1b). There were no PM2.5-monitored data in 2001. The model’s skill is evaluated using qualitatively time series plots and statistics such as bias, normalized errors and correlation coefﬁcient. Figure 2 shows the time series of the daily mean PM10 concentrations simulated by the CHIMERE model and observed at four different type monitoring stations (urban and suburban background, trafﬁc and industrial), located over Porto and Lisbon metropolitan areas. Besides a clear overestimation of the PM10 mean values, their time variations are well captured by the model for the different monitoring sites. The erroneous predicted peaks could be related to an underestimation of the boundary layer depth by the MM5 meteorological model with consequent deﬁcient pollutants dispersion or to an overestimation of the anthropogenic emissions (Hodzic et al., 2005). The mean statistical indicators (bias, RMS, normalized error and correlation coefﬁcient) obtained at different monitoring sites are presented in Table 2. The model shows a relatively good agreement with observations, with correlation coefﬁcients exceeding 0.6 and normalized errors bellow 40%. A systematic negative bias, conﬁrmed by time series in Fig. 2, is observed at almost all stations, suggesting an overestimation of anthropogenic emissions (even at the city-centre area) or a less correct estimation of the boundary layer depth, as previously discussed. The high correlation coefﬁcients indicate that meteorological conditions and trafﬁc emissions, which are effective over all the area, rather than speciﬁc local sources and events, dominate the time variations of the PM concentrations. RMS and normalized errors are higher where correlation coefﬁcients are lower, with the small errors at close suburban area. The model exhibits its poorest skill at the Lavradio industrial station. It should be also noticed that despite slight differences between near-city and city-centre stations, the model bears quite similar features for different categories of stations. Error compensations between particle components are certainly responsible for a part of these good results, already observed by Seigneur (2001). In order to better understand the model behaviour, two seasonal periods (summer and winter) were distinguished and evaluated separately. Figure 3 shows the statistical results found for each period. In summer,

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Table 2. Statistical analysis between observed and simulated PM10 hourly mean concentrations Station

Station typea

ERM CUS LEC VNT LAR REB AVL ENT VER LAV

SB SB SB SB UB UB TR TR TR IND

Mean obs. (mg m–3)

Mean mod. (mg m–3)

Bias (mg m–3)

Normalized error (%)

RMSE (mg m–3)

Correlation

48.84 50.03 53.89 45.37 43.12 38.19 61.90 41.04 65.40 30.03

61.31 65.73 62.05 49.22 66.04 59.52 78.07 72.27 61.47 51.93

14.4 16.7 8.1 3.8 18.9 16.3 16.1 17.2 3.9 14.4

37 39 38 37 40 40 33 43 38 45

30.1 35.4 32.8 27.0 38.6 35.4 36.2 41.9 32.2 33.3

0.61 0.61 0.60 0.62 0.65 0.66 0.56 0.55 0.60 0.57

Bias (mg m3) ¼ (1/N)Si(OiMi) and NE (%) ¼ (100/N)Si|(OiMi)/Oi|, where N is the number of samples, Oi are observations and Mi are model predictions. a SB: suburban background; UB: urban background; TR: trafﬁc; IND: industrial.

relatively low bias and normalized error (o5 mg m–3 and o36%, respectively) exist at almost all stations, compared with the winter season where large bias (30 mg m–3) and normalized error (45%) values are found. In winter, discrepancies between the model and observations are more frequent, particularly at urban sites where the model predicts several signiﬁcant PM peaks inconsistent with observations (see Fig. 2). These erroneous peaks are responsible for the important model overestimation and the poor mean correlation coefﬁcient (o0.6) obtained at urban stations. These higher deviations are probably due to an emission overestimation in winter and to a deﬁcient boundary layer height simulation. Bigger measurement errors in winter (Hodzic et al., 2005) could also explain a part of this model overestimation. Nevertheless, the correlation coefﬁcients at suburban background stations are higher in winter reaching 0.7, but inferior for urban sites (LAR, OLI and REB), where the correlation drops down to 0.55. This suggests that in summer, emissions from natural sources (e.g., dust from North Africa) could be a dominant event for the time variations of the PM concentrations and responsible for the less correct model results.

4. Simulation results

The spatial distribution of the annual and the 35th maximum daily mean PM10 levels predicted by the model is shown in Fig. 4, following the limit

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values required by the EU directives on air quality. The highest concentrations are found over the main cities of Lisbon and Porto and close suburban areas, due to the concentration of primary anthropogenic emissions, clearly exceeding the EU target limits for PM10 established for 2005 and 2010. Rural areas, away from important emission sources, undergo background aerosol pollution levels, except for a few locations which are inﬂuenced by point source emissions. The magnitude of the PM10 annual mean values is similar to the one found in Spain and Europe (Rodrı´ guez et al., 2004; Van Dingenen et al., 2004) for rural areas (7–10 mg m3), urban background (30–40 mg m3) and trafﬁc (45–55 mg m3). Simulation results (Fig. 4c) show PM2.5/PM10 mean ratios in the range 70–80%, similar to the average found for Europe (Van Dingenen et al., 2004). The higher ratios close to urban places suggest that secondary organic aerosols (which produce ﬁne particles) should be predominant in these sites (Querol et al., 2001). Besides that, the spatial distribution of PM10 average and the PM2.5/PM10 ratio shows that simulated PM2.5 displays similar spatial structures, and that this ratio increases with PM10 levels. This indicates that these polluted areas are predominantly due to increases in the PM2.5 mass concentration. In the south of the domain, this ratio is expected to be overestimated due to resuspended and windblown dust particles (coarse particles) in dry regions not taken into account (Vautard et al., 2005). However, the lack of PM2.5 stations does not allow the conﬁrmation of these conclusions. PM10 levels at rural sites present higher values in summer season (Fig. 4d) due to the frequency and intensity of the regional episodes, in contrast to the urban places with a high frequency of local urban pollution events (Rodrı´ guez et al., 2004.).

5. Summary and conclusion

In this paper, the skill of the CHIMERE aerosol chemistry-transport model in simulating particulate matter has been evaluated over Portugal during the year 2001. Compared to ground-level available data, model results seem to fairly reproduce the PM10 spatial and temporal variability with satisfactory normalized errors and correlation coefﬁcients (often exceeding 0.60) particularly for suburban background stations. Summer and winter time periods are distinguished and, in general, fewer deviations are obtained during the summer period. However, the correlation coefﬁcients are signiﬁcantly better in winter for near-city stations, often exceeding 0.65. This could suggest that in summer, emissions from natural sources (North Africa dust) could be a dominant event for the time variation and episodes of PM concentrations, and the

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lack of transport of these emissions in model simulation may lead to deﬁcient statistical correlation. The negative biases at urban background stations indicate that the PM10 levels are overestimated, mainly in winter. This can be attributed to several factors including (i) the measurements uncertainties, (ii) the underprediction of the vertical mixing in urban area and (iii) overestimation of PM urban emissions and emission inventory inaccuracies. Simulation results show that the actual monitoring network has an adequate spatial distribution, suggesting that it would be more interesting to expand the monitoring network to other PM components (specially PM2.5) than using ﬁnancial resources to increase the number of PM10 stations. This work shows that this modelling system can be used in the future as a tool for air pollution abatement strategies as well as for scientiﬁc purposes to investigate the transport of PM and its formation at the regional scale.

Discussion

T. Odman:

A. Monteiro:

P. Kishcha:

A. Monteiro:

D. Steyn:

Why is there a difference between your national emissions and EMEP inventories? Aren’t you reporting your national emissions to EMEP? The EMEP database is based in two different sources of emissions inventory: the ‘‘ofﬁcial’’ and the ‘‘expert’’ databases. The ‘‘ofﬁcial’’ data considers the national emissions reports delivered by each EU member state. The ‘‘expert’’ emissions database is build taking into account the EMEP emissions model. In this way, different emissions values are estimated for the Portugal region, which are compared in the present study. It was found that local pollution in winter dominates that in summer. How can it be explained from the point of view of typical seasonal synoptic situations? In fact, PM10 modelled levels at urban sites present lower values in summer season, in opposition to the rural zones. This could be explained with the high frequency of local urban pollution events, caused by an incomplete vertical mixing of pollutants due to low urban boundary layers, which occurs often at wintertime. Your network may adequately resolve the peaks in your two urban regions, but it cannot resolve nationwide spatial variability of PM10 over all of Portugal.

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A. Monteiro:

Yes, it is true; our national monitoring network only covers the main urban centres (namely Porto and Lisbon). In this sense, the network should be increased (or modify) in order to have a more adequate and correct spatial representation. Nevertheless, model results showed that the main critical areas of PM10 concentrations are at those two urban centres.

ACKNOWLEDGMENTS

The authors wish to thank the National Institute for the Environment for ﬁnancing and providing monitoring data, and the Portuguese Ministe´rio da Cieˆncia e do Ensino Superior, for the PhD grant of A. Monteiro (SFRH/BD/10922/2002). The authors are also grateful to the Network of Excellence ACCENT.

REFERENCES Ackermann, I.J., Hass, H., Memmesheimer, M., Ebel, A., Binkowski, F.S., Shankar, U., 1998. Modal aerosol dynamics model for Europe: Development and ﬁrst applications. Atmos. Environ. 32(17), 2981–2999. Bessagnet, B., Hodzic, A., Vautard, R., Beekmann, M., Cheinet, S., Honore´, C., Liousse, C., Rouil, L., 2004. Aerosol modeling with CHIMERE—Preliminary evaluation at the continental scale. Atmos. Environ. 38, 2803–2817. Borrego, C., Monteiro, A., Miranda, A.I., Vautard, R, 2005. Air quality assessment for Portugal. In: Sokhi, R., Milla´n, M, Moussiopoulos, N. (Eds.), 5th International Conference on Urban Air Quality, University of Hertfordshire, UK. Dudhia, J., 1993. A nonhydrostatic version of the Penn State/NCAR mesoscale model: Validation tests and simulation of an Atlantic cyclone and clod front. Mon. Weather Rev. 121, 1493–1513. Hass, H., Van Loon, M., Kessler, C., Stern, R., Matthijsen, J., Sauter, F., Zlatev, Z., Langner, J., Foltescu, V., Schaap, M., 2003. Aerosol modeling: Results and intercomparison from European Regional scale modeling systems. GLOREAM, EUROTRAC 2 Report. Hodzic, A., Vautard, R., Bessagnet, B., Lattuatic, M., Moreto, F., 2005. Long-term urban aerosol simulation versus PM observations. Atmos. Environ. 39, 5851–5864. IA—Instituto do Ambiente, 2005. Portuguese National Inventory Report on Greenhouse Gases, 1990–2003. Available at www.iambiente.pt Moshammer, H., Neuberger, M., 2003. The active surface of suspended particles as a predictor of lung function and pulmonary symptoms in Austrian school children. Atmos. Environ. 37, 1737–1744. Querol, X., Alastuey, A., Rodrı´ guez, S., Plana, F., Ruiz, C., Cots, N., Massague´, G., Puig, O., 2001. PM10 and PM2.5 source apportionment in the Barcelona Metropolitan area, Catalonia, Spain. Atmos. Environ. 35, 6407–6419.

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Rodrı´ guez, S., Querol, X., Alastuey, A., Viana, M., Alarco´n, M., Mantilla, E., Ruiz, C., 2004. Comparative PM10-PM2.5 source contribution study at rural, urban and industrial sites during PM episodes in Eastern Spain. Sci. Total Environ. 328, 95–113. Schmidt, H., Derognat, C., Vautard, R., Beekmann, M., 2001. A comparison of simulated and observed O3 mixing ratios for the summer of 1998 in Western Europe. Atmos. Environ. 35, 6277–6297. Seigneur, C., 2001. Current status of air quality models for particulate matter. J. Air Waste Manage. Assoc. 51, 1508–1521. Van Dingenen, R., Raes, F., Putaud, J.P., Baltensperger, U., Charron, A., Facchini, M.C., Decesari, S., Fuzzi, S., Gehrig, R., Hansson, H.C., Harrison, R.M., Huglin, C., Jones, A.M., Laj, P., Lorbeer, G., Maenhaut, W., Palmgren, F., Querol, X., Rodriguez, S., Schneider, J., Brink, H., Tunved, P., Torseth, K., Wehner, B., Weingartner, E., Wiedensohler, A., Wahlin, P., 2004. A European aerosol phenomenology—1: Physical characteristics of particulate matter at kerbside, urban, rural and background sites in Europe. Atmos. Environ. 38, 2561–2577. Vautard, R., Bessagnet, B., Chin, M., Menut, L., 2005. On the contribution of natural Aeolian sources to particulate matter concentrations in Europe: Testing hypotheses with a modelling approach. Atmos. Environ. 39(18), 3291–3303. Vestreng, V., 2003. Review and revision of emission data reported to CLRTAP, EMEP Status Report, July 2003.

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Chapter 5.7 Radiative effects of natural PMs on photochemical processes in the Mediterranean Region Marina Astitha, George Kallos, Petros Katsafados, Ioannis Pytharoulis and Nikos Mihalopoulos Abstract Particulate matter of anthropogenic and/or natural origin in the atmosphere is considered a parameter causing multiple effects on the environment of local and remote locations. The presence of absorbing and/or scattering aerosols in the lower troposphere can affect the atmospheric radiation through the modiﬁcation of both short- and long-wave components. On a shorter term, aerosols can cause the modiﬁcation of environmental conditions by inﬂuencing atmospheric temperature and dynamics. Atmospheric photochemistry is also affected by the presence of increased particle concentrations due to the alteration of UV and visible radiation ﬂuxes and therefore the modiﬁcation of the j-values (photolysis rates) of the photochemical reactions. Physiographic characteristics and climatic conditions in the Mediterranean Region are followed by excessive solar radiation leading to high photochemical activity in the Region. In addition long-range transport of ﬁne particles is very common in the area. In this work we focus on the distribution of ﬁne desert particles in the greater Mediterranean Region since Saharan dust is the desert responsible for many severe dust outbreaks that inﬂuence the Region. We examine the impact of increased desert dust concentration on the photochemistry of the region. For this purpose, advanced atmospheric and photochemical models are implemented with the aid of air pollutant measurements from stations in the region. The models used are the RAMS atmospheric model, the SKIRON/ Eta atmospheric modeling system with the implementation of dust module, and the CAMx photochemical model. Sensitivity tests performed with and without the inﬂuence of dust load on the photolysis rates showed some interesting results in the formation and production of gases like ozone, nitrogen oxides, and nitric acid, and aerosols

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like sulfates and nitrates. The preliminary results of these simulations will be thoroughly discussed in this presentation. 1. Introduction

Atmospheric particulate matter of anthropogenic and/or natural origin is considered a parameter causing multiple effects on the environment of local and remote locations. The presence of absorbing and/or scattering aerosols in the lower troposphere can affect the atmospheric radiation through the modiﬁcation of both short- and long-wave components. On a shorter term, aerosols can cause the modiﬁcation of environmental conditions by inﬂuencing atmospheric temperature and dynamics. Atmospheric photochemistry is also affected by the presence of increased particle concentrations due to the alteration of UV and visible radiation ﬂuxes and therefore the modiﬁcation of the j-values (photolysis rates) of the photochemical reactions. Physiographic characteristics and climatic conditions in the Mediterranean Region are followed by excessive solar radiation leading to high photochemical activity in the Region. In addition long-range transport of ﬁne particles is very common in the area (Luria et al., 1996; Kallos et al., 1999). The naturally produced particulate like desert dust contributes signiﬁcantly on air quality degradation, especially in Southern Europe and of course, North Africa. Several studies indicate that violations of air quality standards due to high PM concentrations in South European cities are associated to Saharan dust transport episodes for 30–70% of the cases, depending on the location (Rodriguez et al., 2001, Papadopoulos et al., 2003). This is considered an important issue in many European cities because it is difﬁcult to meet EU air quality standards not only for ozone but also for PM. Desert dust impacts on air quality are deﬁned in several ways, through the direct and indirect effects on radiation and through heterogeneous chemical processes that lead to the formation of new types of aerosols. In that sense, the effort to identify all the possible effects of natural particles in air quality and climate with modeling techniques is a very complicated and serious task considering that the implications on air quality, water budget and climate are not well known. The main focus of this work is on the distribution of ﬁne desert particles in the greater Mediterranean Region since Saharan dust is the desert responsible for many severe dust outbreaks that inﬂuence the area. We examine the impact of increased desert dust concentration on the photochemistry of the region. For this purpose, advanced atmospheric and photochemical models are implemented with the aid of air pollutant measurements from stations in the

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region. The models used are the RAMS atmospheric model, the SKIRON/Eta atmospheric modeling system with the implementation of dust module and the CAMx photochemical model. Sensitivity tests performed with and without the inﬂuence of dust load on the photolysis rates showed some interesting results in the formation and production of gases like ozone, nitrogen oxides and nitric acid and aerosols like sulfates. 2. Model description

A short description of the modeling systems used for performing simulations is provided below: The SKIRON/ETA is a modeling system developed at the University of Athens from the Atmospheric Modeling and Weather Forecasting Group (Kallos, 1997; Nickovic et al., 2001; Kallos et al., 2006). It has enhanced capabilities with a unique quality to simulate the dust cycle (uptake, transport, deposition). RAMS (Regional Atmospheric Modeling System) is considered as one of the most advanced atmospheric models. Detailed information about RAMS model can be found in Cotton et al. (2003) and references therein. The Comprehensive Air Quality Model with Extensions (CAMx) (Environ, 2003) is an Eulerian photochemical model that allows for integrated assessment of air-pollution over many scales ranging from urban to super-regional (http://www.camx.com). CAMx has also model structures for modeling aerosols, processes that are linked to the CB4 gas phase chemical mechanism, science modules for aqueous chemistry (RADM-AQ), inorganic aerosol thermodynamics/partitioning (ISORROPIA) and secondary organic aerosol formation/partitioning (SOAP). 3. Methodology

The method followed for the identiﬁcation of the feedback effects in species concentration due to excessive dust load is described herein. Saharan dust can inﬂuence physicochemical processes in various ways. One possible way is the shading effect that alters the radiation and causes modiﬁcations of photolysis rates. In that way, desert dust inﬂuences the production of species like ozone, nitrogen oxides and particulate sulfate. A more complicated impact of increased dust load in the atmosphere is through heterogeneous chemical reactions that occur on the surface of a wet dust particle. Such heterogeneous reactions lead to the formation of new types of aerosols, like a dust particle coated with sulfate.

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SKIRON/Eta modeling system is used to calculate the dust load for particle sizes with bins centered on 1.3 and 12.1 mm. Dust optical depth (DOD) at 340 nm is calculated at the preprocessing stage of CAMx air quality model by multiplying the dust load (g m2) with the extinction coefﬁcient (m2 g1) relevant to each particle size. By calculating the optical depth of desert dust instead of using aerosol optical depth values from bibliography, appears a certain feedback in the calculation of the photolysis rates of speciﬁc photochemical reactions that are shown below: NO2 ! NO þ Oð3PÞ CH2 O ! H þ HCO CH2 O ! H2 þ CO O3 ! O2 þ Oð1DÞ CH3 CHO ! CH3 þ HCO ISPD þ hn ! products

ð1Þ

ISPD ¼ Isoprene product (lumped methacrolein, methylvinyl ketone, etc.). The simulations were performed for a severe desert dust episode that occurred in eastern Mediterranean during April 17, 2005, as shown in Fig. 1 from NASA/GSFC satellite. Two sensitivity simulations were performed: one using the usual j-values, without any input from the desert dust load. The second simulation was performed with the altered j-values

Figure 1. Saharan dust episode for April 17, 2005. (a) Dust over Greece, picture taken from NASA/GSFC satellite (2005/107–04/17 at 11:40 UTC, http://www.gsfc.nasa.gov). (b) Total dust load as simulated from SKIRON/Eta dust modeling system.

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due to the modeled dust optical depth. The results from the simulations and the feedback in the photochemical species are discussed in the next sections of this paper.

4. Impacts of desert dust on ozone and particulate sulfate

Most of the Saharan dust events that transport signiﬁcant amounts of dust towards the Mediterranean Sea and Europe occur during the low index circulation period of the year (cold and transient seasons as is described in Rodriguez et al. (2001); Kallos et al. (2005, 2006). The synoptic conditions during April 17, 2005, are indicated in Fig. 2, showing the south-southwesterly winds from the Africa Region ending up in eastern Mediterranean. During that day, no signiﬁcant amount of rainfall or cloud cover occurred in the area so the effects of desert dust particles in the photolysis reactions are the main inﬂuence that dust had on the air quality of the area. The desert dust load for April 17, 2005, at 1200 UTC is evident in Fig. 3a, as well as the calculated DOD in Fig. 3b. The peak values for dust load and optical depth are in the area of the Libyan Sea, in the south of Crete Island. The dust plume is extended towards the Northeast, reaching the Black Sea. These plots are a result from SKIRON/Eta modeling system with the implementation of the dust module, as described in the previous section. Measurements in the Athens area from the Hellenic Ministry for the Environment, Physical Planning and Public

Figure 2. Synoptic conditions during the Saharan dust episode of April 17, 2005 as simulated from SKIRON/Eta modeling system. (a) 6h-accumulated precipitation and sea level pressure and (b) Wind ﬁeld (m s1) at 850 hPa.

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Figure 3. (a) Saharan dust load for April 17, 2005 at 1200 UTC. (b) Dust optical depth for April 17, 2005 at 1200 UTC.

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Figure 4. (a) Hourly ozone change (ppm) for April 17, 2005 at 1100 UTC. (b) Hourly percentage change in initial ozone concentration for April 17, 2005 at 1100 UTC. Simulations were performed with CAMx model.

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Figure 5. (a) Hourly ﬁne particulate sulfate change (mg m3) for April 17, 2005 at 1200 UTC. (b) Hourly percentage change in initial ﬁne particulate sulfate concentration for April 17, 2005 at 1200 UTC. Simulations were performed with CAMx model.

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Works, showed peak values for PM10 in the order of 1500–2000 mg m3, while in northern Crete Island PM10 measurements from the University of Crete showed values that reached 2500 mg m3 in that day. The sensitivity tests preformed with the CAMx model consisted of two types of simulations, as already discussed in Section 3 of this paper. In Figs. 4 and 5, hourly differences and hourly percentage differences for ozone and ﬁne particulate sulfate are presented. More speciﬁcally, in Fig. 4a the difference of the ozone concentration between the simulation with and without the altered j-values due to dust load is shown. The blue colors denote ozone reduction in the area. The maximum reduction in ozone concentration was approximately 2–3% of the initial concentration, a result that agrees with the work of Tang et al. (2004) during the ACE-ASIA experiment. Taken as example the Athens area, Fig. 6, shows the comparison between measured and modeled ozone concentration. It is quite evident that no signiﬁcant difference appeared between the dust and non-dust sensitivity test with the CAMx model and the comparison with the observations is considered as satisfactory. Desert dust radiative impacts were also evident in the ﬁne particulate sulfate concentration. An example is shown in Fig. 5 for the hourly concentration at 1200 UTC, during April 17, 2005. The maximum sulfate

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reduction reached 3% of the initial concentration. In both surface ozone and sulfate appeared an increase in the initial value in speciﬁc locations in the domain, instead of the expected decrease. This fact is attributed to species formed during the previous day and not destroyed due to the shading effect of excessive desert dust load on April 17, 2005. The heterogeneous chemical processes were not included in the present work, but they are of major importance for the identiﬁcation of the role that desert dust plays on the air quality degradation in the Mediterranean Region. This task is under work by our group (Atmospheric Modeling and Weather Forecasting Group), awaiting the forthcoming results. 5. Conclusions

At the framework of this work an effort was devoted to implement the effects of desert dust excessive load in the photochemical processes in the eastern Mediterranean Region. This was succeeded with the aid of advanced modeling systems like SKIRON/Eta, RAMS and CAMx. As it was found, desert dust particles alter the photolysis rates and resulted in a minor reduction of ozone and ﬁne particulate sulfate concentrations at the surface. The heterogeneous reactions are much more complicated and should be taken into account in order to have a more complete picture of the actual effects that dust has on species production/destruction. In synergy with air quality observations these state-of-the-art models can provide an integrated modeling approach for the investigation and/or prediction of air quality degradation episodes in key-sensitive areas like the Mediterranean Region and southern Europe. Discussion

J.W. Kaminski: M. Astitha:

S.T. Rao:

Do you consider enhancement of j-values above the dust layer? The code developed takes into consideration the dust radiative impacts in the entire vertical column up to the model top. Therefore, the j-values are modiﬁed above, below and inside the dust cloud. Given the high frequency of Saharan dust events impacting air quality over Athens, would your air quality management agency give a variance to the number of exceedances of the PM standards if it can be shown that this is a natural contribution and that

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exceedance is not triggered by anthropogenic emissions in Athens? The Greek Ministry of the Environment, City Planning and Public Works is aware of this impact and granted our group the project to analyze the dust impacts on daily base. According to our analysis in almost 2/3 of the exceedances, the Saharan dust transport is partially responsible. The same is true for the Iberian Peninsula and in general for South European countries. As it was stated in our presentation, the Saharan dust transport is a contributing factor not taken into account in the imposed EU limits. Nevertheless, the problem is well posed from our analysis and EU started to take it into consideration during the last years. There is a clear impact of Saharan dust on air quality in Athens. On average there are about 150 days a year where the PM 50 mg m3 level is exceeded. How much would this number of days drop if there wasn’t Saharan dust? This question requires a quantitative analysis of our results such as to subtract the predicted dust amounts from the measurements at monitoring points. Unfortunately, such analysis cannot be done due to the fact that the monitoring network provides measurements of PM10 where the larger particles are ﬁltered out and because chemical composition analysis is very rare. In general, a single removal of modeled dust concentrations from monitored PM10 is not correct. It is worth mentioning that the severity of dust outbreaks varies signiﬁcantly. In serious dust episodes the monitored and/or modeled concentrations are highly above the limits of 50 mg m3. In minor episodes, dust particles are of ﬁne mode (mainly PM2.5) and in these cases the modeled quantities should be subtracted from the monitored ones. Such detailed analysis has not been performed yet, due to the reasons mentioned, but we plan to do so as long as we have the necessary monitoring datasets.

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ACKNOWLEDGMENTS

This work is funded by the Greek General Secretariat for Research and Technology (GSRT) under the project PENED 2003. Measurements of air pollutants from several stations in Athens area were provided by the Hellenic Ministry for the Environment, Physical Planning and Public Works. The authors would also like to thank the Athens International Airport, the Environmental Department for providing air quality measurements and especially Michael O’Connor for his valuable help. REFERENCES Cotton, W.R., Pielke, R.A. Sr., Walko, R.L., Liston, G.E., Tremback, C.J., Jiang, H., McAnelly, R.L., Harrington, J.Y., Nicholls, M.E., Carrio, G.G., McFadden, J.P., 2003. RAMS 2001: Current status and future directions. Meteorol. Atmos. Phys. 82(1–4). Environ., 2003. User’s Guide to the Comprehensive Air Quality Model with Extensions (CAMx). Version 4.00. Prepared by ENVIRON. Intern. Corporation, Novato, CA. Kallos, G., 1997. The regional weather forecasting system SKIRON: An overview. Proceedings of the symposium on regional weather prediction on parallel computer environments, University of Athens, Greece, pp. 109–122. Kallos, G., Katsafados, P., Spyrou, C., Papadopoulos, A., 2005. Desert dust deposition over the Mediterranean Sea estimated with the SKIRON/Eta. 4th EuroGOOS Conference, 6–9 June 2005, Brest, France. Kallos, G., Kotroni, V., Lagouvardos, K., Papadopoulos, A., 1999. On the transport of air pollutants from Europe to North Africa. Geophys. Res. Lett. 25(5), 619–622. Kallos, G., Papadopoulos, A., Katsafados, P., Nickovic, S., 2006. Trans-Atlantic Saharan dust transport: Model simulation and results. J. Geophys. Res.-Atmos. 111, doi:10.1029/2005JD006207. Luria, M., Peleg, M., Sharf, G., Siman Tov-Alper, D., Schpitz, N., Ben Ami, Y., Gawi, Z., Lifschitz, B., Yitzchaki, A., Seter, I., 1996. Atmospheric sulphur over the East Mediterranean region. J. Geophys. Res. 101(25917). Nickovic, S., Kallos, G., Papadopoulos, A., Kakaliagou, O., 2001. A model for prediction of desert dust cycle in the atmosphere. J. Geophys. Res. 106(16), 18113–18129. Papadopoulos, A., Katsafados, P., Kallos, G., Nickovic, S., Rodriguez, S., Querol, X., 2003. Contribution of desert dust transport to air quality degradation of urban environments. Recent Model Developments. 26th NATO/CCMS ITM Proceedings on Air Pollution Modeling and its Application, Istanbul, Turkey. Rodriguez, S., Querol, X., Alastuey, A., Kallos, G., Kakaliagou, O., 2001. Saharan dust inputs to suspended particles time series (PM10 and TSP) in Southern and Eastern Spain. Atmos. Environ. 35(14), 2433–2447. Tang, Y., Carmichael, G.R., Kurata, G., Uno, I., Weber, R.J., Song, C., Guttikunda, S.K., Woo, J., Streets, D.G., Wei, C., Clarke, A.D., Huebert, B., Anderson, T.L., 2004. Impacts of dust on regional tropospheric chemistry during the ACE-Asia experiment: A model study with observations. J. Geophys. Res. 109(D19), doi:10.1029/ 2003JD003806.

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Chapter 5.8 Modelling of mineral dust emissions and transport with the CHIMERE-DUST model: Preliminary analysis of dust events for the AMMA ﬁeld campaign L. Menut, C. Schmechtig, R. Vautard and B. Marticorena Abstract The atmospheric transport of mineral dust is difﬁcult to reproduce using global circulation model because their coarse spatial and temporal resolutions limit their ability to represent the small-scale processes that control the generation and the deposition of such particles. The mineral dust transport model CHIMERE-DUST enables to generate concentration ﬁelds of mineral dust at high temporal (1h) and spatial (a few kilometres) resolutions for long time periods (several years). The modelled areas include the dust sources of western Africa and the zone of long-range dust transport in the North Atlantic. We present an estimation of the mineral dust concentrations uncertainties as estimated by various model conﬁgurations. For selected dust storms that occured over western Africa, the accuracy of the results is discussed in term of sensitivity to the surface emission ﬂuxes (depending on the parameterization employed, the sandblasting process used), to the number and values of dust bins used to account for the particle size distribution and to the dry and wet deposition processes. The modelled results are ﬁnally compared to SeaWiFS images and AERONET data. With CHIMERE-DUST used in forecast conﬁguration, some preliminary results will be presented in the framework of the international African Monsoon campaign AMMA.

1. Introduction

Dust emitted from arid and semiarid areas of the Earth strongly affect the biogeochemical cycles and the Earth radiative budget. The evaluation of

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these dust impacts requires to use three-dimensional models in order to obtain regional or global assessments. Many processes to account for in these models are size dependent and thus most of the transport models use a particle size-resolved bin scheme. In addition, the main source of these dust, their emissions, are very difﬁcult to estimate due to their physical principle based on threshold phenomena. For all these reasons, sensitivity tests have to be performed if we want to estimate more precisely their concentrations. In this context, the CHIMEREDUST model was developed step by step, testing every process to account for such as emissions, deposition, etc. (Fig. 1). The transport model CHIMEREDUST was developed on the basis of the chemistry-transport model CHIMERE (Vautard et al., 2001; Bessagnet et al., 2004). The meteorological ﬁelds are those of the MM5 model forced by the NCEP analysis ﬁelds with a horizontal resolution of 11 for the Atlantic domain. Vertically, 15 levels are deﬁned from the surface to 200 hPa. Turbulent parameters as u*, the friction velocity and h, the boundary layer depth are estimated from the mean meteorological parameters. The horizontal transport is performed using the VanLeer scheme (VanLeer, 1979) and the vertical mixing is estimated from the calculation of the bulk Richardson number as extensively described in Menut (2003) for this model. The simulations are performed with an hourly timestep. The dust emissions scheme employed in the model is the Marticorena and Bergametti’s (1995) scheme. It computes horizontal ﬂuxes from wind velocities and surface features for the emissions area. The vertical ﬂuxes are computed by using the Alfaro and Gomes (2001) parameterization, numerically optimized following Menut et al. (2005) (see next sections). The vertical ﬂuxes are calculated corresponding to three dust size modes, then redistributed into the model size distribution using the following mass partition scheme (Fig. 2).

2. Sensitivity of dust concentrations to the size distribution scheme

Another very sensitive point for the dust modelling is the selection of the size distributions bins. This corresponds to the discretization of the particle size distribution into a ﬁnite number of classes. This discretization is conserved all along a simulation from the dust emission (the source) to the terminal deposition (the sink). The computing cost is a key parameter in climate modelling. Since each size bin has to be transported independently, the simulation time is directly dependent of the number of size bins used in the model. Thus, most of the models try to limit the number of transported particle size bins. In the same manner, a lot of parameterizations try to

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limit the computational cost linked to the emissions ﬂuxes. These two points are crucial to limit the uncertainty in modelled dust concentrations. By contrast, greater the number of bins is, less the resulting concentrations will be affected by numerical inaccuracies. Thus, compromises have to be made and the selected particle size bin scheme mainly depends on the capacities of the model and on the objectives of the simulation. In the literature, the number of size bins used in dust transport models ranges from 1 to 12, roughly ranging from 0.1 to 10 um. Within this interval, the number of four size bins seems to be the most frequently used. In all cases, models always use isolog bins to represent the dust size distribution. In a recent paper, Foreˆt et al. (2006) showed that it is possible to optimize the selection of these size bins in order to drastically reduce the modelled concentrations error. The main idea is to select the bins following the gradient of the dry deposition velocity (as a function of the friction velocity) instead of a classical (but not physical) isolog distribution. This new scheme was successfully applied in the framework of threedimensional modelling. In order to quantify the beneﬁt of this approach, a comparison was achieved using a reference case obtained with a distribution of 40 bins and four others conﬁgurations with 6 and 12 bins in isolog and 6 and 12 following algorithm of Foreˆt et al. (2006). We ﬁrst conclude that more the bins number is large more accurate are the concentrations ﬁelds. In addition, and for all studied sites, the adaptative approach of Foreˆt et al. (2006) always gives better results than the isolog distribution.

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This improvement is not negligible since the concentration errors are reduced by a factor of 2 with the new approach. Second, we conclude that a minimum of bins number is required, even if we use an optimized size distribution. We compared the concentrations estimated with 12 bins in isolog (log12) and only 6 with the new scheme (Grad6). The results are not constant in space and time: near the sources and within the boundary layer, the results may be better with the Grad6 conﬁgurations. But, more generally, the results are better with size distributions using 12 bins (isolog or optimized). Finally, and knowing that mineral dust size distributions evolve a lot from the source to a few thousand of kilometres, we have to recommend to use at least 12 bins to avoid a too large underestimation of the simulated mass. Third, we have to notice that the new gradient method, even if this leads to better results, is limited by an important hypothesis. Since it is deﬁned using a ﬁxed dry deposition velocity function, it is required to ﬁx a mean friction velocity. This assumption leads to errors if the chosen friction velocity is not representative of the studied environment. Thus, we recommend to select the friction velocity the most often diagnosed near the sources of the studied site. More the errors are important near the source and more they will be transported all along the simulation over others sites. In conclusion, these results also show that a numerical choice may be at the origin of a non-negligible part of the uncertainty linked to the mineral dust concentrations transported over long distances. This approach is easy to implement and reduces by a factor of 2 the model error due to the dry deposition process (Fig. 3). Capo Verde 1.10 Ratio of dust conc.

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Figure 3. Modelled ratios of concentrations between the reference case (40 bins) and the size distribution with 12 bins in isolog or isogradient distributions.

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3. The AMMA experiment and the CHIMEREDUST forecast during the winter 2005–2006

The AMMA (African Monsoon Multidisciplinary Analysis) experiment is an international project to improve our knowledge and understanding of the West African Monsoon (WAM) and its variability. AMMA is motivated by an interest in fundamental scientiﬁc issues and by the societal need for improved prediction of the WAM and its impacts on West African nations. Recognizing the societal need to develop strategies that reduce the socioeconomic impacts of the variability of the WAM, AMMA will facilitate the multidisciplinary research required to provide improved predictions of the WAM and its impacts. AMMA will promote a multiyear campaign over West Africa and the tropical Atlantic and will develop close partnerships between those involved in basic research of the WAM, operational forecasting and decision making. Further informations may be found at http://amma.mediasfrance.org/. During the winter 2005–2006, a experimental forecast model platform was designed in order to estimate dust concentrations over the whole northern Atlantic and for the next three days. Every morning, numerous maps, issued of the previous night runs, were available on a dedicated web site at the Laboratoire de Meteorologie Dynamique. The web site presented all informations necessary to estimate the potential generation of a dust event or not. Numerous ﬁgures of maps and vertical slices were presented, including meteorological parameters and dust concentrations parameters. In addition, an historic of the estimated aerosol optical thickness was added to be directly compared with the AERONET measurements for several sites; sites of importance for the AMMA experimentalists: Niamey, CapoVerde, Dakar, Banizoumbou, etc. The day-to-day comparisons showed a systematic overestimation of the AOT modelled compared with the AERONET stations. During the SOP0 period, one moderate dust event was observed over western Africa. The model forecasted two dust events, including the one observed. But, a false alert was thus diagnosed. These results were mainly used under the form of aerosol optical thickness as presented in Fig. 4 (a comparison between the model forecasts and the AERONET measurements in Ilorin during the whole month of February 2006). The main problem is that direct surface concentration measurements need some work to be available and no comparisons are possible at this time. Some other comparisons were done with the help of the DREAM model. The main patterns and concentration intensities were found to be in good accordance between the two models (of course, not a proof of validity, but a ﬁrst improvement of model

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566 Ilorin , N 08 19'12", E 04 20'24" , Alt 350 m, PI : Rachel_T._Printer , [email protected]. edu Level 1.5 AOT ; Data from FEB 2006

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stability in forecast mode). Further comparisons will be done with the future AMMA SOP0 database. 4. Conclusion and perspectives

In this paper, the development of the CHIMEREDUST model was described and some sensitivity experiments, done to evaluate the

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state-of-the-art parameterizations, were presented and discussed. We showed (not presented here) that an optimized approach enables to reduce by a factor of 10 (at the maximum, not constant) the errors that can be made on dust emission ﬂuxes calculations. In addition, we showed that the selection of the dust size distribution bins according to the gradient of the dry deposition velocity may reduce by a factor of 2 the modelled concentrations (compared with a reference case) all along the transport. These previous points, as well as systematic comparisons with AERONET data, conﬁrmed the validation of CHIMEREDUST. During the winter 2005–2006, the model was used for an experimental forecast experience. Every day, the model delivered numerous maps and slices over Africa in order to estimate dust events for the next three days. After an analysis of the differences between the model and the measurements, the experiment will be reconducted during the next winter 2006–2007 for additional intensive observation periods over the western Africa. REFERENCES Alfaro, S.C., Gomes, L., 2001. Modeling mineral aerosol production by wind erosion: Emission intensities and aerosol size distribution in source areas. J. Geophys. Res. 106, 18075–18084. Bessagnet, B., Hodzic, A., Vautard, R., Beekmann, M., Cheinet, S., Honore, C., Liousse, C., Rouil, L., 2004. Aerosol modeling with chimere: Preliminary evaluation at the continental scale. Atmos. Environ. 38, 2803–2817. Foreˆt, G., Bergametti, G., Dulac, F., Menut, L., 2006. An optimized particle size bin scheme for modeling mineral dust aerosols. J. Geophys. Res. 111, D17310, doi: 10.1029/ 2005JD006797. Marticorena, B., Bergametti, G., 1995. Modeling the atmospheric dust cycle: 1. Design of a soil derived dust production scheme. J. Geophys. Res. 100, 16415–16430. Menut, L. (2003), Adjoint modelling for atmospheric pollution processes sensitivity at regional scale during the ESQUIF IOP2, J. Geophys. Res. 108(D17), 8562, doi:10.1029/ 2002JD002549. Menut, L., Schmechtig, C., Marticorena, B., 2005. Sensitivity of the sandblasting ﬂuxes calculations to the soil size distribution accuracy. J. Atmos. Ocean. Technol. 22(12), 1875–1884. VanLeer, B., 1979. Towards the ultimate conservative difference scheme. V. A second order sequel to Godunov’s method. J. Computat. Phys. 32, 101–136. Vautard, R., Beekmann, M., Roux, J., Gombert, D., 2001. Validation of a hybrid forecasting system for the ozone concentrations over the Paris area. Atmos. Environ. 35, 2449–2461.

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Chapter 5.9 Comparison of modelled and measured aerosol optical depth over southwestern Germany R. Rinke, D. Ba¨umer, B. Vogel, St. Versick and Ch. Kottmeier Abstract The optical properties of aerosols are subject of many experimental and theoretical studies. One of these properties is the aerosol optical depth (AOD), a vertical integrated value for the aerosol content in the atmosphere. In cooperation with the Remote Sensing Group at the Institute of Geography of the University of Berne, AOD data derived from TERRA/MODIS measurements were compared with numerical simulations for selected weather situations. Additionally, we use our AERONET sunphotometer station data for comparison the simulations. The model system KAMM/DRAIS/MADEsoot (Adrian and Fiedler, 1991; Vogel et al., 1995; Riemer et al., 2003) is used to simulate the three-dimensional and time-dependent distributions of physical and chemical variables. The non-hydrostatic model KAMM gives the meteorological variables in the model domain which is situated in South-Western Germany. The module DRAIS simulates the transport and diffusion of reactive chemical species. The gas-phase chemistry is computed with a modiﬁed RADM2 mechanism (Stockwell et al., 1990). The aerosol model MADEsoot (Riemer et al., 2003) which is based on the aerosol model MADE (Ackermann et al., 1998; Schell et al., 2001), is used to calculate the spatial and temporal distribution of aerosol particles. The size distributions and the chemical composition of the particles are taken into account. The aerosol particles are described by ﬁve individual modes. The optical properties as the extinction coefﬁcient, the single scattering albedo, and the phase function in terms of Legendre polynomials are calculated for each grid box applying Mie theory using a code according to Bohren and Huffman (1983). The results will be presented and especially the differences between the satellite, AERONET data and the numerical simulations will be shown.

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1. Introduction

The optical properties of aerosols are the subject of many experimental and theoretical studies. Our interest is focused on the validation of numerically simulated optical properties using experimental data. One of these properties is the aerosol optical depth (AOD), a vertically integrated measure for the aerosol content in the atmosphere and a good measure to quantify the atmospheric turbidity caused by aerosols. An efﬁcient way to determine aerosol optical properties on the temporal and spatial scales is the use of satellite observations. An interesting property of satellite measurements to validate numerical simulations is the data distribution over a great area. In cooperation with the Remote Sensing Group at the Institute of Geography of the University of Berne, AOD data derived from AVHRR satellite data were compared with numerical simulations for selected weather situations. Because of the problems of satellite measurements as an uncertain earth surface albedo, we used additionally AOD derived from our AERONET sunphotometer station (81250 4000 E and 49150 3400 N) for comparisons with the simulations. Also, we determined the particle size distribution by in situ measurements at Forschungszentrum Karlsruhe using scattering mobility particle sizer (SMPS) and condensation particle counter (CPC). These data are also should be used to validate the numerical simulations. The model system KAMM/DRAIS/MADEsoot (Adrian and Fiedler, 1991; Vogel et al., 1995; Riemer et al., 2003) is used to simulate the threedimensional and time-dependent distributions of physical and chemical variables. The non-hydrostatic model KAMM gives the meteorological variables in the model domain which is situated in southwestern Germany. The module DRAIS simulates the transport and diffusion of reactive chemical species. The gas-phase chemistry is computed with a modiﬁed RADM2 mechanism (Stockwell et al., 1990). The aerosol model MADEsoot (Riemer et al., 2003), which is based on the aerosol model MADE (Ackermann et al., 1998), is used to calculate the spatial and temporal distribution of aerosol particles. The size distributions and the chemical composition of the particles are taken into account. The optical properties such as the extinction coefﬁcient, the single scattering albedo and the phase function in terms of Legendre polynomials are calculated for each grid box applying Mie theory using a code according to Bohren and Huffman (1983). The simulations are situated in a speciﬁc period in summer (August). The results will be presented and especially the differences between the satellite data, the AERONET data, the in situ measurements and the numerical simulations will be shown.

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2. Model description

Since the model system KAMM/DRAIS/MADEsoot has been described in detail in previous papers (Vogel et al., 1995; Riemer et al., 2003), we only give a short summary with the focus on the aerosol model MADEsoot. 2.1. KAMM/DRAIS

The comprehensive KAMM/DRAIS model system uses the non-hydrostatic mesoscale model KAMM (Adrian and Fiedler, 1991) as the meteorological driver. It is coupled with a surface vegetation model developed by Scha¨dler (1989). This model part gives the lower boundary conditions for temperature and humidity. The submodule DRAIS calculates the transport and diffusion of the reactive trace gases and the aerosol particles. For the treatment of the chemical reactions, the RADM2 gas-phase chemistry mechanism (Stockwell et al., 1990) is incorporated. The photolysis rate coefﬁcients are determined with the radiation scheme of Ruggaber et al. (1994). The anthropogenic emissions are precalculated, and the biogenic VOC (volatile organic compounds) emissions are calculated depending on the land use, the model temperatures, and modelled radiative ﬂuxes (Vogel et al., 1995). For the parameterization of the NO emissions from the soil a modiﬁed scheme of is employed (Ludwig et al., 2001). Dry deposition is parameterized by means of a bigleaf multiple resistance model. In the past, KAMM/DRAIS has been extensively validated against observations. 2.2. MADEsoot

The aerosol model MADEsoot (Riemer et al., 2003) is used to calculate the size distribution and the chemical composition of the atmospheric particles. It is based on the Modal Aerosol Dynamics Model for Europe (MADE; Ackermann et al., 1998). In MADEsoot ﬁve several overlapping modes represent the aerosol population, which are approximated by lognormal functions. Two modes represent secondary inorganic particles consisting of sulphate, ammonium, nitrate, secondary organic compounds and water, one mode represents pure soot and two more modes represent particles consisting of sulphate, ammonium, nitrate, organic compounds, water and soot. The modes if, jf, ic and jc are assumed to be internally mixed. Thus, the modes if and jf represent soot-free particles, whereas the modes ic and jc represent soot-containing particles, or, in other words, the aged soot particles. All modes are subject to condensation and coagulation. The growth rate of the particles due to condensation is calculated following Binkowski and Shankar (1995) depending

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on the available mass of the condensable species and the size distribution of particles. With coagulation, the assignment to the individual modes follows two different ways described by Whitby et al. (1991). Those particles formed by intramodal coagulation stay in their original modes and particles formed by intermodal coagulation are assigned to the mode with the larger median diameter. Furthermore, a thermodynamic equilibrium of gas phase and aerosol phase is applied to calculate the concentrations of sulphate, nitrate, ammonium and water. The calculation of the thermodynamic equilibrium follows a bulk approach. This means that the aerosol concentrations summed over all modes enter the calculation. After the equilibrium concentrations are obtained, the concentrations of ammonium, nitrate and water are distributed over the modes depending on the mass fraction of sulphate. The source of the secondary inorganic particles in modes if and jf is the binary nucleation of sulphuric acid and water. The secondary organic compounds are treated according to Schell et al. (2001). The soot particles in mode js are directly emitted into the atmosphere. The source of the particles in modes ic and jc is due to the ageing process described below. Additionally, sedimentation, advection and turbulent diffusion can modify the aerosol distributions. 2.3. Calculation of the aerosol optical depth

The aerosol optical depth tl is a measure for the atmospheric turbidity caused by aerosol particles. It is determined by the extinction coefﬁcient bext,l and is given by the vertical integration of the extinction coefﬁcient Z topat Z S bext;l dz ¼ bext;l mS dS b (1) tl ¼ 0

0

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L X

bext;l ð~ x; ml ; lÞ

(2)

p 2 x; ml ; d p ; lÞnl ð~ x; d p Þdd p d p Qe ð~ 1 4

(3)

l¼1

Z x; ml ; lÞ ¼ bext;l ð~

1

where ml is the refraction index of the particles in mode l, dp the diameter of the particles, l the wavelength of the incident radiation and Qe the extinction efﬁciency. The extinction efﬁciency Qe is the sum of the

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scattering efﬁciency Qs and the absorption efﬁciency Qa: Qe ¼ Qs þ Qa

(4)

Correspondingly, the volume extinction coefﬁcient bext is composed of the volume scattering coefﬁcient bs and of the volume absorption coefﬁcient ba: bext ¼ bs þ ba

(5)

Since we assume spherical particles, the extinction efﬁciency can be calculated using Mie theory. This method provides the partitioning of the extinction coefﬁcient into the absorption and the scattering coefﬁcient. For our study, we use a Mie code according to Bohren and Huffman (1983) for homogeneous and coated spheres. Since we treat internally mixed particles, a mixing rule is needed to calculate the composite refractive index as it is described, for instance, by Jacobsen (1997). In the case of solutions as it applies to the particles in the soot-free modes if and jf and shell of the soot-containing modes ic and jc, the composite refractive index m ¯ can be computed as a volume-weighted average of the nspec individual refractive indices mn: m ¯ ¼

nspec X

f n mn

(6)

n¼1

where fn is the volume fraction of the nth component. For dry ammonium sulphate and ammonium nitrate, the value mdry ¼ 1.53 is used as refraction index, and for water the value is mH2 O ¼ 1:33: For soot particles, recent wavelength-dependent measurements of the refraction index of diesel soot made in the AIDA chamber are used (Kamm et al., 1999; Schnaiter et al., 2003). The extinction coefﬁcient for the whole aerosol population is given by the sum of the extinction coefﬁcients of the individual modes. Moreover, the soot-containing particles are assumed to consist of an insoluble core and a soluble shell where the core is centred. The integral in Eq. (3) is solved using a Gauss–Hermite method. With the procedure described in this section, a three-dimensional distribution of extinction coefﬁcients can be calculated for every point in time. We derived the aerosol optical depth from the vertical integration of the extinction coefﬁcient at the wavelength from 550 nm for each point. 3. Results

In this chapter, we present selected results of the simulations. A typical summer situation is simulated starting on the 17th of August with a time

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period of 72 h. The shown results are from the 18th of August at 1200 CEST. The model is initialized with a geostrophic wind of 3 m s1 blowing from the northeast (401). The horizontal grid size is 4 4 km2. In the vertical direction 25 layers are used. The vertical grid size varies from 20 m close to the surface up to 400 m at the top of the model domain at 8 km above sea level. The time steps are on the order of seconds, and the whole model system runs in a coupled mode. The model is applied to an area in southwestern Germany. It covers main parts of Baden–Wu¨rttemberg and the adjacent regions (248 248 km2). In Fig. 1, the topography of the model domain is displayed. 3.1. Meteorology

Figure 1 shows the wind ﬁeld as it is predicted at 20 m above the surface at 1200 CEST. The characteristics of the wind ﬁeld are the channelling of the airﬂow in the Rhine Valley and the thermal secondary ﬂow systems on the slopes of the mountains. Figure 2 shows the temperature ﬁeld (left) and the relative humidity (right). The maximum temperature near Karlsruhe reaches about 251C. The relative humidity in the lowest layer varies between 42% and 55%.

Figure 1. Topography of the model domain and wind ﬁeld at 20 m above the surface.

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Figure 2. Horizontal distribution of the simulated temperature (left) and the simulated relative humidity (right) 20 m above the surface at 1200 CEST.

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Mannheim

* Saarbrücken Saarbrücken

km -1

*

200

Karlsruhe Karl

0.08 0.08 0.075 0.075

* Stuttgart

150

*

y in km

Strasbourg Strasb our g

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0.07 0.07 0.065 0.065 0.06 0.06 0.055 0.055

100

0.05 0.05 0.045 0.045 0.04 0.04 0.035 0.035

50 Basel

0.03 0.03

* 0 0

50

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2.5 km -1 2.0

0.1 0.1

z in km

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1.5

0.07 0.07 0.06 0.06 0.05 0.05

1.0

0.04 0.04 0.03 0.03

0.5

0.02 0.02 0.01 0.01 00

0.0 0

50

100

150 x in km

200

Figure 3. Horizontal distribution of the extinction coefﬁcient at 20 m above the surface (left) and vertical proﬁle of the extinction coefﬁcient y ¼ 80 km (right) at 1200 CEST.

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µg m-3

Mannheim

*

12

Saarbrücken

11.5

*

200

11

Karlsruhe

*

10.5 Stuttgart

150

*

y in km

Strasbourg

10 9.5

*

9

100

8.5 8 7.5

50

7 6.5

Basel

*

0 0

50

6 100 150 x in km

200

Figure 4. Horizontal distribution of the simulated dry aerosol mass on the 18th of August at 1200 CEST.

Figure 5. Horizontal distribution of the aerosol optical depth simulated on the 18th of August at 1200 CEST.

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Figure 6. AOD derived from AVHRR on 08/18/2005 at 1200 CEST. Table 1. Comparison of simulated and measured AOD for the 18th of August 2005 Daytime 6:00 12:00 18:00

Sunphotometer

KAMM

0.28 0.18 0.23

0.16 0.13 0.16

3.2. Extinction coefficient

Figure 3 (left) shows the horizontal distribution of the extinction coefﬁcient for the wavelength of 550 nm 20 m above the ground. The highest value in this layer occurs in the rural area of Lake Constance where ammonium nitrate aerosol is present and also the water content of the aerosol is high. Hence the particles grow to a size where they effectively scatter light. Figure 3 (right) presents a vertical section of the extinction coefﬁcients for y ¼ 80 km. The extinction coefﬁcient increases with height because of the increasing water content in the particles. This in turn is due to increasing relative humidity with height. 3.3. Aerosol properties

Figure 4 shows the horizontal distribution of the dry aerosol mass over all modes. The values vary between 6 mg m3 over the Vosges mountains and 12 mg m3 in the Rhine Valley.

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Comparision AOD Photometer/Kamm 18.08.05 0.35 0.3

Photometer Kamm

0.25

AOD

0.2 0.15 0.1 0.05 0 0

6

12

18

24

t in h Figure 7. Comparison of AERONET sunphotometer and simulated AOD on 08/18/2005.

In Fig. 5, the horizontal distribution of the aerosol optical depth is displayed. The aerosol optical depth varies between 0.09 and 0.32. The values are decreased in the mountains of the Black Forest and the Vosges mountains and they are increased in the Rhine Valley and over the Lake Constance with a maximum in the Rhine Valley near Freiburg. Figure 6 shows the aerosol optical depth derived from AVHRR satellite data; on 08/18/2005 at 1200 CEST, it shows similar structures as the simulation in Fig. 5. The values are increased in the Rhine Valley with a maximum of 0.45. A detailed comparison of the AOD values can be found in Table 1. Figure 7 displays a comparison of the simulated aerosol optical depth at the gridpoint x ¼ 34 (8.351E) and y ¼ 45 (49.171N) and the derived one from our AERONET sunphotometer on the 18th of August. The simulated values are lower than the measured values, which is partially caused by the limited emitted species in the model. Figure 8 shows the particle size distribution (left) and the volume distribution (right) as a comparison of the simulated particles (dashed line) at the gridpoint x ¼ 34 and y ¼ 45, and the SMPS data (solid line).

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Anzahlgrößenverteilung 18.08.05 12:00 CEST 6,00E+09 5,00E+09

dN/dln(dp)

4,00E+09

3,00E+09

2,00E+09

1,00E+09

0,00E+00 1,00E-02

1,00E-01

1,00E+00

dln(dp) in µm Volume size distribution 18.08.05 12:00 CEST 4,00E-11 3,50E-11

dV/dln(dp) in m³/m³

3,00E-11 2,50E-11 2,00E-11 1,50E-11 1,00E-11 5,00E-12 0,00E+00 1,00E-02

1,00E-01

1,00E+00

1,00E+01

dln(dp) in µm Figure 8. Particle size distribution (left) and volume size distribution (right) as a comparison of the simulation at the gridpoint x ¼ 34 and y ¼ 45 (dashed line) and SMPS measurements (solid line) at 1200 CEST for the 18th of August 2005.

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The simulated particle size distribution and the volume distribution are lower than the measured ones, also partially caused by the limited emitted species in the model. The maximum (median particle diameter) of the simulated particle size distribution is at 0.1 mm with a particle number of 2.62 109 and for the SMPS data about 0.09 mm with a particle number of 5.1 109. The maximum for the simulated volume distribution is situated at a diameter of 0.4 mm and for the SMPS data at 0.46 mm.

4. Summary

The aerosol extinction coefﬁcient and the aerosol optical depth for a special summer period (17th–19th of August) were investigated using the comprehensive mesoscale model system KAMM/DRAIS coupled with MADEsoot. With this model system, both meteorology and threedimensional distribution of gas-phase and particle-phase species can be simulated. The aerosol model includes the species ammonium, sulphate, nitrate, soot and water. On the basis of these simulated distributions, the aerosol optical properties were derived using Mie calculations. The extinction coefﬁcients show a strong spatial variation, both horizontally and vertically. The characteristic distribution of the extinction coefﬁcients has important implications for the diurnal development of the aerosol optical depth. We compared the simulated aerosol optical depth with derived values from AERONET sunphotometer and TERRA/MODIS satellite. Also, the simulated particle size distributions were validated with SMPS measurements to get a qualitative agreement.

REFERENCES Ackermann, I., Has, H., Memmesheimer, M., Ebel, A., Binkowski, F., Shankar, U., 1998. Modal aerosol dynamics model for Europe: Development and ﬁrst applications. Atmos. Environ. 32, 2981–2999. Adrian, G., Fiedler, F., 1991. Simulation of unstationary wind and temperature ﬁelds over complex terrain and comparison with observations. Beitr. Phys. Atmos. 64, 27–48. Binkowski, F., Shankar, U., 1995. The regional particulate matter model: 1.Model description and preliminary results. J. Geophys. Res. 100, 26191–26209. Bohren, C., Huffman, D., 1983. Absorption and Scattering of Light by Small Particles. Wiley, New York. Jacobsen, M., 1997. Development and application of a new air pollution modeling system: II. Aerosol module structure and design. Atmos. Environ. 31, 131–144. Kamm, S., Mo¨hler, O., Naumann, K.-H., Saathoff, H., Schurath, U., 1999. Heterogenous interaction of ozone, NO2 and N2O5 with soot aerosol. Atmos. Environ. 33, 4651–4661.

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Ludwig, J., Meixner, F.X., Vogel, B., Fo¨rstner, J., 2001. Soil–air exchange of nitric oxide: An overview of processes, environmental factors, and modeling studies. Biogeochemistry 52, 225–258. Riemer, N., Vogel, H., Vogel, B., Fiedler, F., 2003. Modeling aerosols on the mesoscale g: Treatment of soot aerosol and its radiative effects. J. Geophys. Res. 109, 4601, doi:10.1029/2003JD003448. Ruggaber, A., Dlugi, R., Nakajiima, T., 1994. Modeling radiation quantities and photolysis frequencies in the troposphere. J. Atmos. Chem. 18, 171–210. Scha¨dler, G., 1989. Triggering of atmospheric circulations by moisture inhomogeneities of the Earth’s surface. Bound.-Layer Meteorol. 51, 1–29. Schnaiter, M., Horvath, H., Mohler, O., Naumann, K.-H., Saathoff, H., Schock, O.W., 2003. UV-VIS-NIR spectral optical properties of soot and soot-containing aerosols. J. Aerosol Sci. 34(10), 1421–1444. Schell, B., Ackermann, I., Binkowski, F., Ebel, A., 2001. Modeling the formation of secondary organic aerosol within a comprehensive air quality model system. J. Geophys. Res. 106, 28275–28293. Stockwell, W., Middleton, P., Chang, J.S., 1990. The second generation regional acid deposition model chemical mechanism for regional air quality modelling. J. Geophys. Res. 95, 16343–16367. Vogel, B., Fiedler, F., Vogel, H., 1995. Inﬂuence of topography and biogenic volatile organic compounds emissions in the state of Baden-Wu¨rttemberg on ozone concentrations during episodes of high air temperatures. J. Geophys. Res. 100, 22907–22928. Whitby, E., McMurray, P., Shankar, U., Binkowski, F., 1991. Modal aerosol dynamics modeling, Technical Report 600/3-91/020, Atmospheric Research and Exposure Assessment Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06510-2

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Chapter 5.10 On the direct aerosol forcing of nitrate over Europe: Simulations with the new LOTOS-EUROS model Martijn Schaap, Ferd Sauter and Peter Builtjes Abstract Nitrate is an important component of ﬁne aerosols in Europe. We present model simulations for the year 1995 accounting for the formation of the ammonium nitrate, a semi-volatile component. For this purpose we have used the new LOTOS-EUROS model. During winter, fall and especially spring, high nitrate levels are projected over north-western, central and eastern Europe. During winter, nitrate concentrations are highest in the Po valley, Italy. Appreciable ammonium nitrate concentrations in summer are limited to those areas with high ammonia emissions, e.g., the Netherlands, since high ammonia concentrations are necessary to stabilise this aerosol component at high temperatures. Veriﬁcation with measurements shows that the model is able to reproduce the basic features of nitrate and sulphate concentrations over Europe. Already for 1995, the annual direct forcing by nitrate over Europe is calculated to be about 20% of that by sulphate. In summer, nitrate is found to be regionally important, e.g. equally in the Netherlands. In winter, spring and fall, the nitrate forcing over Europe is about half that by sulphate. Over north-western Europe and the alpine region the forcing by nitrate was calculated to be similar to that of sulphate. Overall, nitrate forcing is signiﬁcant and should be taken into account to estimate the impact of regional climate change in Europe. 1. Introduction

Aerosols of an anthropogenic origin play a key role in changing the earth’s radiation budget. Aerosols directly scatter and/or absorb solar radiation. Indirectly, they inﬂuence the micro-physical properties of clouds and therewith their effective albedo. Over polluted continental regions the direct forcing of sulphate alone can be as large as those of the

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combined greenhouse gases, but opposite of sign (e.g., Charlson et al., 1992; Kiehl and Briegleb, 1993). In the last decade, the inﬂuence of a number of other aerosol components, like organic carbon, black carbon and mineral dust, on the radiation budget has also been shown (IPCC, 2001 and references therein). However, IPCC (2001) did not present the best estimate for the direct forcing by nitrate, mostly because of a lack of reliable measurement data on this semi-volatile compound. A recent critical assessment of nitrate observations in Europe showed that nitrate signiﬁcantly contributes to the aerosol concentration in northern Europe (Schaap et al., 2002). Especially in winter (October– March), large contributions of nitrate to the total aerosol mass were found in western Europe, where nitrate concentrations often exceeded those of sulphate. At continental sites nitrate is mainly present in the ﬁne aerosol mode (Ten Brink et al., 1997; Heintzenberg et al., 1998). The aerosols in this size range scatter UV-VIS light most efﬁciently, which indicates that nitrate could exert a signiﬁcant climate forcing over continental Europe in winter (Ten Brink and Schaap, 2002) and regionally even during summer (Ten Brink et al., 1997). We evaluate the aerosol nitrate ﬁeld over Europe using the new threedimensional (3D), 25 km resolution, European scale, LOTOS-EUROS model (Schaap et al., 2005). The LOTOS-EUROS model is a combination of the TNO LOTOS model and RIVM EUROS model. Because forcing efﬁciencies for sulphate vary more than a factor of 3 (Adams et al., 2001), forcing calculations were performed to assess the relative forcing of nitrate compared to that of sulphate. We performed our calculations for 1995 as that year coincides with studies to the nitrate distribution over Europe mentioned above.

2. Model description

In this study, the domain of LOTOS-EUROS was chosen to bound at 351N and 701N and 101W and 401E. The projection is normal longitude– latitude and the standard grid resolution is 0.501 longitude 0.251 latitude, approximately 25 25 km2. In the vertical there are three dynamic layers and an optional surface layer. The model extends in vertical direction 3.5 km above sea level. The lowest dynamic layer is the mixing layer, followed by two reservoir layers. The height of the mixing layer is part of the diagnostic meteorological input data. The heights of the reservoir layers are determined by the difference between the mixing layer height and 3.5 km. Both reservoir layers are equally thick with a minimum of 50 m. Simulations were performed with the optional surface

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layer of a ﬁxed depth of 25 m. Hence, this layer is always part of the dynamic mixing layer. The transport consists of advection in three dimensions, horizontal and vertical diffusion and entrainment/detrainment. The recently improved and highly accurate, monotonic advection scheme developed by Walcek (2000) is used to solve the advection. The number of steps within the advection scheme is chosen such that the courant restriction is fulﬁlled. Each hour the vertical structure of the model is adjusted to the new mixing layer depth. After the new structure is set the pollutant concentrations are redistributed using linear interpolation. Vertical diffusion is described using the standard Kz theory. Vertical exchange is calculated employing the new integral scheme by Yamartino et al. (2004). In this study we used the TNO CBM-IV (Schaap et al., 2005) scheme which is a modiﬁed version of the original CBM-IV (Whitten et al., 1980). The scheme includes 28 species and 66 reactions, including 12 photolytic reactions. N2O5 hydrolysis is computed following Dentener and Crutzen (1993) and Jacob (2000). Aerosol chemistry is represented using ISORROPIA (Nenes et al., 1999). The dry deposition in LOTOS-EUROS is parameterised following a resistance approach (Erisman et al., 1994). Below, cloud scavenging is described using simple scavenging coefﬁcients for gases (Schaap et al., 2005) and following Simpson et al. (2003) for particles. In-cloud scavenging is neglected due to the limited information on clouds. Neglecting in-cloud scavenging results in too low wetdeposition ﬂuxes but has a very limited inﬂuence on ground level concentrations (see Schaap et al., 2004). The standard meteorological data for Europe are produced at the Free University of Berlin employing a diagnostic meteorological analysis system based on an optimum interpolation procedure on isentropic surfaces (Kerschbaumer and Reimer, 2003). The anthropogenic emissions used in this study are a combination of the TNO emission database (Visschedijk and Denier van der Gon, 2005) and EMEP emissions for 1995. For each source category and each country, we have scaled the country totals of the TNO emission database to those of the EMEP emissions. Hence, we use the ofﬁcial emission totals as used within the LRTAP protocol but we beneﬁt from the higher resolution of the TNO emission database (0.25 0.125 longitude–latitude). For a detailed description of the model and the input data we refer to Schaap et al. (2005). 3. Modelled distributions and validation

In Fig. 1 the annual averaged ﬁelds of aerosol nitrate and sulphate for 1995 are presented. Nitrate, in our model present as ammonium nitrate,

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Figure 1. Annual average concentrations (mg m 3) of sulphate and nitrate concentrations in 1995 at measuring height.

is a continental phenomenon, since its concentration rapidly trails off from coast to open sea. Maximum nitrate concentrations are found in the Po Valley. In an area over the Netherlands, Belgium and north-western Germany modelled concentrations range between 4 and 7 mg m 3. Elevated concentrations can also be identiﬁed over England and southern Germany, where the annual averaged concentrations exceed 4 mg m 3. These areas, incidentally, are characterised by high ammonia emissions. Except for northern Italy nitrate concentrations over southern Europe do not exceed 2 mg m 3. Due to low nitric acid formation annual average nitrate concentrations are calculated to be lower than 0.5 mg m 3 over most of Scandinavia. In case of sulphate, high concentrations, 3–7 mg m 3, are calculated for Poland and south-eastern Europe. Secondary maxima can be observed in northern Spain, central England, the Ruhr area and the Po Valley. In more remote regions the concentration ranges between 1 and 2 mg m 3. In northern Scandinavia the modelled concentrations are less than 1 mg m 3. A clear seasonal trend can be observed in the nitrate to sulphate concentration ratio with the lowest values in summer. In this season nitrate is conﬁned to western Europe (Benelux and the UK) and the Po Valley. The ratio ranges between 10% and 30% for the latitude band between 461N and 561N. The ambient conditions, i.e., high temperature and low relative humidity, in eastern and southern Europe do not favour ammonium nitrate formation. In summary, in summer concentrations of sulphate are much higher than those of nitrate in most regions. In the winter, spring and fall nitrate shows a different behaviour than in the summer, despite the fact that the concentrations of sulphate are

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marginally different in the various seasons. In the mentioned seasons the nitrate concentration ﬁeld shows a large area of high nitrate concentrations over western and central Europe. High concentrations during winter and early spring are calculated in the Po Valley, where they are almost everywhere above 7 mg m 3. The nitrate to sulphate ratio ranges between 30% and 120% between 401N and 601N. In western Europe the nitrate concentrations exceed those of sulphate, whereas they are slightly lower than those of sulphate in eastern Europe. The higher nitrate concentrations as compared to the summer can be explained by the much higher stability of ammonium nitrate at low(er) ambient temperatures and higher relative humidity. The maximum contribution of nitrate occurs in spring, which coincides with the maximum ammonia emissions in this season. Annual mean modelled concentrations are compared to those observed in the EMEP network in Fig. 2. For sulphate the model has a slight tendency (20%) to underestimate the observed concentrations. The average correlation coefﬁcient over all stations is 0.56. Aerosol nitrate and ammonium data are sparse. Hence we also included total nitrate (aerosol NO3 plus gaseous HNO3) and total ammonium (aerosol NH4

Figure 2. Comparison between modelled and measured annual average concentrations (mg m 3) for sulphate, (total) nitrate and (total) ammonium.

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plus gaseous NH3) in the comparison. Annual average aerosol and total nitrate concentrations are reproduced quite well. The average correlation coefﬁcient for total nitrate is 0.52. Correlations tend to be lowest in Spain and in central Norway. This is well illustrated for aerosol nitrate: the average correlation coefﬁcient rises from 0.4 to 0.53 when excluding the Norwegian stations. Ammonium data are shown for completeness. Average correlations coefﬁcients are 0.56 and 0.48 for ammonium and total ammonium, respectively. 4. Radiative forcing due to nitrate and sulphate

The direct forcing of nitrate for cloud free conditions was assessed using the parameterisation of van Dorland et al. (1997), which relates aerosol optical depth (AOD) and direct aerosol forcing. These authors used optical properties for the Koepke et al. (1997) water soluble aerosol type, which includes some absorption. Therefore, our calculated forcing values will be lower limits. The AOD was calculated from the computed aerosol burden using a dry scattering coefﬁcient of 4 m2 g 1 accounting for aerosol water following Veefkind et al. (1996). Seasonal values for the surface albedo were taken from Matthews (1984). The annual average nitrate forcing over Europe between 451N and 571N is about 0.21 W m 2 as compared to 0.7 W m 2 for sulphate (Fig. 3). In areas with high ammonia emission densities, such as northwestern Europe and northern Italy, the nitrate forcing is enhanced. In (more) remote locations, such as the Iberian Peninsula, the nitrate forcing is low whereas the sulphate forcing is still appreciable. Over Europe as a whole, the averaged forcing by nitrate is 0.12 W m 2, about 20% that of

Figure 3. Modelled annual average forcing of sulphate (left panel) and nitrate (right panel).

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sulphate ( 0.61). The absolute numbers are rather small as the calculations also include some absorption. Various authors have assumed different optical properties and used different ways to account for water uptake. As a consequence, the forcing efﬁciencies for sulphate for instance vary more than a factor of 3 (Adams et al., 2001). The nitrate to sulphate forcing ratio is relatively independent, since most uncertainties are divided out. Therefore, the nitrate to sulphate forcing ratio is most probably the most robust indicator to assess the importance of nitrate. The ratio basically gives a weighted ratio of the column burdens where the weight is determined by the solar zenith angle, which controls the fraction of the light scattered back into space. In Fig. 4 we present the nitrate to sulphate forcing ratio as function of season. In spring, the ratio of nitrate to sulphate forcing is 50% or more

Figure 4. Distribution of the nitrate to sulphate forcing ratio as function of season.

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over most of continental Europe with equal importance over the Benelux, England, Po Valley and the Alpine region. In fall, the importance of nitrate is somewhat lower than in spring. The maximum ratios in summer are found in the same regions as in spring and fall. However, the thermal instability of ammonium nitrate causes the ratio to be lower especially in more remote locations. During winter, the distribution of the ratio shows small gradients due to the high stability of ammonium nitrate. Over continental Europe, the nitrate forcing in winter is about 50% of that of sulphate. 5. Synthesis

The direct aerosol forcing of nitrate is not well established. We presented calculations with the new LOTOS-EUROS model to assess the nitrate and sulphate burdens over Europe for 1995. Veriﬁcation with measurements shows that the model is able to reproduce the basic features of nitrate and sulphate concentrations over Europe. Nitrate concentrations exceed those of sulphate in western Europe. Based on our simulations, the annual forcing by nitrate is calculated to be in the order of 20% of that by sulphate. In summer nitrate is found to be only regionally important, e.g., in the Netherlands, where the forcing of nitrate equals that by sulphate. In winter the nitrate forcing over Europe is about half the sulphate forcing. Over north-western Europe and the alpine region, the forcing by nitrate was calculated to be similar to that of sulphate. These results are in agreement with estimates based on measured data (Ten Brink et al., 1997; Schaap et al., 2002). Overall, nitrate forcing is signiﬁcant and should be taken into account to estimate the impact of regional climate change in Europe. Discussion

S. Andreani-Aksoyoglu: M. Schaap:

What is the uncertainty in NH3 emissions? The uncertainty in the ammonia emissions is in the order of 50%. Not only the totals are uncertain. Especially, the timing of the emissions are difﬁcult to represent correctly. We use a seasonal emission function representative for the Netherlands and Germany. However, in countries with different climatological conditions and farming practices the assumed seasonal variation may be crude.

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I. Tegen:

M. Schaap:

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Could the additional sulphate in coastal sites (e.g., the Netherlands) be due to DMS-sulphate? I have compared my model results to non-sea salt sulphate data. Nss-sulphate concentrations in air masses from the Atlantic are pretty low. We use a boundary condition of 0.8 mg m 3 for sulphate, which in principle should incorporate a small contribution by DMS. So I do not think DMS-sulphate plays an important role in the explanation of the underestimation of sulphate in our model. We see this underestimation throughout the domain, also at very large transport distances from open sea. You are assuming nitrates internally mixed with sulphates, so it is in accumulation mode. Do you need to also consider nitrate deposition on coarse mode? Yes, in principle one needs to do this. Especially, in coastal areas the impact from sea salt may be large. In the model these areas are relatively ammonia poor, especially over open sea. Hence, a large part of the nitrate evaporates to nitric acid, which in reality should be scavenged by sea salt. On the other hand, nitrate concentrations peak in continental air masses and in these air masses it has been shown that nitrate is for the largest part in the ﬁne aerosol model with the same size distribution as sulphate. We are working on the inclusion of a coarse mode nitrate.

REFERENCES Adams, P.J., Seinfeld, J.H., Koch, D.M., Mickley, L., Jacob, D., 2001. General circulation model assessment of direct radiative forcing by the sulphate-nitrate-ammoniumwater inorganic aerosol system. J. Geophys. Res. 102, 1097–1111. Charlson, R.J., Schwartz, S.E., Hales, J.M., Cess, R.D., Coakley, J.A., Hansen, J.E., Hofmann, D.J., 1992. Climate forcing by anthropogenic aerosols. Science 255, 423–430. Dentener, F.J., Crutzen, P.J., 1993. Reaction of N2O5 on tropospheric aerosols: Impact on the global distributions of NOx, O3, and OH. J. Geophys. Res. 98, 7149–7163. Erisman, J.W., van Pul, A., Wyers, P., 1994. Parametrization of surface-resistance for the quantiﬁcation of atmospheric deposition of acidifying pollutants and ozone. Atmos. Environ. 28, 2595–2607.

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Heintzenberg, J., Muller, K., Birmili, W., Spindler, G., Wiedensohler, A., 1998. Massrelated aerosol properties over the Leipzig basin. J. Geophys. Res. 103, 13125–13135. IPCC, 2001. Climate Change 2001: The Scientiﬁc Basis. Cambridge University Press, Cambridge, UK. Jacob, D.J., 2000. Heterogeneous chemistry and tropospheric ozone. Atmos. Environ. 34, 2131–2159. Kerschbaumer, A., Reimer, E., 2003. Preparation of Meteorological input data for the RCGmodel, UBA-Rep. 299 43246. Free Univ. Berlin Inst. for Meteorology, Germany. Kiehl, J.T., Briegleb, B.P., 1993. The relative roles of sulfate aerosols and greenhouse gases in climate forcing. Science 260, 311–314. Koepke, P., Hess, M., Schult, I., Shettle, E.P., 1997. Global aerosol data set. Max-Planck Institut fu¨r Meteorologie, Report No. 243, Hamburg, Germany. Matthews, E., 1984. Vegetation, land-use and seasonal albedo data sets: Documentation and archived data tape. NASA Tech. Memo, 86107. Nenes, A., Pilinis, C., Pandis, S.N., 1999. Isorropia: A new thermodynamic model for multiphase multicomponent inorganic aerosols. Aquatic Geochem. 4, 123–152. Schaap, M., Muller, K., Ten Brink, H.M., 2002. Constructing the European aerosol nitrate concentration ﬁeld from quality analysed data. Atmos. Environ. 36, 1323–1335. Schaap, M., Roemer, M., Sauter, F., Boersen, G., Timmermans, R., Builtjes, P.J.H., 2005. LOTOS-EUROS Documentation, TNO report B&O 2005/297, TNO, Apeldoorn, the Netherlands. Schaap, M., van Loon, M., Ten Brink, H.M., Dentener, F.J., Builtjes, P.J.H., 2004. Secondary anorganisch ae¨rosol simulations for Europe with special attention to nitrate. Atmos. Chem. Phys. 4, 857–874. Simpson, D., Fagerli, H., Jonson, J.E., Tsyro, S., Wind, P., Tuovinen, J-P., 2003. Transboundary Acidiﬁcation, Eutrophication and Ground Level Ozone in Europe, Part 1: Uniﬁed EMEP Model Description. EMEP Report 1/2003, Norwegian Meteorological Institute, Oslo, Norway. Ten Brink, H.M., Kruisz, C., Kos, G.P.A., Berner, A., 1997. Composition/size of the lightscattering aerosol in the Netherlands. Atmos. Environ. 31, 3955–3962. Ten Brink, H.M., Schaap, M., 2002. Aerosol Nitrate, a Dominant Atmospheric Trace Component in Europe?! Proceedings of the workshop: Air Pollution as a Climate Forcing, April 29–May 3, 2002, Honolulu, Hawaii, http://www.giss.nasa.gov/ meetings/pollution02/d2/tenbrink.html, 2002 van Dorland, R., Dentener, F.J., Lelieveld, J., 1997. Radiative forcing due to tropospheric ozone and sulphate aerosols. J. Geophys. Res. 102, 28079–28100. Veefkind, J.P., van der Hage, J.C.H., Ten Brink, H.M., 1996. Nephelometer derived and directly measured aerosol optical depth of the atmospheric boundary layer. Atmos. Res. 41, 217–228. Visschedijk, A.J.H., Denier van der Gon, H.A.C., 2005. Gridded European anthropogenic emission data for NOx, SOx, NMVOC, NH3, CO, PPM10, PPM2.5 and CH4 for the year 2000, TNO-Report B&O-A R 2005/106. Walcek, C.J., 2000. Minor ﬂux adjustment near mixing ratio extremes for simpliﬁed yet highly accurate monotonic calculation of tracer advection. J. Geophys. Res. 105(D7), 9335–9348. Whitten, G., Hogo, H., Killus, J., 1980. The carbon bond mechanism for photochemical smog. Environ. Sci. Technol. 14, 14690–14700. Yamartino, R.J., Flemming, J., Stern, R.M., 2004. Adaption of analytic diffusivity formulations to eulerian grid model layers ﬁnite thickness. 27th ITM on Air Pollution Modelling and its Application. Banff, Canada, October 24–29, 2004.

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Chapter 5.11 Causes for spread between global models w.r.t. Lifetime and distribution of particulate sulphate Øyvind Seland and Trond Iversen Abstract Understanding the effects of aerosols on climate and clouds remain one of the most important problems in climate science. Global models, both chemistry transport models and general circulation models, include a large number of aerosol species, but the spread of concentrations and lifetimes are considerable even for sulphur, as documented, e.g., in COSAM (Barrie, L.A., Yi, Y., Leaitch, W.R., Lohmann, U., Kasibhatla, P., Roelofs, G.-J., Wilson, J., McGovern, F., Benkovitz, C., Melieres, M.A., Law, K., Prospero, J., Kritz, M., Bergmann, D., Bridgeman, C., Chin, M., Christensen, J., Easter, D., Feichter, J., Land, C., Jeuken, A., Kjellstrom, E., Koch, D., Rasch, P., 2001. A comparison of large-scale atmospheric sulphate aerosol models (COSAM): Overview and highlights. Tellus 53B, 615–645.) and the Aerocom intercomparison (Textor, C., Schulz, M., Guibert, S., Kinne, S., Balkanski, Y., Bauer, S., Berntsen, T., Berglen, T., Boucher, O., Chin, M., Dentener, F., Diehl, T., Easter, R., Feichter, J., Fillmore, D., Ghan, S., Ginoux, P., Gong, S., Grini, A., Hendricks, J., Horowitz, L., Huang, P., Isaksen, I., Iversen, T., Kloster, S., Koch, D., Kirkeva˚g, A., Kristja´nsson, J.E., Krol, M., Lauer, A., Lamerque, J.F., Liu, X., Montanaro, V., Myhre, G., Penner, J., Pitari, G., Reddy, S., Seland, Ø., Stier, P., Takemura, T., Tie, X., 2006. Analysis and quantiﬁcation of the diversities of aerosol life cycles within AeroCom. Atmos. Chem. Phys. 6(7), 1777–1813.). 1. Introduction

In previous studies, we have documented that a major source of model uncertainty is the parameterization of convective transport (Iversen and Seland, 2002, 2004). However, differences in the treatment of convective transport do not explain the entire difference. Parts of the problem may

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be explained by different techniques for solving the advection equation. Here, we hypothesize that it is likely that uncertain parameterizations of various physical and chemical processes contribute more. Furthermore, differences in meteorological ﬁelds may contribute more than the physical parameterizations. In this paper, we present results from extended versions of the NCARCommunity Atmospheric Models, called CAM-Oslo. The basic parameterizations for sulphur and black carbon and their forcing effects are similar to those we used in CCM-Oslo (Iversen and Seland, 2004). More aerosol species, organic carbon, mineral dust, and sea-salt are now included in addition to sulphate and black carbon. To estimate the importance of differences between models, we have used two different versions of CAM-Oslo: CAM2 and CAM3. The advection equation tests have been done within the framework of CAM2 only, while the variation of chemical parameters is done in CAM3. 2. Possible uncertainties in sulphate modelling

Due to the complex nature of the sulphur lifecycle which includes gasphase and aqueous-phase chemistry, turbulent mixing, and in-cloud and below-cloud scavenging, the spread in results w.r.t. concentrations, lifetime, optical, and water activity properties remain large. This may be due to the representation of the sulphur cycle itself, as well as the transport, cloud representation, and precipitation processes in the various models. In this work we attempt to estimate the importance of model-to-model variations in cloud processes and dynamical representations compared with internal variations in the sulphur parameterization itself. 2.1. Sulphur parameterization in CAM-Oslo

Except for the convective parameterization, the sulphur cycle is the same as described in Iversen and Seland (2002) with some minor adjustments as described in Iversen and Seland (2004). Based on results shown in the latter paper, the convective transport is changed from no transport at all in the paper from 2002, to convective transport modiﬁed by an enhanced scavenging in the lower part of the atmosphere, and mixing between incloud updrafts and downdrafts. Sulphur species are mainly emitted as DMS or SO2. DMS reacts with OH in a gas-phase reaction producing either SO2 or MSA. The latter is assumed to undergo rapid deposition. SO2 are oxidized either through gas-phase oxidation with OH, or in aqueous phase with H2O2, O3, or by O2 catalysed by iron or manganese.

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These two sources of sulphate aerosols are separated into different particle size modes. In addition, a minor percentage of the gas-phase reaction is assumed to create new particles by nucleation and coagulation. The aqueous-phase oxidation is calculated using the Henry law constants equilibria and reaction rates from Seinfeld and Pandis (1998). We do, however, lower the effective H2O2 concentration with a factor of 0.8 to account for swift depletion in clouds (e.g., Barth et al., 1989). In addition, we deplete H2O2 in the entire cloudy grid-volume, to take into account the replenishment rate of H2O2 and SO2 as described in Iversen and Seland (2002). The oxidation agents are prescribed from the global Oslo CTM1 (Berntsen and Isaksen, 1997). The effective in-cloud oxidation rate is thus: kH2 O2 ¼ 0:8H H2 O2 ð1 þ 0:8H H2 O2 Þ1 k0_H2 O2

(1)

where the parameter k0_H2 O2 is taken from Eq. (6.84) in Seinfeld and Pandis (1998). 2.2. Differences between CAM2 and CAM3

Both CAM2 (Collins et al., 2002) and CAM3 (Collins et al., 2004) are the atmospheric model components of the NCAR community climate system models, CCSM2 and CCSM3, and are here run in stand-alone modus. The models are available in a number of resolutions, although in general we have chosen to use the same horizontal resolution, T42, as in CCMOslo. The number of layers has been increased from 18 to 26 and is the same in both CAM2 and CAM3. Both CAM2 and CAM3 offer a choice of three dynamical cores, eulerian, ﬁnite volume, and semi-lagrangian, although we have only tested the eulerian and ﬁnite volume for this work. The standard version of the spectral eulerian uses the classic spectral method in the horizontal discretization and 2nd order ﬁnite differences in the vertical. A semilagrangian method is applied for tracer transport. The ﬁnite volume is based on Lin and Rood (1996). As opposed to the eulerian version, this method is fully mass conservative, but it has not yet been sufﬁciently tested and validated as a standard dynamical core. This method employs a latitude–longitude grid with a horizontal resolution of 2 2.51. Although CAM2 and CAM3 are based on the same model structure, and use a number of similar physical representations, the results for cloud processes are quite different. Figure 1 compares zonal averages of cloud volume fractions and cloud water in CAM2 and CAM3 taken from a one-year simulation. It should be noted that CAM3 differentiates between cloud ice and cloud water, but here they have been added for the comparison. There is a marked increase in cloud volume almost

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Cloud volume fraction CAM2;E1

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Figure 1. Zonally averaged cloud volume fractions and cloud water mixing ratios in CAM2 and CAM3. Upper left: cloud fraction CAM2. Lower left: cloud fraction CAM3. Upper right: cloud water CAM2. Lower right: cloud water+cloud ice from CAM3.

everywhere from the boundary layer up to the middle troposphere. The pattern is particularly striking over the tropical and sub-tropical regions. Also of interest for the sulphur cycle is the different partition between cloud water and cloud ice. In CAM2, one assumes a linear increase in fractional cloud ice from 0 to 251C when calculating aerosol processes. In CAM3, the general ice-water partitioning starts out from 100% water at 101C decreasing linearly to 0% at 401C. In order to separate the effects on scavenging from aqueous-phase chemistry, the old partitioning is kept for scavenging in both CAM2 and CAM3. Some differences may also be found in the convective treatment of tracers. While the parameterization of convective clouds is the same in CAM3 and CCM3, the parameterization of aerosol scavenging in

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convective clouds suggested by NCAR in CAM3 is different from the old one we used in CCM3 and CAM2 (Iversen and Seland, 2004). Since differences in scavenging efﬁciency easily mask other changes in cloud parameterizations, we have continued to use the old scavenging parameterization for almost all the tests done in this work. CAM3 gives an increased scavenging simply due to the increased cloud volume. The enhanced scavenging efﬁciency in convective clouds proposed in Iversen and Seland (2004) may therefore lead to overestimated scavenging. 3. Experiments

We ran six experiments to test properties of the dynamics scheme, cloud amounts, and chemical replacement rate in clouds, on the global distribution of sulphate. Statistics are given for one year after a two-year spinup. The experiments are:

E1: a standard run with CAM2 and the eulerian transport scheme; E2: as E1, but with a ﬁnite volume dynamical core; E3: a standard run with CAM3 and the eulerian transport scheme; E4: as E3, but with the ice-/liquid-water fraction used in CAM2; i.e., a linear increase in the ice-fraction from 0 at 01C to 1 at 251C, instead of 0 at 101C to 1 at 401C; E5: as E3 but a reduced replenishment efﬁciency of H2O2 in Eq. (1) from 0.8 to 0.2; E6: as E3 but with the new reduced convective scavenging rates for CAM3. (Unfortunately, this experiment is not strictly rigorous because a slight reﬁnement in the H2O2 replacement rate has also been made.) Emissions, both amounts and height distributions are given by the dataset for the Aerocom model intercomparison compiled at the Institute for the Environment and Sustainability, European Commission Joint Research Centre, Ispra, Italy. 4. Selected results and discussion

Table 1 shows global budgets for sulphate calculated for the six experiments. The most striking feature is the difference in burden and turnover time for SO2 between CAM2 and CAM3. The increase in cloud volume and cloud water leads to a much more rapid aqueous-phase chemistry and a reduction of the turnover time by more than 50%. Due to a more rapid chemical production, the sulphate columns are not as much affected as SO2. The lifetime is, however, noticeably shorter.

E1 ¼ CAM2 eulerian E2 ¼ CAM2 ﬁnite vol. E3 ¼ CAM3 eulerian E4 ¼ E3+increased cloud ice E5 ¼ E3+reduced H2O2 eff. E6 ¼ E3+new convect. scav. Rasch et al. (2000) Aerocom median

SOx source (TgS/a)

SO2 dep. (%)

84.0

SO 4 prod. Aq. (%)

Gas. (%)

Burden (TsS)

T (days)

SO4 source (TgS/a)

30.2

49.4

18.3

0.45

2.0

83.9

34.8

44.3

18.8

0.46

84.0

23.7

66.9

7.2

84.0

24.2

65.6

84.1

25.1

65.0

84.1

28.9

56.8

81 –

32a –

55a –

SO2

SO4 Wetdep. (%)

Burden (TgS)

T (days)

58.7

93

0.54

3.4

2.1

54.7

90

0.49

3.3

0.17

0.8

64.1

92

0.48

2.7

8.1

0.20

1.0

63.7

92

0.48

2.8

7.71

0.19

0.8

62.9

93

0.48

2.8

12.2

0.35

1.5

59.8

88

0.65

4.0

12 –

0.4 –

1.9 –

93a 89

0.60 0.64

4.0a 3.9

55a –

Causes for Spread Between Global Models

Table 1. Global budget parameters for the production of airborne particulate sulphate

T is turnover time. SOx sources are emissions of SO2, sulphate, and SO2 oxidised from DMS. a Rasch et al. (2000) assign SO2 oxidised in precipitation as prod. and dep. sulphate.

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Most of the other tests give a much weaker signal. Neither the dynamical core nor the changes in cloud ice representation or the H2O2 replenishment efﬁciency give any clear response. The extremely rapid oxidation of SO2 in CAM3 may, however, decrease the model sensitivity of these parameters. Finally, as shown earlier in Iversen and Seland (2004), any change in the convective treatment is bound to give strong responses in the aerosol lifetime. The lifetime of SO2 is almost doubled, although this is partly due to decreased effective H2O2 concentration near source areas. For sulphate, for which wet scavenging dominates the loss process, we ﬁnd a 40% increase in lifetime, ending up with higher column burden than in any of the other experiments shown. We have also included the median values for sulphate burden and lifetime found in the Aerocom model intercomparison. Figure 1 shows that there are so large differences in cloudiness and cloud water between the two versions of the CAM model that they appear as two different models with regard to these quantities. There is a strong increase in cloud volume from CAM2 to CAM3 in lower and middle troposphere over the tropical and sub-tropical regions, leading to a rapid transition from SO2 to sulphate. At the same time, there is a second maximum in cloud water in the tropical regions of CAM3 in middle troposphere, suggesting an increased scavenging below this region. For extra-tropical regions, the patterns are not quite as clear, cloud volume is being reduced in some areas, while there are general increases in cloud water. The increase in cloud water should, however, induce an increase in aqueous-phase reactions. The effects of the changes over tropical regions are quite clear in Fig. 2. CAM3 has much less SO2 than CAM2, in particular in the middle and lower troposphere. This is probably due to both aqueous-phase chemical loss and precipitation scavenging. In the upper troposphere, the concentration of SO2 in CAM3 is markedly lower than observations, although it should be noted that the emissions may have been different at the time of the measurements. Likewise, for sulphate, the concentration near the ground is higher for the CAM3 experiment, while being lower in the upper troposphere. As expected, experiment E4 does not have much effect near the ground in tropical areas. Effects on ground concentration are only noticeable on the polar sides of 501 latitudes, increasing from 10% to a 10-fold increase near the poles. The effects on sulphate concentration are much smaller, typically around 10%. Turning off the enhanced convective deposition in E6 lead, as expected, to a strong increase in low level concentrations of sulphate. Due to the different parameterization of convective deposition,

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SO2 concentration GUAM Meas. E1 E3 E4 E6

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Figure 2. SO2 (left) and sulphate (right) measured in a ﬂight campaign in February over Guam given as an example of a tropical station. Data from the Paciﬁc Exploratory Mission (PEM) (Barth et al., 2000). Calculations for February year 3 are shown as lines. Experiment E4 is not included for sulphate since it is indistinguishable from experiment E3. SO2, Union County, Kentucky (38N,88W) 20

Sulphate,Union County, Kentucky (38N,88W 4.0

E1

SO2 (pptv)

15

E3 E6

10 5 0

1 2 3 4 5 6 7 8 9 10 11 12 Month No.

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Obs

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1 2 3 4 5 6 7 8 9 10 11 12 Month No.

Figure 3. Measured and modelled ground-level concentrations of SO2 and sulphate for experiments E1, E3, and E6 at an extra-tropical station, Union County, Kentucky, USA. Experiment E4 is not included since the calculated concentrations are close to identical with E3.

the concentration falls off quite rapidly, and has the lowest concentration of all the experiments in the upper troposphere. The comparison with ground measurements on the extra-tropical site in Fig. 3 show much of the same effect as in the tropics. A reduction in SO2 concentration from experiment E1 to E3, and then an increase again to experiment E6. As opposed to the tropical station, the last experiment seems to give the best values both with regard to concentration and yearly variation. It should, however, be mentioned that the change in winter

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concentration may be due to a decrease in efﬁcient H2O2 concentration as well as effects of convection. ACKNOWLEDGMENTS

This work is a part of the AerOzClim project ﬁnanced by the Research Council of Norway. Computational costs are covered by a grant from the Research Council’s Programme for Supercomputing. REFERENCES Barth, M.C., Hegg, D.A., Hobbs, P.V., Walega, J.G., Kok, G.L., Heikes, B.G., Lazarus, A.L., 1989. Measurements of atmospheric gas-phase and aqueous phase-phase hydrogen peroxide concentrations in winter on the east coast of the United States. Tellus 41B, 61–69. Barth, M.C., Rasch, P.J., Kiehl, J.T., Benkowitz, C.M., Schwartz, S.E., 2000. Sulfur chemistry in the National Center for Atmospheric Research Community Climate Model: Description, evaluation, features, and sensitivity to aqueous chemistry. J. Geophys. Res. 105, 1387–1415. Berntsen, T.K., Isaksen, I.S.A., 1997. A global three-dimensional chemical transport model for the troposphere, 1. Model description and CO and ozone results. J. Geophys. Res. 102, 21239–21281. Collins, W., Hack, J., Boville, B., Rasch, P., Williamson, D., Kiehl, J., Brieglev, B., McCaa, J., 2002. Description of the NCAR community atmosphere model (CAM2). NCAR Technical Note. Collins, W., Rasch, P., Boville, B., Hack, J., McCaa, J., Williamson, W., Kiehl, J., Briegleb, B., 2004. Description of the NCAR community atmosphere model (CAM2). NCAR Technical Note NCAR/TN-464+STR. Iversen, T., Seland, Ø., 2002. A scheme for process-tagged SO4 and BC aerosols in NCAR CCM3: Validation and sensitivity to cloud processes. J. Geophys. Res. 107(D24), 4751, 10.1029/2001JD000885. Iversen, T., Seland, Ø., 2004. In: Borrego, C., Incecik, S. (Eds.), The role of cumulus parameterisation in global and regional sulphur transport NATO CCMS ITM air pollution and its application XVI. Lin, S.-J., Rood, R., 1996. Multidimensional ﬂux-form semi-Lagrangian transport schemes. Mon. Weather Rev. 124, 2046–2070. Rasch, P.J., Barth, M.C., Kiehl, J.T., Schwartz, S.E., Benkovitz, C.M., 2000. A description of the global sulfur cycle and its controlling processes in the National Center for Atmospheric Research Community Climate Model, Version 3. J. Geophys. Res. 105, 1367–1385. Seinfeld, J.H., Pandis, S.N., 1998. Atmospheric chemistry and physics. From air pollution to climate change. Wiley, USA, p. 1326.

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Chapter 5.12 Modelling of aerosol composition using the MARS/MUSE dispersion model A. Arvanitis, E. Debry, I. Douros and N. Moussiopoulos Abstract Among aerosol characteristics, their chemical composition is by far one of the most important and at the same time quite difﬁcult to follow, as it gathers several inorganic and lots of organic species. We present here the aerosol module inserted in the MARS-MUSE 3D dispersion model and some preliminary results for urban areas. In this module, the aerosol phase is described as a multimodal (ﬁne, accumulation and coarse) internally mixed distribution. For inorganics, an equilibrium model has been built especially for dry, coastal and urban regions, which contains common inorganic species and also crustal species. For secondary organics, the SORGAM module has been incorporated into the model. The simulations conducted revealed the capability of the model to describe PM pollution in Southern California. Most deviation from the observed data are probably not exclusively related to the module, but to inaccuracies in emission input data and simulation of meteorological parameters. 1. Introduction

Atmospheric aerosols are nowadays a key issue in atmospheric sciences because of their impact on health, gas pollutant concentrations (gas-toparticle conversion) and atmospheric radiative balance (light scattering and cloud formation). The impact of aerosols on human health arises from their associated adverse health effects, particularly in the case of PM10 and PM2.5, which are linked by several epidemiological studies to increased morbidity and mortality rates. Of particular importance is also the effect of aerosols on climate, mainly due to their radiative forcing processes. They directly affect the thermal structure of the atmosphere by scattering and absorbing solar and terrestrial radiation in both cloud-free and cloudy conditions. In this study, the incorporation of a modal aerosol dynamics

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and chemistry module (Arvanitis, 2004) as well as of the SORGAM module for secondary organics into the MARS/MUSE (Moussiopoulos, 1995) 3D dispersion model is described and simulation results for the Greater Los Angeles Area in Southern California and the Greater Milan area in Northern Italy are presented.

2. Methodology

The Eulerian 3D non-hydrostatic prognostic mesoscale model MEMO (Moussiopoulos, 1995) has been used in the present study to provide meteorological input data required by the MARS-aero/MUSE dispersion modelling system. MARS is a 3D Eulerian dispersion model for reactive species in the local-to-regional scale, in which an aerosol module was incorporated in this study. Processes of emission, dispersion, transformation and deposition of pollutants are calculated on a staggered grid in terraininﬂuenced co-ordinates. The possibility of selecting the level of complexity with regard to the numerical algorithm used is offered. 3D chemical transport models like MARS/MUSE calculate concentration changes for each species of the chemical mechanism in use by simultaneously solving the following equation in each grid of the computational domain: @ci @2 ci @ci @ci @ci ¼ Dturb 2 þ v þ Q_ i þ j internal þ j @t @x @x @t processes @t deposition

(1)

On the right side of this equation the ﬁrst term represents change due to turbulent diffusion, the second term change due to advection, the third term emissions, the fourth term change due to internal processes (chemistry, condensation and coagulation) and the ﬁfth term change due to deposition. The important difference between particles and gases during the application of this equation is that the mass concentration of particles is dependent on their size and that internal processes have to take account of mass transfer between gas and particle phase, as well as the coagulation of particles. In the new aerosol module, the aerosol phase is described as a multimodal 3-mode (nucleation, accumulation and coarse) or 2-mode (accumulation and coarse) internally mixed distribution (Fig. 1). In each of these modes, the size distribution is a function of three parameters: the number concentration, the geometric mean diameter and the standard deviation: " # mi 1 ðln d p ln d pgi Þ2 (2) exp ni ðln d p Þ ¼ pﬃﬃﬃﬃﬃﬃ 2 ln2 sgi 2p ln sgi

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Figure 1. Particle mass distribution with respect to particle size for the three-mode module.

where mi is the mass concentration of the ith mode, dp the particle diameter, dpgi the geometric mean diameter of particle population of the ith mode and sgi the standard deviation of the ith mode distribution. Suitable equations can be derived for the moments of each mode (Binkowski and Shankar, 1995) according to the main dynamic processes for aerosols: coagulation and condensation/evaporation. In the case of condensation/evaporation, each species is driven by the difference between the bulk gas concentration and that of the aerosol surface, which results from the thermodynamic equilibrium between gas and aerosol. The coagulation of coarse particles is neglected, as the number of coarse particles under normal atmospheric conditions is limited. The population dynamics model was validated by comparison to other well tested models (GATOR, CIT, UAM-AERO) and was found to perform quite satisfactorily (Fig. 2). In this type of application, it is important for a model to be able to reproduce the small peak at about 0.1 mm, which is due to particles occurring through the process of nucleation. For inorganics, an equilibrium model (SINE) has been built (Arvanitis et al., 2001) especially for dry, coastal and urban regions. Therefore it contains common inorganic species and also crustal species. The equilibrium model has a key role in the aerosol module because of its ability to calculate the equilibrium partial pressures even under dynamic conditions. This is particularly important as the difference between the calculated and real partial pressures determines the evaporation rate. In Fig. 3, SINE is compared with similar equilibrium models (SCAPE, AIM).

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Figure 2. Particle volume and number distributions according to several validated aerosol models (Zhang et al., 1999).

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RH 90%

Total

80

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70 60 H2O (*0.1)

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0 SINE

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0

Figure 3. Validation of the SINE equilibrium model for the 90% relative humidity case.

Organic matter of aerosols may constitute up to 90% of aerosols in urban areas. This latter gathers both primary organic particles, directly emitted in the atmosphere, and semi-volatile organic species, produced by gas-phase chemistry. The handling of secondary organic aerosols (SOA) in this study was carried out with the incorporation into MARS/MUSE of the SORGAM organic aerosols model (Schell et al., 2001). Some oxidisation reactions have also been added to the RACM (Stockwell et al., 1997) gas chemistry mechanism in order to supply for low-volatility condensable products. The aerosol organic phase is assumed to be one quasi-ideal solution of eight classes of organic species. The gas-to-particle process of organic species is identical to that of inorganic species.

3. Case studies

For the validation of the inorganics aerosol module in MARS, a case study for the period 26–27 August 1987 covering the Los Angeles basin in Southern California was selected. The domain dimensions for the MARS/ MUSE application was 40 30 5 with a horizontal resolution of 4.565 5.095 km2. Initial and limit concentrations and spatially deﬁned hourly emissions were also derived from SCAQS campaign data (Lawson, 1990). For the validation of the implementation of SORGAM into MARS/ MUSE, the case of the Greater Milan area was investigated, for which sets of measured data for PM10 and the gaseous pollutants for the year 1999 (CityDelta European Modelling Exercise) as well as a very detailed emissions inventory for VOCs and primary PM were available. The simulation time was one week starting on the 1st of April 1999, while the dimensions

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of the studied domain were 100 100 5 with horizontal resolution of 1 1 km2. Initial and boundary conditions are derived from corresponding EMEP model results (URL 1).

4. Results

Figure 4 shows the hourly variations of PM10 at Anaheim (ANAH), central Los Angeles (CELA), Claremont (CLAR) and Riverside Rubidoux (RIVR) on the 27th of August. Results are presented here for the complex (3-mode) and the simple (2-mode) aerosol modules. Concentrations are realistically reproduced by both versions of the model, apart from the case of Riverside valley (RIVR), where concentrations are underestimated. There are good reasons to believe that the latter is due to the overestimation of the mixing height, resulting in the dilution of pollutants. The contribution of secondary organics at the urban PM levels is illustrated in Fig. 5 for the station at Meda, Milan. Simulations with and without SOA are compared to the corresponding information coming

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Figure 4. Model validation for PM10 at four distinct locations of the Los Angeles study area. Results of both the simple and the complex models are shown.

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Figure 5. Particulate matter time average at the Meda station.

Table 1. Statistical analysis of model results for PM10 with and without considering SOA at various stations in Milan Station

BIAS

RMSE

Correlation coefﬁcient

+13.81 +11.42

24.99 23.22

0.15 0.14

Meda (with SOA) Meda (without SOA)

2.44 4.94

16.86 7.34

0.1886 0.1483

Vimercate (with SOA) Vimercate (without SOA)

0.38 2.15

13.90 13.38

0.4280 0.4373

Magenta (with SOA) Magenta (without SOA)

3.46 4.99

15.15 15.42

0.0183 0.033

Limito (with SOA) Limito (without SOA)

from measurements. One can note the SOA increment in PM10 concentrations between simulations with and without SOA. The fact that this increment also appears for PM2.5 predictions in equivalent proportions indicates that secondary organics mainly lies on smaller aerosols. In order to have a better insight on the acquired results, we present comparisons between predicted and observed time series at four stations of the Greater Milan area, namely Limito, Meda, Vimercate and

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Magenta. The BIAS and RMSE are computed as follows: vﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ u N N u 1 X pre 1X pre 2 obs BIAS ¼ ðyi yi Þ; RMSE ¼ t ðy yobs i Þ N i¼1 N i¼1 i

(3)

Table 1 contains the values of BIAS, RMSE and the correlation coefﬁcient computed for the PM10 time series. The simulation with SOA slightly reduces the error compared to the simulation without SOA, except for the Limito station, but in both cases the error remains of the same order of magnitude. The impact of SOA on the BIAS values is more evident. 5. Conclusions

An aerosol dynamics module using the modal approach has proven to reproduce effectively the population distribution of particles for a test case. Results from the SINE inorganics equilibrium model were also very close to similar advanced models. The simulations, conducted with MARS/ MUSE by using a simple and complex version of the aerosol module reveal the capability of the model to describe PM pollution in Southern California with only minor drawbacks that in most cases are probably not exclusively related to the module, but to inaccuracies in emission input data and simulation of meteorological parameters. The additional consideration of the chemistry of secondary organics has been shown to noticeably affect particulate matter concentrations, revealing their importance in PM calculations. Although the lack of detailed emissions data for primary organic gas and PM species signiﬁcantly hinders this effort, these results have to be conﬁrmed with analyses for other cases. Discussion

P. Builtjes: I. Douros:

A.-L. Norman: I. Douros:

You have a quite low contribution of SOA. Is that caused by the low terpene emissions in the Milan area? This could indeed be the case, although we have not performed a sensitivity analysis in order to assess the impact of biogenics on SOA production. What is the effect if you consider MSA in your oxidation scheme? Having not considered MSA in our oxidation scheme, it is difﬁcult to assess its potential effect; however, we think that the absence of sea in our case study domain would diminish the importance of the DMS cycle and MSA.

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REFERENCES Arvanitis, A., 2004. Mathematical description of aerosol dispersion in urban areas, PhD thesis. Arvanitis, A., Moussiopoulos, N., Kephalopoulos, S., 2001. Proceedings of the 2nd Conference on Air Pollution Modelling and Simulation, APMS ‘01, Champs-sur-Marne, 277–288, 9–12 April. Binkowski, F.S., Shankar, U., 1995. The regional particulate matter model I: Model description and preliminary results. J. Geophys. Res. 100, 26191–26209. Lawson, D.R., 1990. The Southern California air quality study. J. Air Waste Manage. Assoc. 40, 156–165. Moussiopoulos, N., 1995. The EUMAC Zooming Model, a tool for local-to-regional air quality studies. Meteorol. Atmos. Phys. 57, 115–133. Schell, B., Ackermann, I.J., Hass, H., Binkowski, F.S., Ebel, A., 2001. Modeling the formation of secondary organic aerosol within a comprehensive air quality model system. J. Geophys. Res. 106, 28275–28293. Stockwell, W.R., Kirchner, F., Kuhn, M., Seefeld, S., 1997. A new mechanism for regional atmospheric chemistry modelling. J. Geophys. Res. 102, 25847–25879. URL 1: EMEP Model, http://www.emep.int Zhang, Y., Seigneur, C., Seinfeld, J.H., Jacobson, M.Z., Binkowski, F.S., 1999. Simulation of aerosol dynamics: A comparative review of algorithms used in air quality models. Aerosol Sci. Technol. 31, 487–514.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06513-8

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Chapter 5.13 Interpretation of new particle formation bursts in the planetary boundary layer using a high-order columnar model Olaf Hellmuth Abstract New particle formation plays a key role in the spatio-temporal evolution of atmospheric aerosols. In the present study the question of interest is the genesis of particle formation bursts in the planetary boundary layer. Two ‘‘clean-air mass’’ scenarios for both binary and ternary nucleation in a cloudless continental boundary layer with an emission source at the ground and very low background concentrations of aerosol are simulated within the framework of a conceptual study. It is demonstrated, that bursts of ultraﬁne condensation nuclei can originate from a complex interaction between gas-phase chemistry, nucleation, and boundary layer turbulence. 1. Introduction

New particle formation (NPF) plays a key role in the spatio-temporal evolution of atmospheric aerosols. NPF events often exhibit a typical burst-like evolutionary pattern characterised by a strong increase in the concentration of ultraﬁne condensation nuclei (UCN) (diameter o10 nm, particle number concentration 4 104 cm3), a subsequent shift in the mean size of the nucleated particles and the gradual disappearance of particles over several hours (see e.g., Hellmuth, 2006a). The question how multiscale transport processes inﬂuence NPF is not yet answered and subject of ongoing research. A columnar modelling approach is proposed to elucidate the circumstances, under which NPF does occur in the convective boundary layer (CBL). 2. General model description

The CBL model includes predictive equations for the horizontal wind components u, v, the potential temperature y and the water-vapour

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mixing ratio q (Andre´ et al., 1976a, b, 1978, 1982; Verver et al., 1997). The surface energy budget, the friction velocity and the skin properties are determined according to Holtslag (1987). The determination of the surface Reynolds stresses, the kinematic and convective heat and humidity ﬂuxes and the variances and covariances of temperature and humidity follows the approach proposed by Andre´ et al. (1978). The diabatic heating/cooling rate due to longwave and shortwave radiation is calculated according to Krishnamurti and Bounoua (1996). Vertical advection due to large-scale subsidence is considered by an empirically prescribed subsidence velocity w: ¯ The chemical model consists of predictive equations for NH3, SO2 and H2SO4, which consider emission, gas-phase oxidation, condensation loss on nucleation, Aitken and accumulation mode particles, molecule loss due to homogeneous nucleation and dry deposition. The time-height evolution of the hydroxyl radical is diagnostically prescribed (Liu et al., 2001). The aerosol model is based on a monodisperse approach proposed by Kulmala et al. (1995), Pirjola and Kulmala (1998) and Pirjola et al. (1999, 2003). It consists of predictive equations for two moments (number and mass concentration) in three modes (nucleation, Aitken, accumulation mode) and considers homogeneous nucleation, condensation onto the particle surfaces, intraand intermode coagulation and dry particle deposition. For the calculation of the homogeneous binary H2O/H2SO4 nucleation rate, a computer code of Wilck (1998) is used and for the ternary H2O/ H2SO4/NH3 nucleation rate, a parameterisation of Napari et al. (2002). The gas-phase NH3 concentration is diagnostically determined for given total ammonium and sulphate concentration (gas+particle phase) using the inorganic aerosol thermodynamical equilibrium model ISORROPIA of Nenes et al. (2000), whereas total ammonium and sulphate concentration are prognostically determined. The condensation coefﬁcient inclusive Fuchs–Sutugin correction for the transition regime was parameterised according to Seinfeld and Pandis (1998), Clement and Ford (1999) and Liu et al. (2001). The determination of the Brownian coagulation coefﬁcient follows Seinfeld and Pandis (1998). The water uptake of dry aerosol is considered by applying the empirical humiditygrowth factor of Birmili and Wiedensohler (2004). The size-segregated particle dry deposition velocity is parameterised according to Zhang et al. (2001). The kinematic and convective tracer ﬂuxes as well as the variances, covariances of temperature, water-vapour mixing ratio and tracer concentration are determined according to Andre´ et al. (1978) and Verver et al. (1997). A comprehensive description of the modelling approach can be found in Hellmuth (2006a).

Olaf Hellmuth

612 3. Description of physicochemical processes

We consider a set of N predictive chemical and aerosol-dynamical variables {wa} ¼ {[NH3]tot, [SO2], [H2SO4], N1, N2, N3, M1, M2, M3}, a ¼ 1, y, N, representing the concentration of total ammonia, sulphur dioxide and sulphuric acid in molecules per [m3] as well as the number and mass concentrations of nucleation, Aitken and accumulation mode aerosols in [m3] and [kg m3], respectively. The total time rates of change of these variables are given by the following governing equations: d t ½NH3 tot ¼ QNH3 ;emission d t ½SO2 ¼ k1 ½OH½SO2 þ QSO2 ;emission d t ½H2 SO4 ¼ C cond ½H2 SO4 N 1 C cond ½H2 SO4 N 2 C cond ½H2 SO4 N 3 þ k1 ½OH½SO2 J nuc nH2 SO4

ð1Þ

d t N 1 ¼ J nuc 0:5C coag N 1 N 1 C coag N 1 N 2 C coag N 1 N 3 d t N 2 ¼ 0:5C coag N 2 N 2 C coag N 2 N 3 d t N 3 ¼ 0:5C coag N 3 N 3 d t M 1 ¼ C cond mH2 SO4 ½H2 SO4 N 1 C coag m1 N 1 N 2 C coag m1 N 1 N 3 þ J nuc ðnH2 SO4 mH2 SO4 þ nNH3 mNH3 Þ d t M 2 ¼ C cond mH2 SO4 ½H2 SO4 N 2 þ C coag m1 N 1 N 2 C coag m2 N 2 N 3 d t M 3 ¼ C cond mH2 SO4 ½H2 SO4 N 3 þ C coag m1 N 1 N 3 þ C coag m2 N 2 N 3

ð2Þ

Here, QNH3 ;emission and QSO2 ;emission denote the emission strengths of total ammonia and sulphur dioxide, respectively, k1 is the second-order rate coefﬁcient for the reaction of the hydroxyl radical with sulphuric acid in [m3 molecules1 s1], Ccond and Ccoag denote the respective condensation and Brownian coagulation coefﬁcients in [m3 s1], mH2 SO4 and mNH3 are the respective masses per sulphuric acid and ammonia molecule in [kg], m1 and m2 denote the respective mean dry particle masses of UCN and Aitken mode aerosol in [kg], Jnuc is the nucleation rate in [m3 s1] and nH2 SO4 and nNH3 are the numbers of sulphuric acid and ammonia molecules in the critical embryo. In a generalised form, this set of governing equations for wa, a ¼ 1, y, N reads d t wa ¼ Ra þ Qa þ Qa;emission ;

Ra ¼

N X N X

kamn wm wn

(3)

m¼1 n¼m

Here, Ra, Qa and Qa,emission denote time rates of change due to physicochemical interactions, source processes and emissions, respectively. The

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physicochemical interactions between these variables are described by the matrix of coupling parameters ðkamn Þ: From Reynolds’ decomposition and averaging approach of any ﬂow variable, a set of governing equations for ﬁrst-, second- and third-order moments, respectively, is obtained. 4. First-order moment equations

@t u¯ þ w@ ¯ z u¯ ¼ @z w0 u0 þ f ð¯v vg Þ @t v¯ þ w@ ¯ z v¯ ¼ @z w0 v0 f ð¯u ug Þ ¯ rad @t y¯ þ w@ ¯ z y¯ ¼ @z w0 y0 þ ð@t yÞ @t q¯ þ w@ ¯ z q¯ ¼ @z w0 q0 @t w¯ a þ w@ ¯ z w¯ a ¼ @z w0 w0a þ

N X N X

a ¯a k¯ mn ð¯wm w¯ n þ w0m w0n Þ þ Q

ð4Þ

m¼1 n¼m

5. Second-order moment equations

Reynolds stresses fu0i u0j ; ðui ; uj Þ ¼ ðu; v; wÞg: @t u0i u0j þ w@ ¯ z u0i u0j ¼ @z u0i u0j w0 þ f ðik3 u0j u0k þ jk3 u0i u0k Þ 2 2 u0i u0j dij e¯ dij C4 3 3 e¯ ð1 C 5 Þðu0i w0 @z u¯ j þ u0j w0 @z u¯ i bðd3j u0i y0v þ d3i u0j y0v ÞÞ 2 þ C 5 dij ðbw0 y0v u0 w0 @z u¯ v0 w0 @z v¯ Þ 3 u0k u0k C 1 ðLturb Þ¯e3=2 e¯ ¼ ; ¼ 2 Lturb

ð5Þ

Scalar ﬂuxes fu0i a0 ; ui ¼ ðu; v; wÞ; a ¼ ðy; q; wa Þ; a ¼ 1; . . . ; Ng: @t u0i a0 þ w@ ¯ z u0i a0 ¼ @z u0i w0 a0 þ ik3 f u0k a0 u0i w0 @z a¯ C 6 u0i a0 ð1 C 7 Þðw0 a0 @z u¯ i bd3i y0v a0 Þ e¯ ! þ@t u0i a0 reac

ð6Þ

The reaction term appears in the tracer ﬂux equation only for fa ¼ wa g: N X N X a ! @t u0i w0a reac ¼ kmn ð¯wm u0i w0n þ w¯ n u0i w0m þ u0i w0m w0n Þ m¼1 n¼m

(7)

Olaf Hellmuth

614

Scalar correlations fa0 b0 ; ða; bÞ ¼ ðy; q; wa ; wb Þ; ða; bÞ ¼ 1; . . . ; NÞg: @t a0 b0 þ w@ ¯ z a0 b0 ¼ @z w0 a0 b0 ðw0 a0 @z b¯ þ w0 b0 @z a¯ Þ C 2 a0 b0 R þ ! @t a0 b0 e¯ reac R ¼ cR y 0 y 0 ;

cR ½s1 ð0:036 m s1 Þ

ð8Þ

(9) e¯ The respective interaction terms between a passive and a reactive scalar fa ¼ ðy; qÞ; b ¼ wa g and two reactive scalars fða; bÞ ¼ ðwa ; wb Þg read: 3=2

N X N X a kmn ð¯wm a0 w0n þ w¯ n a0 w0m þ a0 w0m w0n Þ ! @t a0 w0a reac ¼ m¼1 n¼m

! @t w0a w0b

N X N X

¼

reac

a

kmn ð¯wm w0b w0n þ w¯ n w0b w0m þ w0b w0m w0n Þ

m¼1 n¼m

þ

N X N X

b

kmn ð¯wm w0a w0n þ w¯ n w0a w0m þ w0a w0m w0n Þ

ð10Þ

m¼1 n¼m

6. Third-order moment equations

Turbulent transport of momentum ﬂuxes fu0i u0j w0 ; ðui ; uj Þ ¼ ðu; v; wÞg: @t u0i u0j w0 þ w@ ¯ z u0i u0j w0 ¼ ðu0i w0 w0 @z u¯ j þ u0j w0 w0 @z u¯ i Þ ðw0 w0 @z u0i u0j þ u0i w0 @z u0j w0 þ u0j w0 @z u0i w0 Þ C 8 u0i u0j w0 e¯ ð1 C 11 Þbðu0i u0j y0v þ d3j u0i w0 y0v þ d3i u0j w0 y0v Þ Turbulent transport of scalar fu0i u0j a0 ; a ¼ ðy; q; wa Þ; a ¼ ð1; . . . ; NÞg:

ﬂuxes

(‘‘ﬂuxes

of

ð11Þ

ﬂuxes’’)

@t u0i u0j a0 þ w@ ¯ z u0i u0j a0 ¼ ðu0i u0j w0 @z a¯ þ u0i w0 a0 @z u¯ j þ u0j w0 a0 @z u¯ i Þ ðw0 a0 @z u0i u0j þ u0i w0 @z u0j a0 þ u0j w0 @z u0i a0 Þ þ bðd3j u0i y0v a0 þ d3i u0j y0v a0 Þ 1 þ C 8 ðu0i u0j a0 dij u0k u0k a0 Þ 3 e¯ 0 0 0 u0 u0 a0 þ C 9 dij uk uk a C 10 dij k k e¯ e¯ 3 2 0 0 0 0 C 11 bðd3j ui yv a þ d3i uj y0v a0 dij w0 y0v a0 Þ 3

ð12Þ

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Turbulent transport of scalar correlations (‘‘ﬂuxes of correlations’’) fu0i a0 b0 ; ða; bÞ ¼ ðy; q; wa ; wb Þ; ða; bÞ ¼ ð1; . . . ; NÞg: @t u0i a0 b0 þ w@ ¯ z u0i a0 b0 ¼ ðw0 a0 b0 @z u¯ i þ u0i w0 a0 @z b¯ þ u0i w0 b0 @z a¯ Þ ðu0i w0 @z a0 b0 þ w0 a0 @z u0i b0 þ w0 b0 @z u0i a0 Þ ð13Þ C 8 u0i a0 b0 ð1 C 11 Þd3i by0v a0 b0 e¯ Turbulent transport of scalar correlations fa0 b0 c0 ; ða; b; cÞ ¼ ðy; q; wa ; wb ; wg Þ; ða; b; gÞ ¼ ð1; . . . ; NÞg: @t a0 b0 c0 þ w@ ¯ z a0 b0 c0 ¼ ðw0 a0 b0 @z c¯ þ w0 a0 c0 @z b¯ þ w0 b0 c0 @z a¯ Þ ðw0 a0 @z b0 c0 þ w0 b0 @z a0 c0 þ w0 c0 @z a0 b0 Þ C 10 a0 b0 c0 e¯

ð14Þ

Buoyancy ﬂuxes yv ¼ yð1 þ 0:61qÞ ¼ y þ C T 0 q; a0 y0v

¼

a0 y0

þ CT0

a0 q0 ;

a0 b0 y0v

¼

C T 0 0:61T 0 a0 b0 y0

þ C T 0 a0 b0 q0

ð15Þ

7. Results from a conceptual study

Two ‘‘clean-air mass’’ scenarios for both binary and ternary nucleation in a cloudless CBL with an emission source at the ground and very low background concentrations of aerosol are simulated within the framework of a conceptual study. Such situations are possible to occur in anthropogenically inﬂuenced CBLs depleted from air pollutants in connection with frontal-air mass change and post-frontal advection of fresh polar and subpolar air, respectively. The ground emission source provides the precursor gases required for the production of nucleating vapours. The low background concentration of aerosol particles ensures the absence of condensation sinks, which condensable vapours prevent from nucleating or thermodynamically stable clusters (TSCs) prevent from growing to detectable size. The time evolution of UCN number concentration in the Prandtl layer reveals for both the binary as well as the ternary nucleation the formation of a UCN burst in the course of the day (Fig. 1, top). The UCN number concentration due to ternary nucleation is by several orders of magnitude larger than the binary NPF burst. The time shift between the bursts appearing in both scenarios indicates a different origin of NPF. The NPF bursts are accompanied by downward directed vertical ﬂuxes of the UCN number concentration

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(Fig. 1, bottom). The time-height cross sections of the UCN evolution (Fig. 2) reveal a completely different genesis of the burst behaviour in both scenarios. In the presentation, the interaction of turbulent, gasphase chemical and aerosol-dynamical processes in NPF as well as the consequences with respect to the interpretation of NPF observations will be discussed. Details can be found in Hellmuth (2006b–d). 106 105

N1 [cm−3]

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(a)

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2

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10

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Time [h]

Figure 1. Time-series of UCN properties in the Prandtl layer during a NPF burst for binary (subscript ‘‘bin’’) and ternary nucleation (subscript ‘‘ter’’). UCN number concentration (top), turbulent vertical ﬂux of UCN number concentration (bottom) (after Hellmuth, 2006c).

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[N1] [#/m3] 1.7*109 1800

1.4*109

Height [m]

1500 1200

1.0*109

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6.9*108

600 3.5*108

300

1.2*105

0 3

6

9

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2.2*1012

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1.6*1012

900

1.1*1012

600 5.4*1011 300 1.2*105

0 3 (b)

6

9

12 Time [h]

15

18

Figure 2. Time-height cross section of UCN number concentration in the CBL. Binary nucleation scenario (subscript ‘‘bin’’) (top), ternary nucleation scenario (subscript ‘‘ter’’) (bottom) (after Hellmuth, 2006c).

Discussion

Ø. Seland:

Do you have enough condensation mass to grow nucleation mode particles into Aitken mode sizes? O. Hellmuth: The aerosol model includes predictive equations for the number and mass concentration of the nucleation mode, the Aitken mode and the accumulation mode. The mean particle size in each mode is diagnostically determined

618

Olaf Hellmuth

from mass and number concentration, in this way changing with time. The growth rate (and corresponding characteristic time scale) was not determined. Under the condition of a very low pre-existing particle surface concentration, as investigated here, the sulphuric acid concentration sharply drops after the onset of nucleation owing to subsequent condensation loss. Hence, in the present case, the sulphuric acid concentration is insufﬁcient to grow the particles to Aitken mode. Organic vapours, which are not considered in the present study, are suspected to contribute to the major part to particle growth and formation of cloud condensation nuclei. P. Builtjes: Are there possibilities to determine a parameterisation of the detailed process for inclusion in 3D Eulerian grid models? O. Hellmuth: The effort to predict high-order moments of meteorological and physicochemical properties is mainly motivated by the need to consider nonlinear effects in the parameterisation of the nucleation rate in 3D Eulerian grid models. Owing to the nonlinearity, the mean (gridscale) nucleation rate can exceed the nucleation rate at mean state conditions (e.g., determined from gridscale temperature, humidity and acidity) by several orders of magnitude. While there exist parameterisations of the turbulence-induced enhancement of the nucleation rate, a remaining challenge for 3D Eulerian grid models is the determination of the high-order moments required for the parameterisation input. By means of the present model, it is possible to derive simple enough expressions of variances and co-variances of temperature, humidity and acidity for advanced nucleation parameterisations in 3D Eulerian grid models. U. Uhrner: What are the sensitive parameters of the two nucleation mechanisms suggested? O. Hellmuth: Owing to the high number of meteorological and physicochemical parameters, it was not possible to perform a comprehensive sensitivity study with parameter ranking. The most sensitive parameter for the occurrence of NPF is the number concentration of the pre-existing particles. It controls the supersaturation required for nucleation. To get a signiﬁcant signal of binary homogeneous nucleation, the concentration of the

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pre-existing particle surface, and consequently the condensation sink for sulphuric acid vapour, must be very low. For typical ambient concentrations of the preexisting particle surface, there was no evaluable signal of homogeneous binary nucleation. In the case of ternary nucleation, NPF was found to occur also at typical ambient concentrations of the pre-existing particle surface. Here, also the relative humidity and the diurnal evolution of the ammonia concentration were found to have a strong impact on the burst evolution.

REFERENCES Andre´, J.C., De Moor, G., Lacarre`re, P., Therry, G., Du Vachat, R., 1976a. Turbulence approximation for inhomogeneous ﬂows: Part I. The clipping approximation. J. Atmos. Sci. 33, 476–481. Andre´, J.C., De Moor, G., Lacarre`re, P., Therry, G., Du Vachat, R., 1976b. Turbulence approximation for inhomogeneous ﬂows: Part II. The numerical simulation of a penetrative convection experiment. J. Atmos. Sci. 33, 481–491. Andre´, J.C., De Moor, G., Lacarre`re, P., Therry, G., Du Vachat, R., 1978. Modeling the 24-hour evolution of the mean and turbulent structures of the planetary boundary layer. J. Atmos. Sci. 35, 1861–1883. Andre´, J.C., Lacarre`re, P., Traore´, K., 1982. Pressure effects on triple correlations in turbulent convective ﬂows. In: Bradbury, L.J.S., Durst, F.J., Launder, B.E., Whitelaw, F.W., Whitelaw, J.H. (Eds.), Turbulent Shear Flows. Springer-Verlag Berlin, 3, pp. 243–252. Birmili, W., Wiedensohler, A., 2004. Feuchtewachstumsfaktor als Funktion der Partikelgro¨Xe und relativen Feuchte. Leibniz-Institut fu¨r Tropospha¨renforschung. Personal communication. Clement, C.F., Ford, I.J., 1999. Gas-to-particle conversion in the atmosphere: II. Analytical models of nucleation bursts. Atmos. Environ. 33, 489–499. Hellmuth, O., 2006a. Columnar modelling of nucleation burst evolution in the convective boundary layer – ﬁrst results from a feasibility study. Part I: Modelling approach. Atmos. Chem. Phys. 6, 4175–4214. Hellmuth, O., 2006b. Columnar modelling of nucleation burst evolution in the convective boundary layer – ﬁrst results from a feasibility study. Part II: Meteorological characterisation. Atmos. Chem. Phys. 6, 4215–4230. Hellmuth, O., 2006c. Columnar modelling of nucleation burst evolution in the convective boundary layer – ﬁrst results from a feasibility study. Part III: Preliminary results on physicochemical model performance using two ‘‘clean air mass’’ reference scenarios. Atmos. Chem. Phys. 6, 4231–4251. Hellmuth, O., 2006d. Columnar modelling of nucleation burst evolution in the convective boundary layer – ﬁrst results from a feasibility study. Part IV: A compilation of previous observations for valuation of simulation results from a columnar modelling study. Atmos. Chem. Phys. 6, 4253–4274.

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Holtslag, A.A.M., 1987. Surface Fluxes and Boundary Layer Scaling. Scientiﬁc reports WR-nr 87-2, Koninklijk Nederlands Meteorologisch Instituut. Krishnamurti, T.N., Bounoua, L. (Eds.), 1996. An Introduction to Numerical Weather Prediction Techniques. CRC Press LLC, Boca Raton. Kulmala, M., Kerminen, V.-M., Laaksonen, A., 1995. Simulations on the effect of sulphuric acid formation on atmospheric aerosol concentrations. Atmos. Environ. 29, 377–382. Liu, X., Hegg, D.A., Stoelinga, M.T., 2001. Numerical simulation of new particle formation over northwest Atlantic using MM5 mesoscale model coupled with sulfur chemistry. J. Geophys. Res. 106, 9697–9715. Napari, I., Noppel, M., Vehkama¨ki, H., Kulmala, M., 2002. Parameterization of ternary nucleation rates for H2SO4–NH3–H2O vapors. J. Geophys. Res. 107, AAC 6-1–6-6, doi:10.1029/2002JD002132. Nenes, A., Pilinis, C., Pandis, S.N., 2000. ISORROPIA V1.5 Reference Manual. University of Miami, Carnegie Mellon University. Pirjola, L., Kulmala, M., 1998. Modelling the formation of H2SO4–H2O particles in rural, urban and marine conditions. Atmos. Res. 46, 321–347. Pirjola, L., Kulmala, M., Wilck, M., Bischoff, A., Stratmann, F., Otto, E., 1999. Formation of sulphuric acid aerosols and cloud condensation nuclei: An expression for signiﬁcant nucleation and model comparison. J. Aerosol Sci. 30, 1079–1094. Pirjola, L., Tsyro, S., Tarrason, L., Kulmala, M., 2003. A monodisperse aerosol dynamics module, a promising candidate for use in long-range transport models: Box model tests. J. Geophys. Res., 108, AAC 1-1–1-16, doi:10.1029/2002JD002867. Seinfeld, J.H., Pandis, S.N., 1998. Atmospheric Chemistry and Physics. From Air Pollution to Climate Change. John Wiley & Sons, Inc, New York. Verver, G.H.I., van Dop, H., Holtslag, A.A.M., 1997. Turbulent mixing of reactive gases in the convective boundary layer. Boundary-Layer Meteorol. 85, 197–222. Wilck, M., 1998. Modal Modelling of Multicomponent Aerosols. Ph.D. dissertation, Universita¨t Leipzig, Leipzig. Zhang, L., Gong, S., Padro, J., Barrie, L., 2001. A size-segregated particle dry deposition scheme for an atmospheric aerosol module. Atmos. Environ. 35, 549–560.

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Chapter 5.14 Mixing of plumes with ambient background air: Effects of particle size variations close to the source T. Engelke, A. Hugo, E. Renner, F. Schmidt, R. Wolke and J. Zoboki Abstract Directly after the release of stack emissions into the atmosphere, particle size and number concentrations change depending on surrounding conditions. Coagulation and condensation are identiﬁed as the main aerosol dynamic processes in the plume. High resolution numeric calculations were carried out to predict plume conditions and resulting particle dynamic effects for different plant types, such as heating plants (o1 KW) and power stations (10 MW). The model results are compared with measurements at different locations in the ﬂue gas tract and in the plume for two different plants. The simulation results show a general dependence of the particle evolution on volume ﬂow and mixing conditions. In priciple, for small plants only small effects are quantiﬁed; for larger power plant plumes the predicted effects are more signiﬁcant.

1. Introduction

Dispersion models are used to determine ambient air concentrations from emission amounts and meteorological dispersion conditions. High-resolution numerical calculations are carried out to predict nearsource conditions and resulting particle dynamic effects for different plant types, such as heating plants (350 kW) and power stations (10 MW). The CFD system FLUENT with the coupled ﬁne particle model (FPM) is used for the simulation of formation, transport, and transformation of multimodal-multicomponent aerosol systems. During the early plume development, inside the stack and after release into the atmosphere, changes in temperature and concentration occur. Depending on these conditions, particle sizes increase and particle number concentration changes. Our examinations concentrate on hot

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waste gas from power stations and heating plants. Coagulation, water condensation, and the uptake of other gaseous species are identiﬁed as the main aerosol processes in the plume during these transport steps. To evaluate the effectiveness of processes in the plume, an additional sensitivity study is performed with the parcel model SPACCIM (SPectral Aerosol Cloud Chemistry Interaction Model) which describes aerosol dynamic effects and multiphase chemistry more in detail (Wolke et al., 2005). The results of the calculations are compared with the results of measurements at different locations in the ﬂue gas tract and in the plume for two selected plants. A general depending of particle number concentration and particle size on volume ﬂow and mixing conditions is observed. In principle, the data for small plants show only small effects on particle size; for larger power plant plumes the predicted effects are more signiﬁcant. This is in agreement with experimental studies. Furthermore, the inﬂuence of near-source processes on the aerodynamic and chemical properties of emitted particles is discussed for varying atmospheric and plume conditions.

2. Plume characterization 2.1. Combustion emission

Primary combustion products are particles and gases, which can condense on or nucleate to particles under characteristic atmospheric conditions. Typically, four classes of combustion particles formed from gas or vapor precursors: – – – –

Inorganic particles produced at high temperatures Sulfuric acid produced at exhaust temperatures Soot produced at high temperatures Condensable organic particles produced at exhaust temperatures

Particle size distributions for power plant emissions depend mainly on the characteristics of the fuel. For coal combustion, particle modes mostly are above 1 mm with a broad distribution between 3 and 5 mm. 5% are particles of cenosphere character. The mass fraction of submicron modes is 5–20% of total particle mass. For pulverized coal combustion, typically modes can be identiﬁed as 0.05 mm (ash condensation mode), 0.4, 2, and 15 mm (Kaupinen, 1991).

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2.2. Plume development

In the plume, different areas with typical conditions can be distinguished (Kerminen and Wexler, 1995): Source like Background like

ci42cB cip2cB

Here, the background concentration is denoted by cB. The value ci stands for the position-dependent concentration in the plume. The mixing ratio deﬁnes the contour of the areas and is inﬂuenced by the ﬂow rate, the initial velocity of the plume, the atmospheric turbulence, and thermal conditions. There are typical concentration gradients in vertical and horizontal directions along the plume axis. Far from the source, a plume can be characterized as vertically well mixed, so the concentrations are mostly equal to background air concentrations. Size characteristic changes occur according to chemical reactions (secondary aerosol processes). Close to the source, concentrations and temperature are different to ambient air. The gas is still hot, so the turbulence is mainly inﬂuenced by buoyancy. For cooler gas, atmospheric turbulence characterizes plume dilution. Typical plume areas can be classiﬁed according to temperature, concentration, and humidity condition (Kerminen and Wexler, 1995) as follows. The initial stage is characterized by a small fraction of the hot plume mixing with the background because of buoyancy (source-like characteristics). The Gaussian stage consists of cooler gas, mixing occurs mainly because of atmospheric turbulence. The plume is visible because of condensed water droplets. The time average contour of the ﬂuctuating plume is Gaussian like, but not the momentary plume at the point in time. Other classiﬁcations can be made according to the water saturation in the plume. The droplet stage is characterized by a relative humidity of rhX100% and condensing water droplets. The relative humidity stage is determined by values 100%Xrh80%. Dry stage is deﬁned by values for rho80% and is typically achieved well before the plume reaches background conditions (Kerminen and Wexler, 1995). 3. Measurement results of combustion emission characteristics

For source characterization of particle size, an oil ﬁred boiler (heating plant, 230 kW) and a coal ﬁred boiler (power station, 100 MW) were investigated. Measurements of particle concentration and gas conditions were carried out at different stages of the ﬂue gas tract by using an aerosol

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spectrometer (WELAS system, Palas GmbH, Karlsruhe/Germany) and ﬁlter samplers. Table 1 shows the results for the power station for particle and sulfur measurements. In the range of 0.7odpo40 mm, particle concentration of approximately 2400 particles cm 3 were measured. Figure 1 presents the modal approximation of the measured results. As a most important point, there is no signiﬁcant change in particle size distribution during transport within the waste gas channel. Nearly the same results are given from measurements at the heating plant. 4. Simulation results 4.1. FLUENT simulations

Formation, transport, and transformation of the emitted particles inside the stack as well as in the plume are simulated by the commercial CFD system FLUENT (see http://www.ﬂuent.com). The calculations show a Table 1. Particle and sulfur characterization of ﬂue gas (100 MW coal ﬁred) Power plant, 100 MW Parameter O2, i. N. (vol.%) CO2, i. N. (vol.%) Humidity, abs. (g m 3) Gas temperature (1C) Ambient pressure (mbar) Dpstatic (mbar) Waste gas channel (circular) (m2) Pressure in channel (mbar) Gas density, i. N., dry (kg m 3) Velocity (average) (m s 1) Gas ﬂow, hum. (m3 h 1) Gas ﬂow, i. N., dry (m3 h 1) i. N.: 1013 mbar, 01C Particles TSP, mean (mg m mg m 3, hum. SO2 SO3 SO2, mean (mg m mg m 3, hum. SO3, mean (mg m mg m 3, hum.

Place 12.5 m

Place 50 m

5.4 14.4 80.9 169 1002 0.8 5.73 1001.2 1.37 27.6 568.891 315.598

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2.5 Mean, stack height = 15 Mean, stack height = 50

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Figure 1. Particle size distribution waste gas channel, modal approximation of measurement results (average over 20 measurements each).

general dependence of particle number concentration and particle size on volume ﬂow and mixing conditions. At ﬁrst, the condensation on particles from a stack with a given ﬂue gas temperature is examined under special consideration of the relative humidity of ambient air. The results in Fig. 2 show the particle diameter against the temperature of dew point for two different relative humidities. Getting a linear dependence is in agreement with experimental studies. In principle, for small plants the data show only small effects on particle size; for larger power plant plumes the predicted effects are more signiﬁcant. Submicron particles dominate the number concentration and often the surface area concentration. Residual ash (organic or inorganic residue of material that remained in a solid or liquid phase throughout combustion) dominates the mass concentration of emitted particulate matter. Our results agree with other studies in literature. Struschka et al. (2001) investigate biomass burning in small heating systems. After ﬂue gas dilution with background air (dilution factor 6, hold up for 3 s), the particle mass increases for fractions with aerodynamic particle diameters of 0.5–1.1 mm, caused by condensation on smaller particles, while the amount of larger particles is constant. Similar investigations are carried out for tail pipe nanoparticle emissions of car engines (Kittelson and Abdul-Khalek, 1999). The nanoparticle number concentration increases by the factor of 100 according to increasing residence time (0.09–0.9 s) or

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particle diameter [µm]

7.0 6.0 5.0 4.0 3.0 2.0

ambient temperature = 5°C, 70% relative humidity, gas temperature = 105°C ambient temperature = 5°C, 65% relative humidity, gas temperature = 105°C

1.0 47.0

47.5

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temperature of dew point [°C] Figure 2. Particle diameter as a function of dew point temperature and relative humidity.

decreasing temperature. Changes in relative humidity will have only small effects on particle number concentration in this study. 4.2. Parcel model simulations

In addition to the FLUENT simulations, the inﬂuence of microphysical and multiphase chemical processes on changes of the particle size and composition is analyzed more in detail. Beside the microphysical processes condensation, evaporation, and coagulation, the inﬂuence of gas-phase scavenging and multiphase chemical transformations on the size distribution and the chemical properties of the particles (e.g., pH value) are examined. The feedback of the chemical processes on microphysics is explored particularly with regard to the modiﬁcation of the emitted particles. This sensitivity study is performed by using the parcel model SPACCIM (Wolke et al., 2005). The results are given for a ﬁne-resolved particle spectrum. The movement of the air parcel can follow a predeﬁned trajectory (e.g., simulated by a 3D atmospheric model). Entrainment and detrainment processes are included in a parameterized form (Sehili et al., 2007). FLUENT simulations are also used for the predeﬁnition of the trajectories within the exhaust plume and the determination of gas phase as well as thermodynamic surrounding conditions (e.g., supersaturation, temperature). Three different trajectories are deﬁned for the ﬂight path of

Mixing of Plumes with Ambient Background Air center

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280

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240 0.60 200 0

100

200

300

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0

100

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Figure 3. The three prescribed trajectories in the exhaust plume (left) and the time series of the corresponding saturations (right). All distances are given in meter.

the parcel (see Fig. 3, left). Additionally, the accompanying relative humidities along the trajectories are shown in Fig. 3 (right). The mixing is simulated for particles as well as for gas-phase species. In all cases, the exhaust plume is completely mixed with the ambient air after 500 m. The mixing rate between ambient and plume air for each trajectory is also derived from FLUENT simulations. In the basic scenarios, a relative humidity of 65% is assumed for the ambient air. Additionally, simulations with 75% relative humidity are performed. The surrounding gas phase and particle concentrations are deﬁned according to scenarios given by Poppe et al. (2001). The initial particle size distribution, sulfate concentration, and the thermodynamic conditions are taken from the measurements (see Table 1). The particle composition is derived from literature data sets (Mohr et al., 1996; Rose et al., 1999). In the sensitivity study, the dependency of the effective emitted particle population against variations of the ﬂight path within the plume, the surrounding conditions, the mixing rate, and the initial particle distribution (size and composition) is investigated by repeated SPACCIM simulations. The main results are summarized in the following: The main questions are: Which mass condenses inside the plume from the gas phase on particles and how does this processing change the dynamic, microphysical, and chemical behavior of the emitted particles? As expected, particle mass increase is recognized during the drop phase due to gas-phase uptake. With the evaporation most of this mass again becomes gaseous. However, a mass increase is observed especially for particles at the end of the spectrum. This fact is also observed by Sehili et al. (2005). Overall, the modiﬁcations in the plume appear from low inﬂuence on the aerodynamic behavior of the emitted particles.

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center upper lower 75 rh

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110 130

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Figure 4. Temporal development of the sulfate concentration summarized over all size bins (left) and the mean pH value for the three different trajectories and higher relative humidity (right).

Therefore, a modiﬁcation of power plant emission data seems to be not necessary for use in chemistry-transport models. Due to the large supersaturation in the plume, nearly the whole particle spectrum is activated. The growth behavior is similar for all emitted particles and depends strongly on the trajectory of the particle (see Fig. 4). Ambient particles mixed into the plume are also activated and grow to bigger drops. In all cases, the inﬂuence of the coagulation is very small and can be neglected. Variations of the initial particle distribution as well as particle composition induce only small differences in the particle growth and the connected mass increase. The observed sensitivity against changes in the emitted gas-phase concentrations and thermodynamic conditions is much higher (Fig. 4, left). The inﬂuence of the mixing ratio on the number and mass distribution is evident. This implies also noticeable changes in the aerosol processing in the plume. In contrast to the small inﬂuence on the aerodynamic properties, the chemical behavior of the particles can be altered substantially by multiphase near-source processes. Temporal developments of the pH value for different scenarios are shown in Fig. 4 (right). A change in acidity of the emitted particles can occur with respect to the considered scenario. However, a general ‘‘classiﬁcation’’ of this respective change is difﬁcult. Obviously, the pH values are closely connected with the sulfate chemistry (Fig. 4). By contrast, the acidity is mainly determined by the sulfate fraction of the particles. Otherwise, the pH values control mainly the uptake behavior of SO2 and, therefore, the mass increase of the particles. A more precise analysis shows that other gaseous precursors contribute also to the particle growth in the plume.

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5. Conclusions

The results of the simulations show a general dependence of particle number concentration and particle size on volume ﬂow and mixing conditions. This is in agreement with experimental studies. In principle, for small plants the data show only small effects on particle size; for larger power plant plumes the predicted effects are more signiﬁcant. Parcel simulations with a detailed description of near-source multiphase processes are performed for quantifying the effects on the emitted particles. The modiﬁcations in the plume prove to be of small inﬂuence on the aerodynamic behavior and, hence, on the further atmospheric transport. Remarkably, the mass increase is observed especially for particles at the end of the spectrum. The chemical particle properties, in particular the pH value, can change substantially. However, this will also alter their ability to act as cloud condensation nuclei. ACKNOWLEDGMENTS

The project was supported by the German Federation of Industrial Research Associations ‘‘Otto von Guericke’’ in cooperation with VEU e. V. and founded by the German Federal Ministry of Economics and Technology (FV-No. 13871). REFERENCES Kaupinen, E.I., 1991. Aerosol formation in coal combustion process. J. Aerosol Sci. 22(Suppl. 1), 451–454. Kerminen, V.-M., Wexler, A.S., 1995. The interdependence of aerosol processes and mixing in point source plumes. Atmos. Environ. 29(3), 361–375. Kittelson, D., Abdul-Khalek, I., 1999. Formation of nanoparticles during exhaust dilution. Univ. of Minnesota, Dep. of. Mech. Eng.; EFI-Conference ‘‘Fuels, Lubricants Engines, & Emissions,’’ January 18–20. Mohr, M., Yla¨talo, S., Klippel, N., Kauppinen, E.I., Riccius, O., Burtscher, H., 1996. Submikron ﬂy ash penetration through electrostatic precipitators at two coal power plants. Aerosol Sci. Technol. 24, 191–203. Poppe, D., Aumont, B., Ervens, B., Geiger, H., Herrmann, H., Roeth, E.-P., Seidl, W., Stockwell, W.R., Vogel, B., Wagner, S., Weise, W., 2001. Scenarios for modeling multiphase tropospheric chemistry. J. Atmos. Chem. 40, 77. Rose, N.L., Juggins, S., Watt, J., 1999. The characterisation of carbonaceous ﬂy-ash particles from major European fossil-fuel types and applications to environmental samples. Atmos. Environ. 33, 2699–2713. Sehili, A.-M., Wolke, R., Knoth, O., Simmel, M., Tilgner, A., Herrmann, H., 2005. Comparison of different model approaches for the simulation of multiphase processes. Atm. Env. 39, 4403–4417.

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Sehili, A.M., Wolke, R., Helmert, J., Simmel, M., Schro¨der, W., Renner, E., 2007. Cloud chemistry modeling: Parcel and 3D simulations. In: Borrego, C., Norman, A.L. (Eds.), Air Pollution Modeling and Its Application XVII. Springer, New York, pp. 340–350. Struschka, M., Starub, D., Pfeiffer, F., Baumbach, G., 2001. Untersuchung der Feinstaubemissionen einer Kleinfeuerungsanlage mittels Simulation der Ausbreitungs- und Austrittsbedingungen der Rauchgase. ISBN 3-928123-42-4, IVD-Bericht 46-2001, University of Stuttgart, Germany. Wolke, R., Sehili, A.M., Simmel, M., Knoth, O., Tilgner, A., Herrmann, H., 2005. SPACCIM: A parcel model with detailed microphysics and complex multiphase chemistry. Atmos. Environ. 39, 4375–4388.

Interactions between climate change and air quality Chairpersons: Bernard Fisher Rapporteurs: Keiya Yumimoto

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Chapter 6.1 Examining the impact of changing climate on regional air quality over the U.S.$ Ellen J. Cooter, Robert Gilliam, William Benjey, Chris Nolte, Jenise Swall and Alice Gilliland Abstract The Climate Impact on Regional Air Quality (CIRAQ) project is a collaborative research effort involving multiple federal agencies and academic institutions to assess the impact of present and future climate on regional air quality across the United States. Preliminary results are presented which highlight model biases, variability and change of present (2000) and future (2050) regional climate, emissions and air quality model results for the summer season (June– August). The regional climate scenario derived for CIRAQ appears to reasonably represent large-scale summer-season climate means and variability in the western United States, but it fails to replicate some key summertime features in the eastern United States. A comparison of future and current climate simulation reveals that even though the general weather patterns change little in the future, the summer temperatures are on average 2–3 K warmer over the southwest quadrant of the United States. Other regions of United States are also warmer, but generally by less than 1 K. Preliminary analyses of the interannual and seasonal variability of biogenic and mobile emissions driven by current climate scenarios indicate isoprene and biogenic NO emissions are more temporally and spatially variable than are model generated on-road mobile source emissions. Comparison of these results with modeled emissions under future climate reveals similar spatial and temporal patterns, but elevated levels of biogenic emissions are present in the future simulation due to the warmer future summer temperatures throughout the study domain. $

This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reﬂect their policies or views.

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Preliminary air quality modeling results identify regional differences in the response of current simulated 8-h maximum ozone concentrations and interannual variability to future climate change. Annual average ﬁne particulate matter (PM2.5) concentrations and the frequency and duration of elevated particulate matter episodes decrease in the future period relative to current period simulations. 1. Introduction

There is concern that global climate change over the next hundred years may lead to altered weather patterns that, along with changes in land use and source emissions could signiﬁcantly impact tropospheric air quality. The U.S. Environmental Protection Agency (EPA)/National Oceanic and Atmospheric Administration (NOAA) Climate Impact on Regional Air Quality (CIRAQ) project assesses the impact of present-day and future (ca. 2050) climate on regional ozone and particulate matter (PM2.5) in North America. Downscaled regional climate conditions are derived from a global climate model (GCM) to deﬁne present and future climate scenarios. These regional climate scenarios are then used to drive the Community Multiscale Air Quality (CMAQ) model (Byun and Schere, 2006). In CIRAQ Phase I, anthropogenic emissions that do not directly respond to climate conditions are maintained at present levels in order to isolate the sensitivity of air quality to the climate scenario alone. CIRAQ Phase II will use alternative anthropogenic emission inventories that include future economic, population and technological change in the continental U.S. 2. Development and analysis elements

The CIRAQ project involves the development and analysis of: (1) a decade of present-day (ca. 2000) and future (ca. 2050) regional climate model (RCM) data; (2) 5 years of present and future climate driven emission scenarios and (3) 5 years of present and future CMAQ simulations. Each is discussed below. 2.1. Regional climate scenarios 2.1.1. Description

The NASA Global Institute for Space Studies (GISS) version II0 GCM (Rind et al., 1999) was run assuming the IPCC SRES A1B global

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emissions scenario. Regional air quality models require information at ﬁner horizontal and vertical resolutions than is typically available from GCM simulations. The Fifth Generation Pennsylvania State University/ National Center for Atmospheric Research Mesoscale Meteorological Model (MM5; Grell et al., 1994) was used to generate physically consistent downscaled regional climate scenarios (MM5/RCM) from the coarse GCM data over a 36 km 36 km gridded domain (e.g., Fig. 1). These downscaled simulations do not necessarily reproduce day-to-day

Figure 1.

Summer (JJA) mean sea-level pressure (mb) for (A) MM5/RCM and (B) NARR.

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and year-to-year observed variations but rather, they represent climatological time periods under speciﬁed greenhouse gas forcing. Without the assimilation of observed data to constrain the GCM and mesoscale models, careful evaluation against observed climate conditions is essential to identify meteorological biases in the downscaled data that will impact CMAQ model predictions.

2.1.2. Results

Ten years of MM5/RCM downscaled summer season mean sea-level pressure and 2 m temperature data at 1800 UTC representing current climate have been compared to gridded North American Regional Reanalysis (NARR; Mesinger et al., 2006) data from 1996 to 2005. Mean summer NARR and MM5/RCM sea-level pressure patterns (Fig. 1) compare well along and off the western coast and across the southwestern U.S., indicating the MM5/RCM is simulating the dominant synoptic ﬂow pattern correctly. The MM5/RCM also correctly simulates higher pressure over the eastern U.S. and lower mean pressure over the western U.S. Conversely, the mean NARR pattern indicates the presence of a persistent sub-tropical high-pressure system off the eastern U.S. coastline that is absent in the MM5/RCM data. The MM5/RCM also erroneously simulates low pressure in the Gulf of Mexico and just off the eastern U.S. coast, and increasing mean pressure from the Mississippi River Valley northward to the Great Lakes and Canada. Spatial patterns of simulated current and future MM5/RCM mean sea-level pressure (not shown) are in general agreement for the summer period. Surface temperatures have been shown to correlate well with ambient concentrations of several common pollutants, e.g., ozone. MM5/RCM 2 m summertime temperatures in the northeastern U.S., Florida and southern Texas are up to 3 K cooler on average than the NARR (Fig. 2). The cooler temperatures over Texas and Florida seem related to increased afternoon cloudiness. Cooler conditions in the northeastern U.S. are related to the MM5/RCM dominant high pressure located over the Great Lakes and Ohio River Valley, resulting in dominant northerly ﬂow and cooler afternoon temperatures. The MM5/RCM is 7–9 K cooler than the NARR pattern over the upper Great Plains. Isolated areas of very large temperature differences along the western U.S. coast and Rocky Mountains are most likely interpolation artifacts and should be ignored. Future period summertime MM5/RCM 2 m temperature simulations average 2–3 K warmer across much of the southwestern U.S., Rocky Mountains and the Paciﬁc Northwest. Over the eastern U.S., the future

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Figure 2. Difference (K) in the mean summer (JJA) 1800 UTC 2 m temperature between the MM5/RCM and the NARR (computed as MM5/RCM-NARR).

summer climate is an average of 1 K warmer. Areas of the central and northern U.S. are less than 0.5 K warmer in the future simulation. 2.2. Emissions scenarios 2.2.1. Description

The current emission inventory is represented by version 2001ad of the 2001 National Emission Modeling Inventory (U.S. Environmental Protection Agency, 2004). Biogenic and mobile source meteorologically dependent emissions and plume rise are modeled using the same MM5/RCM data analyzed in Section 2.1. The biogenic emissions are modeled using Biogenic Emission Inventory System (BEIS) version 3.13 and on-road mobile source emissions are modeled by the U.S. EPA MOBILE6 model. 2.2.2. Preliminary results

Analysis of meteorologically inﬂuenced emission rates during 5 years of current-period downscaled data shows peak isoprene emission ﬂuxes are an order of magnitude greater and more variable in the eastern U.S. than in the West (Benjey and Cooter, 2005). Biogenic NO emission rates follow the same spatial and temporal trends, but are less variable. On-road

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Table 1. Annualized statistics of area-weighted (eastern/western region) mean hourly meteorologically inﬂuenced current and future emission rates reported as kg year1 km2 Minimum

Isoprene—Eastern U.S. Isoprene—Western U.S. Biogenic NO—Eastern U.S. Biogenic NO—Western U.S. Mobile NO2—Eastern U.S. Mobile PM2.5—Eastern U.S.

Median

Maximum

C

F

Interquartile Range

C

F

C

F

C

F

0.0 0.0 0.0

0.0 0.0 0.0

8.0 2.7 60.8

11.4 3.4 68.4

0.0

0.0

29.0

33.7

77.5

79.5

31.4

32.5

4.9

4.8

23.6

23.9

53.6

54.1

24.6

24.8

0.9

0.9

9.3

9.5

20.5

20.5

10.5

10.6

10,915.2 11,408.5 463.2 654.1 3474.0 4717.8 132.5 192.1 202.5 228.0 61.8 63.4

C represents the 5-year current period; F represents the 5-year future period.

mobile source emissions (principally NOx and PM2.5) are higher in the East, but exhibit less temporal and spatial variability because of the effects of non-meteorological variables and temperature averaging in the MOBILE6 model. Modeled emission rates for 5 years of future-period downscaled data (Table 1) identify larger and more variable isoprene and NO emission ﬂuxes that reﬂect the general increase in 2 m temperature signal (see Section 2.1.2). Future median isoprene values are 21% (West) and 43% (East) greater than current rates. Figure 3 shows that increase in emission rates and their associated interquartile ranges are focused on the spring and summer seasons. The pattern of change for biogenic NO emission rates is similar, but of lesser magnitude. Modeled mobile source emissions, as represented by NOx and PM2.5 emission rates, change relatively little between the current and future periods (Table 1). There is little change between seasons or years due to meteorology. This lack of response is likely a product of the limited sensitivity to temperature of the MOBILE6 model with respect to the other model input variables and temperature averaging. 2.3. Air quality scenarios 2.3.1. Description

The air quality modeling scenarios are generated using the U.S. EPA CMAQ model, version 4.5 (Byun and Schere, 2006). The horizontal

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Figure 3. Box plots of (A) current period and (B) future period seasonal isoprene emission rates for the eastern U.S. The vertical bars deﬁne the interquartile range (IQR). The horizontal bars mark the median. The vertical dashed line represents the upper range of emission rates (1.5 times the IQR). The small circles represent outlier values.

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model domain covers the contiguous U.S. at a 36 km grid resolution. Current and future simulations are 5 years each in length to account for interannual variability. Chemical boundary conditions were obtained from global chemical transport models (CTMs) driven by the same GCM used to drive the MM5/RCM downscaling (Section 2.1). Initial and domain boundary conditions for ozone, NOx and VOC species concentrations were obtained from Mickley et al. (1999), while aerosol species concentrations were computed from the uniﬁed tropospheric chemistry-aerosol model of Liao et al. (2003). Evaluation of a related global CTM at this spatial scale, i.e., 41 51, has shown spatial prediction patterns that were quite good but local maxima that were compromised (Fiore et al., 2003). Since we are using the global CTM predictions as background monthly average values, the coarse resolution should be sufﬁcient. Preliminary results for ozone and PM2.5 are presented here; more complete analyses, including a comparison with the results of Hogrefe et al. (2004), will be presented in a forthcoming paper.

2.3.2. Preliminary results

Surface-level ozone concentrations are of concern primarily during the summer months. Empirical cumulative distribution functions (CDFs) of maximum 8-h average ozone concentrations for June 1–August 31 of each current and future year simulated are plotted in Fig. 4. Future summer season ozone concentrations in the northeastern U.S. show no signiﬁcant change from current period simulations. Simulated future concentrations in the southeastern and western (not shown) U.S. are higher (shifted right) than current period estimates. Figure 5 shows a comparison of the frequency and duration of O3 episodes (deﬁned as grid cells where 8-h maximum concentrations exceeded 80 ppb). Again, future simulations in the northeastern U.S. show little change from the current period, but the frequency and duration of these events increase in the southeastern and western (not shown) U.S. data. Although average annual concentrations of future PM2.5 concentrations are somewhat lower than the current period, the spatial patterns are quite consistent (Fig. 6). Future frequency and duration of 24-h average PM2.5 concentration episodes, which exceed 35 mg m3 are reduced from those of the current period data (Fig. 7). The differences in PM2.5 are likely resulting from differences in transport or other meteorological factors. The speciﬁc causes are currently under investigation.

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Figure 4. Empirical CDFs of modeled maximum 8-h average surface ozone concentration (ppb) June 1–August 31 for ﬁve current and ﬁve future downscaled climate years for the northeastern and southeastern U.S.

3. Summary

Climate analysis results for the current (ca. 2000) summer season indicate that the MM5/RCM does not replicate the dominant summer weather pattern off the eastern portion of the continental U.S. domain. Although general weather patterns change little in the future (ca. 2050), 2 m temperatures are on average 2–3 K warmer over the southwest quadrant of

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Figure 5. Domain-wide frequency and duration of events during which modeled peak 8-h ozone concentrations exceeded 80 ppb during ﬁve current and future downscaled climate years for the northeastern and southeastern U.S.

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Figure 6. Annual average PM2.5 concentrations (mg m3) for (A) current and (B) future downscaled climate years.

the U.S. Other regions of the U.S. are also warmer, but generally by less than 1 K. A preliminary comparison of modeled current to future biogenic and mobile emission rates reﬂect expected geographic and interannual variability and a general increase in biogenic emissions in response to warmer future temperatures. Preliminary air quality modeling results identify regional differences in the response of current simulated 8-hour maximum ozone concentrations to future climate change ranging

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Figure 7. Domain-wide frequency and duration of days during which modeled 24-h average PM2.5 concentrations exceeded 35 mg m3 during ﬁve current and ﬁve future downscaled climate years for the northeastern and western U.S.

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from no difference (northeastern U.S.) to increased concentrations (southeastern and western U.S.). Annual average particulate matter concentrations and the frequency and duration of elevated particulate matter episodes decrease in the future period relative to current period simulations.

Discussion

A.-L. Norman: E.J. Cooter:

A. Ebel: E.J. Cooter:

D.W. Byun:

E.J. Cooter:

The decrease in PM2.5 with climate change is counterintuitive—is it related to decreases in sulphate? The decrease in PM2.5 is not due to changes in sulfate emissions, because anthropogenic emissions are held constant in these simulations. The decrease is evident in all components of PM and is not speciﬁc to sulfate. We suspect the decrease is due to a combination of increased precipitation and changes in ventilation, but cannot say for certain as yet. We hope to answer this question more deﬁnitively after further analysis. How are land surface changes treated in the emission scenario simulations? Land cover (type) and land use is held constant at current conditions in the results reported here. A Phase II study will begin shortly that will include alternative anthropogenic emission futures. We have the capacity to include appropriate population and/or economically driven land cover and land use changes associated with those future scenarios if they are provided to us. One statement you have made is that the regional climate model may not inherit the features of the global climate model due to the problem of downscaling. When we tested downscaling, depending on the method used, sometimes the regional model was ‘‘blind’’ to the change in the global model simulations. What do you do to ensure the climate change is still well presented during the downscaling? Dr. Ruby Leung of the DOE Paciﬁc Northwest National Laboratory generated our downscaled climate data using a regional climate version of MM5. Her previous experience working with the PCM Global Climate Model suggested an initial set of

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mesoscale model parameterizations that would best preserve the large-scale global model results. Testing prior to the ﬁnal production runs indicated changes were needed to the westward domain extent of the coarse 108 km rectangular mesoscale grid, increasing it from 67 89 grid points to 67 109 grid points. An alternative convection parameterization scheme (switch from Kain-Fritsch to Grell) was needed to preserve the NASA/GISS II’ model solution. It is important to note that a conscious decision was made to preserve the large-scale global model information, even if that meant poor or degraded performance relative to the observed present-day climate.

ACKNOWLEDGMENTS

The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and U.S. Department of Commerce’s National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW13921548. REFERENCES Benjey, W.G., Cooter, E.J., 2005. Interannual and Seasonal Variability of MeteorologicallyInﬂuenced Emissions. Proceedings of the 14th Emission Inventory Conference, LasVegas, Nevada, April 11–14, 2005. http://www.epa.gov/ttn/chief/conference/ei14/ session11/benjey.pdf Byun, D., Schere, K.L., 2006. Appl. Mech. Rev. 59, 51–59. Fiore, A.M., Jacob, D.J., Mathur, R., Martin, R.V., 2003. J. Geophys. Res. 108(D14), 4431. Grell, G.A., Dudhia, J., Stauffer, D., 1994. A description of the ﬁfth-generation Penn State/NCAR Mesoscale Model (MM5). In: NCAR Technical Note, NCAR/ TN-398+STR, National Center for Atmospheric Research, Boulder. Hogrefe, C., Lynn, B., Civerolo, K., Ku, J.-Y., Rosenthal, J., Rosensweig, C., Goldberg, R., Gafﬁn, S., Knowlton, K., Kinney, P., 2004. J. Geophys. Res. 109(D2), 2301, doi:10.1029/2004JD004690. Liao, H., Adams, P.J., Chung, S.H., Seinfeld, J.H., Mickley, L.J., Jacob, D.J., 2003. J. Geophys. Res. 108(D1), 4001, doi:10.1029/2001JD001260. Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P.C., Ebisuzaki, W., Jovic, D., Woollen, J., Robers, E., Berbery, E., Ed, M., Fan, Y., Grumbine, R., Higgins, W., Li, H., Lin, Y., Mankin, G., Parrish, D., Shi, W., 2006. Bull. Am. Meteorol. Soc. 87, 343–360.

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Mickley, L.J., Murti, P.P., Jacob, D.J., Logan, J.A., Koch, D.M., Rind, D., 1999. J. Geophys. Res. 104, 30153–30172. Rind, D., Lerner, J., Shah, K., Suozzo, R., 1999. J. Geophys. Res. 104(D8), 9151–9168. U.S. Environmental Protection Agency, 2004. EPA 2001 Modeling Platform. U. S. Environmental Protection Agency, Ofﬁce of Air Quality Planning and Standards, Research Triangle Park, NC 27711. Available on the Internet at: http://www.epa.gov/ ttn/chief/emch/invent/index.html (Accessed March 29, 2005).

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06062-7

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Chapter 6.2 Analyzing the impacts of climate change on ozone and particulate matter with tracer species, process analysis, and multiple regional climate scenarios$ C. Hogrefe, D. Werth, R. Avissar, B. Lynn, C. Rosenzweig, R. Goldberg, J. Rosenthal, K. Knowlton and P.L. Kinney Abstract This paper describes the application of tracer species and process analysis to study the simulated effects of regional climate change on air quality over the eastern United States. The coupled modeling system utilized in this study consists of the NASA Goddard Institute for Space Studies General Circulation Model (GISS-GCM), Fifth Generation Pennsylvania State University/National Center for Atmospheric Research Mesoscale Model (MM5), Sparse Matrix Operator Kernal Emissions (SMOKE) modeling system, and the US Environmental Protection Agency Community Multiscale Air Quality (CMAQ) model. Results show that climate change for the 2050s is expected to cause an increase in summertime average concentrations for a variety of primary and secondary pollutants. Through the use of CO-like chemically inert tracer species it is found that changes in physical parameters such as boundary layer ventilation and stagnation cause an increase in primary pollutant concentrations, but that actual CO concentrations show an even larger increase that points to a contribution from increased chemical reaction as well. This ﬁnding is corroborated through the use of process analysis that reveals increased chemical production of O3 and total odd oxygen as well as an increase in the rate of radical initiation. In an attempt to study the robustness of the simulated changes in pollutant concentrations towards the choice of $

Although the research described in this article has been funded in part by the U.S. Environmental Protection Agency and the National Oceanic and Atmospheric Administration, it has not been subjected to their required peer and policy review. Therefore, the statements, ﬁndings, conclusions, and recommendations are those of the authors and do not necessarily reﬂect the views of these agencies and no ofﬁcial endorsement should be inferred.

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physics options in the MM5 regional climate model, CMAQ simulations were performed with two sets of MM5 conﬁgurations under both current and future climate scenarios. While the magnitude of changes in climate parameters and pollutant concentrations shows differences between the two sets of simulations, the directionality of concentrations changes was found to be robust towards the choice of physics options in the MM5 regional climate model. This ﬁnding implies that performing future regional climate ensemble modeling studies could help to quantify the uncertainty around simulated pollutant changes as a result of regional climate change. 1. Introduction

Possible changes in regional-scale O3 and PM2.5 concentrations due to climate change are the result of a complex interplay of physical and chemical processes such as changes in chemical reaction rates, changes in biogenic emissions, and changes in transport patterns and mixing heights. The results presented in this paper build upon recent studies by Hogrefe et al. (2004a, b, 2005) who showed increased O3 and PM2.5 concentrations over the eastern United States in future decades for a speciﬁc regional climate change scenario. In this paper, we expand upon this work by studying the relative contributions of various physical and chemical processes to simulated changes in pollutant concentrations through the use of tracer species and the process analysis feature contained in the Community Multiscale Air Quality (CMAQ) model. Furthermore, in an attempt to study the robustness of the results toward the choice of physics options in the MM5 regional climate model, CMAQ simulations were performed for two sets of MM5 conﬁgurations under both current and future climate scenarios. 2. Modeling setup

Emissions projections for greenhouse gases are used as inputs to the global and regional climate models to simulate future climate conditions. In this paper, we utilize the greenhouse gas projections of the IPCC SRES A2 marker scenarios, one of the more pessimistic SRES marker scenarios characterized by a large increase of CO2 emissions (IPCC, 2000). As described in Hogrefe et al. (2004a, b), the county-level U.S. EPA 1996 National Emissions Trends (NET96) inventory processed by SMOKE was used for the air quality simulations under both the current and future climate scenarios. Current and future year regional climate ﬁelds were obtained by coupling the MM5 mesoscale model (Grell et al., 1994) to the Goddard

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Institute for Space Studies (GISS) 41 51 resolution Global AtmosphereOcean Model (GISS-GCM) (Russell et al., 1995) through initial conditions and lateral boundaries. Simulations were performed for ﬁve consecutive summer seasons (June–August) in the 1990s and 2050s at a horizontal resolution of 36 km over the eastern United States. To test the sensitivity of the air quality simulations toward uncertainties in the regional climate model, we utilized two sets of MM5 conﬁgurations for both decades. In one conﬁguration, convective clouds were parameterized by the Betts–Miller scheme (Betts, 1986) and in the other conﬁguration the Grell scheme (Grell et al., 1994) was used. Hereafter, these two conﬁgurations are referred to as MM5-BM and MM5-G. The analysis presented in Sections 3.1 and 3.2 is based on simulations with the MM5-BM conﬁguration, while Section 3.3 compares results obtained by using either MM5-BM or MM5-G. Further details on the setup of this modeling system and results of the future regional climate simulations are described in Lynn et al. (2004). Using the processed emissions and MM5 regional climate simulations, air quality simulations were performed using the CMAQ model (Byun and Ching, 1999). Details of the model setup are described in Hogrefe et al. (2004a, b). Here, we expanded upon this earlier work by including the aerosol, tracer species, and process analysis modules into the CMAQ simulations (Jeffries and Tonnesen, 1994; Byun and Ching, 1999). Two chemically inert tracer species, CO_T1 and CO_T2, were deﬁned as follows: CO_T1 is subject to all transport and removal processes within the modeling domain and its only source are initial and boundary conditions identical to those of CO. CO_T2 is deﬁned similarly, except that it also has an emissions source term identical to that of CO.

3. Results and discussion 3.1. Changes in regional climate and pollutant concentrations

Figure 1 shows the changes in summertime average surface level climate parameters and selected pollutant concentrations between the 2050s and 1990s. The results displayed in this ﬁgure are for the MM5-BM/CMAQ simulations and are averaged over all hours and all non-water grid cells which are located at least 10 grid cells away from the domain boundary. Out of the climate parameters, temperature, solar radiation, cloud cover, and water vapor all show an increase. By contrast, wind speed, boundary layer height, and boundary layer ventilation calculated as wind speed vertically integrated throughout the boundary layer show a decrease, and the persistence of low-ventilation events (determined as the number of

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Nitrate(0.5 ug/m3)

OC (0.8 ug/m3)

Sulfate (6 ug/m3)

PM2.5 (10.8 ug/m3)

Ozone (49 ppb)

CO (230 ppb)

Crustal (1.2 ug/m3)

EC (0.3 ug/m3)

CO_T2 (223 ppb)

CO_T1 (76 ppb)

Persistence (1.32 d)

Water Vapor (11g/kg)

Radiation (222 W/m2)

Clouds (45%)

Ventilation (7200 m2/s)

-20.0

PBL Height (946 m)

0.0 Wind Speed (3.2 m/s)

Change 2050s - 1990s (%)

20.0

Temperature (23.1 C)

40.0

-40.0

Figure 1. Changes in summertime average surface level climate parameters and selected pollutant concentrations between the 2050s and 1990s. The results displayed in this ﬁgure are for the MM5-BM/CMAQ simulations and are averaged over all hours and all non-water grid cells which are located at least 10 grid cells away from the domain boundary. Average values for the 1990s are also shown for each variable for reference.

consecutive days with boundary layer ventilation below the 10th percentile) shows an increase. This is indicative of a future climate in which there is less exchange of air masses and the potential for an increase in the rate of temperature- and radiation-dependent chemical reactions. Indeed, the chemically inert pollutants CO_T1, CO_T2, EC, and crustal PM2.5 all show increases that are consistent with the notion of decreased vertical mixing, boundary layer ventilation, and increased persistence of low ventilation events. The chemically active pollutants CO and O3 both show increases that are larger than those for the inert pollutants. Total PM2.5 and sulfate also show increases, while nitrate and organic carbon concentrations show decreases. The increase of sulfate concentrations is consistent with a more active photochemical regime indicated by higher temperatures and O3 concentrations. Increased temperatures are also likely the cause for the decreases in nitrate and organic carbon concentrations since the gas/particle partitioning of these species is highly temperature dependent. In the following sections, the changes in pollutant concentrations shown in Fig. 1 are further analyzed utilizing tracer species and process analysis, and the results are compared with those obtained from a simulation with alternate regional climate ﬁelds.

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3.2. Utilizing tracer species and process analysis to study simulated changes in pollutant concentrations

Figure 2 displays spatial patterns of percentage changes in the concentrations of the two chemically inert tracer species CO_T1 and CO_T2 described in Section 2 as well as results for the chemically active species CO for comparison. The left panel in Fig. 2 illustrates that regional climate change simulated by MM5-BM for the 2050s causes a slight increase of CO_T1 over the northern portion of the modeling domain, while there is a slight decrease over the ocean in the southeastern corner. The center panel shows results for the tracer CO_T2, and it is evident that concentration changes are generally positive except for the extreme southern portion of the domain and are larger than for the tracer species CO_T1. This indicates that chemically inert pollutants having emissions sources within the modeling domain are expected to show a larger increase in concentrations than those being advected into the modeling domain through the domain boundaries only. The right panel displaying concentration changes for the chemically active species CO reveals even larger percentage increases in pollutant concentrations, indicating that enhanced chemical formation contributes to higher CO concentrations beyond the effects of physical processes affecting increases in CO_T1 and CO_T2. All panels illustrate that the effects of climate change exhibit spatial variability, but generally lead to increased pollutant concentrations over the modeling domain. To further study the relationship between regional climate change and simulated changes in pollutant concentrations, we employed the integrated process rate (IPR) and integrated reaction rate (IRR) features of CMAQ (Jeffries and Tonnesen, 1994; Byun and Ching, 1999). The purpose of IPR is to track the contribution of various physical and chemical processes to the hourly rates of change of pollutant concentrations, while the purpose of IRR is to track the contribution of individual chemical reactions. In the context of the present study, it is important to note that changes between concentrations in the current and future climate scenarios cannot be observed on an hour-by-hour basis because the meteorological regime for any given hour in the current climate does not correspond to the same meteorological regime for the same hour in the future climate case. Therefore, just as the effect of climate change on the pollutant concentrations displayed in Fig. 1 was quantiﬁed by taking the differences of summertime average concentrations, the individual process rates from the two climate scenarios have to be aggregated temporally and were also aggregated spatially as for Fig. 1. Furthermore, for the present analysis, the individual IPR terms were aggregated to a physics term (sum of horizontal and vertical advection and

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Figure 2. Spatial patterns of percentage changes in the concentrations of the two chemically inert tracer species CO_T1 (left) and CO_T2 (center) described in Section 2 as well as results for the chemically active species CO (right).

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diffusion, mass adjustment, dry deposition, emissions, scavenging, aqueousphase chemistry, and vertical cloud mixing), and a chemistry term (gasphase or aerosol module chemical production and loss). Figure 3 shows the average diurnal cycles of the physics and chemistry terms for surface level O3 determined from IPR. The chemistry process rates are positive during daytime and negative during nighttime, indicating daytime photochemical production and nighttime depletion by titration with NO. Physics process rates are positive during the early morning hours indicating downward transport of higher O3 levels aloft but turn negative as vertical transport becomes a net sink of surface level O3 after 10 a.m. local time. The strength of both the physics and chemistry term increases from the 1990s to the 2050s. The greater level of chemical production of O3 is consistent with the higher temperature and radiation displayed in Fig. 1. Table 1 provides further detail on the rates of chemical production, chemical loss, and net changes due to chemistry determined from IRR for O3 and total odd oxygen Ox (the sum of O3, NO2, 2 NO3, O, O1D, PAN, 3 N2O5, HNO3, NHO4, and organic nitrate) as well as the rate of radical initiation and the OH propagation ratio and chain length; all values were calculated for daytime hours. Net average hourly O3 production rates increased from 2.44 to 3.17 ppb h1. While some of this net increase in chemical production of O3 is balanced by an increased sink through physical processes as displayed in Fig. 2, the remaining portion 6 Chemistry_1990s Chemistry_2050 Physics_1990s Physics_2050

5 4 3

ppb/hr

2 1 0 1

3

5

7

9

11 13 15 17 19 21 23

-1 -2 -3 -4 Figure 3. Average diurnal cycles of the IPR physics and chemistry terms for surface level O3 for the 1990s and 2050s as deﬁned in the text.

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Table 1. Aggregated chemical production and loss terms determined from IRR

Net O3 production Total Ox production Total Ox loss Net Ox production Total new OH Total OH reacted OH terminated OH yield per HO2 OH chain length

1990s (ppb h1)

2050s (ppb h1)

2050s–1990s (ppb h1)

Percentage change

2.44 5.02 0.79 4.23 0.62 3.42 0.36 0.80 4.09

3.17 6.05 1.03 5.02 0.77 4.09 0.40 0.76 3.94

+0.73 +1.03 +0.24 +0.79 +0.15 +0.67 +0.04 0.04 0.15

+29.9 +20.5 +30.0 +18.7 +24.2 +19.5 +9.7 5.0 3.6

of this increased net production likely causes the overall increase in average O3 concentrations displayed in Fig. 1. For total Ox, hourly net production rates change by 0.79 ppb h1 from 4.23 to 5.04 ppb h1. It can also be seen that there is a 24.2% increase in the rate of new OH production from the 1990s to the 2050s, as well as an increase in OH propagation and termination. Within the OH/HO2 cycle, there is decrease in the yield of OH per HO2, leading to an overall decrease in the OH chain length (Jeffries and Tonnesen, 1994) from 4.09 to 3.94. These results further demonstrate that regional climate change as simulated by MM5BM over the eastern United States causes increased concentrations of secondary pollutants not only through a modiﬁcation of transport patterns and airmass exchange, but also through a more active photochemical system. A further application of IRR is illustrated in Fig. 4. To examine the likely reasons behind the decrease of total particulate OC shown in Fig. 1, the total change in the oxidation of total VOC and isoprene by OH between the 1990s and 2050s was determined from IRR and was found to be positive (Fig. 4). Changes in primary OC stemming from emissions were also positive, while changes in secondary OC stemming from the oxidation and subsequent condensation of anthropogenic and biogenic VOC were found to be negative. This supports the notion that a shift in the gas/particle partitioning due to increased temperatures is responsible for the decrease in SOC as well as total OC despite an increase in the oxidation of gas-phase VOC. 3.3. Alternate regional climate fields

Lynn et al. (2004) have shown that changing the cumulus cloud parameterization in MM5 from the Betts–Miller scheme to the Grell scheme

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656 40 Isoprene with OH

Percentage Change

All VOC with OH

20

POC

0 Total OC

SOC_Anthro

SOC_Bio

-20 Figure 4. Total change in the oxidation of all VOC and isoprene by OH between the 1990s and 2050s as from IRR as well as changes in concentrations of primary organic carbon (POC), secondary organic carbon from anthopogenic and biogenic sources (SOC_Anthro and SOC_Bio), and total OC.

causes differences in the simulated spatial patterns of average temperatures, precipitation, and clouds over the eastern United States for both the 1990s and 2050s. Furthermore, the spatial patterns and magnitude of changes in temperatures and other climate parameters also vary between the models, with the MM5-BM conﬁguration typically predicting a larger degree of warming for the 2050s A2 scenario compared with the MM5-G conﬁguration (Lynn et al., 2004). However, it should be noted that the predictions of climate change from both MM5-BM and MM5-G lie within the span of climate predictions from several global models for the 2050s A2 scenario (Houghton et al., 2001). Therefore, these different conﬁgurations should be viewed as two members of a larger ensemble of possible future regional climate scenarios for the purpose of this study. Figure 5 displays the spatially averaged changes in summertime surfacelevel climate parameters and selected pollutant concentrations. While the magnitude of changes in the different parameters varies depending on the MM5 conﬁguration utilized, the directionality of these changes is consistent for all variables except solar radiation. Compared with the MM5BM results shown above, the MM5-G results are characterized by less warming, a stronger decrease in wind speeds and boundary layer height,

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Crustal

EC

Sulfate

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PM25

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Radiation

CO

MM5-BM MM5-G

Ozone

Temperature

0 Wind

Change (%)

12.5

WaterVapor

25

-25

Nitrate

-12.5

Figure 5. Spatially averaged changes in summertime surface-level climate parameters and selected pollutant concentrations between the 1990s and 2050s as simulated by MM5-BM/ CMAQ and MM5-G/CMAQ.

and a stronger increase in cloudiness. This causes a slightly larger percentage increase in the concentrations of inert PM2.5 species and a smaller increase in CO and O3. While the consistent directionality of the simulated changes is encouraging, these results point to the need to perform ensemble modeling studies of future regional climate and air quality to better estimate the uncertainty of simulated changes and their spatial variability.

4. Summary

This paper describes the application of tracer species and process analysis to study the simulated effects of regional climate change on air quality over the eastern United States. Results show that climate change under the IPCC A2 emissions scenario for the 2050s is expected to cause an increase in summertime average concentrations for a variety of primary and secondary pollutants. Through the use of CO-like chemically inert tracer species, it is found that changes in physical parameters such as boundary layer ventilation and stagnation cause an increase in primary

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pollutant concentrations, but that actual CO concentrations show an even larger increase that points to a contribution from increased chemical reaction as well. This ﬁnding is corroborated through the use of IPR and IRR process analysis that reveals increased chemical production of O3 and total odd oxygen as well as an increase in the rate of radical initiation. In an attempt to study the robustness of the simulated changes in pollutant concentrations toward the choice of physics options in the MM5 regional climate model, CMAQ simulations were performed with two sets of MM5 conﬁgurations under both current and future climate scenarios. While the magnitude of changes in climate parameters and pollutant concentrations shows differences between the two sets of simulations, the directionality of concentrations changes was found to be robust toward the choice of physics options in the MM5 regional climate model. This ﬁnding implies that performing future regional climate ensemble modeling studies could help to quantify the uncertainty around simulated pollutant changes as a result of regional climate change.

Discussion

P. Suppan:

C. Hogrefe:

S. Andreani-Aksoyoglu:

C. Hogrefe:

O. Hellmuth:

The results are based on a ﬁve-year period. From a climate point of view, this is a very short time to be statistically robust. Do you think the results will change if you use a longer period to overcome the short-term uncertainty? I would expect possible changes in the magnitude but not the directionality of the results. Comment: SOC decrease in future is based on partitioning theory. If you include polymerization processes, the direction might change. Your observation is correct. Polymerization processes were not considered in the version of the model we were using for our study, but their inclusion might diminish or even reverse the SOC decrease present in our simulations. Can you comment on the link between increasing temperature and decreasing ventilation index? Increased O3

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production in the lowest layer is expected to be over-compensated by loss due to increased vertical exchange. VC ¼ zi jV h j

C. Hogrefe: C. Mensink:

C. Hogrefe:

(a) increased exchange coefﬁcient leads to an increased zi and thus to increased VC; (b) decreased jVhj leads to decreased VC. I guess that the increased temperature reﬂects the more frequent occurrence of high pressure systems which are associated with calms, hence decreasing VC. The increased vertical exchange is overcompensated by decreased wind velocity, so the net effect is the decrease in VC. I agree with your interpretation of this phenomenon. In the ﬁgure showing the changes 1990–2050 for PM2.5, is there any feedback on the biogenic emissions included? Biogenic emissions were computed taking into account the changes in climate ﬁelds between 1990 and 2050. Therefore, we did include the effect of increased temperature on biogenic emissions. However, we did not modify the landuse data base to include the possible feedback of climate change on vegetation distributions.

ACKNOWLEDGMENTS

This work has been supported by the U.S. Environmental Projection Agency under STAR grant R-82873301 and the National Oceanic and Atmospheric Administration under award NAO40AR4310185185 and contract EA133R-05-SE-5953. REFERENCES Betts, A.K., 1986. A new convective adjustment scheme. Part I: Observational and theoretical basis. Q. J. R. Meteorol. Soc. 112, 677–692. Byun, D.W., Ching, J.K.S. (Eds.), 1999. Science algorithms of the EPA Models-3 Community Multiscale Air Quality Model (CMAQ) modeling system. EPA/600/R-99/030.

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U.S. Environmental Protection Agency, Ofﬁce of Research and Development, Washington, DC. Grell, G.A., Dudhia, J., Stauffer, D., 1994. A description of the ﬁfth-generation Penn State/ NCAR Mesoscale Model (MM5). NCAR Technical Note. TN-398+STR, National Center for Atmospheric Research, Boulder, CO, p. 138. Hogrefe, C., Biswas, J., Lynn, B., Civerolo, K., Ku, J.-Y., Rosenthal, J., Rosenzweig, C., Goldberg, R., Kinney, P.L., 2004. Simulating regional-scale O3 climatology over the Eastern United States: Model evaluation results. Atmos. Environ. 38, 2627–2638. Hogrefe, C., Lynn, B., Civerolo, K., Ku, J.-Y., Rosenthal, J., Rosenzweig, C., Goldberg, R., Kinney, P.L., 2004b. Simulating changes in regional air pollution due to changes in global and regional climate and emissions, J. Geophys. Res. 109(D22), D22301, 10.1029/2004JD004690. Hogrefe, C., Lynn, B., Rosenzweig, C., Goldberg, R., Civerolo, K., Ku, J.-Y., Rosenthal, J., Knowlton, K., Kinney, P.L., 2005. Utilizing CMAQ Process Analysis to Understand the Impacts of Climate Change on Ozone and Particulate Matter, Models-3 Users’ Workshop, September 26–28, Chapel Hill, NC. Available online http://www.cmascenter.org/html/2005_conference/abstracts/3_2.pdf Intergovernmental Panel on Climate Change, 2000. In: Nacenovic, N., Swart, R., (Eds.), Special Report on Emissions Scenarios. Cambridge University Press, Cambridge, UK, p. 612. Jeffries, H.E., Tonnesen, S., 1994. A comparison of two photochemical reaction mechanisms using mass balance and process analysis. Atmos. Environ. 28, 2991–3003. Lynn, B.H., Druyan, L., Hogrefe, C., Dudhia, J., Rosenzweig, C., Goldberg, R., Rind, D., Healy, R., Rosenthal, J., Kinney, P., 2004. On the sensitivity of present and future surface temperatures to precipitation characteristics. Climate Res. 28, 53–65. Russell, G.L., Miller, J.R., Rind, D., 1995. A coupled atmosphere-ocean model for transient climate change studies. Atmos. –Ocean. 33, 683–730.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06063-9

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Chapter 6.3 Dimethylsulﬁde (DMS) ﬂux and DMS oxidation over the North Atlantic: Comparison of a top-down and a bottom-up approach A.L. Norman, M.A. Wadleigh, S. Eaton, C. Burridge, C. Zaganescu, J.P. Blanchet, M. Scarratt, S. Michaud, M. Levasseur, A. Merzouk, M. Lizotte, S. Sharma and R. Leaitch Abstract Dimethylsulﬁde (DMS) is a gas that forms in some biologically productive regions of the surface ocean. Only a few percent of the total DMS in surface waters is released to the atmosphere but, because it has the potential to form new CCN on oxidation to sulfate, this ﬂux may be an important variable to understand in a changing climate scenario. DMS ﬂuxes in this study were used in a comparative approach to constrain DMS oxidation pathways. A traditional approach to calculate DMS ﬂux (bottom-up) used DMS concentrations in surface waters and the parameterization of Wanninkhof and McGillis (1999) for piston velocity. The bottom-up surface water DMS ﬂuxes were calculated for the SOLAS SABINA Spring cruise over the North Atlantic: 30–601N from April 25–May 14, 2003. These ﬂuxes were compared to a top-down approach using a steady-state oxidation model where corresponding DMS mixing ratios in the air over the Atlantic, and boundary layer heights from the NARCM model were incorporated. In the simplest scheme, using OH alone as an oxidant, the top-down and bottom-up approaches showed poor agreement in terms of both the magnitude of DMS ﬂux and its temporal variations. Better sensitivity for DMS ﬂux was achieved using OH plus NO3 oxidation, but temporal variations were still poorly represented. Adding very small amounts of BrO to the oxidation scheme improved the agreement between the top-down and bottom-up approaches, but only slightly. Transport of DMS was examined during a high atmospheric DMS concentration event using

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estimates for the spatial distribution of atmospheric DMS from the NARCM model using the Kettle et al. (1999) database and could be responsible for the poor agreement between ﬂux estimates during high wind regimes. However, the poor agreement between the two approaches during low wind regimes suggests that the oxidation pathway for DMS over the North Atlantic is more complex than at low latitudes where OH oxidation is dominant.

1. Introduction

Aerosol formation in the remote marine atmosphere is postulated to be an important negative feedback to a warmer global climate. Charlson et al. (1987) proposed that oxidation of atmospheric dimethylsulﬁde (DMS), a gas released from the ocean during turnover of microalgae populations in the ocean’s surface, could form new aerosols and precipitate a cooling response to warmer ocean surface waters. New aerosols in the submicron diameter range form cloud condensation nuclei (CCN) that inﬂuence the radiation budget directly through scattering of incident radiation back to space, and indirectly by affecting cloud properties. DMS oxidation is another important source of SO2 and aerosol sulfate that may contribute to dimming, but the spatial and temporal extent of changes in DMS concentrations in the atmosphere is poorly constrained. A comparison of seasonal DMS ﬂuxes and atmospheric concentrations for the North Atlantic is described using both modeling and measurement approaches. The sea-to-air ﬂux (bottom-up) method incorporated measured surface ocean DMS concentrations from a seasonal study over the North Atlantic in 2003 as part of the Canadian Surface Ocean Lower Atmosphere Study (C-SOLAS) Study of the Air–Sea Biogeochemical Interactions in the North Atlantic (SABINA) program (e.g., Le Clainche et al., 2005). Fluxes were calculated from measurements of DMS in surface waters using Wanninkhof and McGillis’s (1999) parameterization. These ﬂuxes were compared to those calculated from atmospheric DMS concentrations (top-down) in a steady-state model similar to one described by Chen et al. (2000) and Kouvarakis and Mihalopoulos (2002) using boundary layer heights from the Northern Aerosol Regional Climate Model (NARCM). Further, atmospheric DMS mixing ratios from the cruise were compared to similar values for surface level DMS from the NARCM, which uses the Kettle database.

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1.1. Bottom-up approach 1.1.1. DMS flux: Sea-to-air transfer

Calculations of DMS ﬂuxes over the remote ocean are typically based on surface water DMS concentrations (Cw) and a parameterization based on wind speed. In this study, we have used the parameterization of Wanninkhof and McGillis (1999) to calculate ventilation rates (KWM). Flux (F) is calculated using the difference in surface water and atmospheric concentration (in mmol L1) divided by the dimensionless Henry’s Law constant (H). Atmospheric DMS concentrations (Ca) are often too small to impact ﬂux calculations and the second term is often neglected and this was true for the results presented here. However, the companion SOLAS study of the North Paciﬁc (Sub-Arctic Iron Enrichment Study, SERIES, Wadleigh, M., personal communication, June 2004) showed that DMS ﬂuxes changed by as much as 50% if Ca was included. Ca F SA ¼ K WM C w H 1.1.2. DMS flux: Oxidation model

A second method used to calculate ﬂux was based on a steady-state model using atmospheric DMS concentrations, NARCM boundary layer height (BLH), OH as a function of temperature, plus NO3 and BrO oxidation as input parameters (Chen et al., 2000; Wadleigh et al., 2004). A diurnal pattern was assumed for the hydroxyl and nitrate radicals with maximum values of 9.85 105 and 1.75 107 molecules cm3 during the day and night, respectively (Fig. 1). Maximum OH number density was based on OH concentrations during July at 501N (Lawrence et al., 2001). Gaseous nitrate was not measured in this study but aerosol nitrate was. The lowest aerosol NO3 concentration, reduced by a factor of 50, was used as a ﬁrst estimate of the maximum value for gaseous nitrate. Entrainment ﬂux (E), representing mixing between the MBL and the buffer layer, was calculated assuming a buffer layer DMS concentration of 10% of that in the boundary layer (Andreae et al., 1993; Faloona et al., 2005). The model considers that a steady state was achieved (d[DMS] BLH/dt ¼ 0) between production (DMS ﬂux, F), loss (oxidation by OH, NO3, and BrO), and entrainment (E). Fluxes derived from sea-to-air transfer (bottom-up) and identiﬁed with the subscript SA, were compared to the DMS ﬂux from the oxidation model (top-down),

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Figure 1. Diel cycle assumed for OH and NO3 number density.

subscript AIR: d½DMS BLH ¼ F ½DMS BLH ðK OH ½OH þ K NO3 ½NO3 Þ dt E DMS F AIR ¼ C a BLH ðK OH ½OH þ K NO3 ½NO3 þ K BrO ½BrOÞ E where E SO2 ¼ 0:1 ½DMS ve ; ve ¼ 0.35 cm s1 (Andreae et al., 1993; Faloona et al., 2005), KOH ¼ 1.13 1011 e(253/T) cm3 molecule1 s1 (DeMore et al.,1997), and K NO3 ¼ 1:1 1012 cm3 molecule1 s1 ; and KBrO ¼ 2.54 1014 e(850/T) cm3 molecule1 s1 (Ingham et al., 1999) 1.1.3. NARCM atmospheric DMS concentrations

Kettle et al. (1999), Kettle and Andreae (2000) compiled information on discrete shipboard and coastal DMS measurements for use in an oceanic DMS ﬂux database. This database was used in the NARCM to determine atmospheric DMS concentrations and DMS ﬂux (FMO) for comparison with measurements and the bottom-up DMS ﬂux approach described above. The model, driven by NCEP reanalysis on lateral boundary, was run at 50-km horizontal resolution using a polarstereographic grid with 79 points on the X-axis and 67 points on the Y-axis centered at 451N, 451W for the spring SABINA cruise. A full description of the aerosol module of the model is provided by Gong et al. (2003). Surface layer atmospheric DMS concentrations for select vertical proﬁles from the spring transect were computed to compare with measurements.

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1.2. DMS oxidation products

Modeling DMS oxidation and subsequent biogenic sulfate nucleation over the remote ocean has been the subject of numerous modeling exercises previously, but none of these studies were able to distinguish anthropogenic from biogenic SO2 and sulfate as reported here (Yvon and Saltzman, 1996; Capaldo et al., 1999; Davis et al., 1999; Chen et al., 2000; Sciare et al., 2000; Yoon and Brimblecomb, 2002; von Glasow and Crutzen, 2004). In addition to measurements of DMS and aerosol MSA concentrations in this study, we have used isotope apportionment techniques to calculate DMS-derived SO2 and aerosol sulfate to compare with model outputs. The timing in DMS oxidation products should be coincident with peaks in DMS ﬂux derived from the three approaches.

2. Methods

A seasonal transect measuring DMS in and above the North Atlantic from 301N to 601N was conducted as part of the C-SOLAS SABINA program in 2003 (Fig. 2). Five-minute atmospheric DMS samples were collected each hour throughout the spring (April 25–May 14), cruise providing more than 400 analyses for comparison with models. Surface water DMS concentrations were measured each hour from May 5 to May 10, throughout the cruise. Details of measurement methods for atmospheric DMS and ocean DMS concentrations can be found in Wadleigh, M., personal communication, June 2004 and Levasseur et al. (2004), respectively. Total aerosol and size fractionated sulfate and MSA, as well as SO2, were collected using a suite of four high-volume samplers located on the topmost deck of the ship. Two samplers were used to collect total and size segregated aerosols and SO2 during the day, and two more were used for nighttime sampling. Sampling frequency was limited by the minimum amount of sample needed for isotope analysis, 10 mg of S; therefore in some cases, samples were collected during the day (night) on consecutive days to provide sufﬁcient material for analysis. In total, more than 18 aerosol and SO2 sample suites were collected. Sample extraction and preparation for isotope and ion and elemental analysis has been documented elsewhere (Norman et al., 2004). The isotope composition and ion and elemental concentrations were corrected for ﬁeld blanks. Isotope apportionment was performed based on mass balance calculations assuming Na as a conservative tracer of seawater

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Figure 2. Map showing a series of Lagrangian (L) and Transect (T) stations at and between which DMS and aerosol sampling was conducted.

sulfate (SS): d34 S ¼ d34 SSS f SS þ d34 SA f A þ d34 SB f B d34S values express the difference in the ratio of 34S to 32S in a sample relative to an international standard in parts per thousand (%). This value is assumed to be constant for each of the three sources: sea salt d34S ¼ +21%, anthropogenic d34SA ¼ +2%, and biogenic d34SB ¼ +18%. The fractional contribution (f) from each of these

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sources is then calculated using the mass ratio of SO4/Na ¼ 0.252 as a constraint for seawater. Measurement uncertainties (1s) for isotope ratios were 70.4% based on replicate measurements. Uncertainties in concentrations for SO2, sulfate, sodium, and MSA were 710%.

3. Results and discussion 3.1. Top-down versus bottom-up

Atmospheric DMS mixing ratios for the spring averaged 0.1470.05 ppbv, with little evidence of a diurnal cycle. Companion measurements of surface water DMS concentrations were 1.771.4 nmol L1. The simplest model for the top-down approach in calculating DMS ﬂux (FAIR) for comparison with ﬂuxes calculated using the Wanninkhof and McGillis (1999) parameterization for FSA (bottom-up: connected points in Fig. 3) considered only OH oxidation during the day as a ﬁrst step in the analysis. Fluxes calculated using OH only ranged from 0.85 to 1.04 mmol m3 day1 and showed no relationship to FSA. This suggested another oxidation term was required. Higher values for OH under cloudy conditions in spring could not be rationalized. Note that ﬂuxes were orders of magnitude too large if entrainment was ignored. Polluted air masses traveling over the North Atlantic from eastern North America are expected to contribute to high NO3 conditions in the MBL, particularly during the night when NO2 photolysis is shut down. Addition of NO3 as an oxidation pathway for DMS did not give good agreement between FAIR and FSA (r2 ¼ 0.17, Fig. 3, dark solid line) and values for FAIR were generally too small. Since NO3 concentrations were unknown, the model was tested using varying NO3 and OH concentrations. The model was sensitive to NO3 and it was found that values an order of magnitude higher than those shown in Fig. 1 gave roughly the correct magnitude for DMS ﬂux as determined by FSA (Fig. 3, dashed line). However, maxima and minima for the two approaches did not coincide. Both OH and NO3 oxidation are expected to contribute to a dial cycle for DMS oxidation (Fig. 3). The model FAIR using OH and 10 NO3 is in poor agreement with the magnitude and the variation in FSA and peaks in DMS ﬂux using the two approaches were not coincident except on May 7 in the afternoon. Von Glasow and Crutzen (2004) have discussed BrO as a possible oxidant for DMS. When very low BrO mixing ratios were added to the model (12.5 molecules cm3), trends for DMS ﬂux from

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Figure 3. DMS ﬂux from the bottom-up approach (FSA—squares connected by a line) compared to selected top-down approaches (FAIR) described in the text.

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the two approaches were in better agreement. In fact the agreement was best (r2 ¼ 0.34) when BrO was used in combination with 10 NO3 and OH and an offset of 37 h was applied. The rationale for applying an offset is described below. Figure 3 shows that the combination of OH, 10 NO3, and BrO with an offset (light colored solid line), approximates the magnitude of DMS ﬂux in late evening on May 5, 6, and 7 (Julian Date (JD) 124126), more closely than any of the other models. However peaks in DMS ﬂux using FAIR in late morning on May 6 and May 9 are not seen in FSA. Transport of DMS in the atmosphere may be one reason that daytime and nighttime ﬂuxes using the two models did not correspond. Atmospheric and surface ocean measurements typically do not represent the same spatial scales and this is reﬂected in the offset. An offset of 37 h provided the best ﬁt between the top-down (FAIR) and bottom-up (FSA) results. When the ship encountered air masses from regions upwind, and these regions were represented by the surface waters sampled later, then results for FAIR and FSA should coincide with some lag time. This could explain the reasonable agreement in FAIR and FSA with a time lag of 37 h on speciﬁc occasions. As an example, the late afternoon of May 6 was examined below to see whether atmospheric transport was feasible.

3.2. Atmospheric DMS measurements: Model comparison

Atmospheric DMS concentrations at select points along the ship’s trajectory were obtained from the surface level values where vertical proﬁles were extracted from the NARCM runs. Figure 4 shows the vertical proﬁle for DMS concentration at approximately 401390 N, 481010 W on May 6, 2003 (JD 126) at 19:30 UTM. Typically, maximum DMS concentrations were observed at sea level, and this is consistent with the negative value for entrainment in the equations for FAIR above. A comparison between atmospheric DMS concentrations from the NARCM, measurements, and DMS oxidation products is shown in Fig. 5. On average, NARCM mixing ratios were 0.35 ppbv higher than measured values, though smoothing of the measured data should be performed to better reﬂect the larger scales represented by the NARCM results. Clear diurnal cycles in biogenic SO2 and MSA were observed at the beginning of the experiment but were not reﬂected in atmospheric DMS concentrations. Wind speeds during the cruise were high, averaging 7.7 m s1. Differences in lifetime for DMS and its oxidation products may be responsible for a lack of observed correspondence between

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Figure 4. Vertical proﬁle for DMS concentrations above 401390 N, 481010 W on May 6, 2003, at 19:30 UTM.

Figure 5. Comparison of atmospheric DMS concentrations from measurements (dark line), to those from NARCM (circles). MSA and biogenic biogenic SO2 are shown for comparison.

DMS parameters. Biogenic SO2 and MSA may have been transported from an upwind site and atmospheric DMS sampled may not have experienced sufﬁcient oxidation to have inﬂuenced the measurements for SO2 and MSA.

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The average atmospheric DMS concentrations in the boundary layer and the average DMS ﬂux from the NARCM for the period of the spring SABINA cruise are plotted in Fig. 6. As described in the previous section, a peak in DMS ﬂuxes (FAIR) was modeled for May 5 (JD 125—not shown in Fig. 3), but a FSA peak of the same magnitude and duration occurred 37 h later (May 7, JD 127) and potentially could reﬂect atmospheric transport. It is worthwhile examining the ship’s position and the wind direction relative to the maximum DMS concentrations from the NARCM to see whether the DMS gradient was positive during that time. The ship was at the southernmost station T2 on JD 125 and transiting to T3 and T4 on days 126 and 127: as can been seen from Fig. 6, the ship was clearly moving toward regions with higher atmospheric DMS concentration during this portion of the cruise and toward regions with higher ﬂux. Winds were mainly from the south on May 5 (JD 125) switching to the NE on May 6 and 7 (JD 126, 127) strengthening the argument that DMS was transported from regions of higher ﬂux toward the ship during this period and providing justiﬁcation for the 37 h offset between FSA and FAIR, as shown in Fig. 4. This offset may be useful in providing an estimate for the scaling factors appropriate for atmospheric and surface water DMS measurements at wind speeds near 8 m s1. Further work to evaluate scaling will be possible using the combined data sets for the spring summer and fall SABINA cruises. Further information on scaling can potentially be obtained by comparing peaks in MSA and biogenic SO2 with maps of DMS ﬂux and concentration from the NARCM but remains to be explored.

Figure 6. Average DMS concentrations in the MBL (ﬁrst panel) and average DMS ﬂux (second panel) for the spring SABINA cruise.

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4. Conclusions

A combined top-down, bottom-up approach was used to investigate DMS ﬂux and atmospheric concentrations during the spring SABINA study of the North Atlantic in 2003. Sea-to-air ﬂuxes (FSA) calculated using Wanninkhof and McGillis’s (1999) parameterization scheme were compared to those calculated using a simple oxidation scheme (FAIR) involving gas phase reactions with OH, NO3, and BrO. DMS oxidation by OH alone and OH plus NO3 alone did not account for the variations in observed DMS ﬂux. Including extremely small BrO number densities (12.5 molecules cm3) resulted in a much better match between the magnitude of FSA and FAIR. Peaks in FSA and FAIR were offset by 37 h, however, and this was consistent with atmospheric transport: higher concentrations representative of ﬂuxes several degrees latitude and longitude upwind were sampled approximately 1.5 days earlier than peaks in ﬂuxes derived from surface water DMS concentrations. These results provide information that can be used to determine the approximate scaling factors between atmospheric and surface ocean DMS measurements. Analysis of a single event in this study suggests atmospheric DMS measurements may represent regions several degrees upwind. However, this interpretation may be reﬁned to include DMS oxidation products MSA and biogenic SO2 to assess scales that atmospheric measurements represent in future. Discussion

S.-E. Gryning:

A.-L. Norman:

Did you consider the stability dependence on the drag coefﬁcient (relation between ﬂux and gradient)? If not, this might explain some of the spread. Differences in DMS ﬂux calculated using the NARCAM and the sea–air parameterization scheme (bottom-up) and steady-state (top-down) models can be attributed to two factors. First, how closely does the Kettle database (surface water DMS concentrations used in the NARCAM DMS ﬂux calculation) represent conditions that were present during the sampling period? A comparison of the Kettle database and measured DMS concentrations in surface waters from May 2003 shows reasonable agreement with respect to the spatial distribution of maximum and minimum values for surface water

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measurements. However, the magnitude of measured values was lower, on average, than the Kettle database. This difference contributed to NARCM atmospheric DMS concentrations that were higher than measured values. Second, what is the appropriate value to use for entrainment (which affects the gradient and hence the ﬂux)? The disagreement between the top-down and the bottom-up approaches could not be due to stability dependence on the drag coefﬁcient since a drag coefﬁcient was not used in either of these models (steady-state conditions were used for the top-down approach). Instead, the gradient was conditional on a 4-h average DMS concentration at the surface. Several values for entrainment velocity appear in the literature and model sensitivity to this parameter was tested. Fluxes calculated using the steady-state, topdown approach were relatively insensitive to entrainment velocity, largely because buffer layer concentrations were ﬁxed at 10% of surface values. On reﬂection, perhaps a better measure for buffer layer concentrations could be used in the model—one that uses a much longer time-averaged surface concentration, for example, might result in a better estimate of the gradient, and hence ﬂux.

ACKNOWLEDGMENTS

This study was supported by a grant from NSERC and the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) to the C-SOLAS program. SOLAS colleagues and secretariat as well as the Canadian Coast Guard and research assistants Wing Tang and Tiffany Zwarich are thanked for their support. It is with great sadness we report that colleague Moire Wadleigh passed away before this work was published. REFERENCES Andreae, T.W., Andreae, M.O., Bingemer, H.G., Leck, C., 1993. Measurements of dimethyl sulﬁde and H2S over the western North Atlantic and the tropical Atlantic. J. Geophys. Res. 98(D12), 23389.

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Capaldo, K.P., Kasibhatla, P., Pandis, S.N., 1999. Is aerosol production within the remote marine boundary layer sufﬁcient to maintain observed concentrations? J. Geophys. Res. 104, 3483–3500. Charlson, R.J., Lovelock, J.E., Andraea, M.O., Warren, S.G., 1987. Oceanic plankton, atmospheric sulphur, cloud albedo and climate. Nature 326, 655–661. Chen, G., Davis, D.D., Kastibhatla, P., Bandy, A.R., Thornton, D.C., Huebert, B.J., Clarke, A.D., Blomquist, B.W., 2000. A study of DMS oxidation in the tropics: Comparison of Christmas Island ﬁeld observations of DMS, SO2, and DMSO with model simulations. J. Atmos. Chem. 37, 137–160. Davis, D., Chen, G., Bandy, A., Thornton, D., Eisele, F., Mauldin, L., Tanner, D., Lenschow, D., Fuelberg, H., Huebert, B., Heath, J., Clarke, A., Blake, D., 1999. Dimethyl sulﬁde oxidation in the equatorial Paciﬁc: Comparison of model simulations with ﬁeld observations for DMS, SO2, H2SO4(g), MSA(g), MS, and NSS. J. Geophys. Res. 104(D5), 5765–5784. DeMore, W.B., Sander, S.P., Golden, D., Hampson, R.F., Kurylo, M.J., Howard, C.J., Ravishankara, A.R., Kolb, C.E., Molina, M.J., 1997. Chemical kinetics and photochemical data for use in stratospheric modeling. Evaluation no.12, JPL Publication. 97-4. Faloona, I., Lenschow, D.H., Campos, T., 2005. Observations of entrainment in eastern Paciﬁc stratocumulus using three conserved tracers. J. Atmos. Sci. 62, 3265–3268. Gong, S.L., Barrie, L.A., Blanchet, J.-P., von Salzen, K., Lohmann, U., Lesins, G., Spacek, L., Zhang, M., Girard, E., Lin, H., Leaitch, R., Leighton, H., Chylek, P., Huang, P. 2003. Canadian aerosol module: A size-segregated simulation of atmospheric aerosol processes for climate and air quality models. 1. Module development. J. Geophys. Res. 108, doi.10.1029/2001JD002002. Ingham, T., Bauer, D., Sander, R., Crutzen, P.J., Crowley, J.N., 1999. Kinetics and products of the reactions BrO+DMS and Br+DMS at 298K. J. Phys. Chem. A 103, 7199–7209. Kettle, A.J., Andreae, M.O., 2000. Flux of dimethylsulﬁde from the oceans: A comparison of updated data sets and ﬂux models. J. Geophys. Res. 105(D22), 26793–26808. Kettle, A.J., Andreae, M.O., Amouroux, D., Andreae, T.W., Bates, T.S., Berresheim, H., Bingemer, H., Boniforti, R., Curran, M.A.J., DiTullio, G.R., Helas, G., Jones, G.B., Keller, M.D., Kiene, R.P., Leck, C., Levasseur, M., Malin, G., Maspero, M., Matrai, P., McTaggart, A.R., Mihalopoulos, N., Nguyen, B.C., Novo, A., Putaud, K.P., Rapsomanikis, S., Roberts, G., Schebeske, G., Sharma, S., Simo, R., Staubes, Turner, S., Uher, G., 1999. A global database of sea surface dimethylsulﬁde (DMS) measurements and a procedure to predict sea surface DMS as a function of latitude, longitude, and month. Global Biogeochem. Cycles 13, 399–444. Kouvarakis, G., Mihalopoulos, N., 2002. Seasonal variation of dimethylsulﬁde in the gas phase and methanesulfonate and non-sea-salt sulfate in the aerosols phase in the Eastern Mediterranean atmosphere. Atmos. Environ. 36, 929–938. Lawrence, M.G., Jo¨ckel, P., von Kuhlmann, P., 2001. What does the mean global OH concentration tell us? Atmos. Chem. Phys. 1, 37–49. Le Clainche, Y., Levasseur, M., Vezina, A., Bouillon, R.-C., Merzouk, A., Michaud, S., Scarratt, M., Wong, C.S., Rivkin, R., Boyd, P.W., Harrison, P.J., Miller, W.L., Law, C.S., Saucier, F.J. (2005). Modeling analysis of the effect of iron enrichment on dimethyl sulﬁde analysis of the effect of iron enrichment on dimethyl sulﬁde dynamics in the NE Paciﬁc (SERIES experiment). J. Geophys. Res. Oceans 111, C01011, doi:10.1029/2005JC002947.

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Levasseur, M., Scarratt, M., Michaud, S., Merzouk, A., Boyd, P.W., Rivkin, R., Hale, M., Le Clainche, Y., Wong, C.S., Law, C.S., Sherry, N., Tsuda, A., Takeda, S., Matthews, P., Harrison, P.J., Miller, W., Kiene, R., Kiyosawa, H., Arychuk, M., Li, W.K.W., Vezina, A., 2004. Iron enrichment decreases DMS production in the subarctic Northeast Paciﬁc, submitted to Nature. Norman, A.L., Belzer, W., Barrie, L.A., 2004. Insights into the biogenic contribution to total sulphate in aerosol and precipitation in the Fraser Valley afforded by isotopes of sulphur and oxygen. J. Geophys. Res. 109 D05311, doi:10.1029/2002JD003072. Sciare, J., Baboukas, E., Kanakidou, M., Krischeke, U., Belviso, S., Bardouki, H., Mihalopoulos, N., 2000. Spatial and temporal variability of atmospheric sulfurcontaining gases and particles during the Albatross campaign. J. Geophys. Res. 105(D11), 14433–14448. von Glasow, R., Crutzen, P.J., 2004. Model study of multiphase DMS oxidation with a focus on halogens. Atmos. Chem. Phys. 4, 589–608. Wanninkhof, R., McGillis, W.R., 1999. A cubic relationship between air-sea CO2 exchange and wind speed. Geophys. Res. Lett. 26, 1889–1892. Yoon, Y.J., Brimblecomb, P., 2002. Modelling the contribution of sea salt, dimethyl sulﬁde derived aerosol to marine CCN. Atmos. Chem. Phys. 2, 17–30. Yvon, S.A., Saltzman, E.S., 1996. Atmospheric sulfur cycling in the tropical Paciﬁc marine boundary layer (121S, 1351W): A comparison of ﬁeld data and model results. 2. Sulfur dioxide. J. Geophys. Res. 101(D3), 6911–6918.

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Chapter 7.1 Calculations of personal exposure to particulate matter in urban areas Inga Fløisand, Herdis Laupsa, David Broday, Trond Bøhler, Werner Holla¨nder, Susanne Lu¨tzenkirchen, Christos Housiadas, Thanos Stubos and Harold McInnes Abstract A numerical tool for calculations of personal exposure to particulate matter has been developed and integrated into an existing Air Quality Management System, AirQUIS. Based on deﬁned daily routes, the hourly concentration of particulate matter is calculated for various microenvironments. The outdoor concentrations are calculated using an Eulerian dispersion model. The indoor concentrations are calculated on the basis of both outdoor concentrations and contributions from selected indoor sources. Based on the concentrations, activity level, gender and age, the respiratory deposition for various particle sizes is calculated as hourly values. The accumulated dose for a given period can be calculated from the hourly values. A case study for Oslo is presented and shows how the personal exposure and respiratory dose of a male adult and a 5-year old child are affected for a trafﬁc abatement scenario. 1. Introduction

One of the most important environmental concerns of today is the negative impact of pollution on human health. Environmental changes affect human health directly or indirectly through multiple pathways such as air, water and food and the resulting health impact may not simply be the sum of the individual effects through various pathways of exposure. Exposure to particulate matter (PM) in air is one pathway of concern. The health effects of PM are thought to be strongly associated with particle size, composition and concentration. Long-term exposure to the current urban levels of especially ﬁne particles (PM2.5) is associated with increased mortality (Pope et al., 2002). People are exposed to PM from

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many sources as they go about their daily activities, spending time in their homes, at work, in recreation and when travelling. Some individuals or groups of the population are more susceptible than others to PM exposures, due to factors such as respiratory habits, pre-existing diseases or genetics (Davidson et al., 2005). The aim of the EU funded project ‘‘Integrated exposure management tool characterizing air pollution-relevant human exposure in urban environment’’ (Urban Exposure, EVK4-CT-2002-00090) was to study human exposure to air-pollution compounds through two important pathways of exposure, namely inhalation and dermal absorption, and further to quantify exposure speciﬁcally for PM and chloroform in European urban areas (Coulson et al., 2005). For this purpose, a comprehensive computer tool for calculation of personal exposure to PM in indoor and outdoor environments as well as water disinfection by-products from tap water and swimming pools has been developed. This paper describes the Urban Exposure management tool, with emphasis on the calculation of exposure to PM, and presents a case study from Oslo to illustrate the utilisation of the tool.

2. Description of the tool

The Urban Exposure management tool has been implemented within the air quality management system AirQUIS (AirQUIS, 2005). Particle concentrations of PM10, PM2.5 and PM1, in both outdoor and indoor environments and resulting deposition in the respiratory system are calculated using a dispersion model, a speciﬁc indoor concentration model and a respiratory deposition model (Fløisand, 2006). The calculations are performed for individuals moving along predeﬁned daily routes using daily activity patterns. Selected indoor sources can be activated over certain periods of the day in the various microenvironments. Three environmental models have been integrated as part of the new tool: an indoor model for calculation of indoor concentrations (Holla¨nder et al., 2004), an inhalation model for calculation of respiratory deposition (Broday, 2004) and a dermal absorption model for calculation of uptake through the skin (Psychogios et al., 2004). The Urban Exposure user interface is shown in Fig. 1. A scenario is deﬁned for a speciﬁed time period in terms of the subject’s gender and age, daily route and time spent in the various microenvironments: home, work or school/kindergarten, travel to and travel from and leisure. In addition, the subject’s activity level is deﬁned in order to calculate the resulting respiratory deposition of particles. The interface has three sub

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Figure 1. The Urban Exposure tool user interface, featuring the form for deﬁning person characteristics and daily routine. The microenvironment and activity level is selected from a drop down list. The main functionalities of the tool are accessed through the toolbar at the top of the interface.

forms. In the ﬁrst one, the user deﬁnes the subject’s microenvironments and daily routines for each hour throughout the day shown in Fig. 1. The second one is for deﬁnition and activation of indoor sources in the indoor environments shown in Fig. 2. The third one is for deﬁning the input parameters for calculation of multi pathway gas uptake (not shown). The ambient concentrations determine the outdoor exposure and contribute to the indoor exposure. The dispersion model EPISODE (e.g., Laupsa and Slørdal, 2003) in AirQUIS calculates the outdoor concentrations of both PM10 and PM2.5 for the various microenvironments. The particle concentrations are divided into 48 size bins, logarithmically equidistant from 10 nm to 100 mm. Particle size distributions are based on lognormal density distributions of PM10 and PM2.5 using actual ambient concentrations of PM10 and PM2.5. A list of the deﬁned receptor points and the corresponding microenvironments are presented in the form showing the geographical route throughout the day (see Fig. 1). The geographical position of the various microenvironments can be deﬁned using the GIS functionality of AirQUIS.

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Figure 2. The form indoor sources. The various sources are activated by using the check boxes on the left hand side. The user speciﬁes which hours the sources are activated throughout the day.

One needs to deﬁne the subject’s activity level in order to calculate the respiratory deposition of PM. The different activity level options are sleeping, sitting, light exercise and heavy exercise, and are selectable from a drop down list menu. The indoor module is activated for the calculation hours the subject spends indoors. Various indoor sources can be selected and thereby contribute to the indoor concentrations for speciﬁed hours (Fig. 2). The indoor module calculates discrete one-hour source contributions using the indoor concentration model (Fløisand, 2006). The indoor microenvironments are home and work/school/kindergarten. In addition, travel to and travel from are assumed to be indoor environments if the activity levels are sleeping or sitting. In these cases, one assumes travelling by public transport or car. If the activity level is either light or heavy exercise, it is assumed that the person is outdoors, for example, moving by bike or foot. For each of the indoor environments, there is a list of various sources that can be added (see Table 1). The microenvironment leisure is always an outdoor environment. The user deﬁnes the size of the room and whether the house is old or new (Fig. 2). The latter affects the penetration rate of outdoor air. The respiratory deposition for various particle sizes is calculated on the basis of the microenvironmental concentrations, activity level, gender and

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Table 1. Deﬁned indoor sources and source characteristics Smoking Gas stove Indoor heating Vacuuming Pets Filter cleaning

Fine fraction, passive smoking Mostly ﬁne fraction Deﬁned as coal ﬁre heating in the current data set (based on measurements from Katowice) Coarse fraction Mostly coarse fraction A sink for particles indoors

age. The aggregated daily dose is calculated from the hourly values (Fløisand, 2006).

3. Effects on exposure from trafﬁc velocity reduction on an urban highway

To illustrate the possible application of the Urban Exposure management tool, case study calculations based on an abatement scenario for Oslo have been carried out. Oslo is the capital of Norway and the main emission sources for PM are trafﬁc and resuspension of PM as well as wood burning for domestic heating. In many Norwegian cities, PM10 exceeds the European limit value for daily average several times a year, especially during early springtime. One of the main reasons for this is trafﬁc-induced resuspension of road dust due to the extended use of studded tyres, contributing mainly to the coarse fraction of PM10. In order to investigate this further, the Oslo department of the Norwegian Public Roads Administration carried out a ﬁeld experiment over a 2-year winter-spring period in Oslo. The aim was to identify the effect of a reduced speed limit on the PM levels. Measurements were carried out along one of the main roads entering Oslo (RV4). During the ﬁrst measurement period, the speed limit was 80 km h 1. The following year the speed limit was reduced to 60 km h 1. Analysis of the measurements showed a reduction of the PM10 level of approximately 35% due to the reduced speed limit, and a reduction of the coarse fraction of approximately 40% (Hagen et al., 2005). The effect of the abatement measures on personal exposure for two individuals were simulated: a male adult and a 5-year-old child. They live close to the main road and the male adult travels along the main road to his work in downtown Oslo. In the evening, he plays football on a pitch close to home. His 5-year-old child spends his day in kindergarten near their home. He is indoors during the morning hours and outdoors in the afternoon. The time activity patterns and activity level for the two

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Table 2. Time activity pattern and activity level for male adult and 5-year-old child living in Grorud Hours of the day 1–5 6–7 8 9–15 16 17–18 19–20 21 22–24

Male adult

Home sleeping Home sitting Travel to work sitting Work sitting Travel from work sitting Home sitting Leisure heavy exercise Home sitting Home sleeping

Hours of the day 1–5 6–7 8 9–11 12–15 16 17–19 20–24

5-year-old child

Home sleeping Home sitting Walking to kindergarten light exercise Kindergarten light exercise Kindergarten light exercise Walking from kindergarten light exercise Home sitting Home sleeping

Regular font indicates indoor environments and italic is time spent outside.

Figure 3. Map of Oslo showing home, work and kindergarten as well as travel routes for male adult and 5-year-old child.

individuals are described in Table 2 and the routes are shown in Fig. 3. Calculations have been performed for 22 March 2003. The estimated PM10 concentration for speed limits of 60 and 80 km h 1 are presented in Fig. 4 and compared with measurements at an urban background station in Oslo. Home is situated very close to the main road (15 m) and the reduced speed limit results in a signiﬁcant reduction in

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the outdoor concentration of PM10 at this location. For the kindergarten situated approximately 350 m from the road, however, the ambient concentration is not much reduced. The levels at the kindergarten are similar to the measured levels at the closest urban background station Skøyen (Fig. 4). This station is located 10 km from their home in the southwest direction. The coarse particles are removed efﬁciently when the outdoor air passes the building shell. The difference in the indoor concentrations for the two speed limits is therefore smaller than the difference in the corresponding outdoor concentrations. The aggregated exposure over time is therefore strongly dependent on the amount of time spent outdoors vs. indoors. However, if indoor sources such as smoking were activated, this contribution would elevate the resulting indoor concentration considerably. Figures 5 and 6 show the calculated outdoor PM10 concentrations and resulting respiratory deposition for the two individuals. The respiratory deposition is based on either indoor or outdoor concentrations depending on the hour of the day (see Table 2). For the male adult, the largest impact of the reduced speed and resulting concentration reduction is calculated for the afternoon hours when the outdoor concentrations are highest. The two hours he spends in leisure (25 m from the main road), doing heavy exercise in the evening contribute substantially to his total respiratory dose, but is not much affected by the reduced speed limit because the leisure site is upwind of the main road during the leisure hours.

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For the 5-year-old child, the hour spent along the main road going back from kindergarten has an important impact on the overall exposure level and the effect of the reduced speed limit is signiﬁcant. Overall, the child receives the highest dose while walking back and forth to the kindergarten and playing outside in the afternoon.

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4. Summary

A numerical tool for calculation of personal exposure to PM has been developed and integrated into an existing AQMS, AirQUIS. Based on deﬁned daily routes, the hourly concentration of PM is calculated for various indoor and outdoor microenvironments. The respiratory deposition for various particle sizes is calculated on the basis of the concentrations, activity level, gender and age. Results from a case study are presented. Discussion

U. Schlink: I. Fløisand:

F. Sauter:

I. Fløisand:

Did you consider organic compounds in the exposure study? No, the current study only includes particles. In principle, it would be possible to perform calculations of personal exposure for organic compound, provided we have the input data, e.g., emissions. However, population exposure studies have previously been performed, for e.g., benzene, using the AirQUIS system. Do you see possibilities to generalise this approach to more individuals, since the present approach is very sensitive to the deﬁned individual? The aim is to be able to classify individuals in groups that describe the population of a city, thus proceeding from individual exposure via group exposure to population exposure. This will provide more realistic population exposure calculation compared with the more traditional approach that assumes that people spend the whole day outside, at one single location.

REFERENCES AirQUIS, 2005. AirQUIS. URL: www.airquis.com Broday, D.M., 2004. Deposition of ultraﬁne particles and gaseous pollutants at carinal ridges of the upper bronchial airways. Aerosol. Sci. Technol. 38, 991–1000. Coulson, G., Bartonova, A., Bøhler, T., Broday, D.M., Colbeck, I., Fløisand, I., Fudala, J., Holla¨nder, W., Housiadas, C., Lazaridis, M., Smolik, J., 2005. Exposure risks from pollutants in domestic environments: The Urban Exposure project. Indoor Built Environ. 14, 209–213.

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Davidson, R.I., Phalen, R.F., Solomon, P.A., 2005. Airborne particulate matter and human health: A review. Aerosol Sci. Technol. 39, 737–749. Fløisand, I. (Ed.), 2006. Urban Exposure. Final report, Kjeller (NILU OR 1/2006). Hagen, L.O., Larssen, S., Schaug, J., 2005. Environmental Speed Limit in Oslo. Effects on Air Quality of Reduced Speed Limit on RV 4. Kjeller (NILU OR 41/2005). Holla¨nder, W., Windt, H., Dunkhorst, W., 2004. Modelling indoor aerosol equilibrium concentrations; European Aerosol Conference, Budapest, September 6–10, 2004. J. Aerosol Sci. Abstr. EAC 2004, II, S929–S930. Laupsa, H., Slørdal, L.H., 2003. Applying model calculations to estimate urban air quality with respect to the requirements of the EU directives on NO2, PM10 and C6H6. Int. J. Environ. Pollut. 20(1–6), 309–320. Pope, C.A., Burnett, R.T., Thun, M.J., Calle, E.E., Krewski, D., Ito, K., Thurston, G.D., 2002. Lung cancer, car diopulmonary mortality, and long-term exposure to ﬁne particulate air pollution. J. Am. Med. Assoc. 287(9), 1132–1141. Psychogios, J., Kainourgiakis, M., Charalambopoulou, G., Steriotis, T., Stubos, A.K., 2004. Prediction of porcine stratum corneum transport properties from USANS data and numerical simulations. In: Proceedings from the 9th International Perspectives in Percutaneous Penetration Conference, La Grande Motte, France, 13–17 April 2004.

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Chapter 7.2 Lagrangian particle model simulation to assess air quality along the Brenner transit corridor through the Alps D. Oettl, P. Sturm, D. Anfossi, S. Trini Castelli, P. Lercher, G. Tinarelli and T. Pittini Abstract Dispersion from line sources in complex terrain is still a challenging task. Nevertheless in environmental impact assessments often detailed information about the expected pollution levels besides roads is required. In this work two different methods are demonstrated. One method utilized the models RAMS-MINERVE/SPRAY and a two-way nesting procedure starting at the continental scale and ending up with a ﬁnal horizontal resolution for the innermost domain of 100 m for the 3D wind and concentrations ﬁelds. Seven episodes, representative of the most frequently occurring circulation systems, were simulated and from their weighted mean, the annual average concentrations were deduced. The second method used the models GRAMM/GRAL which were driven by local observations of wind and stability class. Instead of simulating episodes to recover annual mean concentrations, steady-state wind and concentration ﬁelds were calculated by establishing a simple classiﬁcation of meteorological situations based on wind speed, wind direction and stability class. The horizontal resolution used in the latter approach was 100 m for the wind ﬁeld computation and 10 m for the dispersion calculations. Both Lagrangian dispersion models used a new set of Langevin equations especially suited for all wind speed regimes, which have been recently developed. The results of the computed concentration ﬁelds were compared with observed NO2 and PM10 levels recorded by four air quality monitoring stations (NO2, PM10) and about 15 passive samplers (NO2). The main goal of this presentation is to outline the differences/similarities, the effectiveness and implications of the two different methods on achieving the objectives of the project, with a special emphasis on their application to the epidemiological study.

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1. Introduction

The motorways A13 and A22 are the most important corridors for transit trafﬁc through the eastern Alps. In order to reduce noise and pollutant concentrations along this corridor, a new 56-km-long railway tunnel is planned connecting Austria and Italy. In the frame of a comprehensive environmental assessment study, the dispersion of NOx (NO2) and PM10 emitted by road trafﬁc is investigated for the Austrian and Italian areas by two different approaches. The results of the computed concentration ﬁelds were compared with observed NO2 and PM10 levels recorded by four air-quality-monitoring stations (NO2, PM10) and 15 passive samplers (NO2). Based on these results, an epidemiologic study on the doses to the population living in those alpine valleys is currently being performed, which serves subsequently to assess the health beneﬁts once the railway tunnel is under operation.

2. Methods

Dispersion modeling in complex terrain (Fig. 1) and for situations with low wind speeds is still considered to be a challenging task among scientists. During low wind speed conditions, large horizontal atmospheric motions develop (so-called meandering), which inﬂuence the dispersion process signiﬁcantly. A hypothesis for the development of meandering as well as a sound physical treatment of that process is just appearing in literature (see these proceedings: Anfossi et al., 2007). Complex topography strongly affects the 3-dimensional wind ﬁelds. Although theoretically well known since decades, the accurate simulation of slope winds, mountain/valley breezes, gravity waves, etc. is still difﬁcult because of the many not well known parameters inﬂuencing these processes. In addition, these processes appear at different spatial scales from local to regional, which cannot be effectively solved by a model at once currently. In this work, both methodologies use prognostic non-hydrostatic Eulerian wind ﬁeld models coupled with a Lagrangian dispersion model. The main differences between the two modeling approaches concern the wind ﬁeld simulations, which are outlined in the following section. In addition, calculations of vehicle emissions from the highways A13 and A22 had to be performed by applying a suitable model. Here, the model NEMO (Network Emission Model) has been used. The model has been developed in several international and national projects, namely, the EU 5th research framework program ARTEMIS, the COST 346 initiative and the German–Austrian–Swiss cooperation on the Handbook of

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Figure 1. Topography and selected model domains north (left) and south (innermost nesting domain; right) of the Brenner Pass.

Emission Factors (Hausberger, 2002; Hausberger et al., 2003). It is able to compute emissions of NOx, PM10 (exhaust and non-exhaust), and other pollutants for each road section. Both highways were divided into several thousand sections to account for their exact position (horizontally and vertically) and slope. Further inputs to the model were trafﬁc volumes, ﬂeet composition for the speciﬁc years, and average speed. 2.1. Italian side

One of the most appealing ways to model air pollution in areas with complex terrain is to compute time series of 3-dimensional wind-, turbulence-, and concentration ﬁelds (Anfossi et al., 1998; Tinarelli et al., 2000; Carvalho et al., 2002; Trini Castelli et al., 2003 and 2004; Finardi and Pellegrini, 2004). In order to account for the synoptic circulation and

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to scale down to regional and local scales, the atmospheric models are initialized with the outputs of a global scale model, in this case the ECMWF analyses. In this work, the well known atmospheric model RAMS was used to simulate the regional circulation. Using four nested grids, the simulated ﬁelds were scaled down to a grid resolution of 1 km. This technique allows to describe both the mesoscale forcing and the local circulations. A resolution of the order of 1 km is generally considered an actual limit of mesoscale models. For this reason, in cascade to RAMS, the diagnostic mass-consistent model MINERVE (ARIA Technologies, 2001) was used to obtain meteorological and concentration ﬁelds with a resolution of 100 m. We recall that due to very sharp concentration gradients usually found near highways and construction features like bridges, viaducts, dams, etc., it is necessary to go down to at least say 200 m 200 m horizontal grid spacing (Oettl et al., 2003). The main disadvantage of this approach is the high demand on computer resources. Consequently, considering the numerical resources generally available, it is hardly possible now to perform simulations at high resolution for periods of one year, particularly in the frame of operational applications (Oettl and San Jose, 2004). A current solution to overcome this basic problem is to select a subset of periods shorter than one year (seven periods were chosen in this study), which in their ensemble represent the concentration statistics for a whole year in terms of annual mean, percentiles, maximum concentrations, etc. (Finardi et al., 2002). The limitation of this methodology lies in the statistical approach itself, both in the pre-selection of the periods, based on the available observed data not necessarily representative for the full domain, and in the approximation linked to the ﬁnite number of periods considered. However, this approach has been successfully used in several studies (Finardi et al., 2002). The major advantages of this method are its scientiﬁcally soundness and basically the independence on local meteorological measurements, apart from evaluating the model results. We add that locally observed data can be inserted in the simulation chain, either in the ﬁnest grid of the prognostic model (via assimilation processes) or in the successive downscaling performed with the mass-consistent model. However, in general the number of measurement points and their reliability (in relation to their location with respect to the circulation features that are important at the scale reached by the model) can be insufﬁcient or generate strong contrasts with the background simulated ﬁelds. In these conditions, measurements hardly represent a correct input to the model chain in real atmospheric applications, since the criterion (many observation points and correctly positioned for modeling aims) is not fulﬁlled, thus

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worsening the simulation. On the other hand, the efﬁciency and reliability of numerical simulations performed with prognostic models are affected by the quality of some input data, such as soil moisture, soil temperature, snow cover, etc., which are often not well known at all scales, and on the actual progress in the parameterization schemes. 2.2. Austrian side

Wind ﬁeld simulations for the rather complex nocturnal wind ﬁeld of the city of Graz, Austria, ﬁrst brought up the idea of a simpler method to compute wind ﬁelds in complex terrain with a high horizontal resolution. In the study of Oettl et al. (2000), the Froude-number-dependent ﬂow in the Graz basin could be successfully modeled and explained by simply initializing the prognostic non-hydrostatic wind ﬁeld model GRAMM with a single-point measurement of wind and simple assumptions about the vertical wind- and temperature proﬁles. In other words, the wind ﬁeld model was initialized with a horizontally homogenous wind ﬁeld and then a steady-state wind ﬁeld was computed by solving the Navier–Stokes equations (RANS). It has to be mentioned that this is the common way of computing building inﬂuenced ﬂows in the microscale (Schaedler et al., 1996). The most important point is of course to ﬁnd or mount a meteorological wind station, which is representative for the large-scale wind (e.g., the mountain/valley breeze). This leads already to the main disadvantage of the method, as it is based on local wind observations, which are often not available for practical applications. Another disadvantage is that the wind ﬁeld might be captured only well in the surface layer but not in the whole boundary layer. However, provided that one representative point measurement of wind is available, the method offers distinctive advantages compared to the method described before. When using a locally observed atmospheric ‘‘stability’’ parameter (e.g., radiation balance), some simple assumption can also be made about the near-surface vertical temperature and wind proﬁles in that area. This means that the model can already be fully initialized by the use of three locally observed input parameters, namely wind speed, wind direction, and a kind of atmospheric ‘‘stability.’’ Due to this limited number of required input parameters it is still possible to establish a simple classiﬁcation of the meteorological conditions. For instance, when using 36 wind sectors, ﬁve wind speed classes, and three stability classes, the number of meteorological situations observed over some years reduces usually to 200–500. As locally observed wind directions and speeds are used as input to the model (one local station for each of the ﬁve domains), the main

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characteristics of the pollutant advection are already captured (frequency distribution of wind speed, wind direction). The main assumption is that all other parameters in a mesoscale model are of minor importance once the large-scale wind is used as input. The subsequent computation of a steady-state ﬂow ﬁeld for each classiﬁed meteorological condition using constant boundary conditions derived from the locally observed largescale wind aims mainly at the simulation of very local topographical inﬂuences on the large-scale wind (e.g., development of eddies in the wake of mountains). In this study, a horizontal resolution of 100 m is used for the wind ﬁeld calculations and 10-m horizontal resolution is used for the dispersion calculations. If, for instance, one considers stable atmospheric stratiﬁcation, GRAMM is initialized with a temperature inversion. Without constantly cooling the surface in the model, the temperature inversion would be eroded due to the vertical diffusion. Thus, in GRAMM, a sensible surface heat ﬂux of 10 W m 2 is assumed for such situations. In this way, the temperature inversion is maintained near the ground—especially in basins—while at the top of mountain ridges, where wind speeds are higher, the temperature proﬁle becomes almost neutral in the course of the simulation. In convective conditions, a positive surface heat ﬂux is used, which is calculated taking into account shading effects of topography. This method has been applied successfully in more than 30 environmental assessment studies in complex terrain (e.g., Oettl et al., 2005, 2006).

3. Results and conclusions

Figure 2 depicts the simulated annual mean NOx concentrations (without background) for both areas for the year 2004. In both cases, steep concentration gradients are suggested by both modeling approaches. Close to highway bridges, much lower concentrations can be found. Figure 3 shows a comparison of observed and modeled annual mean NO2 concentrations for the areas north of the Brenner Pass. A reasonable agreement regarding the spatial concentration variations can be found. It has to be stated that most of the data included in the graph are passive sampler observations, which usually provide concentrations much more uncertain compared to conventional air quality analyzers. The European air quality standard of 40 mg m 3 is only exceeded very close to the highway. It can be said that both methods provide robust and consistent spatial patterns of concentrations, although concentrations very close to the highways are higher with decreasing horizontal resolution (10 m north and 100 m south of the Brenner Pass).

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Figure 2. Simulated annual mean NOx concentrations (mg m 3) for selected areas north (left) and south (right) of the Brenner Pass for 2004.

Figure 3. Comparison of observed and modeled annual mean NO2 concentrations north of the Brenner Pass.

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Discussion

J. Kaminski: U. Uhrner:

What was the impact of elevated emissions? The plot shows you concentrations on the near-surface level, i.e., near the ground. However, the emissions at viaducts are several decameters above the ground, so due to better dispersion conditions and the distance to the ground much lower near-surface concentrations result.

REFERENCES Anfossi, D., Desiato, F., Tinarelli, G., Brusasca, G., Ferrero, E., Sacchetti, D., 1998. TRANSALP 1989 experimental campaign—Part II: Simulation of a tracer experiment with Lagrangian particle models. Atmos. Environ. 32, 1157–1166. Anfossi, D., Alessandrini, S., Trini Castelli, S., Ferrero, E., Oettl, D., Degrazia, G., 2007. Lagrangian particle model simulation of tracer dispersion in stable low wind speed conditions. In: Borrego, C., Renner, B. (Eds.), Developments in Environmental science, Volume 6. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06072-X. ARIA Technologies. 2001. Minerve Wind Field Models version 7.0, General Design Manual, ARIA Report. May 2001. Carvalho, J.C., Anfossi, D., Trini Castelli, S., Degrazia, G.A., 2002. Application of a model system for the study of transport and diffusion in complex terrain to the TRACT experiment. Atmos. Environ. 36, 1147–1161. Finardi, S., Brusasca, G., Calori, G., Nanni, A., Tinarelli, G., Agnesod, G., Pession, G., Zublena, M., 2002. Integrated air quality assessment of an alpine region: Evaluation of the Mont Blanc tunnel re-opening effects. 8th Conference on Harmonization within Atmospheric Dispersion Modeling for Regulatory Purposes. Soﬁa, 14–17 October, pp. 404–408. Finardi, S., Pellegrini, U., 2004. Systematic analysis of meteorological conditions causing severe urban air pollution episodes in the central Po valley. In: Proceedings of the Ninth International Conference on Harmonisation within atmospheric dispersion modelling for regulatory purposes. Forschungszentrum Karlsruhe, GarmischPartenkirchen, Germany. Vol. 2, pp. 75–79. Hausberger, S., 2002. Update of the Emission Functions for Heavy Duty Vehicles in the Handbook Emission Factors for Road Trafﬁc. Institute for Internal Combustion Engines and Thermodynamics, Inffeldgasse 21A, Graz. Hausberger, S., Rodler, J., Sturm, P., Rexeis, M., 2003. Emission factors for heavy-duty vehicles and validation by tunnel measurements. Atmos. Environ. 37, 5237–5245. Oettl, D., Almbauer, R.A., Sturm, P.J., Piringer, M., Baumann, K., 2000. Analysing the nocturnal wind ﬁeld in the city of Graz. Atmos. Environ. 35, 379–387. Oettl, D., Hausberger, S., Rexeis, M., Sturm, P.J., 2006. Simulation of trafﬁc induced NOx-concentrations near the A 12 highway in Austria. Atmos. Environ. 40, 6043–6052. Oettl, D., Kurz, Ch., Hafner, W., Sturm, P., 2005. PM10 source apportionments within the city of Klagenfurt, Austria. 10th International Conference on Harmonisation within

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Atmospheric Dispersion Modeling for Regulatory Purposes, October 11–14, Sissi, Greece, pp. 638–642. Oettl, D., San Jose, R., 2004. The future of air pollution modelling. In: Zannetti, P. (Ed.), Air Pollution Modelling, EnviroComp Institute and Air & Waste Management Association. Fremont, California, pp. 325–354. Oettl, D., Sturm, P.J., Pretterhofer, G., Bacher, M., Rodler, J., Almbauer, R.A., 2003. Lagrangian dispersion modeling of vehicular emissions from a highway in complex terrain. J. Air Waste Manage. Assoc. 53, 1233–1240. Schaedler, G., Baechlin, W., Lohmeyer, A., Van Wees, Tr., 1996. Comparison and judgement of currently available microscale ﬂow and dispersion models (German). Rep. No. FZKA-PEF 138, Forschungszentrum Karlsruhe, p. 201. Tinarelli, G., Anfossi, D., Bider, M., Ferrero, E., Trini Castelli, S., 2000. A new high performance version of the Lagrangian particle dispersion model SPRAY, some case studies. In: Gryning, S.E., Batchvarova, E. (Eds.), Air Pollution Modelling and Its Applications XIII. Kluwer Academic/Plenum Press, New York, pp. 499–507. Trini Castelli, S., Anfossi, D., Ferrero, E., 2003. Evaluation of the environmental impact of two different heating scenarios in urban area. Int. J. Environ. Pollut. 20, 207–217. Trini Castelli, S., Morelli, S., Anfossi, D., Carvalho, J., Zauli Sajani, S., 2004. Intercomparison of two models, ETA and RAMS, with TRACT ﬁeld campaign data. Environ. Fluid Mech. 4, 157–196.

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Chapter 7.3 Optimum exposure ﬁelds for epidemiology and health forecasting Bill Physick, Martin Cope, Sunhee Lee and Peter Hurley Abstract Perhaps the weakest aspect of epidemiological studies is the exposure component. How accurate is our assessment of the dose received by a person or population on short- or long-term scales? Most epidemiological analyses involving air quality use data from a ﬁxed location monitor(s), with the implicit assumption that pollutant concentration is spatially uniform across the study domain. This is not the situation in reality, nor do individuals remain at the one location, even over short periods. We have developed a methodology which takes account of spatial variation in air quality and reduces uncertainty in ambient exposure estimates. This approach, using elliptical inﬂuence functions, involves the blending of observations from a monitoring network with gridded meteorological and pollution ﬁelds predicted by the complex air quality model TAPM. Examples from exposure ﬁelds developed on a 1.5 km-spaced grid for each day of a 6-year period (1998–2003) for Brisbane will be shown. 1. Introduction

Probably the greatest uncertainty in an epidemiological study is associated with the estimate of each individual’s exposure to the pollutant of interest. In urban air quality studies, the traditional approach is to assume that each person in a city has the same exposure. For an acute effects time-series study, the exposure assigned to each morbidity or mortality outcome is generally a pollutant concentration that is obtained from one air quality monitor (or sometimes averaged across several monitors) and that is assumed to be representative of the city’s daily exposure (Schwarz, 1994; Morgan et al., 1998). In chronic effects studies, the exposure of a whole city population (or members of a cohort study) is

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generally assumed to be the average value of the pollutant concentration over the period of the study (Dockery et al., 1993). It is well recognised that there are two assumptions in the above approach that are not strictly true. First, air quality on any day is not uniform across a city, i.e., there are spatial gradients, and second, most people do not remain in one location over the study period, be it one day or one year or more. The end result is that an individual’s true personal exposure is often quite different to that determined from the ‘uniform air quality/ﬁxed site’ approach outlined above. In this paper, we present a technique that produces gridded daily (or hourly if necessary) spatially varying exposure ﬁelds across a city, which when used with post-coded health outcomes, provide more information for epidemiological analyses than the traditional uniform air-quality approach. The technique takes computed air pollutant ﬁelds from a complex meteorological and air quality model (TAPM) and blends them in an optimal manner with observations from sites within an air quality network. Computing speed, storage capacity and cluster technology for PCs these days are such that simulations at relatively ﬁne grid spacings (down to 1 km) can be done for periods of many years. 2. Air quality model

For many years, the models used to assess the impact of emissions on air quality have been the relatively simple Gaussian plume or puff models. These models were ﬁrst developed in the late 1970s when available computing power was orders of magnitude less than now, and simulations of the full set of equations governing atmospheric dispersion were not feasible. However, the recent huge advances in information technology have led to much activity in high-resolution three-dimensional meteorological modelling, while recognition of the health and economic beneﬁts of understanding and predicting atmospheric pollution has also spurred the development of complex air quality models (Kumar and Russell, 1996). In some countries, coupled meteorology and air quality models are run daily to produce forecasts of photochemical smog constituents, particles and various air toxics (see Cope and Hess, 2005 for a review). In this study, The Air Pollution Model (TAPM) is used to produce gridded hourly ﬁelds of pollutants (Hurley et al., 2005). TAPM is a threedimensional prognostic model that solves the equations governing the behaviour of the atmosphere and the dispersion of emissions to predict meteorological ﬁelds and ground-level concentrations at local and regional scales. TAPM predicts concentrations for any non-reactive pollutant, primary and secondary particulate matter (PM10 and PM2.5),

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and the reactive pollutants nitrogen dioxide, ozone and sulphur dioxide. To illustrate the modelled and observed data blending approach presented in this paper, TAPM was run for the coastal city of Brisbane (Australia), using an hourly varying gridded (1 km spacing) emissions inventory supplied by Environment Protection Authority of Queensland (EPAQ). Emission sources include motor vehicles, industry, commercial and domestic, and biogenic (vegetation). TAPM has been veriﬁed against data in a number of studies (Hurley et al., 2005), including the urban study for Melbourne, where it was judged to have performed well (Hurley et al., 2003). 3. Blending methodology

TAPM was run for a 12-month period (1998) in a nested mode, with grid spacings of 30, 10, 3 and 1.5 km for the meteorological and pollutant grids. The innermost grid (90 90 km) is shown in Fig. 1, and includes the locations of air quality stations from the Brisbane monitoring network. Final exposure ﬁelds are produced by applying the blending methodology to daily meteorological and concentration ﬁelds from this grid, in a post-processing mode. An alternative blending approach involves the insertion of observations during a simulation, a process often referred to as four-dimensional data assimilation or data nudging. Our aim is to interpolate observations into the daily-modelled ﬁelds in such a way that they not only ‘correct’ the model prediction for the observing site, but also inﬂuence the model solution at surrounding gridpoints. An important parameter for consideration is the distance to which the inﬂuence extends, a scale that is dependent on the pollutant under consideration, its sources and the distribution of monitors within the network. The optimal scale choice for each pollutant is determined a priori in the manner described below and involves minimising a gross error and maximising a correlation. is calculated as At a gridpoint (i,j), the ﬁnal blended value fblended ij fblended ¼ fmod þ ij ij

PN

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mod is the modelled value at gridpoint (i,j), fobs are the where fmod ij k and fk observed and modelled values respectively at monitor k, N is the number of monitors and wijk is the inﬂuence or weighting function. An elliptical spatial weighting scheme is used, which has the following form within

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the ellipse: wijk

x x 2 y y 2 n ij k ij k ¼ 1 a b

(2)

This has the behaviour wijk(xijxk,yijyk) ¼ 1 for (0,0) and ¼ 0 for (7a,0) and (0,7b). Here a and b are the length scales of the ellipse in the x (west–east) and y (south–north) directions, respectively. xijxk, yijyk are the Cartesian distances between a monitoring site k and an interpolation point (i,j). n is the shape factor, and determines how quickly wijk-0 as |xijxk|-a and/or |yijyk|-b. The effect of increasing n is to make wijk-0 faster. After some preliminary experiments, a value of n ¼ 2 was chosen.

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Note that our approach is to derive the optimum blended ﬁelds by adjusting each model gridpoint value by an amount that is linked to the difference between model and observation at all monitoring sites, rather than just interpolating the observed value itself. In this way, problems with variables that are a function of height above sea level, such as temperature, are avoided. Prior to blending, the modelled ﬁelds are compared to daily observations at each monitor to look for any bias. This is done on a seasonal basis, with the daily-model ﬁelds at each monitor gridpoint adjusted by the associated mean 3-month bias (seasonal correction factor). Model values at surrounding gridpoints are adjusted by amounts determined by a Kriging procedure with zero (bias) boundary conditions assumed at the four corners of the grid. These points were deemed sufﬁciently far from the sources that zero concentration values (or background for ozone) could be assumed.

4. Optimally blended exposure ﬁelds

The ﬁrst task is to select optimum values of a and b for each pollutant such that wijk is best matched to the spatial layout of the observation network. This has been done by considering a range of a and b between 10 and 100 km at 10 km intervals and evaluating how well wijk performs by interpolating difference data (model minus observed) from all but one monitoring site to the omitted site. The interpolated value is then compared with the observed difference at that site. The site selected for omission is then cycled through each of the sites in the network in turn, yielding a comparison data set which can be used to optimise a and b. For example, consider the variable of daily-mean NO2—calculated each day from 24 hourly averaged values—from an annual simulation for Brisbane for 1998. Ten observation sites are available, each for (a maximum of) 365 days per year. This means that 10 365 10 (possible values of a) 10 (possible values of b) data points can be generated and used to optimise a and b. Indicators used to determine optimal a and b are gross error (interpolated daily-mean NO2 difference minus ‘observed or actual’ NO2 difference) and correlation. For most variables, the optimal spatial scale pairs are the same for gross error and correlation, and are always within 10 km of each other. When they differ, the correlation pair is chosen for application in the blending procedure. An annual simulation (1998) was carried out for the Brisbane region, followed by a blending of daily observations into the gridded ﬁelds using

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Figure 2. Scatter plots of observed 24-h NO2 concentration at Rocklea site for 1998 against TAPM modelled predictions (top left), TAPM predictions corrected with all observations except Rocklea (top right) and TAPM predictions corrected with all observations (bottom left).

the optimal (a, b) values. An indication of the validity of the blending approach can be gained by comparing observed data at a monitor to the TAPM prediction corrected by using the weighting function. A scatter plot of modelled against observed data at the Rocklea (ROC) monitor is shown for daily-mean NO2 in Fig. 2 (top right), in which observations from all sites but Rocklea have been blended into the model results. Although the original (uncorrected) predictions from the model, plotted against the observations in Fig. 2 (top left), indicate that TAPM shows reasonable skill in the difﬁcult task of predicting the temporal behaviour of a pollutant concentration at a point, the values resulting from the blending procedure (Fig. 2 (top right)) agree very well with the observations. When the Rocklea observations are included in the blending, the comparison is of course even better (Fig. 2 (bottom left)). It is not perfect

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because other stations are also contributing to the estimated value at Rocklea, albeit with smaller weights. Nevertheless, the results indicate that some improvements to predictions on an entire TAPM grid can be expected. A tendency to under predict at this site is largely remedied by the blending procedure, and the variance between observations and predictions is reduced. A linear equation of best ﬁt and correlation squared R2 (coefﬁcient of determination) is shown on each plot, with R2 values improving from 0.36 for model predictions only, to 0.70 when all but Rocklea observations are blended, to 0.83 when all observations are blended. Time-series plots for the ﬁrst 180 days of 1998 are shown in Fig. 3 for daily maximum temperature at the Brisbane airport site. As is to be expected, TAPM predicts this variable more accurately than it does a pollutant, and once the observations have been included, there is excellent agreement. R2 values increase from 0.91 to 0.98 to 0.99.

5. Summary and discussion

We have presented a methodology for computing daily pollutant concentration ﬁelds that takes account of spatial variation in air quality and reduces uncertainty in exposure estimates. This approach, using elliptical inﬂuence functions, involves the optimum blending of observations from a monitoring network with gridded meteorological and pollution ﬁelds predicted by the complex air quality model TAPM. Such ﬁelds allow more information to be incorporated in the exposure ﬁelds used in epidemiological studies, rather than having to assume that exposure is the same across a whole city and that individuals remain at the one location for the period of a study. These daily exposure ﬁelds enable epidemiological cohort studies to take advantage of information in daily activity diaries. As well as obvious application in time-series studies examining short-term effects of exposure to pollutants, today’s computing technology is such that simulations can also be done for cohort and longitudinal studies that involve chronic effects and take place over a number of years. When a metric such as annual population exposure for a city is required, this can be compiled from appropriate weightings of the personal exposures of many individuals whose daily (weekly) trip proﬁles are representative of population subgroups. This will give a different result from the standard method of using a single concentration for a whole city. The daily exposure ﬁelds for temperature, humidity, NO2, ozone and particles are currently being used by the authors in collaboration with the

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Figure 3. Time series at the Brisbane Airport site of observed and modelled daily maximum temperature (upper), and observed and blended maximum temperature without Brisbane Airport observations (lower). X-axis indicates day number (1 ¼ 1 January 1998).

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National Centre for Epidemiology and Population Health at Australian National University, Canberra to develop a health forecasting model for various diseases.

Discussion

D.W. Byun:

B. Physick:

B. Fisher:

B. Physick:

You have a new 4D AQ ﬁeld, which may represent observation much better than the direct AQ model output. Do you see any pitfalls of using such data for a purpose other than the exposure study? I am asking because the optimal blending method is a diagnostic (instead of dynamic) assimilation method, so it may change the physical/chemical balance of the concentration ﬁeld. I think that if the ﬁelds were used as an initial ﬁeld for a simulation, there is the possibility of problems arising from chemical imbalances (e.g., between NO, NO2 and O3). Has anyone compared the risk associated with personal exposure to PM of a commuter who drives some hours a day with the health risk associated with trafﬁc accidents? I daresay someone somewhere has attempted to do so, but I’m not aware of it.

ACKNOWLEDGMENTS

We are appreciative of the provision of emissions inventories and monitoring data by EPA Queensland, and to CSIRO’s Preventative Health Flagship for funding this work.

REFERENCES Cope, M., Hess, D., 2005. Air quality forecasting: A review and comparison of the approaches used internationally and in Australia. Clean Air Environ. Qual. 39(1), 39–45. Dockery, D.W., Pope, C.A., Xu, X., Spengler, J.D., Ware, J.H., Fay, M.E., Ferris, B.G., Speizer, F.E., 1993. An association between air pollution and mortality in six U.S. cities. N. Engl. J. Med. 329(24), 1753–1759.

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Hurley, P.J., Manins, P.C., Lee, S.H., Boyle, R., Ng, Y.L., Dewundege, P., 2003. Year-long, high-resolution, urban airshed modelling: Veriﬁcation of TAPM predictions of smog and particles in Melbourne, Australia. Atmos. Environ. 37, 1899–1910. Hurley, P.J., Physick, W.L., Luhar, A.K., 2005. TAPM: A practical approach to prognostic meteorological and air pollution modeling. Environ. Model. Software 20, 737–752. Kumar, N., Russell, A.G., 1996. Multiscale air quality modelling of the northeastern United States. Atmos. Environ. 30, 1099–1116. Morgan, G., Corbett, S., Wlodarczyk, J., 1998. Air pollution and hospital admissions in Sydney, Australia, 1990 to 1994. Am. J. Public Health 88(12), 1761–1766. Schwarz, J., 1994. Air pollution and hospital admissions for the elderly in Birmingham, Alabama. Am. J. Epidemiol. 139, 589–598.

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Chapter 7.4 On inﬂuence of long-range transport of pollen grains onto pollinating seasons Pilvi Siljamo, Mikhail Sofiev, Elena Severova, Hanna Ranta and Svetlana Polevova Abstract Pollen grains can be transported over hundreds and even thousands of kilometres and signiﬁcantly affect pollen concentration in many regions making it less dependent upon the local conditions. In this study we have used the Finnish operational dispersion model SILAM to delineate birch pollen source areas as well as to forward simulations for pollen long-range transport. We have considered an episode of very high birch pollen concentration in Finland in April 1999 both in forward and inverse point of view. As a representative of easterly located areas, we considered also Moscow region. Source delineation for early pollen peaks is done for years 1999, 2002 and 2004. 1. Introduction

Pollen is a known source of allergy-related diseases. Observational evidence and a theoretical ground are mounting that the pollen grains, despite their large size, can be transported overt hundreds and even thousands of kilometres and signiﬁcantly affect pollen concentrations in many regions making it less dependent upon the local conditions. Here, we present some results of a research project of the Academy of Finland studying the long-range transport of pollen grains and its main consequences. As shown in preceding study (Siljamo et al., 2004; Soﬁev et al., 2006a), large-scale transport of pollen from the southern or western European region can shift the high-concentration period in Finland for as long as a few weeks. However, consideration of consecutive springs 2002–2005 showed that there is no systematic pattern in such episodes: they are fully determined by speciﬁc meteorological conditions during the European

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ﬂowering season in speciﬁc year. In some years, the situation can favour the transport and the episodes will be very strong while conditions in some other years may appear less favourable and the early concentration peaks will be small or non-existent. Pasken and Pietrowicz (2005) have studied long-range transport modelling of oak pollen in the United States. The current study was performed within the project of Academy of Finland that aims at comprehensive investigation of the phenomenon over Europe, with a speciﬁc task of highlighting the areas where the longrange transport signiﬁcantly affects the pollinating seasons and where its inﬂuence is small (http://pollen.fmi.ﬁ). As a representative of easterly located areas, we considered Moscow region. It is located in the middle of the large mixed forest area (spruce and birch, but birch usually dominates) with long birch ﬂowering seasons and usually high pollen concentrations. As we show below, one of the reasons for the long birch pollen season is both pre-ﬂowering and post-ﬂowering pollen transport from the remote areas. The second area considered in this work is territory of Finland that is the Nordic region highly vulnerable to early-spring transport from the south and also serving as a source of late pollen at the end of the European ﬂowering period. 2. Atmospheric dispersion model and data

Our analytical tool was a dispersion modelling system SILAM (Soﬁev et al., 2006b), which can solve various air quality-related problems including consequences of radioactive release, air pollution with chemical species and various aerosols, in particular, pollen. It is a Lagrangian random-walk model that is capable of solving both forward and inverse dispersion problems. Meteorological input data can be taken from operational NWP HIRLAM and ECMWF models, but in these studies ECMWF data has only been used. Pollen observations are from Moscow (years 1992–2005) and Finland, where we have got six sites in 1999. 3. Inverse case studies for long-range transported pollen to Moscow area

In this part of the study, we performed SILAM inverse simulations to delineate potential source areas of long-range transported pollen to Moscow region and to quantify the importance of the effect. Usually, the ﬁrst pollen grains are observed in Moscow in early April. The birch pollen season usually starts in the end of April, when the concentrations rapidly

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increase. High concentration period lasts for 2–4 weeks, after which low levels can be still observed until the end of summer. In 1999, a long high-concentration episode of birch pollen was observed (Fig. 1). Results of source apportionment studies are shown in Fig. 2. The maps in Fig. 2 delineate the areas with possible locations of sources, which created the pollen observed during the different parts of the period. In particular, it is seen (Fig. 2a) that during the very beginning of the period Moscow was affected by air masses transported from the Moscow region itself and from southwestern birch forests (the latter ones were already ﬂowering). Then, the city was inﬂuenced by the southeastern and southern sources (Fig. 2b, and the 5-day extraction in Fig. 2c), and ﬁnally by the northern ones (Fig. 2d). In all cases, the respective forests were already ﬂowering and thus supplied pollen, which signiﬁcantly increased and widened the Moscow-own pollinating season. Similar inverse simulations for the episodes in 2002 and 2004 (Fig. 3) also showed very diversiﬁed patterns, which, however, conﬁrmed that these episodes were created partly by forests located hundreds and even thousands kilometres far from the Moscow area. These early episodes were selected from the observations for the period preceding or at the very beginning of the local ﬂowering season. Pollen concentrations were much lower (about 100 grains m 3) than 10–14 days later, when they were as high as 3000–6000 grains m 3. We have got also leaf-unfolding Moscow 1999

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Figure 2. Cumulative sensitivity area of Betula pollen in springs 1999 in pre-ﬂowering cases (a–c) and post-ﬂowering case (d) Greys delineate area from where air parcels came to Moscow (+) between (a) April 14–18, 1999; (b) April 19–30, 1999; (c) April 25–30, 1999; and (d) April 30–May 5, 1999.

observations from environs. Birches start to ﬂower before leaf unfolding, but these days were well after the ﬁrst pollen peaks. 4. Forward simulation for spring 1999

For the sake of comparison of the central and northern regions in 1999, we also studied pollen transport to Finland in 1999, when exceptionally high birch pollen concentrations were observed by all six Finnish monitoring sites during the same days as in Moscow. It was an early spring in Finland and birches did not ﬂower yet.

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Figure 3. Cumulative sensitivity area of Betula pollen in springs 2002 and 2004. Greys delineate area from where air parcels came to Moscow (+) (a) 15–19 April 2002 and (b) 19–24 April 2004.

Our previous inverse study showed that pollen came mainly from western and southern Russia and partly from the southern Baltic or Poland and Germany (Ranta et al., 2006). However, the detailed pattern of the episodes is more complicated: it consists of three periods during which Finland was affected by totally different source areas. Figure 4a–d shows new results of forward pollen dispersion simulations with the sources located only in Central Russia (Fig. 4, box area). It can be seen that Finnish pollen stations are inﬂuenced by the sources in that region two times: during the 18th of April and again during the 20th–21st of April. According to observations, these were the main peaks recorded over most of Finland. Some pollen was also observed during the 19th of April, but the concentrations were small and indeed originated from Central Europe and Baltic States. Figure 5 shows birch pollen counts at two Finnish sites— Helsinki and Vaasa—together with SILAM probability function computed for Central Russia sources (this function shows the probability of speciﬁc receptor to be affected by the speciﬁc source). It is well seen that the observed peaks and the probability function are very similar showing that the Russian forests were strong contributors to the episodes while the inﬂuence of the southern Baltic and Central Europe was comparatively small. 5. Conclusions

In a general year, the local ﬂowering produces the bulk of pollen, but its long-range transport can signiﬁcantly change timing and intensity of pollinating seasons. In northern regions, such as Finland, the

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Figure 4. Probability area of Betula pollen on the (a) 18th of April 1999, (b) 19th of April 1999, (c) 20th of April 1999, and (d) 21st of April 1999. The source areas are outlined by the boxes. Computations start since the 16th of April.

pre-ﬂowering transport from southern source areas is the most important transport phenomenon. In southern areas, such as part of Central and Eastern Europe, the transport from the north after the end of the local ﬂowering can be also important due to still-high pollen concentrations in the northern areas. However, this effect is not easy to observe due to overall abundance of pollinating species in air during summer time. In the regions, such as the Baltic Countries, northeastern Europe and Central Russia, the pollinating season can be signiﬁcantly extended due to both early-spring transport from the south and late-spring inﬂuence of the northern forests.

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Forward and inverse simulations using the probability distributions as the main transported tracer provide a good ﬁrst approximation of the source locations and probable pollen concentrations. It also helps distinguishing between the local and non-local pollen observed at monitoring sites and highlights the local- and transport-induced parts of the pollinating seasons.

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However, accurate timing and intensity of birch ﬂowering over speciﬁc areas are of vital importance for forecasting the absolute concentrations of grains. Discussion

G. Kallos: P. Siljamo:

P. Kishcha:

P. Siljamo:

Why you are only looking at birch pollen and not at other trees like e.g., coniferous (pine trees)? Birch is an early ﬂowering tree, therefore redistribution of its pollen with air masses greatly affects the overall allergenic season over most of Europe. For laterﬂowering taxa, the effect is not that visible as many species pollinate simultaneously. In the northern Europe, up to 15% of population is sensitive to birch allergens, but it is unusual that people would be allergy to coniferous pollen. So, birch is an important allergy plan in the northern Europe. Later we are going to look at the other important allergy plant, like alder and olive, but biological part of the modelling is complicated, because there are not European-wide phenological models, which could be used, but you should invent them. Already a map for a tree species needs a lot of work. So we think that it is important to get model working to one species ﬁrst and then thinking about the others. Did you compare the model-simulated distribution of pollen grains with that of allergic illnesses? Which equipment is used in the network for counting pollen grains? The model is not ready yet and an ordinary evaluation is needed ﬁrst. Later, it could be interesting to compare model results to allergic symptoms. Pollen observations are usually made using Burkard volumetric spore trap. They locate on the roof and aerobiologists analyse the results using microscope.

ACKNOWLEDGEMENTS

The current study is a part of a project of Academy of Finland ‘‘Evaluation and forecasting of atmospheric concentrations of allergenic pollen in Europe’’ (http://pollen.fmi.ﬁ).

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Helsinki pollen data is provided by the Helsinki Skin and Allergy Hospital. REFERENCES Pasken, R., Pietrowicz, J.A., 2005. Using dispersion and mesoscale meteorological models to forecast pollen concentrations. Atmos. Environ. 39, 7689–7710. Ranta, H., Kubin, E., Siljamo, P., Soﬁev, M., Likosalo, T., Oksanen, A., Bondestam, K., 2006. Long distance pollen transport cause problems for determining the timing of birch pollen season in Fennoscandia by using phenological observations. Grana, 45, 297–304. Siljamo, P., Soﬁev, M., Ranta, H., 2004. An approach to simulation of long-range atmospheric transport of natural allergens: An example of birch pollen. In: Air Pollution Modelling and Its Applications XVII (in press). Also in pre-prints of 27th International Technical Meeting on Air Pollution Modelling and Its Applications, Banff, 23—30 October 2004, Canada, pp. 395–402. Soﬁev, M., Siljamo, P., Ranta, H., Rantio-Lehtima¨ki, A., 2006a. Towards numerical forecasting of long-range air transport of birch pollen: Theoretical considerations and a feasibility study. Int. J. Biometeorol. 50, 392–402. Soﬁev, M., Siljamo, P., Valkama, I., Ilvonen, M., Kukkonen, J., 2006b. A dispersion modelling system SILAM and its evaluation against ETEX data. Atmos. Environ. 40, 674–685.

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Chapter 7.5 Air quality characterization for environmental public health tracking$ Timothy Watkins, Fred Dimmick, David Holland, Alice Gilliland, Vickie Boothe, Chris Paulu and Andrew Smith Abstract The U.S. Centers for Disease Control and Prevention (CDC) has been given the mandate to develop a national environmental public health tracking network (Tracking Network). The Tracking Network will require both environmental and public health data to be routinely available at a national scale. Historically, the only source of air quality data in the United States that was available on an ongoing and systematic basis at national levels was generated by ambient air monitoring networks put in place for the U.S. Environmental Protection Agency’s (EPA) Air Quality Programs. However, new analysis techniques are being developed to use air quality modeling forecasts and satellite data to provide additional information to characterize air quality on a routine basis. With the public’s expanding interest in the serious health effects associated with ozone and ﬁne particles, public health ofﬁcials are looking for ways to better use the available air quality data for use in the Tracking Network. The EPA and the CDC have conducted a collaborative effort entitled the Public Health Air Surveillance Evaluation (PHASE) to $

The views expressed in these proceedings are those of the individual authors and do not necessarily reﬂect the views and policies of the United States Environmental Protection Agency (EPA). Scientists in the EPA have prepared the EPA sections and those sections have been reviewed in accordance with the EPA’s peer and administrative review policies and approved for presentation and publication. This report does not constitute an endorsement of authors, or organizations by the CDC. The views and opinions of these authors and organizations are not necessarily those of the CDC or the Department of Health and Human Services (HHS). This paper has been reviewed in accordance with the EPA’s peer and administrative review policies and approved for presentation and publication.

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evaluate various air quality data sets, from routinely available sources, for speciﬁc use by public health ofﬁcials. The U.S. EPA generated air quality data sets using ambient monitoring data, modeling results, and statistically combined monitoring and modeling data. The EPA, in collaboration with the National Aeronautics and Space Administration (NASA), is also exploring the integration of satellite data with monitoring and modeling data sets to improve available air quality information. The resulting air quality estimates were provided to the CDC’s Tracking Network State partners to evaluate the use of these air quality data sets in tracking potential associations between air quality and public health impacts (asthma and cardiovascular disease). 1. Introduction

In September of 2000, the Pew Environmental Health Commission issued a report that called on U.S. legislators to create a federally supported Nationwide Health Tracking Network of local, state, and federal public health agencies that tracks trends of priority chronic diseases and relevant environmental factors in all 50 states. According to the report, effective environmental health tracking requires a coordinated approach that identiﬁes hazards, evaluates exposures, and tracks the health of the population (Pew Report, 2000). The term ‘‘tracking’’ was deﬁned by the Commission as synonymous with the concept of public health surveillance, which is, ‘‘the ongoing systematic collection, analysis, and interpretation of outcome-speciﬁc data for use in the planning, implementation, and evaluation of public health practice.’’ (Thacker and Berkelman, 1988). The U.S. Congress responded to the Commission in 2002, funding the U.S. Centers of Disease Control and Prevention (CDC) to develop a national environmental public health tracking network (Tracking Network) and to expand environmental health capacity within state and local health departments. As part of the development process, the CDC evaluated existing environmental data for inclusion in the Tracking Network. Ozone (O3) and ﬁne particle (PM2.5) data from air monitoring networks ranked highest for suitable characteristics and public health importance. These air pollutants, at levels found across the U.S., can have a substantial impact on human health. According to the published peer-reviewed literature, they have been associated with premature death, hospitalizations for acute respiratory and cardiovascular events, decreased lung function, adverse birth outcomes, and lung cancer. In 2002, approximately 126 million people in the United States lived in counties where they are exposed to unhealthy levels of criteria air

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pollutants, mostly in the form of either ozone or ﬁne particles (http:// www.epa.gov/airtrends). In order to implement the Tracking Network, compatible environmental and public health data need to be routinely available across the nation. The CDC, the EPA, and three Tracking Network state public health agencies (New York, Maine, and Wisconsin) collaborated in the Public Health Air Surveillance Evaluation (PHASE) project to identify and evaluate tools, methods, and data sources that can be used to generate daily surrogate measures of exposure to ambient air pollution and to relate those measures to available public health data. This paper presents the approaches used in PHASE to characterize air quality for potential environmental public health tracking (EPHT) applications and provides example results from PHASE analyses that related routinely available air quality and public health data. 2. PHASE air quality measures

Ambient air quality levels are measured across the U.S. in a comprehensive monitoring network. In addition to monitoring data, ambient air quality levels are estimated through various models based on emission estimates, meteorological and other data, and physical/chemical algorithms. The air quality data developed in the PHASE project can be divided into four types of estimates: monitored air quality, interpolated air quality, modeled air quality, and air quality based on a combined or ‘‘fused’’ monitored-model statistic. The data sets were developed within the EPA for ozone and ﬁne particles (PM2.5). The data covered areas encompassing New York, Maine, and Wisconsin for each day of the year in 2001. The PM2.5 estimates represent a 24-h integrated sample and the ozone estimates represent the concentration averaged over the 8-h maximum period in each day. 2.1. Ambient air network monitoring data

Ambient air measurements for ozone and PM2.5 are gathered across the U.S. in a comprehensive population-based network by State and local agencies. Ambient air measurements are often considered the true measure of air quality, although there are uncertainties in these measurements. In 2001, there were 1067 monitoring locations that collected PM2.5 measurements and 883 monitoring locations that collected ozone measurements. The distribution of these monitoring locations is largely determined by the needs of the State and local air pollution control

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agencies operating them; however, some of the monitors are sited to obtain more timely and detailed information about air quality in strategic locations (e.g., areas of maximum concentrations and high population density in urban areas). All ambient monitoring methods or analyzers used in these networks are tested periodically to assess the quality of the data being produced (EPA Technology Transfer Network). There are advantages and disadvantages of using ambient air monitoring data for EPHT. The primary advantage is that these measurements of pollution concentrations are the best characterization of the concentration of a given pollutant at a given time and location. Furthermore, the data are supported by a comprehensive quality assurance program, ensuring data of known quality. A disadvantage of using ambient air monitoring data is that there can be signiﬁcant spatial and temporal gaps. Spatial gaps exist in monitoring data, especially for rural areas, since the monitoring networks are mostly population based. Temporal limits include lack of daily monitored data for PM2.5, since most samples are collected every third day. O3 is monitored daily, but mostly during the O3 season (approximately from April to August). 2.2. Ambient monitoring data with statistical interpolation

Statistical interpolation of monitoring data improves spatial and temporal coverage by providing estimates of air quality in unmonitored locations and time periods. For the PHASE project, the EPA performed ordinary Kriging (i.e., Kriging without the use of covariates) for each day and each pollutant. Kriging is a form of statistical modeling that interpolates data from a known set of sample points to a continuous surface. According to published literature, Kriging is the most common geostatistical technique used in the air pollution research ﬁeld (Jerrett et al., 2001). A major advantage of Kriging over other interpolation methods is the production of both predicted values and their standard errors (Kriging variance) at unsampled locations. These standard errors quantify the degree of uncertainty in spatial predictions at unsampled sites, providing valuable information on where the interpolation is less reliable (Mulholland et al., 1998). 2.3. Air quality modeling

Air quality models have historically been used to develop and evaluate emission control strategies for air quality management activities. These modeling applications have typically been done for particular periods of interest (e.g., major air pollution episode events) and for years that have

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the most thorough emission inventories available. However, the U.S. National Oceanic and Atmospheric Administration (NOAA) is working with the EPA to develop and apply air quality models to provide routinely available, real-time national air quality forecasts. The routine availability of modeled daily air quality concentrations used for forecasting also has potential to support EPHT. Modeled air quality forecasts were not available for 2001, so for the PHASE project, the EPA used readily available air quality modeling data from Community Multiscale Air Quality (CMAQ) modeling applications for the continental U.S. at 36 km 36 km grid cells and for the eastern U.S. at 12 km 12 km grid cells. The CMAQ modeling system has been designed to approach air quality as a whole by including state-of-the-science capabilities for modeling multiple air quality issues, including tropospheric ozone, ﬁne particles, toxics, acid deposition, and visibility degradation (Byun and Schere, 2006; Models 3/CMAQ model). As with ambient monitoring data, there are advantages and disadvantages of using air quality modeling data from CMAQ for EPHT applications. An advantage of using the CMAQ model output is that it has the potential to provide complete spatial and temporal coverage. However, the CMAQ modeling estimates have the potential for more uncertainty relative to monitoring data. This uncertainty is driven by the uncertainties in the emissions and meteorological input data and the chemical process mechanisms embedded in the model. These uncertainties can be most signiﬁcant on the shortest time scales (e.g., intraday) where meteorological variability may not be captured as well as weather patterns over several days and weeks. Additional uncertainties exist because of emissions that are difﬁcult to quantify because their sources are broad in area and cannot be directly measured, unlike mobile and point emission sources. 2.4. Data fusion: Statistically combined monitored and modeled air quality data

Given the limitations in air quality monitoring and modeling data, the EPA is working to integrate or ‘‘fuse’’ these individual data sets to derive a better measure of air quality for air quality management activities and to compare with health outcomes. The PHASE project used a hierarchical-Bayesian (HB) approach to combine ambient monitoring data with output from the CMAQ model to produce predictions of pollution concentrations. Introducing air quality model output that scientiﬁcally estimates air quality in areas without a monitor should allow more accurate prediction in non-monitored areas (e.g., rural areas). The Bayesian method quantiﬁes the uncertainties in both the monitoring

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and modeling data sets to produce an improved estimate of error in the predicted concentrations. It draws on the strengths of each data source by giving more weight to precise monitoring data in areas where monitoring exists, while relying on model output in non-monitored areas. The HB model assumes that each source provides information about the underlying true pollutant surface. Air monitors are assumed to measure the true pollutant surface with some measurement error, but no bias. In contrast, numerical model output is assumed to approximate the variability of the true surface while exhibiting both measurement error and bias (additive and multiplicative) across space and time. Also, the HB model allows for the inclusion of covariates, such as daytime population density to account for possible pollution–population relationships. The HB approach provides an inherent system to predict air quality data for a speciﬁc time and spatial scale using monitoring and modeling data as input, while minimizing the limitations of either of these methods applied separately. The primary advantage of this approach is increased model ﬂexibility and the ability to predict pollution gradients and uncertainties that might otherwise be unknown using interpolation results based solely on relatively sparse monitoring data. Spatial maps of bias in numerical models are another useful output that will allow modelers to improve their models to minimize bias. The major disadvantage is the computational burden of HB models. These models are ﬁt by simulation, and sometimes the solutions are difﬁcult to program and require signiﬁcant computer resources. It requires experience and statistical expertise to ensure that proper modeling assumptions have been used, proper convergence criteria have been used, and the results are reasonable. 2.5. Evaluation of PHASE air quality characterization approaches for tracking network applications

As part of the PHASE project, the EPA, the CDC, and the CDC Tracking Network State partners (Maine, New York, and Wisconsin) evaluated the different air quality characterization approaches discussed above for their application in the Tracking Network. The selection of a method for use in the Tracking Network should be based on quantitative evaluations, or on qualitative evaluations where quantiﬁcation is not possible, and be informed by the usefulness of the ambient estimates for public health assessments. The EPA evaluated how well the estimated air quality concentrations from each method agree with available observations. For CMAQ, operational and diagnostic model evaluation is an ongoing effort in the development and enhancement of the modeling system (Eder and Yu, 2006; Hogrefe et al., 2006). For the statistical methods (Kriging and

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HB approach), the EPA is currently conducting additional quantitative evaluations to compare their performance. In addition to these evaluations, there are other evaluation criteria that need to be considered for the various approaches used in PHASE. These criteria include ease/speed of implementation, costs in software and computer resources, and required expertise for each method. Ultimately, all of these factors need to be considered before choosing the most appropriate approach for the Tracking Network. Figure 1 shows an example of visual qualitative comparisons of two of the air quality characterization techniques used in the PHASE project.

Interpolated Interpolated from monitors from monitors

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Figure 1. Interpolated and fused data from PHASE (NE U.S.).

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The Krigged data surface (top) shows a fairly ‘‘smooth’’ surface gradient, while the fused data surface (bottom) shows a surface that contains more ‘‘texture.’’ This type of information produced by the data fusion approach may prove to be particularly useful for EPHT applications. 3. Measuring associations between health and air quality

As part of the PHASE Project, the three CDC Tracking Network State partners applied statistical models to examine the associations between health outcomes and air pollutant levels. This section provides a brief summary of the health data and statistical models used in these analyses and some example results. 3.1. Health data

To examine the associations between the air quality data and health outcome data, the PHASE project team selected two health outcomes: acute myocardial infarction (AMI) and asthma. The choice was based on the availability of data in Maine, Wisconsin, and New York, and scientiﬁc literature showing associations between these health conditions and ambient air pollution. AMI is a leading cause of illness and death among adults. Epidemiologic studies have shown associations between its incidence and the ambient concentrations of airborne particulate matter, especially PM2.5 (Peters et al., 2001; Sullivan et al., 2005). Asthma is a major cause of illness and disability; among children, it is the number one cause of emergency room visits, hospital admissions, and doctor’s ofﬁce visits. During 1980–1996, the prevalence increased; since 1995, there has been an increase in outpatient visits and emergency department visits and a decrease in mortality and hospitalizations (Mannino et al., 2002). Rates are associated with the levels of both particulate matter and ozone (Koren, 1995; Lebowitz, 1996). 3.2. Statistical analysis of health and air quality data

The PHASE project examined the association between the health outcomes and air quality levels using a case-crossover design. The casecrossover design (Maclure, 1991) compares air quality levels just prior to a case day on which a person is hospitalized with the levels on one or more control days when the person was not hospitalized. The method is well suited for estimating the association between short-term exposures and acute health events shortly thereafter, such as asthma attacks and

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Figure 2. Association between asthma emergency department visits and 8-h maximum ozone concentrations in Maine (example results).

AMI. Since each case provides its own control, behavioral and socioeconomic risk factors as well as unknown personal risk factors are controlled for by design. Figure 2 presents example results from the casecrossover analyses performed by the Maine Department of Health and Human Services (DHHS). In examining the statewide relationship between O3 concentrations and asthma emergency department (ED) visits in 2001, all of the air quality characterization approaches yielded statistically signiﬁcant associations consistent with the peer-reviewed literature. All of the PHASE state partners continue to investigate and evaluate the utility of the fused air quality characterization data for understanding air quality and public health relationships for both Ozone and PM and will be publishing results in the near future.

4. Summary and future analyses

The overall objective of the PHASE project was to develop, evaluate, and demonstrate the advantages and limitations of different methods of generating air quality characterization surveillance data that could be systematically and routinely available to link with public health surveillance data as part of the Tracking Network. In addition, new air quality characterization techniques that combine data sets (e.g., ambient air monitoring and modeling) are being evaluated for potential additional EPHT applications. In the future, additional approaches for characterizing air quality and human exposures for EPHT will be tested. One such

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approach is the use of modeled air quality forecasts from NOAA. Because the year of analysis for the PHASE project was 2001, existing CMAQ simulations were used. However, routinely available modeled air quality forecasting results for ozone and PM are expected in the near future and could be used for the Tracking Network. The EPA and other Federal partners, such as NOAA and the National Aeronautics and Space Administration (NASA), are also exploring the use of remote sensing (satellite) data for various air quality applications, including EPHT. Finally, all of the air quality characterization techniques used in PHASE estimate ambient pollutant concentrations. When associating ambient concentrations to public health endpoints, a key assumption is that ambient concentrations can serve as a surrogate for actual personal exposures to ambient air pollution. The relationship between actual human exposures and ambient air concentrations will depend upon the pollutant being measured, as well as, many other factors such as season and human activity. Research efforts are underway to develop methods to enhance exposure estimates for EPHT applications. Ultimately, through the PHASE project and future activities, the EPA and its Federal and State partners will gain an improved understanding and enhancement in the air quality characterization data sets needed for EPHT and other air quality management activities.

REFERENCES Byun, D., Schere, K.L., 2006. Appl. Mech. Rev. 55, 51–77. EPA. Technology Transfer Network—Ambient Monitoring Technology Information Center www.epa.gov/ttn/amtic Eder, B., Yu, S., 2006. Atmos. Environ. 40, 4811–4824. Hogrefe, C., Porter, P.S., Gego, E., Gilliland, A., Gilliam, R., Swall, J., Irwin, J., Rao, S.T., 2006. Atmos. Environ. 40, 5041–5055. Jerrett, M., Burnett, R.T., Kanaroglou, P., Eyles, J., Finkelstein, N., Giovis, C., Brook, J., 2001. Environ. Plann. A 33, 955–973. Koren, H.S., 1995. Environ. Health Perspect. 103(Suppl 6), 235–242. Lebowitz, M.D., 1996. Eur. Respir. J. 9(5), 1029–1054. Maclure, M., 1991. Am. J. Epidemiol. 133, 144–153. Mannino, D.M., Homa, D.M., Akinbami, L.J., Moorman, J.E., Gwynn, C., Redd, S.C., 2002. Surveill. Summ. MMWR. 51(No SS-1), 1–13. Mulholland, J.A., Butler, A.J., Wilkinson, J.G., Russell, A.G., Tolbert, P.E., 1998. J. Air Waste Manag. Assoc. 48, 418. Peters, A., Dockery, D.W., Muller, J.E., Mittleman, M.A., 2001. Circulation. 103, 2810–2815. Pew Report, 2000. America’s environmental health gap: Why the country needs a nationwide health tracking network, Technical Report, Pew Environmental Health Commission, September 2000.

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Sullivan, J., Sheppard, L., Schreuder, A., Ishikawa, N., Siscovick, D., Kaufman, J., 2005. Epidemiology. 16(1), 41–48. Thacker, S.B., Berkelman, R.L., 1988. Epidemiol. Rev. 10, 164–190. The Models 3/Community Multiscale Air Quality (CMAQ) model (see: http://www.epa.gov/ asmdnerl/CMAQ or http://www.cmascenter.org/index.html).

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Poster 1 Mass ﬂux balance at an urban intersection Sandro Baldi, Matteo Carpentieri and Alan G. Robins Abstract The understanding of the behaviour of pollutants released in urban sites is of paramount importance for a number of reasons, mainly related to human health. Furthermore, the particular present international political situation adds further concerns, as the deliberate discharge of toxic material in populated areas is a serious threat. Wind tunnel experiments were performed in order to study ﬂow and pollutant dispersion in a real urban environment. The work is part of a larger EPSRC funded project (DAPPLE, Dispersion of Air Pollution & Penetration into the Local Environment) involving six British Universities. The study concerned the 350 m-diameter circular area shown above, centred on the intersection between Marylebone Road and Gloucester Place in central London and modelled at 1:200 scale in a 20 35 1.5 m wind tunnel, (Fig. 1). A 2D Laser Doppler Anemometer (LDA) was used to measure mean and turbulent velocities across the site in all three directions, which provided a full three-dimensional velocity mapping of the ﬂow on a 31 31 6 grid over a 600 600 150 mm region. The concentration of pollutant across the area was measured with the use of a ‘‘Fast’’ Flame Ionisation Detector (FFID). The LDA and the FFID devices were then coupled in order to produce simultaneous measurements of velocity and concentration, which permitted the estimation of mean and turbulent pollutant ﬂuxes, thus giving an important insight into the understanding of turbulent diffusion. The mean ﬂow reveals a very complex behaviour across the whole area. Recirculation vortices can be observed in many locations but they are not always well deﬁned as they interact with the along-the-street component of the ﬂow. This is rather important since the wind is directed 511 from Marylebone road and therefore the x and y components of wind velocity

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Sandro Baldi et al.

are rather similar in magnitude. Generally the ﬂow in Gloucester Place appears to be mainly two-dimensional with an almost zero vertical component, both south and north of the intersection. The pattern is rather different as soon as we reach the top of the buildings as wakes and vortices interact quite strongly there. The asymmetric conﬁguration of the building geometry plays an important role in deﬁning the mean and turbulent ﬂow in the upper part of the street canyon. The mean ﬂow along Marylebone Road shows a much more complicated behaviour. Upwind of the intersection (at negative x) the ﬂow in the lower part of the canyon is generally redirected upwards in the direction of the intersection and then down again partly in Gloucester Place (north side) and partly in Marylebone Road (east side). The mean ﬂow in Marylebone Road downwind of the intersection is fully three-dimensional with vortices forming on the building on the lower-right corner of the intersection mixing with the down-ﬂow coming from the upper levels of west Marylebone. Nevertheless, in this region the recirculation vortices are clearly visible and the ﬂow assumes a more ‘‘classical’’ canyon-style pattern. Particular features of the site, mainly towers and tall buildings, affect the ﬂow very strongly, causing strong recirculation areas and wakes. The analysis of the Reynolds stresses shows that the boundary

Figure 1. A schematic view of the central region of the study area, showing the intersection of Marylebone Road and Gloucester Place. The vectors show the direction of the undisturbed ﬂow above roof level.

Mass Flux Balance at an Urban Intersection

733

layer might be affected by the buildings up to heights in excess of three times the tallest building of the intersection. Measurements were then performed employing both LDA and FFID in order to obtain mass ﬂuxes estimations; both mean and turbulent ﬂuxes were measured. Despite the fact that only one wind direction was used, results were quite varied. This was thought to be due to the strong effect exerted by the geometry of the intersection and the surrounding area. Generally, horizontal turbulent mass ﬂuxes were found to be small with respect to the mean mass ﬂux driven by the advection mechanism. Vertical turbulent ﬂuxes results emphasised the increased turbulent exchange at roof level, which conﬁrmed the importance of this region in the general exchange mechanism between the canopy and external ﬂow. A complete and detailed database of the velocity ﬁeld, the concentrations of pollutant released from point sources in the area, the geometry and approach has been assembled. Furthermore, turbulence and ﬂuxes analysis showed that the area is characterised by a number of local turbulent structures, primarily located in the wake of tall buildings, where mixing and mass ﬂuxes appeared to be enhanced. The examination of the results obtained so far suggests that future experiments should be focussed on the measurement of local mean and turbulent ﬂuxes in order to highlight how small regions in the measurement area contribute to and differ from the total mass exchange and mixing mechanism (i.e., the classical street canyon process).

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06802-7

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Poster 2 Wind tunnel experiments of ﬂow and dispersion in idealised urban areas Matteo Carpentieri, Andrea Corti and Paolo Giambini

In this work, experiments were performed in the CRIACIV (Research Centre for Building Aerodynamics and Wind Engineering) boundarylayer wind tunnel (University of Florence), in order to study ﬂow and pollutant dispersion in an idealised urban area. The issue was addressed at both the local (micro) and the neighbourhood (intermediate) scales. The main objectives of these experiments were: To evaluate the inﬂuence of an urban area on the ‘‘far-ﬁeld’’ pollutant dispersion from a point source located upwind of the model. To investigate the spatial variation of concentration associated with various typologies of urban areas with different morphological parameters. To establish mechanism of ﬂow and pollutant exchange between street canyons at urban intersections and between the urban canopy and the air ﬂow above. To build an experimental database for the development and the validation of microscale and intermediate (neighbourhood) scale mathematical models for urban areas. Three urban area models were built, with different morphological parameters, with a 1:250 scaling factor. The urban areas were modelled using regular arrays of buildings; the models can be classiﬁed respectively as ‘‘CC’’, ‘‘BEB’’ and ‘‘R&B’’, using the morphological classiﬁcation scheme proposed by Theurer (1999). Particle Image Velocimetry (PIV) was used in order to map the ﬂow, while tracer concentration measurements were performed by means of a Flame Ionisation Detector (FID), in order to analyse the mean dispersion mechanism. Detailed maps of the ﬂow inside the central intersection have been produced for the ‘‘CC’’ model. An example is shown in Fig. 1. Horizontal

Wind Tunnel Experiments of Flow and Dispersion

735

Figure 1. PIV measurements; wind direction ¼ 22.51 (left) and 451 (right): streamlines and velocity contour plot of the ﬂow in the horizontal plane (h ¼ 1 cm).

Figure 2. Concentration maps with wind direction ¼ 01: ground level concentrations (left) and above-roofs concentrations (right). The wind is coming from the left-hand side.

measurements as well as vertical sections in the canyons are available. The produced maps show the complex 3-dimensional ﬂow in the intersection and highlight in particular the main recirculation vortexes. These maps capture the average behaviour of the ﬂow. An example of tracer concentration measurement results is shown in Fig. 2. The different behaviour due to the channelling effect in the street canyons can be seen. These maps are very useful to analyse the dispersion phenomena at the neighbourhood scale.

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Experimental databases were built for both the microscale and the neighbourhood scale, using different wind directions. These are useful for mathematical models testing and validation. Preliminary validation exercises using operational models were also performed, highlighting the difﬁculties that these models encounter when dealing with complex urban areas and street canyon intersections. In particular, the inﬂuence of the street geometry on the concentration ﬁeld was quantiﬁed, showing the importance of implementing the urban details in urban dispersion models. REFERENCE Theurer, W., 1999. Typical building arrangements for urban air pollution modelling. Atmos. Enviorn. 33, 4057–4066.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06803-9

737

Poster 3 Improving the Martilli’s urban boundary layer scheme: Off-line validation over different urban surfaces R. Hamdi and G. Schayes Abstract In recent local atmospheric models, the representation of the urban boundary layer has undergone important progress in order to better represent the effect of the thermal and mechanical exchanges between the atmosphere and the surface. In this study, we have added a few improvements to already complete parameterization of Martilli which combines the thermal and dynamical effects of the urban canopy. 1. Introduction

The thermal part of the parameterization of Martilli et al. (2002) has already been validated with measurements from two mid-latitude European cities (Hamdi and Schayes, 2005). However, using the measurements obtained during the Basel Urban Boundary Layer Experiment (BUBBLE), one noticed that, even if the main dynamical effects of the urban canopy are reproduced, comparison with measurements indicated that some physical processes were still inadequate in the parameterization. In most of the cases, the model overestimates the wind speed inside the canopy layer and does not simulate the observed maximum of the friction velocity, which appears above the buildings roofs. The wind speed and turbulence inside the urban canopy have a direct impact on the concentrations of primary pollutants, which are emitted in the street. For this reason, the improvement of the dynamical part of the urban parameterization of Martilli is an important point for the accuracy of primary pollutant dispersion and also for imposing a more correct stress to the driving mesoscale model. Therefore, the urban module of Martilli has been extended by taking into account a new drag formulation. This new version is implemented in a mesoscale model (TVM), which is run on 1D column. The goal of the

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present contribution is to validate this new version of the urban module using the data from the BUBBLE campaign carried out in the city of Basel, Switzerland. 2. The new version

The extension of the original version of Martilli’s urban module is done in two ways: i) In the original version, the force induced by the presence of the building is orthogonal to the street canyon. The effect of the ﬂow parallel to the street canyon is not taken into account. In order to correct this, we partition the overall force imparted to a roughened surface by a ﬂuid passing over it, into the force exerted on the roughness elements and the force exerted on the intervening wall surface (East and West wall). ii) In the original version, the drag coefﬁcient is kept constant at every level inside the street canyon, while, the observed drag coefﬁcient for cross canyon ﬂow increases with height inside the street canyon and its maximum value lies just above roof top. In order to correct this, we calculate at every level inside the street canyon the sum of the drag force calculated for this level and the cumulated drag force calculated below. This allows to relate the drag coefﬁcient to a given height.

3. Results

In this study, TVM is run on a vertical column using measurements recorded at each tower top as forcing. The period of the simulation extends from June 16 to June 30, 2002. For site U1 in Basel, the results show that (see Fig. 1): (i) the wind speed proﬁle for along canyon ﬂow is very well reproduced especially inside the urban canopy where a nearly linear wind proﬁle is observed; (ii) for cross canyon ﬂow this new version is able to better ﬁt with observations, especially inside the street canyon where the nearly constant wind proﬁle is reproduced. Also, the new version is able to simulate the increase of the local friction velocity occurring with increasing height inside the urban canopy and the maximum of the friction velocity, which appears above roof level. Running TVM in an off-line mode, allowed this study to focus also on the inﬂuence of surface input parameters. Data collected on air temperatures within urban canyons and surface energy ﬂuxes allowed modeled and observed canyon air temperatures and surface energy ﬂuxes to be

Improving the Martilli’s Urban Boundary Layer Scheme

739

Figure 1. Measured and modelled wind speed for different heights.

compared. The results showed that Martilli’s original urban module overestimates the daily maximum temperature by 31C. When taking into account the urban vegetation, the simulated canyon air temperature is then improved and the model partitions the surfaces energy ﬂuxes appropriately. ACKNOWLEDGMENTS

This research was part of the EU-FUMAPEX research program (2002–2005). We thank A. Martilli for providing us his urban BL code. REFERENCES Hamdi, R., Schayes, G., 2005. Validation of the Martilli’s urban boundary layer scheme with measurements from two mid-latitude European cities. ACPD 5, 4257–5038. Martilli, A., Clappier, A., Rotach, M.W., 2002. An urban surface exchange parameterization for mesoscale models. Bound.Layer Meteorol. 104, 261–304.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06804-0

740

Poster 4 Wind shear distortion of concentration ﬂuctuations from an elevated source Trevor Hilderman and David J. Wilson Abstract Concentration ﬂuctuations in a dispersing plume are important for determining the hazardous effects of the release. For example, toxicity is typically non-linear with exposure concentration and therefore highly sensitive to the peak concentration levels. Modelling the relevant ﬂuctuation statistics is difﬁcult because most exposures of interest occur within 2 m of ground level where the high wind shear levels attenuate and distort the concentration ﬂuctuations. In this paper we propose a shear correction model that leverages the currently available dispersion models. The shear correction model we propose leverages the currently available dispersion models, such as the meandering plume model of Wilson (1995), that do an excellent job of predicting plume statistics in more homogeneous turbulence well above the ground. We developed models for the ﬂuctuation intensity i, intermittency factor g and integral time scale Tc of concentration ﬂuctuations in a well-developed rough surface boundary-layer shear ﬂow based on high spatial and temporal resolution experimental data measured with a linescan laser-induced ﬂuorescence technique in a water channel as described in Hilderman and Wilson (2006). The algebraic model presented can be used with any dispersion model that predicts no-shear concentration ﬂuctuation statistics. A minimal set of information about the surrounding ﬂow is assumed to be available from the local meteorological information or generated by a dispersion models. These required parameters are the following: mean concentration C, vertical velocity proﬁle U and shear proﬁle @U/@z, vertical rms velocity ﬂuctuation w0 rms ;

Wind Shear Distortion of Concentration Fluctuations

741

velocity ﬂuctuation integral time scale Tvel, no-shear prediction of the ﬂuctuation intensity i, vertical plume spread sz. From this information the attenuation of the concentration ﬂuctuation intensity, i, and the integral ﬂuctuation time scale, Tc, under shearing conditions is ﬁt to a power law model of the form: shear statistic (1) ¼ ð1 þ B1 SÞB2 no-shear statistic with the algebraic constants B1 and B2 ﬁt to the water channel data. The non-dimensional shear, S, is deﬁned as w0 rms tt @U (2) S¼ U @z where tt ¼ x/U is the local travel time to the receptor position of interest. The conditional concentration ﬂuctuation intensity, ip (i.e., excluding zero concentration intermittent periods), was found to reach an asymptotic value as a function of shear, S, and the ﬂuctuation intensity, i. The intermittency factor, g, was determined directly from the deﬁnition of i and ip. REFERENCES Hilderman, T., Wilson, D.J., 2006. Predicting plume meandering and averaging time effects on mean and ﬂuctuating concentrations in atmospheric dispersion simulated in a water channel. Accepted for publication in Boundary-Layer Meteorology. Wilson, D.J., 1995. Concentration Fluctuations and Averaging Time in Vapor Clouds. Center for Chemical Process Safety of the American Institute of Chemical Engineers, New York, NY.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06805-2

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Poster 5 Inter-comparison of CFD, wind tunnel and Gaussian plume models for estimating dispersion from a complex industrial site P. Jenkinson, R. Hill, E. Lutman, A. Arnott and T.G. Parker Abstract The British Nuclear Group’s Sellaﬁeld site extends over 2 km2 and includes over 100 buildings of varying dimensions and extending to heights of up to 60 m. Authorised atmospheric releases of radioactivity arise from fugitive emissions from open fuel-storage ponds and stack releases ranging between 20 and 125 m in height. The aim of this study was to compare dispersion predictions from wind tunnel, CFD and advanced Gaussian plume models for six release points of varying height located on the Sellaﬁeld site. 1. Methodology

Dispersion from a fuel storage pond (8 m in height) and ﬁve release points ranging from 20 to 125 m in height was studied for three different wind trajectories using the CFD model Fluidyn PANACHE-PANEIA (Transoft International, France) and the advanced Gaussian plume models UK-ADMS and ISC-AERMOD. Determination of the standard statistics of fraction of model centreline predictions within a factor of 2 of those previously measured in wind tunnel studies, normalised mean square error (NMSE) and mean bias (MB), allowed the effectiveness of the numerical models to be evaluated.

2. Results and conclusions

The results of standard model evaluation statistics carried out on centreline concentrations measured in the wind tunnel and those predicted by the numerical models are presented in Table 1. Of the three numerical models studied, ISC-AERMOD proved more effective at predicting dispersion concentrations when compared to those measured

Inter-Comparison of CFD, Wind Tunnel and Gaussian Plume Models

743

Table 1. Statistical comparison of centreline wind tunnel dispersion factors with those predicted by UK-ADMS (AD), ISC-AERMOD (AE) and CFD models (CFD) Release point

1 (High) 2 (High) 3 (High) 4 (Med.) 5 (Med.) 6 (Low)

Fraction within a factor of 2 of wind tunnel

Normalised mean square error

Mean bias

AD

AE

CFD

AD

AE

CFD

AD

AE

CFD

0.78 0.50 0.78 0.67 0.33 0.89

0.22 0.88 0.22 1.00 0.44 0.89

0.00 0.13 0.89 0.33 0.22 0.89

0.49 1.31 0.44 0.89 1.05 0.25

1.15 0.20 1.40 0.08 0.78 0.20

2.25 2.97 0.18 0.85 0.99 0.41

0.81 1.87 0.58 0.49 0.44 0.73

0.40 1.00 0.35 0.82 0.49 1.13

3.62 3.53 0.89 0.54 0.45 0.64

Numbers in bold indicate the most appropriate model for each of the statistical assessment criteria.

in the wind tunnel for four of the six release points. UK-ADMS proved the most effective model at predicting dispersion from release point 1, whilst the CFD model proved the most effective at predicting dispersion from release point 3. It is concluded that numerical models capable of accounting for the complex effects of buildings and terrain on dispersion are effective at predicting dispersion from release points of varying height located on complex industrial sites. When validated with wind tunnel measurements, the advanced Gaussian plume model ISC-AERMOD proved the more effective of the models studied. ACKNOWLEDGMENT

The authors are grateful to the British Nuclear Group Sellaﬁeld Ltd. for the funding of this work.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06806-4

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Poster 6 Nowcasting and forecasting the street pollution dispersion for Tallinn metropolitan area Marko Kaasik, Triinu Lukk, Kuido Kartau and Tanel Dovnar Abstract Rapidly growing number of cars, after the collapse of the Soviet Union, made the street pollution the most urgent air quality problem. Need for new trafﬁc routes pushes the reconstruction of street network. Computations of maximal and annual mean concentrations of NOx, CO and PM2.5 from street and road trafﬁc were performed applying the AEROPOL model including the present situation in the city of Tallinn. The used dispersion model was based on the Gaussian plume dispersion with analytical integration over the length of street segment. City of Tallinn together with suburbs at the southern coast of the Gulf of Finland has slightly below half a million inhabitants, thus more than 1/3 of the total population of Estonia. Rapidly growing number of cars, after the collapse of the Soviet Union, made the street pollution the most urgent air quality problem. Need for new trafﬁc routes (via Baltica) pushes the reconstruction of street network. Computations of maximal (hourly average) and annual mean concentrations of NOx, CO and PM2.5 from street and road trafﬁc were performed applying the AEROPOL model (Kaasik and Kimmel, 2004), including the present situation and a future scenario with a motorway connecting Tallinn marine port with roads towards Riga and Warsaw (Pan-European Corridor I) and St. Petersburg (link to Corridor IX). The emission model was based on trafﬁc counting, trafﬁc model VIPER (base year 2003, see Technical Assistance for Mainland Connections of Corridor No I at Tallinn in Estonia, 2005) and emission coefﬁcients by Finnish Meteorological Institute (FMI). In the base year, nearly 30% cars were with catalytic converter, 60% without that and 10% had diesel engine. For year 2030, it was assumed that 60% cars have catalytic converter, 20% do not and 20% have diesel

Nowcasting and Forecasting the Street Pollution Dispersion

745

engine. It was assumed as well that until 2020 the number of cars will increase by 4% per year and from 2020 to 2030 2% per year compared to 2003 level. The dispersion model was based on the Gaussian plume dispersion with analytical integration over the length of street segment (method is earlier applied and validated for Helsinki by FMI). That scheme appeared computationally efﬁcient, ﬁtting a computation of dispersion for the whole domain, 34 by 26 km with 75 m spatial resolution (thus, over 1.5 106 grid points) and 1100 road segments of about 670 km of total length, within reasonable time limit, about 5 h (single 2.8 MHz Pentium 4 processor). As the dispersion model does not include non-linear chemical transformations, an empirical assumption about 20% NO2 in NOx (based on long-term average near-street monitoring results in Tallinn) was made. Modelling indicated several crossings of heavy trafﬁc roads, where the concentrations are close to the European maximal hourly average norms and may exceed these (200 mg m 3 for NO2, the threshold value for 2010, and 5000 mg m 3 for CO) in calm and stable atmospheric conditions. It is expected that despite rather rapidly increasing number of cars the concentrations of pollutants in the air will slightly decrease due to technical improvement of vehicles (catalytic converters in ﬁrst order). Comparing the present situation modelling results with measurements of a rather sparse air quality-monitoring network, it was found that requirements of EC directive 1999/30/EC are satisﬁed, concerning the reference year 2003 (Table 1). Although for both NO2 and CO, the modelled values lay in 60% tolerance limits indicated in the directive for hourly average values, the concentrations of NO2 are clearly overestimated and CO underestimated. Such shifts cannot be only or mainly due to dispersion modelling error (as concentration is linear to the emission), but the emission model must play a key role.

Table 1. Comparison of measured and modelled hourly average maximal concentrations in the central street-side monitoring station Viru in Tallinn Pollutant

NO2 CO

Measured maximum

Modelled

2003

2000–2005

Average of annual maximums, 2000–2005

2003

Forecast for 2030

173 6880

177 7700

147 6183

279 4410

253 3800

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Modelling results are to be applied to make vitally important development decisions for the city. REFERENCES Kaasik, M., Kimmel, V., 2004. Validation of the improved AEROPOL model against the Copenhagen data set. Int. J. Environ. Pollut. 20(1–6), 114–120. Technical Assistance for Mainland Connections of Corridor No I at Tallinn in Estonia. 2005. Draft Feasibility Study Report, August 2005 (available in Tallinn City Council).

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06807-6

747

Poster 7 Wind-driven NOx removal by ﬂow-through fences with ACF (Activated Carbon Fiber): Evaluation of the fence’s efﬁciency in reduction of ambient NOx Toshihiro Kitada, Makoto Nagano, Takaaki Shimohara and Takayuki Tokairin Abstract Because of heavy trafﬁc in urban area in Japanese mega cities, air pollution by NO2, SPM, etc., is still severe in near-major-roads’ environment. In this study, we propose to use the activated carbon ﬁber (ACF) in ﬂow-through fence set at roadside as an energy-free NOx remover. Efﬁciency of NOx removal by the ﬂow-through fences was numerically investigated, when a NOx-rich air mass driven by natural wind passes through the fence.

1. Introduction

Because of heavy trafﬁc in urban area in Japanese mega cities, air pollution by NO2, SPM, etc., is still severe in near-major-roads’ environment. For example, percentage of various cancer diseases in the Tokyo Metropolitan area was reported to be about 1.7 times higher for the people who live within 500 m from the road, the trafﬁc volume of which is larger than 5000 cars per half-day. Though, to lower the pollution level locally, several measures such as forced ventilation of the polluted air by pumping, oxidation of NOx on the fence/wall surface coated with TiO2, removal of NO2 by injection of the polluted air into soil layer have been tested, so far they are not so successful; in general these methods are not effective in reduction of pollutant concentration in ambient air and/or need huge energy for their operation. In this study, we propose to use the activated carbon ﬁber (ACF) in ﬂow-through fence set at roadside as an energy-free NOx remover. Efﬁciency of NOx removal by the ﬂow-through fences was numerically investigated, when a NOx-rich air mass driven by natural wind passes through the fence. Cases of both double-decked and

Toshihiro Kitada et al.

748

plain-roads along which the porous (‘‘ﬂow-through’’) fences placed were considered. Some of the obtained results show: (1) Based on ﬁeld and laboratory experiments, the NOx removal by ACF was tentatively evaluated as the ﬁrst order chemical reaction with its rate constant (k) ranging from 2.36 to 6.28 s1 for the ACF packing density of 0.066 g cm3, and also drag force of the ACF layer to air ﬂow was determined. (2) NOx concentration was largely reduced by applying the ﬂow-through fence with ACF; for example, at ground level and 10 m downstream from the road, the concentration was decreased by 30% for the plain-road case with the fences of 10 cm thick and the averaged rate coefﬁcient k ¼ 4.01 s1, compared with the solid fence case. (3) Finally, it was demonstrated that the porous fence with ACF ﬁlled inside can be an effective passive NOx remover without excess energy use. Software used in this study is described elsewhere (Tokairin and Kitada, 2004, 2005).

2. Results

The results are explained in Figs. 1 and 2 as shown below. 400

15

300 [m]

10 200 5 100 0 -5

a

0

5

10

15

20

25

30

0 [ppb]

[m] 15

400 300

[m]

10 200 5 100 0 -5

b

0

5

10

15

20

25

30

0 [ppb]

[m]

Figure 1. (a) Calculated ﬂow ﬁeld and pollutant concentration for ‘‘solid’’ fence. (b) Same as (a) but for ‘‘ﬂow-through ACF’’ fence; Rc ¼ 3700, k ¼ 4 s1.

Wind-Driven NOx Removal by Flow-Through Fences with ACF

749

Figure 2. Vertical proﬁles of NOx concentration at X ¼ 20 m. ‘‘Solid’’ shows solid fence case corresponding to Fig. 1a, i.e., no ﬂow-through and no-ACF. ‘‘Rc ¼ 3700’’ shows normal ‘‘ﬂow-through ACF’’ fence case corresponding to Fig. 1b. ‘‘Rc’’ stands for the coefﬁcient determined for drag of ACF layer to the ﬂow, and ‘‘k’’ is the ﬁrst order chemical reaction rate coefﬁcient for NOx removal by ACF. We used laboratory experimental data (Shimohara et al., 2001) to determine the rate coefﬁcient.

REFERENCES Shimohara, T., Chikara, H., Nakamura, M., Enjoji, T., Shirahama, N., Mochida, I., 2001. 42nd Annual Meeting of the Japan Society for Atmospheric Environment, p. 486, (in Japanese). Tokairin, T., Kitada, T., 2004. J. Wind Eng. Ind. Aerod. 92, 85. Tokairin, T., Kitada, T., 2005. Environ. Monit. Assess. 105, 121.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06808-8

750

Poster 8 Radiative transfers in CFD modelling of the urban canopy Maya Milliez, Luc Musson-Genon and Bertrand Carissimo Abstract The processes that govern air pollution in the urban canopy highly depend on the shape and spacing of the buildings. The dynamical effects have been extensively studied using CFD techniques, usually assuming a neutral atmosphere and neglecting the thermal effects. Nevertheless, radiative transfers play an important role because of their inﬂuence on the urban canopy budget. In order to take into account the radiation budget in simulations of pollution dispersion in urban areas, we have developed a radiative scheme in the atmospheric CFD model Mercure_Saturne. The CFD model Mercure_Saturne is a 3-D model adapted to atmospheric ﬂow and pollutant dispersion simulations, which can perform detailed numerical simulations in urban areas, including explicit building representation. In our simulations, we use an Eulerian approach, with a k-eps turbulence closure and take into account the meteorological conditions (Milliez and Carissimo, 2007). We have adapted to atmospheric radiation a radiative heat transfer scheme. For a grey semi-transparent non-diffusive media, the radiative source term Srad is given by Z Iðx; SÞS dO Srad ðx; SÞ ¼ =: with =:ðIðx; SÞSÞ ¼ KIðx; SÞ þ KI b ðx; SÞ, where dO is the elementary solid angle, S the direction of propagation, x the location vector, I the intensity of radiation, Ib the black body intensity and K the coefﬁcient of absorption. In our simulations, we ﬁrst assume the air to be transparent (K ¼ 0).

Radiative Transfers in CFD Modelling of the Urban Canopy

751

Short-wave radiation: The radiative scheme was adapted to model the atmospheric short-wave (SW) radiation, which is composed of three terms: SW ¼ SWðdirectÞ þ SWðdiffused by atmosphereÞ þ SWðdiffused by environmentÞ where SW (diffused by environment) includes multi-reﬂections on the ground and building surfaces. While the direct short-wave radiation is unidirectional, the diffuse one is assumed isotropic. The short-wave radiation scheme is validated with classical cases found in the literature. Figure 1 shows the comparison with the effective albedo measurements of Aida (1982) for modelled ﬂat and built surfaces. Long-wave radiation: The long-wave (LW) radiation takes also into account multi-reﬂections on the ground and building surfaces. The net long-wave ﬂux L* for each surface is given by h i X LW ðfrom other surfacesÞ sT 4 L ¼ LW ðfrom atmosphereÞ þ

Figure 1. Diurnal albedo variation, June 15 (Aida, 1982).

Maya Milliez et al.

752

and the outgoing ﬂux for each surface is given by L " sT 4 þ ð1 Þ½LW ðfrom atmosphereÞ i X þ LW ðfrom other surfacesÞ Surface temperature: The surface temperature is modelled with a forcerestore method: p dT=dt ¼ ð2oÞ=mF oðT T g=b Þ where o is the earth angular frequency, m the thermal admittance, F* the total net ﬂux and Tg/b either ground or interior building temperature. The long-wave radiative scheme and the temperature model are validated with measurements of surface temperature and net long-wave radiation at night. Figure 2 compares modelled surface temperature at night with the measurements from Nunez and Oke (1977) in a canyon street. To conclude, short-wave and long-wave radiative schemes were developed in the CFD model Mercure_Saturne and validated with classical cases found in the literature. The perspectives are now to study the

Figure 2. Canyon street: evolution of surface temperature at night (Nunez and Oke, 1977).

Radiative Transfers in CFD Modelling of the Urban Canopy

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interaction between the radiative ﬂuxes and the ﬂow dynamics and its impacts on pollutant dispersion. REFERENCES Aida, M., 1982. Urban albedo as a function of the urban structure: A model experiment. Bound.-Layer Meteorol. 23, 405–413. Milliez, M., Carissimo, B., 2007. Numerical simulations of pollutant dispersion in an idealized urban area, for different meteorological conditions. Bound.-Layer Meteorol. 122, 321–342. Nunez, M., Oke, T.R., 1977. The energy balance of an urban canyon. J. Appl. Meteorol. 16, 11–19.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06809-X

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Poster 9 Evaluation of passive pollutants residence time in a deep street canyon by CFD simulations F. Murena and G. Favale Abstract Flow ﬁeld and mass transport in a deep street canyon have been modelled using a CFD software (FLUENT). Simulations have been carried out with the aim to evaluate the residence time of a passive pollutant (CO) as function of several parameters.

1. Introduction

High levels of pollutant concentration at pedestrian level in deep street canyons can be measured (Pfeffer et al., 1995; Vakeva et al., 1999; Chan and Kwok, 2000; Murena and Vorraro, 2003) or evaluated by modelling studies (Vardoulakis et al., 2003). This condition is the consequence of relatively high exhaust vehicles emission rates and ineffective mass exchange with surrounding (upper) atmosphere.

2. Method

The geometry of the modelled street canyon corresponds to the actual geometry of a street in the old centre of Naples (width 5.7 m, height 30 m, aspect ratio H/Wﬃ5.3). The street canyon has been divided into three volumes: the upper vortex, the bottom vortex and the vehicle induced turbulence volume. Carbon monoxide (CO) has been assumed as model passive pollutant. 2D simulations both steady and unsteady state have been carried out. Wind velocity direction was orthogonal to the street axis. Unsteady simulations have been carried out assuming at time t ¼ 0 a uniform CO concentration in the street canyon and a lower but constant CO concentration in the wind entering the dominion beyond the rooftop level. Curves of CO concentration in the canyon vs. time have been

Evaluation of Passive Pollutants Residence Time

755 C/C(0) C/C80) C/C(0) C/C(0)

1.0

Cb=0.5C(0) Cb=0.5C(0) Cb=0.1C(0) Cb=0.1C(0)

C/C(0)

0.8

0.6

0.4

0.2 0.0

0.5

1.0 Time (h)

1.5

2.0

Figure 1. Effect of Cb concentration. Full symbols total volume, empty symbols bottom volume.

obtained (Figs. 1 and 2). From these curves characteristic time has been evaluated. 3. Results

The ﬁrst phenomenon studied was the effect of wind velocity on the ﬂow ﬁeld gene-rated inside the canyon (range 1–5 m s1). The results obtained conﬁrm the presence of two vortices as reported in the literature (upper and bottom vortex volumes). To consider the effect of atmospheric stability several unsteady simulations were carried out by varying the turbulence percentage value in the volume beyond the roof top level in the range 10–30%. In this range the effect of turbulence percentage on the CO reduction rate is limited. Then the effect of CO concentration level beyond the roof top level (Cb) was studied. Simulations were carried out assuming CO at roof top level equal to 0.1 or 0.5 times the concentration at time t ¼ 0 in the canyon: i.e., Cb ¼ 0.1/0.5 CO(0) (Murena and Vorraro, 2003). Results reported in Fig. 1 show that the CO reduction is strongly dependent from the CO level beyond the roof top level. Vehicles moving in the canyon produce some turbulence, which increases mass transfer. The volume where vehicle induced turbulence is signiﬁcant (VIT volume) has

F. Murena and G. Favale

756 1.2 C/C(0) C/C(0) C/C(0) C/C(0)

1.0

Cb=0.5C(0) Cb=0.1C(0) Cb=0.5C(0) Traffic Cb=0.1C(0) Traffic

C/C(0)

0.8

0.6

0.4

0.2

0.0 0.0

0.5

1.0

1.5 time (h)

2.0

2.5

3.0

Figure 2. Effect of vehicle induced turbulence empty symbols in presence of VIT.

Table 1. CO half-life time obtained by simulations (wind sped at roof top level ¼ 2 m s1) Turbulence percentage 10 20 30 20 20 20 20 20

Cb/C(0)

VIT (kg m1 s3)

0 0 0 0.1 0.5 0.1 0.5 0.1

0 0 0 0 0 0.09 0.09 0.09

CO emission rate (kg m3 s1) 0 0 0 0 0 0 0 109

CO0.5 (h) 0.295 0.233 0.189 0.233 0.236 0.263 0.239 0.352

been identiﬁed carrying out some speciﬁc simulations and the turbulence intensity induced by vehicles has been estimated as 0.09 kg (ms3)1. The effect of vehicle induced turbulence is reported in Fig. 2. As last effect the CO emission in the street canyon has been considered. Corresponding to highest trafﬁc ﬂow in the canyon a CO production rate of about 107 kg sm3 has been calculated. Simulations were carried out in the range of CO production rate from 109 to 106 kg sm3. The effect of CO emission on CO vs. time curves is signiﬁcant. Time necessary to reduce CO

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concentration level at half the initial value (CO0.5) obtained by simulations are reported in Table 1. These values are of the same order of residence time of passive gaseous pollutants in the street canyon. REFERENCES Chan, L.Y., Kwok, W.S., 2000. Atmos. Environ. 34, 4403–4412. Murena, F., Vorraro, F., 2003. Atmos. Environ. 37, 4853–4859. Pfeffer, H.U., Friesel, J., Elbers, G., Beier, R., Ellermann, K., 1995. Sci. Total Environ. 169, 7–15. Vakeva, M., Hameri, K., Kulmala, M., Lahdes, R., Ruuskanen, J., Laitinen, T., 1999. Atmos. Environ. 33, 1385–1397. Vardoulakis, S., Fisher, B.E.A., Pericleous, K., Gonzalez-Flesca, N., 2003. Atmos. Environ. 37, 155–182.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06810-6

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Poster 10 Modelling integrated system for urban air quality in Bologna Linda Passoni, Vanes Poluzzi, Marco Deserti, Enrico Minguzzi, Michele Stortini and Giovanni Bonafe` Abstract Bologna, as most of the urban areas located in the Po Valley, is often affected by high pollution mainly by PM10 and NO2. These pollutants are produced by large-scale chemical processes and by direct emissions inside the urban area. The urban pollution was simulated combining the background concentration calculated by a chemical transport model (CTM), with the roadside concentration calculated by an urban dispersion model.

1. Methodology

The advanced Gaussian ADMS-Urban model (CERC, 2003) has been used to simulate the pollutants in urban area, calculating the daily and hourly mean concentrations of PM10 and NO2 during a 1-year period (April 2003–March 2004). The model was run using two meteorological datasets. The ﬁrst one is provided by the meteorological pre-processor CALMET (Deserti et al., 2001), which uses data from surface and upper air stations in northern Italy. The other dataset (LAMA) was provided by the non-hydrostatic meteorological model LAMI with a continuous assimilation of surface and upper air stations data. Trafﬁc emissions on 213 road links were estimated on the basis of: trafﬁc ﬂows (source: Bologna Municipality), emissions factors (sources: Corinair2000 for gases, TNO for PM10), emissions time-varying proﬁles (source: Bologna Municipality Structural Plan). The urban background concentrations were provided by the CTM CHIMERE (50 km horizontal resolution; source: INERIS).

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Table 1. Urban model results (mg m 3) for the annual period, at the monitoring station NO2 annual mean

NO2 18th highest hourly value

PM10 annual mean

PM10 35th highest daily value

68 52

195 123

46 42

68 73

Simulated Observed

Table 2. Urban model results (mg m 3) for the pollution episodes NO2

PM10 Episode

Summer Winter

Simulated (CALMET input)

Simulated (LAMA input)

Observed

Simulated (CALMET input)

Simulated (LAMA input)

Observed

102 63

136 71

35 65

50 55

69 66

32 87

The simulation domain covers a district (2 km 2 km) of the Bologna urban area. Output surface ﬁelds are calculated with a 50-m horizontal resolution. The resulting concentrations were used to calculate, by a deterministic exposure model, the exposure of a population of 333 children.

2. Results

The urban model, combined with the regional CTM, performs quite well to assess long-term averages of PM10 and NO2, less to simulate peak pollution (Table 1). During the summer episode (10–16 June 2003), the PM10 daily concentration is in good agreement with observations, while it is underestimated during the winter episode (12–19 February 2004). NO2 concentrations are in good agreement with observations during winter, when the diurnal cycle is well described, while the summer diurnal cycle is not well simulated (Table 2), probably because of the chemical reaction scheme of the urban model, which is not detailed enough. NO2 personal exposures range from 20 to 50 mg m 3, PM10 exposures from 10 to 25 mg m 3.

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REFERENCES CERC, 2003. ADMS-Urban User Guide (Version 2.0). Cambridge. Deserti, M., Cacciamani, C., Golinelli, M., Kerschbaumer, A., Leoncini, G., Savoia, E., Selvini, A., Paccagnella, T., Tibaldi, S., 2001. Operational meteorological preprocessing at Emilia-Romagna ARPA Meteorological Service as a part of a decision support system for Air Quality Management. In: Coppalle A. (Ed.), Proceedings of the Sixth Workshop on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes. Int. J. Environ. Pollut. 16(1–6).

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06811-8

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Poster 11 Long-term evaluation of secondary atmospheric pollution over Italy M.P. Costa, S. Alessandrini, M. Bedogni, B. Bessagnet, E. Bossi, G. Maffeis, C. Pertot, G. Pirovano and R. Vautard Abstract Italy often suffers for high concentrations of secondary pollutants: ozone during summer and particulate matter in winter. Modelling reconstruction is a really challenging task, due to the presence of complex orographic systems and the interaction with the Mediterranean Sea. As case study, the year 1999 was simulated and evaluated by measuring data. The modelling system is made up of the 3D meteorological model RAMS (Pielke et al., 1992) and the chemical transport model CAMx (ENVIRON, 2003). The simulation domain has an extension of 1400 1600 km2, with a grid step of 25 km. Emissions have been derived from the Italian ofﬁcial inventory and the EMEP database (EMEP, 2004). The initial and boundary conditions have been obtained by CHIMERE model (Vautard et al., 2005) runs. Figure 1 describes an example of the main results obtained. As for ozone, the 95th percentile of the 8-hourly daily maximum is greater than 60 ppb over the whole Italy, thus exceeding everywhere the limit values established by the European and the Italian laws. The highest concentrations occur in the suburban areas of the main cities, favoured by the high levels of the ozone precursors. PM10 yearly mean ranges from 6 to 25 mg m 3, with the highest values localised in the Po valley, due to the high emission density combined with weak circulation conditions. Figure 2 shows, as an example, the time series of the PM10 daily mean for a background station placed in the Roma region. Models show better performances during summer than they do in winter, when stable conditions emphasise local scale effects.

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Figure 1. 95th percentile of the O3 8-hourly daily maximum (l); PM10 yearly mean (r).

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Figure 2. PM10 daily mean in Roma station. Black dots: observations; black line: CAMx; grey line: CHIMERE with dust; dashed line: CHIMERE without dust.

ACKNOWLEDGMENT

CESI and CESI RICERCA contribution has been ﬁnanced by the Research Fund for Italian Electrical System established with Ministry of Industry Decree DM 26/1/2000. REFERENCES EMEP, 2004. http://webdab.emep.int/ ENVIRON, 2003. CAMx—User’s guide version 4.00, Internal report, Environ. Int. Corp. Pielke, R.A., Cotton, W.R., Walko, R.L., Tremback, C.J., Lyons, W.A., Grasso, L.D., Nicholls, M.E., Moran, M.D., Wesley, D.A., Lee, T.J., Copeland, J.H., 1992. A comprehensive meteorological modeling system—RAMS. Meteorol. Atmos. Phys. 49, 69–91. Vautard, R., Bessagnet, B., Chin, M., Menut, L., 2005. On the contribution of natural aeolian sources to particulate matter concentrations in Europe: Testing hypotheses with a modelling approach. Atmos. Environ. 39, 3291–3303.

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Poster 12 Modelling past and future trends in sulphur and nitrogen deposition in the United Kingdom A.J. Dore, M. B!as´, M. Kryza, J. Hall, C.J. Dore, M. Vieno, K.J. Weston and M.A. Sutton Abstract The Fine Resolution Atmospheric Multi-pollutant Exchange model (FRAME) has been applied to model the spatial distribution of sulphur and nitrogen deposition over the UK during a 50-year period from 1970 to 2020. The study demonstrates the importance of the ability to control emissions of ammonia. Land-based emissions of NOx and SO2 from the UK have fallen signiﬁcantly over the last few decades (Fig. 1). SO2 emissions fell from a peak of 3200 kT S in 1970 to 400 kT S in 2005 and are forecast by business-as-usual emissions scenarios to fall to 180 kT by 2020. NOx emissions were at a maximum of 820 kT N in 1979 and fell to 430 by 2005 with a further

SO2 historical emissions SO2 future emissions NOx historical emissions NH3 historical emisions NOx future emissions

SO2 emissions (Mg S)

3000 2500

1000 800

2000

600

1500

400

1000 200

500 0 1965

1975

1985

1995 year

2005

2015

NOx & NH3 emissions (Mg N)

3500

0 2025

Figure 1. Past and future forecast trends in total emissions of SO2, NOx and NH3 for the UK from 1970 to 2020.

Modelling Past and Future Trends in Sulphur and Nitrogen Deposition

765

decrease to 265 kT N forecast by 2020. These large reductions in emissions have not been matched by emissions changes for NH3, which decreased from 305 kT N in 1990 to 247 in 2003. The Fine Resolution Atmospheric Multi-pollutant Exchange model (FRAME) has been applied to model the spatial distribution of sulphur and nitrogen deposition over the UK during a 50-year period from 1970 to 2020. FRAME is a Lagrangian trajectory model that generates annual average 5 km resolution maps of wet and dry deposition of sulphur and oxidised and reduced nitrogen for the British Isles. In this study, emissions from point sources and background sources were generated for the UK and the Republic of Ireland, from international shipping and from European sources for the years 1970, 1980, 1990, 2000, 2005, 2010, 2015 and 2020. Emissions of SO2 and NOx from international shipping were assumed to increase by 2.5% per annum relative to emissions estimates for the year 2000. Nine model simulations were undertaken, one for each emissions year. Total acid deposition was calculated to be the sum of the deposition of SOx, NOy and NHx (which assumes that NHx is oxidised in soil) and total nitrogen deposition as the sum of the deposition of NOy and NHx (excess N deposition as a nutrient can lead to adverse effects on biodiversity and ecosystem function). For the year 1970, sulphur was found to account for over half of total acid deposition to forest. During the period 1970–1990, oxidised nitrogen accounted for 30% of total nitrogen deposition to forest. However, for a recent emissions year (2005), reductions in emissions of SO2 and NOx lead to a changing importance of pollutants, with NHx making the greatest contribution to both acid deposition (64%) and total nitrogen deposition (78%) to forest. Without future reductions in ammonia emissions, NHx deposition is forecast to increasingly dominate acid and total nitrogen deposition. The exceedances of critical loads of acidity and nutrient nitrogen across the UK were calculated using the FRAME data for 1970, 2002 and 2020. Figure 2 illustrates the change in the percentage area of sensitive UK habitats for which critical loads of acidity and nutrient nitrogen were exceeded. For acidity, the habitat areas with deposition exceeding critical loads is seen to fall signiﬁcantly between 1970 and 2020 (from 96% to 22% for dwarf shrub heath). However, for nutrient nitrogen, the percentage area of unmanaged forest exceeded falls only marginally, from 98% to 94% between 1970 and 2020. This is due to the dominant role of dry deposition of ammonia to tall vegetation. The total area of sensitive UK habitats exceeded fell from 89% to 39% for acidity and from 69% to 48% for nutrient nitrogen. Reductions in acid deposition and total nitrogen deposition may provide the conditions in which chemical and biological recovery of sensitive

766

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1970 90

2002

80

2020

% area exceeded

70 60 50 40 30 20 10 0 AG

DSH

BOG

UMW

FW

AG

DSH

BOG

UMW

Nutrient nitrogen exceedances

Figure 2. Percentage of the UK land surface area with exceedance of critical loads for acidic and nutrient nitrogen deposition for selected vegetation types for 1970, 2002 and 2020 (AG, acid grassland; DSH, dwarf shrub heath; UMW, unmanaged woods; FW, fresh water).

A.J. Dore et al.

Acidity exceedances

Modelling Past and Future Trends in Sulphur and Nitrogen Deposition

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habitats can begin, but the timescales of these processes are often very long relative to the timescales for reductions in emissions. The study demonstrates the importance of the ability to control emissions of ammonia. Future work will focus on comparison with measurements of changes in wet deposition and air pollutant concentrations in the UK and on changing patterns in atmospheric oxidation rates.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06813-1

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Poster 13 Long-term simulation and validation of ozone and aerosol in the Po Valley M. Stortini, M. Deserti, G. Bonafe and E. Minguzzi Abstract The Po Valley is characterized by a large number of exceedances of air quality standards for ozone and PM10, as well as very strong air pollution episodes. To assist local authorities in air quality evaluation and management, the Emilia Romagna Environmental Agency (ARPA) has implemented an operational Air Quality forecast system, called NINFA (Northern Italy Network to Forecast Aerosol pollution). NINFA has been run over 1 year, simulating air pollution over Northern Italy for the period April 2003–March 2004. 1. The NINFA system

The system NINFA is based on the chemistry-transport model CHIMERE (Bessagnet et al., 2004). The domains consist of 64 (E-W) by 41(N-S) grid cells, 10 km 10 km each. NINFA runs daily at ARPA-SIM and provides concentration maps of PM10, O3 and NO2 for the previous day (hindcast) and the following 72 h (forecast). The outputs are daily available on the ARPA-SIM web site. In the NINFA conﬁguration, CHIMERE is driven by LAMI, the Italian implementation of the Lokal Modell (Steppeler et al., 2003). LAMI has a horizontal resolution of 7 km and is run twice a day at ARPA-SIM. Cloud water content, mixing height and friction velocity, parameters which are crucial for accurate air quality forecasts, are recalculated with parametric schemes and validated (Bonafe’ et al., 2006). For the hindcast and long-term NINFA simulations, ﬁelds from the LAMI assimilation cycle are used. Anthropogenic emission input data, with a resolution of 5 km, are based on the Italian National Inventory of the year 2000 and prepared by the CTN-ACE. To improve the simulation of power plants emissions, a plume rise module based on CALGRID code (Yamartino et al., 1992) has been added. Suitable interfaces to build CHIMERE meteorological input ﬁle as well

Simulation and Validation of Ozone and Aerosol in the Po Valley

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as emissions ﬁles were constructed. Pollutant concentrations at the boundaries are provided by the air quality modelling system PREV’AIR (www.prevair.org). 2. Results

NINFA has been run over 1 year, simulating air pollution over Northern Italy for the period April 2003–March 2004. It was run on a cluster of six processor Linux. Each processor runs a 2-month simulation in time slices of 1 day. To achieve that concentrations are continuous in time, a spin-up run of 5 days is used. Model simulations are compared with measured O3 and PM10 levels at monitoring station located in rural and suburban areas and to the coarse continental model PREV’AIR. The hourly model outputs were processed in order to assess air quality throughout the Northern Italy territory. A large part of the Po Valley is heavily polluted by ozone, the large amount of exceedances of the target value for the protection of human health (120 mg m 3 maximum daily 8-h mean) are in the sub alpine region and in the plane. The large number of exceedances (up to 120 per year) is located downwind to the large urban agglomerates (Milano and Torino). PM10 annual average is maximum in the plain area, extending from the west sub alpine region to the North-East Adriatic coast, and features small spatial variability. The spatial structure of the simulated ﬁelds contains more information of the coarse model and reproduce the mountain–plain concentration gradients of pollutants. These patterns are linked to the wind regimes, characterized by frequently stagnation of air masses in the plain, and to the emissions distribution. Preliminary results of the validation show that simulated daytime ozone concentrations (1-h and maximum daily 8-h mean) agree very well with the observed ones, with correlation coefﬁcient exceeding 0.7 and relatively low bias (o10 mg m 3) in large part of stations. By contrast, the PM10 annual mean levels are underestimated both at rural and suburban stations, the negative bias are approximately 20 mg m 3, although correlation coefﬁcients for the daily mean are around 0.6. Further tests and comparison with experimental data are planned to improve the system performances.

REFERENCES Bessagnet, B., Hodzic, A., Vautard, R., Beekmann, M., Cheinet, S., Honore´, C., Liousse, C., Rouil, L., 2004. Aerosol modelling with CHIMERE: Preliminary evaluation at the continental scale. Atmos. Environ. 38, 2803–2817.

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Bonafe’, G., Deserti, M., Jongen, J., Minguzzi, E., Stortini, M., 2006. Validation of the meteorological input for air quality simulation in Northern Italy, 28th ITM/NATO CCMS. Steppeler, J., Doms, G., Scha¨ttler, U., Bitzer, H.W., Gassmann, A., Damrath, U., Gregoric, G., 2003. Meso-gamma scale forecasts using the nonhydrostatic model LM. Meteorol. Atmos. Phys. 82, 75–96. Yamartino, R.J., Scire, J.S., Charmicheal, G.R., Chang, Y.S., 1992. The CALGRID mesoscale photochemical grid model-I. Model formulation. Atmos. Environ. 26A, 1493–1512.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06814-3

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Poster 14 Biogenic emission modeling in Lithuania Vidmantas Ulevicius, Vytautas Vebra, Kestutis Senuta and Kristina Plauskaite Abstract A biogenic emission modeling system, BioModel, was developed on the basis of BEIS3 temporal emissions computation algorithms. BioModel uses CORINE land cover database, providing Lithuania land cover data in GeoTIFF geographic data exchange format and MM5 meteorology data in netCDF format and produces spatial– temporal biogenic emissions data, which are rendered immediately (visualized), statistically analyzed and stored in netCDF ﬁle. Biogenic emissions were estimated from 11 land use categories. According to BioModel results, highest average isoprene emission rates were produced by conifer forests: in June, 521 g km 2 h 1; in July, 753 g km 2 h 1. Overall average isoprene emission rates from Lithuania (65,281 km2) were 12,714 kg h 1 in June and 18,282 kg h 1 in July (approximately 34% from conifer forests). 1. Introduction

Biogenic volatile compounds play a prominent role in the chemistry of the atmosphere. Moreover, they may contribute together with anthropogenic NOx and VOC emissions to regional and global changes in the OH-radical budget, ozone as well as new particle formation. Numerous reports and papers (Guenther et al., 2000) have been written on the biogenic emissions model formulation described in BEIS3 (Pierce, 2001). BEIS3 biogenic emissions model estimates temporal isoprene, monoterpene and other VOC and NO emissions and prepares emission data for air quality models (CMAQ). BEIS3 uses temporal temperature, pressure, solar radiation data from meteorology model (MM5), BELD3 land cover database, user-deﬁned normalized emissions proﬁles and leaf area indices per land use category (text ﬁle). User-deﬁned emission ﬂuxes are normalized to standard environment conditions: 301C temperature

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and 1000 mmol m 2 s 1 photosynthetic active radiation. BELD3 database is available only for the USA and Canada, so it is difﬁcult to use BEIS3 for other countries. A biogenic emission modeling system, BioModel, was developed on the basis of BEIS3 temporal emissions computation algorithms.

2. Results and discussion

Isoprene emission ﬂux mostly depends on photosynthetic active radiation and environment temperature. BioModel involves complex canopy model to split photosynthetic active radiation to direct and diffuse beams and compute fractions of leaves that are sunlit and shaded according to pressure, solar radiation, sun altitude and leaf area index and ﬁnally uses Guenther et al.’s (2000) equation to estimate light correction factor for isoprene emission ﬂux computation, taken from BEIS3. For temporal isoprene emission ﬂux estimation, temperature and light correction factors are applied. Monoterpenes and other volatile organic compound (OVOC) emission ﬂuxes mostly depend on temperature; therefore, only temperature correction factor is applied. Fluxes for NO emissions, which result mostly from the agriculture, are approximated according to environment temperature. BioModel uses CORINE land cover database, providing Lithuania land cover data in GeoTIFF geographic data exchange format and MM5 meteorology data in netCDF format and produces spatial–temporal biogenic emissions data, which are rendered immediately (visualized), statistically analyzed and stored in netCDF ﬁle. Static normalized biogenic emission proﬁles and leaf area index per land use category, taken from BEIS3 data ﬁles, are contained in executable module. BioModel implements OpenGIS-speciﬁed grid coverage interfaces, involves many GIS typical low-level procedures, written in assembly language and using processor’s multimedia technologies (MMX, SSE), and uses graphic subsystem to visualize spatial emission data. Highest biogenic emission rates are expected during warm season; therefore, for example, we modeled Lithuanian biogenic emission for June 2004 and July 2004 (Fig. 1). Biogenic emissions were estimated from 11 land use categories. According to BioModel results, highest average isoprene emission rates were produced by conifer forests: in June, 521 g km 2 h 1; in July, 753 g km 2 h 1. Conifer forests cover 7248 km2 area in Lithuania, or 11% of Lithuanian territory. Other high-rate isoprene emission producers were mixed forests and deciduous forests: their respective average emission rates were 368 g km 2 h 1 and

Biogenic Emission Modeling in Lithuania

773

Figure 1. Average emission of isoprene in July 2004.

346 g km 2 h 1 in June and 528 g km 2 h 1 and 498 g km 2 h 1 in July. Mixed forests cover 7196 km2 (11%) and deciduous forests cover 4138 km2 (6.3%) area in Lithuania. Overall average isoprene emission rates from Lithuania (65,281 km2) were 12,714 kg h 1 in June and 18,282 kg h 1 in July (approximately 34% from conifer forests). ACKNOWLEDGMENT

This research was supported by the Lithuanian State Science and Studies Foundation and FP6 project ACCENT. The authors gratefully thank for this assistance. REFERENCES Guenther, A., Geron, C., Pierce, T., Lamb, B., Harley, P., Fall, R., 2000. Natural emissions of non-methane volatile compounds, carbon monoxide and oxides of nitrogen from North America. Atmos. Environ. 34, 2205–2230. Pierce, T.E., 2001. BEIS3 version 0.9. (ftp://ftp.epa.gov/amd/asmd/beis3v09/).

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06815-5

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Poster 15 The use of a photochemical trajectory model to estimate pollution levels within the West Midlands conurbation, UK Helen L. Walker, Jacob Baker, Richard G. Derwent and Rossa G. Donovan Abstract This study develops and validates a photochemical trajectory model (PTM) coupled with the master chemical mechanism (MCM) (Derwent and Malcolm, 2000) to simulate the transportation of an air parcel and the chemical reactions within it. Using the West Midlands, UK conurbation as a case study, a 1 1 km emissions inventory for isoprene and monoterpenes based upon a land class survey (Donovan, 2003) is incorporated into the PTM, leading to the evaluation of the effects of locally produced biogenic VOCs (BVOCs) upon levels of ozone and particulate matter (PM).

1. Introduction

Volatile organic compounds (VOCs) are emitted from both anthropogenic and biogenic sources. On a global scale, the production of biogenic VOCs (BVOCs) greatly exceeds emissions from man-made sources, while anthropogenic sources tend to dominate in urban areas. However, BVOCs have a proportionally greater effect due to their higher reactivity and the rate at which they react, despite their low concentrations in urban environments. Isoprene (C5H8) and monoterpenes (C10H16) are the most abundant BVOCs, and react readily with ozone (O3), the hydroxyl radical (OH) and the nitrate radical (NO3), to form non-volatile oxidation products which partition between the gas and aerosol phases (secondary organic aerosols, SOA) (Odum et al., 1996). Isoprene is a less effective source of SOA because it is small in size and possesses more volatile oxidation products, thus making it a signiﬁcant source of tropospheric O3 regeneration. For monoterpenes, rates of reaction tend to be greater, and both the reactants and products are larger with lower vapour pressures, thus resulting in greater aerosol yields via heterogeneous and/or homogeneous

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nucleation (e.g., Odum et al., 1996). Aromatic compounds such as benzene are the greatest SOA-forming species of the anthropogenic VOCs (AVOCs) (Odum et al., 1996). Without the inclusion of BVOC inventories within air pollution models, the contribution to local formation of O3 from the oxidation of isoprene and the generation of particulate matter (PM) following the oxidation of terpenes are given limited consideration, and their absence is likely to play a signiﬁcant role in the underestimation of ozone and PM, speciﬁcally under stagnant, anti-cyclonic conditions in summer (Utembe et al., 2005). The current study uses an existing Lagrangian model that has been widely used to simulate the formation of photochemical ozone in Europe (e.g., Jenkin et al., 2002), and more recently the formation and composition of SOA in the UK (Derwent and Malcolm, 2000; Utembe et al., 2005).

2. Methodology

The photochemical trajectory model (PTM) which contains the nearexplicit master chemical mechanism (MCM v 3.1; e.g., Jenkin et al., 2003) is used to simulate the chemical evolution of an air mass arriving in the West Midlands at 6-h intervals during the summer 1999 PUMA (Pollution of the Urban Midlands Atmosphere) ﬁeld campaign. The conditions speciﬁed in the model represent the broad meteorological conditions associated with photochemical pollution episodes in the UK (Hough and Derwent, 1987). The recent 1 1 km BVOC emissions inventory for the West Midlands (Donovan, 2003) is added into the PTM, with emission potentials converted to emission rates using Guenther’s algorithms (Guenther et al., 1993). The model is validated using data collected during the PUMA campaign at Pritchatts Road (PR), and an automatic monitoring station at Birmingham East.

3. Results and discussion

Table 1 shows the statistical analysis of the modelled output compared with observed data measured at PR and Birmingham East (BE) from 15 June to 12 July 1999 during the PUMA campaign. The line of best-ﬁt for each data set is given, with a corresponding R2 value (square of the Pearson product–moment correlation coefﬁcient) indicating the proportion of the variance in y (M, modelled) attributable to the variance in x (O, observed). Statistical tests also included are the percentage of the number of modelled-observed pairs (N) within a factor of 2 of each other

776

Table 1. Statistical analysis of the modelled output compared with observed data measured during the PUMA campaign Pollutant

Isoprene (ppbv) Ozone (ppbv) NO2 (ppbv) NO (ppbv) NOx (ppbv) Ox (ppbv) NO2:NO (ppbv) CO (ppbv) SO2 (ppbv) Benzene (ppbv)

Elemental carbona Organic carbona Sulphate PMa Nitrate PMa HNO3a PAN (pptv) a

PR BE PR BE PR BE PR BE PR BE PR BE PR BE PR PR PR BE PR BE PR PR PR PR PR PR

Concentrations in mg m3.

N

% within factor of 2

Line of best ﬁt (x ¼ observed)

R2

Mean bias

RMSE

Wilmott’s index

57 46 98 106 96 95 96 95 92 106 95 107 92 95 79 91 53 94 52 91 27 27 27 27 27 83

35 24 81 80 44 49 33 38 54 51 91 93 38 54 85 36 32 11 29 44 33 70 22 56 41 10

0.1588x+0.0545 0.4877x0.0019 0.4208x+10.051 0.4896x+8.3277 0.4711x+1.3944 0.5976x+7.0846 1.2847x+1.5805 0.2713x+3.3098 0.776x+0.4746 0.3933x+6.2484 0.2692x+18.166 0.3093x+17.663 0.0885x+4.006 1.0135x+1.9423 0.3344x+130.79 1.0949x+1.9882 0.0656x+0.0842 0.1374x+0.0386 0.0683x+0.4678 0.3174x+0.2405 0.5696x0.0809 0.5367x+3.6713 0.3857x+0.0555 1.3992x+0.9309 1.3325x+0.8904 0.0543x+54.812

0.0161 0.1017 0.2715 0.3688 0.2984 0.3171 0.3262 0.1736 0.2851 0.2874 0.2092 0.1997 0.0638 0.0839 0.0838 0.1045 0.0065 0.2389 0.0073 0.3168 0.2364 0.1623 0.4884 0.0523 0.6167 0.2401

0.023 0.047 4.662 4.109 5.592 4.043 2.419 1.802 3.223 5.486 10.418 7.575 7.554 1.980 4.165 2.141 0.131 0.349 0.554 0.488 0.736 0.346 1.116 1.154 1.301 537.73

0.08 0.06 13.45 12.56 9.45 8.50 7.42 9.76 12.83 15.00 16.02 13.21 25.39 6.38 110.48 6.05 0.22 0.44 1.14 0.85 0.94 1.58 1.37 2.38 1.75 850.29

0.5903 0.4846 0.6999 0.7531 0.6845 0.6964 0.5952 0.5712 0.6935 0.6720 0.5821 0.5973 0.3142 0.3070 0.5507 0.3018 0.4501 0.4134 0.4324 0.5957 0.5554 0.1627 0.5574 0.1416 0.6887 0.4465

Helen L. Walker et al.

Ethene (ppbv)

Location

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(PC2, Eq. (1)), mean bias (BMB, Eq. (2)), root mean square error (RMSE, Eq. (3)) and Wilmott’s index of agreement (IOA, Eq. (4)). The mean bias is negative for model under prediction, and positive for over prediction; RMSE varies from zero to inﬁnity, where 0 corresponds to the ideal; Wilmott’s IOA (Wilmott, 1982) describes the skill of the model, where 0 denotes no skill for the prediction of pollutant levels, and 1 indicates a perfect agreement. " # N 1X O PC2 ¼ 100 2OM (1) N i¼1 2 BMB ¼

N 1X ðM i Oi Þ N i¼1

"

N 1X ðM i Oi Þ2 RMSE ¼ N i¼1

"

PN

IOA ¼ PN

i¼1 ðM i

i¼1 ðjM i

(2) #1=2

Oi Þ2

¯ þ ðjOi OjÞ ¯ 2 OjÞ

(3) # (4)

3.1. Temperature and photosynthetically active radiation

The parameterised diurnal temperature variation (Fig. 1) generally over estimates the midday temperature for PR, while the midnight temperature is under estimated. Variation in photosynthetically active radiation (PAR) is given by an idealised diurnal variation for the West Midlands conurbation during the period of 19–24 July 1996 (Donovan, 2003), and assumes a clear sky. Modelled PAR is similar in its diurnal variation to measured radiation, reaching a maximum at midday and zero overnight. However, the assumed PAR was found to be inaccurate and has since been modiﬁed. 3.2. Volatile organic compounds

In general, the amount of isoprene produced is underestimated (Table 1; Fig. 2). The model is at its best at 12:00 h (43 and 39% of points within a factor of 2 for PR and BE, respectively) and weakest for nighttime simulation (13 and 8% within a factor of 2 at PR and BE). The initial inaccurate modelling of PAR—particularly around sunrise and

Helen L. Walker et al.

30

1400 1200

BLH/m PAR temp/C

1000

25 20

800 15 600 10 400 200

temperature (deg C)

boundary layer height (m), photosynthetic active radiation (µmol m-2 s-1)

778

5

0 0 00:00:00 03:00:00 06:00:00 09:00:00 12:00:00 15:00:00 18:00:00 21:00:00 00:00:00 time of day

Figure 1. PTM diurnal variation for temperature, boundary layer height and PAR. 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05

15 /0 16 6/1 /0 99 17 6/1 9 /0 99 18 6/1 9 /0 99 19 6/1 9 /0 99 20 6/1 9 /0 99 21 6/1 9 /0 99 22 6/1 9 /0 99 23 6/1 9 /0 99 24 6/1 9 /0 99 25 6/1 9 /0 99 26 6/1 9 /0 99 27 6/1 9 /0 99 28 6/1 9 /0 99 29 6/1 9 /0 99 30 6/1 9 /0 99 01 6/1 9 /0 99 02 7/1 9 /0 99 03 7/1 9 /0 99 04 7/1 9 /0 99 05 7/1 9 /0 99 06 7/1 9 /0 99 07 7/1 9 /0 99 08 7/1 9 /0 99 09 7/1 9 /0 99 10 7/1 9 /0 99 11 7/1 9 /0 99 12 7/1 9 /0 99 13 7/1 9 /0 99 7/ 9 19 99

0

Figure 2. Levels of isoprene for PTM and PUMA data at Pritchatts Road (PR).

sunset—contributed to this underestimation in isoprene emissions, and has recently been changed. There is, however, a period when isoprene levels are signiﬁcantly overestimated (22–25 June 1999), when there was a prolonged period of weak easterly winds, and the air parcels spent longer than average in the West Midlands conurbation. Levels of ethene and benzene are underestimated at both PR and BE. The PTM employs a 10 10 km inventory for the UK which includes total VOC emissions as an annual average, and speciation of the VOCs is

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taken from the National Atmospheric Emissions Inventory (NAEI). An annual average over such a large area does not take into account ﬂuctuations in pollutants linked with trafﬁc. As vehicle emissions are a major source of these VOCs, it seems likely that modelled levels would improve with the inclusion of a higher resolution emissions grid for the West Midlands conurbation and/or temporal variations in emissions with season, day of week and hour of day (Jenkin et al., 2002). 3.3. Ozone

Ozone is modelled particularly well by the PTM/MCM, which was originally set up to predict and analyse ozone episodes. Table 1 shows that 80% of the modelled data is within a factor of 2 of that observed, and around 90% of afternoon and evening concentrations are within a factor of 2. There was an ozone episode on 26th June 1999, when a period of anti-cyclonic, stagnant conditions and higher than average temperatures persisted (Fig. 3). Model performance is weaker during this period, underestimating [O3], potentially from an underestimation of locally produced isoprene, which has since been redeveloped to better represent local production. Further analysis suggests that the trajectory path is responsible for the PTM missing the ozone episode. 3.4. Nitrogen oxides

The model performs well at midnight and in the early morning for nitrogen oxide (NOx) at PR, with over 70% of points within a factor of 2; however, 100 90

O3 PUMA O3 PTM

80

O3 ppb

70 60 50 40 30 20 10

15

/6

16 /99 /6 17 /99 /6 18 /99 /6 19 /99 /6 20 /99 /6 21 /99 /6 22 /99 /6 23 /99 /6 24 /99 /6 25 /99 /6 26 /99 /6 27 /99 /6 28 /99 /6 29 /99 /6 30 /99 /6 1/ /99 7/ 2/ 99 7/ 3/ 99 7/ 4/ 99 7/ 5/ 99 7/ 6/ 99 7/ 7/ 99 7/ 8/ 99 7/ 9/ 99 7 10 /99 /7 11 /99 /7 12 /99 /7 13 /99 /7 /9 9

0

Figure 3. Levels of ozone for PTM and PUMA data at PR.

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the results are poorer during the evening, with concentrations greatly underestimated. NOx emissions are given as a yearly average on a 10 10 km emissions grid, with no diurnal variation speciﬁed. NO is both a short-lived and highly variable species, and the relatively low resolution emission grid and annual average are not conducive to the accurate modelling of such a species, hence the overestimation of [NO]. In addition, all NOx is emitted as NO, therefore the modelled amounts of NO2 are not so strongly affected by the relatively low resolution of the emissions grid. There is, nevertheless, a general underestimation in NO2, which can be attributed to the missing local sources. Combining and comparing observed and modelled levels of the oxidants, NO2 and O3 ( ¼ Ox) leads to a further improvement on the ozone prediction, with greater than 90% of modelled [Ox] within a factor of 2 of that observed. 3.5. Organic and elemental carbon

The modelled values for organic carbon (OC) are derived from the addition of PM generated from aromatic compounds and total PM from terpene compounds (Fig. 4). The latter dominates the amount predicted, with a negligible amount from the aromatics (o0.1%). The majority of modelled daily averaged data lies within a factor of 2 of observations; however, there is a great deal of scatter, and even an apparent inverse relationship between modelled and measured amounts (Table 1). Elemental carbon (EC) was measured at the PUMA site as a daily average, and is generally underestimated, although the maxima and

total organic carbon (µg m-3)

5.00

4.00

3.00

2.00

1.00 Total OC PUMA OCPTM

14

/6 15 /99 /6 16 /99 /6 17 /99 /6 18 /99 /6 19 /99 /6 20 /99 /6 21 /99 /6 22 /99 /6 23 /99 /6 24 /99 /6 25 /99 /6 26 /99 /6 27 /99 /6 28 /99 /6 29 /99 /6 30 /99 /6 1/ /99 7/ 2/ 99 7/ 3/ 99 7/ 4/ 99 7/ 5/ 99 7/ 6/ 99 7/ 7/ 99 7/ 8/ 99 7/ 9/ 99 7 10 /99 /7 11 /99 /7 12 /99 /7 13 /99 /7 /9 9

0.00

Date

Figure 4. Levels of organic carbon (OC) for PTM and PUMA data at PR.

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minima for the 28-day modelling period are captured. Modelled EC is calculated from the emissions of carbon monoxide (CO), which, outside the UK, are derived from the NOx inventory as both pollutants are emitted broadly from the same sources; a 10 km emissions inventory is used for the UK. The underestimation of NOx has already been discussed, and is due largely to the relatively low resolution grid of the emissions in the current model. Although there is a general underestimation of [CO], a large percentage of modelled data (85%) lies within a factor of 2 of that measured at PR. 3.6. Sulphur containing species

There is an overestimation of sulphur dioxide (SO2), with only around one third of points within a factor of 2. The highest resolution of SO2 emissions are given on a 10 10 km grid and speciﬁed as annual averages. Because the principal emitters of the pollutant are point sources such as power stations, and the measurement campaign took place in the summer, it is likely that the annual averages are an overestimation of emissions for that time of year, when fuel consumption is reduced as a consequence of longer and warmer days. A seasonal factor could therefore be applied to the emissions of SO2, in an effort to improve predictions. The PTM provides a good simulation of daily averaged amounts of sulphate PM in terms of variation from day to day, although the majority of the modelled data lies within a factor of 2–3 below that measured at PR. In the PTM, SO2 is oxidised to form sulphur trioxide (SO3) which is used to derive an ‘upper limit’ for gas phase-produced sulphate PM. No aqueous chemistry is deﬁned however, thereby under predicting the amount of sulphate PM generated. 3.7. Nitrogen containing species

The daily averages of nitrate PM (calculated as a fraction of nitric acid (HNO3) that is partitioned into the aerosol phase) are signiﬁcantly overestimated from 24 to 27 June 1999, coinciding with the ozone episode and stagnant, anti-cyclonic conditions. Omitting this period of high-modelled nitrate levels gives an acceptable simulation in magnitude of nitrate PM (still an overestimation with 35% of points above a factor of 2). HNO3 is over-predicted over the same period of time as nitrate PM, with 41% of points within, and 59% over a factor of 2. The magnitude of HNO3 levels is captured from day to day, implying accurate trajectory paths and chemistry.

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Observed peroxy acetyl nitrate (PAN) is compared (Table 1) with the total amount of all PAN species generated by the PTM. The modelled PAN species are approximately an order of magnitude less than measured PAN, and 90% of modelled data is below a factor of 2 of the measured levels. As nitrate PM and HNO3 are over-predicted, this suggests that the reactive nitrogen containing species (NOy), which include NOx and its oxidation products (HNO3, HONO, NO3, N2O5, HNO4, PAN, RONO2, ROONO2) could be present in the modelled output in different forms. Subsequent model runs will take this into consideration and output total NOy. The steady-state level of PAN is proportional to the NO2:NO ratio which is underestimated at PR but overestimated at Birmingham East. 4. Conclusions

The PTM currently provides a reasonable simulation of pollutant levels, in general underestimating isoprene and other VOCs, O3, NO2, organic and EC, sulphate PM, while overestimating NO, SO2, CO, HNO3 and nitrate PM. As BVOCs and products are the focus of this study, the initial aim is to improve their prediction. The good performance with respect to O3 indicates that the MCM accurately describes the photochemistry occurring in the boundary layer, and that the inaccuracies in model output come from primary emissions, and their determining physical parameters. A weakness in the prediction of C5H8 levels is deﬁned PAR variation. In order to correct the identiﬁed deﬁciencies, a new parameterisation scheme has been implemented (Owen, 2006), with PAR varying as a function of time of day and year with solar zenith angle. Following improvements in [C5H8], the secondary area of development lies in the resolution of the emissions grids. It is expected that model performance for all nitrogen-containing species will be enhanced by the upgrade to a 1 1 km NOx inventory in the West Midlands region. Further improvements in [NOx] will be made by deﬁning diurnal variations in the emissions in order to imitate changes in trafﬁc ﬂow in the region. Subsequently, the ozone- and aerosol-forming potential of the BVOC emissions in the West Midlands will be assessed as well as an estimation of the ozone forming potential of each hydrocarbon in the PTM/MCM for the West Midlands.

REFERENCES Derwent, R.G., Malcolm, A.L., 2000. Photochemical generation of secondary particles in the United Kingdom. Phil. Trans. R. Soc. Lond. A 358, 2643–2657.

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Donovan, R.G., 2003. The development of an urban tree air quality score (UTAQS) and its application in a case study. Unpublished PhD thesis, University of Lancaster, UK. Guenther, A., Zimmerman, P., Harley, P., 1993. Isoprene and monoterpene emission rate variability: Model evaluations & sensitivity analyses. J. Geophys. Res. 98, 12609–12617. Hough, A.M., Derwent, R.G., 1987. Distribution of photochemical ozone production between different hydrocarbons. Atmos. Environ. 21, 2015–2033. Jenkin, M.E., Davies, T.J., Stedman, J.R., 2002. The origin and day-of-week dependence of photochemical ozone episodes in the UK. Atmos. Environ. 36, 999–1012. Jenkin, M.E., Saunders, S.M., Wagner, V., Pilling, M.J., 2003. Protocol for the development of the Master Chemical Mechanism, MCM (v3) (Part B): Tropospheric degradation of aromatic volatile organic compounds. Atmos. Chem. Phys. 3, 181–193. Odum, J., Hoffmann, T., Bowman, F., Collins, D., Flagan, R., Seinfeld, J., 1996. Gas/ particle partitioning & secondary organic aerosol yields. Environ. Sci. Technol. 30, 2580–2585. Owen, S.M., 2006. Personal communication. Utembe, S., Jenkin, M., Derwent, R., Lewis, A., Hopkins, J., Hamilton, J., 2005. Modelling the ambient distribution of organic compounds during the August 2003 ozone episode in the southern UK. Faraday Discuss. 130, 1–16. Wilmott, C.J., 1982. Some comments on the evaluation of model performance. Bull. Am. Met. Soc. 63, 1309–1313.

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Poster 16 Producing high-resolution spatial maps of ambient ozone concentrations in Belgium Stijn Janssen, Jef Hooyberghs, Clemens Mensink, Gerwin Dumont and Frans Fierens Abstract An interpolation model, called RIO, is described. The interpolation scheme is based on the Kriging technique. RIO produces ozone estimates on a 5x5 km grid. Database is of ambient ozone concentrations that are systematically sampled by the three Belgian Regions at more than 30 sites. Ambient ozone concentrations are governed by two different production mechanisms, each acting on a different spatial scale. On the regional level, ﬂuctuations in the ozone concentration pattern are mainly meteorological from origin. Beside this, ambient ozone can have a distinct local character due to air pollution. In Belgium, an increased nitrogen oxide (NO) level usually is accompanied by a reduced ozone concentration. The phenomenon is known as the ‘‘titration-effect’’ and is clearly noticeable in Western and Central European urban areas. Ambient ozone concentrations are systematically sampled by the three Belgian Regions at more than 30 sites. The average distance between nearest measuring stations is about 25 km. In spite of this dense coverage, it remains non-trivial to make an accurate spatial map from these sampling values. We describe an interpolation model, called RIO, that is developed to incorporate both the regional and local scale of the ozone phenomenon and that produces ozone estimates on a 5 5 km grid. In a ﬁrst step, the local differences are reduced: by assimilating the population density, which is a measure for the NO pollution, a spatial trend is estimated for the average ozone value. After removing this trend from the sampling values, all stations are transformed to exhibit the same rural character so that spatial homogeneity is obtained. Under these conditions, a regional interpolation can be performed on the residual values. The interpolation scheme is based

Ambient Ozone Concentrations in Belgium

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on the Kriging technique. An unbiased estimator is used and the RMS error is statistically minimised with respect to an estimated spatial covariance. In contrast to ordinary Kriging, the availability of ozone concentration time series is exploited for the estimation of the correlation function. At the end, each interpolated result is retransformed taking into account the appropriate trend value corresponding to the local population density.

Figure 1. Interpolated daily ozone maximum for Belgium on July 24, 2002. The left panel shows the RIO result, the right panel is obtained with the standard IDW technique. Black squares are monitoring sites.

24 IDW+H RIO

22 20 18

RMSE

16 14 12 10 8 6

urban

rural

4 0

5

10

15

20

25

monitoring site nr Figure 2. RMS error of interpolation tools (RIO versus IDW) evaluated with the ‘‘leaving one out’’ technique. Monitoring sites: ordered according to increasing population density.

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A comparison is made of the RIO model with a standard inverse distance weighting (IDW) interpolation scheme. The results are presented in Figs. 1 and 2. In the ﬁrst plot, interpolated maps are presented for the daily ozone maximum on July 24, 2002. It becomes clear that much more detail is revealed in the RIO scheme compared with the IDW technique. In Fig. 2, the interpolated results are validated by a ‘‘leaving out one’’ technique. At the location of a particular sampling station, both the RIO and IDW technique are applied to produce an interpolated value based on the results of all stations except the one which is estimated. The interpolated value can be compared with the measured one and the error can be calculated. This procedure is repeated for the entire time series and for every station. From this analysis it turns out that RIO clearly outperforms the standard techniques, especially in the Belgian urbanised regions.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06817-9

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Poster 17 2D variational data assimilation of near-surface chemical species Lennart Robertson and Michael Kahnert Abstract Variational methods are common procedures for spatial analysis in numerical weather forecasting systems. In this paper, a twodimensional variational scheme was used to perform spatial analyses of SO2, ozone and base cation data. Variational methods are today the most common procedures for spatial analysis in numerical weather forecasting systems (Ide et al., 1997). The approach offers blending of various data sources where complex relations between model state and observed entities are consistently accounted for. In this work, we have used a two-dimensional variational scheme to perform spatial analyses of SO2, Ozone and base cation data. The variational scheme offers an opportunity to include, for example, conditions on equilibrium of atmospheric chemistry and is thus of general interest for spatial analysis of atmospheric chemistry data. The variational problem is in short set up to minimise a cost function in the following vector form: 1 1 JðxÞ ¼ ðx xb ÞT B1 ðx xb Þ þ ðy hðxÞÞT O1 ðy hðxÞÞ 2 2

(1)

where B and O are the background and observation error covariance matrices, respectively, y the observation state vector, xb a background state and x the state of the system, which is to be determined such as to minimise the cost function. The numerical minimisation of the cost function (1) is carried out for a reduced set of the eigenmodes of the background error covariance matrix. How to set up B assuming isotropic or anisotropic error covariance structures will be discussed. The background ﬁeld is provided by the Multi-Scale Atmospheric Chemistry and Transport Model (MATCH) (Robertson et al., 1999) in two different setups: (a) on the European scale applying a photochemistry

788 Lennart Robertson and Michael Kahnert

Figure 1. SO2 concentration ﬁeld computed with the high-resolution MATCH-Sweden model (left), the low-resolution MATCH-Europe photochemistry model (middle) and the sum of the left panel and the 2DVAR analysis of the long-range contribution (right), red circles marks the observations used in the analysis.

Data Assimilation of Near-Surface Chemical Species

789

scheme and the EMEP European emission inventories (Vestreng, 2003) and (b) a high-resolution run with quasi steady-state Sulphur–Nitrogen chemistry applied solely for the Swedish domestic inventory. Figure 1 demonstrates the use of the 2DVAR analysis in conjunction with the two different MATCH versions. The left panel shows SO2 air concentrations computed on an 11 11 km2 grid and only accounting for Swedish emissions. The middle panel shows a clip-out of the SO2 concentration ﬁeld computed on a 44 44 km2 grid on the European scale. We subtract the Swedish contribution from both the European scale calculations and the observations (thus neglecting non-linear chemistry effects), yielding a long-range background ﬁeld and the long-range component in the observations, which then becomes xb and y in the cost function (1). The combined results from the 2DVAR analysis and the ‘‘MATCH-Sweden’’ model then constitute the total SO2 concentrations, where the long-range component is updated by the observations (right panel in Fig. 1). Ozone and base cation ﬁelds are analysed directly. For base cations, the background error covariance matrix is assumed to be inhomogeneous and anisotropic, thus accounting for the slow variation of base cation concentrations over the sea and the rapid decrease land-inwards. REFERENCES Ide, K., Courtier, P., Ghil, M., Lorenc, A.C., 1997. Uniﬁed notation for data assimilation: Operational, sequential and variational. J. Meteor. Soc. 75, 181–189. Robertson, L., Langner, J., Engardt, M., 1999. An Eulerian limited-area atmospheric transport model. J. Appl. Meteorol. 38, 190–210. Vestreng, V., 2003. EMEP/MSC-W Technical report. Review and Revision. Emission data reported to CRLTAP. MSC-W Status Report 2003. EMEP/MSC-W Note 1/2003. ISSN:0804-2446.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06818-0

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Poster 18 Principal component and multiple linear regressions to predict ozone concentrations Sofia I.V. Sousa, Fernando G. Martins, Jose´ C.M. Pires, Maria C.M. Alvim-Ferraz and Maria C. Pereira Abstract The aim of this study was: (i) to predict next day hourly ozone concentrations, for an urban site with trafﬁc inﬂuence located at Oporto, by applying principal component regression (PCR); and (ii) comparing PCR with multiple linear regression (MLR). The use of principal components as inputs in the models improved the O3 concentrations prediction. The development of models to predict ozone concentrations can be useful, namely because it can provide early warnings to the population, allowing additionally the reduction of the number of measuring sites. Many models have been developed for the prediction of ozone concentrations. The aim of this study was: (i) to predict next day hourly ozone concentrations, for an urban site with trafﬁc inﬂuence located at Oporto, by applying principal component regression (PCR); and (ii) comparing PCR with multiple linear regression (MLR). Correlation coefﬁcients between pollutants and meteorological variables were analysed to evaluate the inﬂuence of each variable on O3 concentrations. Thus, this study considered as predictor variables the previous day hourly concentrations of ozone (O3), nitrogen monoxide (NO) and nitrogen dioxide (NO2), and the previous day hourly means of temperature (T), relative humidity (RH) and wind velocity (WV). The period analysed was June 2003; the development period included the ﬁrst 25 days and the last 5 days were used for the validation period. For both methods, a t-test (signiﬁcance level of 0.05) was applied to statistically evaluate the regression parameters. Considering the statistically valid parameters, new regressions were then performed. Using MLR method, the derived model was as follows: ½O3 ðdÞ ¼ 1:67T ðd1Þ þ 0:22HRðd1Þ þ 0:28½O3 ðd1Þ

(1)

Predicting Ozone Concentrations

791

The results of Bartlett sphericity test showed that the PCR was applicable to the analysed dataset. The eigenvalues obtained, from PC1 to PC6, were 2.89, 1.69, 0.72, 0.37, 0.20 and 0.14, respectively. For PCR, forecasting was performed considering separately the use of two to six PC. Considering two PC, the eigenvalues were higher than one (Kaiser Criterion), being responsible for 76% of the total variance (using six PC, all the variance was accounted). The t-test results showed that the same model was achieved using two, three, four or ﬁve PC. The model obtained with the PCR method which showed the best performance indexes was the one that used two PC, as follows: ½O3 ðdÞ ¼ 48:2 þ 11:96PC1 3:20PC2

(2)

The rotated factor loadings using two PC and the respective communalities are presented in Table 1. The variables associated with PC1 were O3, T, RH and WV, and those associated with PC2 were NO and NO2. Thus, PC1 accounted the inﬂuences of meteorological and accumulation parameters whereas PC2 accounted inﬂuences associated with photochemical parameters. It is also important to point out that the communality values were relatively high. The models behaviour was evaluated by calculating the correlation coefﬁcient (R), the mean bias error (MBE), the mean absolute error (MAE), the root mean squared error (RMSE) and the index of agreement (d2). Table 2 shows the values of the performance indexes using MLR and PCR with two PC, for both development and validation periods. Considering the MLR model, performance indexes were higher in the validation period, with exception for the MBE value that was considerably lower in the development period. Also, with PCR model, the performance indexes were better during the validation phase, with exception for the MBE value, which was lower in the development period. Table 1. Predictions of O3 concentrations for the validation period and the corresponding measured data Variables

NO NO2 O3 T RH WV

Rotated factor loadings PC1

PC2

0.227 0.214 0.812 0.933 0.931 0.576

0.790 0.906 0.326 0.121 0.122 0.420

Communalities

Bold marked loads indicate the variables that mostly inﬂuenced each parameter.

0.68 0.87 0.89 0.88 0.51 0.77

Sofia I.V. Sousa et al.

792

Table 2. Performance indexes for both models during development and validation periods Performance indexes

R MBE MAE RMSE d2

Development period

Validation period

MLR

PCR

MLR

PCR

0.67 1.42 18.4 23.4 0.75

0.68 1.77 107 18.4 23.1 0.79

0.72 4.33 10.5 13.9 0.79

0.73 1.96 10.9 13.6 0.84

90 Data MLR PCR

80

O3 (µg/m3)

70 60 50 40 30 20 10 0 1

8

15 22 29 36 43 50 57 64 71 78 85 92 99 106 113 Hour

Figure 1. Predictions of O3 concentrations for the validation period and the corresponding measured data.

Comparing both models, in development and validation periods, it was observed that the performance indexes were similar, however, MBE and d2 values were signiﬁcantly better using PCR model. Figure 1 shows the predictions of both models as well as the measured data, for the validation period. The ﬁgure shows that the prediction using both models was generally similar, but PCR was more efﬁcient in predicting extreme values. Moreover, PCR was considered better, because it introduced the inﬂuence of all original variables. Concluding, the use of principal components as inputs in the models improved the O3 concentrations prediction, because it reduced the model complexity and also accounted for the inﬂuence of all original variables.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06819-2

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Poster 19 Relationships between nitrogen oxide emissions from electrical generating units in the U.S. and meteorology$ P. Steven Porter, S.T. Rao and E.L. Gego Abstract The correlation between nitrogen oxide (NOX) emissions and heat input (HI) from electrical generating units as well as meteorology was investigated. HI is the energy content of fuel used to generate electricity. Here, we examine time scales common to both HI and meteorology with the goal of improving the performance of air quality modeling and forecasting. 1. Introduction

Nitrogen oxide (NOX) emissions from electrical generating units (EGUs) in the northeast U.S. have declined dramatically during the past few years as a result of a series of air quality rules (RACT rule, Clean Air Act Amendments Title IV, and the NOX SIP call). Progress in reducing NOX emissions is evident in continuous NOX measurements collected by EGUs. These measurements are collected hourly from roughly 1500 EGUs and are archived at EPA’s Clean Air Market Division, (USEPA, 2006a). NOX data collected by EGUs also have value as a source of information important to air quality modeling. Declining NOX emissions are most evident during the summer ozone season when seasonal controls are in effect, and have occurred despite offsetting increases in heat input (HI), the energy content of fuel used to generate electricity (also archived by EPA’s Clean Air Market Division). Weather indirectly affects NOX emissions (and therefore air quality) by inﬂuencing the amount of fuel $

Although the research described in this article has been funded in part by the U.S. Environmental Protection Agency and the National Oceanic and Atmospheric Administration, it has not been subjected to their required peer and policy review. Therefore, the statements, ﬁndings, conclusions, and recommendations are those of the authors and do not necessarily reﬂect the views of these agencies and no ofﬁcial endorsement should be inferred.

794

P. Steven Porter et al.

used for cooling and heating. Here, we examine time scales common to both HI and meteorology with the goal of improving the performance of air quality modeling and forecasting. 2. Data

Time series of HI were gridded on a Lambert coordinate system. The HI time series from all EGUs falling within a given grid were summed. An HI time series with local character was created by summing data from a cluster of EGUs in north-central Pennsylvania. Nearby meteorological data (Kane Experimental Forest, KEF112, USEPA, 2006b), was used to illustrate relationships between HI and meteorology, included temperature, solar radiation, relative humidity, and wind speed. 3. Analysis

A two-week period during the summer of 2002 was used to illustrate some of the relationships between HI and meteorology. Typically, high daily peak HI values result from warm, sunny, humid days, while lower values occur during cool, dry, windy conditions. HI also follows a weekly cycle, with low HI days occurring on weekends. The periodogram of HI for a 10-week period during the summer of 2002 has signiﬁcant peaks at one week (and harmonics) and one day (and harmonics). Wavelet images (local in time periodograms) of HI show that weekly variation is ephemeral, occurring most often during the summer. Weekly forcing was strongest during the summers of 2001 and 2002, periods that coincided with large summertime HI values for the SIP states and high semi-annual forcings. The diurnal cycle in HI appears at times to be modulated by meteorology at one week and semi-annual time scales. The wavelet image of HI diurnal amplitude also shows diurnal HI modulation at semi-annual time scales. Relationships between seasonal HI and meteorology were also examined. Local maxima in HI are accompanied by local maxima in temperature, solar radiation, and local minima in relative humidity. Summer relationships are reversed for the winter heating season. Coherence (local-in-time correlation between temperature and HI across time scales), is strongly positive between temperature and HI during summer months across a wide range of time scales; negative correlation often occurs during the winter. REFERENCES USEPA, 2006a. Clean Air Markets Division. www.epa.gov/airmarkets USEPA, 2006b. Castnet Archives. www.epa.gov/castnet/air

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06820-9

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Poster 20 Validation of the meteorological input for air quality simulations in Northern Italy Giovanni Bonafe`, Marco Deserti, Suzanne Jongen, Enrico Minguzzi and Michele Stortini Abstract This work investigates the operational building of the meteorological input for the chemical transport model (CTM) CHIMERE starting from the outputs of the limited area model LAMI. Crucial meteorological parameters were validated by analysing one-year values from LAMI and comparing them with available observations. 1. Introduction

Chemical transport models (CTM) are a powerful tool for air quality (AQ) evaluation and operational forecasts. The meteorological input is very important in these simulations. In order to produce operational AQ forecasts at local scale, the meteorological input must come from a limited area model (LAM), which is able to predict all the ﬁelds required. Predictions of near-surface turbulence, boundary layer height, cloud water and other parameters, which affect pollutant concentrations, must be used very carefully, as they are rarely veriﬁed. 2. Methodology

This work investigates the operational building of the meteorological input for CTM CHIMERE (Bessagnet et al., 2004) starting from the outputs of the LAM LAMI, the Italian implementation of LM (Steppeler et al., 2003). CHIMERE and LAMI are part of the modelling system NINFA (Stortini et al., 2006). The following parameters, crucial for accurate AQ forecasts, are selected: mixing height, friction velocity, Monin–Obukhov length, sensible heat ﬂux, cloud water and soil moisture. They were validated by analysing one-year values from LAMI and comparing them with available observations.

796

Giovanni Bonafe` et al.

3. Results 3.1. Friction velocity u*, Monin–Obukhov length L* and sensible heat flux H

LAMI output of u*, L* and H have been compared with available observations. Data were collected in three ﬁeld campaigns, carried out with an ultrasonic 3D anemometer, and processed with the eddy covariance method. The frequency of stable conditions is overestimated, and that of unstable conditions underestimated; u* is often overestimated. In the central hours of the day, H values are overestimated by 60%; during night, LAMI predicts negative values, while observations are close to 0. 3.2. Cloud water

LAMI output has been compared with the implemented adiabatic cloud scheme (Slobin, 1982). The Slobin scheme produces more realistic amounts of cloud liquid water and increases the amount of secondary PM. 3.3. Soil moisture

Soil moisture is crucial in the erosion/resuspension parameterisation, and it can signiﬁcantly affect predicted PM10 concentrations. LAMI output has been compared with observed data. Soil moisture is systematically overestimated, by E0.03 m3m 3 during winter and by E0.08 m3m 3 during summer. 3.4. Mixing height

Two scheme are compared: a simple one (Mahrt, 1981), in which nocturnal MH is a function of u*, and one based on critical Richardson number (Troen and Mahrt, 1986). The simpler scheme reproduces better the typical nocturnal values of the Po Valley.

REFERENCES Bessagnet, B., Hodzic, A., Vautard, R., Beekmann, M., Cheinet, S., Honore´, C., Liousse, C., Rouil, L., 2004. Aerosol modeling with CHIMERE: Preliminary evaluation at the continental scale. Atmos. Environ. 38, 2803–2817. Mahrt, L., 1981. Modelling the depth of the stable boundary layer. Bound.-Layer Meteorol. 21, 3–19.

Air Quality Simulations in Northern Italy

797

Slobin, S.D., 1982. Microwave noise temperature and attenuation of clouds: Statistics of these effects at various sites in the United States, Alaska and Hawaii. Radio Sci. 17, 1443–1454. Steppeler, J., Doms, G., Scha¨ttler, U., Bitzer, H.W., Gassmann, A., Damrath, U., Gregoric, G., 2003. Meso-gamma scale forecasts using the nonhydrostatic model LM. Meteorol. Atmos. Phys. 82, 75–96. Stortini, M., Deserti, M., Bonafe`, G., Minguzzi, E., Jongen, S., 2006. Long–term simulation and validation of ozone and aerosol in the Po Valley. 28th ITM/NATO CCMS. Troen, I., Mahrt, L., 1986. A simple model of the atmospheric boundary layer: Sensitivity to surface evaporation. Bound.-Layer Meteorol. 37, 129–148.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06821-0

798

Poster 21 The North American mercury model inter-comparison Study (NAMMIS)$ O. Russell Bullock, Dwight Atkinson, Thomas Braverman, Ashu Dastoor, Didier Davignon, Noelle Selin, Daniel Jacoby, Kristen Lohman, Christian Seigneur, Krish Vijayaraghavan, Tom Myers, Kevin Civerolo and Christian Hogrefe Abstract NAMMIS is an intercomparison of atmospheric Hg models with a focus on North America. Three regional-scale atmospheric Hg models are the prime subjects of the study: the Community Multi-scale Air Quality model (CMAQ) developed by NOAA and EPA, the Regional Modeling System for Aerosols and Deposition (REMSAD) developed by ICFI, and the Trace Element Analysis Model (TEAM) developed by AER. The models were run for the entire year of 2001 using the same initial and boundary condition data. Several air-quality models have been applied to estimate the relative contributions of various sources to mercury (Hg) deposition to the United States and other nations. These models have, at times, offered rather different conclusions. An atmospheric Hg model inter-comparison was previously conducted by the Meteorological Synthesizing Centre— East (MSC-E) with the participation of various models from Europe and North America. This earlier study focused on modeling atmospheric Hg over Europe and is described in detail by MSC-East Technical Reports 2/2001, 1/2004 and 1/2005 (available at http://www.msceast.org/ publications.html). This ﬁrst Hg model inter-comparison provided valuable information about the way the models treated speciﬁc physical and chemical processes and it showed that these differing process treatments

$

This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reﬂect their policies or views.

The North American Mercury Model Inter-Comparison Study (NAMMIS)

799

can lead to signiﬁcantly different modeling results. The NAMMIS is a follow-on effort to apply atmospheric Hg models in a more tightly constrained testing environment, this time with a focus on North America. With each model using the same input data sets for initial conditions, meteorology, emissions and boundary values, and with each model applied to the same horizontal modeling domain (Fig. 1), the separate effects of input data and scientiﬁc process treatments can be better understood and guidance can be provided to the research community regarding which scientiﬁc process uncertainties are contributing most to observed discrepancies in model simulations of Hg deposition. The NAMMIS involves the U.S. National Oceanic and Atmospheric Administration (NOAA), U.S. Environmental Protection Agency (EPA), Environment Canada, the New York State Department of Environmental Conservation (NYSDEC), Atmospheric and Environmental Research, Inc. (AER), Harvard University, and ICF International (ICFI). Three regional-scale atmospheric Hg models are the prime subjects of the study: the Community Multi-scale Air Quality model (CMAQ) developed by NOAA and EPA, the Regional Modeling System for Aerosols and Deposition (REMSAD) developed by ICFI, and the Trace Element Analysis Model (TEAM) developed by AER. CMAQ, REMSAD and TEAM are each being applied for the entire year of 2001 using three sets of initial condition and boundary condition (IC/BC) data developed from

Figure 1. NAMMIS modelling domain.

800

O. Russell Bullock et al.

three global-scale models: the Chemical Transport Model (CTM) from AER, the GEOS-Chem model from Harvard University, and the Global/ Regional Atmospheric Heavy Metals model (GRAHM) from Environment Canada. Emissions of Hg and other relevant substances from anthropogenic sources within the regional modeling domain are based on the inventory for the 2001 time frame used by EPA for its Clean Air Mercury Rule beneﬁts analysis. An inventory of Hg emissions from oceans, land surfaces and volcanic activity developed by AER is also being applied to all three regional models. Meteorological guidance for the three regional-scale models was obtained from the Penn State/NCAR Meteorological Model—Generation 5 (MM5). All regional modeling results are being sent to NYSDEC for inter-comparison analysis and for evaluation against observed data from the Mercury Deposition Network (MDN) in the U.S. and Canada and any other pertinent observations identiﬁed by NYSDEC. Some interesting differences have been observed in the IC/BC data derived from the global models. Average lateral-boundary air concentration proﬁles were found to differ signiﬁcantly. GRAHM shows slightly lower elemental mercury (Hg0) air concentrations than the other two global models, especially at higher altitudes. For reactive gaseous mercury (RGM), CTM tends to show higher concentrations than the other global models in the middle troposphere, but lower concentrations at higher altitudes. For particulate Hg (HgP), GRAHM simulates higher concentrations overall, especially at higher altitudes, while the other two global models show very little HgP at any height. GRAHM appears to simulate a strong oxidation of Hg0 by stratospheric ozone to produce HgP in addition to RGM at high altitudes while the other two global models appear to produce RGM but almost no HgP. CMAQ and REMSAD regional modeling results for all three IC/BC cases have been compared to one another and to Hg wet deposition observations from the MDN. While both models tend to over-estimate annual Hg wet deposition ﬂux, CMAQ is closer to the observed value in two of the three cases. CMAQ also performs best on the average predicted-to-observed ratio for annual wet deposition ﬂux at MDN sites. REMSAD shows much less Hg dry deposition than CMAQ. This is certainly a contributing factor to the stronger over-estimation of Hg wet deposition by REMSAD. Unfortunately, there are no observations of Hg dry deposition ﬂux to compare to these modeling results. Simulated Hg concentration in precipitation show largely the same outcomes as for the deposition ﬂux, except for an interesting juxtaposition for the GRAHM IC/BC case where CMAQ has higher concentrations. For both models, there are

The North American Mercury Model Inter-Comparison Study (NAMMIS)

801

observable differences in performance depending on the IC/BC data set used. The TEAM results are expected soon. ACKNOWLEDGMENTS

The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce’s National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW13921548.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06822-2

802

Poster 22 Two-dimensional steady state advection-diffusion equation: An analytical solution Daniela Buske, Marco Tullio Vilhena, Davidson Moreira and Tiziano Tirabassi Abstract Atmospheric air pollution turbulent ﬂuxes can be assumed to be proportional to the mean concentration gradient. This assumption, along with the equation of continuity, leads to the advection-diffusion equation. In the last years, special attention has been devoted to the task of searching analytical solutions for the advection-diffusion equation in order to simulate the pollutant dispersion in the planetary boundary layer (PBL). Presently, analytical solutions of the advectiondiffusion equation are usually obtained by making strong assumptions about the eddy diffusivity coefﬁcients and wind speed proﬁles. In this work, we present a general solution (i.e., for any wind and eddy diffusivity vertical proﬁles) of the two-dimensional steady-state advection-diffusion equation using the general integral Laplace transform technique (GILTT).

The GILTT solution

The crosswind integration of the advection-diffusion equation (in stationary conditions and neglecting the longitudinal diffusion) leads to U

@¯c @ @¯c ¼ Kz @x @z @z

(1)

subject to the boundary conditions of zero ﬂux at the ground and PBL top, and a source with emission rate Q at height Hs Kz

@¯c ¼ 0 at z ¼ 0; h @z

(2)

Two-Dimensional Steady State Advection-Diffusion Equation

803

Table 1. Statistical evaluation of models results for ground-level concentration Experiment Copenhagen Prairie Grass

NMSE

COR

FA2

FB

FS

0.05 0.74

0.91 0.83

1.00 0.66

0.02 0.37

0.14 0.52

U c¯ ð0; zÞ ¼ Qdðz H s Þ at x ¼ 0

(3)

where c¯ represents the average crosswind integrated concentration, U is the mean wind speed in x direction, Kz the eddy diffusivity and h the boundary layer height. Equation (1) is solved by the GILTT method (Wortmann et al., 2005). The mean feature of the GILTT method comprehends the steps: solution of an associate Sturm–Liouville problem, expansion of the pollutant concentration in a series in terms of the attained eigenfunctions, replacement of this expansion in the advection-diffusion equation, and ﬁnally, taking moments. This procedure leads to a set of differential ordinary equations, named the transformed equation. These equations are then solved analytically by application of the Laplace transform technique without any approximation along its derivation. So, the solution of Eq. (1) is c¯ ðx; zÞ ¼

1 X c¯ ðxÞCi ðzÞ 1=2

i¼0

(4)

Ni

where Ci (z) is the eigenfunction associated to the Sturm–Liouville problem R and satisﬁes the orthonormality condition, Ni is represented by N i ¼ v C2i ðzÞdv and c¯ ðx; zÞ comes from the solution of the transformed problem. Bearing in mind the exactness of the analytical solution, we may state that the pollutant concentration calculation by this kind of solution is free of error except for the round-off error. In order to show the behaviour of the solution, it was applied to the dataset of Prairie Grass and Copenhagen dispersion experiments. Table 1 shows the statistical analysis (Hanna, 1989) of the model using the approach (Eq. (4)) with vertical eddy diffusivity given by Degrazia et al. (1997) and power proﬁle of wind. The statistical indices point out that a good agreement is obtained between experimental data and the GILTT model.

REFERENCES Degrazia, G.A., Campos Velho, H.F., Carvalho, J.C., 1997. Nonlocal exchange coefﬁcients for the convective boundary-layer derived from spectral properties. Contrib. Atmos. Phys. 70, 57–64.

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Daniela Buske et al.

Hanna, S.R., 1989. Conﬁdence limit for air quality models as estimated by bootstrap and jacknife resampling methods. Atmos. Environ. 23, 1385–1395. Wortmann, S., Vilhena, M.T., Moreira, D.M., Buske, D., 2005. A new analytical approach to simulate the pollutant dispersion in the PBL. Atmos. Environ. 39, 2171–2178.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06823-4

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Poster 23 The new GIADMT approach to simulate the pollutant dispersion in the planetary boundary layer Camila Costa, Marco Tullio Vilhena, Davidson Moreira and Tiziano Tirabassi Abstract Analytical solutions of equations are of fundamental importance in understanding and describing the phenomenon of turbulent diffusion in the atmosphere. Focusing our attention in this direction, in this work we report a semi-analytical solution for the three-dimensional advection–diffusion equation in order to simulate pollutant dispersion in the atmosphere considering a vertically inhomogeneous PBL. This work relies on the semi-analytical solution for the three-dimensional advection–diffusion equation combining the ADMM (Advection Diffusion Multilayer Model) and GITT (Generalized Integral Transform Technique) methods. We coin this technique as GIADMT (Generalized Integral Advection Diffusion Multilayer Technique). 1. Model application and results

The advection–diffusion equation of air pollution in the atmosphere is written as @c @c @ @c @ @c @ @c þ þ þS þU ¼ Kx Ky Kz @t @x @x @x @y @y @z @z

(1)

where c denotes the average concentration, Kx, Ky and Kz are the Cartesian components of eddy diffusivity, U is the mean wind and S is the source term. The X-axis of the Cartesian coordinate system is aligned in the direction of the actual wind near the surface, the Y-axis is oriented in the horizontal crosswind direction, the Z-axis is chosen vertically upwards and t is the time.

Camila Costa et al.

806

Table 1. Statistical evaluation of model results with Copenhagen data set Model

nmse

r

fa2

fb

fs

GIADMT

0.10

0.90

0.96

0.06

0.22

After the application of the GIADMT method, we get the solution ( " Nj Nk 1 X X P¯ j cosðli yÞ X Pk pﬃﬃﬃﬃﬃﬃ w wk cðt; x; y; zÞ ¼ ¯j t N i k¼1 x j¼1 i¼0 0 139 = An eRz þ Bn eRz þ A 5 ð2Þ @ Q ; ðeRðzHsÞ eRðzH s Þ ÞHðz H s Þ þ 2Ra n where vﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ !ﬃ u 2 u 1 Pk Pk Pj Rin ¼ t K xn Un þ K yn l2i þ x x t K zn vﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ u pﬃﬃﬃﬃﬃﬃ 2 ! P P Pk N i Pj u j k 2 tK zn þ K yn li þ U n K xn Ran ¼ t x x cosðli yo Þ t where the constants An and Bn are determined solving a linear system applying the (2N2) interface conditions, and wk, wj and Pk, Pj are the Gaussian Quadrature parameters tabulated (Stroud and Secrest, 1966), Nk is the number of inversions, Q is emission rate, Hs is the height source, H(zHs) is the Heaviside function Ni is the norm (see Costa et al., 2006) and li is the eigenvalue from the GITT approach. Table 1 shows the statistical analysis of the new model compared with the moderately unstable experiments of Copenhagen (Gryning and Lyck, 1984) for centreline concentrations. Analysing the statistical indices (Hanna, 1989) in Table 1, it is possible to notice that the models simulate satisfactorily the observed concentrations, with nmse (normalised mean square error), fb (fractional bias) and fs (fractional standard deviation) values relatively near to 0 and r (correlation coefﬁcient) and fa2 (factor of two) relatively near to 1.

REFERENCES Costa, C.P., Vilhena, M.T., Moreira, D.M., Tirabassi, T., 2006. Semi-analytical solution of the steady three-dimensional advection–diffusion equation in the planetary boundary layer. Atmos. Environ. 40, 5659–5669.

Pollutant Dispersion in Planetary Boundary Layer

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Gryning, S.E., Lyck, E., 1984. Atmospheric dispersion from elevated source in an urban area: Comparison between tracer experiments and model calculations. J. Climate Appl. Meteorol. 23, 651–654. Hanna, S.R., 1989. Conﬁdence limit for air quality models as estimated by bootstrap and jacknife resampling methods. Atmos. Environ. 23, 1385–1395. Stroud, A.H., Secrest, D., 1966. Gaussian Quadrature Formulas. Englewood Cliffs, NJ, Prentice Hall.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06824-6

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Poster 24 One-dimensional eddy diffusivities for growing turbulence in the convective boundary layer Antonio Goulart, Umberto Rizza, Davidson Moreira, Marco T. Vilhena, Gerva´sio Degrazia and Jonas Carvalho Abstract In this work a general method to derive eddy diffusivities in a convective growing turbulence in the planetary boundary layer is proposed. The method is based in a model for the budget equation describing the 3-D energy density spectrum and the Taylor statistical diffusion theory. 1. Introduction

Exists a vast literature regarding the issues of parameterization and pollutant dispersion simulation in the Planetary Boundary Layer (PBL), but for Convective Boundary Layer (CBL) growing is scarce. In the work a general method to derive eddy diffusivities in a convective growing turbulence in the planetary boundary layer is proposed. The method is based in a model for the budget equation describing the 3-D energy density spectrum and the Taylor statistical diffusion theory. First, on the basis of a dimensional analysis, the unknown inertial transport term present in dynamical equation for the 3D spectrum is parameterized. The 3-D energy density spectrum equation is resolved. The vertical onedimensional vertical spectrum is derived from the 3-D spectrum decaying, employing a weight function that allows to select the magnitude of the vertical spectral component for the production of the decaying 3-D energy density spectrum. The eddy diffusivity is calculated from expression suggested by Goulart et al. (2004). 2. Turbulent energy equation in the growing CBL

In order to derive the spectral form of the turbulent energy equation we must recall that is possible to derive a spectral form of the turbulent

One-Dimensional Eddy Diffusivities for Growing Turbulence

809

energy equation from the momentum conservation law, expressed through the Navier-Stokes equations. Indeed, for a homogeneous turbulent ﬂow, the spectral form of the turbulent energy equation reads like Hinze (1975), @ g (1) Eðk; t; zÞ ¼ W ðk; t; zÞ þ Hðk; t; zÞ 2nk2 Eðk; t; zÞ @t T0 where: (g/T0)H(k, t; z) is the buoyancy term and W(k, t; z) is the energytransfer-spectrum function that represents the contribution due to the inertial transfer of energy among different wave-numbers. Is assumed that H(k, t; z) is pt 2=3 Hðk; t; zÞ ¼ c1 gc 0 k2=3 E 0 ðk; zÞ sin (2) 2tf where c1 is a constant to be determined from experiments or model simulations, and tf is the time at which the height of CBL becomes constant. Pao (1965) parameterized the term W(k, t) on the basis of dimensional analysis, as follows: @ 1 1=3 5=3 @ m2 2=3 1=3 k Eðk; tÞ (3) W ðk; t; zÞ ¼ ða k Eðk; tÞÞ @k @k w h where a is the Kolmogorov constant, e is the rate of molecular dissipation of kinetic energy, w is the velocity scale, h is the height of CBL and m2 is a dimensionless constant determined from initial conditions. Substituting Eqs. (3) and (2) in Eq. (1) yields an expression for the energy spectrum function E(k, t; z). The one-dimensional spectrum components in CLC is calculated as follows: Rt ð1=TÞ 0 F w ðk; t; zÞdt Eðk; t; zÞ (4) F w ðk; t; zÞ ¼ aðkÞ Rt ð1=TÞ 0 Eðk; t; zÞdt where the ratio between the two integrals is a weight function that indicates that the w component takes part in the construction of the 3D spectrum and a(k) is the proportionality constant. The eddy diffusivity is calculated as follows (see Pao, 1965): Z 0:55 1 E w ðk; t; zÞ sw hX k dk (5) K z ðt; zÞ ¼ sin sw 0 k 0:55w

810

Antonio Goulart et al.

REFERENCES Goulart, A., Moreira, D., Carvalho, J., Tirabassi, T., 2004. Derivation of eddy diffusivities from an unsteady turbulence spectrum. Atmos. Environ. 38, 6121–6124. Hinze, J.O., 1975. Turbulence. McGraw-Hill, p. 790. Pao, Y.H., 1965. Structure of turbulent velocity and scalar ﬁelds at large wavenumbers. Physics Fluids 8, 1063.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06825-8

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Poster 25 Impact of meteorological factors on turbulent dispersion over complex terrain S. Saavedra, J.A. Souto and J. Vila`-Guerau de Arellano Abstract Dispersion air quality models require meteorological ﬁelds to calculate the dispersion characteristics that can be provided by mesoscale meteorological models. These characteristics are related to the structure of the atmospheric boundary layer (ABL), and in particular to ABL depth and the atmospheric stability. This study investigates the sensitivity of the dispersion parameters to ABL schemes included in the meteorological model PSU/NCAR MM5 (Grell, G. A., Dudhia, J., Stauffer, D. R., 1995. A description of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5). NCAR/TN-398+STR, Boulder (CO), USA.) applied to a complex terrain at Galicia (NW of Spain). Two days (March 18–19th, 2003) were selected, with high pressures and moderate wind. Using a Gaussian plume model, the inﬂuence of the ABL characteristics is included in the calculation of the dispersion parameters, sy and sz. We focus on two factors that can modify the values of these two parameters: topographical resolution (9.0 vs. 0.9 km) and ABL closure schemes (Eta local vs. MRF non-local). Sensitivity analysis of these factors vs. stability parameter zi/L (zi, ABL depth; L, Monin–Obukhov length) was done. This study was restricted to convective conditions. MM5 results were compared to available aloft (rawinsonde) measurements (Fig. 1a); experimental zi values were calculated by using the critical bulk Richardson number method (Vogelezang and Holtslag, 1996). Comparing zi values (Fig. 1b), atmospheric boundary layer (ABL) shows a larger diurnal development for the MRF scheme, although simulated values are lower than experimental values. Estimations of zi are independent to the topographical resolutions tested.

812

S. Saavedra et al.

Figure 1. (a) Potential temperature proﬁle (March 19th, 2003, 11 UTC) and (b) estimated hourly ABL depth (zi) during March, 18–19th, 2003, from experimental data (’), simulated data obtained with MM5 using MRF (n), and using Eta ( ) schemes.

Figure 2. Sensitivity of Gaussian dispersion parameters sy and sz vs. zi/L (L: Monin– Obukhov length) for the following cases: (a) sy calculated with MRF and Eta schemes at 1.5 and 5.0 km from the source; (b) sz for the same schemes; (c) sy calculated with MRF for high (9.0 km) and low (0.9 km) topographic resolutions at 1.5 and 5.0 km from the source; and (d) sz for the same resolutions. Dots: hourly values. Lines (1–4): linear regression for each case and conditions.

sy and sz were calculated following Dosio et al. (2003), which explicitly includes the effects of buoyancy and shear in convective ABLs. Figure 2 shows the estimated hourly sy and sz vs. zi/L, at two travel distances (1.5 and 5.0 km), for the convective periods. A plume transport height of 400 agl-m was considered. Concerning the boundary layer schemes (Fig. 2a and b), MRF results show a high dependence on both sz and sy to zi/L, as MRF non-local closure is sensitive to the stability type (free convection vs. forced convection). Eta results show a low dependence on

Impact of Meteorological Factors on Turbulent Dispersion

813

both sz and sy, with lower values than MRF results. As it was expected, the non-local closure scheme, MRF, can represent better the inﬂuence of the stability type in the pollutants dispersion than Eta local closure scheme. An increment in the topographical resolution yields a slight reduction of sz (Fig. 2d), especially in convective ABL; this effect (Fig. 2c) is similar in sy, although in this case the reduction is stronger close to the pollutants source (1.5 km). ACKNOWLEDGMENTS

The supports of Endesa, MeteoGalicia, and CESGA are acknowledged. REFERENCES Dosio, A., Vila`-Guerau de Arellano, J., Holtslag, A.M., 2003. J. Appl. Met. 42, 1116. Vogelezang, D.H.P., Holtslag, A.A.M., 1996. Boundary-Layer Meteorol. 81, 245.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06826-X

814

Poster 26 An air pollution model applying a semi-analytical solution for low wind conditions Tiziano Tirabassi, Davidson Moreira, Daniela Buske and Antoˆnio Goulart Abstract Aeolian dust from arid and semi-arid areas contributes signiﬁcantly to the global atmospheric aerosol mass and is expected to impact the climate system by direct and indirect effects. The project Saharan Mineral dust experiment (SAMUM) aims at investigating the properties of Saharan dust. Within this framework, a new regional model system was developed for simulations of the Saharan dust cycle and radiative effects. The model performance is tested for two Saharan dust outbreaks directed to Europe in August and October 2001. The importance of dispersion modelling in low wind conditions lies in the fact that such conditions occur frequently and are crucial for air pollution episodes. The classical approach based on conventional models, such as Gaussian plume or the K-theory with suitable assumptions, are known to work reasonably well during most meteorological regimes, except for weak wind conditions. A steady-state mathematical model for dispersion of contaminants in low wind conditions that takes into account the along-wind diffusion is proposed. The solution of the advection–diffusion equation for these conditions is obtained applying the Laplace transform, considering the planetary boundary layer (PBL) as a multilayer system. The eddy diffusivities used in the K-diffusion model were derived from the local similarity and Taylor’s diffusion theory. The eddy diffusivities are functions of distance from the source and correctly represent the near-source diffusion in weak winds. The GILTT solution

The crosswind integration of the advection–diffusion equation (in stationary conditions) considering the PBL as a multilayer system leads to

Air Pollution Model for Low Wind Conditions

815

(in any nth layer) @¯cn @ @¯cn @ @¯cn un þ ¼ Kx Kz @x @x @x @z @z

(1)

subject to the boundary conditions of zero ﬂux at the ground and PBL top, and a source with emission rate Q at height Hs. c¯ represents the average crosswind integrated concentration, u is the mean wind speed in x direction and Kx and Kz are the eddy diffusivities. Bearing in mind the dependence of the, Kx and Kz coefﬁcients and wind speed proﬁle u on the height h of a PBL is discretized in N sub-intervals in such a way that inside each interval Kx, Kz and u assume constant average values. The analytical solution proposed by Vilhena et al. (1998) is applied to Eq. (1) and results c¯ n ðx; zÞ ¼

8 P

2 wj

j¼1

QHðzH s Þ 2

P

1Pej

1=2

Pj K n un x

Pj x

4An e

0 @e

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ﬃ P

1Pej

Pj un xK n

z

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ﬃ P

P

1Pej

Pj un xK n

z

þ

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ﬃ 13

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ﬃ

ðzH s Þ

P j un xK n

1Pej

þ Bn e

e

ðzH s Þ

P

1Pej

Pj un xK n

A5

(2) where wj and Pj are the weights and roots of the Gaussian quadrature scheme and H(zHs) is the Heaviside function and Pe ¼ unx/Kx is the well-known Peclet number. In Table 1, the performances of the model were evaluated against the ﬁeld experiments carried out at the Idaho National Engineering Laboratory (INEL) (Sagendorf and Dickson, 1974). Furthermore, the study suggests that the inclusion of the along-wind diffusion can improve the description of the turbulent transport of atmospheric contaminants. The model has been used with and without the diffusion along the wind direction outlining so the performances due to the PBL parameterizations and due the capability to represent low wind scenarios. The statistical indices (see Hanna, 1989) point out that the model simulates the observed concentrations satisfactorily. Table 1. Statistical evaluation of models results for ground-level concentration Experiment K-diffusion (with Kx) K-diffusion (without Kx)

NMSE

COR

FA2

FB

FS

0.21 0.31

0.85 0.83

0.92 0.83

0.02 0.09

0.21 0.26

816

Tiziano Tirabassi et al.

REFERENCES Hanna, S.R., 1989. Conﬁdence limit for air quality models as estimated by bootstrap and jacknife resampling methods. Atmos. Environ. 23, 1385–1395. Sagendorf, J.F., Dickson, C.R., 1974. Diffusion under low wind-speed, inversion conditions. National Oceanic and Atmospheric Administration Technical Memorandum ERL ARL-52. Vilhena, M.T., Rizza, U., Degrazia, G.A., Mangia, C., Moreira, D.M., Tirabassi, T., 1998. An analytical air pollution model: Development and evaluation. Contrib. Atmos. Phys. 71(3), 315–320.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06827-1

817

Poster 27 Modeling of Saharan dust events within SAMUM: On the description of the Saharan dust cycle using LM-MUSCAT Bernd Heinold, Ju¨rgen Helmert, Ina Tegen, Olaf Hellmuth and Ralf Wolke Abstract The multiscale model system LM-MUSCAT consists of two online coupled codes. The operational forecast model LM (Local Model) of the German Weather Service and the chemistry transport model MUSCAT (Multi-Scale Atmospheric Transport Model). The coupler provides MUSCAT with meteorological ﬁelds like temperature, humidity and density from LM. An improved coupling scheme was developed to optimize the parallel efﬁciency of the model system. Due to considerable uncertainties in quantifying distribution and optical properties of dust, the understanding of these effects remains poor (IPCC, 2001). The project Saharan Mineral dust experiment (SAMUM) aims at investigating the properties of Saharan dust. Within this framework, a new regional model system was developed for simulations of the Saharan dust cycle and radiative effects. The regional dust model is based on the ‘Lokal Modell’ (LM; Doms and Scha¨ttler, 1999), which is the operational weather prediction model of the German Weather Service (DWD), the Multiscale Chemistry Aerosol Transport model (MUSCAT; Wolke et al., 2004) and a dust emission scheme (Tegen et al., 2002). The model domain covers major parts of the Sahara desert and Europe at 14 km horizontal grid spacing. The modeled dust is transported as dynamic tracer in ﬁve independent size classes, and is removed from the atmosphere by dry and wet deposition. The modeled dust load is evaluated by comparisons with satellite data, lidar proﬁles from the European Aerosol Research Lidar Network (EARLINET) and sunphotometer measurements. For computing the online dust radiative feedback, in the LM radiation routine the aerosol type ‘desert’, originally ﬁxed in time and space, was replaced by the modeled dust distribution and its optical properties from Sokolik and Toon (1999)

818

Bernd Heinold et al.

Figure 1. Saharan dust on August 2, 2001. Map of the cloud-screened Total Ozone Mapping Spectrometer Aerosol Index (TOMS AI; left), model-derived DOT (550 nm; middle), vertical proﬁle of dust backscatter coefﬁcient at Neuchaˆtel (7:38 UTC; right).

(2% hematite/98% kaolinite; see Helmert et al., this issue). The radiation ﬂuxes are calculated from the continuously updated dust load, and the radiative ﬂux changes are fed back into the LM, modifying atmospheric dynamics. The simulations are carried out with radiatively active desert aerosol and without any dust feedback on the radiation for October 2001. The model is capable of reproducing the location of dust sources and transport patterns fairly well (Fig. 1, left/middle). A good qualitative agreement in magnitude and variability is found comparing the computed DOT with sunphotometer data. Also, the vertical distribution of Saharan dust is well captured by the model (Fig. 1, right). Comparisons of model results including radiatively active dust with those without show a distinct negative effect of dust on the net radiative budget at the top of the atmosphere near the source region and a negative radiative impact on the 10-m wind speeds, in agreement with earlier studies. Due to the reduced wind speeds the dust production is lowered by up to 50% at the location of strongest dust emission in Chad (Bode´le´).

REFERENCES Doms, G., Scha¨ttler, U., 1999. The non-hydrostatic limited-area model LM (Lokal-Modell) of DWD, Tech. Rep. Part I: Scientiﬁc Documentation. DWD, Forschung und Entwicklung. Helmert, J., Heinold, B., Tegen, I., Hellmuth, O., Wolke, R., this issue. Modeling of Saharan dust events within SAMUM: Investigations on regional radiative forcing using LM-MUSCAT.

Modeling of Saharan Dust Events within SAMUM

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IPCC, 2001. Third assessment report of the IPCC, IPCC-International Panel on Climate Change. Cambridge University Press, Cambridge, New York, Melbourne. Sokolik, I.N., Toon, O.B., 1999. Incorporation of mineralogical composition into models of the radiative properties of mineral aerosol from uv to ir wavelengths. JGR 104, 9423–9444. Tegen, I., Harrison, S.P., Kohfeld, K. E., Prentice, I. C., Coe, M., Heimann, M., 2002. Contribution of different aerosol species to the global aerosol extinction optical thickness: Estimation from model results. JGR, 107, doi:10.1029/2001JD000963. Wolke, R., Hellmuth, O., Knoth, O., Schro¨der, W., Heinrich, B., Renner, E., 2004. The chemistry-transport modeling system LM-MUSCAT: Description and CityDelta applications. In Air Pollution Modeling and Its Application XVI, ed. by C. Borrego and S. Incecik, Proceedings of twenty-sixth NATO/CCMS international technical meeting on air pollution modeling and its application.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06828-3

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Poster 28 An improved coupling scheme in the parallel modelling system LM-MUSCAT R. Wolke, M. Lieber, B. Heinold, J. Helmert, W. Schro¨der and E. Renner Abstract The multiscale model system LM-MUSCAT consists of two online coupled codes: the operational forecast model LM (Local Model) of the German Weather Service and the chemistry transport model MUSCAT (Multi-Scale Atmospheric Transport Model). The coupler provides MUSCAT with meteorological ﬁelds like temperature, humidity and density from LM. An improved coupling scheme was developed to optimize the parallel efﬁciency of the model system. 1. Introduction

The state-of-the-art multiscale model system LM-MUSCAT (Wolke et al., 2004a, b) consists of two online coupled codes. The operational forecast model LM (Local Model) of the German Weather Service is a non-hydrostatic and compressible meteorological model (Doms and Scha¨ttler, 1999). Driven by the meteorological model, the chemistry transport model MUSCAT (Multi-Scale Atmospheric Transport Model) treats the atmospheric transport as well as chemical transformations for several gas-phase species and particle populations. The implicit–explicit time integration scheme of MUSCAT operates independently from the meteorological model, thus allowing for autonomous time steps and different horizontal grid resolutions in selected regions of the model domain. The coupler provides MUSCAT with meteorological ﬁelds like temperature, humidity and density from LM. Moreover, a feedback is implemented whereby the aerosol particle distribution calculated by MUSCAT inﬂuences the aerosol optical thickness and, hence, the radiation budget in LM. The analysis of the used coupling scheme shows that the adaptive step size control implemented in MUSCAT leads to a variable workload and, consequently, to load imbalances between the

Parallel Modelling System LM-MUSCAT

821

models. Therefore, an improved coupling scheme was developed to optimize the parallel efﬁciency (Lieber and Wolke, 2006). The proposed approach is applicable to other model systems, which have load imbalances between their models as well. 2. Coupling scheme

In the old ‘‘concurrent’’ coupling scheme, both model codes run mostly independent each on their own disjoint set of processors. The number of processors for meteorology and chemistry transport has to be deﬁned at model startup. The codes are synchronized only for data exchange between LM and MUSCAT. This takes place each explicit time step only. Since this time step is chosen as a fraction of the CFL number, its length varies over the prediction time. LM has to calculate one couple time step in advance. This causes the feedback to reach LM this one step ‘‘too late’’. In contrast to the concurrent coupling, in the so-called ‘‘sequential’’ approach, each model runs on all available processors. Each processor is assigned to carry out one partition of the coupled codes alternately. Since the workload of each model code is distributed equally over all processors, imbalances between the model codes are compensated. In the sequential approach, all processors ﬁrst calculate the meteorology over one coupling interval. Then the meteorological coupling data are exchanged and all processors continue with the calculation of chemistry transport over the same interval. Required arrays for feedback are sent from MUSCAT to LM, before the next coupling step is performed. 3. Performance analysis

The performance of LM-MUSCAT is investigated on an IBM p570 computer with 32 processors. For the comparison, two scenarios with different characteristics have been chosen exemplarily. Figure 1 shows the parallel efﬁciency of LM-MUSCAT for this scenarios. The ‘‘Europe’’ scenario has been utilized to supply boundary values for a scenario in a nested region. The model region comprises central Europe. Since a multitude of chemical reactions are considered and a reﬁned grid is used, the main workload is located in MUSCAT. The load ﬂuctuations in MUSCAT are very strong. The ‘‘Samum’’ scenario is used for investigations of the inﬂuence of Saharan dust particles on the radiation budget (Helmert et al., 2006). The emission, transport and deposition of dust particles without aerosol dynamical processes are considered. A uniform

R. Wolke et al. 1

sequential concurrent

0.9

efficiency E(n)

efficiency E(n)

822

0.8 0.7 0.6 0.5

1 4

8 16 24 number of processors

32

1 0.9 0.8 sequential concurrent

0.7 0.6 0.5

1

4

8 16 number of processors

32

Figure 1. Parallel efﬁciency of LM-MUSCAT with concurrent and sequential scheduling for the ‘‘Europe’’ (left) and ‘‘Samum’’ (right) scenarios.

grid of 150 150 horizontal cells is used in LM and MUSCAT. In contrast to the ‘‘Europe’’ scenario, the main computational load is located in the meteorological model and only small workload variations in MUSCAT can be observed. 4. Conclusions

The sequential coupling scheme is an appropriate method to increase the performance of model systems with high workload variation in one or more of the single models. Through the implementation of the sequential coupling scheme in the air quality model system LM-MUSCAT, promising performance improvements are achieved. An essential advantage of the sequential scheme is that an a priori partitioning of the processors is not necessary. REFERENCES Doms, G., Scha¨ttler, U., 1999. The Nonhydrostatic Limited-Area Model LM (LokalModell) of DWD: Part I: Scientiﬁc Documentation (Version LM-F90 1.35). Deutscher Wetterdienst, Offenbach. Helmert, J., Heinold, B., Tegen, I., Hellmuth, O., Wolke, R. 2006. Modeling of Saharan dust events within SAMUM: Implications for regional radiation balance and mesoscale circulation. 28th NATO/CCMS International Technical Meeting on Air Pollution Modelling and its Application. Leipzig, Germany, 15–19 May. Lieber, M., Wolke, R., 2006. Optimizing the coupling in parallel air quality model systems. Environ. Modell. Software, submitted for publication. Wolke, R., Hellmuth, O., Knoth, O., Schro¨der, W., Heinrich, B., Renner, E., 2004a. The chemistry-transport modeling system LM-MUSCAT: Description and CityDelta applications. In: Borrego, C., Incecik, S. (Eds.), Air Pollution Modeling and Its Application XVI. Kluwer Academic/Plenum Publishers, pp. 427–439. Wolke, R., Knoth, O., Hellmuth, O., Schro¨der, W., Renner, E., 2004b. The parallel model system LM-MUSCAT for chemistry-transport simulations: Coupling scheme, parallelization and applications. In: Joubert, G.R., Nagel, W.E., Peters, F.J., Walter, W.V. (Eds.), Parallel Computing. Elsevier, pp. 363–370.

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Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06829-5

Poster 29 Identiﬁcation of aerosol sources in the Baikal region by receptor modeling methods Ekaterina V. Kuchmenko, Elena V. Molozhnikova, Alexandre V. Keiko and Maxim S. Zarodnyuk Abstract A regression-like approach is used for characterizing source-receptor relationships. This method is applied for the estimation of emissions in the Baikal region. To identify the emission sources, the results of chemical analysis of snow cover samples are used. The problem of identiﬁcation is very topical because its solution makes it possible to determine the contribution of different sources—a company, a city, a country—to the air pollution at a given point (Hopke, 1991). It is assumed that in some territory there are n emission sources; in this case, (Xj, Yj) are the coordinates of the jth source, for example, a boiler house (j ¼ 1, y, n). Each boiler house emits m main components Qij (kg h1), (i ¼ 1, y, m). The amount (mass, kg m h2) of a given component at a given point Mi(Xk,Yk) is equal to the sum of emission multiplied by the weighting factors for all the sources of this component. Taking into account that a component may be carried outside the observed territory, we can write M i ðX k ; Y k Þ ¼

n X j¼1

xjk Qij

n X

Pi Qij þ P0i Q0i

(1)

j¼1

where xjk is a coefﬁcient (1m2) depending, in particular, on the mutual location of the jth source and kth receptor, Pj is the fraction of the jth source emission leaving the territory due to the long-distance transfer, P0i Q0i is the inﬂow of the ith component to a receptor point due to the long transfer. Since both in calculating emissions and in making chemical analyses to determine the quantity of precipitated components, the errors of methods, instruments and so on affect the values of the quantities obtained,

Ekaterina V. Kuchmenko et al.

824

the determination of an error is being made as follows: r X k¼1

d2ik

¼

" r X k¼1

M i ðX k ; Y k Þ

n X j¼1

Qij xik þ

n X

#!2 Qij Pj

j¼1

and must be minimized. Thus, we have the problem for absolute extremum. Taking into account the peculiarities related to the convexity of the constructed distribution, we can assume that the solution of the problem can be found using the rule of the Lagrangian multipliers. First, the components of the vector x are chosen as unknown values. It is assumed that if we set the function of substance distribution from each source using one or another model of its dispersal, the problem reduces to the estimation of model validity. Then we select least square differences

Figure 1. The location of emission sources and sampling points in the city of Slyudyanka (solines show altitudes in kilometers).

Identification of Aerosol Sources in the Baikal Region

825

Figure 2. Weighting factors, characterizing the effect of emissions from separate boiler houses (Center, Building project, Rudo, Pass, Hospital, Shop, Proletarskaya, Bath-house) on the point-receptors (Mountain pass, Left bank, Rudo, Gateway, Talaya, GIBDD, Park, Church).

between the calculated and the experimental data as the sought variable: m X n X ðxjk pjk Þ2

(2)

i¼1 j¼1

where pjk denotes the weighting factors determining the fraction of the source emission Qij, which falls out at a point Mik. This formulation of the problem is, ﬁrst of all, characterized by the simplicity of solution. After calculating the partial derivatives L(x,l) of the Lagrangian function by components of the Kuhn–Tucker vector (x, l), we obtain a set of linear equations with a square matrix of coefﬁcients. To identify the emission sources in Slyudyanka (Fig. 1), the results of chemical analysis of snow cover samples are used. The nonnegative character of derived coefﬁcients is a criterion of reliability and consistency of the solution. For the correct statement of problem, it is necessary to introduce an imaginary negative emission source simulating the long-distance transfer carrying the pollutants outside the domain. Some results of the analysis are shown in Fig. 2. ACKNOWLEDGMENT

The project is supported by RFBR. Project No. 05-05-97233-r_baikal_a. REFERENCES Hopke, P.K., 1991. An introduction to receptor modeling. In: Chemometrics and Intelligent Laboratory Systems, vol. 10. Elsevier Science Publishers BA, Amsterdam.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06830-1

826

Poster 30 Small-scale modeling of PM deposition and re-suspension in inner part of urban area Jiri Pospisil and Miroslav Jicha Abstract A steady solution was used for prediction of PM dispersion processes in an urban area during a time period with stable meteorological and trafﬁc conditions. Deposition processes of PM and accompanying processes of re-suspension of once deposited particles are considered. 1. Introduction

The numerical modelling represents the only tool capable to take into account detail geometry of urban areas, detail description of relevant PM sources and the interaction between moving cars and ambient air (Jicha et al., 2000). PM dispersion processes are mostly solved as the steady situations. The steady solution is convenient for prediction of PM dispersion processes during a time period with stable meteorological and trafﬁc conditions. A deposition process of PM and an accompanying process of re-suspension of once deposited particles is connected with changes of the actual air velocity ﬁeld above the ground surface. The transient solution represents another possible way better describing a re-suspension process. This solution requires knowledge of transient input parameters and a very long computational time. Namely the impractically long computational time requirement leads to utilizing of simpliﬁed solutions taking into account the transient character of input parameters. This paper introduces the simpliﬁed evaluation of PM10 deposition and re-suspension rates in a street canyon during 24 h period.

2. Description of calculation and obtained results

The studied street canyon is located at the centre of the city of Brno (population 350,000). Five-story buildings (20 m high) form both sides of

Small-scale Modeling of PM Deposition and Re-suspension

827

the street canyon. Width of the street is 22 m. Two-way trafﬁc in total four trafﬁc lanes is present in the street. The lowest and the highest day trafﬁc rates are 104 and 1534 cars h 1, respectively. Results of in situ measurements were used as input parameters for two steady numerical calculations corresponding to situations with minimal and peak daily trafﬁc rates. Measurements provided information about trafﬁc parameters, meteorological parameters, PM10 local background concentrations and actual PM concentrations in the studied street canyon for both above-mentioned situations. The numerical predictions add information about air velocity ﬁeld in the studied street canyon and dispersion of PM released from the line source simulating road trafﬁc. The relationship between the trafﬁc rate and time was the only dependence known for the entire 24 h period. The linear dependence is assumed between all other parameters and the trafﬁc rate. In calculations, we presume coarse spherical particles with aerodynamic diameter 10 mm. PM deposition is considered on surfaces with the reference wind velocity smaller than 0.56 m s 1. The deposition ﬂux is obtained from multiplication of PM concentration just above the ground surface and a particles settling velocity. The re-suspension ﬂux takes into account an actual particles silt load and the ground area affected by air velocity bigger than the reference wind velocity. The total PM concentration is calculated as a sum of the background concentration, the contribution from line sources, the deposition ﬂux term and the re-suspension ﬂux term. Figure 1 shows predicted street canyon PM10 concentrations during the working-day period and measured values.

Figure 1. Predicted PM10 concentrations during the working-day period.

828

J. Pospisil and M. Jicha

3. Conclusion

The presented calculation enables to predict transient behavior of PM concentration in a street canyon based on the knowledge of the trafﬁc rate behavior and two steady calculations of PM dispersion. The prediction provides more realistic behavior in case that both deposition and re-suspension processes are included. Comparison of the prediction with results of the measurements shows an insufﬁcient inﬂuence of the considered deposition. For a better agreement, the actual deposition velocity must be signiﬁcantly higher then the particle sedimentation velocity. ACKNOWLEDGMENTS

The Czech Ministry of Transport under the grant 1F54H/098/520 ﬁnancially supported this work. REFERENCE Jicha, M., Katolicky, J., Pospisil, J., 2000. Dispersion of pollutants in street canyon under trafﬁc induced ﬂow and turbulence. J. Environ. Monit. Assess. 65, 343–351.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06831-3

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Poster 31 Enhanced characterization of ambient air quality to study the link between climate variability, air quality, and health$ C. Hogrefe, K. Knowlton, R. Goldberg, J. Rosenthal, C. Rosenzweig, B. Lynn and P.L. Kinney Abstract The work presented in this paper is part of a larger study whose objective it is to analyze the link between climate variability, air quality, and health over New York State and surrounding areas. The speciﬁc aims of the project are to: (1) develop ﬁne-scale gridded maps of hourly surface weather, ozone, and particulate matter (PM) over New York State over the 15-year period 1988–2002 using observations integrated with simulations from a photochemical modeling system, (2) analyze the relationship between climate variability and episodes of extreme PM, ozone, and heat, and (3) measure the independent and joint effects of air quality and weather on acute mortality and hospitalization risks from 1988 to 2002. Models, database, and methods of analysis

In this work we focus on the development of concentration maps using a database of observations augmented by predictions from the MM5 regional-scale meteorological model (Grell et al., 1994) and the Community Multiscale Air Quality (CMAQ) air quality model (Byun and Ching, 1999). The project utilizes hourly meteorological surface observations for the 1988–2002 time period from NCAR’s TDL dataset, hourly surface O3 and PM10 concentrations for the 1988–2002 time period from the U.S. EPA AQS, and total and speciated PM2.5 concentrations for the 1999–2002 time period from the U.S. EPA AQS. In addition, the MM5 $

This work has not been subjected to its required peer and policy review. Therefore, the statements, ﬁndings, conclusions, and recommendations are those of the authors and do not necessarily reﬂect the views of the sponsoring agency and no ofﬁcial endorsement should be inferred.

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meteorological model and the CMAQ air quality model are being utilized to generate hourly gridded climate and air quality ﬁelds for the 1988–2002 time period. The models are applied with a horizontal grid spacing of 36 km over the eastern United States and a ﬁner 12 km grid spacing over New York and surrounding states (Fig. 1). Air quality predictions from CMAQ include O3 and PM2.5. Archived county-level emission inventories from different years are used as input for CMAQ and reﬂect changing anthropogenic emissions over the time period of the study. As a ﬁrst step, spatially continuous meteorological and air quality ﬁelds were derived from observed values using two geostatistical interpolation techniques, namely, inverse distance weighting and kriging. Using crossvalidation procedures, results show that both methods have shortcomings such as the oversmoothing during the kriging procedure and the lack of an interpolation error estimate with the inverse distance weighting approach. As a next step, methods to integrate observations with predictions from photochemical modeling systems were explored. For this purpose, the model bias is determined at each monitor station, a variogram is constructed to describe the functional form of the spatial correlation structure of the model bias, the model bias is then interpolated spatially using weights determined from the variogram ﬁtting procedure,

Figure 1. 36 km (gray) and 12 km (ck) modeling domains for MM5 (solid) and CMAQ (dashed).

Link between Climate Variability, Air Quality, and Health

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and ﬁnally the map of the interpolated bias is added to the original gridded concentration map as an estimate of ambient ozone concentrations integrating both observations and model predictions. In the next phase of this research, daily gridded estimates of weather, ozone, and PM will be matched with daily mortality and hospital admissions data at the county level estimate to pollutant-speciﬁc relative risk coefﬁcients. ACKNOWLEDGMENT

This work has been supported by the National Oceanic and Atmospheric Administration under award NAO40AR4310185185. REFERENCES Byun, D.W., Ching, J.K.S. (Eds.), 1999. Science algorithms of the EPA Models-3 Community Multiscale Air Quality Model (CMAQ) modeling system. EPA/600/R-99/030, U.S. Environmental Protection Agency, Ofﬁce of Research and Development, Washington, DC 20460. Grell, G.A., Dudhia, J., Stauffer, D., 1994. A description of the ﬁfth-generation Penn State/ NCAR Mesoscale Model (MM5). NCAR Technical Note, TN-398+STR, National Center for Atmospheric Research, Boulder, CO, p. 138.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06832-5

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Poster 32 Preparatory work for optimised European air quality and health effect monitoring (EURAQHEM) T. Kuhlbusch, A. John, U. Quass, A. Hugo, A. Peters, S. von Klot, J. Cyrys and E. Wichmann Abstract A concept will be presented and discussed on how an improved European-wide monitoring network, air pollution data and health impact assessment could be achieved. The current practise of ambient air quality monitoring has been analysed and limitations have been identiﬁed. The European Commission project ‘‘Analysis and Design of Local Air Quality Measurements’’ has the objective to provide recommendations for new or modiﬁed legislation so that air pollution assessment will be more health relevant and that health effects related to air pollution can be assessed in an adequate way. The project includes a) analysis of the health relevance of the current air pollution assessments based on monitoring and modelling as conducted in air pollution networks, related to the European Air Quality Directive, b) analysis of the current assessments of air-pollution-related health effects, c) proposal of a design of a network and methodology for the assessment of health effects by air pollution, and d) proposal of a design for the systematic assessment of health impacts by air pollution. Within this project, a scheme (Fig. 1) was developed on how an improved European-wide monitoring network, air pollution data and health impact assessment could be achieved. Information has been gathered and analysed in order to describe the current practise of ambient air quality monitoring and health impact assessment and identify limitations and need for improvements with regard to a more health-relevant ambient air

Optimised European Air Quality and Health Effect Monitoring

833

Concentration Response Functions

Health Status Monitoring

Distribution of Population at Risk and Average Disease Rate Spatial Variation

Air Pollution Monitoring

Health Impact Assessment

Population Average Exposure Spatial Variation

Toxicology

Quantitative Estimation of Health Impacts (excess events….)

Figure 1. Information needed for health impact assessment.

quality monitoring and assessment. This concept will be presented and discussed. For AQ-monitoring requirements, the EU directives were analysed as well as the annual reports by the MS together with supplemental data taken from the database Airbase. It can be concluded that the current structure of air quality assessment has to be modiﬁed. This does not mean that all measurement sites and/or equipment have to be changed but that the use of data for AQ assessment has to be changed. One major issue is the current lack of knowledge on the representativeness of measurement sites with regard to population exposure. Direct measurements (monitoring) of health effects due to air pollution are not possible; they can only be calculated (assessed) applying models (based on exposure response functions) and certain assumptions. Air pollution health effects as quantiﬁed by epidemiological studies always are statistical associations, which describe the impact in general, but not on individual events. Hence, to become more health relevant, AQ-monitoring data must be acquired in a way that data can be used for calculation of population exposure. This means that AQ data must be representative for the main situations in which people are exposed to air pollutants. Ideally, it would be possible to construct the exposure distribution curve from the AQ-monitoring data. In summary, a health-related network for monitoring ambient air pollution ideally must 1. give deﬁnitions of population subgroups and—as far as feasible—an assessment of their particular vulnerability towards air pollution compounds,

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2. deﬁne the relevant exposure situations the population subgroups experience, 3. include an assessment of the time fractions which are spent by the different subgroups in these exposure situations, 4. delineate the territory (country, region, area) the assessments are valid for, 5. obtain concentration data measured in environments reﬂecting typical exposure situations, with at least one site per particular environment, 6. provide a calculation scheme to derive an estimate on the mean population exposure from the measured concentrations, which takes into account the factors mentioned in 1–4 (e.g., site speciﬁc weighing factors). Therefore, there is the need for a methodology to group or part these areas in an objective way which can be used to compare the AQ between similar area types in different regions/countries and to deliver indicators which can be used for the decision where monitoring stations shall be installed and which components should be measured. The methodology proposed here comprises four key steps: 1. air pollution modelling for a pre-selected region using an air chemistry transport model with sufﬁcient resolution, 2. identiﬁcation and grouping of areas with similar characteristics regarding the pollution situation by means of (multivariate) statistical analysis, 3. for each grouped area type, determination of representative locations or to place measurement stations, 4. assignment of relevant population numbers to each area type for use as weighing factors in the calculation of the population exposure for the region of interest. The realisation of the above mentioned ideas will be discussed especially in view of hot spot locations.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06833-7

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Poster 33 PM levels and their health implications in Lisbon Hugo Tente, Francisco Ferreira, Luı´sa Nogueira, Carlos Silva Santos and Sandra Moreira Abstract A multidisciplinary methodology was performed in order to characterize suspended particulate matter PM10 levels in Lisbon, and its impact on human health. Four PM10 measuring campaigns were developed: background levels distribution, outdoor versus indoor levels, maximum levels at trafﬁc hotspots and personal exposure assessment. Receptor modelling methods were applied to aerosol samples from Alfragide (near Lisbon), in 2003. Another task developed within the modelling ﬁeld was a health impact assessment for Lisbon using PSAS-9 model, model used by the French Public Health Institute to assess the impact of atmospheric pollution, in general, and PM levels in particular.

1. Introduction

The main objectives of this project were to characterize PM10 distribution within the city and to compare several environments within respect to PM10 levels (indoors, outdoors and personal exposure). Other issues included an evaluation of PM10 trends within the Lisbon’s monitoring network within the last years as well as developing a health impact assessment for typical concentrations using PSAS-9 model and the APHEIS project methodology. Finally, receptor-modelling work was also performed in order to identify and quantify the major sources contributing to Lisbon’s aerosol.

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Figure 1. PM10 daily distribution during 2 days of the background sampling campaign.

2. Main ﬁndings, results and conclusions

Some of the results already available allow presenting a background PM10 distribution across the city for several meteorological situations since daily levels were measured during one week (Fig. 1). Spatial distribution varies accordingly with daily meteo patterns, particularly with wind speeds and directions. Nevertheless, the most important issue is that the central axis of the city always has a tendency for having higher values, which is in accordance with higher trafﬁc levels, as data from the regional emission inventory conﬁrm. Results from a source apportionment model (Thurston and Spengler, 1985), also points out road trafﬁc contribution as being directly responsible for around 50% of the measured PM10 mass, taking into account only primary PM emissions. Another measuring campaign compared several trafﬁc hotspots in order to verify if the maximum levels covered by the monitoring network are representative. This was conﬁrmed for ‘‘Av. Liberdade’’ station, where European limit values are still exceeded frequently. These exceedances are aggravated by Saharan transported dust (Rodriguez et al., 2000). Preliminary health impact assessment using the APHEIS project methodology as reference, and PSAS-9 model as a tool, allowed the estimation of attributable cases to PM pollution for different comparative scenarios, like the work developed for several European cities (Medina et al., 2001).

REFERENCES Medina, S., Plase`ncia, A., Artazcoz, L., Que´nel, P., Katsouyanni, K., Mu¨cke, H.G., De Saeger, E., Krzyzanowsky, M., Schwartz, J., March 2001. Apheis Monitoring the

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Effects of Air Pollution on Public Health in Europe. Scient. report, 1999–2000. Institut de veille sanitaire, p. 136. Rodriguez, S., Que´rol, X., Alastuey, A., Kallos, G., Kakaliagou, O., 2000. Saharan dust contribution to PM10 and TSP levels in Southern and Eastern Spain. Atmos. Environ. 35, 2433–2447. Thurston, G.D., Spengler, J.D., 1985. A quantitative assessment of source contributions to inhalable PM pollution in metropolitan Boston. Atmos. Environ. 19, 9–25.

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06834-9

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Poster 34 Spatial and temporal variations in particulate Polycyclic Aromatic Hydrocarbon (PAH) levels over Menen (Belgium) and their relation with air mass trajectories Khaiwal Ravindra, Eric Wauters and Rene´ Van Grieken Abstract The 16 US EPA priority-listed polycyclic aromatic hydrocarbons (PAHs) were determined, in the border region between Belgium and France, by a fast analytical approach and minimum solvent consumption. Further, the levels of PAHs were evaluated with relation to the various emission sources, and for the ﬁrst time, backward air mass trajectories were used to study the variation in PAHs levels due to local and regional/global activities.

1. Introduction

Polycyclic aromatic hydrocarbons (PAHs) are of major health concern, mainly due to their well-known carcinogenic and mutagenic properties (Ravindra et al., 2001). These adverse properties, together with the still growing presence of PAHs in the environment, demand to assess their concentration, trends, and source proﬁle in the atmosphere to provide an aid to manage regional as well as global air pollution control strategies.

2. Fast analytical approach

Pressurized liquid extraction (PLE) was optimized for the fast recovery of PAHs from quartz ﬁber ﬁlters in less than 30 min with minimum solvent consumption (20 ml) prior to their analysis with high-performance liquid chromatography. Hence, PLE offers a very fast procedure with a minimum consumption of toxic solvents and environmental burden, two crucial parameters in the choice of the extraction technique (Godoi et al., 2004; Ravindra et al., 2006a).

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3. Spatial and temporal variations

Particulate PAH samples were collected from ambient air during 2003 from Menen (Belgium), near the French border. Menen is inﬂuenced by industrialized regions on both sides of the border. PAHs have low vapor pressure and hence some PAH are present at ambient temperature in air, both as gases and associated with particles. The only PAHs with lower molecular weight (MW) noticed in signiﬁcant concentration were Naphthalene (0.15 ng m3) and Phenanthrene (0.19 ng m3), whereas the high MW species like Fluoranthene (0.83 ng m3), Chrysene (0.87 ng m3), Benzo[a]ﬂuoranthene (0.84 ng m3), Benzo[a]pyrene (0.58 ng m3), Benzo[ghi]perylene (0.73 ng m3), and Indeno[1,2,3-cd]pyrene (0.77 ng m3) dominate the total PAH fraction. The annual average concentration of total PAHs was 6.49 ng m3 at Menen, and the monthly average concentrations were noticed to be 5–7 times higher in January, February, and December; in comparison to May, June, and August. 4. Potential toxic fraction (PTF)

The carcinogenicity classiﬁcations veriﬁed by the EPA Carcinogenicity Risk Assessment Endeavor Work Group (Ravindra et al., 2006a, b) show that Benzo[a]anthracene, Chrysene, Benzo[b]ﬂuoranthene, Benzo[k]ﬂuoranthene, Benzo[a]pyrene, Indeno[1,2,3-cd]pyrene, and Dibenz[a,h]anthracene are considered to be probable human carcinogens (PHC). The study of PTF of PAHs shows that around 63% of total particulate PAHs have PHC at Menen. Further, most of the hazardous PAHs are found to be associated with the particulate PAH levels, which raise the risk of human health hazards. During various months, the PTF of PAHs varies from 44% (August) to 70% (November, December). Further estimation shows that the PTF of PAHs increases with the increase in total PAH concentration and has maximum values in winter. 5. Source apportionment studies

(a) Correlation: A strong correlation (po0.01) between prevailing high MW PAHs was found, and hence one of these PAHs can be used as an indication of other PAHs; further, this likely indicates toward a similar source of emission. There was no strong correlation found between low-MW PAH and high-MW PAH species. (b) Diagnostic ratio: The concentrations of speciﬁc PAH compounds, or a group of PAHs, have been used to indicate toward the corresponding emission sources

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(Ravindra et al., 2006b, c). Based on this approach, it can be concluded that the emission from the diesel engines and combustion sources dominates the particulate PAH emission in Menen, whereas the emission from gasoline engines and stationary sources (industrial/coal/wood) also inﬂuences the concentration. The ratio of nonalkylated PAHs to total PAH concentration varies signiﬁcantly with the variation in monthly average concentration of PAHs, and hence the increase in PAH levels during the winter season is likely to be associated with the combustion activities. (c) Factor analysis: Principal component analysis was used as an extraction method to reduce the set of PAH variables. Each of these factors can be identiﬁed as an emission source or a chemical interaction. The results show that three factors explain the main part of the data variance at Menen. Factor 1 has a very high factor loading of Benzo[a]anthracene, Benzo[a]pyrene, Benzo[b]ﬂuoranthene, Benzo[ghi]perylene, and Indeno[123-cd]pyrene, which are identiﬁed as markers of gasoline emission. A relatively higher factor loading for Fluoranthene, Phenanthrene, Anthracene, and Pyrene is indicative for diesel emission. Hence, it can be suggested that vehicular emissions form a major fraction of PAHs at Menen. For factor 2, Acenaphthylene, Phenanthrene, and Anthracene have a loading greater than 0.5. These PAHs have been identiﬁed in coal combustion, coke production, and wood combustion. Factor 3 comprised the low-MW PAHs (Naphthalene and Acenaphthylene), which are prevalent in the vapor phase. Components related to the third factor are not attributed to ‘identiﬁed sources.’

6. Impact of regional–global activities

To the best in our knowledge, for the ﬁrst time, the inﬂuence of regional and global activities and emission source on PAHs levels was studied using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Model. The four-day backward air mass trajectories were computed for 128 days at an arrival point 501470 N, 3160 W in Menen and assigned to eight categories as Belgium, Atlantic, Arctic, Western Europe, Europe, Scandinavia, Maritime, Eastern Europe, and unclassiﬁed. This shows that both local and regional activities inﬂuence the levels of PAHs and hence demand a uniform policy by all European nations to curb PAH pollution. However, the health risk studies (Ravindra et al., 2001) conducted with relation to PAH exposure urges to include these pollutants as top priority for air quality management, but till, date only few countries have proposed a non-mandatory limit for PAHs in air.

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REFERENCES Godoi, A.F.L., Ravindra, K., Godoi, R.H.M., Andrade, S.J., Santiago-Silva, M., Van Vaeck, L., Van Grieken, R., 2004. J. Chromatogr. A. 1027(1-2), 49–53. Ravindra, K., Mittal, A.K., Van Grieken, R., 2001. Rev. Environ. Health. 16, 169–189. Ravindra, K., Godoi, A.F.L., Bencs, L., Van Grieken, R., 2006. J. Chromatogr. A. 1114, 278–281. Ravindra, K., Bencs, L., Wauters, E., de Hoog, J., Deutsch, F., Roekens, E., Bleux, N., Bergmans, P., Van Grieken, R., 2006. Atmos. Environ. 40, 771–785. Ravindra, K., Wauters, E., Taygi, S.K., Mor, S., Van Grieken, R., 2006. Environ. Monit. Assess. 115, 405–417.

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Author index

A Aaltonen, H. 287 Abbott, J. 397 Abdul-Khalek, I. 625 Abonnel, C. 23 Abraham, H.-J. 412 Ackermann, I.J. 76, 159, 407–408, 535, 569–571, 605 Adams, M. 211 Adams, P.J. 640 Addis, R. 275, 335 Adrian, G. 569–570 Agnesod, G. 692 Aida, M. 751 Akimoto, H. 211 Akinbami, L.J. 724 Alapaty, K. 418, 451 Alarco´n, M. 544 Alastuey, A. 544, 549, 552, 836 Alessandrini, S. 352–353, 362, 365, 690, 761 Alfaro, S.C. 561 Allen, D.T. 169–170, 172 Almbauer, R.A. 354, 692–693 Alpert, P. 44–48, 50 Alvim-Ferraz, M.C.M. 790 Amann, M. 96, 134–135, 159, 215–216, 467–469, 471 Ammannato, L. 45 Amstrup, B. 65, 385 Anadranistakis, E. 45 Anderson, B.D.O. 305 Anderson, T.L. 89, 556 Andersson-Sko¨ld, Y. 159 Andonopoulos, S. 335 Andre´, J.C. 611 Andrade, S.J. 838 Andreae, M.O. 662–664 Andreae, T.W. 663–664 Andreani-Aksoyoglu, S. 75–76 Andrieu, H. 65 Andronopoulos, S. 275 Anfossi, D. 33, 352–354, 689–691 Anquetin, S. 56 Aoki, K. 324 Apsley, D.D. 33

Arao, K. 324 Arnott, A. 742 Arranz, J.M. 418 Artazcoz, L. 836 Artin˜ano, B. 384 Arumachalam, S. 339 Arunachalam, S. 451 Arvanitis, A. 601–603 Arychuk, M. 665 Astitha, M. 548 Astrup, P. 66, 275, 335 Atkinson, D. 798 Atlas, E. 326 Aumont, B. 627 Avdyushin, S.I. 284, 287 Avery, M.A. 326, 438 Avissar, R. 648 Aylor, D.E. 483 Ayuso, J.J. 66

B B"as´ , M. 764 Baboukas, E. 665 Bacher, M. 692 Baechlin, W. 693 Baik, J.-J. 33, 41 Bainael, S.M. 525–526 Baker, D. 339, 343 Baker, J. 774 Baklanov, A. 64–65, 275, 383, 385, 393 Baldasano, J.M. 44–45, 48, 50–51, 425–426, 428, 431 Baldi, S. 731 Baldwin, M. 448, 493 Balkanski, Y. 159 Ballagas, R. 220 Balsley, B. 200 Baltensperger, U. 75–76, 78–79, 287, 407, 544 Bandy, A.R. 326, 662–663, 665 Banta, R. 200 Barazzetta, S. 97 Barbu, A.L. 301 Bardouki, H. 665 Barlow, J.F. 56

844 Barnaba, F. 44–45, 47–48, 50, 53 Barnes, I. 159 Barr, S. 251 Barrie, L.A. 610–611, 664–665 Barros, N. 431 Bartels, D. 438–439 Barth, M.C. 437–440, 594, 597, 599 Barthe, C. 438, 441 Bartiz, J. 66 Bartniki, J. 275 Bartonova, A. 680 Bartzis, J.C. 275, 335 Batchelor, G.K. 246, 248 Batchvarova, E. 12, 15–16 Bates, T.S. 323 Bauer, D. 664 Baumann, K. 438–439, 693 Baumbach, G. 625 Ba¨umer, D. 568 Baumhefner, D.P. 334 Beck, M.B. 333 Bedogni, M. 362, 365, 467, 469, 761 Beekmann, M. 22–23, 76, 210–211, 406–408, 504, 536–537, 561, 768, 795 Beier, R. 754 Beitz, K. 332 Bejan, I. 159 Bel, L. 22 Belcher, S.E. 56 Bellasio, R. 275 Belviso, S. 665 Belzer, W. 665 Ben Ami, Y. 549 Bencs, L. 838–840 Benjey, B. 418 Benjey, W.G. 633, 637 Benkovitz, C.M. 597 Benkowitz, C.M. 599 Bennett, B. 284, 287 Benter, T. 159 Bentham, T. 56 Berbery, E. 636 Bergamaschi, P. 216 Bergametti, G. 561, 563 Berge, E. 383–384 Bergin, M.S. 339 Bergmans, P. 839–840 Berkelman, R.L. 718 Berkowicz, R. 467, 469 Berkowitz, C.M. 375

Author Index Bernard, J. 109 Berntsen, T.K. 594 Berthier, E. 65 Bertok, I. 159, 215, 468–469, 471 Bessagnet, B. 76, 293, 298, 363, 407, 467, 469, 503–506, 508, 510, 536–537, 539, 541, 544, 561, 761, 768, 795 Betts, A.K. 650 Bhave, P. 169–170 Bianconi, R. 275, 335 Bider, M. 691 Bingemer, H.G. 663–664 Binkowski, F.S. 76, 159, 407–408, 451, 493–494, 535, 569–571, 601, 603–605 Binkowsky, F. 418 Birmili, W. 611 Bischoff, A. 611 Biswas, J. 649–650 Bitzer, H.W. 768, 795 Bjergene, N. 65, 385 Black, T. 334, 448, 493 Blake, D.R. 326, 665 Blanchet, J.P. 438, 661, 664 Bleux, N. 839–840 Blomquist, B.W. 662–663, 665 Blond, N. 21–22 Blumen, W. 200–201 Blumthaler, M. 47 Bøhler, T. 679–680 Bohren, C. 569, 572 Bompay, F. 275 Bonafe´, G. 383, 385, 758, 768, 795 Bond, T.C. 320, 323, 326–327 Bondestam, K. 712 Bonnardot, F. 275 Boothe, V. 717 Boris, J. 417 Bornstein, R. 129, 258 Borrego, C. 399, 431, 534, 537 Bossi, E. 761 Boucher, O. 159 Bouillon, R.-C. 662 Boukas, L. 45 Bounoua, L. 611 Bouville, A. 284, 287 Bouzom, M. 275 Bowers, J.F. 335 Bowman, F. 407, 774–775 Boyd, P.W. 662, 665

Author Index Boyle, R. 700 Brandt, A. 158 Brandt, J. 285, 467, 469 Brankovic, C. 276 Brauers, T. 159 Braverman, T. 85, 798 Bravo, M. 200, 204 Brehme, K.A. 87, 457–458, 462–464 Bremer, P. 388 Brimblecomb, P. 665 Brink, H. 544 Britter, R. 3, 6, 8, 55–57, 59 Brocheton, F. 212, 467, 469 Broday, D.M. 679–680 Brode, R.W. 344 Brook, J. 720 Brost, R.A. 77 Brown, M.J. 31–32, 59 Bru¨ggemann, E. 503 Brune, W.H. 438 Brunekreef, B. 302 Brusasca, G. 354, 691–692 Buccolieri, R. 3, 6, 8 Buchmann, B. 76 Buckland, A.T. 246, 250–251 Buckley, R. 275 Buenestado, P. 200 Buhr, J.S. 451 Builtjes, P.J.H. 302, 467, 469 Buis, J.P. 528 Buizza, R. 276 Bukowiecki, N. 407 Bullock, O.R. 85, 87, 457–458, 462–464, 798 Burian, S. 59 Burnett, R.T. 679, 720 Burns, S.P. 200 Burridge, C. 661 Burtscher, H. 627 Buske, D. 802–803, 814 Butler, A.J. 720 Byun, D.W. 87, 90, 169, 180, 203, 221, 233, 253–255, 258, 407, 418, 428, 448, 451, 457, 493, 634, 638, 650, 652, 721, 829

C Cabala, R. 159 Cacciamani, C. 758 Cachier, H. 76, 364

845 Calle, E.E. 679 Calori, G. 135, 692 Calvo, J. 65, 200 Campolongo, F. 331, 341 Campos, T. 663–664 Campos Velho, H.F. 803 Cantalapiedra, I.R. 200 Capaldo, K.P. 516, 665 Cardelino, C. 220 Carey, K.F. 493 Carissimo, B. 750 Carlsaw, D.C. 211 Carmichael, G.R. 135, 144, 319–320, 323, 326–327, 341, 375, 556 Carnevale, C. 96, 99 Carpentieri, M. 731, 734 Carrio, G.G. 438, 457, 464, 550 Carruthers, D. 8 Carslaw, K.S. 438 Carter, L.D. 96 Carter, W.P.L. 170 Carvalho, A.C. 399 Carvalho, J.C. 691, 803, 808 Castellanos, N. 418 Castro, I.P. 33 Caton, F. 59 Cats, G. 65 Caughey, M. 418 Causera, G. 368 Cautenet, S. 437 Cermak, J.E. 57 Chai, T. 319 Champion, H. 275 Chan, L.Y. 754 Chan, S.T. 32 Chang, J.C. 333, 339 Chang, J.S. 77, 569–570 Chang, M.E. 220 Chang, Y.S. 96, 169–170, 172, 768 Charalambopoulou, G. 680 Charlson, R.J. 662 Charmicheal, G.R. 768 Charron, A. 544 Chaumerliac, N. 437 Chauvet, C. 4 Chazette, P. 23, 504, 506, 508, 510 Cheinet, S. 76, 504, 536–537, 561, 768, 795 Chen, G. 662–663, 665 Chen, Y. 662–324

Author Index

846 Cheng, F.-Y. 253, 255, 258, 324 Chikara, H. 749 Chin, H.-N. 335 Chin, M. 46–47, 298, 363, 407, 505, 536, 544, 761 Ching, J.K.S. 65, 169, 203, 221, 393, 418, 428, 448, 451, 457, 493, 650, 652, 829 Chino, M. 275 Chirizzi, C. 59 Chiszar, J. 203 Chong, M.S. 248 Christen, A. 12, 15–16 Christensen, J.H. 285 Chuang, H.-Y. 220–221, 448, 493 Chung, S.H. 640 Chylek, P. 664 Cimorelli, A.J. 344 Civerolo, K. 640, 649–650, 798 Clappier, A. 56, 64–65, 129, 737 Clarke, A.D. 326, 556, 662–663, 665 Clement, C.F. 611 Coats, C.J. 418 Coats C.J., Jr. 451 Coe, M. 524, 531, 817 Cofala, J. 96, 135, 159, 216, 468–469, 471 Coffman, D.J. 323 Colbeck, I. 680 Coll, I. 362, 365, 368 Colle, B.A. 384–385, 392 Collins, D. 407, 774–775 Conangla, L. 200 Cooter, E.J. 633, 637 Cope, M. 698–699 Copeland, J.H. 33, 319, 761 Coppalle, A. 467, 469 Corani, G. 97 Corbett, S. 698 Cordle, J. 220 Corsmeier, U. 371 Corti, A. 734 Costa, A.M. 399 Costa, C.P. 805–806 Costa, M.P. 362, 365, 761 Cots, N. 544 Cotton, W.R. 33, 319, 438–439, 457, 464, 550, 761 Coulson, G. 680 Courtier, P. 787 Cousin, F. 76 Covert, D.S. 323

Cowin, J.P. 505 Craig, K.J. 4 Cros, B. 364 Crowley, J.N. 504–505, 664 Crutzen, P.J. 664–665, 667 Csanady, G.T. 245 Cubasch, U. 276 Cuhart, J. 65 Cullen, A.C. 331, 333, 343 Cuvelier, C. 469 Cuxart, J. 200 Cyrys, J. 832 Czader, B. 258

D Degrazia, D. 352–353 Dabberdt, W.F. 335 Dabdub, D. 169, 172, 515–516 Dacic, M. 46 D’Amours, D. 275 Damrath, U. 385, 768, 795 Dastoor, A. 798 Davakis, R. 275 Davidson, K.L. 196 Davidson, P.M. 220–221, 448, 492–493 Davidson, R.I. 680 Davies, H.C. 126 Davies, T.J. 775, 779 Davignon, D. 798 Davis, D.D. 662–663, 665 Davis, J. 31 De Cort, M. 284, 287 De Geer, L.-E. 285–286 de Hoog, J. 839–840 De Kock, D.J. 4 de Leeuw, F. 467, 469 De Moor, G. 611 De Saeger, E. 836 Deaven, D. 448, 493 Debry, E. 601 DeCaria, A.J. 441 Decesari, S. 544 Defer, E. 438, 440 Degrazia, G.A. 352–353, 690–691, 803, 808, 815 Delany, T. 201 Delle Monache, L. 335 Deme, S. 66, 244 DeMore, W.B. 453, 664

Author Index Denby, B. 384, 467, 469 Dennis, R.L. 451 Dentener, F. 211, 216 Deprost, R. 109 Derognat, C. 211, 536 Derwent, R.G. 211–212, 216, 774–775 Deschamps, P.Y. 267 Deserti, M. 758, 768, 795 Desiato, F. 691 Deutsch, F. 514, 839–840 Devaux, C. 267 Devell, L. 284–285 Dewundege, P. 700 Di Sabatino, S. 3, 6, 8, 55, 57, 59 Dickson, C.R. 353–354, 815 Dieguez, J.J. 385 DiMego, G. 220–221, 334, 448, 492–493, 636 Dimmick, F. 717 Dlugi, R. 570 Dobricic, S. 45 Dockery, D.W. 514, 699, 724 Dodd, G.C. 335 Dodge, M.C. 77, 428 Dommen, J. 76, 78, 407 Doms, G. 524, 768, 795, 817, 820 Dong, X. 323–324 Donovan, R.G. 774–775, 777 Dore, A.J. 764 Dore, C.J. 764 Dore, T. 397 Dorn, H.P. 158–159 Dosio, A. 812 Douros, G. 467, 469 Douros, I. 601 Dovnar, T. 744 Downing, D.J. 340 Draxler, R.R. 180, 230, 335 Drobinski, Ph. 21, 23, 364 Druyan, L. 650, 655–656 Du, J. 334 Du Vachat, R. 611 Dubois, G. 284, 287 Dubovik, O. 46–47, 524–526 Dudhia, J. 88, 180, 202–203, 418, 427, 458, 537, 635, 649–650, 655–656, 811, 829 Dulac, F. 563 Dumont, G. 784 Dunkhorst, W. 680

847 Dupont, S. 65, 393 Durand, P. 364 Dye, J.E. 438–441

E Easter, R.C. 47, 375 Eaton, S. 661 Ebel, A. 76, 158–159, 285, 407–408, 535, 569–571, 605 Ebisuzaki, W. 636 Eck, T.F. 47, 524–526, 528 Ed, M. 636 Eder, B.K. 221, 493, 722 Edwards, D.P. 327 Eerola, K. 65 Egami, R.T. 339 Eisele, F. 665 Elbers, G. 754 Eleveld, H. 275 Ellermann, K. 754 Elman, J. 100 Emmenegger, C. 78 Emmons, L. 327 Engardt, M. 197, 787 Engelke, T. 621 Engelstaedter, S. 525–526 Enjoji, T. 749 Erdman, L. 197 Ervens, B. 627 Espinalt, A. 200 Estes, M. 258 Evans, J.S. 514 Evensen, G. 303, 306, 316 Eyles, J. 720

F Facchini, M.C. 47, 544 Fagerli, H. 99, 190, 211 Fairlie, T.D. 89 Fairweather, G. 375 Fall, R. 771–772 Faloona, I. 438, 663–664 Fan, Y. 636 Favale, G. 754 Fay, B. 65, 383, 385, 388, 393 Fay, M.A. 514 Fay, M.E. 699 Fayet, S. 368

Author Index

848 Fehsenfeld, F.C. 438 Fehsenfeld, G. 451 Feichter, J. 159 Feldmann, H. 158–159 Fernandes, S.D. 320, 323, 326–327 Fernau, M.E. 339 Ferna´ndez, A. 200 Ferranti, L. 276 Ferreira, F. 265, 835 Ferreira, J. 399, 534 Ferrero, E. 33, 352–354, 690–691 Ferrero, S. 352–353 Ferrier, B. 334 Ferries, B.G. 514 Ferris, B.G. 699 Fiedler, F. 159, 482, 484, 491, 569–570 Fierens, F. 784 Filiberti, M.A. 212 Finardi, S. 65, 383–384, 691–692 Findlater, P.A. 46 Fine, S. 418 Finkelstein, N. 720 Finzi, G. 96–97, 99 Fiore, A.M. 89, 640 Fischer, P. 302 Fisher, B.E.A. 340, 395, 397, 754 Fisseha, R. 76, 78 Flagan, R.C. 407, 774–775 Flagen, R.C. 97 Flamant, C. 23 Flamant, P.H. 23 Flemming, J. 75 Fløisand, I. 679–680, 682–683 Folberth, G. 212 Foltescu, V. 76, 537 Ford, I.J. 611 Foreˆt, G. 563 Fo¨rstner, J. 570 Fortelius, C. 283 Fortescu, V. 469 Fox, D.G. 332 Fragkou, L. 384 Francois, S. 368 Frankevic, V. 76 Fredenslund, A. 516 Freeman, D.L. 339 Frejafon, E. 371 Frenkiel, F.N. 353 Frey, H.C. 331, 333, 339, 343 Fridman, Sh.D. 284, 287

Fried, A. 326 Friedlander, S.K. 484 Friedli, T.K. 211 Friedrich, R. 21, 23, 97, 190 Friese, E. 158–159 Friesel, J. 754 Fritts, D.C. 200 Fritz, A. 483 Fre´jafon, E. 364 Frohn, L.M. 285 Frost, G. 438 Fu, Q. 320, 323, 326–327 Fuchs, N.A. 484 Fudala, J. 680 Fuelberg, H. 665 Fujita, S. 134–135 Fujiyoshi, Y. 323 Fuzzi, S. 544

G Gabusi, V. 99, 362, 365 Gafﬁn, S. 640 Gage, K.S. 246, 248 Gaggeler, H.W. 78, 287 Galmarini, S. 275, 335 Ganzeveld, L. 159 Gardner, R.H. 340 Garger, E.K. 287 Gassmann, A. 768, 795 Gatski, T.B. 31, 33–34 Gawi, Z. 549 Geertsema, G.T. 275 Gego, E.L. 180, 230, 722, 793 Gehrig, R. 76, 78–79, 544 Geiger, H. 159, 627 Geiger, J. 158 Geiss, H. 285 Geleyn, J.-F. 526 Genikhovich, E.L. 128 Geogdzhayev, I. 47 Georgi, B. 287 Georgopoulos, P.G. 340 Gering, F. 66 Germenchuk, M.G. 284, 287 Geron, C. 771–772 Gery, M.W. 77, 408, 428 Ge´go, E. 177 Ghan, S.J. 47, 375 Ghil, M. 787

Author Index Ghim, Y.S. 96 Giambini, P. 734 Gifford, F.A. 246, 249 Gifford, F.R. 251 Gilbert, J.-C. 22 Gill, D. 203 Gille, G. 327 Gilliam, R. 633, 722 Gilliland, A. 177, 180, 230, 633, 717, 722 Ginoux, P. 46–47 Giovis, C. 720 Gipson, G. 418 Girard, E. 664 Girardeau, P. 301 Giroux, E. 76 Glaab, H. 275 Gobbi, G.P. 44–45, 47–48, 50, 53 Godoi, A.F.L. 838–839 Godoi, R.H.M. 838 Godowitch, J. 177, 180, 230, 418 Goldberg, R. 640, 648–650, 655–656, 829 Goldbohm, S. 302 Golden, D.M. 453, 664 Goldstein, R. 97 Golinelli, M. 758 Gombert, D. 22–23, 561 Gomes, L. 561 Gong, S.L. 610–611, 664 Gonzalez-Flesca, N. 754 Gonza´lez, R.M. 374–376, 416, 418 Goodman, A.L. 504–505 Goodwin, J. 211 Gottlieb, J. 504 Goulart, A. 352–353, 808, 814 Gracheva, I.G. 128 Graff, A. 408, 467, 469 Granier, C. 327 Grassian, V.H. 503–505 Grasso, L.D. 33, 319, 761 Graziani, G. 284 Gregoric, G. 768, 795 Grell, G.A. 180, 202, 418, 458, 635, 649–650, 811, 829 Grell, G.J. 88 Gressel, W. 483 Grifﬁn, R.J. 515–516 Grigoryan, S. 197 Grippa, G. 284 Grosjean, D. 407 Grosso, N. 265, 268

849 Grumbine, R. 636 Gryning, S.-E. 12, 14–16, 806 Guariso, G. 96–97 Guazzotti, S.A. 323 Gue´dalia, D. 23 Guenther, A. 771–772, 775 Guillaume, B. 76 Guilloteau, E. 65 Guo, Y.-R. 203 Gurciullo, C. 77 Guttikunda, S.K. 319, 326, 556 Gutzwiller, L. 78 Gwynn, C. 724 Gyarfas, F. 159, 468–469, 471 Gysel, M. 76, 78–79

H Hafner, W. 694 Hagen, L.O. 683 Hajdas, I. 78 Hakami, A. 221, 319 Hale, M. 665 Hales, J.M. 375 Hall, J. 395–397, 764 Haller, P. 287 Hamdi, R. 737 Hameri, K. 754 Hamill, T.M. 334 Hamilton, J. 775 Hampson, R.F. 453, 664 Hanaki, K. 56 Hance, R. 284 Hanea, R.G. 301 Hanisch, F. 504–505 Hanna, A.F. 418, 451 Hanna, S.R. 331–333, 336, 339, 343, 803, 806, 815 Ha¨nninen, O. 65 Hansen, D.A. 339 Hansen, J.E. 135, 302 Hansson, H.C. 544 Ha¨rko¨nen, J. 191 Harley, P. 771–772, 775 Harman, I.N. 56 Harrington, J.Y. 33, 438–439, 457, 464, 550 Harrison, P.J. 662, 665 Harrison, R.M. 544 Harrison, S.P. 524, 531, 817

Author Index

850 Hart, M. 492 Hartmann, U. 158 Has, H. 569–570 Ha¨seler, R. 159 Hass, H. 76, 159, 285, 407–408, 469, 535, 537, 605 Hati, S.K. 332 Hauglustaine, D.A. 210–212, 215 Hausberger, S. 691, 694 Hayes, J.L. 493 Hayman, G. 397 He, D. 320, 323, 326–327 Healy, R. 650, 655–656 Heath, C.W. 514 Heath, J. 665 Heck, T. 190 Heemink, A.W. 301 Hegg, D.A. 594, 611 Heidegger, A. 159 Heikes, B.G. 438, 594 Heimann, M. 524, 531, 817 Heinold, B. 523, 817–818, 820–821 Heinold, D. 339, 343 Heinrich, B. 817, 820 Helbig, N. 482, 484, 491 Held, T. 76 Hellmuth, O. 467, 469, 523, 610, 817–818, 820–821 Helmert, J. 523, 626, 817–818, 820–821 Helton, J.C. 331 Henbest, S. 248 Henn, D.S. 335 Henrion, M. 331 Henze, D.K. 319 Herman, J. 47 Herman, M. 267 Herrmann, H. 622, 626–627 Herzog, M. 47 Hess, D. 699 Heyes, C. 96, 159, 468–469, 471 Heyes, F.G.C. 215 Heywood, E. 396–397 Higgins, W. 636 Hildemann, L.M. 516 Hilderman, T. 740 Hill, R. 742 Hillamo, R.E. 287, 504 Hinojosa, J. 204 Hinze, J.O. 809 Hobbs, P.V. 594

Hodzic, A. 76, 467, 469, 503–506, 508, 510, 536–537, 539, 541, 561, 768, 795 Hoe, S.C. 64–66, 243 Hoek, G. 302 Hoffman, F.O. 340 Hoffmann, T. 407, 774–775 Hogrefe, C. 177, 640, 648–650, 655–656, 722, 798, 829 Ho¨gstro¨m, U. 247–248 Holben, B. 46–47, 524–526, 528 Holland, D. 717 Holland, E.A. 212 Hollingsworth, A. 276 Holla¨nder, W. 679–680 Holloway, D.D. 451 Holloway, J.S. 438 Holt, T. 417 Holtslag, A.A.M. 16, 611, 812 Homa, D.M. 724 Hongisto, M. 192, 197 Honore´, C. 22–23, 76, 211, 293, 467, 469, 504, 536–537, 561, 768, 795 Hooyberghs, J. 784 Hopke, P.K. 823 Hopkins, J. 775 Hosker, R.P. Jr. 32 Hough, A.M. 775 Hourdin, F. 212 Housiadas, C. 679–680 Houtekamer, P.L. 276, 303 Howard, C.J. 453, 664 Hu, Y. 220–221 Huang, H.-C. 418 Huang, P. 664 Huang, Z. 4, 6 Hubler, G. 438–439 Hu¨bler, M.P. 451 Huebert, B.J. 556, 662–663, 665 Hueglin, C. 76, 78–79 Huffman, D. 569, 572 Huglin, C. 544 Hugo, A. 621, 832 Hunt, J.R.C. 247–248 Hunter, L. 8 Hurley, P.J. 698–700

I Ichikawa, Y. 134–135 Ichinose, T. 56

Author Index Ide, K. 787 Iida, T. 319 Ilari, O. 284 Ilvonen, M. 191, 275, 285, 709 Iman, R.L. 331 Infante, C. 200 Ingham, T. 664 Insley, E.M. 99 Irwin, J.S. 16, 332, 339, 722 Isaksen, I.S.A. 77, 594 Ishikawa, N. 724 Isukapalli, S.S. 340 Ito, K. 679 Iversen, T. 592 Izrael, Yu. A. 284, 287

J Jacob, D.J. 89, 438, 640 Jacob, P. 284 Jacobs, H.J. 285 Jacobson, M.Z. 76, 135, 515, 572, 604 Jacoby, D. 798 Jaegle´, L. 438 Jaffe, D. 89 Jakobs, H.J. 158–159 Jang, J.C. 407 Jang, M. 253 Janjic, Z.I. 45–46 Jankowiak, I. 528 Janssen, L. 514 Janssen, S. 784 Janssens, A. 284, 287 Jantunen, M. 65 Ja¨rvenoja, S. 66 Ja¨rvinen, H. 65 Jaubertie, A. 21, 23 Jeffries, H.E. 407, 650, 652, 655 Jenk, T.M. 78 Jenkin, M. 775 Jenkin, M.E. 775, 779 Jenkinson, P. 742 Jensen, M. 200 Jerrett, M. 720 Jiang, H. 33, 438, 457, 464, 550 Jiang, W. 76 Jicha, M. 826 Jime´nez, P. 425, 431 Jo¨ckel, P. 663 Joergensen, H.E. 200

851 Joffre, S. 64, 197 Johansson, M. 190 John, A. 407, 832 Johnen, F.J. 159 Johnson, G. 8 Johnson, J.E. 323 Jokiniemi, J. 287 Jones, A.M. 275, 544 Jones, A.R. 284, 287 Jones, R.L. 516 Jongen, J. 768 Jongen, S. 383, 385, 795 Jonson, J.E. 99, 190, 211, 467, 469 Jorba, O. 425–426, 428 Joseph, J.H. 44–48, 50 Jost, D.T. 287 Jourdain, L. 212 Jourden, E. 189 Jovic, D. 45–46, 636 Jørgensen, J.U. 65 Juang, H. 334 Juggins, S. 627 Junker, N. 448, 493

K Kaasik, M. 744 Kaduwela, A. 76 Kagawa, N. 324 Kahnert, M. 787 Kainourgiakis, M. 680 Kajino, M. 134 Kakaliagou, O. 44–46, 549–550, 552, 836 Kalberer, M. 76, 78 Kallos, G. 44–46, 48, 50, 457, 548–550, 552, 836 Kalnay, E. 276, 636 Kalthoff, N. 371 Kamm, S. 572 Kanakidou, M. 665 Kanaroglou, P. 720 Kang, D. 221, 447, 492 Kangas, L. 189 Kangas, M. 287 Kanhert, M. 192 Karamchandani, P.K. 97, 375 Karl, M. 159 Karppinen, A. 65, 189, 191, 385, 388 Kartau, K. 744 Karvosenoja, N. 189–190

852 Kashpur, V. 287 Kasibhatla, P. 665 Kasper-Giebl, A. 78 Kastibhatla, P. 662–663, 665 Kastner-Klein, P. 4, 56 Katolicky, J. 826 Katsafados, P. 44–46, 48, 50, 548–550, 552 Katsouyanni, K. 836 Kaufman, Y.J. 524–526, 528, 724 Kauppinen, E.I. 287, 622, 627 Keiko, A.V. 823 Keller, J. 75–76 Kelly, G.N. 284, 287 Kephalopoulos, S. 603 Kerminen, V.M. 504, 611, 623 Kerschbaumer, A. 159, 406–408, 467, 469, 758 Kerschgens, M.J. 159 Kessler, C. 76, 158–159, 469, 537 Kettle, A.J. 664 Khan, M.N. 221 Khattotov, B. 327 Kiehl, J.T. 597, 599 Kiene, R. 665 Killus, J.P. 77, 408, 428 Kim, H. 253 Kim, J.-J. 33, 41 Kim, S.-B. 56, 253, 255, 258 Kim, S.-T. 258 Kimmel, V. 744 Kimura, Y. 169–170, 172 King, M.D. 524–526 Kinne, S. 47, 159 Kinney, P.L. 640, 648–650, 655–656, 829 Kintigh, E. 339, 343 Kirchner, F. 605 Kishcha, P. 44–45, 47–48, 50 Kitada, T. 144–145, 747–748 Kittelson, D. 625 Kitwiroon, N. 384 Kiyosawa, H. 665 Kleeman, M. 76 Klein, M. 159 Kley, D. 21, 23 Klimont, Z. 96, 135, 159, 215–216, 320, 323, 326–327, 468–469, 471 Klinker, E. 276

Author Index Klippel, N. 627 Kloster, S. 159 Klug, W. 275, 284, 335 Kmit, M. 65 Knipping, E.M. 169, 172 Knoth, O. 622, 626–627, 817, 820 Knowlton, K. 640, 648–649, 829 Koch, D.M. 47, 640 Kofﬁ, E. 335 Kohfeld, K.E. 524, 531, 817 Kok, G.L. 594 Kolb, C.E. 453, 664 Kollax, M. 275 Kondo, H. 56 Kondo, Y. 326 Kondragunta, S. 492 Koren, H.S. 724 Koskentalo, T. 191 Kotroni, V. 384, 549 Kottmeier, Ch. 364, 371, 481, 568 Kouvarakis, G. 662 Kovar-Panskus, A. 4 Kozlova, E.G. 287 Krewski, D. 679 Krichak, S.O. 44–48, 50 Krischeke, U. 665 Krishnamurti, T.N. 611 Krueger, B.J. 505 Kryza, M. 764 Krzyzanowsky, M. 836 Ku, J.-Y. 640, 649–650 Kubin, E. 712 Kuchmenko, E.V. 823 Kuhlbusch, T. 407, 832 Kuhn, M. 605 Kukkonen, J. 65, 189, 191, 285, 383–385, 388, 709 Kulmala, M. 191, 611, 754 Kumar, N. 77, 699 Kumari, M. 504 Kumazawa, S. 284 Kunkel, K. 418 Kurata, G. 144–145, 326, 556 Kurylo, M.J. 453, 664 Kurz, Ch. 694 Kusumi, S. 284 Kutzner, K. 412 Kvasnikova, E.V. 284, 287 Kwok, W.S. 754

Author Index

L Laaksonen, A. 611 Labow, G. 47 Lacarre`re, P. 611 Lacis, A.A. 524–525, 528 Lagouvardos, K. 384, 549 Lahdes, R. 754 Laitinen, T. 754 Laj, P. 544 Lakhani, A. 504 Lamarque, J.F. 212, 327 Lamb, B. 771–772 Lamb, R.G. 332 Lang, C. 418 Lang, T. 438 Langner, J. 76, 197, 469, 537, 787 Laroche, P. 438, 441 Larsen, S.E. 66, 244–246 Larson, S. 418 Larssen, S. 683 Laskin, A. 505 Lasry, F. 362, 365, 368 Lathie`re, J. 212 Lattuatic, M. 21, 23, 536, 539, 541 Laube, M. 285 Launder, B. 6 Laupsa, H. 679, 681 Lavenu, F. 528 Law, C.S. 662, 665 Lawrence, M.G. 663 Lawson, D.R. 174, 605 Lazaridis, M. 680 Lazarus, A.L. 594 Lazo, E. 284 Le Clainche, Y. 662, 665 Leach, M.J. 335 Leaitch, R. 661, 664 Lebowitz, M.D. 724 Leck, C. 663–664 Leclerc, N. 109 Lee, P.C.S. 220–221, 448, 492, 493 Lee, R.F. 344 Lee, R.L. 32 Lee, S.H. 698, 700 Lee, T.J. 33, 319, 761 Lefebre, F. 514 Lefebvre, M.P. 23 Legrand, M. 267 Leighton, H. 664 Leitl, B. 5, 32

853 Lemare´chal, C. 22 Lenhart, L. 190 Lenschow, D.H. 663–665 Lenschow, P. 412 Leoncini, G. 758 Lercher, P. 689 Leriche, M. 437 Lerner, J. 634 Lesins, G. 664 Lesponne, C. 368 Leung, L.R. 375 Levasseur, M. 661–662, 665 Lewellen, W.S. 332–333, 336 Lewis, A. 775 Li, H. 636 Li, Q. 89 Li, W.K.W. 665 Liang, X.-Z. 418 Liao, H. 526, 528, 640 Lieber, M. 820–821 Lifschitz, B. 549 Likosalo, T. 712 Lin, H. 664 Lin, J.T. 246 Lin, S.-J. 46, 594 Lin, X. 463 Lin, Y. 448, 493, 636 Lindskog, A. 211 Liousse, C. 76, 504, 536–537, 561, 768, 795 Liston, G.E. 33, 438, 457, 464, 550 Liu, F. 56 Liu, H. 89 Liu, X. 611 Lizcano, G. 525–526 Lizotte, M. 661 Logan, J.A. 89, 640 Lohman, K. 798 Lohmann, U. 47, 664 Lohmeyer, A. 693 Loon, M.v. 76 Lorbeer, G. 544 Lorenc, A.C. 787 Lorenz, E.N. 341 Lossec, B. 23 Loughlin, D.H. 97 Lovelock, J.E. 662 Lu, H. 483 Lu, Z. 339 Ludwig, J. 570 Luecken, D. 168, 170

854 Luhana, L. 384 Luhar, A.K. 699–700 Lukk, T. 744 Lundquist, J.L. 200 Luria, M. 549 Lurmann, F.W. 77 Lusa, K. 191 Lutman, E. 742 Lutz, M. 407, 412 Lu¨tzenkirchen, S. 679 Lyck, E. 14, 806 Lynch, P. 65 Lynn, B.H. 640, 648–650, 655–656, 829 Lyons, W.A. 33, 319, 761

M Maclure, M. 724 Madronich, S. 77 Maenhaut, W. 544 Maffeis, G. 761 Mahrt, L. 200–201, 796 Mahura, A. 64 Makhon´ko, K.P. 287 Malcolm, A.L. 775 Malone, R.C. 251 Mamane, Y. 504 Manins, P.C. 700 Mankin, G. 636 Manning, A.L. 211–212, 275 Manning, K. 203 Mannino, D.M. 724 Manousakis, M. 45 Mantilla, E. 384, 430, 544 Mari, C. 438 Martens, R. 66 Marticorena, B. 560–561 Martilli, A. 4, 56, 65, 737 Martin, D. 23 Martin, F. 4 Martin, R.V. 640 Martins, F.G. 790 Martins, H. 399 Maryon, R.H. 246, 250–251 Mass, C.F. 384–385, 392 Massague´, G. 544 Matejka, T. 438–439 Mathur, R. 220–221, 418, 447–448, 451, 493, 640 Matsui, I. 323–324

Author Index Matsumoto, K. 319 Matthews, P. 665 Matthijsen, J. 76, 469, 537 Matveenko, I.I. 284, 287 Mauldin, L. 665 Maxwell, G.B. 56 McAnelly, R.L. 33, 438, 457, 464, 550 McDonald-Buller, E.C. 169–170, 172 McFadden, J.P. 438, 457, 464, 550 McGillis, W.R. 662–663, 667, 672 McHenry, J.N. 418, 451 McHugh, C. 8 McInnes, H. 679 McKeen, S.A. 451 McKendry, I. 89 McLaughlin, D. 307, 316 McMurry, P.H. 516, 571 McQueen, J.T. 220–221, 334, 448, 492, 493 McRae, D.S. 221 McRae, G.J. 96, 375 Meagher, J.F. 451 Mebust, M.R. 493 Mechler, R. 216 Medina, S. 836 Meixner, F.X. 570 Meleux, F. 293 Memmesheimer, M. 158–159, 285, 535, 569–570 Meng, Z. 516 Mensink, C. 514, 784 Menut, L. 21–23, 210–211, 215, 293, 298, 341, 362–363, 365, 406–407, 503–506, 508, 510, 536, 544, 560–561, 563, 761 Merrill, J.T. 323 Merzouk, A. 661–662, 665 Mesinger, E. 45 Mesinger, F. 636 Mesquita, S. 265 Mestayer, P.G. 56, 64–65 Metcalfe, S. 397 Me´tivier, H. 284 Meyers, M.P. 439 Me´gie, G. 23 Michaud, S. 661–662, 665 Michel, A.E. 503, 505 Mickley, L.J. 640 Middleton, P. 77, 569–570 Mihalopoulos, N. 548, 662, 665

Author Index Mikkelsen, T. 66, 243–246, 275, 335 Milanchus, M. 232 Milford, J.B. 339 Miller, E. 335 Miller, J.R. 650 Miller, T.L. 323 Miller, W.L. 662, 665 Milliez, M. 750 Milla´n, M.M. 384, 389, 430 Mimikou, N. 45 Minguzzi, E. 467, 469, 758, 768, 795 Minikin, A. 158–159 Miranda, A.I. 399, 534, 537 Mircea, M. 47 Mishchenko, M.I. 47, 524–525, 528 Misirlis, N. 45 Misra, P.K. 375 Mitchell, H.L. 303 Mitchell, K. 636 Mittal, A.K. 838, 840 Mittleman, M.A. 724 Miyakoda, K. 126 Mo¨hler, O. 572 Mo¨ller, D. 407–408 Mu¨cke, H.G. 836 Mochida, A. 56 Mochida, I. 749 Mogensen, K.S. 65 Mohr, M. 627 Molina, M.J. 453, 664 Molinie´, G. 438, 441 Molozhnikova, E.V. 823 Molteni, F. 276 Monahan, E.C. 196 Mongia, C. 815 Monks, P. 211 Monn, C. 76, 78–79 Monteiro, A. 399, 534, 537 Moon, N.K. 90 Moore, J.B. 305 Moorman, J.E. 724 Mor, S. 838, 840 Moral, P. 21, 23 Morales, G. 200 Moran, M.D. 33, 319, 761 More, E.J. 251 Moreira, D.M. 802, 803, 805–806, 808, 814–815 Moreira, S. 835 Morelli, S. 691

855 Moreno, J. 375 Moreto, F. 536, 539, 541 Morgan, M.G. 331, 698 Morrison, J.F. 248 Moshammer, H. 535 Moussiopoulos, N. 384, 467, 469, 601–603 Mulholland, J.A. 720 Mullen, S.L. 334 Muller, A. 66 Muller, J.E. 724 Mu¨ller, J-F. 439, 441 Mullins, C.B. 170, 172 Murakami, S. 56–57 Murayama, T. 324 Murena, F. 754–755 Murgatroyd, R.J. 353 Murphey, B. 220 Murti, P.P. 89, 640 Musson-Genon, L. 750 Mutchimwong, A. 397 Myers, T. 798

N Nagano, M. 747 Naja, M. 211 Nakajima, T. 528, 570 Nakamura, M. 749 Namboodiri, M.M. 514 Nanni, A. 692 Napari, I. 611 Nappo, C. 200 Nastrom, D. 246, 248 Naumann, K.-H. 572 Navascue´s, B. 66 Nayfeh, A.H. 126 Nazarov, I.M. 284, 287 Neece, J.D. 169, 170, 172 Neininger, B. 21, 23 Nelson, S.M. 320, 323, 326–327 Nenes, A. 76–77, 407–408, 505, 515, 611 Nester, K. 159 Neuberger, M. 535 Neunha¨userer, L. 383, 385, 388, 393 Newsom, R. 200 Ng, Y.L. 700 Nicholls, M.E. 33, 319, 438, 457, 464, 550, 761 Nickovic, S. 44–46, 48, 50–51, 549–550, 552

856 Nielsen, N.W. 64–65 Nishizawa, M. 145, 324 Nodop, K. 335 Nogueira, L. 835 Nolte, C. 633 Noppel, M. 611 Norman, A.L. 661, 665 Novak, J. 418 Nowak, D. 258 Nunez, M. 752

O Ødegaard, V. 65, 383–385 Odman, M.T. 220–221, 418 Odum, J.R. 407, 774–775 Oettl, D. 352–354, 689–690, 692–694 Oettl, E. 352–353 Ohba, R. 33 Oikawa, S. 56–57 Okada, K. 504 Okamoto, H. 323 Oke, T.R. 56, 752 Oksanen, A. 712 Olerud D.T., Jr. 451 Oncley, S. 201 Ooka, R. 56 Orbe, J. 200 Oreskes, N. 332 Orszag, S.A. 33–34 Ortega, S. 200 Otte, T.L. 65, 221, 393, 447, 448, 493 Otto, E. 611 Owen, S.M. 782 Owens, D. 384–385, 392 Ozsoy, E. 51

P Paccagnella, T. 758 Padro, J. 610–611 Page, T. 395, 397 Pai, P. 97 Paine, R.J. 339, 343, 344 Pajanovic, G. 51 Pakkanen, T.A. 504 Palau, J.L. 383, 385 Palmer, T.N. 276 Palmgren, F. 544 Pandis, S.N. 76–77, 407–408, 505, 515–516, 594, 611, 665

Author Index Pao, Y.H. 809 Papadopoulos, A. 44–46, 549–550, 552 Papageorgiou, J. 45 Paretzke, H.G. 287 Parker, D.J. 438 Parker, S.F. 333, 336 Parker, T.G. 742 Parmar, R.S. 504 Paronis, D. 267 Parrish, D.D. 438–439, 636 Parrish, G. 451 Pasken, R. 709 Pasler-Sauer, J. 66 Pasquill, F. 246 Passant, N. 397 Passoni, L. 758 Patnaik, G. 417 Patterson, L. 220 Paulsen, D. 76 Paulu, C. 717 Pe´cseli, H.L. 244–246 Pen˜a, J.I. 418 Pechinger, U. 275 Pelechano, A. 375–376 Peleg, M. 549 Pellegrini, U. 691 Pellerin, G. 276 Pennell, W.R. 375 Penner, J. 47 Pereira, M.C. 790 Perez, A.T. 534 Perez, C. 44–45, 48, 50–51 Perez-Landa, G. 383, 385 Pericleous, K. 754 Perna, R. 253 Perros, P.E. 23, 364, 371 Perry, A.E. 248 Perry, S.G. 344 Persson, C. 275, 285–286 Pertot, C. 467, 469, 761 Pession, G. 692 Peters, A. 724, 832 Peters, L.K. 144, 375 Peters, W.D. 344 Petersen, C. 64 Petersen, W.B. 339 Pre´voˆt, A.S.H. 76 Petzold, A. 158–159 Peuch, V.H. 293 Pfeffer, H.U. 158, 754

Author Index Pfeiffer, F. 625 Phalen, R.F. 680 Physick, B. 698 Physick, W.L. 699–700 Pickering, K.E. 439, 441 Piekorz, G. 159 Pielke, R.A. 33, 319, 332, 438, 457, 464, 550, 761 Pierce, D. 284 Pierce, T.E. 451, 771–772 Pietarila, H. 191 Pietrowicz, J.A. 709 Pilinis, C. 77, 505, 515–516, 611 Pilling, M.J. 775 Pino, D. 200, 204 Pinty, J.-P. 438, 441 Pires, J.C.M. 790 Piringer, M. 693 Pirjola, L. 189, 191, 611 Pirovano, G. 97, 362, 365, 467, 469, 761 Pison, I. 21 Pisoni, E. 96 Pitari, G. 47 Pittini, T. 689 Plana, F. 544 Plase`ncia, A. 836 Plate, E. 4 Plauskaite, K. 771 Pleim, J. 221, 418, 447 Pleim, J.E. 407, 448, 493 Plinis, C. 407–408 Pohjola, M. 191, 384, 385, 388 Pohl, F. 485–486 Poisson, N. 293 Pokumeiko, M. 284, 287 Polevova, S. 708 Po¨lla¨nen, R. 285, 287 Polreich, E. 275 Poluzzi, V. 758 Ponche, J.L. 364, 368 Pope, C.A. 514, 679, 699 Poppe, D. 627 Porter, P.S. 177, 722, 793 Pospisil, J. 826 Potemski, S. 275 Potra, F.A. 341, 375 Potter, B. 326 Pouliot, G.A. 221, 448, 493 Poulos, G.S. 200 Powell, K. 220

857 Powers, J. 438–439 Prakash, S. 504 Prather, K.A. 323 Prausnitz, J.M. 516 Prentice, I.C. 524, 531, 817 Pretterhofer, G. 354, 692 PreuX, J.-D. 412 Prevot, A.S.H. 75, 407 Pe´rez, C. 426, 428 Pe´rez, J.L. 374, 416, 418 Pe´rez-Landa, G. 385 Price, C. 439, 441 Price, H. 89 Prieto, J.F. 418 Prinn, R.G. 437, 441–442 Prodanova, M. 275 Prosnitz, D. 31 Prospero, J.M. 46 Psychogios, J. 680 Pe´tron, G. 327 Puig, O. 544 Pullen, J. 417 Puls, K.E. 483, 486 Pulvirenti, B. 3, 6, 8 Pun, B.K. 76, 515, 516 Putaud, J.-P. 503, 544 Putaud, M. 47 Pytharoulis, I. 457, 548

Q Quan, H. 324 Quass, U. 832 Querol, X. 544, 549, 552 Quinn, P.K. 323 Que´nel, P. 836 Que´rol, X. 836

R Raes, F. 216, 503, 544 Ranta, H. 708, 712 Rantama¨ki, M. 383–385, 388 Rantio-Lehtima¨ki, A. 708 Rao, K.S. 332 Rao, S.T. 170, 177, 180, 230, 232–233, 339, 722, 793 Rasch, P.J. 597, 599 Raschendorfer, M. 383, 393 Rasmussen, A. 65–66, 383, 385 Ratti, C. 59

Author Index

858 Ratto, M. 331, 341 Raupach, M.R. 46 Ravela, S. 307, 316 Ravindra, K. 838–840 Ravishankara, A.R. 453, 664 Reagan, J.A. 528 Redd, S.C. 724 Redondo, J.M. 200 Rees, J.M. 200 Regmi, R.P. 145 Reichenba¨cher, W. 412 Reimann, S. 78 Reimer, E. 159, 406–408 Reis, S. 97 Reisin, T. 31 Renner, E. 621, 626, 817, 820 Rexeis, M. 691, 694 Reynolds, B. 396–397 Riccius, O. 627 Richardson, F.L. 248 Rickard, A.R. 211 Riddle, A. 8 Ridley, B.A. 438, 440–441 Riemer, N. 159, 569–570 Rind, D. 634, 640, 650, 655–656 Rinke, R. 568 Rivera, M. 323 Riviere, E. 109 Rivkin, R. 662, 665 Rizza, U. 808, 815 Robers, E. 636 Robertson, L. 66, 197, 275, 787 Robin, D. 364 Robins, A.G. 731 Robinson, N.F. 339 Robinson, R.A. 516 Rocadenbosch, F. 426, 428 Rodhe, H. 285–286 Rodler, J. 691–692 Rodrı´ guez, E. 66 Rodrı´ guez, L. 375 Rodrı´ guez, M.A. 375–376 Rodriguez, S. 544, 549, 552, 836 Roekens, E. 839–840 Roemer, M. 211, 302 Roeth, E.-P. 627 Rogers, E. 334, 448, 493 Rontu, L. 65 Rood, R. 594 Rosati, A. 126

Rose, N.L. 627 Roselle, S.J. 418, 451, 493 Rosensweig, C. 640 Rosenthal, J. 640, 648–650, 655–656, 829 Rosenzweig, C. 648–650, 655–656, 829 Rosset, R. 76 Rotach, M.W. 15, 16, 56, 65, 737 Roth, H. 76 Rouil, L. 76, 210–211, 293, 467, 469, 504, 536–537, 561, 768, 795 Roux, J. 22, 561 Roy, A. 340 Ruggaber, A. 570 Ruiz, C. 544 Russel, M. 169 Russell, A.G. 96, 220–221, 339, 699, 720 Russell, G.L. 650 Russell, L.M. 323 Rutledge, S.A. 438 Ruuskanen, J. 754 Ryerson, T. 438

S Saathoff, H. 572 Saavedra, S. 811 Sacchetti, D. 691 Sachse, G.W. 326, 438 Saı¨ d, F. 364, 371 Sagendorf, J.F. 353–354, 815 Sakellaridis, G. 45 Saltbones, J. 275 Saltelli, A. 331, 341 Saltzman, E.S. 665 Salvador, R. 384, 430 Samaali, M. 368 Samburova, V. 78 San Jose´, R. 374–376, 416, 418, 692 Sander, R. 664 Sander, S.P. 453, 664 Sandholm, S.T. 326, 438 Sandu, A. 319, 341 Santabarbara, J. 66 Santese, F. 55 Santiago, J. 4 Santiago-Silva, M. 838 Santos, C.S. 835 Sarwar, G. 168–170 Sass, B. 65 Satake, S. 319, 323

Author Index Sato, M. 302 Satsangi, G.S. 504 Sattler, K. 64, 66 Saucier, F.J. 662 Saunders, S.M. 775 Sau¨t, C. 438 Sauter, F. 76, 469, 537 Savkin, M. 287 Savoia, E. 758 Savory, E. 4 Sax, M. 76 Saxena, P. 516 Saylor, R.D. 375 Scala, L.R. 441 Scarratt, M. 661–662, 665 Schaap, M. 76, 159, 301, 467, 469, 537 Scha¨dler, G. 570 Schaedler, G. 693 Schatzmann, M. 4–5, 32 Schaug, J. 683 Schayes, G. 123, 129, 737 Schell, B. 76, 159, 407–408, 571, 605 Schere, K.L. 87, 180, 220–221, 233, 254, 447, 448, 451, 493, 634, 638, 721 Schillinger, C. 109 Schleiniger, R. 96 Schlosser, E. 159 Schmechtig, C. 560–561 Schmidt, F. 621 Schmidt, H. 211, 536 Schneider, J. 544 Schopp, W. 215 Schpitz, N. 549 Scho¨pp, W. 96, 134–135, 159, 216 Schro¨der, W. 626, 817, 820 Schreuder, A. 724 Scha¨ttler, U. 524, 768, 795, 817, 820 Schuepbach, E. 211 Schulz, M. 159 Schurath, U. 572 Schwartz, J. 836 Schwartz, S.E. 442, 597, 599 Schwarz, J. 698 Sciare, J. 665 Scire, J.S. 99, 768 Seaman, N.L. 220–221, 448, 493 Secrest, D. 806 Seefeld, S. 341, 605 Sehili, A.M. 622, 626–627 Seidl, W. 627

859 Seigneur, C. 76, 515–516, 536, 539, 604, 798 Seinfeld, J.H. 76, 97, 319, 375, 407, 515–516, 526, 528, 594, 604, 611, 640, 774–775 Seland, 592 Selin, N.E. 89, 798 Selvini, A. 758 Senuta, K. 771 Seter, I. 549 Setzer, A. 528 Severova, E. 708 Shaﬁr, H. 45–46 Shafran, P.C. 636 Shah, K. 634 Shankar, U. 418, 451, 535, 569–571, 603 Shao, Y. 46, 145, 483 Sharf, G. 549 Sharma, S. 661 Sharpe, A. 8 Sheppard, L. 724 Sherry, N. 665 Sheu, R.-S. 335 Shi, W. 636 Shih, J. 96 Shimizu, A. 323–324 Shimodozono, K. 56 Shimohara, T. 747, 749 Shirahama, N. 749 Shirakawa, Y. 144 Shrader-Frechette, K. 332 Shtivelman, A. 44–45, 47–48, 50 Sicard, M. 23 Sifakis, N. 267 Siljamo, P. 191, 283, 285, 708–709, 712 Siman Tov-Alper, D. 549 Simmel, M. 622, 626–627 Simmonds, P.G. 211–212 Simpson, D. 99, 159, 190, 211 Singh, H.B. 438 Sini, J.-F. 56 Sinkki, K. 287 Sinnaeve, J. 284 Sinyuk, A. 524–526 Siscovick, D. 724 Sitak, V.A. 284, 287 Sivertsen, B. 16 Skamarock, W.C. 438–440 Skefﬁngton, R. 396–397 Skouloudis, A. 65

Author Index

860 Slaper, H. 275 Slingo, J.M. 526 Slobin, S.D. 796 Slørdal, L.H. 65, 384, 681 Slutsker, I. 47, 524–526, 528 Smedman, A. 247–248 Smiatek, G. 159 Smirnov, A. 47, 524–526, 528 Smith, A. 717 Smith, F.B. 246 Smolik, J. 680 Snyder, C. 334 Snyder, W.H. 57 Snyman, J.A. 4 Sodeman, D.A. 323 Soﬁev, M.A. 128, 189, 191–192, 197, 275, 283, 285, 385, 708–709, 712 Sokhi, R.S. 65, 383, 384, 397 Sokka, N. 287 Sokolik, I.N. 524–526, 528, 817 Solazzo, E. 55, 57 Solberg, S. 211 Soler, M.R. 200, 204 Solomon, P.A. 680 Solvberg, S. 211 Song, C. 253, 556 Sorensen, J.H. 64–66, 275 Soriano, C. 431 Souchkevitch, G. 284 Soulakellis, N. 267 Sousa, S.I.V. 790 Souto, J.A. 811 Spacek, L. 664 Spain, G. 211–212 Spalding, D. 6 Speizer, F.E. 514, 699 Spengler, J.D. 514, 699, 836 Speziale, C.G. 31, 33–34 Spiel, D.E. 196 Spittler, M. 159 Spyrou, C. 44–45, 48, 50, 552 Srivastava, R.K. 221 Srivastava, S.S. 504 St John, J. 220 Staehelin, J. 211 Starub, D. 625 Staudt, A. 438 Stauffer, D.R. 88, 180, 202, 418, 458, 635, 649–650, 811, 829 Stedman, J.R. 775, 779

Stein, A.F. 336 Steinbacher, M. 76 Stenchikov, G.L. 441 Steppeler, J. 768, 795 Steriotis, T. 680 Stern, R. 76, 159, 407–408, 467, 469, 537 Stetson, S. 258 Stevens, D.E. 32 Stevenson, D. 216 Stier, P. 159 Stith, J.L. 438–441 Stocker, J. 8 Stockwell, W.R. 77, 341, 569–570, 605, 627 Stoelinga, M.T. 611 Stogner, J. 220 Stokes, R.H. 516 Stollar, O.A. 31 Storm, H. 287 Stortini, M. 758, 768, 795 Strader, R. 77 Stratmann, F. 611 Straume, A.G. 335 Streets, D.G. 96, 134–135, 319–320, 323, 326–327, 556 Stroud, A.H. 806 Struschka, M. 625 Stuart, A.L. 438–439 Stubos, A.K. 680 Stubos, T. 679 Stukin, E.D. 284, 287 Stull, R.B. 335 Sturm, P.J. 354, 689, 691–694 Sugimoto, N. 323–324 Sullivan, J. 724 Sun, J. 200–201 Suozzo, R. 634 Sutton, M.A. 764 Swall, J. 633, 722 Sykes, R.I. 331–333, 335–336 Synal, H.-A. 78 Syrakov, D. 275 Szidat, S. 78 Szopa, S. 210–211, 215

T Tabachny, L. Ya. 284, 287 Takeda, S. 665

Author Index Takemura, T. 47 Talat Odman, M. 451 Talbot, R.W. 326 Tan, D. 438 Tanaka, P. 169–170, 172 Tang, Y. 319, 326, 556 Tanimoto, H. 319 Tanner, D. 665 Tanre´, D. 47, 267, 524–526, 528 Tao, W.-K. 439, 441 Tao, Y. 463 Tao, Z. 418 Tarantola, S. 331, 341 Tarrason, L. 191–192, 467, 469, 611 Tassone, C. 284 Taygi, S.K. 840 Tchepel, O. 399 Tegen, I. 46–47, 523–524, 531, 817–818, 821 Tegen, L. 159 Tente, H. 835 Terada, H. 275 Terradellas, E. 200 Thacker, S.B. 718 Thangam, S. 33–34 Theloke, J. 21, 23 Therry, G. 611 Thery, C. 438 Theurer, W. 734 Thomson, D.J. 353 Thongboonchoo, N. 326 Thornton, D.C. 662–663, 665 Thorton, D. 326 Thun, M.J. 514, 679 Thunis, P. 129, 469 Thurauf, J. 371 Thurston, G.D. 679, 836 Thykier-Nielsen, S. 66, 243–244 Tibaldi, S. 276, 758 Tilgner, A. 622, 626–627 Tilmes, S. 159 Tinarelli, G. 352–354, 689, 691–692 Tirabassi, T. 802, 805–806, 808, 814–815 Tisler, P. 388 Todd, M.C. 525–526 Toivonen, H. 285 Tokairin, T. 747–748 Tolbert, P.E. 720 Toll, I. 431 Tompkins, M. 96

861 Tonnesen, S. 407, 650, 652, 655 Toon, O.B. 524–526, 528, 817 Toriyama, S. 323 Torres, O. 47, 524–526 Torseth, K. 544 Toth, Z. 276, 334 Toupance, G. 23, 364 Tracton, M.S. 334 Trainer, M. 438 Traore´, K. 611 Travis, L.D. 524 Tremback, C.J. 33, 319, 438, 457, 464, 550, 761 Trickl, T. 164 Trini Castelli, G. 352–353 Trini Castelli, S. 31, 33, 352–353, 689–691 Troen, I. 796 Tsai, N.Y. 320, 323, 326–327 Tsang, T.T. 375 Tsaturov, Yu. S. 284, 287 Tschiersch, J. 287 Tsidulko, M. 45–46, 220–221, 448, 492–493 Tsuda, A. 665 Tsyro, S.G. 99, 190–191, 474, 611 Tuck, A. 438 Tunved, P. 544 Tuovinen, J.-P. 190 Turner, D.B. 339

U Uchiyama, A. 324 Ueda, H. 134–135 Uehara, K. 56–57 Uematsu, M. 319 Ulevicius, V. 771 Ullerstig, A. 66 Unden, P. 65, 287 Underwood, G.M. 504–505 Uno, I. 318–319, 323–324, 326, 556 Usher, C.R. 503, 505 Utembe, S. 775

V Vakeva, M. 754 Valkama, I. 191, 275, 283, 285, 385, 709 van den Brandt, P.A. 302 Van der Auwera, L. 275 Van der Gon, D. 471 Van Dingenen, R. 47, 503, 544

Author Index

862 van Dop, H. 611 Van Grieken, R. 838–840 van Leeuwen, P.J. 306 van Loon, M. 302, 469, 474, 504, 537 Van Vaeck, L. 838 Van Wees, Tr. 693 Vanderlei, M. 525–526 Vankerkom, J. 514 VanLeer, B. 561 Vardoulakis, S. 754 Vautard, R. 22–23, 76, 210–211, 215, 293, 298, 362–363, 365, 407, 469, 503–506, 508, 510, 534, 536–537, 539, 541, 544, 560–561, 761, 768, 795 Vebra, V. 771 Vehkama¨ki, H. 611 Venkatram, A. 97, 331–332, 336, 344, 375 Venticinque, M. 438 Vermote, E. 528 Versick, St. 568 Verver, G.H.I. 611 Vestreng, V. 211–212, 216, 516, 537, 789 Vezina, A. 662, 665 Viana, M. 544 Vickers, D. 200 Vieno, M. 764 Vignati, E. 467, 469 Vijayaraghavan, K. 76, 515, 798 Viktorsson, C. 284 Vila` Guerau de Arellano, J. 200, 204, 811–812 Vilhena, M.T. 802–803, 805–806, 808, 815 Vinagati, E. 159 Vincent, K. 397 Visscherdijk, A. 471 Vogel, B. 159, 481–482, 484, 491, 568–570, 627 Vogel, H. 159, 481–482, 484, 491, 569–570 Vogt, R. 12, 15–16 Volokitin, A.A. 287 Volta, M. 96–97, 99, 467, 469 Volz, F.E. 524–526 von Glasow, R. 665, 667 von Klot, S. 832 von Kuhlmann, P. 663 von Salzen, K. 664 Vonmont, H. 76, 78–79 Vorraro, F. 754–755 Voudouri, A. 457 Vukovich, J. 339

W Wachter, R. 483 Wadleigh, M.A. 661 Wadsworth, R. 395–397 Wagatani, K. 144 Wagner, F. 159 Wagner, S. 627 Wagner, V. 775 Wahlin, P. 544 Wahner, A. 159 Waight, P. 284 Wakamatsu, S. 56–57 Walcek, C.J. 77 Walden, J. 191 Walega, J.G. 594 Walker, H.L. 774 Walko, R.L. 33, 319, 438–439, 457, 464, 550, 761 Walters, S. 212 Wand, M.Q. 320, 323, 326–327 Wang, C. 437, 441–442 Wang, J. 4, 6 Wang, T. 326 Wang, W. 203 Wang, Y. 439, 441 Wang, Z. 319 Wanninkhof, R. 662–663, 667, 672 Ware, J.H. 514, 699 Warner, T.T. 335 Warren, S.G. 662 Washington, R. 525–526 Watkins, T. 717 Watson, J.G. 339 Watt, J. 627 Wauters, E. 838–840 Wayland, R.A. 221, 493 Weber, R.J. 556 Wehner, B. 544 Wei, C. 556 Wei, M. 276 Weil, J.C. 332, 336, 344 Weingartner, E. 76, 78, 407, 544 Weise, W. 627 Wennberg, P.O. 438 Werner, M. 159 Werth, D. 648 Wesely, M.L. 77 Wesley, D.A. 33, 319, 761 Weston, K.J. 764 Westrick, K. 384–385, 392

Author Index Wexler, A.S. 623 Wheeler, N. 339 Whitby, E. 571 White, A. 326 White, L. 467, 469 Whitehead, P. 396–397 Whitten, G.Z. 77, 170, 428 Whyatt, J.D. 397 Wichmann, E. 832 Wiedensohler, A. 544, 611 Wilck, M. 611 Wilkinson, J.G. 339, 720 Williams, A. 418 Willows, R. 395 Wilmott, C.J. 777 Wilson, D.J. 159, 740 Wilson, R.B. 344 Wind, P. 99, 190, 467, 469 Windt, H. 680 Winiwarter, W. 215 Witten, G. 408 Wlodarczyk, J. 698 Wolke, R. 523, 621–622, 626–627, 817–818, 820–821 Wong, C.S. 662, 665 Wong, D.C. 220–221, 448, 493 Woo, J.-H. 319–320, 323, 326–327, 556 Woodﬁeld, M. 397 Woollen, J. 636 Wortham, H. 364 Wortmann, S. 803 Wotawa, G. 451 Wu, P.-M. 504 Wu, S.-Y. 76, 515–516 Wuebbles, D. 418 Wurzler, S. 158 Wyngaard, J.C. 336

X Xie, X. 4, 6 Xiping, X. 514 Xiu, A. 418, 451 Xu, X. 699

Y Yagu¨e, C. 200 Yakhot, V. 33–34 Yamamoto, K. 144

863 Yamartino, R.J. 99, 408, 467, 469, 768 Yamazaki, A. 324 Yantosca, B. 86, 89 Yantosca, R.M. 89 Yarber, K.F. 320, 323, 326–327 Yarmatino, R. 408 Yarwood, G. 168–170, 172 Yienger, J.J. 319 Yin, D. 76 Yin, Y. 438 Ying, Q. 76 Ying, R. 33 Yitzchaki, A. 549 Yla¨talo, S. 627 Yocke, M. 170 Yoon, Y.J. 665 Yoshioka, K. 319 Young, J.O. 221, 418, 447, 448, 451, 493 Young, T. 417 Yu, S. 221, 447, 722 Yu, Y. 383 Yudin, V. 327 Yumimoto, K. 318–319, 323 Yvon, S.A. 665

Z Zaganescu, C. 661 Zarodnyuk, M.S. 823 Zauli Sajani, S. 691 Zelazny, R. 275 Zenobi, R. 76 Zerr, R. 438 Zhang, L. 611 Zhang, M. 664 Zhang, Y. 76, 515, 604 Zhao, Q. 448, 493 Zhou, B. 334 Zhu, Y. 276 Zilitinkevich, S. 64 Zimmer-Dauphinee, S. 220 Zimmerman, P. 775 Zimmermann, J. 159 Zinder, B. 287 Zlatev, R. 469 Zlatev, Z. 76, 537 Zoboki, J. 621 Zuber, A. 467, 469 Zublena, M. 692 Zurbenko, I.G. 232–233

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865

Subject index

AERMOD, 331 Aerosol budget, 411 Aerosol composition, 144 Aerosol concentration, 144 Air quality, 689, 699 Air quality forecast, 253, 448 AirQUIS, 680 Analytical solution, 802 AOD, 492, 568, 587 AOT, 265 AOT40, 97 Aqueous phase chemistry, 437 Asian dust, 323 Atmospheric transport model, 331 BEIS3, 771 Biogenic emissions, 771 CAFE, 468 CALGRID, 77 CALMET, 758 CAMax, 76 CBL, 610, 808 CBM-IV, 584 CEDVAL, 4 CFD model, 417, 742 Chemical transport model, 144 Chernobyl accident, 283 CHIMERE, 21, 109, 211 CHIMEREDUST, 560 Chlorine chemistry, 168 CIRAQ, 634 City-Delta, 469 Clean Air Act, 468 Climate change, 634, 648 CMAQ, 85, 180, 221 Coastal area, 362 Complex geometries, 3 Complex terrain, 811 Cost function, 97 Critical load, 395 Data assimilation, 318, 787 Dispersion modelling, 3 DMS, 661

Down scaling, 123 DREAM, 44 Elemental carbon, 780 EMEP/CORINAIR, 97 EMEP uniﬁeded model, 99 Emission reduction, 211, 469 Ensemble Kalman Filter, 302 Ensemble smoothers, 301 Ensemble weather forecast, 275 ESCOMPTE, 363 ESQUIF, 21 EURAD, 159 Finite array, 8 FLUENT, 3 FRAME, 764 Fuzzi methods, 340 Gaussian Plume Model, 17 Great Lakes, 492 Grid resolution, 467 Health forecasting, 698 Heterogeneous chemistry, 503 High-order model, 610 High-resolution simulation, 383 HIRLAM, 64, 277 HYSPLIT model, 235 Iberian Peninsula, 425 Indirect acidiﬁcation, 134 Inverse modellling, 21 ISORROPIA, 77, 470 KAMM/DRAIS, 569 Kriging interpolation, 272 Lagrangian particle model, 352, 689 Lagrangian timescale, 245 LM, 482, 523 LM-ART, 482 LOAD-FEM, 144 Long range transport, 708 Long-term simulation, 158

Subject index

866 LOTOS-EUROS, 582 Low wind speed, 814 MADEsoot, 568 MADRID2, 515 MARS/MUSE, 601 MCM, 774 Mediterranean Region, 548 Mercury deposition, 88, 800 Mercury emissions, 86 Microscale urban ﬂow, 31 Mineral dust, 560 MINERVE, 689 Mixing height, 12 MM5/RCM, 635 MODIS, 265 Multi-objective analysis, 96 MUSCAT, 524, 817, 820 Nested grid, 221 Neural-Network model, 97 Nitrate concentration, 503 Nitrate deposition, 134 One-way nesting, 427 Organic carbon, 780 Ozone analysis, 293 Ozone concentrations, 784 Ozone control policies, 230 Ozone exposure, 96 Ozone maxima, 374 PAH, 838 Particle formation, 610 Particle size variation, 621 Particulate sulfate, 592 Personal exposure, 679 Photochemical simulation, 365 Photochemical smog, 210 Physico chemical processes, 550, 612 Planetary boundary layer, 610 Plume dispersion, 16

PM10, 75, 158, 189 PM2.5, 76, 189, 220 Point sources, 235 Pollen dispersion, 481 PREV’AIR, 293 Public health, 717 Puff growth, 243 Radiation balance, 523 Radioactive pollutants, 283 RADM, 77 RAMS, 31, 437, 457 RAMS-Hg, 457 Receptor modelling, 823 Regional climate scenarios, 648 REM3, 79 REM-Calgrid, 406, 467 Remote sensing data, 265 Risk assessment, 395 Saharan dust, 523, 548 SAMUM, 523, 817 Secondary organic aerosols, 75 Sensitivity analysis, 331 SILAM, 189, 283 SKIRON, 44 Small scale modeling, 826 SORGAM, 601 SPACCIM, 622 Spatial aggregation, 23 SPM, 149 Surface roughness, 12 TKE, 201 Trajectory modelling, 235 Urban area, 679, 750 Urban ﬂow, 31 Urban heat island, 55 Wind tunnel experiments, 734

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