Air Pollution XIX (Transactions on Ecology and the Environment) (Wit Transactions on Ecology and the Environment)

Air Pollution XIX

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NINETEENTH INTERNATIONAL CONFERENCE ON MODELLING, MONITORING AND MANAGEMENT OF AIR POLLUTION

AIR POLLUTION XIX CONFERENCE CHAIRMEN C. A. Brebbia Wessex Institute of Technology, UK J.W.S. Longhurst University of the West of England, UK

V. Popov Wessex Institute of Technology, UK

INTERNATIONAL SCIENTIFIC ADVISORY COMMITTEE A. Berezin C. Booth C. Borrego M. Jicha F. Patania E. Petrovsky R. San Jose

ORGANISED BY Wessex Institute of Technology, UK

SPONSORED BY WIT Transactions on Ecology and the Environment

WIT Transactions Transactions Editor Carlos Brebbia Wessex Institute of Technology Ashurst Lodge, Ashurst Southampton SO40 7AA, UK Email: [email protected]

Editorial Board B Abersek University of Maribor, Slovenia Y N Abousleiman University of Oklahoma,

G Belingardi Politecnico di Torino, Italy R Belmans Katholieke Universiteit Leuven,

P L Aguilar University of Extremadura, Spain K S Al Jabri Sultan Qaboos University, Oman E Alarcon Universidad Politecnica de Madrid,

C D Bertram The University of New South

USA

Spain

A Aldama IMTA, Mexico C Alessandri Universita di Ferrara, Italy D Almorza Gomar University of Cadiz, Spain

B Alzahabi Kettering University, USA J A C Ambrosio IDMEC, Portugal A M Amer Cairo University, Egypt S A Anagnostopoulos University of Patras, Greece

M Andretta Montecatini, Italy E Angelino A.R.P.A. Lombardia, Italy H Antes Technische Universitat Braunschweig, Germany

M A Atherton South Bank University, UK A G Atkins University of Reading, UK D Aubry Ecole Centrale de Paris, France H Azegami Toyohashi University of Technology, Japan

A F M Azevedo University of Porto, Portugal J Baish Bucknell University, USA J M Baldasano Universitat Politecnica de Catalunya, Spain J G Bartzis Institute of Nuclear Technology, Greece A Bejan Duke University, USA M P Bekakos Democritus University of Thrace, Greece

Belgium

Wales, Australia

D E Beskos University of Patras, Greece S K Bhattacharyya Indian Institute of Technology, India

E Blums Latvian Academy of Sciences, Latvia J Boarder Cartref Consulting Systems, UK B Bobee Institut National de la Recherche Scientifique, Canada

H Boileau ESIGEC, France J J Bommer Imperial College London, UK M Bonnet Ecole Polytechnique, France C A Borrego University of Aveiro, Portugal A R Bretones University of Granada, Spain J A Bryant University of Exeter, UK F-G Buchholz Universitat Gesanthochschule Paderborn, Germany

M B Bush The University of Western Australia, Australia

F Butera Politecnico di Milano, Italy J Byrne University of Portsmouth, UK W Cantwell Liverpool University, UK D J Cartwright Bucknell University, USA P G Carydis National Technical University of Athens, Greece

J J Casares Long Universidad de Santiago de Compostela, Spain

M A Celia Princeton University, USA A Chakrabarti Indian Institute of Science, India

A H-D Cheng University of Mississippi, USA

J Chilton University of Lincoln, UK C-L Chiu University of Pittsburgh, USA H Choi Kangnung National University, Korea A Cieslak Technical University of Lodz, Poland

S Clement Transport System Centre, Australia M W Collins Brunel University, UK J J Connor Massachusetts Institute of Technology, USA

M C Constantinou State University of New York at Buffalo, USA

D E Cormack University of Toronto, Canada M Costantino Royal Bank of Scotland, UK D F Cutler Royal Botanic Gardens, UK W Czyczula Krakow University of Technology, Poland

M da Conceicao Cunha University of Coimbra, Portugal

L Dávid Károly Róbert College, Hungary A Davies University of Hertfordshire, UK M Davis Temple University, USA A B de Almeida Instituto Superior Tecnico, Portugal

E R de Arantes e Oliveira Instituto Superior Tecnico, Portugal L De Biase University of Milan, Italy R de Borst Delft University of Technology, Netherlands G De Mey University of Ghent, Belgium A De Montis Universita di Cagliari, Italy A De Naeyer Universiteit Ghent, Belgium W P De Wilde Vrije Universiteit Brussel, Belgium L Debnath University of Texas-Pan American, USA N J Dedios Mimbela Universidad de Cordoba, Spain G Degrande Katholieke Universiteit Leuven, Belgium S del Giudice University of Udine, Italy G Deplano Universita di Cagliari, Italy I Doltsinis University of Stuttgart, Germany M Domaszewski Universite de Technologie de Belfort-Montbeliard, France J Dominguez University of Seville, Spain K Dorow Pacific Northwest National Laboratory, USA W Dover University College London, UK C Dowlen South Bank University, UK

J P du Plessis University of Stellenbosch, South Africa

R Duffell University of Hertfordshire, UK A Ebel University of Cologne, Germany E E Edoutos Democritus University of Thrace, Greece

G K Egan Monash University, Australia K M Elawadly Alexandria University, Egypt K-H Elmer Universitat Hannover, Germany D Elms University of Canterbury, New Zealand M E M El-Sayed Kettering University, USA D M Elsom Oxford Brookes University, UK F Erdogan Lehigh University, USA F P Escrig University of Seville, Spain D J Evans Nottingham Trent University, UK J W Everett Rowan University, USA M Faghri University of Rhode Island, USA R A Falconer Cardiff University, UK M N Fardis University of Patras, Greece P Fedelinski Silesian Technical University, Poland

H J S Fernando Arizona State University, USA

S Finger Carnegie Mellon University, USA J I Frankel University of Tennessee, USA D M Fraser University of Cape Town, South Africa

M J Fritzler University of Calgary, Canada U Gabbert Otto-von-Guericke Universitat Magdeburg, Germany

G Gambolati Universita di Padova, Italy C J Gantes National Technical University of Athens, Greece

L Gaul Universitat Stuttgart, Germany A Genco University of Palermo, Italy N Georgantzis Universitat Jaume I, Spain P Giudici Universita di Pavia, Italy F Gomez Universidad Politecnica de Valencia, Spain

R Gomez Martin University of Granada, Spain

D Goulias University of Maryland, USA K G Goulias Pennsylvania State University, USA

F Grandori Politecnico di Milano, Italy W E Grant Texas A & M University, USA

S Grilli University of Rhode Island, USA

R H J Grimshaw Loughborough University,

K L Katsifarakis Aristotle University of

D Gross Technische Hochschule Darmstadt,

J T Katsikadelis National Technical

R Grundmann Technische Universitat

E Kausel Massachusetts Institute of

A Gualtierotti IDHEAP, Switzerland R C Gupta National University of Singapore,

H Kawashima The University of Tokyo,

UK

Germany

Dresden, Germany

Singapore J M Hale University of Newcastle, UK K Hameyer Katholieke Universiteit Leuven, Belgium C Hanke Danish Technical University, Denmark K Hayami University of Toyko, Japan Y Hayashi Nagoya University, Japan L Haydock Newage International Limited, UK A H Hendrickx Free University of Brussels, Belgium C Herman John Hopkins University, USA I Hideaki Nagoya University, Japan D A Hills University of Oxford, UK W F Huebner Southwest Research Institute, USA J A C Humphrey Bucknell University, USA M Y Hussaini Florida State University, USA W Hutchinson Edith Cowan University, Australia T H Hyde University of Nottingham, UK M Iguchi Science University of Tokyo, Japan D B Ingham University of Leeds, UK L Int Panis VITO Expertisecentrum IMS, Belgium N Ishikawa National Defence Academy, Japan J Jaafar UiTm, Malaysia W Jager Technical University of Dresden, Germany Y Jaluria Rutgers University, USA C M Jefferson University of the West of England, UK P R Johnston Griffith University, Australia D R H Jones University of Cambridge, UK N Jones University of Liverpool, UK D Kaliampakos National Technical University of Athens, Greece N Kamiya Nagoya University, Japan D L Karabalis University of Patras, Greece M Karlsson Linkoping University, Sweden T Katayama Doshisha University, Japan

Thessaloniki, Greece

University of Athens, Greece Technology, USA Japan

B A Kazimee Washington State University, USA

S Kim University of Wisconsin-Madison, USA D Kirkland Nicholas Grimshaw & Partners Ltd, UK

E Kita Nagoya University, Japan A S Kobayashi University of Washington, USA

T Kobayashi University of Tokyo, Japan D Koga Saga University, Japan S Kotake University of Tokyo, Japan A N Kounadis National Technical University of Athens, Greece

W B Kratzig Ruhr Universitat Bochum, Germany

T Krauthammer Penn State University, USA C-H Lai University of Greenwich, UK M Langseth Norwegian University of Science and Technology, Norway

B S Larsen Technical University of Denmark, Denmark

F Lattarulo Politecnico di Bari, Italy A Lebedev Moscow State University, Russia L J Leon University of Montreal, Canada D Lewis Mississippi State University, USA S lghobashi University of California Irvine, USA

K-C Lin University of New Brunswick, Canada

A A Liolios Democritus University of Thrace, Greece

S Lomov Katholieke Universiteit Leuven, Belgium

J W S Longhurst University of the West of England, UK

G Loo The University of Auckland, New Zealand

J Lourenco Universidade do Minho, Portugal J E Luco University of California at San Diego, USA

H Lui State Seismological Bureau Harbin, China

C J Lumsden University of Toronto, Canada L Lundqvist Division of Transport and

Location Analysis, Sweden T Lyons Murdoch University, Australia Y-W Mai University of Sydney, Australia M Majowiecki University of Bologna, Italy D Malerba Università degli Studi di Bari, Italy G Manara University of Pisa, Italy B N Mandal Indian Statistical Institute, India Ü Mander University of Tartu, Estonia H A Mang Technische Universitat Wien, Austria G D Manolis Aristotle University of Thessaloniki, Greece W J Mansur COPPE/UFRJ, Brazil N Marchettini University of Siena, Italy J D M Marsh Griffith University, Australia J F Martin-Duque Universidad Complutense, Spain T Matsui Nagoya University, Japan G Mattrisch DaimlerChrysler AG, Germany F M Mazzolani University of Naples “Federico II”, Italy K McManis University of New Orleans, USA A C Mendes Universidade de Beira Interior, Portugal R A Meric Research Institute for Basic Sciences, Turkey J Mikielewicz Polish Academy of Sciences, Poland N Milic-Frayling Microsoft Research Ltd, UK R A W Mines University of Liverpool, UK C A Mitchell University of Sydney, Australia K Miura Kajima Corporation, Japan A Miyamoto Yamaguchi University, Japan T Miyoshi Kobe University, Japan G Molinari University of Genoa, Italy T B Moodie University of Alberta, Canada D B Murray Trinity College Dublin, Ireland G Nakhaeizadeh DaimlerChrysler AG, Germany M B Neace Mercer University, USA D Necsulescu University of Ottawa, Canada F Neumann University of Vienna, Austria S-I Nishida Saga University, Japan H Nisitani Kyushu Sangyo University, Japan B Notaros University of Massachusetts, USA

P O’Donoghue University College Dublin, Ireland

R O O’Neill Oak Ridge National Laboratory, USA

M Ohkusu Kyushu University, Japan G Oliveto Universitá di Catania, Italy R Olsen Camp Dresser & McKee Inc., USA E Oñate Universitat Politecnica de Catalunya, Spain

K Onishi Ibaraki University, Japan P H Oosthuizen Queens University, Canada E L Ortiz Imperial College London, UK E Outa Waseda University, Japan A S Papageorgiou Rensselaer Polytechnic Institute, USA

J Park Seoul National University, Korea G Passerini Universita delle Marche, Italy B C Patten University of Georgia, USA G Pelosi University of Florence, Italy G G Penelis Aristotle University of Thessaloniki, Greece

W Perrie Bedford Institute of Oceanography, Canada

R Pietrabissa Politecnico di Milano, Italy H Pina Instituto Superior Tecnico, Portugal M F Platzer Naval Postgraduate School, USA D Poljak University of Split, Croatia V Popov Wessex Institute of Technology, UK H Power University of Nottingham, UK D Prandle Proudman Oceanographic Laboratory, UK

M Predeleanu University Paris VI, France M R I Purvis University of Portsmouth, UK I S Putra Institute of Technology Bandung, Indonesia

Y A Pykh Russian Academy of Sciences, Russia

F Rachidi EMC Group, Switzerland M Rahman Dalhousie University, Canada K R Rajagopal Texas A & M University, USA T Rang Tallinn Technical University, Estonia J Rao Case Western Reserve University, USA A M Reinhorn State University of New York at Buffalo, USA

A D Rey McGill University, Canada D N Riahi University of Illinois at UrbanaChampaign, USA

B Ribas Spanish National Centre for

Environmental Health, Spain K Richter Graz University of Technology, Austria S Rinaldi Politecnico di Milano, Italy F Robuste Universitat Politecnica de Catalunya, Spain J Roddick Flinders University, Australia A C Rodrigues Universidade Nova de Lisboa, Portugal F Rodrigues Poly Institute of Porto, Portugal C W Roeder University of Washington, USA J M Roesset Texas A & M University, USA W Roetzel Universitaet der Bundeswehr Hamburg, Germany V Roje University of Split, Croatia R Rosset Laboratoire d’Aerologie, France J L Rubio Centro de Investigaciones sobre Desertificacion, Spain T J Rudolphi Iowa State University, USA S Russenchuck Magnet Group, Switzerland H Ryssel Fraunhofer Institut Integrierte Schaltungen, Germany S G Saad American University in Cairo, Egypt M Saiidi University of Nevada-Reno, USA R San Jose Technical University of Madrid, Spain F J Sanchez-Sesma Instituto Mexicano del Petroleo, Mexico B Sarler Nova Gorica Polytechnic, Slovenia S A Savidis Technische Universitat Berlin, Germany A Savini Universita de Pavia, Italy G Schmid Ruhr-Universitat Bochum, Germany R Schmidt RWTH Aachen, Germany B Scholtes Universitaet of Kassel, Germany W Schreiber University of Alabama, USA A P S Selvadurai McGill University, Canada J J Sendra University of Seville, Spain J J Sharp Memorial University of Newfoundland, Canada Q Shen Massachusetts Institute of Technology, USA X Shixiong Fudan University, China G C Sih Lehigh University, USA L C Simoes University of Coimbra, Portugal A C Singhal Arizona State University, USA P Skerget University of Maribor, Slovenia

J Sladek Slovak Academy of Sciences, Slovakia

V Sladek Slovak Academy of Sciences, Slovakia

A C M Sousa University of New Brunswick, Canada

H Sozer Illinois Institute of Technology, USA D B Spalding CHAM, UK P D Spanos Rice University, USA T Speck Albert-Ludwigs-Universitaet Freiburg, Germany

C C Spyrakos National Technical University of Athens, Greece

I V Stangeeva St Petersburg University, Russia

J Stasiek Technical University of Gdansk, Poland

G E Swaters University of Alberta, Canada S Syngellakis University of Southampton, UK J Szmyd University of Mining and Metallurgy, Poland

S T Tadano Hokkaido University, Japan H Takemiya Okayama University, Japan I Takewaki Kyoto University, Japan C-L Tan Carleton University, Canada E Taniguchi Kyoto University, Japan S Tanimura Aichi University of Technology, Japan

J L Tassoulas University of Texas at Austin, USA

M A P Taylor University of South Australia, Australia

A Terranova Politecnico di Milano, Italy A G Tijhuis Technische Universiteit Eindhoven, Netherlands

T Tirabassi Institute FISBAT-CNR, Italy S Tkachenko Otto-von-Guericke-University, Germany

N Tosaka Nihon University, Japan T Tran-Cong University of Southern Queensland, Australia

R Tremblay Ecole Polytechnique, Canada I Tsukrov University of New Hampshire, USA R Turra CINECA Interuniversity Computing Centre, Italy

S G Tushinski Moscow State University, Russia

J-L Uso Universitat Jaume I, Spain E Van den Bulck Katholieke Universiteit Leuven, Belgium

D Van den Poel Ghent University, Belgium R van der Heijden Radboud University, Netherlands

R van Duin Delft University of Technology, Netherlands

A Yeh University of Hong Kong, China J Yoon Old Dominion University, USA K Yoshizato Hiroshima University, Japan T X Yu Hong Kong University of Science & Technology, Hong Kong

P Vas University of Aberdeen, UK R Verhoeven Ghent University, Belgium A Viguri Universitat Jaume I, Spain Y Villacampa Esteve Universidad de

M Zador Technical University of Budapest,

F F V Vincent University of Bath, UK S Walker Imperial College, UK G Walters University of Exeter, UK B Weiss University of Vienna, Austria H Westphal University of Magdeburg,

R Zarnic University of Ljubljana, Slovenia G Zharkova Institute of Theoretical and

Alicante, Spain

Germany

J R Whiteman Brunel University, UK Z-Y Yan Peking University, China S Yanniotis Agricultural University of Athens, Greece

Hungary

K Zakrzewski Politechnika Lodzka, Poland M Zamir University of Western Ontario, Canada

Applied Mechanics, Russia

N Zhong Maebashi Institute of Technology, Japan

H G Zimmermann Siemens AG, Germany

Air Pollution XIX

Editors C. A. Brebbia Wessex Institute of Technology, UK J.W.S. Longhurst University of the West of England, UK

V. Popov Wessex Institute of Technology, UK

Editors C. A. Brebbia Wessex Institute of Technology, UK J.W.S. Longhurst University of the West of England, UK V. Popov Wessex Institute of Technology, UK Published by WIT Press Ashurst Lodge, Ashurst, Southampton, SO40 7AA, UK Tel: 44 (0) 238 029 3223; Fax: 44 (0) 238 029 2853 E-Mail: [email protected] http://www.witpress.com For USA, Canada and Mexico WIT Press 25 Bridge Street, Billerica, MA 01821, USA Tel: 978 667 5841; Fax: 978 667 7582 E-Mail: [email protected] http://www.witpress.com British Library Cataloguing-in-Publication Data A Catalogue record for this book is available from the British Library ISBN: 978-1-84564-528-1 eISBN: 978-1-84564-529-8 ISSN: (print) 1746-448X ISSN: (on-line) 1743-3541 The texts of the papers in this volume were set individually by the authors or under their supervision.Only minor corrections to the text may have been carried out by the publisher. No responsibility is assumed by the Publisher, the Editors and Authors 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. The Publisher does not necessarily endorse the ideas held, or views expressed by the Editors or Authors of the material contained in its publications. © WIT Press 2011 Printed in Great Britain by Martins the Printer, UK. 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.

Preface

This volume contains the peer-reviewed papers accepted for the nineteenth International Conference on Modelling, Monitoring and Management of Air Pollution held on Malta in September 2011. This successful international meeting builds upon the prestigious outcomes of the 18 preceding conferences beginning with Monterrey, Mexico in 1993 and most recently in Kos, Greece in 2010. These meetings have attracted outstanding contributions from leading researchers from around the world. The presented papers have been permanently stored in the WIT eLibrary as Transactions of the Wessex Institute (see http://library.witpress.com). These collected conference papers provide an important record of the development of science and policy pertaining to air pollution. Despite the long history of attempts to manage the consequences of air pollution it remains one of the most challenging problems facing the international community. Air pollution is widespread and growing in importance and has clear and known impacts on health and the environment. The human need for transport, manufactured goods and services brings with it often unintended, but none the less real, impacts on the atmospheric environment at scales from the local to the global. Whilst there are good examples of regulatory successes in minimising such impacts the continuing development of the global economy bring new pressures upon the ability of the atmosphere to process pollutants and to safely remove them. Where the natural processing systems of the atmosphere become overloaded and the systems are unable to process inputs to the atmosphere at the rate they are added then pollution results. This brings risks to human health and the environment. The willingness of governments to move quickly to regulate air pollution is often balanced by concerns over the economic impact of such regulation. This frequently results in a lag between the scientific knowledge about the nature, scale and effect of air pollution and the implementation of appropriate, targeted and timely legislation. Science remains the key to identifying the nature and scale of air pollution impacts and is essential in the formulation of policy relevant information for regulatory

decision-making. Continuous improvements in our knowledge of the fundamental science of air pollution and its application are necessary if we are to properly predict, assess and mitigate the air pollution implications of emissions to the atmosphere. Science must also be able to provide the evidence of improvements to air quality that result from implementation of the mitigation measure or the control regulation. The ability to assess and mitigate using the precautionary principle is a challenge that science must grasp and position itself to convince decision makers that uncertainty does not mean inertia. The outcomes of such activities must be peerreviewed but they must also be translatable into a suitable format to assist policy makers in reaching sustainable decisions and to build public acceptance and understanding of the nature and scale of the air pollution problem. This important volume brings together contributions from scientist from around the world to present recent work on various aspects of the air pollution phenomena. Notable in each of the nineteen conferences in this series has been the opportunity to foster scientific exchange between participants. New collaborations amongst scientists and between scientists and policy makers or regulators have arisen through contacts made in this series and each meeting has provided a further opportunity for identifying new areas of air pollution science demanding collaborative investigation. Contributions in this the nineteenth volume in the series address a broad range of urgent scientific and technical developments in our understanding of the cause, consequence and management of air pollution. Specifically, papers presented at Air Pollution 2011 provide new data or present critical reviews in the fields of modelling, monitoring and management of air pollution, on emission sources, on the effects of air pollution and on the economic costs of air pollution. The Editors wish to thank the authors for their contributions and to acknowledge the assistance of the eminent members of the International Scientific Advisory Committee with the organisation of the conference and in particular for their support in reviewing the submitted papers. The Editors Malta, 2011

Contents Towards a new framework for air quality management in Nigeria A. O. Olowoporoku, J. W. S. Longhurst, J. H. Barnes & C. A. Edokpayi................................................................................................. 1 Section 1: Air pollution modelling Impact of urban planning alternatives on air quality: URBAIR model application C. Borrego, P. Cascão, M. Lopes, J. H. Amorim, R. Tavares, V. Rodrigues, J. Martins, A. I. Miranda & N. Chrysoulakis .................................................... 13 Air quality model for Barcelona J. Lao & O. Teixidó ........................................................................................... 25 A comparison study between near roadway measurements and air pollutant dispersion simulations using an improved line source model R. Briant, C. Seigneur, M. Gadrat & C. Bugajny .............................................. 37 Regional on-line air pollution modelling system in highly complex terrain P. Mlakar, M. Z. Božnar & B. Grašič................................................................ 47 Identification of potential sources and transport pathways of atmospheric PM10 using HYSPLIT and hybrid receptor modelling in Lanzhou, China N. Liu, Y. Yu, J. B. Chen, J. J. He & S. P. Zhao................................................. 59 Performance evaluation of the ADMS-Urban model in predicting PM10 concentrations at the roadside in Chennai, India and Newcastle, UK S. Nagendra, M. Khare, P. Vijay & S. Gulia ..................................................... 71 Coastal influences on pollution transport D. Peake, H. Dacre & J. Methven ..................................................................... 81

Non-parametric nature of ground-level ozone and its dependence on nitrogen oxides (NOx): a view point of vehicular emissions S. Munir, H. Chen & K. Ropkins ....................................................................... 93 Prediction of TSP concentration in a metallurgical city of Brazil using neural networks M. M. C. Lima.................................................................................................. 105 Section 2: Monitoring and measuring The use of mineral magnetic measurements as a particulate matter (PM) proxy for road deposited sediments (RDS): Marylebone Road, London C. A. Booth, C. J. Crosby, D. E. Searle, J. M. Khatib, M. A. Fullen, A. T. Worsley, C. M. Winspear & D. A. Luckhurst .......................................... 117 Elemental carbon as an indicator to monitor the effectiveness of traffic related measures on local air quality M. H. Voogt, A. R. A. Eijk, M. P. Keuken & P. Zandveld ................................ 129 AMEC multigas passive sampler: a green product for cost-effectively monitoring air pollution indoors and outdoors H. Tang, L. Burns, L. Yang & F. Apon ............................................................ 137 Influence of natural and anthropogenic sources on PM10 air concentrations in Spain M. S. Callén, J. M. López & A. M. Mastral ..................................................... 149 Infrared imaging Fourier-transform spectrometer used for standoff gas detection M. Kastek, T. Piątkowski & H. Polakowski ..................................................... 161 POPs in ambient air from MONET network: global and regional trends I. Holoubek, J. Klánová, P. Čupr, P. Kukučka, J. Borůvková, J. Kohoutek, R. Prokeš & R. Kareš....................................................................................... 173 The EC QA/QC programmes for inorganic gas pollutants testing M. Barbiere, A. Borowiak, F. Lagler, M. Gerboles, M. Kapus & C. Belis ...... 185 GIS for data management of environmental surveys, carried out in Biancavilla (CT) superfund experience S. Bellagamba, F. Paglietti, V. Di Molfetta, F. Damiani & P. De Simone ...... 199 BTEX concentrations in the atmosphere of the metropolitan area of Campinas (São Paulo, Brazil) A. C. Ueda & E. Tomaz ................................................................................... 211

The development of an ESEM based counting method for fine dust particles and a philosophy behind the background of particle adsorption on leaves M. Ottelé, W. J. N. Ursem, A. L. A. Fraaij & H. D. van Bohemen .................. 219 Synthesis of metal oxide nanostructure and its characterization as gas pollutant monitoring B. Yuliarto, M. Faizal, M. Iqbal, S. Julia & T. Nugraha ................................. 231 Buildings as sources of mercury to the atmosphere G. F. M. Tan, E. Cairnsa, K. Tharumakulasingam, J. Lu & D. Yap................ 239 Occupational exposure to perchloroethylene in Portuguese dry-cleaning stores S. Viegas .......................................................................................................... 247 Section 3: Air quality management Health impact assessment of PM10 and EC in 1985–2008 in the city of Rotterdam, The Netherlands M. P. Keuken, P. Zandveld, S. van den Elshout, N. Janssen & G. Hoek ......... 257 Assessing air pollution risk potential: case study of the Tohoku district, Japan Y. A. Pykh & I. G. Malkina-Pykh..................................................................... 267 Assessing the potential for local action to achieve EU limit values J. H. Barnes, T. J. Chatterton, E. T. Hayes, J. W. S. Longhurst & A. O. Olowoporoku...................................................................................... 277 A procedure for the evaluation of the historical trend of atmospheric pollution in an urban area F. Murena & M. Urciuolo ............................................................................... 287 Section 4: Aerosols and particles Correlation between the mass of PM2,5 and the chemical composition of acid aerosols in the northwest of the metropolitan zone of Mexico City Y. I. Falcón, E. Martinez & L. Cortes.............................................................. 301

Characteristics of aerosol particle size distributions in urban Lanzhou, north-western China Y. Yu, S. P. Zhao, D. S. Xia, J. J. He, N. Liu & J. B. Chen .............................. 307 Particulates in the atmosphere of Makkah and Mina valley during the Ramadan and Hajj seasons of 2004 and 2005 A. R. Seroji....................................................................................................... 319 Section 5: Emissions studies Effect of biodiesel and alkyl ether on diesel engine emissions and performances D. L. Cursaru, C. Tănăsescu & V. Mărdărescu .............................................. 331 Emissions of selected gas pollutants in the application of the additive EnviroxTM F. Bozek, J. Mares, H. Gavendova & J. Huzlik ............................................... 343 Non-thermal plasma abatement of trichloroethylene with DC corona discharges A. M. Vandenbroucke, A. Vanderstricht, M. T. Nguyen Dinh, J.-M. Giraudon, R. Morent, N. De Geyter, J.-F. Lamonier & C. Leys ............ 353 Monitoring of atmospheric dust deposition by using a magnetic method A. Kapička, E. Petrovský & H. Grison ............................................................ 363 Improving car environmental and operational characteristics using a multifunctional fuel additive E. Magaril........................................................................................................ 373 Section 6: Global and regional Application of methanotrophic biofilters to reduce GHG generated by landfill in Quebec City (Canada) N. Turgeon, Y. Le Bihan, G. Buelna, C. Bourgault, S. Verreault, P. Lessard, J. Nikiema & M. Heitz ..................................................................................... 387 A study of the atmospheric dispersion of an elevated release with plume rise in a rural environment: comparison between field SF6 measurements and computations of Gaussian models (Briggs, Doury and ADMS 4.1) C. Leroy, F. Derkx, O. Connan, P. Roupsard, D. Maro, D. Hébert & M. Rozet....................................................................................................... 399

Ozone pollution during stratosphere-troposphere exchange events over equatorial Africa K. Ture & G. Mengistu Tsidu .......................................................................... 411 Section 7: Economics of air pollution control Environmental tools of atmospheric protection in the Czech Republic O. Malíková & M. Černíková .......................................................................... 423 Environmentally related impacts on financial reporting: the case of pollution permits in Czech legislative conditions J. Horák & O. Malíková .................................................................................. 433 Section 8: Health effects Analysis of lung cancer incidence relating to air pollution levels adjusting for cigarette smoking: a case-control study P. R. Band, H. Jiang & J. M. Zielinski ............................................................ 445 Comparison of fungal contamination between hospitals and companies food units C. Viegas, M. Almeida, C. Ramos, R. Sabino, C. Veríssimo & L. Rosado....... 455 Author Index .................................................................................................. 463

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Air Pollution XIX

1

Towards a new framework for air quality management in Nigeria A. O. Olowoporoku1, J. W. S. Longhurst1, J. H. Barnes1 & C. A. Edokpayi2 1

Air Quality Management Resource Centre, University of the West of England, Bristol, UK 2 Department of Marine Sciences, Faculty of Science, University of Lagos, Nigeria

Abstract Since 1988 the Nigerian Government has introduced environmental legislation aimed at reducing the atmospheric impact of various sources of pollution. Emphasis has often been placed on mitigating pollution from the oil and gas industry. However, various studies indicate significant ambient air pollution from other sources due to vehicular traffic growth in urban areas, increased reliance on petrol and diesel fuelled generators for electricity supply in homes and other public facilities, uncontrolled open incineration of waste and major thermal power stations within the city limits. In this paper, we make the case for the establishment of risk-based air quality management approach based on monitoring, modelling and assessment of these other sources. We outline four important elements that should be considered in order to achieve this recommended approach. These elements are conceptualised within the existing institutional, organisational structures and capacity in Nigeria. Keywords: Nigeria, air quality management, air pollution, air quality standards and objectives, environmental legislation, environmental policy, NESREA, traffic-related emissions.

1 Air pollution as an immediate concern Nigeria has a population of 140 million people, a large percentage of which reside in major cities such as Lagos, Kano, Abuja, Port Harcourt and Kaduna (National Bureau of Statistics [1]). Lagos has been identified as one of the fastest WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line) doi:10 2495/AIR110011

2 Air Pollution XIX growing megacities in the world, with the potential of becoming the most populous city in Africa by 2015 (Gandy [2]; Ibem [3]). However, the cost of population growth is not limited to the demand for water, food and energy resources, but also includes the effect of the increased use of such resources on public health and quality of life. Urban population growth implies that the residents of such cities will increase their demand for journeys through vehicular transport means (Chatterton et al. [4]). Traffic-related pollutants, derived from the use of vehicular transport modes such as cars, are associated with effects ranging from poor public health, built and natural environmental degradation and global climate change (Paulley [5]). Studies have shown that the level of air pollution in Nigeria’s major cities is at a level that could lead to respiratory and cardiovascular diseases in vulnerable individuals (Ogunsola et al. [6]; Efe [7]). Without policy and legislative change in air quality management, increasing numbers of Nigerians living and working in its cities and sprawling urban settlements are at risk from poor air quality. Therefore the policy response must include a rigorous, robust and well-informed strategy of reducing the environmental, social and health impacts of air pollution.

Figure 1:

Map of Nigeria showing major cities (US central intelligence agency [8]).

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2 Institutional and legislative context The institutional and legislative frameworks for pollution control in Nigeria have been viewed as inconsistent and too limited to address the scale and nature of urban air pollution (Achi [9]). High population growth, mass migration to unplanned urban developments and under-regulated industrial pollution in large cities present clear and present threats to the environment as well as to the public health of millions (Adegoroye [10]). Legal and regulatory frameworks are weak and in most cases uncertain on the statutory responsibilities and duties of the government with regard to environmental management and protection (Ogunba [11]). The establishment of the Federal Environmental Protection Agency (FEPA) Act in 1988 provided, for the first time, an attempt at coordinating a statutory and institutional response to environmental pollution (Chokor [12]). However, subsequent policies pursued by the government through the Agency were reactive control measures. Most of the policies were directed at regulating pollution from the oil and gas industries without adequate consideration for other sources and their impacts in densely populated areas. (Adegoroye [10]; Ogunba [11]). The emergence of a new democratic government in 1999 brought, among other things, new hopes for environmental management and protection in Nigeria. The new government created a Federal Ministry of the Environment (FMoE) with a more focused agenda of tackling issues of industrial and urban pollution, marine and coastal resources degradation and the growing threat of desertification. The ministry facilitated major reforms in the environmental legislative and institutional framework. In 2007 the National Assembly repealed the FEPA Act and replaced it with the National Environmental Standards and Regulation Enforcement Agency (NESREA) Act (The Federal Government Printer [13]). The new agency, NESREA, was given the primary responsibility for all environmental laws, guidelines, policies and standards. Part II of the NESREA Act provided statutory enforcement powers and functions of the Agency (The Federal Government Printer [13]). This include responsibilities for “compliance monitoring, the environmental regulations and standards on noise, air, land, seas, oceans and other water bodies other than in the oil and gas sector” (The Federal Government Printer [13]). The corporate strategic plan document published by NESREA identified “improved air quality” as one of the major environmental priorities within its corporate vision (NESREA [14]). In December 2010 the agency undertook a consultation process on various National Environmental Regulations including sections on the Control of Vehicular Emissions from Petrol and Diesel Engines. The establishment of NESREA can thus be seen as a progression from the previous laissez-faire approach to air quality management of previous governments.

3 Air pollution from traffic-related and domestic sources Pollutants from industrial sources, especially from the oil and gas sector in Nigeria have been studied extensively. Sources of emissions include flared gases WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

4 Air Pollution XIX in the Niger Delta, fumes from metal-smelting and cement works, fugitive gases from other chemical and allied industries, and charred particulates and sulphur dioxide emissions from the steel industries (Osuji and Avwiri [15]). These pollutants are not usually confined to the emission point sources. For example, pollutants from flared gases have been observed with concentrations beyond recommended exposure limits in residential communities within 60 m range of the emission source (Obanijesu et al. [16]). Existing Environmental Impact Assessment (EIA) legislation and other pollution control policies have been disproportionately focussed on regulating the oil and gas industries (Ogunba [11]). Conspicuously ignored were the emerging problems from traffic growth, unplanned urban settlements and dependence on wood and kerosene for domestic energy. Various studies conducted in Lagos, Abuja, Port Harcourt, Kano, Calabar, and other major cities in Nigeria, attribute significant emissions to transport, domestic and other industrial sources within close proximity of residential areas (Faboye [17]; Iyoha [18]; Magbagbeola [19]; Oluyemi and Asubiojo [20]). A large proportion of the population are increasingly exposed to air pollution due to growth in vehicular transport and consequent congestion in urban areas, increased reliance on petrol and diesel fuelled generators for electricity supply, and uncontrolled open incineration of waste and major thermal power stations within the city limits (Oluyemi and Asubiojo [20]). Pollution from exhaust pipes is often recognisable without measurements, by reduced visibility, adverse smell and eye irritation on most busy roads (Baumbach et al. [21]). In major cities there are high concentrations of PM10, NO2, CO and VOCs with annual mean concentrations many times greater than the WHO or the Nigerian Ministry of Environment acceptable thresholds (Efe [7]; Koku and Osuntogun [22]). A WHO study in 2007 indicated a growing trend in vehicular-derived air pollution in Lagos due to traffic volume comprising of 2-stroke engines motorcycles (which have higher emissions of particulate matter and un-burnt hydrocarbons than other types of engines) and old imported vehicles (Taiwo [23]). An earlier study also indicated high concentrations of aromatic hydrocarbons, CO and PM especially in areas within close proximity of bus stops and industries within and around Lagos (Baumbach et al. [21]). The level of CO concentrations in Lagos has been shown to be higher than those found in oil-producing cities in the Niger Delta (Abam and Unachukwa [24]). These findings highlight the significance of other sources, such transport, to air pollution beyond that of oil and gas operations. The UK National Centre for Atmospheric Sciences conducted an aerial emissions estimate studies in Lagos using the Atmospheric Research BAe146 aircraft (Capes et al. [25]). The results showed that emissions are attributed to the evaporation of fuels, mobile combustion and natural gas activities around the city. However, Nigeria is among the few countries with no effective procedures or framework for managing ambient air quality (Koku and Osuntogun [22]). There are no coordinated or continuous assessments to inform an appropriate policy framework to manage the local air pollution that residents of cities such as Lagos routinely experience (Taiwo [23]). WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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4 Pathways to a Nigerian air quality management framework While there are various complex political and economic issues that require urgent attention by the Nigerian Government, the need to meet the challenges of urban air pollution is also important. Unlike water, drug or food quality, the impact of urban air pollution is non-discriminatory, and does not recognise the broad social and economic stratum that separate Nigerians. Everyone breathes the same air, including the most vulnerable groups – the children, the elderly and the sick. Managing such a problem requires a cyclic and continuous process. Figure 2 outlines four elements that will be required to initiate and develop a management framework in Nigeria. These elements are conceptualised within the existing institutional, organisational structures and capacity in Nigeria.

Figure 2:

Key elements for developing an air quality management framework in Nigeria.

4.1 Scientific enquiry and monitoring First, there is a need for a government-led scientific inquiry to identify and analyse both the spatial and temporal components of air pollution problems in Nigeria. Such an enquiry will include systematic collation, evaluation and development of an empirical evidence base for ambient air pollution. Deployment of air quality monitoring stations will be necessary across major cities and potential hotspots such as oil and gas production areas. Monitoring air WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

6 Air Pollution XIX quality concentrations across the nation is pivotal to identifying the nature and scale of the air pollution challenge, its sources and impacts. Understanding the science of air pollution provides the ability to assess and mitigate the challenge through robust and evidence-based policies. The body of knowledge on air pollution in Nigeria can be shared and enhanced through research studies and development of professional fora where collaborations and joint-working can be encouraged. An important output of this will be a national emission inventory providing required resources for subsequent air quality assessment, modelling and management options. 4.2 Standards and objectives The scientific recognition of the geography, scale and consequences of the air pollution problem should lead to the determination of relevant standards and objectives against which ambient air quality in Nigeria can be measured. A body similar to the former UK Expert Panel on Air Quality Standards (EPAQS) could be set up and facilitated by NESREA to provide independent advice on concentrations of air pollution at which no or minimal health effects are likely to occur in Nigeria. Although there are still uncertainties with regards to the science of atmospheric pollution, there is sufficient evidence which links poor air quality to a significant public health risk (COMEAP [26]). Based on the best available epidemiological information, the government needs to establish a set of numerical air quality standards and limit values for individual pollutants with the potential to compromise public health. Pollutant concentrations should be riskassessed in relation the costs and benefits of required actions and expressed as air quality objectives setting out the extent to which the government expects the standards to be achieved within a specified timeframe (Longhurst et al. [27]). 4.3 Legislation and regulation Since air quality standards and objectives are designed to protect public health, there is therefore a need for appropriate air pollution regulations to guarantee these standards and objectives. Proposed legislation on air pollution at the National Assembly should include the introduction of regulations, which are shaped by scientific and expert consensus on the definition of the problem. The legislation should introduce a policy framework requiring routine monitoring, assessment and management of ambient air quality to ensure the achievement and maintenance of these standards and objectives. Statutory powers and duties should be conferred on specific governmental institutions such as NESREA or the Federal Ministry of the Environment with regards to air quality. Such powers should include the prohibition and restriction of certain activities or vehicles, the obtaining of information, the levying of fines and penalties, the hearing of appeals and other criteria (HM Government [28]). 4.4 Management and evaluation An important element of the framework is the implementation of legislative requirements to achieve stated air quality objectives. Since the 1980s, the WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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Nigerian Government has introduced reactive legislation and developed institutions aimed at reducing the environmental impact of industrial activities (Chokor [12]). Apart from not being robust enough, subsequent policies emanating from such laws were often impaired by limited technical capacity to implement efficient enforcement and compliance regimes (Adegoroye [10]). For example, there is no specific policy framework for managing or mitigating emissions from light-duty and heavy-duty vehicles and trucks, which are thought to be amongst the most significant contributors to air quality in Nigeria (Taiwo [23]). It is therefore evident that efficient air quality management in Nigeria will rely on suites of proportionate and cost-effective evaluation and management programmes to be undertaken at the local and national level as much as setting standards and regulations. The management framework will need to take account of economic efficiency, practicability, technical feasibility and timescales for achieving legislated air quality objectives. The state government, along with national agencies such as NESREA, will play an important role in setting out and implementing such management procedures. These may include regular reviews and assessments of air quality to identify whether the objectives have been, or will be, achieved at specific geographic locations where public health is, or will be, at risk, by the applicable date. Where applicable, the government should take proactive responsibility for enforcing and implementing appropriate air quality measures that will lead to the achievement of the objectives. This will include source emissions control from both stationary (industries and domestic) and mobile sources (such as transport).

5 Conclusions Establishing an air quality management framework in Nigeria requires the introduction of specific environmental policy reform and legislative changes based on scientific understanding and analysis of the public health risks of air pollution. This paper identifies four important elements that should be considered in order to achieve this. The first element is the development of an empirical evidence base for ambient air pollution through monitoring and analysis of the nature and effect of air pollution problems in Nigeria. The second is the establishment of numerical air quality standards and limit values for individual pollutants with the potential to compromise public health. Third, there is a need for robust legislation and regulations which will guarantee these standards as well as conferring powers and duties on specific governmental institutions such as NESREA and state government agencies with regards to air quality. Last and more importantly, is the introduction of suites of proportionate and cost-effective evaluation and management programmes to be undertaken at the local and national level for achieving the air quality objectives. Significant gains, in terms of quality of life and public health can be achieved if a Nigerian air quality framework is put in place. There are also economic benefits in developing the capacity of Nigerian environmental professional and academic communities to undertake air quality assessment and modelling WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

8 Air Pollution XIX services. The EIA of development projects such as road schemes, commercial and residential developments, industrial developments, airports and mineral extraction, especially in the oil and gas operations will be enhanced by such skills and expertise leading to better environmental outcomes and improved air quality.

References [1] National Bureau of Statistics. Annual Abstracts of Statistics 2009, Federal Republic of Nigeria, 2009. Online http://www nigerianstat.gov.ng/ [Accessed on 10/06/11] [2] Gandy M. Planning, anti-planning and the infrastructure crisis facing metropolitan Lagos. Urban Studies. 43 (2), 371-396, 2006 [3] Ibem E.O. Challenges of disaster vulnerability reduction in Lagos Megacity Area, Nigeria. Disaster Prevention and Management 20(1), 27-40, 2011 [4] Chatterton, T., Coulter, A., Musselwhite, C., Lyons, G. and Clegg, S. Understanding how transport choices are affected by environment and health: views expressed in a study on the use of carbon calculators. Public health. 123(1), 45-49, 2009 [5] Paulley, N. Recent studies on key issues in road pricing. Transport Policy. 9(3), 175-177, 2002 [6] Ogunsola, O J., Oluwole, A F., Asubiojo, O I., Durosinmi, M A., Fatusi, A O., and Ruck, W. Environmental impact of vehicular traffic in Nigeria: health aspects. Science of the Total Environment. 146, 111-116, 1994 [7] Efe, S.I. (2008) Spatial distribution of particulate air pollution in Nigerian cities: implications for human health. Journal of Environmental Health Research, 7(2) online. http://www.cieh.org/jehr/jehr3.aspx?id=14688 [Accessed on 10/06/11] [8] US Central Intelligence Agency. The World Factbook. Online. https://www.cia.gov/library/publications/the-world-factbook/geos/ni html [Accessed on 10/06/11] [9] Achi P. B. U., An update on the Nigerian environment. 3rd International Conference on Quality, Reliability, and Maintenance (QRM 2000) Ed. McNulty GJ Oxford Univ England Consortium Int Activ; Inst Mech Engineers. 2000 [10] Adegoroye, A. The challenges of environmental enforcement in Africa: The Nigerian Experience. The Third International Conference on Environmental Enforcement held in Oaxaca, México, April 25-28, 1994 [11] Ogunba, O.A. EIA systems in Nigeria: evolution, current practice and shortcomings. Environmental Impact Assessment Review. 24, 643–660, 2004 [12] Chokor, B. A. Government policy and environmental-protection in the developing world: the example of Nigeria. Environmental Management. 17 (1) 15-30, 1993

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[13] The Federal Government Printer. National Environmental Standards and Regulations Enforcement Agency (Establishment) Act, 2007. Federal Republic of Nigeria Official Gazette 94(92). 31 July 2007 [14] NESREA. Corporate Strategic Plan 2009-2012: Building Capacity, Enforcing Compliance. A publication of National Environmental Standards and Regulations Enforcement Agency. 2009. Online. http://www.nesrea.org/forms/NESREA%20CSP.pdf [Accessed on 10/06/11] [15] Osuji, L.C., and Avwiri G.O. Flared gases and other pollutants associated with air quality in industrial areas of Nigeria: an overview. 2(10), 1277-89, 2005 [16] Obanijesu, E. O., Adebiyi, F. M., Sonibare, J. A., Okelana, O. A. Air-borne SO2 Pollution Monitoring in the Upstream Petroleum Operation Areas of Niger-Delta. Nigeria. Energy Sources Part A-Recovery Utilization and Environmental Effects. 31 (3), 223-231, 2009 [17] Faboye, O.O., Industrial pollution and waste management. Dimensions of Environmental problems in Nigeria, ed. A. Osuntokun, Davidson Press: Ibadan, pp. 26-35, 1997 [18] Iyoha, M.A., The Environmental effects of oil industry activities on the Nigerian Economy: A theoretical Analysis: Paper presented at National Conference on the management of Nigeria’s petroleum Resources, Department of Economics, Delta State University Nigeria, 2009 [19] Magbagbeola, N. O., The use of Economic Instruments for Industrial pollution Abatement in Nigeria: Application to the Lagos Lagoon. Selected paper, Annual Conferences of the Nigerian Economic Society PortHarcourt, Nigeria, 2001 [20] Oluyemi E.A. and Asubiojo O.I., Ambient air particulate matter in Lagos, Nigeria: A study using receptor modeling with X-ray fluorescence analysis. Bulletin of the Chemical Society of Ethiopia. 15(2), 97-108, 2001 [21] Baumbach, G., Vogt, U., Hein, K.R.G., Oluwole, A.F., Ogunsola, O.J., Olaniyi, H.B., and. Akeredolu, F.A., Air pollution in a large tropical city with a high traffic density - results of measurements in Lagos, Nigeria. The Science of the Total Environment, 169, 25-31, 1995 [22] Koku, C.A., Osuntogun, B.A., Environmental impacts of road transportation in South-Western States of Nigeria. Journal of Applied Sciences. 7 (16), 2536-2360, 2007 [23] Taiwo, O., Carbon Dioxide emission management in Nigerian megacities: the case of Lagos. Presentation at United Nation Environmental Protection. 2009. Online. http://www.unep.org/urban_environment/PDFs/BAQ09_ olukayode.pdf [Accessed on 10/06/11] [24] Abam F.I. and Unachukwu, G.O., Vehicular Emissions and Air Quality Standards in Nigeria. European Journal of Scientific Research. 34 (4), 550560, 2009

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10 Air Pollution XIX [25] Capes, G., Murphy, J. G., Reeves, C. E., McQuaid, J. B., Hamilton, J. F., Hopkins, J. R., Crosier, J. Williams, P. I., and Coe, H., Secondary Organic Aerosol from biogenic VOCs over West Africa during AMMA. Atmospheric Chemistry and Physics, 9, 3841-3850, 2009 [26] COMEAP. Long-Term Exposure to Air Pollution: Effect on Mortality. Report produced by the Health Protection Agency for the Committee on the Medical Effects of Air Pollutants. 2009. Online. http://comeap.org.uk /images/stories/Documents/Reports/mortality%20report%202009.pdf [Accessed on 10/06/11] [27] Longhurst, J.W.S., Beattie, C.I., Chatterton, T.J., Hayes, E.T., Leksmono, N.S. & Woodfield, N.K., Local Air Quality Management as a risk management process: assessing, managing and remediating the risk of exceeding an air quality objective in Great Britain. Environment International 32, 934-947, 2006 [28] HM Government, Environment Act 1995. The Stationary Office: London, 1995

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Section 1 Air pollution modelling

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Impact of urban planning alternatives on air quality: URBAIR model application C. Borrego1, P. Cascão1, M. Lopes1, J. H. Amorim1, R. Tavares1, V. Rodrigues1, J. Martins1, A. I. Miranda1 & N. Chrysoulakis2 1

CESAM & Department of Environment and Planning, University of Aveiro, Portugal 2 Foundation for Research and Technology, Hellas, Greece

Abstract In the last decades, the study of the urban structure impacts on the quality of life and on the environment became a key issue for urban sustainability. Nowadays the relevance of urban planning for the improvement of the interactions between different land uses and economic activities, and also towards a more sustainable urban metabolism, is consensually accepted. A major interest relies on understanding the role of planning on induced mobility patterns and thereafter on air quality, particularly related with the increasing use of private cars. This is one of the main objectives of BRIDGE, a research project funding by the European Commission under the 7th Framework Programme and focused on “SustainaBle uRban plannIng Decision support accountinG for urban mEtabolism”. In this scope, and to evaluate the impact on air quality due to different city planning alternatives (PA), the urban scale air quality modelling system URBAIR was applied to selected areas in Helsinki (Finland), Athens (Greece) and Gliwice (Poland), to estimate traffic related emissions and induced pollutant concentration of different air pollutants, in a hourly basis for the entire year of 2008. For the Helsinki study case the results suggest that urban traffic and building placement considered on the different PA have an influence on local air quality despite no significant concentration levels. In the Athens case study some PA induce a decrease on traffic flows with an improvement of the air quality over the domain. On the contrary, other leads to an increase of PM10 in selected hotspots. The simulations for the Gliwice study case show minor changes between the baseline and the PA, since the proposed interventions do not imply major changes in traffic flows. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line) doi:10 2495/AIR110021

14 Air Pollution XIX URBAIR applications allowed a comparative analysis between current situation and predefined PA in terms of the number of exceedances to air quality thresholds and other parameters established in European legislation. The results provide important information to urban planners and policy makers to choose the best PA according to quality of life standards pursuit by the local authorities. Keywords: sustainability, urban planning, air quality modelling, traffic emissions, integrated air quality system, decision support system.

1 Introduction In the last decades the study of the urban structure impacts on the quality of life and on the environment became a key issue for urban sustainability. Several studies recognize the importance of urban planning for the improvement of the interactions between different land uses and economic activities, and also towards a more sustainable urban metabolism [1]. Urban structure (sprawl or compact) is intimately related with urban fluxes (incoming and outgoing) of material, energy, information, people, etc. [2]. A major interest relies on understanding the role of planning on induced mobility patterns and thereafter on air quality, particularly related with the increasing use of private cars [3]. According to the European Environmental Agency [4] most EU Member States still do not comply with the PM10 limit values (for which the attainment year was 2005 according to Directive 1999/30/EC). Especially in urban areas, the exceedance of the daily mean PM10 limit value is not only a compliance problem but also has important potential adverse effects on human health. The most critical issue for NO2 compliance in European countries is the exceedance of the annual NO2 limit value in urban areas, particularly at measurement stations close to streets [5]. In this context, the current challenge to urban planners and environmental engineers is to reverse the impacts on environment and human health resulting from the problematic cohabitation between intense road traffic and high population densities, as a way to promote a better quality of life to urban populations. Air quality models proves to be an important tool to assess the impact of urban planning alternatives on traffic patterns, on urban air quality allowing the identification and study of hot spots and helping on the definitions of new urban configurations to improve the quality of life for citizens [6–8]. At the same time, the rapid and continuous growth of hardware capabilities opens a vast number of new possibilities to air quality models, especially through the development of online tools, to be implemented in new Decision Support Systems (DSS).

2 Methodology This work presents the development of the Urban Air Quality system (URBAIR) and its implementation, as an on-line tool, into a multi-purpose DSS for sustainable urban planning. In the core of URBAIR system is a second generation Gaussian model, which has been enhanced with a number of WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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functionalities, namely the estimation of road traffic emissions. The model provides air quality patterns for a given spatial domain and time period (usually one year, in compliance with the European Legislation (Directive 2008/50/CE) for different air pollutants, namely: particulate matter with aerodynamic diameter smaller than 10 µm (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2) and carbon monoxide (CO). Because of the capability to simulate the effect of buildings geometry on air pollutants dispersion, URBAIR offers the possibility to assess the impact of urban planning strategies and traffic management scenarios on air quality. 2.1 URBAIR system description URBAIR system integrates a set of pre-processors of urban geometry, meteorological information and air pollutants emission data in a single tool specifically developed to run online in a Decision Support System (DSS) build under a GIS platform. The URBAIR structure is organized into 4 modules as schematically shown in figure 1.

Figure 1:

URBAIR system architecture.

The emission module allows the estimation of road traffic emissions using the code of the Transport Emission Model for Line Sources (TREM) [6], which has been integrated into URBAIR. Because topography and build-up structures characteristics have a significant influence on the dispersion of atmospheric pollutants, in particular in urban areas, transport and dispersion of the emitted air pollutants (gaseous and particles) is modelled applying an improved version of the second generation Gaussian model POLARIS [9], which allows to account for the presence of buildings in the dispersion simulation. In this sense, URBAIR WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

16 Air Pollution XIX requires also the characterization of the spatial variation of terrain surface elevation, buildings 3D coordinates and emission sources location and dimensions, which are usually provided by Geographical Information System maps. The geographic module relies on a Cartesian coordinate system, in which regular and discrete gridded data can be used to characterize and spatially distribute terrain, receptors and sources. Representative terrain-influence heights and ‘projected’ building structures influence are determined following widely used modelling approaches. Topography is specified in the form of terrain heights at receptor locations. The influence of buildings on air pollutants dispersion depends on the orientation of the obstacle relating to the source, the wind direction and the shape of the building. The meteorological pre-processor calculates the parameters needed by the dispersion model, namely the atmospheric turbulence characteristics, mixing heights, friction velocity, Monin-Obukhov length and surface heat flux. The meteorological data needed for this pre-processing stage can be provided by mesoscale meteorological models, or alternatively surface measurements and upper air soundings databases can be used. Meteorological information, geographic and geometric data, and road traffic fluxes constitute the major categories of input data needed by the integrated air quality system URBAIR. The output data includes the estimated emissions from road traffic and pollutant concentration at user-specified receptor points or spatially distributed over a regular grid. The first version of URBAIR was designed for line sources since there are the most important ones in urban environments. New model developments include elevated point sources (such as industrial facilities and combustion activities for residential and services sectors). Different mean averaged concentration values can be defined, depending on the evaluation purposes. 2.2 Study cases description URBAIR system was applied to three European urban areas, selected BRIDGE project case studies, with distinct characteristics namely on dimension and planning attributes: Helsinki, Athens, and Gliwice. With the objective of evaluating the impact on air quality due to different city structure design options, different PA were simulated. The study areas were defined based on detailed information relating the baseline situation and the proposed planning alternatives using ArcGIS maps. Traffic is considered as the main pollutant source in the study areas. Emissions are calculated by the pre-processor TREM using traffic counts provided by each city and average speeds. In URBAIR roads are spatially discretized by defining an adequate number of point sources along each road. Previous sensibility analysis has demonstrated that a spacing of 10 to 15 meters between adjacent point sources guarantees the needed accuracy in the representation of the roads existing in the domain. Meteorological input data, including vertical profiles, were obtained from the WRF mesoscale model simulations over the different case studies domains.

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The intervention area in Helsinki is located in the forest area between current housing of Meri-Rastila and Vartiokylä Bay. The planning objectives for this area are: to provide new housing for the growing metropolitan areas; to provide places of work mixed with housing; to deal with demographic polarization; to move towards more owned dwellings and bigger apartments; to improve services; to maintain sufficient and continuous recreation and habitats; and to improve accessibility to nature areas [10]. Three planning alternatives have been proposed with varying combinations of housing density and office space, and differing relative footprints. These alternatives consider three different building configurations with different number of new roads and, consequently, of traffic fluxes. The URBAIR computational domain, with approximately 4000×4000 m2, and a spatial resolution of 100×100 m2, was defined at the centre of the study area. For the current situation (baseline) the urban built-up area was simplified by considering 234 grouped buildings with different configurations both in geometry and heights. PA1 considers a total of 251 grouped buildings, while in PA2 and PA3, 254 and 263 building blocks, respectively, were defined. All the alternatives imply an increase on the number of roads (see Figure 2). The Athens case study is focused on the municipality of Egaleo, which lies in the Western part of Athens. Five main road axes divide the area in four quarters. One of the quarters is an industrial degraded area called Brownfield (Figure 3). The total area of Egaleo is 650 ha. The intervention area is centred at the Brownfield industrial area. The computational domain has an area of approximately 4000×4000 m2, with a spatial resolution of 100×100 m2. Built-up geometry was simplified by grouping the existing buildings in 151 blocks. No simulations were carried out for PA1, because no changes in urban planning or traffic are foreseen. PA2 implies an increase in the number of buildings. Traffic fluxes were assumed as identical to nearby roads in the Egaleo area. PA3 considers the conversion of the intervention area into a green zone. Consequently, a reduction of 90% in traffic in relation to nearby roads was assumed. Gliwice is a city with an old Town in the central part and residential districts around the centre, with a total area of 134 km2 [6]. The alternatives include: PA1) the construction of a sports hall, which will entail an additional load of people in the area; PA2) the construction of a centre for new technologies, a 7storey building incorporating sustainable energy use (e.g. heat energy from solar collectors, energy recovery, etc.); and PA3) the development of both projects considered in PA1 and PA2. The case study will be mainly assessed with regard to the environmental load in the area (particularly from the point of view of emissions and resource use) and the transport and economic implications to the city. The URBAIR computational domain, with 5400×5400 m2 and a spatial resolution of 100×100 m2, was centred at the intervention zone. 92 rearranged building blocks were defined in URBAIR for the baseline situation. PA1 and PA2 considers the construction of only one additional building (the sports hall and the centre for new technologies, respectively), while for PA3 both were WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

18 Air Pollution XIX defined in URBAIR (Figure 5). The most significant change is the increase of traffic flows due to foreseen attraction of public.

3 Air quality results for baseline and planning alternatives In Figure 2, PM10 simulation results for Helsinki on 25th July 2008 are presented for baseline situation and PA1, PA2 and PA3.

Figure 2:

a)

b)

c)

d)

Comparison of 1.5 m high horizontal 24 hour average [PM10] fields in Helsinki domain, on 25th July 2008 for: a) baseline, b) PA1, c) PA2 and d) PA3. Red rectangle indicates the intervention area. (See online for colour version.)

Comparing the results observed in Figure 2 it is possible to conclude that despite the changes on the number of roads and respective traffic fluxes, and also on the number and location of buildings, the different alternatives do not induce significant modifications on the dispersion patterns. However, and according to the simulations, PA2 and PA3 have a higher influence over the [PM10] in the intervention area and, particularly in PA3, in an area located to the north of the new buildings and roads. In general, [PM10] over the domain stay within the limit value established on legislation for 24 hours average (50 µg.m-3), although some hot-spots are visible where concentrations reach values of 90 µg.m-3 for this particular summer day. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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a)

19

b)

c)

Figure 3:

Comparison of 1.5 m high horizontal 24 hour average [PM10] fields in Athens domain, on 22nd September 2008 for: a) baseline, b) PA2, c) PA3.

Figure 3 presents the simulation results for a specific summer day in Athens, for [PM10] levels, one of the most critical pollutants in this area. Analysing the results presented in figure 3, it is clear that PA3 is the one that presents better results in the intervention area regarding [PM10]. Values as high as 130 µg m-3 were obtained for all the situations, with a strong reduction in the intervention area for PA3. Athens is the only city case in which an air quality monitoring station is located within the study area. Figure 4 presents a time series of simulated and measured [PM10] during the year of 2008. Observed air quality levels were acquired at the Aristotelous air quality monitoring station. The simulated values are from a specific cell of the domain which corresponds to the location of the referred air quality station. In general, simulated values reasonably follow the trend of measured concentrations. However, some underestimation tendency was observed. Possible reasons are the lack of information relating background concentrations and local emission point sources, as well as the no consideration of particulate matter resuspension (only direct exhaust emissions were considered). It can be also inferred from the analysis of Figure 4 that both measured and simulated PM10 concentrations show several exceedances to the legislated limit value of 50 µg m-3. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

20 Air Pollution XIX

Figure 4:

Figure 5:

Comparison of measured and simulated [PM10] in the Aristotelous air quality station for the year 2008 (XY coordinates: 2800 m; 2000 m).

a)

b)

c)

d)

Comparison of 1.5 m high horizontal 24 hour average [PM10] fields in Gliwice domain, on 2nd January 2008 for: a) baseline, b) PA1, c) PA2, d) PA3.

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In figure 5 the PM10 simulation results are presented for Gliwice study case on 2nd January 2008 for baseline situation and PA1, PA2 and PA3. Comparing the results obtained for the baseline situation and planning alternatives, no major differences in [PM10] are visible, showing that the implementation of the new buildings and the increase in traffic fluxes forecast in the nearby roads do not have a significant impact in [PM10]. In order to have a better understanding on the influence of the different alternatives on air quality, table 2 shows the maximum simulated concentrations of PM10, CO, NO2 and SO2 in Helsinki, Athens and Gliwice during 2008. This value corresponds to the maximum concentration calculated by URBAIR for a height of 1.5 meters above ground. From the analysis of the results shown in Table 1 it is possible to conclude that for Helsinki the planning alternatives do not have an influence in the maximum simulated concentrations despite the construction of new roads. Regarding Athens study case, PA2 will lead, according to the simulations, to an increase of the maximum concentrations for all the pollutants considered, while PA3 supports a decrease of the peak concentration when compared with the baseline situation. In Gliwice, baseline scenario and PA1 present the same results, while PA2 and PA3 have lower maximum values. Table 1:

Maximum simulated concentrations of PM10, CO, NO2 and SO2 at 1.5 meters high for Helsinki, Athens and Gliwice in 2008. Study case

Baseline

Helsinki Athens Gliwice

227 248 37

Helsinki Athens Gliwice

1531 5045 451

Helsinki Athens Gliwice

230 382 58

Helsinki Athens Gliwice

84 236 30

Planning alternative 1 2 PM10 [μg.m-3] 227 227 253 37 42 CO [μg m-3] 1531 1532 5526 451 461 NO2 [μg.m-3] 230 230 388 58 68 SO2 [μg m-3] 84 84 240 30 36

3 227 222 42 1532 4995 461 230 370 68 84 228 36

Another analysis was made in terms of the number of exceedances to the limit value of simulated pollutants during the entire year of 2008 for a specific cell of the domain for each study case. This analysis is presented is Table 2. The selected computational cell in Athens and Gliwice corresponds to the location of

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22 Air Pollution XIX the air quality station, although in the latter measurements are not available for 2008. In Helsinki, the selected cell corresponds to the centre of the domain. Table 2:

Number of exceedances to PM10, CO, NO2 and SO2 in Helsinki, Athens and Gliwice during 2008.

Planning alternative Compliance with the Directive? 1 2 3 PM10 Limit value: 50 μg.m-3 [24 hours average] with 35 exceedances allowed Helsinki 0 0 0 0 y Athens 122 122 96 n Gliwice 0 0 0 0 y CO Limit value: 10 mg m-3 [8 hours moving averages] Helsinki 0 0 0 0 y Athens 0 0 0 y Gliwice 0 0 0 0 y NO2 Limit value: 200 μg m-3 [1 hour average] with 18 exceedances allowed Helsinki 0 0 0 0 y Athens 5 5 3 y Gliwice 0 0 0 0 y SO2 Limit value: 350 μg m-3 [1 hour average] with 24 exceedances allowed Helsinki 0 0 0 0 y Athens 0 0 0 y Gliwice 0 0 0 0 y

Study case

Baseline

With the analysis based on the selected cell for each study case, only for the pollutant PM10 in Athens study case were found exceedances in terms of the number permitted by the European legislation. For NO2 some exceedances were forecast but within the accomplishing criteria established in legislation. However, if the selected cell was in a different location, the situation could change and more exceedances might be found. Despite the number of exceedances is beyond the allowed number permitted by the legislation, in PA3 there is a reduction on their number for PM10. With the analysis based on the selected cell for each study case, only in Athens study case and for PM10 were found exceedances to the limit value. Despite the number of exceedances is beyond the allowed number, PA3 can potentially lead to an improvement on the local air quality.

4 Conclusions URBAIR applications allowed a comparative analysis between current situations and predefined planning alternatives in terms of the number of exceedances to air quality thresholds and other parameters established in European legislation. In general, it was concluded from the comparisons of simulated concentrations with WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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measured data that URBAIR presents some underestimation tendency. Among the reasons for this behaviour the followings issues can be raised: • Background concentrations and local emission point sources were not considered, due to lack of information; • Except for Athens study case, average hourly traffic fluxes were calculated from annual values; • Only exhaust emissions were considered (i.e., the contribution of particles resuspension was not taken into account); • Road traffic emissions were estimated based on vehicles count and average speed. This methodology does not allow accounting for the emissions during traffic jams, which can be relevant, especially in Athens, during the peak hours; • Also the contribution of natural events, which can be relevant in some air pollution episodes, was not considered. Despite the small scale of the considered planning alternatives in terms of project dimension and the area of intervention, the results provide important information to urban planners and policy makers to choose the best planning solution according to quality of life standards pursuit by the local authorities.

Acknowledgements The authors would like to acknowledge the financial support of the BRIDGE Project by the European Commission under the 7th Framework Programme, and the Portuguese Ministry of Science, Technology and Higher Education, through the Foundation for Science and Technology (FCT), for the Post-Doc grant of J. H. Amorim (SFRH/BPD/48121/2008) and for the financial support of project INSPIRAR (PTDC/AAC-AMB/103895/2008), supported in the scope of the Competitiveness Factors Thematic Operational Programme (COMPETE) of the Community Support Framework III and by the European Community Fund FEDER.

References [1] Borrego, C., Lopes, M., Valente, V., Neuparth, N., Martins, P., Amorim, J.H., Costa, A.M., Silva, J., Martins, H., Tavares, R., Nunes, T., Miranda, A.I., Cascão, P. & Ribeiro, I., The importance of urban planning on air quality and human health (Chapter 2). Urban Planning in the 21st Century, eds. D.S. Graber & K.A. Birmingham, Nova Science Publishers Inc., 2009. [2] Martins, H., Miranda, A. & Borrego, C., Atmospheric modelling under urban land use changes: meteorological and air quality consequences. 31st NATO/SPS International Technical Meeting on Air Pollution Modelling and its Application, 27 Sept–1 Oct., Torino, Italy. 2010. [3] Amorim, J.H., Lopes, M., Borrego, C., Tavares, R. & Miranda, A.I., Air quality modelling as a tool for sustainable urban traffic management. Air Pollution XVIII. 21-23 June, Kos, 3-14. Greece. WIT Press. 2010.

WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

24 Air Pollution XIX [4] European Environment Agency. Towards a resource-efficient transport system. TERM 2009: indicators tracking transport and environment in the European Union. EEA Report, No 2. 2010. [5] ETC/ACC 2009a: European exchange of monitoring information and state of the air quality in 2007. ETC/ACC Technical Paper 2009/3. [6] Borrego, C., Tchepel, O., Costa. A., Amorim, J. & Miranda, A., Emission and dispersion modelling of Lisbon air quality at local scale. Atmospheric Environment, 37, 5197-5205, 2003. [7] Martins, A., Cerqueira, M., Ferreira, F., Borrego, C. & Amorim, J.H., Lisbon air quality – evaluating traffic hot-spots, International Journal of Environment and Pollution - Vol. 39, Issue 3/4, 306-320, 2009. [8] Borrego, C., Tchepel, O., Salmin, L, Amorim, J.H., Costa, A.M. & Janko, J., Integrated modelling of road traffic emissions: application to Lisbon air quality management, Cybernet. Sys.: An International Journal 35 (5-6), 535-548, 2004. [9] Borrego, C., Martins, J.M, Lemos, S. & Guerreiro, C., Second generation Gaussian dispersion model: the POLARIS model. International Journal of Environment and Pollution - Vol. 8, No.3/4/5/6 pp. 789 – 795, 1997. [10] Bridge Newsletter. Issue 2 may 2010. [11] http://www.bridgefp7.eu/images/pdf/211345_001_DM_NKUA_1_0_2nd_ Newsletter.pdf

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Air quality model for Barcelona J. Lao & O. Teixidó Energy & Air Quality Department, Barcelona Regional, Spain

Abstract Some cities and metropolitan areas have a hard time complying with EU regulations regarding certain pollutant concentration levels. In 2008, Barcelona’s monitoring stations reported NO2 levels above the EU limit of 40 µg/m3. This paper shows the process and results of NO2 dispersion analysis in Barcelona using 2008 as the base year, as well as the results of the 2020 forecast. Barcelona City Council has drawn up an air-quality model as part of the “PECQ” (Energy, Climate Change and Air Quality Plan for Barcelona 2011-2020) to help decision makers implement actions aimed at reducing NO2. In the first stage, a real inventory of vehicles was performed, recording over 90,000 vehicle plates and also measuring 42,000 actual emissions via Remote Sensing Devices. We discovered that the vehicles on the road are newer than the city census vehicles. We also found out that real-world vehicle emissions are 16.2% higher than COPERT. We used GIS tools to compile the geographical inventory of emissions inside and outside the city. The base-year results show that 65.6% of NO2 concentration levels come from vehicles, 8.6% from the residential and commercial, 4.8% from industry, including heat and power production close to the city, 2.1% from Barcelona Port, and 0.1% from Barcelona Airport. The local background contribution was calculated as 10.1% and the regional background accounts for 8.6%. The PECQ Plan will run projects from 2011 until 2020 aimed at reducing NOX emissions in various sectors. Improvements in vehicle technology are also expected. The 2020 forecast scenario shows that NO2 concentration levels will drop by 35% to reach EU standards. Keywords: air quality, air pollution modelling, validation, Barcelona, dispersion modelling, NO2, NOX, PECQ, emission inventory, RSD.

1 Introduction The Barcelona PECQ 2011-2020 (acronym in Catalan of Barcelona’s Energy, Climate Change and Air Quality Plan [1]) is an action plan directed by Barcelona WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line) doi:10 2495/AIR110031

26 Air Pollution XIX city council and developed by Barcelona Regional (a public company) together with the City Council. The general objectives are: to reduce the increase in energy consumption, to reduce the increase in greenhouse-gas emissions associated with the municipality, and to improve air quality in the city, especially as regards NO2 and PM10, with a specific reduction target of 26% for NOX and 39% for PM10 emissions, in order to achieve European objectives for air-quality levels. The PECQ methodology includes a historical analysis of energy, GHG emissions and air quality in Barcelona city, plus a battery of projects and proposals for the next 10 years. It also contains the expected future scenario, including an in-depth dispersion modelling analysis of the city and its surroundings. The PECQ includes other interesting aspects such as the analysis of social attitudes towards energy consumption, and the effects of the PECQ Action Plan on the local and regional economy. The PECQ development process also included extensive consultation with citizens and stakeholders, from the design stage through to drafting of the Action Plan. This paper will focus on the NO2 air-quality model for the city of Barcelona. We will show the methodology, validation process, results of the base case (2008) and Barcelona’s expected air quality by 2020 according to the various policies and measures adopted under the PECQ Action Plan.

2 Barcelona air quality Like other cities, Barcelona exceeds the annual average NO2 concentration thresholds established by the EU to protect human health. This means that cities, regions and countries must adopt new strategies, on various levels, aimed at improving air quality in metropolitan areas. This includes vehicle manufacturers, legislators, citizens, companies, and so on. For years, Barcelona City Council and other public bodies have been working to improve air quality through various measures involving industry and power plants and by promoting renewable energy. Examples of this include the Solar Thermal Bylaw [2] of 1999 or the application of the Barcelona Energy Improvement Plan (2001-2010) [3]. Given that the main source of pollutants is road transport, Barcelona metropolitan area has made remarkable efforts to achieve a modal split change. This has included promoting the integration of public transport fares and the improvement of public transport networks (bus, metro, trams). Barcelona has also expanded the city’s bicycle network and created “Bicing” – a public bicyclerental service with a very low-cost flat rate. Another measure was to increase the roll-out of parking meters for surface parking, in order to make private transport systems less competitive. Despite all the policies designed to make public transport more attractive and to stimulate the modal split change from private vehicles to the public system, there is still some way to go, since the city does not yet fall within the air-quality standards set by Europe. The EU limit value for annual average nitrogen dioxide concentration in 2009 was 42 µg/m3 (including a tolerance margin of 2 µg/m3 WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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© Barcelona Regional, 2010

applicable in 2009). Four out of a total of six measurement stations inside the city exceeded the annual average limit. As shown in Figure 1, NO2 station measurements for Barcelona city and surrounding municipalities exceed EU NO2 limits, meaning that new initiatives are required.

45 µg/m3 El Prat de Llobregat

42 µg/m3 Cornellà de Llobregat

43 µg/m3 L’Hospitalet de Llobregat

Figure 1:

41 µg/m3

62 µg/m3

Barcelona: Sants

Barcelona: Eixample 3

63 µg/m

Barcelona: Gràcia-St Gervasi

46 µg/m3

50 µg/m3

46 µg/m3

Barcelona: Ciutadella

Sant Adrià de Besòs

Badalona

51 µg/m3 Barcelona Poblenou

44 µg/m3 3

40 µg/m

Barcelona: Parc Vall d’Hebrón

Montcada i Reixac

44 µg/m3

Santa Coloma de Gramenet

Annual average NO2 concentration at measurement stations in Barcelona and surroundings (2009).

3 Barcelona urban air-dispersion model Within the PECQ, in order to focus the Action Plan most effectively, it was essential to determine what activities and sectors are responsible for high NO2 concentration levels. A detailed inventory of emissions by sectors has been developed for the base case year 2008 and distributed throughout the territory. This emission inventory was one of the key inputs of the Barcelona AirDispersion Model, a tool that helps decision-makers know what is happening with air quality and what needs to be done in order to improve it. 3.1 Methodology For atmospheric dispersion modelling we used ADMS-Urban [4], developed by CERC in the UK. ADMS-Urban allowed us to calculate NO2 concentration levels based on a Gaussian dispersion model with photochemical reactions and an integrated street canyon model. The entire model is fully integrated in a WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

28 Air Pollution XIX Geographical Information System (GIS) database of emission sources, terrain configurations and other relevant aspects. The main features of the dispersion model used are:  Specific dispersion model for urban and metropolitan areas with resolution down to street level.  Includes a meteorological pre-processing model.  Uses the “FlowStar” module, a processing module designed for hourly flows and turbulence for high-resolution complex plots.  Can use hourly, daily and monthly input profile schedules for each source emission.  Works with the OSPM model, specifically to assess the “Street Canyon” effect resulting from the recirculation of air turbulence among buildings.  Uses the GRS chemistry scheme, a semi-empirical photochemical model which includes the reactions of NO, NO2, O3 and many organic compounds. After gathering the data and in order to map an air-quality model, a highresolution grid was created with up to 150,000 virtual grid points across the territory, plus 50,000 points next to roads using “intelligent gridding” software capability. The result was a mean grid resolution of 35.2 metres in outlying parts of the city and an estimated mean grid resolution of 17.6 metres in the city centre in order to ensure higher accuracy. More than twelve processors were used, working constantly for 30 days. Figure 7 (left) shows the map of NO2 concentration levels after model calibration for 2008. Keep in mind that reality is more complex than the “typical profiles” or mean behaviours introduced in the model. Therefore, unusual traffic jams, fires, construction work, unknown emissions, and other situations can cause deviations between the models and actual data, meaning that model calibration must always be carried out. Actual hourly data from the measurement stations was also compared with modelling results for virtual point detectors. Table 1 shows a comparison of annual mean values and Figure 6 contains a monthly example of hourly comparison. 3.2 Characteristics and emissions of vehicles in Barcelona Since the road transport sector is the main emitter of pollutants, the PECQ established a clear difference with previous studies of vehicle emissions. A realworld characterisation of traffic was developed in order to find out what types of vehicles are driven around the city, as well as their emissions. It is important to mention that previous studies used the vehicle census of the city [5]. We will show below that there is a major “gap” between the traffic on the streets and what is listed in the city census via road tax. An example of this is that the vehicles that use the streets everyday are newer on average than the census vehicles. This is a key point for drawing up effective policies and calculating related NOX reductions. It is also important to have a better idea of actual emissions in order to obtain a better estimate in the air-dispersion model. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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Since it is a diffuse sector, traffic emissions must be determined indirectly, based on vehicle km/year, total number of vehicles, average speed, and methodologies based on emission factors (EF) by vehicle characteristics (fuel type, engine size, weight and technology of the vehicle), such as CORINAIR [6]/COPERT [7]. To improve EF methodology, Barcelona City Council conducted an ad-hoc study [8] over 32 days in May and June 2009, setting up 16 roadside points for measuring vehicle exhaust emissions, with a detector system called “RSD” (Remote Sensing Device [9]). This technology can detect pollutant emissions from vehicle exhaust pipes instantly and in a non-intrusive manner, using infrared and ultraviolet light according to the Lambert-Beer law. This means that vehicles do not have to modify their normal driving patterns and thousands of vehicle license plates and exhaust vehicle emission data can be gathered in just a few hours. The emission data shows the actual emissions of the vehicles, unlike other methodologies based on standard emission factors, and the license plate shows the vehicle type. This makes it possible to know the brand, vehicle model, technical characteristics (power, weight, fuel, age, etc.), and city of residence (census). We gathered 90,000 vehicle license plates and emissions data for more than 42,000 vehicles after RSD exhaust data validation. This study revealed some important aspects:  The average age of all vehicles is 5.7 years.  The cars driving around the city are newer (with an avg. age of 5.53 years) than the city vehicle census (9.13 years). This does not mean that the census does not work properly. The difference only shows that older vehicles get driven less than newer vehicles or, in other words, that people who use their car every day tend to have newer vehicles.  Petrol cars are older (7.58 years) than diesel cars (4.43 years) due to a social trend. In Spain, it is typical to buy a diesel car if your annual mileage is very high, so daily car users buy more diesel cars than weekend car users.  The pre-EURO class displays an interesting behaviour pattern. 20% of the cars in the city vehicle census are pre-EURO; by contrast, the EURO class only represents 8% of cars driven daily in the city streets.  The taxi fleet has an average age of 3.4 years, and the average age of trucks is 6.5 years.  The most common fuel used by vehicles in the city is diesel at 55.1%, followed by petrol at 44.1%, biodiesel at 0.6% and, finally, natural gas at 0.3%.  52% of the vehicles come from outside Barcelona city (they are not included in the municipal census). As for cars, 51% come from outside the city. In this group, 41% are petrol cars and 56% are diesel cars, since it is more cost-efficient to own a diesel car if your annual mileage is very high, as diesel is cheaper and more efficient than petrol. This makes sense considering that 62% [10] of daily car trips in Barcelona are made by people that live outside the city and commute in to work (or vice-versa). This is a very important point, since municipal policies focusing on diesel WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

30 Air Pollution XIX cars (emitting more pollutants than petrol cars) included in the municipal census will not be as effective as regional policies. According to EURO class, 39.2% of vehicles are EURO IV, 34.8% are EURO III, 14.4% are EURO II, 5.5% are EURO I and 5.6% are pre-EURO. It should be noted that the number of EURO V vehicles is symbolic – 0.4% – since this classification only applied to buses and trucks in 2009 (not to cars, vans or motorcycles) and the measurements were taken in mid-2009. The segments with the highest percentage of EURO II vehicle and older are petrol vans (LDV) with 44.2%, diesel buses and coaches with 34.7%, and petrol cars with 32.1%. Average age (in years) of vehicles in Barcelona MEAN

5,66

Special vehicles

3,58

BUS (Natural Gas)

4,86

BUS+COACH (Diesel)

6,85

HDV (Diesel)

6,55

MDV (Diesel)

EURO IV 39,2%

7,11

LDV (mean)

EURO V 0,4%

Pre-EURO 5,6%

EURO I 5,5% EURO II 14,4%

5,85

LDV (Diesel)

5,64

LDV (Petrol)

9,14

MOTORB KES (Petrol)

5,54

CARS (mean)

EURO III 34,8%

5,53

CARS (Híbrid)

1,38

CARS (Diesel)

4,43

CARS (Petrol)

7,58 0

Figure 2:

EURO Class distribution of vehicle traffic in Barcelona (2008): 4.439,16 Mveh-km/year

7,41

MDV + HDV (mean)

2

4

6

8

years

10

Average age of different categories of vehicles (left) and EURO class distribution of vehicles driving around city streets. © Barcelona Regional, 2010.

As mentioned above, we gathered actual exhaust pipe emissions from 42,000 vehicles driving around the streets of Barcelona, and we compared actual emissions from the “RSD” system with COPERT methodology. The average result was that RSD measured 16.2% higher NOX emissions than COPERT considering the same number of vehicles in city driving mode, with an average speed of 21.3 km/h and the weather conditions for May/June. RSD actual vehicle emission data with the annual share of vehicular traffic shows that 34.2% of vehicle NOX emissions are from cars (29.3% from diesel cars and 4.9% from petrol cars), followed by vans (LDV) 17.4%, medium and heavy trucks (MDV and HDV) 15.7%, motorcycles and mopeds 12.3% (in Barcelona, 22.8% of all private transport is by motorbike), private buses and coaches 12.2%, and local public buses 8.2%. The average emission factor for total traffic was 1.1297 grams of NOX per kilometre. Figure 3 shows total emissions and emission ratio by vehicle class, and Figure 4 contains the distribution according to mobility, vehicle emission and trips. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

Air Pollution XIX

1.600

tn NOx/any

NOx emissions of vehicles in Barcelona by vehicle type (2008). TOTAL: 5.014,72 tn/year [COPERT+RSD] [total mean (♦): 1,1297 g/km]

1.400

g/km

11,0090

31 14 12

9,9671

1.200

8,6364

1.000

10 8

800 6

4,4817

600

4

400 200

0,2711

0,8982

0,6074

0,8311

MOTORB KE (Petrol)

LDV (Petrol)

0,0064

1,3240

2

0

0 CAR (Petrol)

Figure 3:

CAR (Diesel)

CAR (Hybrid)

LDV (Diesel)

HDV (Diesel)

NTERURBAN URBAN BUS BUS (Diesel) (Diesel+NatGas)

NOX emissions and emission ratio by vehicle type for Barcelona. © Barcelona Regional, 2010.

4,9% 6,6%

3,3% 8,2%

5,3%

PM

33,0%

veh-km 36,9%

20,3% 16,1%

5,2%

trips

29,3%

CARS (Diesel)

11,5% 16,7%

25,6%

LDV (Diesel)

0,9%

NTERCITY BUS (Diesel) 10,6% 1,4%

8,3%

% 4,2

22,8%

12,3%

Co lo urs w th ho rizo ntal stripes when the main function of the vehicle is to transpo rt goo ds, not people.

1,0%

12% 11% 11%

10,8%

NOx (sorting criteria)

29,3%

5,0%

MOTORB KES (Petrol) HDV (Diesel)

14,2% 21,1%

16,7%

12,2%

Figure 4:

MDV (Diesel)

URBAN BUS (Diesel+NatGas) MDV (Diesel) CARS (Petrol) LDV (Petrol)

Related share of NOX emissions (sorting criteria), PM10 emissions, mobility (veh-km) and trips. © Barcelona Regional, 2010.

3.3 Barcelona 2008 emissions inventory Database quality is one of the most important aspects of air-quality modelling. All the emission sources input into the air-dispersion model are shown below. The base-case year for the emissions inventory is 2008.  Road transport: we used the COPERT emission model plus the XTRA RSD recorded exhaust emission factors in order to include actual emissions from vehicles. Hourly, daily, weekly and monthly traffic profiles were also used.  Residential and commercial: we used the CORINAIR emission factor to estimate NOX emissions from natural gas and LPG. Hourly and monthly profiles were also implemented for this sector.  Industry and power plants: Actual emissions data for isolated emission sources with continuous environmental control was used. Other industrial emissions were estimated using CORINAIR methodology. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

32 Air Pollution XIX  Port: Land traffic, sea traffic and auxiliary vehicles in Barcelona’s Port were taken into account using the COPERT+XTRA RSD system for the first, and the CORINAIR methodology for the second and third. Monthly profiles were implemented for this sector.  Airport: Barcelona Airport is 12 km from the city centre (outside the city limits). NOX emissions were estimated using CORINAIR methodology and taking into account LTO cycle for airplanes and auxiliary vehicles. Hourly and monthly profiles were used for the airport. All these data provide an inventory of NOX emissions for Barcelona and surroundings covering 1,476 km2, of which Barcelona makes up only 7%, with 102 km2. Air-quality modelling involves studying a larger area, since pollution does not respect municipal boundaries. Thus, total NOX emissions for the entire area covered were 34,186 tonnes in 2008, while NOX emissions for Barcelona city were 10,413 tn/y. As regards Barcelona emissions, vehicle are responsible for 5,015 tn/y (4,299 according to COPERT plus 716 according to RSD), making up 48.2% of the city emissions. The second source of emissions was Barcelona Port, with 3,078 tonnes or 29.5% (2,512.5 for sea traffic plus 565,7 for land activity). The third source was industry and power plants, with 1,394 tn/y or 13.4%. And, finally, the residential and services sectors, with 926 tn/y or 8.9%. Figure 5 shows the georeferenced emissions inventory. NOX (kg/year)

Barcelona City emissions (tn/y) AIRPORT

Model area emissions map

n/a

PORT [ships]

2.512,5 (24,1%) 565,7 (5,4%)

PORT [land vehicles]

1.394,5 (13,4%)

INDUSTRY & POWER PLANTS

925,8 (8,9%)

RESIDENTIAL & COMMERCIAL

715,9 (6,9%)

ROAD TRANSPORT [ RSD]

8.000

7.000

6.000

4.000

3.000

2.000

0

1.000

5.000

4.298,8 (41,3%)

ROAD TRANSPORT [COPERT]

Model area emissions (tn/y) AIRPORT

1.607,5 (4,7%)

PORT [ships]

2.512,5 (7,3%)

PORT [land vehicles] 565,7 (1,7%) 10.012,3 (29,3%)

INDUSTRY & POWER PLANTS 1.462,0 (4,3%)

RESIDENTIAL & COMMERCIAL

2.573,5 (7,5%)

ROAD TRANSPORT [ RSD]

© Barcelona Regional, 2010

25.000

20.000

10.000

5.000

0

Figure 5:

15.000

15.453,2 (45,2%)

ROAD TRANSPORT [COPERT]

NO2 emissions map for 2008 (right) and source apportionment for Barcelona city (top left) and for model area (bottom left).

3.4 Structural inputs for the model Certain structural and meteorological inputs were also required in order to model air quality. This is described briefly below:

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33

 Meteorological data: actual hourly weather data for 2008 was used, taken from an urban weather station located in the city.  Cartography: the topography and surface features of the area were taken from the database of the Catalonian Institute of Cartography.  Road infrastructure: city network, highway network and bus network for Barcelona city and surrounding municipalities were introduced in the model with AAWT (annual average weekday traffic).  3-D building model: the 3-D Barcelona building model was used to model the street canyon effect. 3.5 Background concentrations Background concentration levels were based on hourly data from a specific background concentration station located on the coast in the north of Catalonia (Cap de Creus, Girona), 140 km from Barcelona. The annual average concentration levels for 2008 were 4.26 µg/m3 for NO2, 0.30 µg/m3 for NO, and 74.4 µg/m3 for O3 [11]. 3.6 Modelling results and validation process By running the ADMS-Urban model for Barcelona with the geo-referenced emissions inventory and the structural inputs of the modelled area, we obtained an annual average NO2 concentration of 14% below actual concentration levels for virtual point receptors representing real measurement stations. Therefore, 5 µg/m3 of NO2 was added in order to calibrate the model – called “local background concentrations” – and the model was re-run. After the calibration process, a very good estimate was obtained for all station measurement points. Table 1 shows the actual concentration levels for 2008 compared with the modelled values. We also obtained a very good model estimate for hourly concentration levels, as can be seen in Figure 6. Table 1: Barcelona monitoring sites Ciutadella Vall d’Hebrón Eixample Gràcia Poblenou Sants Average value

NO2 model results vs. actual results.

Type of location Urban background Urban background High traffic site High traffic site Moderate traffic site Moderate traffic site ---

Actual NO2 (µg/m3) 42.3 36.5 65.4 62.6 47.4 45.3 49.9

Modelled NO2 (µg/m3) 46.2 37.7 63.2 57.9 41.8 50.0 49.5

Model / Actual (%) 109% 103% 97% 93% 88% 110% 99%

The air-dispersion model for Barcelona revealed than 65.6% of the average annual NO2 concentration level comes from traffic, 8.6% from the residential and services sector, 4.8% from industry and power generation, 2.1% from Barcelona Port, and only 0.1% from Barcelona Airport. In addition, 8.6% comes from background pollution and 10.1% comes from “local background concentrations” after the calibration process. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

34 Air Pollution XIX 200 ug NO2/m3

NO2 REAL HOURLY Barcelona_Gràcia (ug/m3)

160

Weekend

NO2 MODELLED Barcelona_Gràcia (ug/m3)

Weekend

Weekend

Weekend

120 80 40 0 200 160

NO2 REAL ROLLING AVERAGE 24h Barcelona_Gràcia (ug/m3)

ug NO2/m3

NO2 MODELLED ROLLING AVERAGE 24h Barcelona_Gràcia (ug/m3)

120 80 40

Figure 6:

31/10/08 (Fr )

30/10/08 (Thu)

28/10/08 (Tue)

29/10/08 (Wed)

26/10/08 (Sun)

27/10/08 (Mon)

2 /10/08 (Fr )

25/10/08 (Sat)

23/10/08 (Thu)

21/10/08 (Tue)

22/10/08 (Wed)

19/10/08 (Sun)

20/10/08 (Mon)

17/10/08 (Fr )

18/10/08 (Sat)

16/10/08 (Thu)

1 /10/08 (Tue)

15/10/08 (Wed)

12/10/08 (Sun)

13/10/08 (Mon)

10/10/08 (Fr )

11/10/08 (Sat)

9/10/08 (Thu)

7/10/08 (Tue)

8/10/08 (Wed)

6/10/08 (Mon)

/10/08 (Sat)

5/10/08 (Sun)

3/10/08 (Fr )

2/10/08 (Thu)

1/10/08 (Wed)

0

Hourly and 24-hour rolling average actual and modelled NO2 concentration levels for Gràcia measurement station in October 2008. © Barcelona Regional, 2010.

4 Future scenario A Trend Scenario was developed to estimate the future air quality of the city, adding up the effects of the PECQ Action Plan. In general, certain foreseeable aspects were taken into consideration, including: future population, mobility and energy consumption considering new urban projects, social behaviour trends, new infrastructures and public transport systems to be developed over coming years, expected GDP growth, urban waste treatment, future power plants, Barcelona Port and Airport expansion plans, as well as technological improvements, especially in the transport sector with new EURO-class vehicles and the expected penetration of alternative-fuel vehicles. A future reduction of NOX emissions of 26.3% is expected with the “PECQ Scenario” by 2020 (based on the “Trend Scenario” plus the “PECQ Action Plan”). This is equivalent to reducing 2,743 tn/year compared with existing technology. To predict future air quality, we have to assess the impact the PECQ Action Plan will have on future cars, with cleaner vehicles expected thanks to the technological improvements and EU regulations. So a reduction of 1,451 tn/y of emissions is expected by 2020, equivalent to an overall reduction of emissions of 19.2% from 2008 emissions, or an annual average reduction rate of 1.8% from 2008 to 2020. We should point out that the Trend Scenario was calculated with an annual reduction rate of 0.5%. Thus, the PECQ Action Plan will help speed up the reduction rate through projects such as: “Urban mobility action plan”, “High-emissions radar”, “Agreements with the business and transport sectors to reduce diesel use in vehicle fleets”, “New more-efficient bus network”, “Increasing low-emissions vehicles in the urban bus fleet”, “Introducing new power sources for transport, including electric and gas vehicles”, etc. The future PECQ scenario fulfils current EU legislation concerning NO2 air pollution with the modelled annual average below 40 µg/m3 at all city monitoring sites. Table 2 shows expected future concentration levels, while Figure 7 shows the concentration maps for 2008 (left) and PECQ-2020 (right). WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

Air Pollution XIX

Table 2:

35

Expected NO2 concentration levels for future scenario in 2020.

Monitoring site

Type

Ciutadella Vall d’Hebrón Eixample Gràcia Poblenou Sants Average value

Urban background Urban background High traffic site High traffic site Moderate traffic site Moderate traffic site ---

2008 ACTUAL DATA [NO2 µg/m3] 42.3 36.5 65.4 62.6 47.4 45.3 49.9

PECQ-2020 MODEL DATA [NO2 µg/m3] 30.3 24.6 39.9 36.5 29.8 32.7 32.3

© Barcelona Regional, 2010 © Barcelona Regional, 2010 0               10               20     25     30     35      40    45      50               60               70               80                                 100      

NO2  (µg/m3):

Figure 7:

Annual average NO2 concentration map for 2008 (left) and 2020 (right).

5 Discussion and conclusion The Barcelona air-dispersion model was drawn up as part of the Barcelona PECQ 2011-2020 (acronym in Catalan of Barcelona’s Energy, Climate Change and Air Quality Plan). As discussed in the paper, transport is the main culprit for current high NO2 concentrations. This sector is responsible for 48.2% of NOX emissions, and also produces 65.6% of NO2 concentration levels, on an annual average. An important study has been conducted in Barcelona to determine what kinds of vehicles use the city streets every day and their exhaust emissions. The Barcelona air-quality model has been a very important tool for drawing up the PECQ, for understanding local dispersion and detecting main pollutant sectors during the diagnosis stage, and for helping to assess and predict the impact of the measures on the city’s air quality during the policy-making process. With the adoption of the PECQ Action Plan for the year 2020, the Trend Scenario is expected to show an improvement in air quality, with annual NO2 average concentrations below 40 µg/m3 at all city stations.

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36 Air Pollution XIX

Acknowledgements We would like to thank all the institutions that have contributed to the project, particularly to Barcelona Energy Agency and Mobility Depart. of Barcelona City Council, and especially to Barcelona Regional colleagues.

References [1] Barcelona Regional & Energy Agency of Barcelona, PECQ, Pla de l’Energia, el Canvi Climàtic i la Qualitat de l’aire de 2011-2020, March 2011, Barcelona city council, Barcelona. http://www.bcnregional.com/ [2] BOP, Butlletí Oficial de la Província de Barcelona, 14 of march 2006, Num. 62, Pag. 16-22. Barcelona. [3] Barcelona Regional & Barcelona City Council, PMEB. Pla de Millora Energètica de Barcelona. Ed. Ajuntament de Barcelona & Agència d’Energia de Barcelona, 2002, Barcelona. [4] CERC, http://www.cerc.co.uk/environmental-software/ADMS-Urbanmodel html [5] Barcelona Statistics Dept. http://www.bcn.cat/estadistica/catala/dades/ vehicles/index htm [6] EMEP/EEA air pollutant emission inventory guidebook – 2009, http://www.eea.europa.eu/publications/emep-eea-emission-inventoryguidebook-2009 [7] COPERT, http://www.emisia.com/copert/ [8] Barcelona Regional & TechNet S.L., Caracterització del parc mòbil de la ciutat de Barcelona, October 2010, Ajuntament de Barcelona, Barcelona. [9] Technet S.L. http://www.technetsl.es & ESP, http://www.esp-global.com [10] Serveis de Mobilitat Aj, de Barcelona. Dades bàsiques 2008. http://www.bcn.cat/mobilitatl [11] EEA. European Environment Agency. Air quality statistics at reporting stations. http://www.eea.europa.eu/themes/air/airbase.

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A comparison study between near roadway measurements and air pollutant dispersion simulations using an improved line source model R. Briant1 , C. Seigneur1 , M. Gadrat2 & C. Bugajny2 1CEREA,

Joint Research Laboratory, ´ Ecole des Ponts ParisTech / EDF R&D, Universit´e Paris-Est, France 2 Centre d’Etude ´ ´ technique de l’Equipement (CETE) Nord Picardie, France

Abstract Gaussian plume models, which are widely used to model atmospheric dispersion, provide an exact analytical solution for line sources, such as roads, only when the wind direction is perpendicular to the road. Some approximations have been developed to provide an analytical formula for a line source when the wind direction is not perpendicular to the road; however, such formulas lead to some error and the solution diverges when the wind direction is parallel to the road. A novel approach that reduces the error in the line source formula when the wind direction is not perpendicular to the road was recently developed. This model, combined with a Romberg integration to account for the road section width, has then been used to simulate NOx concentrations in two large case studies (1371 road sections for the first case study and 100 for the second). NO2 , NO and O3 concentrations are then computed using the photostationary-state approximation. Finally, NO2 concentrations were successfully compared with near-roadway measurements made at various locations in the domain area (224 locations for the first case study and 70 locations for the second). Results obtained with a standard model used for regulatory applications, ADMS, are also presented. Keywords: Gaussian plume model, line source, polyphemus, ADMS. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line) doi:10 2495/AIR110041

38 Air Pollution XIX

1 Introduction Atmospheric dispersion models are used to estimate the air quality impacts of road traffic emissions for many purposes, such as attainment of ambient air quality standards, health risk assessment and decision support. It may be used for instance to assess the effect of emission control measures or to help select a new road location. It is thus essential to be able to predict with reasonable accuracy the pollutant concentrations associated with vehicle emissions. To that end, analytical models have been developed to simulate the effect of atmospheric dispersion on pollutant concentrations based on an emission rate from a roadway. In open terrain, Gaussian dispersion models are the most commonly used (e.g., [1–4]). Although the Gaussian dispersion formula provides an exact solution to the atmospheric diffusion equation for the dispersion of a pollutant emitted from a point source given some assumptions on stationarity and homogeneity [5], the Gaussian dispersion formula provides an exact solution for the emissions of a pollutant from a line source only in the case where the wind is perpendicular to the line source [6]. It is, therefore, necessary to develop approximations to model atmospheric dispersion from a line source using a Gaussian formulation. One example of such a formulation is that of [7] which reduces the error in the line source formula of [8] when the wind direction is not perpendicular to the road. Although this model performs well for theoretical cases, it has not been evaluated yet with ambient concentration measurements. Here, we briefly present the model developed in [7] and we combine it with a Romberg integration to simulate the road section width (Section 2). Then in Section 3 we present results of comparison between simulations and measurements. We use this model to simulate NOx concentrations in two large case studies (1371 road sections for the first case study and 100 for the second). NO2 , NO and O3 concentrations are then computed using the photostationarystate approximation and NO2 concentrations are compared with near-roadway measurements made at various locations of the domain area (224 locations for the first case study and 70 locations for the second).

2 Gaussian plume model for line sources The Gaussian formulation of the concentration field for a pollutant emitted from a line source is the result of the integration of the point source solution over the line source: Equation (1) (reflexion terms are neglected for simplicity).


y2

C(x, y, z) = y1

Q exp 2πuσy (s)σz (s)



 −z 2 (y − s)2 − ds 2σz2 (s) 2σy2 (s)

(1)

where C is the pollutant concentration in g.m−3 at location (x, y, z), x is the distance from the source along the wind direction in m, y and z are the crosswind distances from the plume centerline in m, u is the wind velocity in m.s−1 , Q is the emission rate in g.s−1 , y1 and y2 the ordinates of the source extremities, WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

Air Pollution XIX

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Figure 1: Schematic representation of the source (xsource , ysource ) and wind (xwind , ywind ) coordinate systems. The wind angle θ is the angle between the normal to the source and the wind direction.

and σy and σz are the standard deviations representing pollutant dispersion in the cross-wind directions in m, computed here with Briggs’s parameterization. In a perpendicular wind case, both source coordinate system and wind coordinate system are identical (Figure 1). Therefore, the distance of the receptor from the source in the wind direction, needed to compute σy and σz , does not change with the integration variable; so no additional approximation is required. For other wind directions, the dependency of standard deviations on the integration variable makes the integration impossible without approximations. Various approximations can be made [6]; we use here a formulation recently proposed by [8]. The Horst-Venkatram (HV) approximation consists in evaluating the integral by approximating the integrand with its behavior near ywind = 0 (see Figure 1). Solving Equation (1) with the HV approximation leads to Equation (2), which provides the concentration field for all wind directions, except θ = 90◦ . The term ucosθ represents the projection of the wind velocity onto the normal direction to the source. However, when the wind is parallel to the line source (θ = 90◦ ), the term cos θ, on the denominator of the equation, makesEquation (2) diverge.  Q −z 2 × C(x, y, z) = √ exp 2σz2 (deff ) 2 2πu cos θσz (deff ) 

 erf

(y − y1 ) cos θ − x sin θ √ 2σy (d1 )



 − erf

(y − y2 ) cos θ − x sin θ √ 2σy (d2 )

 (2)

This solution to the Gaussian equation for a line source has been shown to lead to small acceptable errors compared to an exact solution [8]; nevertheless, some errors remain due to the approximate nature of the solution, especially when the wind is nearly parallel to the line source. In [7] the error made by Equation (2) was computed and parameterized in order to correct the initial formula. For cases where the wind is parallel to the line source, the use of an WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

40 Air Pollution XIX analytical/discretized line source combination, allows one to minimize the error very effectively. Because this combination is only applied for a small range of wind directions, the increase in the overall computational time is not expected to be significant. The objective of this work was to further improve this solution for the concentration field while retaining a computationally-efficient analytical formulation to the extent possible. It provides some improvement in terms of accuracy over previous formulations of the line source Gaussian plume model without being too demanding in terms of computational resources. In addition to what is presented in [7], the model used here also includes a Romberg integration to simulate the road section width. This model was implemented in the modeling platform Polyphemus [9] which is open source and distributed under GNU GPL (http://cerea.enpc.fr/polyphemus) For simplicity, we refer to this new line source model as Polyphemus hereafter.

3 Comparison to measurements The model presented above is evaluated here with actual concentration measurements made by the French technical study and engineering center CETE Nord Picardie. Here we present results of the comparison of simulation results to two cases studies. Those two case studies includes near-roadway air quality estimations measurements by passive tube (to be distinguished from calibrated methods) along with all necessary data required to conduct simulation with Gaussian dispersion models. 3.1 Case study 1 This first case study includes concentration measurements made in Paris region, France during winter 2007 and summer 2008. The dataset contains: • The coordinates of 1371 road sections divided into 5425 smaller, but straight, sections representing a total of 831 km. • The NOx emission rates associated to each road section computed with European model COPERT 3. • Meteorological data required for a Gaussian model: wind velocity, wind direction, cloud coverage. • The measured concentrations at 224 receptor points, averaged over each overall time period of the measurement campaign (1 month in winter and 1 month in summer). • NO2 and O3 background concentrations computed with the Polyphemus Polair3d model [10]. Meteorological data of the specific time period of the measurement campaign were not available. Therefore, for this preliminary study, a generic meteorology of another year was used instead. Although this induces some uncertainty in the results, the use of values averaged over one-month periods minimize the impact of the meteorology. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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(a) Summer campaign with ‘rural’ option for Polyphemus.

41

(b) Winter campaign with ‘rural’ option for Polyphemus.

(c) Summer campaign with ‘urban’ option for Polyphemus.

(d) Winter campaign with ‘urban’ option for Polyphemus.

Figure 2: NO2 concentrations measured and simulated with Polyphemus and ADMS. (note that ADMS results are annual averages rather than periodspecifics values). The computational time required to simulate a whole month is about 2 to 3 hours with a 2, 4 GHz processor. Moreover, because the meteorological situations are independent, several processors can be used concurrently to decrease the computational burden. Figure 2 shows comparison results for each of the 224 receptor points. Several indicators were computed to estimate the error made by the model: N  (Oi − O)(Mi − M ) • Correlation: r =

i=1 N  i=1

(Oi − O)2

N 

(Mi − M )2

i=1

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42 Air Pollution XIX

N

1 • RM SE (root mean square error): RM SE =  (Mi − Oi )2 N i=1 N 1  Mi − Oi • M N E (mean normalize error): M N E = N i=1 Oi

1  Mi − Oi N i=1 Oi where Mi and Oi are the modeled and observed values, respectively. Both ‘rural’ and ‘urban’ land category cases were tested with the Polyphemus model and Table 1 summarizes the results. Polyphemus with the option ‘rural’ has the best performance for the summer campaign whereas it is with the option ‘urban’ that performance is the best for the winter campaign (except for the RMSE which is better with the option ‘rural’). In addition, it can be seen that results are better in summer than in winter. Finally, in the last column of Table 1, results, obtain by the CETE Nord Picardie, with the atmospheric dispersion model ADMS [11] are presented. ADMS is a standard Gaussian dispersion model that is widely used for regulatory applications. Polyphemus seems to give better results but it should be noted that results of the ADMS simulation were averaged over a whole year and do not correspond to the time periods of the measurement campaign. That is the reason why the curve of ADMS in Figure 2 is the same in winter and in summer. Nevertheless, this N

• M N B (mean normalize bias): M N B =

Table 1: Performance indicators of Polyphemus and ADMS for the case study 1. Polyphemus

Summer

ADMS1

Rural

Urban

0.73

0.72

0.71

) 10.08

14.77

12.03

MNE

0.30

0.39

0.56

MNB

0.09

−0.36

0.47

Winter

Polyphemus Rural Urban

Correlation RMSE (in µg.m

−3

Correlation RMSE (in µg.m

−3

ADMS

0.65

0.68

0.67

) 12.43

15.12

13.94

MNE

0.48

0.34

0.72

MNB

0.28

−0.15

0.62

1

Note that ADMS results, compared to period-specific values rather than annual average values and, therefore, should be seen as preliminary. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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(a) Summer campaign with ‘rural’ option for Polyphemus.

(c) Summer campaign with ‘urban’ option for Polyphemus.

43

(b) Winter campaign with ‘rural’ option for Polyphemus.

(d) Winter campaign with ‘urban’ option for Polyphemus.

Figure 3: NO2 concentrations measured and simulated with Polyphemus and ADMS. (note that ADMS results are annual averages rather than periodspecifics values)

results provide some preliminary estimates of the model performance on a large case study. 3.2 Case study 2 Measurements were made in the Lille (France) region in 2010 and the dataset contains: • The coordinates of 100 road sections divided into 362 smaller, but straight, sections representing a total of 29.6 km. • The NOx emission rates associated to each road sections computed with the European model COPERT 4. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

44 Air Pollution XIX

Table 2: Performance indicators of Polyphemus and ADMS for case study 2. Polyphemus Rural Urban

Summer Correlation

ADMS2

0.47

0.54

0.51

10.39

9.43

7.54

MNE

0.35

0.32

0.22

MNB

0.32

0.27

0.13

RMSE (in µg.m

−3

)

Polyphemus

Winter Correlation RMSE (in µg.m MNE MNB

−3

)

ADMS

Rural

Urban

0.54

0.59

0.45

6.15

6.54

9.27

0.11

0.12

−0.04 −0.07

0.18 −0.16

1 Note that ADMS results, compared to period-specific values rather than annual average values and, therefore, should be seen as preliminary.

• Meteorological data required for a Gaussian model: wind velocity, wind direction, cloud coverage. • The measured concentrations at 70 receptor points, average over each overall time period of the measurement campaign (1 month in winter and 1 month in summer). • NO2 and O3 background concentrations measured at a fixed urban background measurement station. This case study is much smaller than the previous one and, accordingly, the computational time required for these simulations was much smaller (a few minutes). Nevertheless, this case study presents two advantages over the previous one. First, meteorological data in this case study, match the measurement campaigns time periods. In addition, emission rates were computed with the more recent model COPERT4, instead of COPERT3 in the previous case study. Figure 3 shows comparison results for each of the 70 receptor points. Performance indicators were computed and are summarize in Table 2. ADMS results, obtain by the CETE Nord Picardie, gives better results for the summer campaign except for the correlation, which is better with Polyphemus with the ‘urban’ option. For the winter campaign, Polyphemus is better with both ‘rural’ and ‘urban’ options.

4 Conclusion The Gaussian plume model of Polyphemus for line sources has been presented and evaluated with two case studies. The first case study a large roadway system, but WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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meteorological data did not match measurements time periods. The second case study is smaller but with the correct meteorological data. Polyphemus performs well on both cases when confronted to both measurements and to ADMS model results. Ongoing work is now to incorporate this Gaussian model into a 3D Eulerian gridded model to constitute a plume-in-grid model, which would allow us to improve the representation of the impact of roadway traffic in Eulerian models.

References [1] Levitin, J., H¨ark¨onen, J., Kukkonen, J. & Nikmo, J., Evaluation of the caline 4 and car-fmi models against measurements near a major road. Atmos Env, 39, pp. 4439–4452, 2005. [2] Berger, J., Walker, S.E., Denby, B., Berkowicz, R., Fstrøm, P.L., Ketzel, M., H¨ark¨onen, J., Nikmo, J. & Karppinen, A., Evaluation and inter-comparison of open road line source models currently in use in the nordic countries. Boreal Env Res, 15(319–334), 2010. [3] Venkatram, A., Isakov, V., Seila, R. & Baldauf, R., Modeling the impacts of traffic emissions on air toxics concentrations near roadways. Atmos Env, 43, pp. 3191–3199, 2009. [4] Chen, H., Bai, S., Eisinger, D., Niemeier, D. & Claggett, M., Predicting nearroad PM2:5 concentrations: comparative assessment of caline4, cal3qhc, and aermod. Transportation Research Record, Journal of the Transportation Research Board, 2123(26–37), 2009. [5] Csanady, G., Turbulent diffusion in the environment. D Reidel Publishing Company, Dordrecht, The Netherlands, 1973. [6] Yamartino, R., AIR QUALITY MODELING - Theories, Methodologies, Computational Techniques, and Available Databases and Software. Vol IIISpecial Issues. EnviroComp Institute and the Air & Waste Management Association, 2008. [7] Briant, R., Korsakissok, I. & Seigneur, C., An improved line source model for air pollutant dispersion from roadway traffic. Atmos Env, 2010. In press, doi:10.1016/j.atmosenv.2010.11.016. [8] Venkatram, A. & Horst, T., Approximating dispersion from a finite line source. Atmos Env, 40, pp. 2401–2408, 2006. ´ [9] Mallet, V., Qu´elo, D., Sportisse, B., Ahmed de Biasi, M., Debry, E., Korsakissok, I., Wu, L., Roustan, Y., Sartelet, K., Tombette, M. & Foudhil, H., Technical Note: The air quality modeling system Polyphemus. Atmos Chem Phys, 7(20), pp. 5479–5487, 2007. [10] Roustan, Y., Pausader, M. & Seigneur, C., Estimating the effect of onroad vehicle emission controls on future air quality in paris, france. Atmos Environ, 2010. In press, doi:10.1016/j.atmosenv.2010.10.010. [11] McHugh, C., Carruthers, D., Higson, H. & Dyster, S., Comparison of model evaluation methodologies with application to ADMS 3 and U.S. models. Int J Env Pollut, 16(1–6), 2001. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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Regional on-line air pollution modelling system in highly complex terrain P. Mlakar, M. Z. Božnar & B. Grašič MEIS doo., Slovenia

Abstract In the paper, a national project with the title “Prognostic and diagnostic integrated regional air pollution modelling system” is described. It shows that such a project can significantly contribute to the proper understanding of the air pollution in smaller regions with a very complex topography. It also describes how foreseen scientific problems were solved and the necessary testing, improvements and validation were made. The development of a Lagrangian particle model-based air pollution modelling system that works in an on-line diagnostic mode and covers air pollution from several industrial and other sources in the region over a highly complex terrain, is described. To achieve online efficiency some new methods of obtaining high resolution short-range meteorological fields derived from mesoscale models were developed and the implementation of advanced Lagrangian models, acceleration techniques and novel approaches for whole system integration are presented. The project’s test bed was established as a novel approach to the overall treatment of the scientific – applicative project goal. Keywords: on-line regional air pollution modelling system, Lagrangian particle dispersion model, Zasavje region, test bed, complex terrain, non-hydrostatic numerical weather forecast model, nesting.

1 Introduction In some areas of Slovenia, local inhabitants or environmental associations are strongly against the operation of some industrial plants due to the air pollution they cause. These problems are regulated by the European directive of Integrated Pollution Prevention and Control (IPPC) which requires among others that the industrial source’s influence on the ambient air is modelled once to obtain the WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line) doi:10 2495/AIR110051

48 Air Pollution XIX IPPC permit. The future European directives that are now in the preparatory phase also emphasize the use of air pollution models in an on-line mode for informing the local community, as the state-of-the-art science is enabling this already. In this paper, a national project with the title “Prognostic and diagnostic integrated regional air pollution modelling system” is described. It shows that such a project can significantly contribute to the proper understanding of the air pollution in smaller regions with a very complex topography.

2 Zasavje region In Zasavje, air pollution is a very serious problem. Pollutant concentration limit values in the atmosphere are exceeded many times; currently the greatest problem is due to dust particles PM10. Particle PM10 (dust fraction that is regulated by the EU directive) air pollution is one of the most important air pollution problems in Slovenia and also in the EU. Several measurement sites show that the basic norms for daily and yearly PM10 concentrations are not being achieved. Based on this fact, the European commission reminded Slovenia in June 2008 that actions should be made to achieve the EU directives’ requirements for a lower ambient air pollution due to the particles PM10 (especially the new Directive 2008/50/ES on ambient air quality and cleaner air for Europe). Unfortunately the measures taken were not sufficient and therefore in July 2010 the European Commission started suing Slovenia in the Court of the European Union regarding this matter. An air pollution modelling system in the atmosphere provides some answers regarding the causes for pollution and pollution mechanisms; in particular, it gives the answer to the spatial and temporal distribution of pollution. Although measuring stations provide very accurate results, mainly in terms of the highly complex terrain, these data represent only a very small area in the immediate vicinity of the measuring station. Highly complex orography and consequently very complex micrometeorological conditions over the small area of the municipalities of Zasavje represent a considerable scientific challenge for modelling both the meteorological conditions and the spread of pollutants in the atmosphere. This project will contribute towards a new dimension of the general understanding of the problem of the air pollution. From a scientific point of view, one of the important objectives of this project is to demonstrate the correlation (in space and time) between the modelled concentrations and the measured concentrations at the locations of the numerous automatic measuring stations in Zasavje.

3 Regional air pollution modelling system At the European level, the European Commission in cooperation with the European Environment Agency set up a forum FAIRMODE [1], responsible for guidance on good practice and the correct usage of modelling tools for any purpose regarding the regulatory use, that is the use with the aim of modelling the concentration of pollutants in the atmosphere for pollution control purposes. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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The regional air pollution modelling system was developed within this project and is fully consistent with all the requirements imposed by FAIRMODE. Among the most important requirements, there are requirements for a previously successful validation of the modelling system in a similarly complex area (size of domain, terrain complexity and most of all a similar complexity of meteorological conditions) [2, 3]. Furthermore, it requires that when using modelling systems in order to assess the impacts of industrial sources, that the cell size in the horizontal direction is at the most 250m. The air pollution modelling system is a mathematical tool which illustrates the mechanism of spreading the pollutants into the atmosphere. The modelling system based on the input data regarding the meteorological conditions and pollutant emissions, calculates the consequences of these emissions as the concentration in the atmosphere in the area of emission sources are taken into consideration. Emission sources that have not (yet) been entered into the system, are not shown by the modelling system (emissions from other industries, traffic, local furnaces, biomass combustion and combustion of wastes in the countryside etc.). The modelling system that allows these calculations for the shown area in Zasavje is composed of several models and uses different input data. General presentation of the model is illustrated on Figure 2. For the most accurate matching of the modelled concentrations with the measured concentrations, high-quality input data are of key importance, especially the qualitative measured meteorological data in the area discussed. For now, the prognosticated data on their own are not yet a sufficient basis for modelling the air pollution spreading over such a complex terrain as Zasavje. In order to achieve the matching results of the models with the actual measured concentrations in space and time it is also necessary to include the local meteorological data measured by appropriate meteorological models. 3.1 Emission In order to obtain a quality modelling system it is necessary to describe the emission as much as possible. This is only possible with industrial sources which emit pollutants through stacks. Part of the emissions cannot be included in the industrial sources as they cannot control the emissions through the stacks. In 2010, we teamed up with four major industrial sources which release the emissions into the atmosphere of Zasavje: the thermal power plant Termoelektrarna Trbovlje, the cement factory Lafarge Cement, the glass product manufacturing company Steklarna Hrastnik and the building material company IGM. They have presented us with a detailed description of the physical features of the dischargers (height, diameter, flow rate etc.) and the amount of pollutants that can be emitted into the air. These data are from the environmental protection permit which must not be exceeded.

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50 Air Pollution XIX 3.2 Measured meteorological data The test bed of the regional air pollution modelling system which was started in 2010 includes the integration of the meteorological data from the national network for monitoring air quality of the Slovenian Environment Agency (Agencija za okolje Repulike Slovenije) and from the environmental measuring system of Termoelektrarna Trbovlje. Measured environmental data from stations are presented in Table 1. Station locations are shown on the right hand side of Figure 1. Table 1:

Figure 1:

Measured environmental data at the automatic stations.

Zasavje region (left - 3D picture of orography, right - 2D picture with station locations).

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Figure 2:

Regional air pollution modelling system.

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52 Air Pollution XIX 3.3 Meteorological model In order to perform a diagnostic operation of our 3D air pollution modelling system we need prognostic data for the wind prognosis and temperature profile above the domain (as the only available measurements were performed on the ground), as measurements with the balloon are performed only once per day and those are in Ljubljana, and other advanced measurements (SODAR, RASS etc.) are not available for Zasavje. In the previous year, we chose the WRF Weather Research & Forecast model [4], which consists of two Meso models ARW (Advanced Research WRF), maintained by NCAR [5], and the NMM (Nonhydrostatic Mesoscale Model), maintained by NOAA/NCEP [6]. The model frame (data acquisition, data processing) is common; the difference is only in the dynamic core. Both models were installed in different resolutions and with different numbers of nesting on a four core computer (Dual Core Quattro) with a 64 bit system Open SUSE. Boundary and initial data are provided by a global meteorological model GFS (NCEP centre from America). For our project, the module ARW was chosen, which was intended for research and the module NMM was intended for more routine weather prediction – however, we were interested in more specific meteorological features. The configuration of the ARW model which has been running daily from February 2010 is as follows: - two domains; - duration of forecasting: 2 days and 3 hours; - larger domain (Central Europe): 101 × 101 pixels in a resolution of 12km per hour; - smaller domain (Slovenia with surroundings): 76 × 76 pixels in a resolution of 4km per 30min; - the model starts running at 5:00 UTC; - the simulation runs from 3 to 4hours. An example of cloudiness prediction with the nesting method on two areas of different sizes is shown in Figure 3 with an illustrative presentation of the size of each domain. 3.4 Air pollution dispersion model At first, in order to register the meteorological conditions for every 30 minutes in a continuous mode, meteorological data from the automatic measuring stations in this area are used. As it is also essential to describe the vertical profile of the wind, temperature and relative air humidity, an approximation using a profile calculated by a prognostic meteorological model is used. Measurements with the SODAR would be more appropriate, unfortunately they are not available at the moment. All the data are processed by a meteorological pre-processor SurfPro and a three dimensional mass consistent wind model Swift [7–9] (both products by the company Arianet Srl).

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Figure 3:

Example of forecast with the nesting.

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54 Air Pollution XIX The modelling system also implements the data on the terrain altitude and land use, both in the above mentioned resolution. Then, the movement of pollutants from their sources towards the hills and valleys is entered in this three-dimensional meteorological area, resulting in the calculated pollutant concentrations as shown in the Figures. For this part, a numerical Lagrangian particle model Spray [9] by the company Arianet Srl is used. The above-mentioned approach in the current state of science in this area with complex terrain provides the best results. This is evidenced by the articles published in the scientific journals [2, 3]. The examples of the model results are shown in the lower part of Figure 2, on the left hand side is shown one of the modelled ground concentrations and on the right hand side, there is shown a 3D picture of the release of one of the sources.

4 KOOREG - public internet portal All the results from the developed air pollution modelling system for Zasavje are given in real time and are available on-line in the portal site named KOOREG – Air pollution control in the region. The portal site shows a concentration of pollutants SO2, NO2 and PM10 due to the operation of different sources of emissions into the atmosphere of Zasavje (http://www.kvalitetazraka.si). During the first phase of the project, the portal site already shows the concentration of pollutants in the air in real time for each 30 minutes, for the past 2 days. The project specifically authorized the use of their nominal (normal maximum operating) emission values: The thermal power plant Termoelektrarna Trbovlje, the cement plant Lafarge Cement, the glass product manufacturing company Steklarna Hrastnik and the building material company IGM Zagorje. For now, only concentrations in the atmosphere which are the result of emissions from these sources are shown. In the modelling system these values are used as if these facilities are operating 24hours every day of the year, therefore the users of the portal site are asked to check the (non-) operation with the individual participant. In the future, we plan to use the simultaneous and automatic use of the emission values (where this is of interest). Besides the concentration, the portal site also offers a current weather forecast for 48 hours in the future. There are forecasts for precipitation, cloudiness, wind, visibility, air pressure and temperature. The portal is also equipped with a non-technical description of the modelling system and the portal site which is intended for the general public. The description is made in order to offer a concise and as simple and non-technical form as possible so that the end users would be able to become acquainted with the content of the portal site. Figure 4 shows the home page. In the upper menu bar, general descriptions of the portal site, the modelling system and contact information are given.

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Figure 4:

55

Public portal KOOREG (http://www kvalitetazraka.si).

This is followed by the first group of results of the modelling system, consisting of a weather forecast. By clicking on one of the group pictures (e.g. temperature) another window opens, providing a detailed review of the predictions for 48 hours with an animation effect, as shown on the left hand side of Figure 5. The second group of results of the modelling system is the average pollutant concentrations for every 30 minutes for the past 24 hours. By clicking on each picture another window opens with a detailed review of the last available concentrations. This is shown in Figure 6. In addition to the last concentration, there is also the criteria description for each pollutant and a legend which allows the user to determine the area with critical situations in a clear and simple way. By clicking on the animation capture another window opens which provide a detailed review of the predictions for the past 24 hours with an animation effect, as shown on the right hand side of Figure 5.

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Figure 5:

Animation of results (left – temperature forecast for 48 hours in the future, right – air pollution ½-hour concentrations for the last 24 hours) (TEST OPERATION).

Figure 6:

Example of ½-hour air pollution concentration of NO2 from TPP Trbovlje.

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5 Conclusions The regional air pollution modelling system gives a completely new way of monitoring the air pollution with the main purpose of providing a better living environment in Zasavje. The air pollution modelling system in the atmosphere provides several answers regarding the causes and pollution mechanisms, in particular the answer regarding the spatial and temporal distribution of the pollution. It is important that the modelling system used a set of models which had demonstrated in previous validations [2, 3] a well grounded matching of the modelled and the measured concentrations in space and time on this and a similarly complex terrain in Slovenia. The modelling system is intended for both the residents and the polluters. The portal site KOOREG with the modelling results is supposed to help people in order to think about pollution in a more comprehensive manner. They will also be able to find out in the portal site which of the polluters is responsible for the pollution. As one of these four polluters will not always be responsible for the pollution, eventually the system will try to also include smaller sources of air pollution which do not fall under the IPPC Regulation yet are strongly polluting the atmosphere in their immediate vicinity due to their unfavourable location. In the near future, the portal site will also be updated with the pollution prognosis for one day in advance. The pollution forecast will also be of help to the polluters who will be able to reduce the emissions for the approaching day and thereby help to mitigate the concentration of pollutants in the atmosphere.

Acknowledgements The authors gratefully acknowledge the financing of the project by the Slovenian Research Agency, Project No. L1-2082. The authors would like to acknowledge all the four companies mentioned in this article (Termoelektrarna Trbovlje, Lafarge Cement, Steklarna Hrastnik and IGM Zagorje) for their voluntary participation in this project and their permission to use their nominal operating emission values.

References [1] FAIRMODE., Guidance on the use of models for the European Air Quality Directive, working document of the Forum for Air Quality Modelling in Europe FAIRMODE ETC/ACC report Version 6.2, Editor: Bruce Denby, In: FAIRMODE. Available from: http://fairmode.ew.eea.europa.eu/ fol429189/forums-guidance/model_guidance_document_v6_2.pdf, 08.03.2011, 2010 [2] Grašič B., Božnar M. Z., Mlakar P., Re-evaluation of the Lagrangian particle modelling system on an experimental campaign in complex terrain, Il Nuovo Cimento C, Vol. 30, No. 6, pp. 557-575, 2007 [3] Božnar M., Mlakar P., Grašič B., Air pollution dispersion modelling around Thermal power plant Trbovlje in complex terrain: model verification and WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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[4] [5] [6]

[7] [8] [9]

regulatory planning. V: Borrego, C. (ed.), Miranda, A. I. (ed.). Air pollution modeling and its application XIX : [proceedings of the 29th NATO/OCCMS International Technical Meeting on Air Pollution Modelling and its Application, Aveiro, Portugal, 24-28 September 2007], (NATO science for peace and security series, Series C, Environmental security). Dordrecht: Springer, cop. 2008, pp. 695-696, 2010 WRF, The Weather Research & Forecasting Model, Available from: http://www.wrf-model.org/, 10.05.2011 The National Center for Atmospheric Research (NCAR), NCAR ARW WRF Forecast, Available from: http://www.wrf-model.org/plots/realtime_ main.php, 10.05.2011 National Oceanic and Atmospheric Administration (NOAA)/National Centres for Environmental Prediction (NCEP), NMM Model Analyses and Forecasts, Available from: http://www nco ncep.noaa.gov/pmb/nwprod/ analysis/namer/hiresw/12/model_l.shtml Brusasca G., Tinarelli G., Anfossi D., Particle model simulation of diffusion in low windspeed stable conditions", Atmospheric Environment Vol. 26, pp. 707-723, 1992 Anfossi D., Ferrero E., Brusasca G., Marzorati A., Tinarelli G., A simple way of computing buoyant plume rise in Lagrangian stochastic dispersion models, Atmospheric Environment Vol. 27A, pp. 1443-1451, 1993 Tinarelli G., Anfossi D., Bider M., Ferrero E., Trini Casteli S., A new high performance version of Lagrangian particle dispersion model SPRAY, some case studies., Air pollution modelling and its Applications XIII, S. E. Gryning and E. Batchvarova eds., Kluwer Academic / Plenum Press, New York, pp. 499-507, 2000

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Identification of potential sources and transport pathways of atmospheric PM10 using HYSPLIT and hybrid receptor modelling in Lanzhou, China N. Liu1,2, Y. Yu1, J. B. Chen1, J. J. He1,2 & S. P. Zhao1,2 1

Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu, China 2 GraduateUniversity of Chinese Academy of Sciences, Beijing, China

Abstract Three-dimensional 4-day backward trajectories arriving at Lanzhou 500m above ground level were calculated every 6 h using HYSPLIT-4 trajectory model for spring (March, April and May) 2001 to 2008. The 8 years were divided into two categories: high dust years (2001, 2002, 2004 and 2006) and low dust years (2003, 2005, 2007 and 2008). Cluster analysis, potential source contribution function (PSCF) model, and concentration-weighted trajectory (CWT) method were used to evaluate the transport pathways and potential source regions affecting PM10 loadings in Lanzhou in spring season. Results indicate that the western and the northwestern pathways, accounting respectively 33% and 19.4% of all trajectories, were major pathways leading to high springtime PM10 loadings in Lanzhou during high dust years. However, the major pathways were the western and the northern pathways in low dust years, accounting for 23.6% and 18%, respectively. There were six potential source regions affecting PM10 concentration in Lanzhou, e.g. the Tarim Basin and the Turpan Basin in Xinjiang, the Qaidam Basin in Qinghai, the Hexi Corridor in Gansu province, the desert and Gobi areas in the middle and west of Inner Mongolia, and the Loess Plateau in the middle of Shaanxi province and eastern Sichuan. Keywords: HYSPLIT, PM10, trajectories, transport pathways, potential sources.

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1 Introduction With the rapid economic growth and accelerating urbanization, the primary pollutant in most Chinese urban areas has changed from SO2 and TSP (Total Suspended Particles) to PM10 (aerosol particles with an aerodynamic diameter less than 10μm). PM10, as atmospheric aerosols, affects the Earth’s climate directly by absorbing or scattering solar and terrestrial radiation, and indirectly by altering cloud formation, microphysical properties, and lifetimes, [1, 2]. In East Asia, high PM10 loadings could arise from natural processes, e.g. surface dust, sea spray, and volcanic dust etc., anthropogenic activities, [2, 3], e.g. the combustion of fossil fuels and industrial production activities etc., and secondary aerosol particles. Dust storms occurring in the desert and Gobi desert in Central Asia and the northern regions of China, are not only a major weather phenomenon influencing the springtime climate in East Asia, but also a main source of atmospheric particles in spring in China. The desert areas in China, which occupy approximately 13% of China’s land area, are major sources of Asian dust. A large number of observations and studies have shown that dust storms in Asia could not only increase the atmosphere aerosol concentrations in the local and adjacent areas, but also transport to the eastern parts of China, Korea, Japan and even across the Pacific to North America, [4–6]. Lanzhou, located in the northwest arid and semi-arid regions of China, is one of the most polluted cities in China. The PM10 concentrations in spring are dramatically affected by dust storms, [7]. Most of the previous studies on air pollution in Lanzhou were focused on the temporal variability of pollutants, [8], local transport and dispersion characteristics of pollutants, [9], the impact of dust weather, [10, 11], and control measurements, [12, 13], with little or no studies on the transport pathways and the source regions that affect air quality in Lanzhou. In order to control air quality, pollution sources must be identified, emission estimates made, and effective management strategies developed. Many studies have shown the signification correlation of the spatial and temporal variation of pollutants, including PM10, [7], mercury, [14, 15], pollen, [16], and dust outbreak, [17], with air mass transport pathways. Hybrid receptor modeling, such as potential source contribution function (PSCF) model and concentrationweighted trajectory (CWT) method, has been used successfully for potential source region identification for PM10 and TGM (total gaseous mercury), [14, 18]. This research aims at identifying the transport pathways and the potential source regions that lead to elevated PM10 concentrations in Lanzhou and quantifying the relative contribution of the source regions to the PM10 loadings during spring (March, April and May) of 2001-2008. The results from this research would not only provide some scientific basis for improving the air quality in Lanzhou and the ecological environment of the surrounding regions, but also accumulate some experience for cities with similar situation in the world.

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2 Data The PM10 concentration data for Lanzhou in spring 2001 to 2008 were calculated from the air pollution index (API) reports for major Chinese cities, [19]. The process used to calculate PM10 concentrations from the API has been described in Zhang et al. [20]. The dust storm records (daily) were obtained from the Meteorological Administration of Gansu Province for the spring 2001 to 2008. The NCEP/NCAR (National Centers for Environmental Prediction and the National Center for Atmospheric Research) Reanalysis archive data were used as meteorological input data for trajectory calculations. The horizontal resolution of the data are 2.5°×2.5°in latitude and longitude, which are archived four times every day (00, 06, 12, 18 UTC).

3 Methods 3.1 Trajectory clustering analysis Three-dimensional 4-day backward trajectories arriving at Lanzhou (Lat: 36.05N, Lon: 103.85E, 1518m above sea level) 500m above ground level (AGL) were calculated every 6 h (00, 06, 12, 18 UTC) using the National Oceanic and Atmospheric Administration (NOAA) HYSPLIT-4 (Hybrid Single-Particle Lagrangian Integrated Trajectory) model, [21], for the spring 2001 to 2008. The eight years were divided into two categories, i.e. high dust years with more than 6 dust storms in spring (2001, 2002, 2004 and 2006) and low dust years (2003, 2005, 2007 and 2008). The final model outputs were hourly backward trajectory endpoints indicating the geographical location and the height of the air parcel. Ward’s hierarchical clustering method was used for all the eight spring seasons based on the mean angle between all pairs of trajectories, [7, 22]. The major transport pathways leading to the elevated PM10 concentrations in Lanzhou during spring could be obtained by combining the trajectory with the daily PM10 concentration data. 3.2 Potential source contribution function (PSCF) A potential source contribution function (PSCF), [23], was used to identify the potential source regions that affect the PM10 loadings during springtime in Lanzhou. The PSCF values for the grid cells in the study domain were calculated by counting the trajectory segment endpoints terminating within each cell. By defining the number of endpoints that fall in the ijth cell as nij and the number of endpoints that corresponds to a PM10 concentration above an arbitrarily set criterion when arriving at Lanzhou in the same grid cell as mij, the PSCF value for the ijth cell can be defined as: PSCFij  mij / nij . (1) Thus, the PSCF value can be interpreted as a conditional probability describing the potential contributions of a grid cell to the high PM10 loadings in WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

62 Air Pollution XIX Lanzhou. That is, grid cells related to high PSCF values are the potential source regions, and the trajectories passing these cells are the major transport pathways leading to high PM10 loadings during spring in Lanzhou. In this study, the spring time daily-averaged PM10 concentration is used as the criterion for counting mij. To reduce the uncertainty of PSCF resulted from small nij values, an arbitrary weight function Wij is multiplied to the PSCF value to better reflect the uncertainty in the values for cells with small nij. The weight function Wij is defined as follows, [7], 1.00, 40  nij  0.70,10  nij  40 . Wij   0.42,5  nij  10 0.17, n  5 ij 

(2)

The weight function reduced the PSCF value when the total number of endpoints in a particular cell was less than about three times the average value of the endpoints per cell (about 40 in this study), [24]. In this study, geographic areas covered by more than 95% of the backward trajectories were selected as the study domain. For the springtimes in high dust years (2001, 2002, 2004 and 2006), the study domain extends from 55° E to 125° E and from 25° N to 65° N, thus composing 11,200 cells 0.5° × 0.5° in latitude and longitude. The total number of trajectory endpoints located in the study domain is 142,784 so there would be about 13 endpoints per cell on average. That is, it is necessary to reduce the uncertainty of PSCF values by using eqn. (2) when the number of trajectory endpoints nij in a grid cell is less than about 40. 3.3 Concentration-weighted trajectory (CWT) method One limitation of the PSCF method is that grid cells may have similar PSCF values when PM10 concentrations at the receptor site are either only slightly or extremely higher than the average value in spring. The PSCF value can only give the spatial distribution of potential source regions and cannot give information on the relative contribution of different potential source regions. To compensate the limitation, a concentration-weighted trajectory (CWT) method, [18, 25], was used to calculate the trajectory weighted concentration. In the CWT method, each grid cell is assigned a weighted concentration by averaging the sample PM10 concentrations that have associated trajectories crossing the grid cell as follows:

Cij 

1



M

 l 1 ijl

M

C  l 1

l ijl

(3)

where Cij is the average weighted concentration in the ijth cell, l is the index of the trajectory, Cl is the PM10 concentration measured on the arrival of trajectory l, M is the total number of trajectories, and τijl is the time spent in the ijth cell by trajectory l, [18]. The eqn. (2) was also applied to the calculation of CWT to reduce the uncertainties when nij is small. A high Cij value implies that air WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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parcels traveling over the ijth cell would be associated with high PM10 concentration at the receptor site Lanzhou.

4 Results and discussion 4.1 PM10 pollution in spring The occurrence of dust storms during springtime 2001 to 2008 in Lanzhou is summarized in table 1. According to the number of dust storm events, years 2001, 2002, 2004 and 2006 are classified as high dust years and years 2003, 2005, 2007 and 2008 are classified as low dust years. Table 1:

Occurrence of dust storms in spring during 2001–2008.

Year Dust events (num)

2001 15

2002 9

2003 6

2004 11

2005 2

2006 12

2007 5

2008 4

The springtime daily-averaged PM10 concentration for Lanzhou is 238.8μg/m3 in high dust years (fig. 1) and there are 138 days with daily PM10 concentrations higher than the average. The daily-averaged PM10 concentration is 157.2μg/m3 in low dust years (fig. 2) and there are 123 days with higher than average PM10 concentrations. The daily-averaged PM10 concentration for the four spring seasons in high dust years is much higher than the national Grade II

Figure 1:

Figure 2:

Daily-averaged PM10 mass concentrations at Lanzhou in spring for the years 2001, 2002, 2004 and 2006.

Same as fig. 1 but for the years 2003, 2005, 2007 and 2008.

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64 Air Pollution XIX standard for daily mean PM10 concentration of 150μg/m3 (GB3095-1996), while it is slightly higher than the Grade II standard in low dust years. The averages in both categories exceed the Grade II standard by 67.7% and 38.0%, respectively. In addition, there are 51 days (accounted for 13.9%) and 13 days (accounted for 3.5%) when the national Grade V PM10 standard of 420μg/m3 were exceeded during the spring of the high and the low dust years, respectively. 4.2 Cluster-mean backward trajectories 4.2.1 Transport pathways of mean backward trajectories Seven clusters (1 to 7) were produced by the clustering algorithm for the high dust years, and the cluster-mean trajectories are shown in fig. 3(a). Six clusters (1 to 3 and 5 to 7) were obtained for the low dust years (fig. 3(b)). The transport routes and the direction of trajectories indicate the geographical areas traveled by air masses before their arrival at the receptor site. The length of the cluster-mean trajectories indicates the transport speed of air masses. The longer is the clustermean trajectory, the faster is the air mass. It is seen from fig. 3 that the western and the northwestern trajectories (cluster 2 and cluster 3) were longer than trajectories from other directions, indicating that air masses from the west and the northwest moved faster than others.

(a)

Figure 3:

(b)

Cluster-mean back-trajectories arriving at Lanzhou in spring for (a) 2001, 2002, 2004 and 2006 (b) 2003, 2005, 2007 and 2008.

In high dust years, the air mass associated with cluster 1 were from western China, including the north Qinghai-Tibet Plateau, and the Gobi desert around the Qinghai and Xinjiang border. These trajectories moved southeasterly over the Qaidam basin and the border areas of Sichuan and Gansu province, and then turned northerly to Lanzhou. The air masses associated with clusters 2 and 3 were from Xinjiang province. The Cluster 2 air masses were from the Tarim Basin in southern Xinjiang; these trajectories passed the Taklimakan desert, and then moved westerly over the Qaidam Basin in northern Qinghai before arriving at Lanzhou. The Cluster 3 air masses were from the Junggar Basin, north of Xinjiang province; these trajectories moved southeasterly into the Gansu province and travelled along the Hexi Corridor to Lanzhou. The air masses associated with clusters 4 and 5 were initially from the desert, semi-desert and Gobi regions of Mongolia. The cluster 4 air masses moved from the southwestern Mongolia and travelled southeasterly over the Badain Jaran Desert WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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in the west of Inner Mongolia to Lanzhou; in contrast, the cluster 5 moved from the middle of Mongolia and travelled southerly to the middle of the Inner Mongolia, then passed through the Tengger Desert and finally turned southwesterly to Lanzhou. The air masses associated with clusters 6 and 7 were from the Loess Plateau and the border regions between Shaanxi and Sichuan province, respectively. The six clusters in the low dust years are denoted following the high dust years for easy comparison. In the low dust years, the air masses associated with cluster 1 were from the marginal regions south of the Tarim Basin, these trajectories moved westerly through the neighboring regions of Xinjiang, Qinghai and Tibet and crossed the south of Qinghai province, and then turned northerly to Lanzhou. The air masses associated with cluster 3 in the low dust years were from the Junggar Basin, north of Xinjiang province, same as the cluster 3 in the high dust years, but the part of pathway within the Gansu province was different. The air masses associated with cluster 3 in low dust years moved southeasterly over the desert and Gobi desert regions near the border of the Inner Mongolia and the Gansu province to Lanzhou. The trajectories in cluster 5 in low dust years moved over Inner Mongolia and directed to Lanzhou without passing through the Ningxia province, which is different from the one in high dust years. The air masses associated with cluster 6 traveled more southerly in low dust years than the corresponding one in high dust years. The air masses associated with cluster 7 were from Sichuan province in low dust years, as in high dust years, but located more southerly. 4.2.2 Transport pathways of the polluted trajectories All the backward trajectories were divided into two groups, i.e. the polluted and the clean trajectories, according to the sample PM10 concentration when they arrived at the receptor site. The polluted trajectories are those with PM10 concentrations higher than the average in spring seasons, i.e. 238.8μg/m3 and 157.2μg/m3 for the high and the low dust years, respectively. The number of trajectories, the percentage and the corresponding daily PM10 concentrations in each cluster for all trajectories and for polluted trajectories are summarized in tables 2 (for high dust years) and 3 (for low dust years). In high dust years, the average PM10 concentrations of clusters 2 (287.7μg/m3) and 3 (275.5μg/m3) exceed the spring season average 238.8 μg/m3, with more than 50% of trajectories being polluted ones, which indicates that the air masses associated with these clusters would carry more particulate matters and lead to high PM10 loadings in Lanzhou in high dust years. The clusters 2 and 3 represent the western and the northwestern pathways, which passed through major Asian dust source areas, therefore these two pathways, were major pathways carrying dust to Lanzhou. In comparison, fewer trajectories were assigned to clusters 1, 5, and 6 and the corresponding PM10 concentrations are lower than the spring season average, thus these clusters had less effect on PM10 loadings in Lanzhou. Cluster 7 was not a major transport pathway as no dust sources lie along the route. In high dust years, the pathways represented by

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66 Air Pollution XIX clusters 2 and 3 were polluted pathways, while clusters 1, 5, 6 and 7 were relatively clean pathways. In low dust years, about 23.6% of trajectories were assigned to cluster 2, of which 44.0% were polluted trajectories. Cluster 2 represents the most important transport pathways with the highest PM10 concentration (177.1μg/m3) among the six clusters. The second important transport pathway was associated with cluster 5, which had the second high PM10 concentration (170.1μg/m3) and a relatively high percent of polluted trajectories (33.2%). The corresponding average PM10 concentrations of the two pathways, i.e. the western and the northern pathways, represented by clusters 2 and 5, exceeded the spring season average of 157.2 μg/m3. These results indicate that air masses associated with clusters 2 and 5 would carry more particulate matters to Lanzhou and lead to high PM10 loadings in low dust years. The third important pathway was associated with cluster 3. Although 47.8% of the trajectories in cluster 1 were polluted, the number of trajectories assigned to the cluster is small, therefore, the pathway represented by cluster 1 was considered less important for PM10 loadings in Lanzhou. The pathways represented by clusters 2, 5 and 3 were polluted pathways, while clusters 1, 6 and 7 were relatively clean pathways in low dust years. Table 2:

Trajectory number and averaged PM10 concentration of each cluster for high dust years 2001, 2002, 2004 and 2006. All trajectories

Polluted trajectories* PM10 PM10 Percent Percent concentration Number concentration Cluster Number of total of total (μg/m3) (μg/m3) 1 87 5.9 218.1±113.4 21 24.1 381.0±111.1 2 486 33.0 287.7±150.3 256 52.7 399.0±121.8 3 286 19.4 275.5±166.1 148 51.7 402.2±132.5 4 190 12.9 213.3±149.5 61 32.1 389.6±136.7 5 147 10.0 179.9±146.3 29 19.7 431.0±136.1 6 117 7.9 150.5±88.0 11 9.4 362.6±68.5 7 159 10.8 184.8±90.9 26 16.4 342.5±85.8 *Trajectories associated with those concentrations higher than the average (238.8 μg/m3).

Table 3:

Same as table 2 but for low dust years 2003, 2005, 2007 and 2008. All trajectories

Polluted trajectories* PM10 PM10 Percent Percent concentration Number concentration Cluster Number of total of total (μg/m3) (μg/m3) 1 161 10.9 171.6±90.2 77 47.8 235.0±91.9 2 348 23.6 177.1±112.5 153 44.0 255.8±129.5 3 255 17.3 148.9±88.6 78 30.6 247.3±98.4 5 265 18.0 170.1±133.2 88 33.2 312.6±142.4 6 162 11.1 126.3±88.3 33 20.4 259.6±111.8 7 281 19.1 137.3±62.3 63 22.4 224.8±72.5 *Trajectories associated with those concentrations higher than the average (157.2 μg/m3). WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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4.3 Potential source regions and their relative contribution The distributions of PSCF for high and low dust years are shown in fig. 4. In high dust years (fig.4 (a)), cells with high PSCF values appeared mainly in Xinjiang, Qinghai and Gansu provinces, that is, the potential source regions most likely to have effect on high PM10 concentration in springtime in Lanzhou were located in the Tarim Basin and the eastern Turpan Basin in Xinjiang province, the Qaidam Basin in Qinghai and the Hexi Corridor in Gansu. The air masses from these potential source regions traveled along the pathways represented by clusters 2 and 3 to Lanzhou. In low dust years (fig. 4 (b)), cells related to high PSCF values were located in the Tarim Basin in Xinjiang, the Qaidam Basin in Qinghai, the degradated grassland near the borders of Qinghai, Sichuan and Gansu provinces, and the desert and Gobi desert in central and western Inner Mongolia. The air masses from these potential source regions traveled along pathways represented by clusters 1, 2 and 5 to Lanzhou. The effect of potential source regions on PM10 loadings in Lanzhou is lower in low dust years than that in high dust years. Fig. 5 shows the distribution of weighted trajectory concentrations which gives the information on the relative contribution of potential source regions to PM10 loadings in Lanzhou. The potential source regions that are represented by

Figure 4:

Potential source contribution function map of PM10 in spring during (a) 2001, 2002, 2004 and 2006, (b) 2003, 2005, 2007 and 2008. Darker colors indicate greater potential source.

Figure 5:

Concentration-weighted trajectory method analysis map of Lanzhou in spring during (a) 2001, 2002, 2004 and 2006, (b) 2003, 2005, 2007 and 2008. Darker colors indicate greater influence.

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68 Air Pollution XIX CWT values higher than 250μg/m3 (fig. 5(a)) include the Tarim Basin and the Turpan Basin in Xinjiang, the deserts near the border of Xinjiang and Gansu, the Qaidam Basin in Qinghai, and the Hexi Corridor in Gansu. In low dust years (fig. 5(b)), the potential source regions are mainly located in the southern Taklimakan Desert and the Gobi Desert in eastern Xinjiang, the Gobi Desert in central and western Inner Mongolia and the Qaidam Basin in Qinghai. The contribution of these potential source regions to the PM10 loadings in Lanzhou was 150μg/m3 to 200μg/m3 in low dust years. The PSCF and CWT analyses give somewhat different results for Lanzhou. Compared to the PSCF results (fig. 4(a)), CWT results (fig. 5(a)) give more detailed information on source regions. For example, three more source regions can be seen in the CWT results, i.e. the Gobi Desert in Inner Mongolia, the Losses Plateau, and the western Sichuan and regions between Shaanxi and Gansu. The former two regions were not the major potential source regions as they had less affect on the PM10 loadings in Lanzhou. The third regions correspond to the “southerly sources” in Wang et al. [7]. The distribution of source regions in low dust years was similar to that in high dust years. Compared to the PSCF results (fig. 4(b)), the CWT results (fig. 5(b)) reveal more source regions, e.g. the Tengger Desert, the edge of the Gurbantunggut Desert, the northwestern Gansu and Qinghai, the Gobi Desert in Inner Mongolia and the borders of Sichuan, Gansu and Shaanxi.

5 Conclusion The atmospheric pathways, potential source regions and their relative contribution to the high PM10 loadings in Lanzhou were identified by trajectory clustering techniques, potential source contribution function (PSCF) model, and concentration-weighted trajectory (CWT) method. Results indicate that the western and the northwestern pathways, accounting respectively 33% and 19.4% of all trajectories, were major pathways leading to high springtime PM10 loadings for Lanzhou in high dust years. However, the most important and the second important pathways were the western and the northern pathways in low dust years. In high dust years, the potential source regions were mainly located in the Tarim Basin and the Turpan Basin in Xinjiang, the border regions between Qinghai and Gansu, and the Hexi Corridor. In low dust years, the potential source regions were the Tarim Basin and the Turpan Basin in Xinjiang and the border regions among Xinjiang, Qinghai and Gansu. There were also some moderate potential sources including the northerly source, the southerly source and the Loess Plateau source. The northerly source was mainly distributed in the desert and Gobi desert in Inner Mongolia, the southerly source was in the border regions of Sichuan, Gansu and Shaanxi, and the Loess Plateau source was in the central areas of Shaanxi province. Although the potential source regions are similar in the high and the low dust years, the transport pathways and the relative contribution to PM10 loadings in Lanzhou are different. It should be emphasized that the current study was conducted for spring. More research are needed to provide more detailed scientific basis on improving WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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the air quality in Lanzhou, especially the transport pathways and source regions for winter pollutants need to be identified by combining event analysis with high-resolution numerical simulations.

Acknowledgement This research was funded by the Chinese Academy of Sciences through the ‘100 Talent Project’.

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Intergovernmental Panel on Climate Change (IPCC). In: Houghton, J.T., et al., (eds), Climate Change 2001, Cambridge University Press, New York, 2001. Kim J., Yoon S.C. & Jefferson A., et al., Aerosol hygroscopic properties during Asian dust, pollution, and biomass burning episodes at Gosan, Korea in April 2001. Atmospheric Environment, 40(8), pp. 1550-1560, 2006. Chen L.L., Carmichael G.R. & Hong M.S., et al., Influence of continental outflow events on the aerosol composition at Cheju island, south Korea. Journal of Geophysical Research, 102, pp. 28551-28574, 1997. Duce R.A., Unni C.K. & Ray B.J., et al., Long-range atmospheric transport of soil dust from Asia to the tropical North Pacific: temporal variability. Science, 209, pp. 1522-1524, 1980. Uematsu M., Duce R.A. & Prospero J.M., et al., Transport of mineral aerosol from Asia over the North Pacific Ocean. Journal of Geophysical Research, 88, pp. 5342-52, 1983. Husur R.B., Tratt D.M. & Schichtel B.A., et al., Asian dust events of April 1998. Journal of Geophysical Research, 106, pp. 18317-18730, 2001. Wang Y.Q., Zhang X.Y. & Arimoto R., The contribution from distant dust sources to the atmospheric particulate matter loading at XiAn, China during spring. Science of the Total Environment, 368, pp. 875-883, 2006. Yu Y., Xia D.S. & Chen L.H., et al., Analysis of particulate pollution characteristics and its causes in Lanzhou, Northwest China. Environmental Science, 31(1), pp. 22-28, 2010. Chu P.C., Chen Y.C. & Lu S.H., Atmospheric effects on winter SO2 pollution in Lanzhou, China. Atmospheric Research, 89, pp. 365–373, 2008. Wang S.G., Wang J.Y. & Zhou Z.J., et al., Regional characteristics of three kinds of dust storm events in China. Atmospheric Environment, 39(3), pp. 509-520, 2005. Wang S.G., Yuan W. & Shang K.Z., The impacts of different kinds of dust events on PM10 pollution in northern China. Atmospheric Environment, 40(40), pp. 7975-7982, 2006.

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Zhang L., Chen C.H. & Murlis J., Study on winter air pollution control in Lanzhou, China. Water, Air, and Soil Pollution, 127, pp. 351–372, 2001. Chu P.C., Chen Y.C. & Lu S.H., Afforestation for reduction of NOX concentration in Lanzhou China. Environment International, 34, pp. 688– 697, 2008. Xu X. & Akhtar U.S., Identification of potential regional sources of atmospheric total gaseous mercury in Windsor, Ontario, Canada using hybrid receptor modeling. Atmospheric Chemistry and Physics, 10, pp. 7073-7083, 2010. Ci Z.J., Zhang X.S. & Wang Z.W., et al., Atmospheric gaseous elemental mercury (GEM) over a coastal/rural site downwind of East China: Temporal variation and long-range transport. Atmospheric Environment, 45, pp. 2480-2487, 2011. Efstathiou C., Isukapalli S., & Georgopoulos P., A mechanistic modeling system for estimating large-scale emissions and transport of pollen and co-allergens. Atmospheric Environment, 45, pp. 2260-2276, 2011. Escudero M., Stein A.F. & Draxler R.R., et al., Source apportionment for African dust outbreaks over the Western Mediterranean using the HYSPLIT model. Atmospheric Research, 99, pp. 518-527, 2011. Seibert P., Kromp-Kolb H. & Baltensperger U., et al., Trajectory analysis of aerosol measurements at high alpine sites. In: Borrell P.M., et al., (eds), Transport and transformation of pollutants in the troposphere. Academic Publishing, Den Haag, pp. 689-693, 1994. Ministry of Environmental Protection of the People’s Republic of China (MEP). Beijing, www.zhb.gov.cn/index htm. Zhang X.Y., Gong S.L. & Shen Z.X., et al., Characterization of soil dust aerosol in China and its transport/distribution during 2001 ACE-Asia, 1. Network observations. Journal of Geophysical Research, 108, pp. 4261, 2003. Draxler R.P. & Hess G.D. & An overview of the HYSPLIT-4 modeling system for trajectories, dispersion, and deposition. Australian Meteorological Magazine, 47, pp. 295-308, 1998. Sirois A. & Bottenheim J.W., Use of backward trajectories to interpret the 5-year record of PAN and O3 ambient air concentrations at Kejimkujik National Park, Nova Scotia. Journal of Geophysical Research, 100, pp. 2867-2881, 1995. Ashbaugh L.L., Malm W.C. & Sadeh W.Z., A residence time probability analysis of sulfur concentrations at Grand Canyon National Park. Atmospheric Environment, 19, pp. 1263-1270, 1985. Polissar A.V., Hopke P.K. & Harris J.M., Source regions for atmospheric aerosol measured at Barrow, Alaska. Environmental Science and Technology, 35, pp. 4214-4226, 2001. Hsu Y.K., Holsen T.M. & Hopke P.K., Comparison of hybrid receptor models to locate PCB sources in Chicago, Atmospheric Environment, 37, pp. 545-562, 2003.

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Performance evaluation of the ADMS-Urban model in predicting PM10 concentrations at the roadside in Chennai, India and Newcastle, UK S. Nagendra1, M. Khare2, P. Vijay1 & S. Gulia2 1

Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India 2 Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, India

Abstract Recent economic development in many Asian and European countries has shown an increase in vehicle kilometre travel (VKT) in many cities, which has resulted in an increase in the vehicular pollution levels. In particular, particulate matter (PM) concentrations emitted from vehicles are at alarming levels in most of the cities of the world. Therefore, there is growing interest in the formulation of local level air quality management system to tackle vehicular emissions. Many mathematical models have been widely used as tools in urban air quality management. In the present paper, an attempt has been made to evaluate the performance of the ADMS Urban model in predicting roadside PM concentrations at two cities namely Chennai, India and Newcastle, UK during critical winter period of the year 2009. The statistical parameters such as Index of Agreement (IA), Fractional Bias (FB), Normalized Mean Square Error (NMSE), Geometric Mean Bias (MG) and Geometric Mean Variance (VG) have been used to evaluate the ADMS model performance. Results indicated that the roadside PM concentrations predicted by the ADMS model are reasonably accurate for Newcastle than at the roadside in Chennai. Keywords: air quality, particulate matter, model, management, statistical indicator, meteorology.

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1 Introduction Air pollution from motor vehicles is one of the most serious and rapidly growing environmental problems in the large cities of the developing world. In many cases, the monitored pollutant concentrations exceed the world health organization (WHO) air quality guidelines or national ambient air quality standards (NAAQS) set in their countries. The recent trends of air pollution in India and UK are showing a low level in most of the criteria pollutants except particulate matter (PM) and oxides of nitrogen concentrations in both the cities. In the United Kingdom, about 80% of the road emissions are generated from particulate matter of which road transport is the dominant share [1]. Vehicular exhaust derived pollution and its effect on human health is now becoming a matter of concern in many urban areas. According to recent epidemiological and toxicological studies, the high concentrations of airborne particles are associated with significant impacts on human health [2]. This holds especially for the fine and ultrafine particle size ranges due to their ability to penetrate deep into the human body. A study conducted by Srimuruganandam and Nagendra [3] showed that the 24-hour average PM10 and PM2.5 concentration are violating of NAAQS as well as world health organisation standards (WHO) during winter and monsoon season and minimus in summer season. Source apportionment studies of Chennai have also revealed the exceedances of particulate matter concentrations above NAAQS in residential areas [4]. Therefore, it becomes necessary for the authorities to assess the quality of air and implement control policies and strategies. In the recent past, many cities have been developed air quality management (AQM) strategies to achieve a specified set of ambient air quality standards (AAQS) or rules. Air quality models plays an important role in formulating air pollution control and management strategies by providing guidelines for better and more efficient air quality planning. Air Quality Models represent essential computational tools for predicting the air quality impacts of emissions from road traffic and also help in testing the accuracy of monitoring equipments once it is validated. In the present work, dispersion models namely ADMS-Urban has been used to simulate the air quality at selected locations in Chennai and Newcastle cities. Further, model sensitivity with respect to traffic and meteorological characteristics has also been studied.

2 Methodology 2.1 Study region Figure 1 shows the details of study region in Chennai city, India. The study area is located in the premises of Indian Institute of Technology Madras (IITM), Sardar Patel Road, Chennai city in India. Traffic flows on SP road is about 0.17 million vehicles per day. Braking is frequent on SP road due to the presence of two busy traffic intersections within a distance of 700 meters. The terrain of the study region is considered as plain and flat terrain (terrain height is 7.6 m from WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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N

Location of sampling site at IITM

Figure 1:

Description of study area in Chennai, India.

mean sea level). At this site, there is no local stationary source emission except emissions from road traffic. The PM monitoring instrument was kept at 1.2 m height from ground level and 7 m away from the centerline of SP Road. The project site in Newcastle city is located at 540 58’ 40 N and 10 36’ 49” (Figure 2) which is also one the busiest intersection of Newcastle upon Tyne. This intersection comes under air quality control regions (AQCRs). The whole intersection has been divided into three different roads. The monitoring site is situated near to road i.e. 20 m at city centre. The city centre monitoring station works under urban centre air quality station which is operated under Automatic Urban and Rural Network (AURN), the main air quality compliance network for DEFRA. The monitored data of both cities are used for validation of the model.

Figure 2:

Description of study area in Newcastle city, UK.

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74 Air Pollution XIX 2.2 Traffic characteristics The traffic volume count (TVC) has been monitored continuously for a week at a Sardar Patel road using automatic traffic flow recorder (Video). The traffic flow for Newcastle city has been obtained from SCOOT profile. The traffic flow at Chennai intersection is about 1,70,000 and that in the intersection at Newcastle city is about 25000. The morning peak flow occurs between 8am and 10am and afternoon peak occurs between 5pm and 7pm (Figure 3). The fleet composition in Chennai is dominated by two-wheelers (about 51%) followed by cars (34%)

Figure 3:

Figure 4:

Traffic flow on SP road in Chennai city.

Traffic flow Newcastle city centre road in Newcastle city.

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and three-wheelers (6%), respectively. Bus, lorry and van contribute a small percentage on SP road. The traffic pattern in Newcastle is somewhat uniform over weekdays and minimum in weekends as compared to weekdays. The diurnal traffic flow shows that morning peak flow occurs between 7 am and 11 am and evening peak occurs between 5pm and 8pm (Figure 4). The fleet composition of Newcastle is dominated by petrol car about 60% followed by diesel car (30%). HGV, LGV and buses contribute a small percentage. 2.3 PM Emissions PM emission has been estimated using the methodology suggested by Righi et al. [5]. ARAI [6] emission factors for Indian vehicles are used for Chennai site and DfT 2009 is used for Newcastle site [1]. The UK emission factors are speed depended unlike Indian vehicles emission factors. Traffic monitoring data for year 2004 was used to calculate emission rate for the year 2009 (extrapolation) on the basis of business as usual. 2.4 Meteorological data The main input meteorological parameters for ADMS model are wind speed and wind direction plus one of the following parameters, cloud cover, heat flux or reciprocal of Monin-Obukhnov length. The parameter solar radiation will be used only if NOx chemistry has to be used [7]. Sequential hourly meteorological data for the winter period (December 2008-February 2009) were obtained from Laga Systems, Hyderabad. The wind rose for the three months of both the cities is shown in Figures 5 and 6.

Figure 5:

Windrose for Chennai.

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Figure 6:

Windrose for Newcastle.

The windrose diagrams revealed that about 2.5% of winds are calm in nature ( 10 (dash dotted line)

day by coastal outflow and how it varies with tracer lifetime, a, and wind speed to landwidth ratio, b = U/L, for the case of maximum convective boundary layer height equal to twice the marine boundary layer height (c = M AXCBL/M BL = 2). Figure 6 shows that as tracer lifetime is increased, that the proportion of tracer exported by coastal outflow also increases. This occurs as it can reach the coastline from further inland. As the windspeed is increased, the proportion of exported tracer increases until the timescale of advection across the coast is shorter than the diurnal timescale of boundary layer height. Tracer is advected across the coast before it is able to be mixed above the top of the marine boundary layer, leading to an increase in tracer in the MBL and a decrease in the coastal outflow layer. Increasing windspeed leads to a decrease in coastal outflow for tracers that are long lived with respect to the wind speed ab > 10, i.e. when the windspeed U exceeds 10L/a, equivalent to a 1.16ms−1 for L = 10km and a = 86400s. Increasing the boundary layer height ratio c also leads to increasing coastal outflow at all lifetimes and windspeeds, but as already noted this is not the meteorologically dominant factor in controlling coastal outflow. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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4 Conclusions Modelling over the eastern US coast using passive tracers within the UM has shown that the magnitude of ventilation of pollutants by coastal outflow, the export of pollutants from a layer within the continental boundary layer to a layer above the marine boundary layer, occurs on a similar order to ventilation by convection. Due to the diurnal boundary layer height cycle over land, a diurnal cycle is induced in export by coastal outflow. For tracers with modelled 3-hour and 24-hour half lives, 5% and 12% of total tracer emitted over land is exported by coastal outflow. The 3-hour tracer exhibits strong diurnal variability with a maximum in the late afternoon (when the greatest amount of tracer has been mixed above the marine boundary layer top) and a minimum in the early morning (when pollutants have remained trapped near the surface by the shallow nocturnal layer). The 24-hour tracer exhibits little diurnal variation but instead is more variable on a day-to-day timescale, and is strongly correlated with the 850m zonal wind speed (850m being a typical height at which coastal outflow occurs). Based on a simple 2D model, the sensitivity of coastal outflow to wind speed, maximum convective boundary layer and pollutant lifetime has been tested. Increasing any of these variables increases coastal outflow, although it has been shown that the maximum convective boundary layer height varies little from dayto-day and does not explain the variability in coastal outflow for a tracer 24hour lifetime during our simulation. Cross-coastal wind-speed is the dominant mechanism controlling the export of pollutants by coastal outflow. There is a threshold U = 10L/a, where U is the cross-coastal wind speed, L is the width of land and a is the pollutant half life, above which increasing the wind speed leads to a decrease in coastal outflow due to tracer being advected across the coast before having the chance to be mixed to a height above the marine boundary layer. Convection, while active in the UM simulation, has negligible effect on export by coastal outflow.

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for pollutant transport between the boundary layer and free troposphere. J Geophys Res, 106(D8), 2001. Sinclair, V.A., Gray, S.L. & Belcher, S.E., Boundary-layer ventilation by baroclinic life cycles. Q J R Meteorol Soc, 134, pp. 1409–1424, 2008. Dacre, H.F., Gray, S.L. & Belcher, S.E., A case study of boundary layer ventilation by convection and coastal processes. J Geophys Res, 112(D17106), 2007. Verma, S., Boucher, O., Venkataraman, C., Reddy, M.S., M¨uller, D., Chazette, P. & Crouzille, B., Aerosol lofting from sea breeze during the Indian ocean experiment. J Geophys Res, 111(D07208), 2006. Raman, S., Niyogi, D.d.S., Simpson, M. & Pelon, J., Dynamics of the elevated land plume over the arabian sea and the northern Indian ocean during northeasterly monsoons and during the Indian ocean experiment (indoex). Geophys Res Lett, 29(16), 2002. Cullen, M.J.P., The unified forecast/climate model. Meteor Mag, 122, pp. 81– 94, 1993. Lock, A.P., Brown, A.R., Bush, M.R., Martin, G.M. & Smith, R.N.B., A new boundary layer mixing scheme. part i: Scheme description and single-column model tests. Mon Wea Rev, 128, pp. 3187–3199, 2000.

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Non-parametric nature of ground-level ozone and its dependence on nitrogen oxides (NOx): a view point of vehicular emissions S. Munir, H. Chen & K. Ropkins Institute for Transport Studies, University of Leeds, UK

Abstract Ground-level ozone has been studied extensively using classic parametric statistics (most commonly conventional linear regression). Very few researchers have considered ozone distributions and even those that do tend to apply parametric techniques. This study assesses ground-level ozone distributions at six locations in the UK and characterises the correlation of nitrogen oxides (NOx) and ozone at a roadside location. The distribution of ozone is investigated, applying Shapiro-Wilk test and graphical presentations. The histograms are right skewed and show maximum frequency at ozone mixing ratios from 0 to 5 ppb (particularly at urban centers and roadsides locations), which is probably caused by high levels of freshly produced NOx associated with road traffic. There is evidence that ground level ozone is not normally distributed (p-values < 0.05). NOx is a dominant sink for ozone at urban and roadside sites due to its ozone scavenging effects. Consistent with literature ozone is negatively correlated with NOx. The negative correlation is stronger at low NOx levels (up to approximately 80 ppb 24 hour mean, Spearman correlation coefficient R is ‘-0.72’) and becomes weaker as NOx levels increase (over 80 ppb R value is ‘-0.53’). When NOx mixing ratios reach approximately 200 ppb or over the correlations become positive. This study investigates how the associations of ozone and NOx vary at different levels of their mixing ratios and suggests that due to the non-normal distribution of ozone, nonparametric statistics should be applied for ozone modelling. Keywords: ground-level ozone, air pollution, nitrogen oxides, nitrogen oxides and ozone, ozone distribution, vehicular emissions and ozone.

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1 Introduction Traditionally road traffic has been mainly linked with particulates (e.g. PM10 and PM2 5), carbon monoxides (CO), and nitrogen oxides (NOx), whereas ozone has very rarely been studied in connection with traffic. This is because ozone is not directly emitted by traffic or any other combustion processes, rather, it is a secondary pollutant generated by atmospheric chemistry. Ozone is linked with traffic closely and ozone levels are affected by traffic in two main ways: (i) traffic is the main source of ozone precursors i.e. NOx and volatile organic compounds (VOCs) are emitted by traffic that react in sun light and produce ozone; (ii) freshly produced NO react with ozone and destroy it (NO + O3 NO2 + O2) and that is the main reason that ozone concentrations are generally lower at roadsides and urban areas than in the surrounding rural areas [1]. Ozone is important at local levels, as well as at regional and intercontinental levels. At a given location ozone concentration is the sum of ozone produced by photochemical reactions, ozone brought in by regional transport and ozone descended from the stratosphere; minus ozone destroyed by NOx reactions and dry deposition [2]. Due to these sources and sinks ozone has a typical diurnal and seasonal cycle in the UK. The worrying factor regarding tropospheric ozone pollution is that in spite of the decreasing trends in its precursors, background ozone concentrations have been increasing in the UK, particularly in urban areas [3]. The Air Quality Expert Group (AQEG [3]) has expressed their concerns that ozone levels in urban areas are increasing at faster rates by comparison with surrounding rural areas, which in future may result in urban ozone levels as high as in the surrounding rural areas. If that happens it will increase ozone related health and environmental risks in these highly populated areas. Therefore it is vital to understand uncertainties in ozone predictions and quantify accurately the relationship of ozone with its sources and sinks. Tropospheric ozone has been studied extensively throughout the World using classic parametric statistics (most commonly ordinary least square regression). Very few researchers have considered ozone distributions and even those that do tend to apply parametric techniques. The majority of classical statistical tests are based on the assumption that the data to which the tests are applied exhibit a normal distribution (i.e. bell shape, symmetrical and with a common mean and median). If the parametric tests are applied to non-normal data, they can result in biased or even erroneous results [4]. Therefore, before applying a classical test ,it is vital to check data distributions and if the data are non-normally distributed, robust and non-parametric methods should be applied that are not based on such assumptions. This study intends to undertake a statistical analysis based investigation into ground-level ozone to determine whether ozone data are normally distributed or not. Ozone data distribution is compared spatially (6 monitoring sites) as well as temporally (different months and years). Variations in ozone concentrations are explained in terms of its correlation with NOx.

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2 Methodology The study is based on the statistical analysis of ozone and NOx data measured at several air quality monitoring sites in the UK. The sites include a roadside (Kirkstall Road Leeds), a kerbside (Marylebone Road London), 2 urban centres (Nottingham and Leeds centre), 1rural (Harwell) and 1 remote (Strath Vaich) air quality monitoring sites. All the sites, except Kirkstall are part of the UK Automatic Urban and Rural Network (AURN). The Kirkstall site is part of facilities available at Institute for Transport Studies (ITS) University of Leeds for the monitoring of air pollution, traffic and meteorological variables. The Kirkstall site [53°48'31.38"N and 1°35'21.40"W] is located on Kirkstall Road (A65), Leeds. Kirkstall Road runs North-West to South-East through the city of Leeds. At all these sites ozone is measured by ultraviolet absorption analyser and NOx by Chemiluminescent analyser, which are the standard methods for measurement of ozone and NOx in the Europe and UK. The details of AURN sites can be found at reference [5]. Ozone data distributions have been studied using simple graphical methods and statistics tests. The graphical methods used in this study include histograms, scatter diagrams and time variation plots. In addition to graphical presentation, the Shapiro-Wilk test has also been applied to estimate ozone normality. Spearman Rank correlation, which is a non-parametric or distribution free approach, has been applied to estimate the degree of co-variance between ozone and NOx. The statistical language R and the associated ‘openair package’ have been used for performing statistical analysis and making diagrams; see [6] for more details of these software.

3 Results and discussions 3.1 Ozone data distribution Firstly ozone data from the Kirkstall site have been analysed in details and then compared with data from AURN sites. The Kirkstall data analysed are for a 2 years periods (Nov. 2007 to Oct. 2009). Figure 1 shows a histogram of hourly ozone mixing ratios collected at Kirkstall site and shows that ozone data are not normally distributed (p-value for Shapiro-Wilk test is less than 0.01). The histogram shows very high frequency (nearly 2500) at ozone levels 0 to 5 ppb (first column). The frequency when ozone mixing ratio is 40 or over is relatively low. The histogram is skewed towards right. The first bar of the histogram needs investigations to prove that it comes from genuine measurements and is not due to an error or artefact. 3.1.1 Ozone distribution when ozone is less than 5ppb This section explains whether the first bar of the histogram (in Figure 1) is a result of genuine ozone measurements or not. Ozone (ppb) hourly average data from Kirkstall site had 16194 data points (excluding missing values). Out of the total 2462 data points have ozone mixing ratios less than 5ppb and 20 data points WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

96 Air Pollution XIX have ozone concentrations even less than 1 ppb. Firstly, the lower detection limit of the monitor (photometric ozone analyser, model 400E) is checked, which is < 0.6 ppb and hence it is low enough to give an accurate ozone measurements for any mixing ratios higher than 0.6 ppb. Secondly whether these data points are distributed over all 12 months or condensed only in 1 or 2 months. Thirdly to find out if these 2462 data points lie where ozone mixing ratios are expected to be low (e.g. winter months, night hours) or not. If these data points mostly lie in winter months (day or night) or in summer night time then we can say that they are genuine, otherwise they will be considered due to an artefact and discarded.

Figure 1:

Histogram of mean hourly ozone ppb from Kirkstall site Nov. 2007 to Oct. 2009 indicates that the data are not normally distributed (pvalue < 0.010 for Shapiro-Wilk test).

Figure 2 (bottom right) shows that the 2462 data points when ozone < 5 ppb is distributed in all 12 months regardless of winter or summer season. However the majority of these hours come from winter months (Nov. to Feb.). Data from summer months (not shown here) indicate that ozone mixing ratios less than 5 ppb mostly occur during night time hours. Figure 2 (bottom left) shows high frequency over night hours (including early morning and evening) and low during day time (especially 10:00 to 16:00). Figure 2 (top right) shows similar frequencies for most days except Sunday. Ozone levels in the UK are generally higher during the summer and lower during the winter, a trend demonstrated for the Kirkstall dataset later in section 3.2.2. Likewise ozone levels are generally observed to be lower at night and higher in the daytime. The reason for low levels of ozone during the winter and at night is most probably the lack (or reduced level) of solar radiation and lower temperatures which are responsible for reduce photochemical ozone production rates. In addition, dry deposition of ozone during the night can further reduce WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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ozone levels. Ozone levels are also linked to traffic activity by the NOx scavenging effect, which are generally low on Sunday, therefore Figure 2 (top right) shows low frequency of low ozone on Sunday as compared to other days. The above explanations clearly indicate that the 2462 data points are genuine measurements. This will become clearer in section 3.1.3, where the distributions of ozone data from different monitoring stations at which different instruments are used are compared.

Figure 2:

Histograms showing the frequency of months, hours and weekdays when ozone (O3) < 5 ppb.

3.1.2 Ozone distribution for different months Shapiro-Wilk test of normality gives p-values less than 0.01 for each month of the year 2008, which reveals that hourly ozone data do not follow a normal distribution in any month of the year at Kirkstall site. Histograms of ozone for each month January to December 2008 are shown in Figure 3, showing how ozone distributions vary in different months. There are 2 main categories of histograms in Figure 3 winter and summer months. In winter months (January, February, September, October, November, December) the highest frequency of ozone is found when the mixing ratios of ozone are 0 to 5 ppb, whereas in the rest of the months (March to Aug) the highest frequency can be observed when ozone mixing ratios are approximately 30 ppb. The winter months can be again subdivided into 2 groups i.e. September, October, November when the distribution is somewhat bimodal (highest frequency of ozone can be observed at 0 to 5 ppb and also at around 30 ppb) and

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98 Air Pollution XIX January, February, December where the highest frequency of ozone is mainly at 0 to 5 ppb. Although the ozone distribution during the summer months seems closer to normal distribution, statistically it still appears to be non-normal (p values < 0.05). The reason for the difference in ozone distributions during summer and winter months is most probably the difference in the amount of solar radiation and temperature. Solar radiation and temperature are the 2 main meteorological factors responsible for photochemical ozone production and that is why the amount of ozone is mostly lower in winter months. In contrast, during the summer months photochemical ozone production is high which results in high ozone mixing ratios as shown in Figure 3.

Figure 3:

The distribution of ozone during different months of the year, hourly mean ozone data from Kirkstall site.

3.1.3 Ozone distribution at different sites Ozone distributions vary spatially from place to place in the UK depending on the nature of the monitoring site. Roadside and urban centre monitoring sites are generally characterized by high levels of fresh NOx that react with ozone and keep ozone levels low at these sites. In contrast, rural and remote monitoring sites have generally low levels of NOx and high levels of ozone.

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Figure 4 shows ozone distributions at 6 different monitoring sites. Ozone distributions at kerbside and roadside monitoring sites (Marylebone and Kirkstall) show high frequencies for low ozone mixing ratios i.e. the first column of the histogram is taller than the rest of the columns, which shows that low ozone mixing ratios occur more frequently than higher ozone mixing ratios. Particularly at the Marylebone site the frequency of higher ozone mixing ratios is very low; the reason probably is that Marylebone site is situated in London at the kerb of very busy road where road traffic exhausts produce a huge amount of fresh NOx [7]. Although the Kirkstall site is a roadside monitoring site, the traffic levels on this road in Leeds is not as high as at Marylebone Road and hence the difference in ozone mixing ratio is clear. In contrast, the Harwell and Strath Vaich monitoring sites have totally different ozone distributions; the histograms almost look like a bell shaped symmetric diagram (statistically it is still non-normal, as p-values < 0.05). The higher frequencies of ozone at rural and remote sites occur at about 30 ppb (60µg m-3). The reason is probably that these sites are far away from busy roads and receive very low fresh NOx inputs. The other two sites Leeds and Nottingham centre (urban centre sites) are intermediate between rural and roadside monitoring sites. These sites although are urban, receive reasonable high levels of fresh NOx which could reduce ozone mixing ratios but still the frequency of high ozone concentrations are higher than the roadside monitoring sites.

Figure 4:

Ozone mean hourly data distribution at different monitoring sites in the UK, where KS, RS, UC, Re and R define the type of monitoring site and stand for kerbside, roadside, urban centre, remote and rural respectively.

After the comparison of different monitoring sites and studying ozone distributions during different months, it can be concluded that ozone data in the UK do not follow a normal distribution and hence non-parametric statistics should be used for its analysis. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

100 Air Pollution XIX 3.2 Ozone and NOx correlation The scatter plot of 24 h average NOx and ozone data from the Kirkstall site Leeds is shown in Figure 5, and exhibits a clear negative correlation between NOx and ozone and as NOx mixing ratios increase ozone mixing ratios decrease. Most probably the negative correlation is due to NO reactions with ozone which destroys ozone molecules and produces NO2: (NO + O3  NO2 + O2). In Figure 5 along the x-axis 3 main segments can be observed. In the first segment (NOx ppb < 80, left circle - solid line) the negative correlation between ozone and NOx seems very strong (Spearman correlation coefficient (R) value is ‘-0.72’) and ozone mixing ratios decrease linearly with increases in NOx mixing ratios. In this area the data points are dense, as on this monitoring site most data points lie in this segment. In the next segment (middle circle - dashed line, 80 < NOx ppb < 220) the negative correlation is still there but not linear. In this section data points are relatively sparse and the negative correlation is weaker (R value ‘-0.53’). In the last segment (right circle – dotted line, NOx ppb > 220) the negative correlation between ozone and NOx disappears and the curve becomes almost totally horizontal. The negative correlation turns into positive correlation (R value ‘+0.30’). The positive correlation between ozone and NOx at atypically high NOx concentrations is most probably due to NO2 oxidation which gives rise to ozone formation. As this correlation is based on 24 h mean ozone and NOx data, the correlation will be further investigated in section 3.2.1 using 1 minute and hourly data from the Kirkstall monitoring site.

Figure 5:

Scatter plot between 24 h mean ozone and NOx data from KS site (01/11/2007 to 31/10/ 2009).

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3.2.1 Correlation between NOx and ozone for 1 minute and hourly data When NOx mixing ratios reach about 200 ppb the correlation between ozone and NOx becomes positive as demonstrated in section 3.2. In this section those 6 days (12–15 December 2007; 11–12 February 2008) when NOx mixing ratios were 365, 304, 250, 198, 271 and 354 ppb, respectively, are further investigated. Figure 6 shows the scatter plot along with their R values for 1 minute data for the 6 days. Over these days ozone and NOx have positive correlation coefficients, except on 13/12/2007 where the correlation is negative despite the fact that NOx mixing ratio is as high as 304 ppb. Figure 6 (top row, middle column) shows that the scatter plot for 13/12/2007 looks somewhat different from the other scatter plots. The main difference is that for some reason there are some higher ozone mixing ratios at the start of the scatter plot. Hourly mean NOx and ozone mixing ratios show more clearly those high ozone mixing ratios points (not shown here). To find out an explanation for these data points, the author investigated the meteorological variables to see if there was some explanation, as only NOx and ozone correlation cannot provide an answer. It was found from the observations of meteorological variable that on 13 December 2007 the sun came out about 09.00 am and was shining until 12.00. The solar radiation triggered photochemical ozone formation and the ozone mixing ratios reached the highest level of the day (7 to 8 ppb); the average ozone on the day was about 3ppb. As a high level of NOx (304 ppb) was present on the day, probably NOx reacted with photochemically formed ozone and brought the ozone level down, explaining the negative correlation on 13 December.

Figure 6:

Scatter plot of ozone vs. NOx for the six days when NOx mixing ratios were nearly as high as 200 ppb or over and NOx is mostly positively correlated with ozone; R stands for Spearman correlation coefficients.

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102 Air Pollution XIX 3.2.2 Temporal variations in NOx and ozone mixing ratios In this section the correlation between ozone and NOx has been investigated using time variation charts (Figure 7). These diagrams depict the association of ozone and NOx showing how their mixing ratios change on average during different hours of the days, days of the week or months of the year. Figure 7 reveals that ozone mixing ratios are normally higher during spring and summer (March, April, May and June) and lower during winter months (January, February, November and December), where as NOx mixing ratios are higher in winter (January, February, November and December) and lower during summer months (May, June, July and August). In winter the high NOx ratios are probably resulted by slow chemical reactions and slower pollutant dilution due to stagnant atmospheric conditions; the opposite happens in summer (better chemistry and dilution). On the other hand ozone is a secondary pollutant and is mostly produced in the atmosphere by photochemical reactions of NOx and VOCs driven by solar radiation (Ultraviolet radiation – UV). Therefore in winter because of low UV radiation and temperature ozone production is minimal (if any at all); and whatever ozone is present is consumed by freshly produced NO (remember UV radiations are required for ozone production but not for its destruction). But in summer high UV radiation and temperature are responsible for the relatively higher levels of ozone.

Figure 7:

Time variation plot of ozone and NOx mixing ratios (ppb) hourly mean data from Kirkstall site Nov 2007 to Oct 2009.

On a weekly basis traffic volume seems to be the dominant factor for controlling NOx and ozone mixing ratios. On Kirkstall Road the volume of road traffic is higher during weekdays and lower during the weekend; as many companies and institutes do not operate at the weekend. Figure 7 clearly shows the lowest NOx and highest ozone levels on Sunday, followed by Saturday. As WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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on Sunday low traffic volumes produce less NOx which result in less ozone destruction and hence ozone levels are higher. Higher ozone levels at the weekend are called the ozone weekend effect (OWE) and is studied by several authors (e.g. 8, 9, 10, and 11). The diurnal changes in NOx and ozone levels seem to be linked with both traffic volume and meteorology. Although NOx and ozone are strongly correlated and both of them are strongly linked with the volume of road traffic, the diurnal average trend of ozone seems to be dominated by solar radiation. The highest ozone mixing ratios were observed at 13.00 to 15.00 hour when UV radiation is often at a maximum; and lowest ozone levels at 06.00 to 07.00 hour in the morning due to the overnight dry deposition and NOx scavenging effect. NOx levels are strongly linked with traffic volume and reached a maximum level at 08.00 to 09.00 am when roads traffic activity is at peak. After that NOx levels come down but rise again at about 17.00 to 18.00 hours in the evening, probably due to late afternoon traffic peak hours. It was observed that correlation between NOx and ozone is stronger during winter (R for January was ‘-0.80’) and weaker during summer (R for May ‘-0.40’). NOx is probably the dominant controlling factor for ozone levels in winter when there is not much photochemical ozone production and as solar radiation and temperature increase they weaken the correlation between NOx and ozone. Therefore for ozone prediction in addition to NOx it is essential to quantify the role of meteorology and traffic flow in controlling ozone levels, which is part of our future plan.

4 Conclusion This study investigates ozone distribution and its association with NOx at a roadside monitoring site, where most of the NOx is believed to be emitted by road traffic. The study demonstrates that ozone distribution is not a fixed phenomenon and rather it varies both spatially and temporally. Our data show that ground-level ozone is not normally distributed and hence should be studied by using non-parametric or distribution free statistics. The study also shows that generally ozone is negatively correlated with NOx, although the strength and nature of correlation may vary as NOx level changes. The negative correlation is strongest at NOx levels up to 80 ppb and becomes weaker afterward. The correlation changes to positive when NOx levels go as high as 200 ppb or over. Moreover, the correlation is stronger in winter months and night times; and weaker in summer months and daytimes probably due to solar radiation. Ongoing investigations are intended to explore the associations between ozone and traffic flow using traffic volume, speed and fleet composition characteristics for better understanding the relationship of ozone with road traffic, which may be helpful in accurate ozone prediction. Accurate prediction of ozone episodes may help to pre-warn the public of the potential high levels of ozone and aid policy makers the development of effective mitigation strategies.

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Acknowledgement We gratefully acknowledge Economic and Social Research Council (ESRC) for providing funding for this study, which is a part of my PhD project.

References [1] Air Quality Expert Group (AQEG). Trends in primary nitrogen dioxide in the UK, the fourth report prepared by the air quality expert group 2007. DEFRA Publication London, 2007. [2] Cape, J.N., Surface ozone concentrations and ecosystem health: past trends and a guide to future projections, the Science of the Total Environment, 400, pp. 257-269, 2008. [3] Air Quality Expert Group (AQEG). Ozone in the UK, the fifth report produced by air quality expert group (AQEG), 2009. DEFRA Publication London, 2009. [4] Reimann, C., Filzmoser, P., Garrett, R. and Dutter, R., Statistical data analysis explained: applied environmental statistics with R. John Wiley and Sons, Ltd, 2008. [5] UK automatic urban and rural network. Department for Environment, Food and Rural Affairs. www.aurn.defra.gov.uk. Accessed July 28, 2010. [6] Carslaw, D.C., Ropkins, K., Openair-project: NERC knowledge transfer. www.openair-project.org. Accessed July 28, 2010. [7] Carslaw, D.C and Beevers, S.D., Estimations of road vehicle primary NO2 exhaust emission fractions using monitoring data in London, Atmospheric Environment, 39, pp. 167–177, 2005. [8] Chang, S.C. and Lee, C.T., Ozone variations through vehicle emissions reductions based on air quality monitoring data in Taipei city, Taiwan, from 1994 to 2003, Atmospheric Environment, 40, pp. 3513–3526, 2006. [9] Bronnimann, S. and Neu, U., Weekend weekday differences of near surface ozone concentration in Switzerland for different meteorological conditions, Atmospheric Environment, 31, pp. 1127–1135, 1996. [10] Pont, V. and Fontan, J., Comparison between weekend and weekday ozone concentration in large cities in France, Atmospheric Environment, 35, pp. 1527–1535, 2000. [11] Gao, O.H., Holmen, B.A., and Niemeier, D.A., Non-parametric factorial analysis of daily weight in motion traffic: implication for the ozone “weekend effect” in Southern California, Atmospheric Environment, 39, pp. 1669–1682, 2005.

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Prediction of TSP concentration in a metallurgical city of Brazil using neural networks M. M. C. Lima Research and Development Centre, Usiminas, Brazil

Abstract The aim of this study was to predict Total Suspended Particulate concentration (TSP) in the main areas of Ipatinga, a metallurgical city located in Minas Gerais state, southeast of Brazil. Artificial neural networks (ANN) were the modelling tool used. This model is able to predict pollutant concentration just by training the input and output parameters. The input parameters were meteorological such as wind direction, wind speed, rain, and ambient temperature and also seasonal such as, summer and winter. The output parameter used was the historical data of the total suspended particulate concentration taken between 1996 and 2004. In the modelling, the multilayer perceptron (MLP) model was tested. Among the MLP configurations evaluated, the topology 13-7-6 was chosen. The validation of the model was done by comparing the simulated with the observed values. The results of this model were also compared with the industrial source complex short-term dispersion model (ISCST3). The four statistical tools used to evaluate the fitting were mean squared error (MSE), fractional bias (FB), index of agreement (IA) and linear correlation coefficient (R). Comparing the results it was seen that the predicted values were better in some boroughs and were overestimated in others. Besides, the predicted results of the ANN model were better than the ISCST3 dispersion model. Keywords: artificial neural networks modelling, multilayer perceptron, total suspended particulate concentration, prediction, ISCST3 dispersion.

1 Introduction This paper introduces the study of prediction of Total Suspended Particulate concentration (TSP) in Ipatinga city using artificial neural networks. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line) doi:10 2495/AIR110101

106 Air Pollution XIX Ipatinga is located in the Vale do Aço Region, in Minas Gerais state of Brazil where the main industrial activity is iron and steel making. In the iron and steel making process, the raw material handling and fuel combustion are the main causes of particulate emission. Depending on the particulate concentration, the air quality can be modified and causes a lot of damage, such as increasing the dust in residential areas, visibility impairment and harmful to health effects (USEPA [1]). Mathematical models are often used to estimate environmental impacts, saving money from air quality monitoring. One of the most important features of models is the ability to predict or simulate future impact scenarios. The dispersion or diffusion models have been traditionally applied to atmospheric mathematical modelling. These models are able to predict the pollutant concentration using mass balance of statistical data (pollutant emission, wind direction and speed, ambient temperature) and introduce a Gaussian mathematical equation as a solution of pollutant dispersion. Mitkiewicz [2] predicted TSP concentration in Ipatinga using industrial source complex shortterm dispersion model (ISCST3). Recently, artificial neural networks model (ANN) has been used in modelling complex problems. ANN, such as the ISCST3 model is able to predict air pollutant concentration just by training a set of input and output variables. It offers a mathematical solution by adjusting the weights in such a way that output will be close to real data. Comparing ANN to ISCST3 models, the first one has the advantage in adapting few variables to evaluate air pollutant dispersion. It can be seen in many papers published about this subject: Linyan and Wang [3], Wal and Janssen [4], Perez and Reyes [5], Viotti et al. [6], Zickus et al. [7], Perez and Reyes [8], Podnar et al. [9], Ordieres et al. [10], Hooyberghs et al. [11]. There are a variety of artificial neural networks models being used in modelling. Among them, the multilayer perceptron (MLP) is the most cited in air dispersion modelling (Gardner and Dorling [12]). The MLP structure consists of processing elements and connections. The processing elements, called neurons, are arranged in layers such as an input layer, one or more occult layers and an output layer (Haykin [13]). They are all interconnected. In this context, this study aimed to develop ANNs using MLP. The input parameters were meteorological data and the output was the TSP concentration data. This model was also compared with the ISCST3 model. Specifically, it intended to: a) predict TSP concentration using ANNs in six air quality monitoring stations distributed in Ipatinga, b) determine the main variables responsible for the measured TSP concentration; c) investigate the behaviour of the created ANN due to different configuration proposals; d) validate the ANN model using the comparison between the simulated and measured values and e) compare simulated results between the ANN and ISCST3 models.

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Material and methodology

2.1 Sampling TSP concentrations were collected weekly between 1996 and 2004 from six air quality monitoring stations using High Volume sampler (HiVol) distributed in six districts around Ipatinga named: CA, BR, BA, NC, EC and CS. Meteorological variables (wind speed (m/s), wind direction, rain volume (mm), ambient temperature (oC)) were collected hourly during the same period and converted to daily variables. The wind directions evaluated were north (N), south (S), east (E), west (W), northeast (NE), southeast (SE), southwest (SW) and northwest (NW). Further variables were also created as calm hours (wind speed less than 1 m/s) and seasonal cycle (winter and summer) during the same period from the same database to evaluate their effect on the TSP concentrations. The database contained 400 data. 2.2 Artificial neural modelling Mathematical routine was developed using the software Matlab [14]. MLP was the artificial neural network model used. It had been tested lots of models with different configurations. The best topology was defined by the MSE (mean square error) analysis. This statistical analysis is suggested by Zhang et al [15] as being efficient in evaluating the artificial neural networks models. The meteorological and seasonal variables were used in the input layer. The choice for the model type and the input variables were due to a lot of published studies in literature as mentioned above. Two models with thirteen and six input data were evaluated. The thirteen data were: daily eight wind directions frequency, daily mean wind speed, daily mean rain volume, daily mean temperature, daily calm hours and seasonal cycle (winter = 1 and summer = 2) frequencies. The six were obtained using the principal component analysis (PCA). The purpose of this analysis is to obtain a small number of linear combinations which account for most of the variability in the data (Haykin [13]). In this case, six components had been extracted from the thirteen input data and they were evaluated in the modelling as input data. TSP concentrations, measured in the six monitoring sites, were introduced in the output layer. Only one occult layer was considered in the modelling. The number of neurons in the occult layer was changed as suggested by Zhang et al [15] and Kóvacs [16]. First, an exploratory data analysis was made, detecting if there were outliers in the database. After that the data were normalized and separated in “training” and “validation” sets. The training set was equivalent to 80% of the database and the validation set to 20% (Zhang et al [15]). The learning algorithms used during the training test were Levenberg-Marquardt and Backpropagation. The early stopping criterion was applied to stop the training. In the mathematical routine, expected error, initial weight, bias, activation function, learning algorithm, momentum term and the iteration cycles were established as usual. Finally, after comparing the real values to the simulated ones by MSE analysis, the best topology was defined. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

108 Air Pollution XIX 2.3 Performance evaluation The model validation was done by comparing between the real and simulated values. These simulated values were the model results obtained from the validation set. The average percentage error (E) was determined as shown in eqn. (1).

E

1 n  Cr  Ce  n i 1  C r

  * 100  

(1)

where n is the sampling size, Ce , Cr simulated and real values, respectively. The tendency of the simulated results was evaluated by quantile-quantile plot and type I (false negative) and II (false positive) error analyses. In the type I error, the model under predicts the values, when they were supposed to be above a critical value. In this case, the critical value (TSP concentration) was the air quality standard of 80g/m3 applied in Minas Gerais state. In the other hand, in the type II error, the model over predicts the values. So they were supposed to be below a critical value. The cluster analysis was applied to verify the effect of input data in the output data. This multivariate statistical technique aims to classify the observations or variables due to their similarity, applying a distance measure algorithm. 2.4 Comparison between ANN and ISCST3 models The comparison between ANN and ISCST3 models was made. For running ISCST3 model, it was collected the meteorological parameters, topography and air emission sources characterization. The comparison among the two simulated results and real values, registered in the six monitoring sites, was made. The statistical evaluation tools used were linear correlation coefficient (R), mean square error (MSE), mean fractional bias (FB) and mean index of agreement (IA) (Olesen [17]). The linear correlation coefficient is shown in eqn. (2):





1 n i1 Ce  Ce C r  Cr n R

C C e



(2)

r

The mean square error is shown in eqn. (3):

 C MSE  n

i 1

r

 Ce 

2

n

(3)

The mean fractional bias is shown in eqn. (4):

FB 

Ce  C r 1 n  i 1 n 0.5Ce  Cr 

The mean index of agreement of (IA) is shown in eqn. (5):

WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

(4)

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Ce  Cr  1 n 1  i 1 n Ce  C r  C r  C r

109

2

IA 



 



2

(5)

where Ce : averaged simulated concentration, Cr : averaged real concentration, n is the sampling size, Ce , Cr simulated and real values, respectively,  Cr : real concentration standard deviation,  Ce : simulated concentration standard deviation.

3 Results The best ANN configurations results considering the MSE analysis are shown in table 1. Table 1: Model Neurons

MLP models results. Alg. MSE

Output neurons

Input Occult Output CA BA BR EC NC CS 1 13 7 6 LM 466.7 + + + + + + 2 13 27 6 BP 636.5 + + + + + + 3 13 14 5 LM 354.7 + + + + + 4 13 19 1 LM 251.5 + 5 13 10 1 LM 385.1 + 6 13 7 1 LM 208.4 + 7 13 7 1 LM 205.7 + 8 13 10 1 LM 911.3 + 9 13 9 1 LM 336.2 + 10 6 4 6 LM 509.1 + + + + + + 11 6 13 1 LM 323.5 + 12 5 8 1 LM 386.4 + 13 6 6 1 LM 220.9 + 14 6 10 1 LM 188.6 + 15 6 4 1 LM 869.5 + 16 6 3 1 LM 382.0 + (Alg.) Algorithm, (LM) Levenberg Marquardt, (BP) Backpropagation, (+) simulated, (-) not simulated. Few ANN models had in the occult layer a half of neurons of the input layer while other ones (models 2 and 11) had more than double of neurons of the input layer as commented by Kóvacs [16]. The results obtained from models 1, 2 and 10, considering 6 neurons in the output layer, had the same order of magnitude. Leaving the NC air monitoring site out of evaluation (models 1 and 3), the MSE was reduced. Models 4 to 9 and 11 to 16 were created to evaluate each result from each air monitoring station. The results were very similar except for NC. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

110 Air Pollution XIX Model 12 had five input data due to PCA results. The results obtained from models 8 and 15 were probably due to other variables that were not introduced in the model, since the used variables were not able to explain entirely the TSP concentrations. Using MSE approach for comparison results between model 1 and 2, it was showed that Levenberg-Marquardt algorithm was better than Backpropagation. On balance, the MSE results among the models were very similar with the same order of magnitude. For this reason model 1 was chosen to describe the subsequent results. Comparing between models 1 and 10, the use of principal components as input data did not altered the MSE results significantly. 3.1 Performance evaluation The average percentage errors for CA, BA, BR, EC, NC, and CS air quality monitoring sites were 35%, 24%, 27%, 31%, 42% and 37%, respectively. BA simulated results showed a good agreement with measured values. NC had the worst results. In regular meteorological conditions, BA is used in suffering particulate emissions from Usiminas more than the other sites, as it is located downwind from it. So the simulated results were better in BA than in the other ones. The result obtained in NC was the worst due to other variables that were not introduced in the model. Mitckiewicz [1] had also got the worst simulated results in NC using ISCST3 modelling. The cluster analysis is shown in fig. 1. Analysing the distance measurement it is possible to identify three groups. Dendrogram Ward's Method, Squared Euclidean 800

Distance

600

400

200

Cluster 3

Cluster 1

Cluster 2

W

SW

S

NW

SE

Rain

seasoncycle

Calm hours

Figure 1:

Temperature

E

windspeed

N

---------------- ---------------- --------------------NE

0

Cluster analysis.

Cluster 1, representing 50% of the output data, was characterized by N, NE, E wind directions, wind speed less than 1m/s, strong raining storms, ambient temperature about 21oC and the presence of two seasonal cycles. In other words, WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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50% of TSP concentrations results were grouped due to similar input data mentioned above. Cluster 2 grouped 19% of the output data due to input variables: SE, S, NW, SW, W wind directions, wind speed more than 1,1m/s, weak raining, ambient temperature about 21oC and winter cycle. Finally, cluster 3 (24% of output data) was characterized by N, NE, and E wind directions, wind speed more than 2,1m/s, frequent raining, ambient temperature about 25oC and the presence of summer cycle. The analysis of quantile-quantile plot and type I and II error was made for all air quality monitoring sites, but in this paper, it will be only shown BA and NC results (figs. 2 and 3). The other ones can be seen in more details in Lima, M. M. C. [18]. In fig. 2, according to quantile-quantile plot analysis, the tendency of 52% of the predicted values was to overestimate the real values. They occurred in meteorological conditions from cluster 1. Considering the analysis of false positive and false negative errors, both were verified. But, the type I error was more often and was determined by the variables characterized by cluster 3. One hypothesis that could probably explain is the location of main particulate emission sources from Usiminas in relation to BA air quality monitoring site. Under those meteorological conditions (characterized by cluster 3), BA air monitoring quality site was downwind from them and if there was an emission increase during that period, the model was not able to evaluate it, as they were not introduced in the modelling. It could explain why predicted values was lower than real. 200

negative false error

Cluster 1 Cluster 2

180

Cluster 3 160

TSP real values (g/m3) (BA)

140

120

100

80

60

40

20

positive false error 0 0

20

40

60

80

100

120

140

160

180

200

TSP predicted values (g/m3) (BA)

Figure 2:

Comparison between real and simulated values for BA.

According to fig. 3, quantile-quantile plot analysis showed that a great part of simulated values was overestimated if compared to the real values. They also occurred in meteorological conditions described in cluster 1. The type II error was more common than the other one. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

112 Air Pollution XIX 240

negative false error

Cluster 1 Cluster 2 Cluster 3

TSP real values (g/m3) (NC)

200

160

120

80

40

positive false error 0 0

40

80

120

160

200

240

TSP predicted values (g/m3) (NC)

Figure 3:

Comparison between real and simulated values for NC.

The possible cause was its location is not favourable and exposed to extra contributions. NC site is close to a paved road with heavy traffic. This situation was not modelled. 3.2 Comparison between ANN and ISCST3 model The comparison between ANN and ISCST3 model results is shown is table 2. Table 2:

Statistical analysis results.

Site Statistical analysis

CA BA BR EC NC CS

FB ISCST3 -0.29 -0.06 0.42 -0.61 -0.82 0.29

ANN 0.06 -0.07 0.12 0.11 0.15 0.04

IA ISCST3 0.36 0.53 0.21 0.19 0.53 0.27

ANN 0.66 0.62 0.69 0.70 0.76 0.61

R ISCST3 0.02 0.37 0.02 0.18 0.44 0.08

ANN 0.48 0.47 0.56 0.54 0.65 0.44

MSE ISCST3 1754.86 2944.57 6124.87 1652.21 4003.12 14616.33

ANN 334.79 671.00 173.68 202.97 1027.18 390.40

FB and MSE usually measure bias of a model. R is the correlation between the observed and simulated values and IA shows the degree that the model predictions are error free. The ideal model would result in values of MSE = 0, R = 1, FB = 0 and IA = 1. Analyzing the mean fractional bias results, NC, BR and EC air quality monitoring stations had the worst results in both models. The mean index of agreement (IA) showed the EC and CS results were the worst in ISCST3 and ANN models, respectively. The values of IA and FB obtained in WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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ANN were better than the ISCST3 dispersion modelling. The values of R and MSE obtained in ANN were also better than the ISCST3 model. For this reason, ANN model should be considered closer to the ideal model.

4 Conclusions This study showed that ANN can be a powerful data analysis tool to evaluate air pollutant dispersion. Even though, in the modelling, particulate emissions from Usiminas were not introduced as input variable, ANN was able to predict the TSP concentration in Ipatinga atmosphere using meteorological and seasonal cycle data. On balance, the tendency of simulated values was to overestimate the real values. The best modelling results were obtained in BR, BA and CA. BA had the best result and NC, the worst. BA monitoring site is favourable to suffer particulate emissions from Usiminas more than the other sites, as it is located downwind from it in regular meteorological conditions. It could explain why the simulated results were better in BA than in the other ones. The simulated result obtained in NC was the worst due to other variables that were not introduced in the model and its unfavourable location. The type I and II errors results were not representative in the modelling. The type II error only occurred in NC monitoring site and type I error was more common in BA site. Those errors were caused by their location probably. According to statistical analysis of MSE, FB, IA and R, the predicted results from the ANN model were better than the ISCST3 dispersion model.

References [1] United States Environmental Protection Agency (USEPA). Air Quality Criteria for Particulate Matter – Vol. II – EPA/600/P-99/002a-f, 2004, www.epa.gov/pmresearch/. [2] Mitkiewicz, G. F., Metodologia para avaliação da dispersão atmosférica de poluentes provenientes de um complexo siderúrgico industrial, 2002, Departamento de Engenharia Sanitária e Ambiental. (Dissertação de Mestrado em Meio Ambiente), Escola de Engenharia da UFMG, Belo Horizonte, Brasil. [3] Linyan, S. Wang, Y., A neural network model for environmental predication: case study for China. Computers and Industrial Engineering, China, 31, pp. 879-883, 1995. [4] Wal, J.T., Janssen, L.H.J.M., Analysis of spatial and temporal variations of PM10 concentrations in the Netherlands using Kalman filtering. Atmospheric Environment, 34, pp. 3675-3687, 2000. [5] Perez, P. Reyes, J., Prediction of particulate air pollution using neural techniques. Neural Computing & Applications, Chile, 10, pp. 165-171, 2001. [6] Viotti, P.; Liuti, G.; Genova, P. D., Atmospheric urban pollution: applications of an artificial neural network (ANN) to the city of Perugia. Ecological Modelling, Rome, 143, pp. 27-46, 2002. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

114 Air Pollution XIX [7] Zickus, M., Greig, A.J., Niranjan, M., Comparison of four machine learning methods for predicting PM10 concentrations in Helsinki, Finland. Water, Air, and Soil Pollution, 2, pp. 717-729, 2002. [8] Perez, P., Reyes, J., Prediction of maximum of 24-h average of PM10 concentrations 30 h in advance in Santiago, Chile. Atmospheric Environment, 36, pp. 4555-4561, 2002. [9] Podnar, D., Koracin, D., Panorska, A., Application of artificial neural networks to modelling the transport and dispersion of tracers in complex terrain. Atmospheric Environment, 36, pp. 561-570, 2002. [10] Ordieres, J.B., Vergara, E.P., Capuz, R.S., Salazar, R.E., Neural network prediction model for fine particulate matter (PM2.5) on the US-Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua). Environmental Modeling & Software, 20, pp. 547-559, 2005. [11] Hooyberghs, J., Mensink, C., Dumont, G., Fierens, F., Brasseur, O., A neural network forecast for daily average PM10 concentrations in Belgium. Atmospheric Environment, 39, pp. 3279-3289, 2005. [12] Gardner, W. M.; Dorling, R. S., Artificial neural networks (the multilayer perceptron) – a review of applications in the atmospheric sciences. Atmospheric Environment, 32(14/15), pp. 2627-2636, 1998. [13] Haykin, S., Neural networks: a comprehensive foundation, Prentice-Hall Inc.: Canada, pp. 1-842, 1999. [14] Matlab R12, Version 5.1, The language of technical computing: getting started with Matlab, The Mathworks Inc., pp. 1-86, 1997. [15] Zhang, G. Patuwo, B.E. HU, M. Y., Forecasting with artificial neural networks: the state of the art. International journal of forecasting, 14, pp. 35-62, 1998. [16] Kovács, Z.H., Redes neurais artificiais: fundamentos e aplicações, Editora Livraria da Física: São Paulo, pp. 1-174, 2002. [17] Olesen, H., Model validation kit – status and outlook, National Environmental Research Institute: Denmark, 1997. [18] Lima, M. M. C., Estimativa de concentração de material particulado em suspensão na atmosfera por meio da modelagem de redes neurais artificiais (Dissertação de Mestrado em Meio Ambiente), Escola de Engenharia da UFMG, Belo Horizonte, Brasil.

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Section 2 Monitoring and measuring

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The use of mineral magnetic measurements as a particulate matter (PM) proxy for road deposited sediments (RDS): Marylebone Road, London C. A. Booth1, C. J. Crosby1, D. E. Searle1, J. M. Khatib1, M. A. Fullen2, A. T. Worsley3, C. M. Winspear1 & D. A. Luckhurst2 1

STech, University of Wolverhampton, UK SAS, University of Wolverhampton, UK 3 NGAS, Edge Hill University, UK 2

Abstract Road deposited sediments (RDS) are a recognised pollution problem and a worrying public health concern of many urban environments. Linkages between the magneto characteristics of RDS and their particle size properties have been explored to determine the extent to which magnetic technologies can be utilised as a proxy for proffering insights to address pollution challenges. Samples (n = 60) were collected (May, 2008) along both sides of a busy urban road (Marylebone Road) in central London, UK. Magnetic concentration parameters (LF, χARM and SIRM) reveal high levels of magnetic material, when compared to previous urban RDS studies. Correlation analysis between the magnetic parameters and textural parameters (LF, χARM, SIRM and PM1.0, PM2.5, PM10) show significantly strong relationships but, unlike earlier studies, the trends display negative correlations. Despite this kinship not adhering to previously identified trends, this does not mean that mineral magnetic measurements cannot be used as a proxy. Moreover, it simply implies that the nature of any trends needs to be established for specific places before it can be reliably applied as a proxy. Keywords: environmental magnetism, particle size, street dust, built environment, epidemiology, public health. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line) doi:10 2495/AIR110111

118 Air Pollution XIX

1 Introduction Road deposited sediments (RDS) (sometimes referred to as street dust) can be toxic [1–3] and contribute to the particulate matter (e.g. PM1 0, PM2 5, PM10) loadings of urban sediments. Given the microscopic characteristics of these grains they are easily re-suspended and can become a significant human exposure source. When particles are absorbed through inhalation it can lead to serious health problems, such as cardiovascular disease and respiratory illness [4–6]. In the UK, for instance, it is estimated that 24,000 deaths occur annually due to poor air quality [7]. From an environmental perspective, RDS also cause urban drainage system issues, where urban runoff transfers the mix of sediments and toxic substances to receiving drainage systems and/or watercourses, causing detrimental effects on water quality and the health of the natural environment [8–10]. Other studies have highlighted linkages between road surface runoff and the deleterious influence of inorganic metal toxicants on benthic community structure and function in receiving water bodies [11, 12]. This is because the composition of RDS comprises a variety of both natural materials (quartz, clay and carbonates) and anthropogenic particles [13, 14]. Typically, anthropogenic sediments are derived from industrial and vehicle-generated sources, causing them to contain both metals (metallic fragments and iron oxides) and organic matter [13, 15]. Previous studies [16–18] have demonstrated the distribution of heavy metals within the built environment, in particular their proximity to roadsides and urban catchments. Heavy metals have been found to be associated with road traffic in urban areas and are known to contain particles associated with vehicle wear (such as tyres, body, brake linings), road surface wear, road paint degradation, vehicle fluids, and particulate emissions [19–22]. There is growing awareness of the issues associated with PM pollution [23], particularly within the built environment arena. As such, programmes of PM monitoring are now commonplace in major towns and cities of many countries. For instance, the UK currently has a network of 64 automatic monitoring sites (using gravimetric analysers of PM concentrations). The suite of sites, forming the ‘Automatic Urban and Rural Network’ (AURN), operated by the Department for Environment, Food and Rural Affairs (DEFRA), are positioned at strategic urban locations where data is continually measured and monitored (www.airquality.co.uk), recording hourly and daily measurements of PM10, PM2 5, Nitrogen oxides, Sulphur dioxide and Ozone, amongst others. The European Air Quality Framework Directive (96/62/EC) and the First Air Quality Daughter Directive (1999/30/EC) legislation require PM10 levels not to exceed 50 gm-3 for more than 35-days per year and set the maximum annual mean limit at 40 gm-3. That said, Marylebone Road is one of the most widely publicised air pollution sites in the UK, where PM concentrations are known to regularly exceed regulatory standards [24–27]. Therefore, this venue was selected so as to validate the potential of adopting an alternative technology for monitoring the PM sizes of RDS. Previous RDS studies, elsewhere, have already shown kinships exist between particle sizes and heavy metal content [28–31] but WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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the magnetic signature of RDS has only recently been identified to exhibit significant correlations with these characteristics [32, 33]. However, magnetotechniques have not been widely applied to sites where there are known PM issues and, thus, allow new findings, herein, to be compared with other studies. This work aims to demonstrate the extent to which particular magnetic concentration parameters can be used as a particle size proxy for urban RDS and attempt to highlight whether any data associations follow the predictable trends of similar studies.

2 Case study Marylebone Road (Fig. 1) is a major arterial route (A501) for traffic (up to seven lanes, including bus lanes) and pedestrians within the city of Westminster (central London) that forms part of the inner London ring-road and marks the northern limit of the London congestion charging zone. Roadside buildings create an asymmetric street canyon with a height to width ratio of ~0.8 [34]. The road has consistently high daily mean PM10 levels that regularly exceed legislative requirements (e.g. 185 incidents 2002–2004 [34]; 47 (a)

(b)

(c)

(d)

Figure 1: Views of Marylebone Road (May, 2008): (a) Site 9, East facing (Grid reference: 527833 181940); (b) Site 14, East Facing (Grid reference: 527963 181995); (c) Site 29, West facing (Grid reference: 528783 182200); and (d) Site 61, East facing (Grid reference: 526243 180535). WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

120 Air Pollution XIX incidents in 2007; 29 incidents in 2008; and 36 incidents in 2009). These exceedences are thought to be due to congestion and high traffic flows [35], with over 80,000 vehicles per day using the road [34]. This has led to a number of independent studies on PM [25, 34, 36–39], which reveal increases in iron rich dusts [39] and a greater frequency of PM10 exceedences during weekdays when traffic conditions peak [34].

3 Materials and methods 3.1 Sample collection and preparation Street dust was collected from the pavements (sidewalks) at regular spacings along both sides of the road. Typically, 10–50 g dust samples were collected (from ~1 m2) by brushing with a small hand-held fine-bristle brush. Dust was then transferred to clean, pre-labelled, self-seal, airtight plastic bags. In the laboratory, samples were visibly screened to remove macroscopic traces of hair, animal and plant matter [21]. 3.2 Mineral magnetic measurements All samples were subjected to the same preparation and analysis procedure. Samples were dried at room temperature (0.05) among fungal contamination and the following parameters: temperature, relative humidity and number of meals served in hospitals’ and companies’ food units. Keywords: air, surfaces, fungal contamination, hospitals, companies.

1 Introduction Fungal spores are complex agents that may contain multiple hazardous components. Health hazards may differ across species because fungi may produce different allergens and mycotoxins, and some species can infect humans [1]. Exposure effects to fungi are dependent on the species present, the metabolic products produced, concentration and duration of the exposure, and also individual susceptibility [2]. The main source of fungi in office environments is outdoor air. As outdoor air is often filtered before entering in the ventilation system and fungi settle due to lower air velocities in buildings than outdoors, common indoor fungal levels are expected to be lower than levels in outdoor air [1]. However, in hospital settings there are diverse possible sources of fungal contamination including ventilation or air-conditioning systems, decaying organic material, dust ornamental plants, food, water and, particularly, building works in and around hospitals [3, 4]. Despite the possibility of adverse health effects due to exposure to fungal products, no health-based exposure limits have yet been proposed. In part this is due to the difficulty of accurately characterizing cumulative fungal spore concentrations [5] and also because, epidemiological studies have failed to establish a causal relation of the extent of fungal presence, exposure time and specific effects on health or frequency and severity of symptoms reported. Studies tend to show only existence of a link between exposure to fungi and development of symptoms, especially respiratory ones [2]. Attempts have been made only to identify fungi responsible for specific symptoms attributed to mould exposure, such as allergenic [6], inflammatory [7] or mycotoxic [8] effects. In Portugal, the prevalence of diseases such as asthma and rhinoconjunctivitis in the general population varies from 15% to 25% and from 10% to 15%, respectively, and in recent years has been increasing [9]. Various causes have been considered, including indoor air pollution caused by fungal contamination. This investigation was designed to compare air and surfaces fungal contamination in ten hospitals’ food units and two food units from companies and explore possible associations with independent variables.

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2 Materials and methods A descriptive study was developed to compare fungal contamination of air and surfaces analyzed from ten hospitals’ food units and two food units from companies. Fifty air samples of 250 litres were collected through impaction method from hospitals’ food units. Forty one swab samples from surfaces were also collected, using a 10 by 10 cm square stencil. Regarding the two companies, ten air samples and eight swab samples were collected using the methodology previously described. Air and swab samples were collected in food storage facilities, kitchen, food plating and canteen. Simultaneously, temperature and relative humidity were also monitored through the equipment Babouc, LSI Sistems and according to the International Standard ISO 7726 – 1998. The number of meals served in each case was also registered. Air samples were collected at one meter tall with a flow rate of 140 L/minute, onto malt extract agar supplemented with the antibiotic chloramphenicol (MEA), in the facilities, and also outdoor, since this is the place regarded as reference. Concerning surfaces samples they were collected by swabbing the surfaces of the same indoor places, using a 10 by 10 cm square stencil disinfected with 70% alcohol solution between samples according to the International Standard ISO 18593 – 2004. Subsequently, all the collected samples were incubated at 27 ºC for 5 to 7 days. After laboratory processing and incubation of the collected samples, quantitative (CFU/m3 and CFU/m2) and qualitative results were obtained, with identification of the isolated fungal species. Whenever possible, filamentous fungi were identified to the species level, since adverse health effects vary according to fungal species [10, 11]. Identification of filamentous fungi was carried out on material mounted in lactophenol blue and achieved through morphological characteristics listed in illustrated literature [11] and yeasts were identified through biochemical API test [12]. Tables with frequency distribution of isolated fungal species were made with the obtained data. Fungal concentration dependence in the two monitored environmental parameters – temperature and relative humidity– and also number of meals served was analyzed.

3 Results Concerning air from hospitals’ food units, 32 species of fungi were identified, being the two most commonly isolated genera Penicillium sp. (43.6%) and Cladosporium sp. (23.2%). Regarding yeasts, only Rhodotorula sp. (84.2%) and Trichosporon sp. (15.8%) were isolated. Considering surfaces from the same places, twenty one fungal species were identified, being also Penicillium sp. (69.1%) and Cladosporium sp. (8.25%) the genera most frequently found. Candida parapsilosis (36.3%) and Rhodotorula sp. (25.7%) were the most prevalent yeasts species. In the two companies, nine fungal species were identified in air, being Cladosporium sp. the most frequent genus (71.2%) followed by Penicillium sp. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

458 Air Pollution XIX (13.0%). Concerning yeasts only Candida famata was identified. Eight fungal species and three yeasts were identified in the analyzed surfaces, being moulds and yeasts most frequently found Penicillium sp. (97.2%) and Candida famata (42.9%), respectively. Regarding hospitals’ food units, there was coincidence between prevailing genera in indoor and outdoor air/environment. Nevertheless, all ten food units presented fungal species different from the ones isolated from outdoor. Moreover, nine from the ten food units presented Aspergillus species. Regarding the comparison of concentrations found in air, for indoor and outdoor environments, nine of the ten food units showed higher levels of contamination in indoor air. Concerning companies’ food units, there was no coincidence between prevailing genera in indoor and outdoor and the two food units presented fungal species different from the ones isolated from outdoor. However, only one of them presented Aspergillus species and none showed higher levels of contamination in indoor air when compare with outdoor air levels. Aspergillus species, such as A. ochraceus, A. versicolor, A. candidus, A. fumigatus and A. niger were isolated in hospitals’ food units, whereas companies’ food units only A. glaucus was isolated. There was no significant relation (p>0,05) between fungal contamination and temperature, relative humidity and number of meals served in hospitals’ and companies’ food units.

4 Discussion The most predominant genus found in hospital air was Penicillium. Regarding this genus, there are different potential risks associated with their inhalation, due to the toxins release [13]. Regarding the most frequent genus in companies’ air – Cladosporium – is probably the fungus that occurs more frequently around world, especially in temperate climates [14] such as in Portugal and is deeply connected to indoor condensation problems [15]. Both of the referred genus were also the more frequent in a study realized in a Portuguese poultry [16]. It is suggested that fungal levels found indoors should be compared, quantitatively and qualitatively, with those found outdoors, because the first are dependent on the last [2]. Nevertheless, when it comes to fungal levels, it should be taken into account that indoor and outdoor environments are quite different which, by itself, justifies diversity of species between different spaces. However, with regard to fungal contamination, there are no stipulated thresholds which makes essential to compare fungal levels indoors and outdoors. Thus, indoor air quality that significantly differs from the outdoor could mean that there are infiltration problems and that exist a potential risk for health. It is worth mentioning that as outdoor air is a major source of the fungi found indoors, it is no coincidence that, in the case of hospitals’ food units, the prevailing genera, Penicillium sp. and Cladosporium sp., are the same in both these environments [13]. Nonetheless, in the companies’ case there was no coincidence between prevailing genera in indoor and outdoor air. Besides that, WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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all the monitored food units (hospitals and companies) had one or more spaces with fungal species that differed from the ones isolated outdoor, suggesting fungal contamination from within [13]. Moreover, according to American Industrial Hygiene Association (AIHA), in 1996, for determination of biological contamination in environmental samples, The confirmed presence of the species Aspergillus flavus and Aspergillus fumigatus (both identified in hospitals’ food units), requires implementation of corrective measures [17]. Regarding comparison of spore concentrations found in air, for indoor and outdoor environments, nine of the ten hospitals’ food units showed higher levels of contamination in indoor air, whereas all the companies’ food units presented higher levels of fungal concentrations in outdoor air. This fact could be explained by the discrepancy of the number of institutions analyzed or maybe because possible sources of fungal dissemination include hospitals’ ventilation or air-conditioning systems [4, 18]. Taking into account what is mentioned in Portuguese law, this value was 500 CFU/m3 is the maximum reference concentration in indoor air, was exceeded only in four indoor spaces from the hospitals’ food units analyzed in this study. Regarding what is mentioned in Portuguese Technician Norm NT-SCE-02, the presence of opportunistic fungi from Aspergillus genus, shows a lack of air quality in indoor space. Aspergillus species are frequently present on food and thus can be an indirect source of airway or digestive tract colonization of the patients and workers [19]. Results related to environmental variables are not consistent with what is expected [20]. It was found that the relation between the fungal air contamination and the temperature, relative humidity, and also number of meals served was not statistically significant (p>0,05). This may be justified by the effect of other environmental variables also influencing fungal spreading, namely patients and workers, who may carry a great diversity of fungal species [21], as well the developed activities that may also affect fungal concentration [22].

5 Conclusions With this study, it was possible to characterize fungal distribution in ten hospitals’ food units and two food units from companies and evaluate the association of environmental variables and also number of meals served with this distribution. It was also possible to observe that hospitals’ food units presented more evidence that fungal contamination comes from within than in the companies’ case. Unlike other studies, environmental variables monitored (temperature and relative humidity) and also number of meals served, did not show the expected association with fungal concentration, which may possibly have resulted from other variables not investigated in this study.

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References [1] Eduard, W., Fungal spores: A critical review of the toxicological and epidemiological evidence as a basis for occupational exposure limit setting. Informahealthcare, 2008. [2] Goyer N, Lavoie J, Lazure L & Marchand G., Bioaerosols in the Workplace: Evaluation, Control and Prevention Guide. Institut de Recherche en Santé et en Sécurité du Travail du Québec, 2001. [3] Van Den Bergh M, Verweij P & Voss A., Epidemiology of nosocomial fungal infections: invasive aspergillosis and the environment. Diagnostic Microbiology and Infectious Disease, 34, 3, pp. 221–227, 1999. [4] Boukline A, Lacroix C, Roux N, Gangneux J & Derouin F., Fungal contamination of food in hematology units. J Clin Microbiol, 38, pp. 4272– 4273, 2000. [5] Bartlett K, Kennedy S, Brauer M, Van Netten C &Dill B., Evaluation and predictive model of airborne fungal concentrations in school classrooms. Ann Occup Hyg, 48, pp., 547–554, 2004. [6] Cruz A, Saenz de Santamaria M, Martinez J, Martinez A, Guisantes J & Palácios R., Fungal allergens from important allergenic fungi imperfecti. Allergol Immunopathol, 25, pp., 153–158, 1997. [7] Rylander R, Persson H, Goto H, Yuasa K & Tanaka S., Airborne beta (1,3)glucans may be related to symptoms of sick buildings. Indoor Environment, 1, pp., 263–267, 1992. [8] Hodgson M, Morey P, Leung W, Morrow L, Miller D, Jarvis B, Robbins H, Halsey J & Storey E., Building associated pulmonary disease from exposure to Stachybotrys chartarum and Aspergillus versicolor. J Occup Environm Med, 40, pp., 241 – 249, 1998. [9] Nunes C & Ladeira S., Estudo da Qualidade de Ambiente Fúngico em Escolas e Edifícios Públicos no Algarve. Revista Portuguesa Imunoalergologia,15, pp. 411–422, 2007. [10] Rao C, Burge H & Chang J., Review of quantitative standards and guidelines for fungi in indoor air. J Air Waste Manage Assoc., 46, pp. 899– 908, 1996. [11] Hoog C, Guarro J, Gené G & Figueiras M., (2th ed). Atlas of Clinical Fungi. Centraalbureau voor Schimmelcultures, 2000. [12] Ghannoum M, Hajeh R, Scher R, Konnikov N, et al., A large-scale North American study of fungal isolates from nails: The frequency of onychomycosis, fungal distribution and antifungal susceptibility patterns. J. Am. Acad. Dermatol, 43, pp. 641–648, 2000. [13] Kemp P, Neumeister-Kemp H, Esposito B, Lysek G & Murray F., Changes in airborne fungi from the outdoors to indoor air; Large HVAC systems in nonproblem buildings in two different climates. American Industrial Hygiene Association, 64, pp. 269–275, 2003. [14] Cooley J, Wong W, Jumper C & Straus D., Correlation between the prevalence of certain fungi and sick building syndrome. Occup. Environ Med, 55, pp., 579–584, 1998. WIT Transactions on Ecology and the Environment, Vol 147, © 2011 WIT Press www witpress com, ISSN 1743-3541 (on-line)

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[15] Garret M, Rayment P, Hooper M, Abranson M & Hooper B., Indoor Airborne Fungal Spores, House Dampness and Associations with Environmental Factors and Respiratory Health in Children. Clinical and Experimental Allergy, 28, pp., 459–467, 1998. [16] Viegas C, Veríssimo C, Rosado L & Silva Santos C., Poultry fungal contamination as a public health problem. Environmental Toxicology III. WIT Transactions on Biomedicine and Health, 2010. [17] American Industrial Hygiene Association: Field Guide for the Determination of Biological Contaminants in Environmental Samples. AIHA, 1996. [18] Beggs C & Kerr K., The threat posed by airborne micro-organisms. Indoor and Built Environment, 9, 5, pp. 241–245, 2000. [19] Sarfati J, Jensen H & Latgé J., Route of infections in bovine aspergillosis. J Med Vet Mycol, 34, pp., 379–383, 1996. [20] Kakde U, Kakde H & Saoji A., Seasonal Variation of Fungal Propagules in a Fruit Market Environment, Nagpur (India). Aerobiologia, 17, pp. 177– 182, 2001. [21] Scheff P, Pulius V, Curtis L & Conroy L., Indoor air quality in a middle school, Part II: Development of emission factors for particulate matter and bioaerosols. Applied Occupational and Environmental Hygiene, 15, pp. 835–842, 2000. [22] Buttner M & Stetzenbach L., Monitoring Airborne fungal spores in an experimental indoor environment to evaluate sampling methods and the effects of human activity on air sampling. Applied and Environmental Microbiology, 59, pp. 219–226, 1993.

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Author Index Almeida M. .............................. 455 Amorim J. H. ............................. 13 Apon F. .................................... 137 Band P. R. ................................ 445 Barbiere M. .............................. 185 Barnes J. H. .......................... 1, 277 Belis C. .................................... 185 Bellagamba S. .......................... 199 Booth C. A. .............................. 117 Borowiak A.............................. 185 Borrego C. ................................. 13 Borůvková J. ............................ 173 Bourgault C.............................. 387 Bozek F. ................................... 343 Božnar M. Z. .............................. 47 Briant R...................................... 37 Buelna G. ................................. 387 Bugajny C. ................................. 37 Burns L. ................................... 137 Cairnsa E.................................. 239 Callén M. S. ............................. 149 Cascão P. ................................... 13 Černíková M. ........................... 423 Chatterton T. J. ........................ 277 Chen H. ...................................... 93 Chen J. B............................ 59, 307 Chrysoulakis N. ......................... 13 Connan O. ................................ 399 Cortes L. .................................. 301 Crosby C. J. ............................. 117 Čupr P. ..................................... 173 Cursaru D. L. ........................... 331 Dacre H. ..................................... 81 Damiani F. ............................... 199 De Geyter N. ............................ 353 De Simone P. ........................... 199 Derkx F. ................................... 399 Di Molfetta V........................... 199

Edokpayi C. A. ............................ 1 Eijk A. R. A. ............................ 129 Faizal M. .................................. 231 Falcón Y. I. .............................. 301 Fraaij A. L. A........................... 219 Fullen M. A. ............................ 117 Gadrat M. ................................... 37 Gavendova H. .......................... 343 Gerboles M. ............................. 185 Giraudon J.-M.......................... 353 Grašič B. .................................... 47 Grison H. ................................. 363 Gulia S. ...................................... 71 Hayes E. T. .............................. 277 He J. J. ............................... 59, 307 Hébert D. ................................. 399 Heitz M. ................................... 387 Hoek G..................................... 257 Holoubek I. .............................. 173 Horák J..................................... 433 Huzlik J.................................... 343 Iqbal M. ................................... 231 Janssen N. ................................ 257 Jiang H. .................................... 445 Julia S. ..................................... 231 Kapička A. ............................... 363 Kapus M. ................................. 185 Kareš R. ................................... 173 Kastek M. ................................ 161 Keuken M. P. ................... 129, 257 Khare M. .................................... 71 Khatib J. M. ............................. 117 Klánová J. ................................ 173 Kohoutek J. .............................. 173 Kukučka P. .............................. 173

464 Air Pollution XIX Lagler F.................................... 185 Lamonier J.-F........................... 353 Lao J. ......................................... 25 Le Bihan Y............................... 387 Leroy C. ................................... 399 Lessard P.................................. 387 Leys C. ..................................... 353 Lima M. M. C. ......................... 105 Liu N. ................................. 59, 307 Longhurst J. W. S. ............... 1, 277 Lopes M. .................................... 13 López J. M. .............................. 149 Lu J. ......................................... 239 Luckhurst D. A. ....................... 117 Magaril E. ................................ 373 Malíková O. ..................... 423, 433 Malkina-Pykh I. G. .................. 267 Mărdărescu V........................... 331 Mares J..................................... 343 Maro D..................................... 399 Martinez E. .............................. 301 Martins J. ................................... 13 Mastral A. M............................ 149 Mengistu Tsidu G. ................... 411 Methven J. ................................. 81 Miranda A. I. ............................. 13 Mlakar P. ................................... 47 Morent R. ................................. 353 Munir S. ..................................... 93 Murena F.................................. 287 Nagendra S. ............................... 71 Nguyen Dinh M. T. .................. 353 Nikiema J. ................................ 387 Nugraha T. ............................... 231 Olowoporoku A. O. ............. 1, 277 Ottelé M. .................................. 219 Paglietti F. ................................ 199 Peake D. ..................................... 81 Petrovský E. ............................. 363 Piątkowski T. ........................... 161

Polakowski H........................... 161 Prokeš R................................... 173 Pykh Y. A. ............................... 267 Ramos C. ................................. 455 Rodrigues V. .............................. 13 Ropkins K. ................................. 93 Rosado L. ................................. 455 Roupsard P............................... 399 Rozet M. .................................. 399 Sabino R. ................................. 455 Searle D. E. .............................. 117 Seigneur C. ................................ 37 Seroji A. R. .............................. 319 Tan G. F. M. ............................ 239 Tănăsescu C. ............................ 331 Tang H. .................................... 137 Tavares R. .................................. 13 Teixidó O. .................................. 25 Tharumakulasingam K. ........... 239 Tomaz E................................... 211 Ture K...................................... 411 Turgeon N. ............................... 387 Ueda A. C. ............................... 211 Urciuolo M. ............................. 287 Ursem W. J. N. ........................ 219 van Bohemen H. D. ................. 219 van den Elshout S. ................... 257 Vandenbroucke A. M. ............. 353 Vanderstricht A........................ 353 Veríssimo C. ............................ 455 Verreault S. .............................. 387 Viegas C. ......................... 247, 455 Vijay P. ...................................... 71 Voogt M. H. ............................. 129 Winspear C. P. ......................... 117 Worsley A. T. .......................... 117 Xia D. S. .................................. 307

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Yang L. .................................... 137 Yap D....................................... 239 Yu Y. ................................. 59, 307 Yuliarto B. ............................... 231

465

Zandveld P. ...................... 129, 257 Zhao S. P. .......................... 59, 307 Zielinski J. M. .......................... 445

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Environmental Health Risk VI Edited by: C.A. BREBBIA, Wessex Institute of Technology, UK and M. EGLITE, Riga Stradins University, Latvia

Health problems related to the environment are causing increasing concern all over the world. The health of the population depends upon society’s ability to ensure good quality air, water, soil, and food and to eliminate or considerably reduce hazards from the human environment. Society’s ability to achieve these objectives is greatly dependent on the development of modelling and interpretive techniques that allow decision-makers to assess the risk posed by various factors, as well as to suggest improvements. Environmental Health Risk VI contains contributions presented at the Sixth International Conference on the Impact of Environmental Factors on Health. The successful biennial series began in 1997 and covers such topics as: Air Pollution; Water and Soil Quality Issues; Risk Prevention and Monitoring; Ecology and Health; Food Safety; Toxicology Analysis; Occupational Health; Control of Pollution Risk; Mitigation Problems; Disaster Management and Preparedness; Epidemiological Studies and Pandemics; Radiation Fields; Waste Disposal; Industrial Safety and Hygiene; Social and Economic Issues; Accidents and Man-made Risks; The Built Environment and Health; Designing for Health; Contamination in Rural Areas; Climate Change and Adaptation; Educational Projects; Environmental Education and Risk Abatement. WIT Transactions on Biomedicine and Health, Vol 15 ISBN: 978-1-84564-524-3 eISBN: 978-1-84564-525-0 Published 2011 / 512pp / £220.00

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Air Pollution is one of the most challenging problems facing the international community; it is widespread and growing in importance and has clear and known impacts on health and the environment. The human need for transport, manufactured goods and services brings with it impacts on the atmospheric environment at scales from the local to the global. The rate of development of the global economy brings new pressures and the willingness of governments to regulate air pollution is often balanced by concerns over the economic impact of such regulation. Science is the key to identifying the nature and scale of air pollution impacts and is essential in the formulation of policies for regulatory decision-making. Continuous improvements in our knowledge of the fundamental science of air pollution and its application are necessary if we are to predict, assess and mitigate the air pollution implications to local, regional, national and international economic systems. The Eighteenth Annual Meeting in the successful series of International Conferences dealing with Modelling, Monitoring and Management of Air Pollution discussed papers dealing with a wide variety of topics, including: Air Pollution Modelling; Air Quality Management; Emission Studies; Monitoring and Measuring; Aerosols and Particles; Innovative Indoor Air Quality Techniques; Indoor Air Pollution; Exposure and Health Effects; Air Pollution Mitigation. WIT Transactions on Ecology and the Environment, Vol 136 ISBN: 978-1-84564-450-5 eISBN: 978-1-84564-451-2 Published 2010 / 464pp / £176.00

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Over the last sixteen years, the Wessex Institute of Technology has convened annually the International Conference on Urban Transport and the Environment in order to facilitate the sharing of research on a subject of growing importance to cities around the world. Urban transportation systems can enhance or degrade the quality of urban life by their impact on the environment as well as their operation and accessibility. Municipal authorities put a priority on transportation systems that minimize ecological and environmental impacts, are sustainable, and help to improve the socio-economic fabric of the city. At the same time, systems must ensure the safety and security of the public while retaining system efficiency. All of these concerns are addressed by research presented at the conference and contained in this book. Topics covered include: Urban Transport Planning and Management; Transportation Demand Analysis; Traffic Integration and Control; Intelligent Transport Systems; Transport Modelling and Simulation; Land Use and Transport Integration; Public Transport Systems; Environmental and Ecological Aspects;Air and Noise Pollution; Safety and Security; Energy and Transport Fuels; Economic and Social Impact; and Advanced Transport Systems. WIT Transactions on the Built Environment, Vol 116 ISBN: 978-1-84564-520-5 eISBN: 978-1-84564-521-2 Published 2011 / 736pp / £316.00

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