Articles | Volume 21, issue 24
https://doi.org/10.5194/acp-21-18195-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-18195-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Dec 2021
Research article |
| 15 Dec 2021
| 15 Dec 2021
Why is the city's responsibility for its air pollution often underestimated? A focus on PM2.5
Philippe Thunis
CORRESPONDING AUTHOR
Joint Research Centre, European Commission, Ispra, Italy
Alain Clappier
Laboratoire Image Ville Environnement, Université de Strasbourg, Strasbourg, France
Alexander de Meij
MetClim, Varese, Italy
Enrico Pisoni
Joint Research Centre, European Commission, Ispra, Italy
Bertrand Bessagnet
Joint Research Centre, European Commission, Ispra, Italy
Leonor Tarrason
NILU – Norwegian Institute for Air Research, Kjeller, Norway
Related authors
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
Geosci. Model Dev., 18, 4231–4245, https://doi.org/10.5194/gmd-18-4231-2025, https://doi.org/10.5194/gmd-18-4231-2025, 2025
Short summary
Short summary
We assess relevance and utility indicators by evaluating nine Copernicus Atmospheric Monitoring Service models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and winter–summer gradients reveal issues. O3 evaluation shows that seasonal gradients are useful. Overall, the indicators reveal model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Philippe Thunis, Jeroen Kuenen, Enrico Pisoni, Bertrand Bessagnet, Manjola Banja, Lech Gawuc, Karol Szymankiewicz, Diego Guizardi, Monica Crippa, Susana Lopez-Aparicio, Marc Guevara, Alexander De Meij, Sabine Schindlbacher, and Alain Clappier
Geosci. Model Dev., 17, 3631–3643, https://doi.org/10.5194/gmd-17-3631-2024, https://doi.org/10.5194/gmd-17-3631-2024, 2024
Short summary
Short summary
An ensemble emission inventory is created with the aim of monitoring the status and progress made with the development of EU-wide inventories. This emission ensemble serves as a common benchmark for the screening and allows for the comparison of more than two inventories at a time. Because the emission “truth” is unknown, the approach does not tell which inventory is the closest to reality, but it identifies inconsistencies that require special attention.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, Enrico Pisoni, and Bertrand Bessagnet
Geosci. Model Dev., 17, 587–606, https://doi.org/10.5194/gmd-17-587-2024, https://doi.org/10.5194/gmd-17-587-2024, 2024
Short summary
Short summary
In our study the robustness of the model responses to emission reductions in the EU is assessed when the emission data are changed. Our findings are particularly important to better understand the uncertainties associated to the emission inventories and how these uncertainties impact the level of accuracy of the resulting air quality modelling, which is a key for designing air quality plans. Also crucial is the choice of indicator to avoid misleading interpretations of the results.
Lina Vitali, Kees Cuvelier, Antonio Piersanti, Alexandra Monteiro, Mario Adani, Roberta Amorati, Agnieszka Bartocha, Alessandro D'Ausilio, Paweł Durka, Carla Gama, Giulia Giovannini, Stijn Janssen, Tomasz Przybyła, Michele Stortini, Stijn Vranckx, and Philippe Thunis
Geosci. Model Dev., 16, 6029–6047, https://doi.org/10.5194/gmd-16-6029-2023, https://doi.org/10.5194/gmd-16-6029-2023, 2023
Short summary
Short summary
Air quality forecasting models play a key role in fostering short-term measures aimed at reducing human exposure to air pollution. Together with this role comes the need for a thorough assessment of the model performances to build confidence in models’ capabilities, in particular when model applications support policymaking. In this paper, we propose an evaluation methodology and test it on several domains across Europe, highlighting its strengths and room for improvement.
Philippe Thunis, Alain Clappier, Enrico Pisoni, Bertrand Bessagnet, Jeroen Kuenen, Marc Guevara, and Susana Lopez-Aparicio
Geosci. Model Dev., 15, 5271–5286, https://doi.org/10.5194/gmd-15-5271-2022, https://doi.org/10.5194/gmd-15-5271-2022, 2022
Short summary
Short summary
In this work, we propose a screening method to improve the quality of emission inventories, which are responsible for large uncertainties in air-quality modeling. The first step of screening consists of keeping only emission contributions that are relevant enough. In a second step, the method identifies large differences that provide evidence of methodological divergence or errors. We used the approach to compare two versions of the CAMS-REG European-scale inventory over 150 European cities.
Philippe Thunis, Alain Clappier, Matthias Beekmann, Jean Philippe Putaud, Cornelis Cuvelier, Jessie Madrazo, and Alexander de Meij
Atmos. Chem. Phys., 21, 9309–9327, https://doi.org/10.5194/acp-21-9309-2021, https://doi.org/10.5194/acp-21-9309-2021, 2021
Short summary
Short summary
Modelling simulations are used to identify the most efficient emission reduction strategies to reduce PM2.5 concentration levels in northern Italy. Results show contrasting chemical regimes and important non-linearities during wintertime, with the striking result that PM2.5 levels may increase when NOx reductions are applied in NOx-rich areas – a process that may have contributed to the absence of significant PM2.5 decrease during the COVID-19 lockdowns in many European cities.
Bart Degraeuwe, Enrico Pisoni, and Philippe Thunis
Geosci. Model Dev., 13, 5725–5736, https://doi.org/10.5194/gmd-13-5725-2020, https://doi.org/10.5194/gmd-13-5725-2020, 2020
Short summary
Short summary
To make decisions on how to improve air quality, it is useful to identify the main sources of pollution for an area of interest. Often these sources of pollution are identified with complex models that, even if accurate, are time consuming and complex. In this work we use another approach, simplified models, to accomplish the same task. The results, computed with two different set of simplified models, show the main sources of pollution for selected cities, and the associated uncertainties.
Manjola Banja, Monica Crippa, Diego Guizzardi, Marilena Muntean, Federico Pagani, and Enrico Pisoni
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-385, https://doi.org/10.5194/essd-2025-385, 2025
Preprint under review for ESSD
Short summary
Short summary
Global efforts to decrease emissions rely on inventories that differ widely in scope and methodology. Alongside national inventories, independent databases provide yearly globally consistent emission inventories. Comparing independent inventories with countries submissions provides clear and consistent track of the real progress. Improvement of emissions inventories, reporting timelines, and statistical systems are essential to ensure reliable and comparable data.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
Geosci. Model Dev., 18, 4231–4245, https://doi.org/10.5194/gmd-18-4231-2025, https://doi.org/10.5194/gmd-18-4231-2025, 2025
Short summary
Short summary
We assess relevance and utility indicators by evaluating nine Copernicus Atmospheric Monitoring Service models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and winter–summer gradients reveal issues. O3 evaluation shows that seasonal gradients are useful. Overall, the indicators reveal model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Diego Guizzardi, Monica Crippa, Tim Butler, Terry Keating, Rosa Wu, Jacek W. Kamiński, Jeroen Kuenen, Junichi Kurokawa, Satoru Chatani, Tazuko Morikawa, George Pouliot, Jacinthe Racine, Michael D. Moran, Zbigniew Klimont, Patrick M. Manseau, Rabab Mashayekhi, Barron H. Henderson, Steven J. Smith, Rachel Hoesly, Marilena Muntean, Manjola Banja, Edwin Schaaf, Federico Pagani, Jung-Hun Woo, Jinseok Kim, Enrico Pisoni, Junhua Zhang, David Niemi, Mourad Sassi, Annie Duhamel, Tabish Ansari, Kristen Foley, Guannan Geng, Yifei Chen, and Qiang Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-601, https://doi.org/10.5194/essd-2024-601, 2025
Preprint under review for ESSD
Short summary
Short summary
The global air pollution emission mosaic HTAP_v3.1 is the state-of-the-art database for addressing the evolution of a set of policy-relevant air pollutants over the past 2 decades. The inventory is made by the harmonization and blending of seven regional inventories, gapfilled using the most recent release of EDGAR (EDGARv8). By incorporating the best available local information, the HTAP_v3.1 mosaic inventory can be used for policy-relevant studies at both regional and global levels.
Monica Crippa, Diego Guizzardi, Federico Pagani, Marcello Schiavina, Michele Melchiorri, Enrico Pisoni, Francesco Graziosi, Marilena Muntean, Joachim Maes, Lewis Dijkstra, Martin Van Damme, Lieven Clarisse, and Pierre Coheur
Earth Syst. Sci. Data, 16, 2811–2830, https://doi.org/10.5194/essd-16-2811-2024, https://doi.org/10.5194/essd-16-2811-2024, 2024
Short summary
Short summary
Knowing where emissions occur is essential for planning effective emission reduction measures and atmospheric modelling. Disaggregating national emissions over high-resolution grids requires spatial proxies that contain information on the location of different emission sources. This work incorporates state-of-the-art spatial information to improve the spatial representation of global emissions with the Emissions Database for Global Atmospheric Research (EDGAR).
Philippe Thunis, Jeroen Kuenen, Enrico Pisoni, Bertrand Bessagnet, Manjola Banja, Lech Gawuc, Karol Szymankiewicz, Diego Guizardi, Monica Crippa, Susana Lopez-Aparicio, Marc Guevara, Alexander De Meij, Sabine Schindlbacher, and Alain Clappier
Geosci. Model Dev., 17, 3631–3643, https://doi.org/10.5194/gmd-17-3631-2024, https://doi.org/10.5194/gmd-17-3631-2024, 2024
Short summary
Short summary
An ensemble emission inventory is created with the aim of monitoring the status and progress made with the development of EU-wide inventories. This emission ensemble serves as a common benchmark for the screening and allows for the comparison of more than two inventories at a time. Because the emission “truth” is unknown, the approach does not tell which inventory is the closest to reality, but it identifies inconsistencies that require special attention.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev., 17, 3645–3665, https://doi.org/10.5194/gmd-17-3645-2024, https://doi.org/10.5194/gmd-17-3645-2024, 2024
Short summary
Short summary
This study is about the modelling of the atmospheric composition in Europe during the summer of 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impacts of two modelling processes that are able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, Enrico Pisoni, and Bertrand Bessagnet
Geosci. Model Dev., 17, 587–606, https://doi.org/10.5194/gmd-17-587-2024, https://doi.org/10.5194/gmd-17-587-2024, 2024
Short summary
Short summary
In our study the robustness of the model responses to emission reductions in the EU is assessed when the emission data are changed. Our findings are particularly important to better understand the uncertainties associated to the emission inventories and how these uncertainties impact the level of accuracy of the resulting air quality modelling, which is a key for designing air quality plans. Also crucial is the choice of indicator to avoid misleading interpretations of the results.
Lina Vitali, Kees Cuvelier, Antonio Piersanti, Alexandra Monteiro, Mario Adani, Roberta Amorati, Agnieszka Bartocha, Alessandro D'Ausilio, Paweł Durka, Carla Gama, Giulia Giovannini, Stijn Janssen, Tomasz Przybyła, Michele Stortini, Stijn Vranckx, and Philippe Thunis
Geosci. Model Dev., 16, 6029–6047, https://doi.org/10.5194/gmd-16-6029-2023, https://doi.org/10.5194/gmd-16-6029-2023, 2023
Short summary
Short summary
Air quality forecasting models play a key role in fostering short-term measures aimed at reducing human exposure to air pollution. Together with this role comes the need for a thorough assessment of the model performances to build confidence in models’ capabilities, in particular when model applications support policymaking. In this paper, we propose an evaluation methodology and test it on several domains across Europe, highlighting its strengths and room for improvement.
Jean-Philippe Putaud, Enrico Pisoni, Alexander Mangold, Christoph Hueglin, Jean Sciare, Michael Pikridas, Chrysanthos Savvides, Jakub Ondracek, Saliou Mbengue, Alfred Wiedensohler, Kay Weinhold, Maik Merkel, Laurent Poulain, Dominik van Pinxteren, Hartmut Herrmann, Andreas Massling, Claus Nordstroem, Andrés Alastuey, Cristina Reche, Noemí Pérez, Sonia Castillo, Mar Sorribas, Jose Antonio Adame, Tuukka Petaja, Katrianne Lehtipalo, Jarkko Niemi, Véronique Riffault, Joel F. de Brito, Augustin Colette, Olivier Favez, Jean-Eudes Petit, Valérie Gros, Maria I. Gini, Stergios Vratolis, Konstantinos Eleftheriadis, Evangelia Diapouli, Hugo Denier van der Gon, Karl Espen Yttri, and Wenche Aas
Atmos. Chem. Phys., 23, 10145–10161, https://doi.org/10.5194/acp-23-10145-2023, https://doi.org/10.5194/acp-23-10145-2023, 2023
Short summary
Short summary
Many European people are still exposed to levels of air pollution that can affect their health. COVID-19 lockdowns in 2020 were used to assess the impact of the reduction in human mobility on air pollution across Europe by comparing measurement data with values that would be expected if no lockdown had occurred. We show that lockdown measures did not lead to consistent decreases in the concentrations of fine particulate matter suspended in the air, and we investigate why.
Laurent Menut, Arineh Cholakian, Guillaume Siour, Rémy Lapere, Romain Pennel, Sylvain Mailler, and Bertrand Bessagnet
Atmos. Chem. Phys., 23, 7281–7296, https://doi.org/10.5194/acp-23-7281-2023, https://doi.org/10.5194/acp-23-7281-2023, 2023
Short summary
Short summary
This study is about the wildfires occurring in France during the summer 2022. We study the forest fires that took place in the Landes during the summer of 2022. We show the direct impact of these fires on the air quality, especially downstream of the smoke plume towards the Paris region. We quantify the impact of these fires on the pollutants peak concentrations and the possible exceedance of thresholds.
Monica Crippa, Diego Guizzardi, Tim Butler, Terry Keating, Rosa Wu, Jacek Kaminski, Jeroen Kuenen, Junichi Kurokawa, Satoru Chatani, Tazuko Morikawa, George Pouliot, Jacinthe Racine, Michael D. Moran, Zbigniew Klimont, Patrick M. Manseau, Rabab Mashayekhi, Barron H. Henderson, Steven J. Smith, Harrison Suchyta, Marilena Muntean, Efisio Solazzo, Manjola Banja, Edwin Schaaf, Federico Pagani, Jung-Hun Woo, Jinseok Kim, Fabio Monforti-Ferrario, Enrico Pisoni, Junhua Zhang, David Niemi, Mourad Sassi, Tabish Ansari, and Kristen Foley
Earth Syst. Sci. Data, 15, 2667–2694, https://doi.org/10.5194/essd-15-2667-2023, https://doi.org/10.5194/essd-15-2667-2023, 2023
Short summary
Short summary
This study responds to the global and regional atmospheric modelling community's need for a mosaic of air pollutant emissions with global coverage, long time series, spatially distributed data at a high time resolution, and a high sectoral resolution in order to enhance the understanding of transboundary air pollution. The mosaic approach to integrating official regional emission inventories with a global inventory based on a consistent methodology ensures policy-relevant results.
Philippe Thunis, Alain Clappier, Enrico Pisoni, Bertrand Bessagnet, Jeroen Kuenen, Marc Guevara, and Susana Lopez-Aparicio
Geosci. Model Dev., 15, 5271–5286, https://doi.org/10.5194/gmd-15-5271-2022, https://doi.org/10.5194/gmd-15-5271-2022, 2022
Short summary
Short summary
In this work, we propose a screening method to improve the quality of emission inventories, which are responsible for large uncertainties in air-quality modeling. The first step of screening consists of keeping only emission contributions that are relevant enough. In a second step, the method identifies large differences that provide evidence of methodological divergence or errors. We used the approach to compare two versions of the CAMS-REG European-scale inventory over 150 European cities.
Svetlana Tsyro, Wenche Aas, Augustin Colette, Camilla Andersson, Bertrand Bessagnet, Giancarlo Ciarelli, Florian Couvidat, Kees Cuvelier, Astrid Manders, Kathleen Mar, Mihaela Mircea, Noelia Otero, Maria-Teresa Pay, Valentin Raffort, Yelva Roustan, Mark R. Theobald, Marta G. Vivanco, Hilde Fagerli, Peter Wind, Gino Briganti, Andrea Cappelletti, Massimo D'Isidoro, and Mario Adani
Atmos. Chem. Phys., 22, 7207–7257, https://doi.org/10.5194/acp-22-7207-2022, https://doi.org/10.5194/acp-22-7207-2022, 2022
Short summary
Short summary
Particulate matter (PM) air pollution causes adverse health effects. In Europe, the emissions caused by anthropogenic activities have been reduced in the last decades. To assess the efficiency of emission reductions in improving air quality, we have studied the evolution of PM pollution in Europe. Simulations with six air quality models and observational data indicate a decrease in PM concentrations by 10 % to 30 % across Europe from 2000 to 2010, which is mainly a result of emission reductions.
Juan Cuesta, Lorenzo Costantino, Matthias Beekmann, Guillaume Siour, Laurent Menut, Bertrand Bessagnet, Tony C. Landi, Gaëlle Dufour, and Maxim Eremenko
Atmos. Chem. Phys., 22, 4471–4489, https://doi.org/10.5194/acp-22-4471-2022, https://doi.org/10.5194/acp-22-4471-2022, 2022
Short summary
Short summary
We present the first comprehensive study integrating satellite observations of near-surface ozone pollution, surface in situ measurements, and a chemistry-transport model for quantifying the role of anthropogenic emission reductions during the COVID-19 lockdown in spring 2020. It confirms the occurrence of a net enhancement of ozone in central Europe and a reduction elsewhere, except for some hotspots, linked with the reduction of precursor emissions from Europe and the Northern Hemisphere.
Laurent Menut, Bertrand Bessagnet, Régis Briant, Arineh Cholakian, Florian Couvidat, Sylvain Mailler, Romain Pennel, Guillaume Siour, Paolo Tuccella, Solène Turquety, and Myrto Valari
Geosci. Model Dev., 14, 6781–6811, https://doi.org/10.5194/gmd-14-6781-2021, https://doi.org/10.5194/gmd-14-6781-2021, 2021
Short summary
Short summary
The CHIMERE chemistry-transport model is presented in its new version, V2020r1. Many changes are proposed compared to the previous version. These include online modeling, new parameterizations for aerosols, new emissions schemes, a new parameter file format, the subgrid-scale variability of urban concentrations and new transport schemes.
Gaëlle Dufour, Didier Hauglustaine, Yunjiang Zhang, Maxim Eremenko, Yann Cohen, Audrey Gaudel, Guillaume Siour, Mathieu Lachatre, Axel Bense, Bertrand Bessagnet, Juan Cuesta, Jerry Ziemke, Valérie Thouret, and Bo Zheng
Atmos. Chem. Phys., 21, 16001–16025, https://doi.org/10.5194/acp-21-16001-2021, https://doi.org/10.5194/acp-21-16001-2021, 2021
Short summary
Short summary
The IASI observations and the LMDZ-OR-INCA model simulations show negative ozone trends in the Central East China region in the lower free (3–6 km column) and the upper free (6–9 km column) troposphere. Sensitivity studies from the model show that the Chinese anthropogenic emissions contribute to more than 50 % in the trend. The reduction in NOx emissions that has occurred since 2013 in China seems to lead to a decrease in ozone in the free troposphere, contrary to the increase at the surface.
Philippe Thunis, Alain Clappier, Matthias Beekmann, Jean Philippe Putaud, Cornelis Cuvelier, Jessie Madrazo, and Alexander de Meij
Atmos. Chem. Phys., 21, 9309–9327, https://doi.org/10.5194/acp-21-9309-2021, https://doi.org/10.5194/acp-21-9309-2021, 2021
Short summary
Short summary
Modelling simulations are used to identify the most efficient emission reduction strategies to reduce PM2.5 concentration levels in northern Italy. Results show contrasting chemical regimes and important non-linearities during wintertime, with the striking result that PM2.5 levels may increase when NOx reductions are applied in NOx-rich areas – a process that may have contributed to the absence of significant PM2.5 decrease during the COVID-19 lockdowns in many European cities.
Jean-Philippe Putaud, Luca Pozzoli, Enrico Pisoni, Sebastiao Martins Dos Santos, Friedrich Lagler, Guido Lanzani, Umberto Dal Santo, and Augustin Colette
Atmos. Chem. Phys., 21, 7597–7609, https://doi.org/10.5194/acp-21-7597-2021, https://doi.org/10.5194/acp-21-7597-2021, 2021
Short summary
Short summary
To determine the impact of the COVID lockdown on air quality in northern Italy, measurements of atmospheric pollutants (NO2, PM10, O3, NO, SO2 ) were compared to the output of a model ignoring the lockdown. We found that NO2 decreased on average by −30 % to −40 %. Unlike NO2, PM10 was not significantly affected due to the compensation of decreased emissions from traffic by increased emissions from domestic heating and/or by changes in atmospheric chemistry enhancing secondary aerosol formation.
Bart Degraeuwe, Enrico Pisoni, and Philippe Thunis
Geosci. Model Dev., 13, 5725–5736, https://doi.org/10.5194/gmd-13-5725-2020, https://doi.org/10.5194/gmd-13-5725-2020, 2020
Short summary
Short summary
To make decisions on how to improve air quality, it is useful to identify the main sources of pollution for an area of interest. Often these sources of pollution are identified with complex models that, even if accurate, are time consuming and complex. In this work we use another approach, simplified models, to accomplish the same task. The results, computed with two different set of simplified models, show the main sources of pollution for selected cities, and the associated uncertainties.
Cited articles
AAQD2008: Directive 2008/50/EC of the European Parliament and of the Council
of 21 May 2008 on ambient air quality and cleaner air for Europe, No. 152,
Official Journal, Publications Office of the European Union,
Luxembourg, 2008.
Alberti, V., Alonso Raposo, M., Attardo, C., Auteri, D., Ribeiro Barranco,
R., Batista e Silva, F., Benczur, P., Bertoldi, P., Bono, F., Bussolari, I.,
Louro Caldeira, S., Carlsson, J., Christidis, P., Christodoulou, A., Ciuffo,
B., Corrado, S., Fioretti, C., Galassi, M., Galbusera, L., Gawlik, B.,
Giusti, F., Gomez Prieto, J., Grosso, M., Martinho Guimaraes Pires Pereira,
A., Jacobs, C., Kavalov, B., Kompil, M., Kucas, A., Kona, A., Lavalle, C.,
Leip, A., Lyons, L., Manca, A., Melchiorri, M., Monforti-Ferrario, F.,
Montalto, V., Mortara, B., Natale, F., Panella, F., Pasi, G., Perpia
Castillo, C., Pertoldi, M., Pisoni, E., Roque Mendes Polvora, A., Rainoldi,
A., Rembges, D., Rissola, G., Sala, S., Schade, S., Serra, N., Spirito, L.,
Tsakalidis, A., Schiavina, M., Tintori, G., Vaccari, L., Vandyck, T.,
Vanham, D., Van Heerden, S., Van Noordt, C., Vespe, M., Vetters, N., Vilahur
Chiaraviglio, N., Vizcaino, M., Von Estorff, U., and Zulian, G., The Future
of Cities, Vandecasteele, I., Baranzelli, C., Siragusa, A., and Aurambout, J.
(Eds.): The future of cities, EUR 29752 EN, Publications Office of the European Union, Luxembourg, JRC116711, https://doi.org/10.2760/375209, 2019.
Amann, M., Purohit, P., Bhanarkar, A. D., Bertok, I., Borken-Kleefeld, J.,
Cofala, J., Heyes, C., Kiesewetter, G., Klimont, Z., Liu, J., Majumdar, D.,
Nguyen, B., Rafaj, P., Rao, P. S., Sander, R., Schöpp, W., Srivastava, A., and Vardhan, B. H.: Managing future air quality in megacities: A case study for Delhi, Atmos. Environ., 161, 99–111, 2017.
Amato, F., Cassee, F. R., Denier van der Gon, H. A. C., Gehrig, R., Gustafsson, M., Hafner, W., Harrison, R. M., Jozwicka, M., Kelly, F. J., Moreno, T., Prevot, A. S. H., Schaap, M., Sunyer, J., and Querol, X.: Urban air quality: The challenge of traffic non-exhaust emissions, J. Hazard. Mater., 275, 31–36,
https://doi.org/10.1016/j.jhazmat.2014.04.053,
2014.
ApSimon, H., Oxley, T., Woodward, H., Mehlig, D., Dore, A., and Holland, M.: The UK Integrated Assessment Model for source apportionment and air pollution
policy applications to PM2.5, Environ. Int., 153, 106515, https://doi.org/10.1016/j.envint.2021.106515, 2021.
ATMO2003: The ATMO index: an air quality indicator for developed areas in
France, Eur. Ann. Allergy Clin. Immunol., 35, 166-9, available at: https://pubmed.ncbi.nlm.nih.gov/12838780/ (last access: 14 December 2021), 2003.
Belis, C. A., Pernigotti, D., Pirovano, G., Favez, O., Jaffrezo, J. L.,
Kuenen, J., Denier van Der Gon, H., Reizer, M., Riffault, V., Alleman, L. Y.,
Almeida, M., Amato, F., Angyal, A., Argyropoulos, G., Bande, S., Beslic, I.,
Besombes, J.-L., Bove, M. C., Brotto, P., Calori, G., Cesari, D., Colombi,
C., Contini, D., De Gennaro, G., Di Gilio, A., Diapouli, E., El Haddad, I.,
Elbern, H., Eleftheriadis, K., Ferreira, J., Garcia Vivanco, M., Gilardoni,
S., Golly, B., Hellebust, S., Hopke, P. K., Izadmanesh, Y., Jorquera, H.,
Krajsek, K., Kranenburg, R., Lazzeri, P., Lenartz, F., Lucarelli, F.,
Maciejewska, K., Manders, A., Manousakas, M., Masiol, M., Mircea, M.,
Mooibroek, D., Nava, S., Oliveira, D., Paglione, M., Pandolfi, M., Perrone,
M., Petralia, E., Pietrodangelo, A., Pillon, S., Pokorna, P., Prati, P.,
Salameh, D., Samara, C., Samek, L., Saraga, D., Sauvage, S., Schaap, M.,
Scotto, F., Sega, K., Siour, G., Tauler, R., Valli, G., Vecchi, R.,
Venturini, E., Vestenius, M., Waked, A., and Yubero, E.: Evaluation of receptor and chemical transport models for PM10 source apportionment, Atmos. Environ., 5, 100053, https://doi.org/10.1016/j.aeaoa.2019.100053, 2020.
Bessagnet, B. and Allemand, N.: Review on Black Carbon (BC) and Polycyclic Aromatic Hydrocarbons (PAHs) emission reductions induced by PM
emission abatement techniques (Informal document), Citepa – TFTEI Techno-Scientific Secretariat – UNECE, Paris, France, 2020.
Bhave, P. V., Pouliot, G. A., and Zheng, M.: Diagnostic model evaluation for
carbonaceous PM2.5 using organic markers measured in the southeastern U.S., Environ. Sci. Technol., 41, 1577–1583, 2007.
Burr, M. J. and Zhang, Y.: Source apportionment of fine particulate matter over the Eastern U.S. Part II: source sensitivity simulations using CAMX/PSAT and comparisons with CMAQ source sensitivity simulations, Atmos. Pollut. Res., 2, 318–336, 2011.
Choma, E. F., Evans, J. S., Hammitt, J. K., Gómez-Ibáñez, J. A., and Spengler, J. D.: Assessing the health impacts of electric vehicles through air pollution in the United States, Environ. Int., 144, 106015, https://doi.org/10.1016/j.envint.2020.106015, 2020.
Clappier, A., Pisoni, E., and Thunis, P.: A new approach to design
source–receptor relationships for air quality modelling, Environ.
Model. Softw., 74, 66–74, 2015.
Clappier, A., Belis, C. A., Pernigotti, D., and Thunis, P.: Source apportionment and sensitivity analysis: two methodologies with two different purposes, Geosci. Model Dev., 10, 4245–4256, https://doi.org/10.5194/gmd-10-4245-2017, 2017.
Daellenbach, K. R., Uzu, G., Jiang, J., Cassagnes, L.E., Leni, Z., Vlachou,
A., Stefenelli, G., Canonaco, F., Weber, S., Segers, A., Kuenen, J., Schaap,
M., Favez, O., Albinet, A., Aksoyoglu, S. Dommen, J., Baltensperger, U.,
Geiser, M., Haddad, I., Jaffrezo, J. L., and Prévôt, A. S. H: Sources of
particulate-matter air pollution and its oxidative potential in Europe,
Nature, 587, 414–419, https://doi.org/10.1038/s41586-020-2902-8, 2020.
de Bruyn, S. and de Vries, J.: Health costs of air pollution in European cities and the linkage with transport, No. 20.190272.134, CE Delft, Delft, the Netherlands, 2020.
Degraeuwe, B., Pisoni, E., Peduzzi, E., De Meij, A., Monforti-Ferrario, F.,
Bodis, K., Mascherpa, A., Astorga-Llorens, M., Thunis, P., and Vignati, E.:
Urban NO2 Atlas, EUR 29943 EN, Publications Office of the European Union, Luxembourg, JRC118193, https://doi.org/10.2760/43523, 2019.
De Meij, A., Wagner, S., Gobron, N., Thunis, P., Cuvelier, C., Dentener, F., and
Schaap, M.: Model evaluation and scale issues in chemical and optical
aerosol properties over the greater Milan area (Italy), for June 2001,
Atmos. Res., 85, 243–267, 2007.
de Meij, A., Gzella, A., Cuvelier, C., Thunis, P., Bessagnet, B., Vinuesa, J. F., Menut, L., and Kelder, H. M.: The impact of MM5 and WRF meteorology over complex terrain on CHIMERE model calculations, Atmos. Chem. Phys., 9, 6611–6632, https://doi.org/10.5194/acp-9-6611-2009, 2009.
De Meij, A., Bossioli, E., Vinuesa, J. F., Penard, C., and Price, I.: The effect of SRTM and Corine Land Cover on calculated gas and PM10 concentrations in WRF-Chem, Atmos. Eniron., 101, 177–193, 2015.
De Meij, A., Zittis, G., and Christoudias, T.: On the uncertainties introduced by land cover data in high-resolution regional simulations, Meteorol. Atmos. Phys., 131, 1213–1223, https://doi.org/10.1007/s00703-018-0632-3, 2018.
EEA: Air Quality in Europe: 2020 Report, European Environment Agency,
Publications Office, https://doi.org/10.2800/786656, 2020.
EMEP2017: Transboundary particulate matter, photo-oxidants, acidification
and eutrophication components, Joint MSC-W & CCC & CEIP Report, EMEP
Status Report 1/2017, Norwegian Meteorological Institute, Oslo, Norway, 2017.
Grewe, V., Tsati, E., and Hoor, P.: On the attribution of contributions of atmospheric trace gases to emissions in atmospheric model applications, Geosci. Model Dev., 3, 487–499, https://doi.org/10.5194/gmd-3-487-2010, 2010.
Grewe, V., Dahlmann, K., Matthes, S., and Steinbrecht, W.: Attributing ozone to NOx emissions: Implications for climate mitigation measures, Atmos.
Environ., 59, 102–107, 2012.
Grigoratos, T. and Martini, G.: Non-exhaust traffic related emissions – Brake and tyre wear PM, EUR 26648g, Publications Office of the
European Union, Luxembourg, JRC89231, https://doi.org/10.2790/22000, 2014.
Guo, H., Kota, S. H., Sahu, S. K., Hu, J., Ying, Q., Gao, A., and Zhang, H.:
Source apportionment of PM2.5 in North India using source-oriented air
quality models, Environ. Pollut., 231, 426–436, 2017.
Hendriks, C., Kranenburg, R., Kuenen, J., van Gijlswijk, R., Wichink Kruit,
R., Segers, A., Denier van der Gon, H., and Schaap, M.: The origin of ambient
particulate matter concentrations in the Netherlands, Atmos. Environ., 69, 289–303, https://doi.org/10.1016/j.atmosenv.2012.12.017, 2013.
Huang Y., Deng, T., Li, Z., Wang, N., Yin, C., Wang, S., and Fan, S.: Numerical simulations for the sources apportionment and control strategies of PM2.5
over Pearl River Delta, China, part I: Inventory and PM2.5 sources
apportionment, Sci. Total Environ., 634, 1631–1644, 2018.
Huszar, P., Belda, M., and Halenka, T.: On the long-term impact of emissions from central European cities on regional air quality, Atmos. Chem. Phys., 16, 1331–1352, https://doi.org/10.5194/acp-16-1331-2016, 2016.
Huszar, P., Karlický, J., Marková, J., Nováková, T., Liaskoni, M., and Bartík, L.: The regional impact of urban emissions on air quality in Europe: the role of the urban canopy effects, Atmos. Chem. Phys., 21, 14309–14332, https://doi.org/10.5194/acp-21-14309-2021, 2021.
Itahashi, S., Hayami, H., Yumimoto, K., and Uno, I.: Chinese province-scale
source apportionments for sulfate aerosol in 2005 evaluated by the tagged
tracer method, Environ. Pollut., 220, 1366–1375, 2017.
Jiang, L., Bessagnet, B., Meleux, F., Tognet, F., and Couvidat, F.: Impact of
physics parameterizations on high-resolution air quality simulations over
the Paris region, Atmosphere, 11, 618, https://doi.org/10.3390/atmos11060618, 2020.
Keuken, M., Moerman, M., Voogt, M., Blom, M., Weijers, E. P., Röckmann,
T., and Dusek, U.: Source contributions to PM2.5 and PM10 at an urban background and a street location, Atmos. Environ., 71, 26–35, 2013.
Khomenko, S., Cirach, M., Pereira-Barboza, E., Mueller, N.,
Barrera-Gómez, J., Rojas-Rueda, D., de Hoogh, K., Hoek, G., and
Nieuwenhuijsen, M.: Premature mortality due to air pollution in European
cities: a health impact assessment, Lancet Planetary Health, 3,
S2542519620302722, https://doi.org/10.1016/S2542-5196(20)30272-2, 2021.
Kiesewetter, G. and Amann, M.: Urban PM2.5 levels under the EU Clean Air
Policy Package, IIASA TSAP Report 12, IIASA, Vienna, Austria, 2014.
Kiesewetter, G., Borken-Kleefeld, J., Schöpp, W., Heyes, C., Thunis, P., Bessagnet, B., Terrenoire, E., Fagerli, H., Nyiri, A., and Amann, M.: Modelling street level PM10 concentrations across Europe: source apportionment and possible futures, Atmos. Chem. Phys., 15, 1539–1553, https://doi.org/10.5194/acp-15-1539-2015, 2015.
Kole, P. J., Löhr, A. J., Van Belleghem, F., and Ragas, A.: Wear and Tear of Tyres: A Stealthy Source of Microplastics in the Environment, Int. J. Env. Res. Pub. He., 14, 1265, https://doi.org/10.3390/ijerph14101265, 2017.
Kranenburg, R., Segers, A. J., Hendriks, C., and Schaap, M.: Source apportionment using LOTOS-EUROS: module description and evaluation, Geosci. Model Dev., 6, 721–733, https://doi.org/10.5194/gmd-6-721-2013, 2013.
Kwok, R. H. F., Napelenok, S. L., and Baker, K. R.: Implementation and evaluation of PM2.5 source contribution analysis in a photochemical model, Atmos. Environ., 80, 398–407, 2013.
Lenschow, P., Abraham, H.-J., Kutzner, K., Lutz, M., Preu, J.-D., and
Reichenbacher, W.: Some ideas about the sources of PM10, Supplement No. 1, Atmos. Environ., 35, 23–33, 2001.
Li, Y., Henze, D. K., Jack, D., Henderson, B. H., and Kinney, P. L.: Assessing public health burden associated with exposure to ambient black carbon in the United States, Sci. Total Environ., 539, 515–525, 2016.
Liu, F., Klimont, Z., Zhang, Q., Cofala, J., Zhao, L., Huo, H., Nguyen, B., Schöpp, W., Sander, R., Zheng, B., Hong, C., He, K., Amann, M., and Heyes, C.: Integrating mitigation of air pollutants and greenhouse gases in Chinese cities: development of GAINS-City model for Beijing, J. Clean. Prod., 58, 25–33, https://doi.org/10.1016/j.jclepro.2013.03.024, 2013.
Luo, H., Yang, L., Yuan, Z., Zhao, K., Zhang, S., Duan, Y., Huang, R., and Fu, Q.: Synoptic condition-driven summertime ozone formation regime in Shanghai and the implication for dynamic ozone control strategies, Sci. Total Environ., 745, 141130, https://doi.org/10.1016/j.scitotenv.2020.141130, 2020.
Menut, L., Bessagnet, B., Khvorostyanov, D., Beekmann, M., Blond, N., Colette, A., Coll, I., Curci, G., Foret, G., Hodzic, A., Mailler, S., Meleux, F., Monge, J.-L., Pison, I., Siour, G., Turquety, S., Valari, M., Vautard, R., and Vivanco, M. G.: CHIMERE 2013: a model for regional atmospheric composition modelling, Geosci. Model Dev., 6, 981–1028, https://doi.org/10.5194/gmd-6-981-2013, 2013.
Mertens, M., Grewe, V., Rieger, V. S., and Jöckel, P.: Revisiting the contribution of land transport and shipping emissions to tropospheric ozone, Atmos. Chem. Phys., 18, 5567–5588, https://doi.org/10.5194/acp-18-5567-2018, 2018.
Met.No: EMEP code 4.34, GitHub [code], available at https://github.com/metno/emep-ctm, last access: 24 November 2021.
Ntziachristos, L. and Boulter, P.: EMEP/EEA air pollutant emission inventory
guidebook 2019 – 1.A.3.b.vi Road transport: Automobile tyre and brake wear – 1.A.3.b.vii Road transport: Automobile road abrasion, European Environment
Agency, Copenhagen, Denmark, 2019.
OECD: Redefining Urban: a new way to measure metropolitan areas, OECD
report, Paris, France, ISBN 9789264174054, 148 pp., 2012.
Ortiz, S. and Friedrich, R.: A modelling approach for estimating background
pollutant concentrations in urban areas, Atmos. Pollut. Res., 4, 147–156,
https://doi.org/10.5094/APR.2013.015, 2013.
Osada, K., Ohara, T., Uno, I., Kido, M., and Iida, H.: Impact of Chinese anthropogenic emissions on submicrometer aerosol concentration at Mt. Tateyama, Japan, Atmos. Chem. Phys., 9, 9111–9120, https://doi.org/10.5194/acp-9-9111-2009, 2009.
Park, M., Joo, H. S., Lee, K., Jang, M., Kim, S. D., Kim, I., Borlaza, L. J. S., Lim, H., Shin, H., Chung, K. H., Choi, Y. H., Park, S. G., Bae, M. S., Lee, J., Song, H., and Park, K.: Differential toxicities of fine
particulate matters from various sources, Sci. Rep., 8, 17007, https://doi.org/10.1038/s41598-018-35398-0, 2018.
Petetin, H., Beekmann, M., Sciare, J., Bressi, M., Rosso, A., Sanchez, O., and Ghersi, V.: A novel model evaluation approach focusing on local and advected contributions to urban PM2.5 levels – application to Paris, France, Geosci. Model Dev., 7, 1483–1505, https://doi.org/10.5194/gmd-7-1483-2014, 2014.
Pey, J., Querol, X., and Alastuey, A.: Discriminating the regional and urban
contributions in the North-Western Mediterranean: PM levels and composition,
Atmos. Environ., 44, 1587–1596, 2010.
Pisoni, E.: Why is the city's responsibility for its air pollution often underestimated? A focus on PM2:5, Zenodo [code], https://doi.org/10.5281/zenodo.5770956, 2021a.
Pisoni, E.: Why is the city's responsibility for its air pollution often underestimated? A focus on PM2:5, Zenodo [data set], https://doi.org/10.5281/zenodo.5770975, 2021b.
Pisoni, E., Clappier, A., Degraeuwe, B., and Thunis, P.: Adding spatial
flexibility to source-receptor relationships for air quality modelling,
Environ. Model. Softw., 90, 68–77, 2017.
Pisoni, E., Thunis, P., de Meij, A., and Bessagnet, B.: A new methodology to
evaluate the effectiveness of local policies during high PM2.5 episodes:
application on 10 European cities, submitted to Atmos. Chem. Phys., 2021.
Pommier, M., Fagerli, H., Schulz, M., Valdebenito, A., Kranenburg, R., and Schaap, M.: Prediction of source contributions to urban background PM10 concentrations in European cities: a case study for an episode in December 2016 using EMEP/MSC-W rv4.15 and LOTOS-EUROS v2.0 – Part 1: The country contributions, Geosci. Model Dev., 13, 1787–1807, https://doi.org/10.5194/gmd-13-1787-2020, 2020.
Qiao, X., Ying, Q., Li, X., Zhang, H., Hu, J., Tang, Y., and Chen, X.: Source
apportionment of PM2.5 for 25 Chinese provincial capitals and municipalities using a source-oriented Community Multiscale Air Quality model, Sci. Total Environ., 612, 462–471, 2018.
Raifman, M., Russell, A. G., Skipper, T. N., and Kinney, P. L.: Quantifying the health impacts of eliminating air pollution emissions in the city of boston, Environ. Res. Lett., 15, 094017, https://doi.org/10.1088/1748-9326/ab842b, 2020.
Simpson, D., Benedictow, A., Berge, H., Bergström, R., Emberson, L. D., Fagerli, H., Flechard, C. R., Hayman, G. D., Gauss, M., Jonson, J. E., Jenkin, M. E., Nyíri, A., Richter, C., Semeena, V. S., Tsyro, S., Tuovinen, J.-P., Valdebenito, Á., and Wind, P.: The EMEP MSC-W chemical transport model – technical description, Atmos. Chem. Phys., 12, 7825–7865, https://doi.org/10.5194/acp-12-7825-2012, 2012.
Simpson, D., Fagerli, H., Colette, A., van der Gon, H. D., Dore, C., Hallquist, M., Hansson, H. C., Maas, R., Rouil, L., Allemand, N., Bergström, R., Bessagnet, B., Couvidat, F., Durif, M., Haddad, I. E., Safont, J. G., Grieshop, A., Fraboulet, I., Kausch, F., Hallquist, A., Hamilton, J., Juhrich, K., Klimont, Z., Kregar, Z., Mawdsely, I., Megaritis, A., Ntziachristos, L., Pandis, S., Prévôt, A. S. H., Schindlbacher, S., Seljeskog, M., Sirina-Leboine, N., Sommers, J., and Aström, S.: How should condensables be included in PM emission inventories reported to EMEP/CLRTAP? Report of the expert workshop on condensable organics organised by MSC-W, Gothenburg, 17–19 March 2020, EMEP Technical Report MSC-W 4/2020, Norwegian Meteorological Institute, 2020.
Squizzato, S. and Masiol, M.: Application of meteorology-based methods to
determine local and external contributions to particulate matter pollution:
A case study in Venice (Italy), Atmos. Environ., 119, 69–81, 2015.
Timmermans, R. M. A., Denier van der Gon, H. A. C., Kuenen, J. J. P., Segers,
A. J., Honoré, C., Perrussel, O., Builtjes, P. J. H., and Schaap, M.:
Quantification of the urban air pollution increment and its dependency on
the use of down-scaled and bottom-up city emission inventories, Urban
Climate, 6, 44–62, 2013.
Timmermans, R., Kranenburg, R., Manders, A., Hendriks, C., Segers, A., Dammers, E., Zhang, Q., Wang, L., Liu, Z., Zeng, L., Denier van der Gon, H., and Schaap, M.: Source apportionment of PM2.5 across China using LOTOS-EUROS, Atmos. Environ., 164, 370–386, 2017.
Thunis, P.: On the validity of the incremental approach to estimate the
impact of cities on air quality, Atmos. Environ., 173, 210–222,
2018.
Thunis, P., Degraeuwe, B., Pisoni, E., Ferrari, F., Clappier, A., On the design and assessment of regional air quality plans: The SHERPA approach,
J. Environ. Manage., 183, 952–958, https://doi.org/10.1016/j.jenvman.2016.09.049, 2016.
Thunis, P., Degraeuwe, B., Peduzzi, E., Pisoni, E., Trombetti, M., Vignati,
E., Wilson, J., Belis, C., and Pernigotti, D.: Urban PM2.5 Atlas: Air Quality in European cities, EUR 28804 EN, Publications Office of the European Union, Luxembourg, JRC108595, https://doi.org/10.2760/336669, 2017.
Thunis, P., Degraeuwe, B., Pisoni, E., Trombetti, M., Peduzzi, E., Belis,
C. A., Wilson, J., Clappier, A., and Vignati, E.: PM2.5 source allocation in European cities: A SHERPA modelling study, Atmos. Environ., 187, 93–106, 2018.
Thunis, P., Clappier, A., Tarrason, L., Cuvelier, C., Monteiro,
A., Pisoni, E., Wesseling, J., Belis, C. A., Pirovano, G., Janssen, S.,
Guerreiro, C., and Peduzzi, E.: Source apportionment to support air
quality planning: Strengths and weaknesses of existing approaches, Environ. Int., 130, 104825, https://doi.org/10.1016/j.envint.2019.05.019, 2019.
Tobías, A., Carnerero, C., Reche, C., Massagué, J., Via, M.,
Minguillón, M. C., Alastuey A., and Querol, X.: Changes in air quality
during the lockdown in barcelona (spain) one month into the SARS-CoV-2
epidemic, Sci. Total Environ., 726, 138540,
https://doi.org/10.1016/j.scitotenv.2020.138540, 2020.
UN2018: The World's Cities in 2018 – Data Booklet (ST/ESA/SER.A/417), United Nations, Department of Economic and Social Affairs,
Population Division, New York, USA, 2018.
Van Dingenen, R., Dentener, F., Crippa, M., Leitao, J., Marmer, E., Rao, S., Solazzo, E., and Valentini, L.: TM5-FASST: a global atmospheric source–receptor model for rapid impact analysis of emission changes on air quality and short-lived climate pollutants, Atmos. Chem. Phys., 18, 16173–16211, https://doi.org/10.5194/acp-18-16173-2018, 2018.
Viana, M., Querol, X., Alastuey, A., Ballester, F., Llop, S., Esplugues, A.,
Fernandez-Patier, R., Garcia dos Santos, S., and Herce, M. D.: Characterising
exposure to PM aerosols for an epidemiological study, Atmos. Environ., 42, 1552–1568, https://doi.org/10.1016/j.atmosenv.2007.10.087, 2008.
Wagstrom, K. M., Pandis, S. N., Yarwood, G., Wilson, G. M., and Morris, R.
E.: Development and application of a computationally efficient particulate
matter apportionment algorithm in a three dimensional chemical transport
model, Atmos. Environ., 42, 5650–5659, 2008.
Wang, L., Wei, Z., Wei, W., Fu, J. S., Meng, C., and Ma, S.: Source
apportionment of PM2.5 in top polluted cities in Hebei, China using the CMAQ model, Atmos. Environ., 122, 723–736, 2015.
Wang, L. T., Wei, Z., Yang, J., Zhang, Y., Zhang, F. F., Su, J., Meng, C. C., and Zhang, Q.: The 2013 severe haze over southern Hebei, China: model evaluation, source apportionment, and policy implications, Atmos. Chem. Phys., 14, 3151–3173, https://doi.org/10.5194/acp-14-3151-2014, 2014.
Wang, Z. S., Chien, C.-J., and Tonnesen, G. S.: Development of a tagged species source apportionment algorithm to characterize three-dimensional transport and transformation of precursors and secondary pollutants, J. Geophys. Res., 114, D21206, https://doi.org/10.1029/2008JD010846, 2009.
WHO2005: Air Quality Guidelines Global Update 2005. Particulate matter,
ozone, nitrogen dioxide and sulfur dioxide, World Health Organisation, Geneva, Switzerland, ISBN 9289021926, 2005.
Wu, Q. Z., Wang, Z. F., Gbaguidi, A., Gao, C., Li, L. N., and Wang, W.: A numerical study of contributions to air pollution in Beijing during CAREBeijing-2006, Atmos. Chem. Phys., 11, 5997–6011, https://doi.org/10.5194/acp-11-5997-2011, 2011.
Yarwood, G., Morris, R. E., and Wilson, G. M.: Particulate Matter Source
Apportionment Technology (PSAT) in the CAMx Photochemical Grid Model.
Proceedings of the 27th NATO/CCMS International Technical Meeting on Air
Pollution Modeling and Application, 24–29 October 2004, Banff,
Alberta, Canada, Springer, Boston, MA, USA, 2004.
Short summary
Air pollution's origin in cities is still a point of discussion, and approaches to assess the city's responsibility for its pollution are not harmonized and thus not comparable, resulting in sometimes contradicting interpretations. We show that methodological choices can easily lead to differences of a factor of 2 in terms of responsibility outcome and stress that methodological choices and assumptions most often lead to a systematic and important underestimation of the city's responsibility.
Air pollution's origin in cities is still a point of discussion, and approaches to assess the...
Altmetrics
Final-revised paper
Preprint