Articles | Volume 24, issue 7
https://doi.org/10.5194/acp-24-4347-2024
© Author(s) 2024. 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-24-4347-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Apr 2024
Research article |
| 15 Apr 2024
| 15 Apr 2024
Modeling the drivers of fine PM pollution over Central Europe: impacts and contributions of emissions from different sources
Department of Atmospheric Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, 180 00 Prague 8, Czech Republic
Peter Huszár
Department of Atmospheric Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, 180 00 Prague 8, Czech Republic
Jan Karlický
Department of Atmospheric Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, 180 00 Prague 8, Czech Republic
Ondřej Vlček
Czech Hydrometeorological Institute, Na Šabatce 2050/17, 143 06 Prague 4, Czech Republic
Kryštof Eben
Czech Academy of Sciences, Institute of Computer Science (ICS), Pod Vodárenskou věží 271/2, 182 00 Prague 8, Czech Republic
Related authors
Lukáš Bartík, Peter Huszár, Jan Peiker, Jan Karlický, Ondřej Vlček, and Petr Vodička
EGUsphere, https://doi.org/10.5194/egusphere-2025-167, https://doi.org/10.5194/egusphere-2025-167, 2025
Short summary
Short summary
This study investigates how to better understand and predict organic aerosols, which are tiny particles in the air that can affect our health and climate. By using advanced computer models, we examined the impact of different emissions and environmental conditions on these aerosols in Central Europe. Our findings show that including specific emissions significantly improved the accuracy of our predictions.
Marina Liaskoni, Peter Huszár, Lukáš Bartík, Alvaro Patricio Prieto Perez, Jan Karlický, and Kateřina Šindelářová
Atmos. Chem. Phys., 24, 13541–13569, https://doi.org/10.5194/acp-24-13541-2024, https://doi.org/10.5194/acp-24-13541-2024, 2024
Short summary
Short summary
The impact of biogenic emissions of hydrocarbons from vegetation on ozone, as well as on overall oxidative capacity of air, is analyzed for central European cities for a present-day period using a chemistry transport model. Moreover, the analysis evaluates the partial role of urban vegetation in impacting all biogenic emissions. We found substantial increases in ozone due to these emissions, and about 10% of this increase is attributable to vegetation within urban areas.
Peter Huszar, Alvaro Patricio Prieto Perez, Lukáš Bartík, Jan Karlický, and Anahi Villalba-Pradas
Atmos. Chem. Phys., 24, 397–425, https://doi.org/10.5194/acp-24-397-2024, https://doi.org/10.5194/acp-24-397-2024, 2024
Short summary
Short summary
Urbanization transforms rural land into artificial land, while due to human activities, it also introduces a great quantity of emissions. We quantify the impact of urbanization on the final particulate matter pollutant levels by looking not only at these emissions, but also at the way urban land cover influences meteorological conditions, how the removal of pollutants changes due to urban land cover, and how biogenic emissions from vegetation change due to less vegetation in urban areas.
Marina Liaskoni, Peter Huszar, Lukáš Bartík, Alvaro Patricio Prieto Perez, Jan Karlický, and Ondřej Vlček
Atmos. Chem. Phys., 23, 3629–3654, https://doi.org/10.5194/acp-23-3629-2023, https://doi.org/10.5194/acp-23-3629-2023, 2023
Short summary
Short summary
Wind-blown dust (WBD) emissions emitted from European soils are estimated for the 2007–2016 period, and their impact on the total particulate matter (PM) concentration is calculated. We found a considerable increase in PM concentrations due to such emissions, especially on selected days (rather than on a seasonal average). We also found that WBD emissions are strongest over western Europe, and the highest impacts on PM are calculated for this region.
Pavel Krč, Michal Belda, Martin Bureš, Kryštof Eben, Jan Geletič, Jelena Radović, Hynek Řezníček, and Jaroslav Resler
EGUsphere, https://doi.org/10.5194/egusphere-2025-4120, https://doi.org/10.5194/egusphere-2025-4120, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
PALM is a highly versatile open-source microscale atmospheric modelling system. One of its most useful applications is modelling detailed street-level urban climate, e.g. for evaluation of climate change adaptation and mitigation measures in cities. However, to produce real-case microscale simulations, they need to be forced by real or realistic weather conditions. The presented tool enables PALM to use meteorological inputs from a large selection of meteorological models and other sources.
Lukáš Bartík, Peter Huszár, Jan Peiker, Jan Karlický, Ondřej Vlček, and Petr Vodička
EGUsphere, https://doi.org/10.5194/egusphere-2025-167, https://doi.org/10.5194/egusphere-2025-167, 2025
Short summary
Short summary
This study investigates how to better understand and predict organic aerosols, which are tiny particles in the air that can affect our health and climate. By using advanced computer models, we examined the impact of different emissions and environmental conditions on these aerosols in Central Europe. Our findings show that including specific emissions significantly improved the accuracy of our predictions.
Petra Bauerová, Josef Keder, Adriana Šindelářová, Ondřej Vlček, William Patiño, Pavel Krč, Jan Geletič, Hynek Řezníček, Martin Bureš, Kryštof Eben, Michal Belda, Jelena Radović, Vladimír Fuka, Radek Jareš, Igor Esau, and Jaroslav Resler
Atmos. Chem. Phys., 25, 4477–4504, https://doi.org/10.5194/acp-25-4477-2025, https://doi.org/10.5194/acp-25-4477-2025, 2025
Short summary
Short summary
The study explored urban air quality in Prague using low-cost sensors and highlighted the multivariate adaptive regression splines (MARS) correction method's effectiveness in enhancing accuracy. Results showed traffic's impact on nitrogen dioxide levels and atmospheric dynamics on particulate matter. The research confirmed MARS-corrected sensors as cost-effective for monitoring, despite challenges like sensor ageing and data quality control.
Jan Karlický, Jáchym Bareš, and Peter Huszár
EGUsphere, https://doi.org/10.5194/egusphere-2025-388, https://doi.org/10.5194/egusphere-2025-388, 2025
Preprint archived
Short summary
Short summary
Our study includes results of WRF model simulations focused to evaluate sensitivity of local climate in cities on urban surface characteristics. Summer urban heat island is mostly impacted by changes in vegetation cover in city, albedo of roofs and irrigated green roofs. Results are usable also to reveal suitable mitigation strategies for reduction of negative aspects of local climate in cities.
Marina Liaskoni, Peter Huszár, Lukáš Bartík, Alvaro Patricio Prieto Perez, Jan Karlický, and Kateřina Šindelářová
Atmos. Chem. Phys., 24, 13541–13569, https://doi.org/10.5194/acp-24-13541-2024, https://doi.org/10.5194/acp-24-13541-2024, 2024
Short summary
Short summary
The impact of biogenic emissions of hydrocarbons from vegetation on ozone, as well as on overall oxidative capacity of air, is analyzed for central European cities for a present-day period using a chemistry transport model. Moreover, the analysis evaluates the partial role of urban vegetation in impacting all biogenic emissions. We found substantial increases in ozone due to these emissions, and about 10% of this increase is attributable to vegetation within urban areas.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
Short summary
Short summary
Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
Geosci. Model Dev., 17, 3867–3878, https://doi.org/10.5194/gmd-17-3867-2024, https://doi.org/10.5194/gmd-17-3867-2024, 2024
Short summary
Short summary
For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms, and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure, facilitating further processing to allow for emission processing from the continental to the street scale.
Jelena Radović, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024, https://doi.org/10.5194/gmd-17-2901-2024, 2024
Short summary
Short summary
Boundary conditions are of crucial importance for numerical model (e.g., PALM) validation studies and have a large influence on the model results, especially when studying the atmosphere of real, complex, and densely built urban environments. Our experiments with different driving conditions for the large-eddy simulation model PALM show its strong dependency on boundary conditions, which is important for the proper separation of errors coming from the boundary conditions and the model itself.
Peter Huszar, Alvaro Patricio Prieto Perez, Lukáš Bartík, Jan Karlický, and Anahi Villalba-Pradas
Atmos. Chem. Phys., 24, 397–425, https://doi.org/10.5194/acp-24-397-2024, https://doi.org/10.5194/acp-24-397-2024, 2024
Short summary
Short summary
Urbanization transforms rural land into artificial land, while due to human activities, it also introduces a great quantity of emissions. We quantify the impact of urbanization on the final particulate matter pollutant levels by looking not only at these emissions, but also at the way urban land cover influences meteorological conditions, how the removal of pollutants changes due to urban land cover, and how biogenic emissions from vegetation change due to less vegetation in urban areas.
Marina Liaskoni, Peter Huszar, Lukáš Bartík, Alvaro Patricio Prieto Perez, Jan Karlický, and Ondřej Vlček
Atmos. Chem. Phys., 23, 3629–3654, https://doi.org/10.5194/acp-23-3629-2023, https://doi.org/10.5194/acp-23-3629-2023, 2023
Short summary
Short summary
Wind-blown dust (WBD) emissions emitted from European soils are estimated for the 2007–2016 period, and their impact on the total particulate matter (PM) concentration is calculated. We found a considerable increase in PM concentrations due to such emissions, especially on selected days (rather than on a seasonal average). We also found that WBD emissions are strongest over western Europe, and the highest impacts on PM are calculated for this region.
Peter Huszar, Jan Karlický, Lukáš Bartík, Marina Liaskoni, Alvaro Patricio Prieto Perez, and Kateřina Šindelářová
Atmos. Chem. Phys., 22, 12647–12674, https://doi.org/10.5194/acp-22-12647-2022, https://doi.org/10.5194/acp-22-12647-2022, 2022
Short summary
Short summary
Urbanization turns rural land cover into artificial land cover, while due to human activities, it introduces a great quantity of emissions. We attempt to quantify the impact of urbanization on the final air pollutant levels by looking not only at these emissions, but also the way urban land cover influences meteorological conditions, how the removal of pollutants changes due to urban land cover, and how biogenic emissions from vegetation change due to less vegetation in urban areas.
Katerina Sindelarova, Jana Markova, David Simpson, Peter Huszar, Jan Karlicky, Sabine Darras, and Claire Granier
Earth Syst. Sci. Data, 14, 251–270, https://doi.org/10.5194/essd-14-251-2022, https://doi.org/10.5194/essd-14-251-2022, 2022
Short summary
Short summary
Three new datasets of global emissions of biogenic volatile organic compounds (BVOCs) emitted into the atmosphere from terrestrial vegetation were developed for air quality modelling using the Model of Emissions of Gases and Aerosols from Nature (MEGANv2.1) driven by European Centre for Medium-Range Weather Forecasts meteorological reanalyses for the years 2000–2019. The datasets include updates of the isoprene emission factors in Europe and study the impact of land cover change on emissions.
Peter Huszar, Jan Karlický, Jana Marková, Tereza Nováková, Marina Liaskoni, and Lukáš Bartík
Atmos. Chem. Phys., 21, 14309–14332, https://doi.org/10.5194/acp-21-14309-2021, https://doi.org/10.5194/acp-21-14309-2021, 2021
Short summary
Short summary
Urban areas are strong hot spots of emissions influencing local and regional air quality. Cities furthermore influence the meteorological conditions due to their characteristic surface properties and geometry. We found that if these latter effects are not included in the quantification of the impact of urban emissions on regional air quality, this impact will be overestimated, and this overestimation is mainly due to the enhanced turbulence that is present in cities compared to rural areas.
Jaroslav Resler, Kryštof Eben, Jan Geletič, Pavel Krč, Martin Rosecký, Matthias Sühring, Michal Belda, Vladimír Fuka, Tomáš Halenka, Peter Huszár, Jan Karlický, Nina Benešová, Jana Ďoubalová, Kateřina Honzáková, Josef Keder, Šárka Nápravníková, and Ondřej Vlček
Geosci. Model Dev., 14, 4797–4842, https://doi.org/10.5194/gmd-14-4797-2021, https://doi.org/10.5194/gmd-14-4797-2021, 2021
Short summary
Short summary
We describe validation of the PALM model v6.0 against measurements collected during two observational campaigns in Dejvice, Prague. The study focuses on the evaluation of the newly developed or improved radiative and energy balance modules in PALM related to urban modelling. In addition to the energy-related quantities, it also evaluates air flow and air quality under street canyon conditions.
Michal Belda, Jaroslav Resler, Jan Geletič, Pavel Krč, Björn Maronga, Matthias Sühring, Mona Kurppa, Farah Kanani-Sühring, Vladimír Fuka, Kryštof Eben, Nina Benešová, and Mikko Auvinen
Geosci. Model Dev., 14, 4443–4464, https://doi.org/10.5194/gmd-14-4443-2021, https://doi.org/10.5194/gmd-14-4443-2021, 2021
Short summary
Short summary
The analysis summarizes how sensitive the modelling of urban environment is to changes in physical parameters describing the city (e.g. reflectivity of surfaces) and to several heat island mitigation scenarios in a city quarter in Prague, Czech Republic. We used the large-eddy simulation modelling system PALM 6.0. Surface parameters connected to radiation show the highest sensitivity in this configuration. For heat island mitigation, urban vegetation is shown to be the most effective measure.
Jan Karlický, Peter Huszár, Tereza Nováková, Michal Belda, Filip Švábik, Jana Ďoubalová, and Tomáš Halenka
Atmos. Chem. Phys., 20, 15061–15077, https://doi.org/10.5194/acp-20-15061-2020, https://doi.org/10.5194/acp-20-15061-2020, 2020
Short summary
Short summary
Cities are characterized by their impact on various meteorological variables. Our study aims to generalize these modifications into a single phenomenon – the urban meteorology island (UMI). A wide ensemble of Weather Research and Forecasting (WRF) and Regional Climate Model (RegCM) simulations investigated urban-induced modifications as individual UMI components. Significant changes are found in most of the discussed meteorological variables with a strong impact of specific model simulations.
Peter Huszar, Jan Karlický, Jana Ďoubalová, Tereza Nováková, Kateřina Šindelářová, Filip Švábik, Michal Belda, Tomáš Halenka, and Michal Žák
Atmos. Chem. Phys., 20, 11655–11681, https://doi.org/10.5194/acp-20-11655-2020, https://doi.org/10.5194/acp-20-11655-2020, 2020
Short summary
Short summary
The paper shows how extreme meteorological conditions change due to the urban land-cover forcing and how this translates to the impact on the extreme air pollution over central European cities. It focuses on ozone, nitrogen dioxide, and particulate matter with a diameter of less than 2.5 μm and shows that, while for the extreme daily maximum 8 h ozone, changes are same as for the mean ones, much larger modifications are calculated for extreme NO2 and PM2.5 compared to their mean changes.
Cited articles
Aksoyoglu, S., Keller, J., Barmpadimos, I., Oderbolz, D., Lanz, V. A., Prévôt, A. S. H., and Baltensperger, U.: Aerosol modelling in Europe with a focus on Switzerland during summer and winter episodes, Atmos. Chem. Phys., 11, 7355–7373, https://doi.org/10.5194/acp-11-7355-2011, 2011. a
Anderson, J. O., Thundiyil, J. G., and Stolbach, A.: Clearing the Air: A Review of the Effects of Particulate Matter Air Pollution on Human Health, J. Med. Toxicol., 8, 166–175, https://doi.org/10.1007/s13181-011-0203-1, 2012. a
Apte, J. S., Marshall, J. D., Cohen, A. J., and Brauer, M.: Addressing Global Mortality from Ambient PM2.5, Environ. Sci. Technol., 49, 8057–8066, https://doi.org/10.1021/acs.est.5b01236, 2015. a
Arasa, R., Domingo-Dalmau, A., and Vargas, R.: Using a Coupled Air Quality Modeling System for the Development of an Air Quality Plan in Madrid (Spain): Source Apportionment and Analysis Evaluation of Mitigation Measures, J. Geosci. Environ. Protect., 4, 46–61, https://doi.org/10.4236/gep.2016.43005, 2016. a, b
Benešová, N., Belda, M., Eben, K., Geletič, J., Huszár, P., Juruš, P., Krč, P., Resler, J., and Vlček, O.: New open source emission processor for air quality models, in: Proceedings of Abstracts 11th International Conference on Air Quality Science and Application, edited by: Sokhi, R., Tiwari, P. R., Gállego, M. J., Craviotto Arnau, J. M., Castells Guiu, C., and Singh, V., Published by University of Hertfordshire, paper presented at Air Quality 2018 conference, Barcelona, 12–16 March, p. 22, https://doi.org/10.18745/PB.19829, 2018. a
Bougeault, P. and Lacarrere, P.: Parameterization of orography-induced turbulence in a mesobeta-scale model, Mon. Weather Rev., 117, 1872–1890, 1989. a
Bove, M., Brotto, P., Cassola, F., Cuccia, E., Massabò, D., Mazzino, A., Piazzalunga, A., and Prati, P.: An integrated PM2.5 source apportionment study: Positive Matrix Factorisation vs. the chemical transport model CAMx, Atmos. Environ., 94, 274–286, https://doi.org/10.1016/j.atmosenv.2014.05.039, 2014. a
Bressi, M., Cavalli, F., Putaud, J., Fröhlich, R., Petit, J.-E., Aas, W., Äijälä, M., Alastuey, A., Allan, J., Aurela, M., Berico, M., Bougiatioti, A., Bukowiecki, N., Canonaco, F., Crenn, V., Dusanter, S., Ehn, M., Elsasser, M., Flentje, H., Graf, P., Green, D., Heikkinen, L., Hermann, H., Holzinger, R., Hueglin, C., Keernik, H., Kiendler-Scharr, A., Kubelová, L., Lunder, C., Maasikmets, M., Makeš, O., Malaguti, A., Mihalopoulos, N., Nicolas, J., O'Dowd, C., Ovadnevaite, J., Petralia, E., Poulain, L., Priestman, M., Riffault, V., Ripoll, A., Schlag, P., Schwarz, J., Sciare, J., Slowik, J., Sosedova, Y., Stavroulas, I., Teinemaa, E., Via, M., Vodička, P., Williams, P., Wiedensohler, A., Young, D., Zhang, S., Favez, O., Minguillón, M., and Prevot, A.: A European aerosol phenomenology – 7: High-time resolution chemical characteristics of submicron particulate matter across Europe, Atmos. Environ., 10, 100108, https://doi.org/10.1016/j.aeaoa.2021.100108, 2021. a
Burr, M. J. and Zhang, Y.: Source apportionment of fine particulate matter over the Eastern U.S., Part II: source apportionment simulations using CAMx/PSAT and comparisons with CMAQ source sensitivity simulations, Atmos. Pollut. Res., 2, 318–336, https://doi.org/10.5094/APR.2011.037, 2011a. a, b
Burr, M. J. and Zhang, Y.: Source apportionment of fine particulate matter over the Eastern U.S., Part I: source sensitivity simulations using CMAQ with the Brute Force method, Atmos. Pollut. Res., 2, 300–317, https://doi.org/10.5094/APR.2011.036, 2011b. a
Byun, D. W. and Ching, J. K. S.: Science Algorithms of the EPA Model-3 Community Multiscale Air Quality (CMAQ) Modeling System, Office of Research and Development, U.S. EPA, North Carolina, EPA/600/R-99/030, 1999. a
Chang, J. S., Brost, R. A., Isaksen, I. S. A., Madronich, S., Middleton, P., Stockwell, W. R., and Walcek, C. J.: A three-dimensional Eulerian acid deposition model: Physical concepts and formulation, J. Geophys. Res.-Atmos., 92, 14681–14700, https://doi.org/10.1029/JD092iD12p14681, 1987. a
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface–Hydrology Model with the Penn State – NCAR MM5 Modeling System, Part I: Model Implementation and Sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001. a
Chen, G., Canonaco, F., Tobler, A., Aas, W., Alastuey, A., Allan, J., Atabakhsh, S., Aurela, M., Baltensperger, U., Bougiatioti, A., De Brito, J. F., Ceburnis, D., Chazeau, B., Chebaicheb, H., Daellenbach, K. R., Ehn, M., El Haddad, I., Eleftheriadis, K., Favez, O., Flentje, H., Font, A., Fossum, K., Freney, E., Gini, M., Green, D. C., Heikkinen, L., Herrmann, H., Kalogridis, A.-C., Keernik, H., Lhotka, R., Lin, C., Lunder, C., Maasikmets, M., Manousakas, M. I., Marchand, N., Marin, C., Marmureanu, L., Mihalopoulos, N., Močnik, G., Nęcki, J., O'Dowd, C., Ovadnevaite, J., Peter, T., Petit, J.-E., Pikridas, M., Matthew Platt, S., Pokorná, P., Poulain, L., Priestman, M., Riffault, V., Rinaldi, M., Różański, K., Schwarz, J., Sciare, J., Simon, L., Skiba, A., Slowik, J. G., Sosedova, Y., Stavroulas, I., Styszko, K., Teinemaa, E., Timonen, H., Tremper, A., Vasilescu, J., Via, M., Vodička, P., Wiedensohler, A., Zografou, O., Cruz Minguillón, M., and Prévôt, A. S.: European aerosol phenomenology – 8: Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets, Environ. Int., 166, 107325, https://doi.org/10.1016/j.envint.2022.107325, 2022. a, b
Ciarelli, G., Aksoyoglu, S., El Haddad, I., Bruns, E. A., Crippa, M., Poulain, L., Äijälä, M., Carbone, S., Freney, E., O'Dowd, C., Baltensperger, U., and Prévôt, A. S. H.: Modelling winter organic aerosol at the European scale with CAMx: evaluation and source apportionment with a VBS parameterization based on novel wood burning smog chamber experiments, Atmos. Chem. Phys., 17, 7653–7669, https://doi.org/10.5194/acp-17-7653-2017, 2017. a, b
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. a, b, c
Coelho, S., Ferreira, J., Rodrigues, V., and Lopes, M.: Source apportionment of air pollution in European urban areas: Lessons from the ClairCity project, J. Environ. Manag., 320, 115899, https://doi.org/10.1016/j.jenvman.2022.115899, 2022. a
Denier van der Gon, H., Hendriks, C., Kuenen, J., Segers, A., and Visschedijk, A.: Description of current temporal emission patterns and sensitivity of predicted AQ for temporal emission patterns, EU FP7 MACC deliverable report D_D-EMIS_1.3, https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf (last access: 25 January 2023), 2011. a
Denier van der Gon, H. A. C., Bergström, R., Fountoukis, C., Johansson, C., Pandis, S. N., Simpson, D., and Visschedijk, A. J. H.: Particulate emissions from residential wood combustion in Europe – revised estimates and an evaluation, Atmos. Chem. Phys., 15, 6503–6519, https://doi.org/10.5194/acp-15-6503-2015, 2015. a
Donahue, N. M., Robinson, A. L., Stanier, C. O., and Pandis, S. N.: Coupled Partitioning, Dilution, and Chemical Aging of Semivolatile Organics, Environ. Sci. Technol., 40, 2635–2643, https://doi.org/10.1021/es052297c, 2006. a
Donahue, N. M., Epstein, S. A., Pandis, S. N., and Robinson, A. L.: A two-dimensional volatility basis set: 1. organic-aerosol mixing thermodynamics, Atmos. Chem. Phys., 11, 3303–3318, https://doi.org/10.5194/acp-11-3303-2011, 2011. a
Donahue, N. M., Kroll, J. H., Pandis, S. N., and Robinson, A. L.: A two-dimensional volatility basis set – Part 2: Diagnostics of organic-aerosol evolution, Atmos. Chem. Phys., 12, 615–634, https://doi.org/10.5194/acp-12-615-2012, 2012. a
EBAS: EBAS database [data set], https://ebas-data.nilu.no/default.aspx (last access: 8 April 2024), 2023. a
EEA: Air quality in Europe 2022, Report no. 05/2022, https://www.eea.europa.eu//publications/air-quality-in-europe-2022, (last access: 25 January 2023), 2022. a
EEA: Air Quality e-Reporting products on EEA data service: E1a and E2a data sets, European Environment Agency, Copenhagen, Denmark [data set], https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm (last access: 25 January 2023), 2023. a
Fountoukis, C., Racherla, P. N., Denier van der Gon, H. A. C., Polymeneas, P., Charalampidis, P. E., Pilinis, C., Wiedensohler, A., Dall'Osto, M., O'Dowd, C., and Pandis, S. N.: Evaluation of a three-dimensional chemical transport model (PMCAMx) in the European domain during the EUCAARI May 2008 campaign, Atmos. Chem. Phys., 11, 10331–10347, https://doi.org/10.5194/acp-11-10331-2011, 2011. a
Giani, P., Balzarini, A., Pirovano, G., Gilardoni, S., Paglione, M., Colombi, C., Gianelle, V. L., Belis, C. A., Poluzzi, V., and Lonati, G.: Influence of semi- and intermediate-volatile organic compounds (S/IVOC) parameterizations, volatility distributions and aging schemes on organic aerosol modelling in winter conditions, Atmos. Environ., 213, 11–24, https://doi.org/10.1016/j.atmosenv.2019.05.061, 2019. a, b, c
Giorgi, F., Coppola, E., Solmon, F., Mariotti, L., Sylla, M., Bi, X., Elguindi, N., Diro, G., Nair, V. S., Giuliani, G., Turuncoglu, U., Cozzini, S., Güttler, I., O’Brien, T., Tawfik, A., Shalaby, A., Zakey, S., Steiner, A., Stordal, F., and Brankovic, C.: RegCM4: Model description and preliminary tests over multiple CORDEX domains, Clim. Res., 52, 7–29, 2012. a
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012. a
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. a, b
Hertel, O., Berkowicz, R., Christensen, J., and Hov, Ø.: Test of two numerical schemes for use in atmospheric transport-chemistry models, Atmos. Environ. Pt. A, 27, 2591–2611, https://doi.org/10.1016/0960-1686(93)90032-T, 1993. a
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. a
Huszar, P., Karlický, J., Ďoubalová, J., Nováková, T., Šindelářová, K., Švábik, F., Belda, M., Halenka, T., and Žák, M.: The impact of urban land-surface on extreme air pollution over central Europe, Atmos. Chem. Phys., 20, 11655–11681, https://doi.org/10.5194/acp-20-11655-2020, 2020a. a, b, c, d
Huszar, P., Karlický, J., Ďoubalová, J., Šindelářová, K., Nováková, T., Belda, M., Halenka, T., Žák, M., and Pišoft, P.: Urban canopy meteorological forcing and its impact on ozone and PM2.5: role of vertical turbulent transport, Atmos. Chem. Phys., 20, 1977–2016, https://doi.org/10.5194/acp-20-1977-2020, 2020b. a
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. a, b
Huszar, P., Karlický, J., Bartík, L., Liaskoni, M., Prieto Perez, A. P., and Šindelářová, K.: Impact of urbanization on gas-phase pollutant concentrations: a regional-scale, model-based analysis of the contributing factors, Atmos. Chem. Phys., 22, 12647–12674, https://doi.org/10.5194/acp-22-12647-2022, 2022. a
Huszar, P., Prieto Perez, A. P., Bartík, L., Karlický, J., and Villalba-Pradas, A.: Impact of urbanization on fine particulate matter concentrations over central Europe, Atmos. Chem. Phys., 24, 397–425, https://doi.org/10.5194/acp-24-397-2024, 2024. a, b, c
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by longlived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res., 113, 2–9, https://doi.org/10.1029/2008JD009944, 2008. a
Jiang, J., Aksoyoglu, S., Ciarelli, G., Oikonomakis, E., El-Haddad, I., Canonaco, F., O'Dowd, C., Ovadnevaite, J., Minguillón, M. C., Baltensperger, U., and Prévôt, A. S. H.: Effects of two different biogenic emission models on modelled ozone and aerosol concentrations in Europe, Atmos. Chem. Phys., 19, 3747–3768, https://doi.org/10.5194/acp-19-3747-2019, 2019a. a
Jiang, J., Aksoyoglu, S., El-Haddad, I., Ciarelli, G., Denier van der Gon, H. A. C., Canonaco, F., Gilardoni, S., Paglione, M., Minguillón, M. C., Favez, O., Zhang, Y., Marchand, N., Hao, L., Virtanen, A., Florou, K., O'Dowd, C., Ovadnevaite, J., Baltensperger, U., and Prévôt, A. S. H.: Sources of organic aerosols in Europe: a modeling study using CAMx with modified volatility basis set scheme, Atmos. Chem. Phys., 19, 15247–15270, https://doi.org/10.5194/acp-19-15247-2019, 2019b. a, b, c, d
Jiang, J., El Haddad, I., Aksoyoglu, S., Stefenelli, G., Bertrand, A., Marchand, N., Canonaco, F., Petit, J.-E., Favez, O., Gilardoni, S., Baltensperger, U., and Prévôt, A. S. H.: Influence of biomass burning vapor wall loss correction on modeling organic aerosols in Europe by CAMx v6.50, Geosci. Model Dev., 14, 1681–1697, https://doi.org/10.5194/gmd-14-1681-2021, 2021. a, b, c, d
Jiménez-Guerrero, P.: What Are the Sectors Contributing to the Exceedance of European Air Quality Standards over the Iberian Peninsula? A Source Contribution Analysis, Sustainability, 14, 2759, https://doi.org/10.3390/su14052759, 2022. a, b
Juda-Rezler, K., Reizer, M., Maciejewska, K., Błaszczak, B., and Klejnowski, K.: Characterization of atmospheric PM2.5 sources at a Central European urban background site, Sci. Total Environ., 713, 136729, https://doi.org/10.1016/j.scitotenv.2020.136729, 2020. a
Kain, J. S.: The Kain–Fritsch Convective Parameterization: An Update, J. Appl. Meteorol., 43, 170–181, 2004. a
Karamchandani, P., Long, Y., Pirovano, G., Balzarini, A., and Yarwood, G.: Source-sector contributions to European ozone and fine PM in 2010 using AQMEII modeling data, Atmos. Chem. Phys., 17, 5643–5664, https://doi.org/10.5194/acp-17-5643-2017, 2017. a, b, c, d
Karlický, J., Huszár, P., Nováková, T., Belda, M., Švábik, F., Ďoubalová, J., and Halenka, T.: The “urban meteorology island”: a multi-model ensemble analysis, Atmos. Chem. Phys., 20, 15061–15077, https://doi.org/10.5194/acp-20-15061-2020, 2020. a
Koo, B., Wilson, G. M., Morris, R. E., Dunker, A. M., and Yarwood, G.: Comparison of Source Apportionment and Sensitivity Analysis in a Particulate Matter Air Quality Model, Environ. Sci. Technol., 43, 6669–6675, https://doi.org/10.1021/es9008129, 2009. a, b, c
Koo, B., Knipping, E., and Yarwood, G.: 1.5-Dimensional volatility basis set approach for modeling organic aerosol in CAMx and CMAQ, Atmos. Environ., 95, 158–164, https://doi.org/10.1016/j.atmosenv.2014.06.031, 2014. a
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. a
Kuenen, J., Dellaert, S., Visschedijk, A., Jalkanen, J.-P., Super, I., and Denier van der Gon, H.: Copernicus Atmosphere Monitoring Service regional emissions version 4.2 (CAMS-REG-v4.2), Copernicus Atmosphere Monitoring Service, ECCAD [data set], https://doi.org/10.24380/0vzb-a387, 2021. a, b
Kuenen, J. J. P., Visschedijk, A. J. H., Jozwicka, M., and Denier van der Gon, H. A. C.: TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling, Atmos. Chem. Phys., 14, 10963–10976, https://doi.org/10.5194/acp-14-10963-2014, 2014. a
Lanz, V. A., Prévôt, A. S. H., Alfarra, M. R., Weimer, S., Mohr, C., DeCarlo, P. F., Gianini, M. F. D., Hueglin, C., Schneider, J., Favez, O., D'Anna, B., George, C., and Baltensperger, U.: Characterization of aerosol chemical composition with aerosol mass spectrometry in Central Europe: an overview, Atmos. Chem. Phys., 10, 10453–10471, https://doi.org/10.5194/acp-10-10453-2010, 2010. a
LMD: Chimere: Chemistry-transport model v2020r1 (Documentation), LMD/INERIS/LISA, https://www.lmd.polytechnique.fr/chimere/ (last access: 25 January 2023), 2022. a
Nenes, A., Pandis, S. N., and Pilinis, C.: ISORROPIA: A New Thermodynamic Equilibrium Model for Multiphase Multicomponent Inorganic Aerosols, Aquat. Geochem., 4, 123–152, https://doi.org/10.1023/A:1009604003981, 1998. a
Nenes, A., Pandis, S. N., and Pilinis, C.: Continued development and testing of a new thermodynamic aerosol module for urban and regional air quality models, Atmos. Environ., 33, 1553–1560, https://doi.org/10.1016/S1352-2310(98)00352-5, 1999. a
Odum, J. R., Hoffmann, T., Bowman, F., Collins, D., Flagan, R. C., and Seinfeld, J. H.: Gas/particle partitioning and secondary organic aerosol yields, Environ. Sci. Technol., 30, 2580–2585, https://doi.org/10.1021/es950943+, 1996. a
Passant, N.: Speciation of UK Emissions of Non-methane Volatile Organic Compounds, DEFRA, Oxon, UK, https://uk-air.defra.gov.uk/assets/documents/reports/empire/AEAT_ENV_0545_final_v2.pdf (last access: 25 January 2023), 2002. a
Pepe, N., Pirovano, G., Balzarini, A., Toppetti, A., Riva, G. M., Amato, F., and Lonati, G.: Enhanced CAMx source apportionment analysis at an urban receptor in Milan based on source categories and emission regions, Atmos. Environ., 2, 100020, https://doi.org/10.1016/j.aeaoa.2019.100020, 2019. a
Putaud, J.-P., Van Dingenen, R., Alastuey, A., Bauer, H., Birmili, W., Cyrys, J., Flentje, H., Fuzzi, S., Gehrig, R., Hansson, H. C., Harrison, R. M., Herrmann, H., Hitzenberger, R., Hüglin, C., Jones, A. M., Kasper-Giebl, A., Kiss, G., Kousa, A., Kuhlbusch, T. A. J., Löschau, G., Maenhaut, W., Molnar, A., Moreno, T., Pekkanen, J., Perrino, C., Pitz, M., Puxbaum, H., Querol, X., Rodriguez, S., Salma, I., Schwarz, J., Smolik, J., Schneider, J., Spindler, G., ten Brink, H., Tursic, J., Viana, M., Wiedensohler, A., and Raes, F.: A European aerosol phenomenology – 3: Physical and chemical characteristics of particulate matter from 60 rural, urban, and kerbside sites across Europe, Atmos. Environ., 44, 1308–1320, https://doi.org/10.1016/j.atmosenv.2009.12.011, 2010. a
Pültz, J., Banzhaf, S., Thürkow, M., Kranenburg, R., and Schaap, M.: Source attribution of particulate matter in Berlin, Atmos. Environ., 292, 119416, https://doi.org/10.1016/j.atmosenv.2022.119416, 2023. a, b, c
Ramboll: CAMx version 7.10, Ramboll [code], https://www.camx.com/download/source/ (last access: 8 April 2024), 2022b. a
Robinson, A. L., Donahue, N. M., Shrivastava, M. K., Weitkamp, E. A., Sage, A. M., Grieshop, A. P., Lane, T. E., Pierce, J. R., and Pandis, S. N.: Rethinking Organic Aerosols: Semivolatile Emissions and Photochemical Aging, Science, 315, 1259–1262, https://doi.org/10.1126/science.1133061, 2007. a
Schaap, M., Timmermans, R. M., Roemer, M., Boersen, G., Builtjes, P., Sauter, F., Velders, G., and Beck, J.: The LOTOS-EUROS model: description, validation and latest developments, Int. J. Environ. Pollut., 32, 270–290, https://doi.org/10.1504/IJEP.2008.017106, 2008. a
Schneider, C., Pelzer, M., Toenges-Schuller, N., Nacken, M., and Niederau, A.: ArcGIS basierte Lösung zur detaillierten, deutschlandweiten Verteilung (Gridding) nationaler Emissionsjahreswerte auf Basis des Inventars zur Emissionsberichterstattung, Dessau. Roßlau Retrieved, 27, 2019, 2016. a
Schwarz, J., Cusack, M., Karban, J., Chalupníčková, E., Havránek, V., Smolík, J., and Ždímal, V.: PM2.5 chemical composition at a rural background site in Central Europe, including correlation and air mass back trajectory analysis, Atmos. Res., 176/177, 108–120, https://doi.org/10.1016/j.atmosres.2016.02.017, 2016. a
Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, John Wiley & Sons, Inc., New York, ISBN: 9780471178156, 1998. a
Simmons, A. J., Willett, K. M., Jones, P. D., Thorne, P. W., and Dee, D. P.: Low-frequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences from reanalyses and monthly gridded observational data sets, J. Geophys. Res.-Atmos., 115, D01110, https://doi.org/10.1029/2009JD012442, 2010. a
Sindelarova, K., Granier, C., Bouarar, I., Guenther, A., Tilmes, S., Stavrakou, T., Müller, J.-F., Kuhn, U., Stefani, P., and Knorr, W.: Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years, Atmos. Chem. Phys., 14, 9317–9341, https://doi.org/10.5194/acp-14-9317-2014, 2014. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D., and Huang, X.: A Description of the Advanced Research WRF Version 4, NCAR Tech. Note NCAR/TN-556+STR, https://doi.org/10.5065/1dfh-6p97, 2019. a
Skyllakou, K., Murphy, B. N., Megaritis, A. G., Fountoukis, C., and Pandis, S. N.: Contributions of local and regional sources to fine PM in the megacity of Paris, Atmos. Chem. Phys., 14, 2343–2352, https://doi.org/10.5194/acp-14-2343-2014, 2014. a, b
Skyllakou, K., Fountoukis, C., Charalampidis, P., and Pandis, S. N.: Volatility-resolved source apportionment of primary and secondary organic aerosol over Europe, Atmos. Environ., 167, 1–10, https://doi.org/10.1016/j.atmosenv.2017.08.005, 2017. a
Strader, R., Lurmann, F. W., and Pandis, S. N.: Evaluation of secondary organic aerosol formation in winter, Atmos. Environ., 33, 4849–4863, 1999. a
Szigeti, T., Óvári, M., C., D., Kelly, F. J., Lucarelli, F., and Záray, G.: Changes in chemical composition and oxidative potential of urban PM2.5 between 2010 and 2013 in Hungary, Sci. Total Environ., 518/519, 534–544, https://doi.org/10.1016/j.scitotenv.2015.03.025, 2015. a
Tagaris, E., Sotiropoulou, R. E. P., Gounaris, N., Andronopoulos, S., and Vlachogiannis, D.: Effect of the Standard Nomenclature for Air Pollution (SNAP) Categories on Air Quality over Europe, Atmosphere, 6, 1119–1128, https://doi.org/10.3390/atmos6081119, 2015. a, b
Terrenoire, E., Bessagnet, B., Rouïl, L., Tognet, F., Pirovano, G., Létinois, L., Beauchamp, M., Colette, A., Thunis, P., Amann, M., and Menut, L.: High-resolution air quality simulation over Europe with the chemistry transport model CHIMERE, Geosci. Model Dev., 8, 21–42, https://doi.org/10.5194/gmd-8-21-2015, 2015. a
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme, Part II: Implementation of a New Snow Parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008. a
Turner, M. C., Andersen, Z. J., Baccarelli, A., Diver, W. R., Gapstur, S. M., Pope III, C. A., Prada, D., Samet, J., Thurston, G., and Cohen, A.: Outdoor air pollution and cancer: An overview of the current evidence and public health recommendations, CA – Cancer J. Clin., 70, 460–479, https://doi.org/10.3322/caac.21632, 2020. a
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, https://doi.org/10.1016/j.atmosenv.2008.03.012, 2008. a
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.-Atmos., 114, D21206, https://doi.org/10.1029/2008JD010846, 2009. a
WRF: WRF Version 4.2, GitHub [code], https://github.com/wrf-model/WRF/releases/tag/v4.2 (last access: 8 April 2024), 2023. a
Yarwood, G., Morris, R. E., and Wilson, G. M.: Particulate matter source apportionment technology (PSAT) in the CAMx photochemical grid model, in: Air Pollution Modeling and Its Application XVII, edited by: Borrego, C. and Norman, A.-L., Springer US, 478–492, https://doi.org/10.1007/978-0-387-68854-1_52, 2007. a, b, c, d
Yarwood, G., Jung, J., Whitten, G. Z., Heo, G., Mellberg, J., and Estes, E.: Updates to the Carbon Bond Mechanism for Version 6 (CB6), Presented at the 9th Annual CMAS Conference, Chapel Hill, North Carolina, USA, October 11–13, 2010, https://www.cmascenter.org/conference/2010/abstracts/emery_updates_carbon_2010.pdf (last access: 8 April 2024), 2010. a
Zhang, L., Gong, S., Padro, J., and Barrie, L.: A size-segregated particle dry deposition scheme for an atmospheric aerosol module, Atmos. Environ., 35, 549–560, https://doi.org/10.1016/S1352-2310(00)00326-5, 2001. a
Zhang, L., Brook, J. R., and Vet, R.: A revised parameterization for gaseous dry deposition in air-quality models, Atmos. Chem. Phys., 3, 2067–2082, https://doi.org/10.5194/acp-3-2067-2003, 2003. a
Zhao, Y., Nguyen, N. T., Presto, A. A., Hennigan, C. J., May, A. A., and Robinson, A. L.: Intermediate Volatility Organic Compound Emissions from On-Road Diesel Vehicles: Chemical Composition, Emission Factors, and Estimated Secondary Organic Aerosol Production, Environ. Sci. Technol., 49, 11516–11526, https://doi.org/10.1021/acs.est.5b02841, 2015. a
Zhao, Y., Nguyen, N. T., Presto, A. A., Hennigan, C. J., May, A. A., and Robinson, A. L.: Intermediate Volatility Organic Compound Emissions from On-Road Gasoline Vehicles and Small Off-Road Gasoline Engines, Environ. Sci. Technol., 50, 4554–4563, https://doi.org/10.1021/acs.est.5b06247, 2016. a
Short summary
The presented study deals with the attribution of fine particulate matter (PM2.5) concentrations to anthropogenic emissions over Central Europe using regional-scale models. It calculates the present-day contributions of different emissions sectors to concentrations of PM2.5 and its secondary components. Moreover, the study investigates the effect of chemical nonlinearities by using multiple source attribution methods and secondary organic aerosol calculation methods.
The presented study deals with the attribution of fine particulate matter (PM2.5) concentrations...
Altmetrics
Final-revised paper
Preprint