Articles | Volume 23, issue 6
https://doi.org/10.5194/acp-23-3629-2023
© Author(s) 2023. 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-23-3629-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Mar 2023
Research article |
| 27 Mar 2023
| 27 Mar 2023
Modelling the European wind-blown dust emissions and their impact on particulate matter (PM) concentrations
Marina Liaskoni
CORRESPONDING AUTHOR
Department of Atmospheric Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, 18000, Prague 8, Czechia
Peter Huszar
Department of Atmospheric Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, 18000, Prague 8, Czechia
Lukáš Bartík
Department of Atmospheric Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, 18000, Prague 8, Czechia
Alvaro Patricio Prieto Perez
Department of Atmospheric Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, 18000, Prague 8, Czechia
Jan Karlický
Department of Atmospheric Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, 18000, Prague 8, Czechia
Ondřej Vlček
Czech Hydrometeorological Institute, Na Šabatce 2050/17, 143 00 Prague 12, Czechia
Related authors
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.
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.
Lukáš Bartík, Peter Huszár, Jan Karlický, Ondřej Vlček, and Kryštof Eben
Atmos. Chem. Phys., 24, 4347–4387, https://doi.org/10.5194/acp-24-4347-2024, https://doi.org/10.5194/acp-24-4347-2024, 2024
Short summary
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.
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.
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.
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
Ansmann, A., Bösenberg, J., Chaikovsky, A., Comerón, A., Eckhardt, S., Eixmann, R., Freudenthaler, V., Ginoux, P., Komguem, L., Linné, H., López Márquez, M. Á., Matthias, V., Mattis, I., Mitev, V., Müller, D., Music, S., Nickovic, S., Pelon, J., Sauvage, L., Sobolewsky, P., Srivastava, M. K., Stohl, A., Torres, O., Vaughan, G., Wandinger, U., and Wiegner, M.: Long-range transport of Saharan dust to northern Europe: The 11–16 October 2001 outbreak observed with EARLINET, J. Geophys. Res., 108, 4783, https://doi.org/10.1029/2003JD003757, 2003. a
Astitha, M., Lelieveld, J., Abdel Kader, M., Pozzer, A., and de Meij, A.: Parameterization of dust emissions in the global atmospheric chemistry-climate model EMAC: impact of nudging and soil properties, Atmos. Chem. Phys., 12, 11057–11083, https://doi.org/10.5194/acp-12-11057-2012, 2012. a
Belmonte Rivas, M. and Stoffelen, A.: Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT, Ocean Sci., 15, 831–852, https://doi.org/10.5194/os-15-831-2019, 2019. a
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., https://doi.org/10.18745/PB.19829, 27 pp., University of Hertfordshire, Air Quality 2018 conference, 12–16 March 2018, Barcelona, 2018. a
Bessagnet, B., Menut, L., Aymoz, G., Chepfer, H., and Vautard, R.: Modelling dust emissions and transport within Europe: the Ukraine March 2007 event, J. Geophys. Res., 113, D15202, https://doi.org/10.1029/2007JD009541, 2008. a
Birmili, W., Schepanski, K., Ansmann, A., Spindler, G., Tegen, I., Wehner, B., Nowak, A., Reimer, E., Mattis, I., Müller, K., Brüggemann, E., Gnauk, T., Herrmann, H., Wiedensohler, A., Althausen, D., Schladitz, A., Tuch, T., and Löschau, G.: A case of extreme particulate matter concentrations over Central Europe caused by dust emitted over the southern Ukraine, Atmos. Chem. Phys., 8, 997–1016, https://doi.org/10.5194/acp-8-997-2008, 2008. a
Bougeault, P. and Lacarrère, P.: Parameterization of orography-induced turbulence in a meso-beta-scale model, Mon. Weather Rev., 117, 1872–1890, https://doi.org/10.1175/1520-0493(1989)117<1872:POOITI>2.0.CO;2, 1989. a
Buchholz, R. R., Emmons, L. K., Tilmes, S., and The CESM2 Development Team: CESM2.1/CAM-chem Instantaneous Output for Boundary Conditions, UCAR/NCAR – Atmospheric Chemistry Observations and Modeling Laboratory, Subset used Lat: 10 to 80, Lon: −20 to 50, December 2014–January 2017, UCAR/NCAR Boulder, CO [data set], https://doi.org/10.5065/NMP7-EP60, 2019. a
Bullard, J. E., Baddock, M., Bradwell, T., Crusius, J., Darlington, E., Gaiero, D., Gassó, S., Gisladottir, G., Hodgkins, R., McCulloch, R., McKenna-Neuman, C., Mockford, T., Stewart, H., and Thorsteinsson, T.: High-latitude dust in the Earth system, Rev. Geophys., 54, 447–485, 2016. 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
CAMx: Comprehensive Air Quality Model With Extensions version 7.10 code, Ramboll US Corporation, Novato, CA 94945, USA [code], http://camx-wp.azurewebsites.net/download/source (last access: 30 November 2022), 2020. 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., 92, 14681–14700, 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, S. and Sun, W.: A one-dimensional time dependent cloud model, J. Meteorol. Soc. Jpn., 80, 99–118, https://doi.org/10.2151/jmsj.80.99, 2002. a
Dorigo, W. A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P., Hirschi, M., Ikonen, J., De Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sens. Environ., 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017. a
Durán, O., Claudin, P., and Andreotti, B.: On aeolian transport: Grain-scale interactions, dynamical mechanisms and scaling laws, Aeolian Res., 3, 243–270, https://doi.org/10.1016/j.aeolia.2011.07.006, 2011. a
Emery, C., Jung, J., Koo, B., and Yarwood, G.: Improvements to CAMx Snow Cover Treatments and Carbon Bond Chemical Mechanism for Winter Ozone, Utah Department of Environmental Quality, Division of Air Quality, Salt Lake City, UT, Ramboll Environ, Novato, CA, https://www.camx.com/files/udaq_snowchem_final_6aug15.pdf (last access: 20 March 2023), 2015. a
Emmons, L. K., Schwantes, R. H., Orlando, J. J., Tyndall, G., Kinnison, D., Lamarque, J.-F., Marsh, D., Mills, M. J., Tilmes, S., Bardeen, C., Buchholz, R. R., Conley, A., Gettelman, A., Garcia, R., Simpson, I., Blake, D. R., Meinardi, S., and Pétron, G.: The Chemistry Mechanism in the Community Earth System Model version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001882, https://doi.org/10.1029/2019MS001882, 2020. a
Fairlie, T. D., Jacob, D. J., Dibb, J. E., Alexander, B., Avery, M. A., van Donkelaar, A., and Zhang, L.: Impact of mineral dust on nitrate, sulfate, and ozone in transpacific Asian pollution plumes, Atmos. Chem. Phys., 10, 3999–4012, https://doi.org/10.5194/acp-10-3999-2010, 2010. a, b, c
Fu, X., Wang, S., Chang, X., Cai, S., Xing, J., and Hao, J.: Modeling analysis of secondary inorganic aerosols over China: pollution characteristics, and meteorological and dust impacts, Nature Scientific Reports, 6, 35992, https://doi.org/10.1038/srep35992, 2016. a
Francis, D., Fonseca, R., Nellia, N., Bozkurtbf, D., and BinGuande, G. P.: Atmospheric rivers drive exceptional Saharan dust transport towards Europe, Atmos. Res., 266, 105959, https://doi.org/10.1016/j.atmosres.2021.105959, 2022. a
Giannadaki, D., Pozzer, A., and Lelieveld, J.: Modeled global effects of airborne desert dust on air quality and premature mortality, Atmos. Chem. Phys., 14, 957–968, https://doi.org/10.5194/acp-14-957-2014, 2014. a
GMTED: Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), USGS EROS Archive [data set], https://doi.org/10.5066/F7J38R2N, 2010. a
Grell, G.: Prognostic evaluation of assumptions used by cumulus parameterizations, Mon. Weather Rev., 121, 764–787, https://doi.org/10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2, 1993. a
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019. a
Gudmundsson, L. and Seneviratne, S. I.: Anthropogenic climate change affects meteorological drought risk in Europe, Environ. Res. Lett., 11, 044005, https://doi.org/10.1088/1748-9326/11/4/044005, 2016. 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
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: Complete ERA5 from 1979: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service (C3S) Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2017. 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., Belda, M., Karlický, J., Bardachova, T., Halenka, T., and Pisoft, P.: Impact of urban canopy meteorological forcing on aerosol concentrations, Atmos. Chem. Phys., 18, 14059–14078, https://doi.org/10.5194/acp-18-14059-2018, 2018. a, b
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, 2020. 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 long-lived greenhouse gases: Calculations with the aer radiative transfer models, J. Geophys. Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Janjic, Z. I.: The step-mountain eta coordinate model: Further developments of the 172 convection, viscous sublayer, and turbulence closure schemes, Mon. Weather Rev., 122, 927–945, https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2, 1994. a
Kakavas, S. and Pandis, S. N.: Effects of urban dust emissions on fine and coarse PM levels and composition, Atmos. Environ., 246, 118006, https://doi.org/10.1016/j.atmosenv.2020.118006, 2021. a, b, c, d
Karlický, J., Huszár, P., Halenka, T., Belda, M., Žák, M., Pišoft, P., and Mikšovský, J.: Multi-model comparison of urban heat island modelling approaches, Atmos. Chem. Phys., 18, 10655–10674, https://doi.org/10.5194/acp-18-10655-2018, 2018. a
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
Karydis, V. A., Tsimpidi, A. P., Lei, W., Molina, L. T., and Pandis, S. N.: Formation of semivolatile inorganic aerosols in the Mexico City Metropolitan Area during the MILAGRO campaign, Atmos. Chem. Phys., 11, 13305–13323, https://doi.org/10.5194/acp-11-13305-2011, 2011. a, b, c, d
Keet, A. C., Keller, P. J., and Peng, D. R.: Long-Term Coarse Particulate Matter Exposure Is Associated with Asthma among Children in Medicaid, Am. J. Resp. Crit. Care, 197, 737–746, https://doi.org/10.1164/rccm.201706-1267OC, 2018. a
Klingmüller, K., Metzger, S., Abdelkader, M., Karydis, V. A., Stenchikov, G. L., Pozzer, A., and Lelieveld, J.: Revised mineral dust emissions in the atmospheric chemistry–climate model EMAC (MESSy 2.52 DU_Astitha1 KKDU2017 patch), Geosci. Model Dev., 11, 989–1008, https://doi.org/10.5194/gmd-11-989-2018, 2018. a, b
Klose, M. and Shao, Y.: Stochastic parameterization of dust emission and application to convective atmospheric conditions, Atmos. Chem. Phys., 12, 7309–7320, https://doi.org/10.5194/acp-12-7309-2012, 2012. a
Korcz, M., Fudała, J., and Kliś, C.: Estimation of wind blown dust emissions in Europe and its vicinity, Atmos. Environ., 43, 1410–1420, https://doi.org/10.1016/j.atmosenv.2008.05.027, 2008. a, b, c
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
Kusaka, H., Kondo, K., Kikegawa, Y., and Kimura, F.: A simple single-layer urban canopy model for atmospheric models: Comparison with multi-layer and slab models, Bound.-Lay. Meteorol., 101, 329–358, 2001. a
Lecœur, È. and Seigneur, C.: Dynamic evaluation of a multi-year model simulation of particulate matter concentrations over Europe, Atmos. Chem. Phys., 13, 4319–4337, https://doi.org/10.5194/acp-13-4319-2013, 2013. a
Malaguti, A., Mircea, M., La Torretta, T. M. G., Telloli, C., Petralia, E., Stracquadanio, M., and Berico, M.: Chemical Composition of Fine and Coarse Aerosol Particles in the Central Mediterranean Area during Dust and Non-Dust Conditions, Aerosol Air Qual. Res., 15, 410–425, https://doi.org/10.4209/aaqr.2014.08.0172, 2015. a, b, c
Meinander, O., Dagsson-Waldhauserova, P., Amosov, P., Aseyeva, E., Atkins, C., Baklanov, A., Baldo, C., Barr, S. L., Barzycka, B., Benning, L. G., Cvetkovic, B., Enchilik, P., Frolov, D., Gassó, S., Kandler, K., Kasimov, N., Kavan, J., King, J., Koroleva, T., Krupskaya, V., Kulmala, M., Kusiak, M., Lappalainen, H. K., Laska, M., Lasne, J., Lewandowski, M., Luks, B., McQuaid, J. B., Moroni, B., Murray, B., Möhler, O., Nawrot, A., Nickovic, S., O’Neill, N. T., Pejanovic, G., Popovicheva, O., Ranjbar, K., Romanias, M., Samonova, O., Sanchez-Marroquin, A., Schepanski, K., Semenkov, I., Sharapova, A., Shevnina, E., Shi, Z., Sofiev, M., Thevenet, F., Thorsteinsson, T., Timofeev, M., Umo, N. S., Uppstu, A., Urupina, D., Varga, G., Werner, T., Arnalds, O., and Vukovic Vimic, A.: Newly identified climatically and environmentally significant high-latitude dust sources, Atmos. Chem. Phys., 22, 11889–11930, https://doi.org/10.5194/acp-22-11889-2022, 2022. a
Metzger, S., Steil, B., Abdelkader, M., Klingmüller, K., Xu, L., Penner, J. E., Fountoukis, C., Nenes, A., and Lelieveld, J.: Aerosol water parameterisation: a single parameter framework, Atmos. Chem. Phys., 16, 7213–7237, https://doi.org/10.5194/acp-16-7213-2016, 2016. a
Nenes, A., Pilinis, C., and Pandis, S. N.: ISORROPIA: A New Thermodynamic Model for Multiphase Multicomponent Inorganic Aerosols, Aquat. Geochem., 4, 123–152, https://doi.org/10.1023/A:1009604003981, 1998. a
Nenes, A., Pilinis, C., and Pandis, S. N.: 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
Passant, N.: Speciation of UK Emissions of Non-methane Volatile Organic Compounds, DEFRA, AEAT/ENV/R/0545 Issue 1, https://uk-air.defra.gov.uk/assets/documents/reports/empire/AEAT_ENV_0545_final_v2.pdf (last
access: 20 March 2023), 2002. a
Ryder, C. L., Highwood, E. J., Rosenberg, P. D., Trembath, J., Brooke, J. K., Bart, M., Dean, A., Crosier, J., Dorsey, J., Brindley, H., Banks, J., Marsham, J. H., McQuaid, J. B., Sodemann, H., and Washington, R.: Optical properties of Saharan dust aerosol and contribution from the coarse mode as measured during the Fennec 2011 aircraft campaign, Atmos. Chem. Phys., 13, 303–325, https://doi.org/10.5194/acp-13-303-2013, 2013. a
Shangguan, W., Dai, Y., Duan, Q., Liu, B., and Yuan, H.: A global soil data set for earth system modeling, J. Adv. Model. Earth Sy., 6, 249–263, https://doi.org/10.1002/2013MS000293, 2014. 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 datasets, J. Geophys. Res., 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. M., and Huang, X.-Y.: A Description of the Advanced Research WRF Version 4, NCAR Tech. Note NCAR/TN-556+STR, 145 pp., https://doi.org/10.5065/1dfh-6p97, 2019. a
Song, Q., Zhang, Z., Yu, H., Kok, J. F., Di Biagio, C., Albani, S., Zheng, J., and Ding, J.: Size-resolved dust direct radiative effect efficiency derived from satellite observations, Atmos. Chem. Phys., 22, 13115–13135, https://doi.org/10.5194/acp-22-13115-2022, 2022. a
Stagge, J. H., Kingston, D. G., Tallaksen, L. M., and David, M. H.: Observed drought indices show increasing divergence across Europe, Sci. Rep., 7, 14045, https://doi.org/10.1038/s41598-017-14283-2, 2017. a
Strader, R., Lurmann, F., and Pandis, S. N.: Evaluation of secondary organic aerosol formation in winter, Atmos. Environ., 33, 4849–4863, 1999. a
Tsyro, S., Aas, W., Colette, A., Andersson, C., Bessagnet, B., Ciarelli, G., Couvidat, F., Cuvelier, K., Manders, A., Mar, K., Mircea, M., Otero, N., Pay, M.-T., Raffort, V., Roustan, Y., Theobald, M. R., Vivanco, M. G., Fagerli, H., Wind, P., Briganti, G., Cappelletti, A., D'Isidoro, M., and Adani, M.: Eurodelta multi-model simulated and observed particulate matter trends in Europe in the period of 1990–2010, Atmos. Chem. Phys., 22, 7207–7257, https://doi.org/10.5194/acp-22-7207-2022, 2022.
a
Tyagi, B., Magliulo, V., Finardi, S., Gasbarra, D., Carlucci, P., Toscano, P., Zaldei, A., Riccio, A., Calori, G., D'Allura, A., and Gioli, B.: Performance Analysis of Planetary Boundary Layer Parameterization Schemes in WRF Modeling Set Up over Southern Italy, Atmosphere, 9, 272, https://doi.org/10.3390/atmos9070272, 2018. a
van der Gon, H. D., 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:
20 March
2023), 2011. a
Wagner, R., Jähn, M., and Schepanski, K.: Wildfires as a source of airborne mineral dust – revisiting a conceptual model using large-eddy simulation (LES), Atmos. Chem. Phys., 18, 11863–11884, https://doi.org/10.5194/acp-18-11863-2018, 2018. a
Wang, K., Zhang, Y., Nenes, A., and Fountoukis, C.: Implementation of dust emission and chemistry into the Community Multiscale Air Quality modeling system and initial application to an Asian dust storm episode, Atmos. Chem. Phys., 12, 10209–10237, https://doi.org/10.5194/acp-12-10209-2012, 2012. a, b, c, d, e
Wang, Q., Gub, J., and Wang, X.: The impact of Sahara dust on air quality and public health in European countries, Atmos. Environ., 241, 117771, https://doi.org/10.1016/j.atmosenv.2020.117771, 2020. a
Wang, T., Liu, Y., Cheng, H., Wang, Z., Fu, H., Chen, J., and Zhang, L.: Significant formation of sulfate aerosols contributed by the heterogeneous drivers of dust surface, Atmos. Chem. Phys., 22, 13467–13493, https://doi.org/10.5194/acp-22-13467-2022, 2022. a, b, c
WRF: Weather Research and Forecast model code, version 4.0 source code, WRF [code], https://www2.mmm.ucar.edu/wrf/src/WRFV4.0.TAR.gz, last access 30 November 2022. a
Yarwood, G., Jung, J., Whitten, G. Z., Heo, G., Mellberg, J., and Estes, E.: Updates to the Carbon Bond Mechanism for Version 6 (CB6), 9th Annual CMAS Conference, October 2010, Chapel Hill, 2010. a
Yuan, H., Dai, Y., Xiao, Z., Ji, D., and Shangguan, W.: Reprocessing the MODIS Leaf Area Index Products for Land Surface and Climate Modelling, Remote Sens. Environ., 115, 1171–1187, https://doi.org/10.1016/j.rse.2011.01.001, 2011. a, b
Zhang, L., Xin, J., Yin, Y., Chang, W., Xue, M., Jia, D., and Ma, Y.: Understanding the Major Impact of Planetary Boundary Layer Schemes on Simulation of Vertical Wind Structure, Atmosphere, 12, 777, https://doi.org/10.3390/atmos12060777, 2021. a
Zheng, B., Zhang, Q., Zhang, Y., He, K. B., Wang, K., Zheng, G. J., Duan, F. K., Ma, Y. L., and Kimoto, T.: Heterogeneous chemistry: a mechanism missing in current models to explain secondary inorganic aerosol formation during the January 2013 haze episode in North China, Atmos. Chem. Phys., 15, 2031–2049, https://doi.org/10.5194/acp-15-2031-2015, 2015.
a, b
Zittis, G., Almazroui, M., Alpert, P., Ciais, P., Cramer, W., Dahdal, Y., Fnais, M., Francis, D.,
Hadjinicolaou, P., Howari, F., Jrrar, A., Kaskaoutis, D. G., Kulmala, M., Lazoglou, G.,
Mihalopoulos, N., Lin, X., Rudich, Y., Sciare, J., Stenchikov, G., Xoplaki, E., and Lelieveld, J.: Climate change and weather extremes in the Eastern Mediterranean and Middle East, Rev. Geophys., 60, e2021RG000762, https://doi.org/10.1029/2021RG000762, 2022.
a
Groot Zwaaftink, C. D., Aas, W., Eckhardt, S., Evangeliou, N., Hamer, P., Johnsrud, M., Kylling, A., Platt, S. M., Stebel, K., Uggerud, H., and Yttri, K. E.: What caused a record high PM10 episode in northern Europe in October 2020?, Atmos. Chem. Phys., 22, 3789–3810, https://doi.org/10.5194/acp-22-3789-2022, 2022. a
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.
Wind-blown dust (WBD) emissions emitted from European soils are estimated for the 2007–2016...
Altmetrics
Final-revised paper
Preprint