Sources of organic aerosols in Europe: A modeling study using CAMx with modified volatility basis set scheme

Source apportionment of organic aerosols (OAs) is of great importance to better understand the health impact and climate effects of particulate matter air pollution. Air quality models are used as potential tools to identify OA components and sources at high spatial and temporal resolution; however,...

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Autores: Jiang, Jianhui, Paglione, Marco, Minguillón, María Cruz, Favez, Olivier, Baltensperger, Urs, Prévôt, André S.H.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/198865
Acceso en línea:http://hdl.handle.net/10261/198865
Access Level:acceso abierto
Palabra clave:Aerosols
Air quality
Smog chamber
Aerosol formation
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oai_identifier_str oai:digital.csic.es:10261/198865
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Sources of organic aerosols in Europe: A modeling study using CAMx with modified volatility basis set scheme
title Sources of organic aerosols in Europe: A modeling study using CAMx with modified volatility basis set scheme
spellingShingle Sources of organic aerosols in Europe: A modeling study using CAMx with modified volatility basis set scheme
Jiang, Jianhui
Aerosols
Air quality
Smog chamber
Aerosol formation
title_short Sources of organic aerosols in Europe: A modeling study using CAMx with modified volatility basis set scheme
title_full Sources of organic aerosols in Europe: A modeling study using CAMx with modified volatility basis set scheme
title_fullStr Sources of organic aerosols in Europe: A modeling study using CAMx with modified volatility basis set scheme
title_full_unstemmed Sources of organic aerosols in Europe: A modeling study using CAMx with modified volatility basis set scheme
title_sort Sources of organic aerosols in Europe: A modeling study using CAMx with modified volatility basis set scheme
dc.creator.none.fl_str_mv Jiang, Jianhui
Paglione, Marco
Minguillón, María Cruz
Favez, Olivier
Baltensperger, Urs
Prévôt, André S.H.
author Jiang, Jianhui
author_facet Jiang, Jianhui
Paglione, Marco
Minguillón, María Cruz
Favez, Olivier
Baltensperger, Urs
Prévôt, André S.H.
author_role author
author2 Paglione, Marco
Minguillón, María Cruz
Favez, Olivier
Baltensperger, Urs
Prévôt, André S.H.
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv European Commission
Ministerio de Economía y Competitividad (España)
Minguillón, María Cruz [0000-0002-5464-0391]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Aerosols
Air quality
Smog chamber
Aerosol formation
topic Aerosols
Air quality
Smog chamber
Aerosol formation
description Source apportionment of organic aerosols (OAs) is of great importance to better understand the health impact and climate effects of particulate matter air pollution. Air quality models are used as potential tools to identify OA components and sources at high spatial and temporal resolution; however, they generally underestimate OA concentrations, and comparisons of their outputs with an extended set of measurements are still rare due to the lack of long-term experimental data. In this study, we addressed such challenges at the European level. Using the regional Comprehensive Air Quality Model with Extensions (CAMx) and a volatility basis set (VBS) scheme which was optimized based on recent chamber experiments with wood burning and diesel vehicle emissions, and which contains more source-specific sets compared to previous studies, we calculated the contribution of OA components and defined their sources over a wholeyear period (2011). We modeled separately the primary and secondary OA contributions from old and new diesel and gasoline vehicles, biomass burning (mostly residential wood burning and agricultural waste burning excluding wildfires), other anthropogenic sources (mainly shipping, industry and energy production) and biogenic sources. An important feature of this study is that we evaluated the model results with measurements over a longer period than in previous studies, which strengthens our confidence in our modeled source apportionment results. Comparison against positive matrix factorization (PMF) analyses of aerosol mass spectrometric measurements at nine European sites suggested that the modified VBS scheme improved the model performance for total OA as well as the OA components, including hydrocarbonlike (HOA), biomass burning (BBOA) and oxygenated components (OOA). By using the modified VBS scheme, the mean bias of OOA was reduced from-1:3 to-0:4 μgm-3 corresponding to a reduction of mean fractional bias from-45% to-20 %. The winter OOA simulation, which was largely underestimated in previous studies, was improved by 29% to 42% among the evaluated sites compared to the default parameterization. Wood burning was the dominant OA source in winter (61 %), while biogenic emissions contributed ∼55% to OA during summer in Europe on average. In both seasons, other anthropogenic sources comprised the second largest component (9% in winter and 19% in summer as domain average), while the average contributions of diesel and gasoline vehicles were rather small (∼5 %) except for the metropolitan areas where the highest contribution reached 31 %. The results indicate the need to improve the emission inventory to include currently missing and highly uncertain local emissions, as well as further improvement of VBS parameterization for winter biomass burning. Although this study focused on Europe, it can be applied in any other part of the globe. This study highlights the ability of long-term measurements and source apportionment modeling to validate and improve emission inventories, and identify sources not yet properly included in existing inventories. © Author(s) 2019.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/198865
url http://hdl.handle.net/10261/198865
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/FP7/262254
https://doi.org/10.5194/acp-19-15247-2019
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Copernicus Publications
publisher.none.fl_str_mv Copernicus Publications
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
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spelling Sources of organic aerosols in Europe: A modeling study using CAMx with modified volatility basis set schemeJiang, JianhuiPaglione, MarcoMinguillón, María CruzFavez, OlivierBaltensperger, UrsPrévôt, André S.H.AerosolsAir qualitySmog chamberAerosol formationSource apportionment of organic aerosols (OAs) is of great importance to better understand the health impact and climate effects of particulate matter air pollution. Air quality models are used as potential tools to identify OA components and sources at high spatial and temporal resolution; however, they generally underestimate OA concentrations, and comparisons of their outputs with an extended set of measurements are still rare due to the lack of long-term experimental data. In this study, we addressed such challenges at the European level. Using the regional Comprehensive Air Quality Model with Extensions (CAMx) and a volatility basis set (VBS) scheme which was optimized based on recent chamber experiments with wood burning and diesel vehicle emissions, and which contains more source-specific sets compared to previous studies, we calculated the contribution of OA components and defined their sources over a wholeyear period (2011). We modeled separately the primary and secondary OA contributions from old and new diesel and gasoline vehicles, biomass burning (mostly residential wood burning and agricultural waste burning excluding wildfires), other anthropogenic sources (mainly shipping, industry and energy production) and biogenic sources. An important feature of this study is that we evaluated the model results with measurements over a longer period than in previous studies, which strengthens our confidence in our modeled source apportionment results. Comparison against positive matrix factorization (PMF) analyses of aerosol mass spectrometric measurements at nine European sites suggested that the modified VBS scheme improved the model performance for total OA as well as the OA components, including hydrocarbonlike (HOA), biomass burning (BBOA) and oxygenated components (OOA). By using the modified VBS scheme, the mean bias of OOA was reduced from-1:3 to-0:4 μgm-3 corresponding to a reduction of mean fractional bias from-45% to-20 %. The winter OOA simulation, which was largely underestimated in previous studies, was improved by 29% to 42% among the evaluated sites compared to the default parameterization. Wood burning was the dominant OA source in winter (61 %), while biogenic emissions contributed ∼55% to OA during summer in Europe on average. In both seasons, other anthropogenic sources comprised the second largest component (9% in winter and 19% in summer as domain average), while the average contributions of diesel and gasoline vehicles were rather small (∼5 %) except for the metropolitan areas where the highest contribution reached 31 %. The results indicate the need to improve the emission inventory to include currently missing and highly uncertain local emissions, as well as further improvement of VBS parameterization for winter biomass burning. Although this study focused on Europe, it can be applied in any other part of the globe. This study highlights the ability of long-term measurements and source apportionment modeling to validate and improve emission inventories, and identify sources not yet properly included in existing inventories. © Author(s) 2019.Funding text #1 1Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen PSI, Switzerland 2Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, USA 3TNO, Department of Climate, Air and Sustainability, Utrecht, the Netherlands 4Italian National Research Council – Institute of Atmospheric Sciences and Climate, Bologna, Italy 5Institute of Environmental Assessment and Water Research (IDAEA), CSIC, 08034 Barcelona, Spain 6Institut National de l’Environnement Industriel et des Risques (INERIS), Verneuil-en-Halatte, France 7Laboratoire des Sciences du Climat et de l’Environnement (LSCE), Gif-sur-Yvette, France 8Aix-Marseille Univ, CNRS, LCE, Marseille, France 9Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland 10Department of Chemical Engineering, University of Patras, 26500 Patras, Greece 11School of Physics, Ryan Institute’s Centre for Climate and Air Pollution Studies, and Marine Renewable Energy Ireland, National ...View all Funding text #2 Acknowledgements. We would like to thank the European Centre for Medium-range Weather Forecasts (ECMWF) for access to the meteorological data, the European Environmental Agency (EEA) for the air quality data, the National Aeronautics and Space Administration (NASA) and its data-contributing agencies (NCAR, UCAR) for the TOMS and MODIS data, the global air quality model data and the TUV model. We acknowledge the continuous support of CAMx by RAMBOLL. Simulation of WRF and CAMx models were performed at the Swiss National Supercomputing Centre (CSCS). Anna Alastuey and Andrés Ripoll from IDAEA-CSIC are acknowledged. Funding text #3 surements were supported by EPA-Ireland (2016-CCRP-MS-31), Emilia-Romagna region’s Supersito project (DRG 428/10; DGR 1971/2013), Generalitat de Catalunya (AGAUR 2017 SGR41), the Spanish Ministry of Economy, Industry and Competitiveness (Ramón y Cajal fellowship awarded to María Cruz Minguil-lón), EU-FP7 ACTRIS project (grant agreement no. 262254), and the COST Action CA16109 Chemical On-Line cOmpoSition and Source Apportionment of fine aerosoL (COLOSSAL).Peer reviewedCopernicus PublicationsEuropean CommissionMinisterio de Economía y Competitividad (España)Minguillón, María Cruz [0000-0002-5464-0391]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202020202019info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/198865reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/FP7/262254https://doi.org/10.5194/acp-19-15247-2019Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1988652026-05-22T06:33:51Z
score 15,811543

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