Source apportionment of PM10 based on offline chemical speciation data at 24 European sites
Abstract This study applied Positive Matrix Factorization (PMF) to PM10 speciation datasets from 24 urban sites across six European countries (France, Greece, Italy, Portugal, Spain, and Switzerland) to perform a detailed source apportionment (SA) analysis. By using a consistent source apportionment...
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2025-07-01
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| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-025-01097-7 |
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| author | Xiansheng Liu Xun Zhang Bowen Jin Tao Wang Siyuan Qian Jin Zou Vy Ngoc Thuy Dinh Jean-Luc Jaffrezo Gaëlle Uzu Pamela Dominutti Sophie Darfeuil Olivier Favez Sébastien Conil Nicolas Marchand Sonia Castillo Jesús D. de la Rosa Stuart Grange Christoph Hueglin Konstantinos Eleftheriadis Evangelia Diapouli Manousos-Ioannis Manousakas Maria Gini Silvia Nava Giulia Calzolai Célia Alves Marta Monge Cristina Reche Roy M. Harrison Philip K. Hopke Andrés Alastuey Xavier Querol |
| author_facet | Xiansheng Liu Xun Zhang Bowen Jin Tao Wang Siyuan Qian Jin Zou Vy Ngoc Thuy Dinh Jean-Luc Jaffrezo Gaëlle Uzu Pamela Dominutti Sophie Darfeuil Olivier Favez Sébastien Conil Nicolas Marchand Sonia Castillo Jesús D. de la Rosa Stuart Grange Christoph Hueglin Konstantinos Eleftheriadis Evangelia Diapouli Manousos-Ioannis Manousakas Maria Gini Silvia Nava Giulia Calzolai Célia Alves Marta Monge Cristina Reche Roy M. Harrison Philip K. Hopke Andrés Alastuey Xavier Querol |
| author_sort | Xiansheng Liu |
| collection | DOAJ |
| description | Abstract This study applied Positive Matrix Factorization (PMF) to PM10 speciation datasets from 24 urban sites across six European countries (France, Greece, Italy, Portugal, Spain, and Switzerland) to perform a detailed source apportionment (SA) analysis. By using a consistent source apportionment tool for all datasets, the study enhances the comparability of PM10 SA results across urban Europe. The results identified seven major PM10 sources including road traffic, biomass burning, crustal/mineral sources, secondary aerosols, industrial emissions, sea salt, and heavy oil combustion (HOC). Road traffic emerged as the predominant source of PM10 in urban areas, with contributions varying by location, but representing as much as 41% in high-traffic zones. Biomass burning was detected at 23 sites, contributing 8% to 41% on yearly averages, with substantial increase in winter. Crustal sources were present at all sites (3–33%). Industrial sources contributed relatively less PM10 mass, which was identified at 10 sites with contributions ranging from 2% to 14%. Secondary inorganic and organic aerosol, consisting primarily of ammonium nitrates and sulfates, and organic matter, formed a portion of the PM10 mass (5–41%). These secondary factors are primarily influenced by anthropogenic emissions, including the various combustion processes. Sea salt, predominantly found in coastal areas, contributed between 4% and 21%, reflecting the impact of the marine environments on air quality. This source was very often ‘aged’ (mixed with anthropogenic pollutants from different origins). Additionally, HOC, especially emits from shipping activities, and traced by V and Ni, was also a frequent contributing source (2–15% for 9 sites), indicating a need for more stringent emission controls. The chemical comparison is performed which indicates road traffic and secondary aerosols, showed consistent chemical profiles across sites, while industrial, HOC, and crustal sources displayed significant site-specific variability. These findings underscore the need for tailored air quality strategies according to local sources of emissions and the importance of long-term PM speciation monitoring for effective pollution control. |
| format | Article |
| id | doaj-art-43c3dbcfdcc345bb80915f4108bd1f1b |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | npj Climate and Atmospheric Science |
| spelling | doaj-art-43c3dbcfdcc345bb80915f4108bd1f1b2025-08-20T03:45:24ZengNature Portfolionpj Climate and Atmospheric Science2397-37222025-07-018111410.1038/s41612-025-01097-7Source apportionment of PM10 based on offline chemical speciation data at 24 European sitesXiansheng Liu0Xun Zhang1Bowen Jin2Tao Wang3Siyuan Qian4Jin Zou5Vy Ngoc Thuy Dinh6Jean-Luc Jaffrezo7Gaëlle Uzu8Pamela Dominutti9Sophie Darfeuil10Olivier Favez11Sébastien Conil12Nicolas Marchand13Sonia Castillo14Jesús D. de la Rosa15Stuart Grange16Christoph Hueglin17Konstantinos Eleftheriadis18Evangelia Diapouli19Manousos-Ioannis Manousakas20Maria Gini21Silvia Nava22Giulia Calzolai23Célia Alves24Marta Monge25Cristina Reche26Roy M. Harrison27Philip K. Hopke28Andrés Alastuey29Xavier Querol30Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Institute of Environmental Health and Pollution Control, Guangdong University of TechnologySchool of Computer and Artificial Intelligence, Beijing Technology and Business UniversitySchool of Computer and Artificial Intelligence, Beijing Technology and Business UniversityShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Fudan UniversitySchool of Computer and Artificial Intelligence, Beijing Technology and Business UniversitySchool of Computer Science and Artificial Intelligence, Xinjiang HeTian CollegeUniv. Grenoble Alpes, IRD, CNRS, INRAE, Grenoble INP, IGE, UMR 5001Univ. Grenoble Alpes, IRD, CNRS, INRAE, Grenoble INP, IGE, UMR 5001Univ. Grenoble Alpes, IRD, CNRS, INRAE, Grenoble INP, IGE, UMR 5001Univ. Grenoble Alpes, IRD, CNRS, INRAE, Grenoble INP, IGE, UMR 5001Univ. Grenoble Alpes, IRD, CNRS, INRAE, Grenoble INP, IGE, UMR 5001INERIS, Parc Technologique Alata, BP 2ANDRA DISTEC/EES Observatoire Pérenne de l’EnvironnementAix Marseille Univ, CNRS, LCEDepartment of Applied Physics, University of GranadaAssociate Unit CSIC-UHU “Atmospheric Pollution, ” University of HuelvaSwiss Federal Laboratories for Materials Science and Technology (Empa)Swiss Federal Laboratories for Materials Science and Technology (Empa)ENRACT Lab, National Centre for Scientific Research “Demokritos”ENRACT Lab, National Centre for Scientific Research “Demokritos”ENRACT Lab, National Centre for Scientific Research “Demokritos”ENRACT Lab, National Centre for Scientific Research “Demokritos”INFN Division of Florence and Department of Physics and Astronomy, University of FlorenceINFN Division of Florence and Department of Physics and Astronomy, University of FlorenceDepartment of Environment and Planning, Centre for Environmental and Marine Studies (CESAM), University of AveiroInstitute of Environmental Assessment and Water Research (IDAEA-CSIC)Institute of Environmental Assessment and Water Research (IDAEA-CSIC)School of Geography Earth and Environmental Sciences, University of BirminghamDepartments of Public Health Sciences and Environmental Medicine, University of Rochester School of Medicine and DentistryInstitute of Environmental Assessment and Water Research (IDAEA-CSIC)Institute of Environmental Assessment and Water Research (IDAEA-CSIC)Abstract This study applied Positive Matrix Factorization (PMF) to PM10 speciation datasets from 24 urban sites across six European countries (France, Greece, Italy, Portugal, Spain, and Switzerland) to perform a detailed source apportionment (SA) analysis. By using a consistent source apportionment tool for all datasets, the study enhances the comparability of PM10 SA results across urban Europe. The results identified seven major PM10 sources including road traffic, biomass burning, crustal/mineral sources, secondary aerosols, industrial emissions, sea salt, and heavy oil combustion (HOC). Road traffic emerged as the predominant source of PM10 in urban areas, with contributions varying by location, but representing as much as 41% in high-traffic zones. Biomass burning was detected at 23 sites, contributing 8% to 41% on yearly averages, with substantial increase in winter. Crustal sources were present at all sites (3–33%). Industrial sources contributed relatively less PM10 mass, which was identified at 10 sites with contributions ranging from 2% to 14%. Secondary inorganic and organic aerosol, consisting primarily of ammonium nitrates and sulfates, and organic matter, formed a portion of the PM10 mass (5–41%). These secondary factors are primarily influenced by anthropogenic emissions, including the various combustion processes. Sea salt, predominantly found in coastal areas, contributed between 4% and 21%, reflecting the impact of the marine environments on air quality. This source was very often ‘aged’ (mixed with anthropogenic pollutants from different origins). Additionally, HOC, especially emits from shipping activities, and traced by V and Ni, was also a frequent contributing source (2–15% for 9 sites), indicating a need for more stringent emission controls. The chemical comparison is performed which indicates road traffic and secondary aerosols, showed consistent chemical profiles across sites, while industrial, HOC, and crustal sources displayed significant site-specific variability. These findings underscore the need for tailored air quality strategies according to local sources of emissions and the importance of long-term PM speciation monitoring for effective pollution control.https://doi.org/10.1038/s41612-025-01097-7 |
| spellingShingle | Xiansheng Liu Xun Zhang Bowen Jin Tao Wang Siyuan Qian Jin Zou Vy Ngoc Thuy Dinh Jean-Luc Jaffrezo Gaëlle Uzu Pamela Dominutti Sophie Darfeuil Olivier Favez Sébastien Conil Nicolas Marchand Sonia Castillo Jesús D. de la Rosa Stuart Grange Christoph Hueglin Konstantinos Eleftheriadis Evangelia Diapouli Manousos-Ioannis Manousakas Maria Gini Silvia Nava Giulia Calzolai Célia Alves Marta Monge Cristina Reche Roy M. Harrison Philip K. Hopke Andrés Alastuey Xavier Querol Source apportionment of PM10 based on offline chemical speciation data at 24 European sites npj Climate and Atmospheric Science |
| title | Source apportionment of PM10 based on offline chemical speciation data at 24 European sites |
| title_full | Source apportionment of PM10 based on offline chemical speciation data at 24 European sites |
| title_fullStr | Source apportionment of PM10 based on offline chemical speciation data at 24 European sites |
| title_full_unstemmed | Source apportionment of PM10 based on offline chemical speciation data at 24 European sites |
| title_short | Source apportionment of PM10 based on offline chemical speciation data at 24 European sites |
| title_sort | source apportionment of pm10 based on offline chemical speciation data at 24 european sites |
| url | https://doi.org/10.1038/s41612-025-01097-7 |
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