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Measurement report: Receptor modeling for source identification of urban fine and coarse particulate matter using hourly elemental composition
The elemental composition of the fine (PM2.5) and coarse (PM2.5−10) fraction of atmospheric particulate matter was measured at an hourly time resolution by the use of a streaker sampler during a winter period at a Central European urban background site in Warsaw, Poland. A combination of multivariat...
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Published in: | Atmospheric chemistry and physics 2021-09, Vol.21 (19), p.14471-14492 |
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description | The elemental composition of the fine (PM2.5) and coarse
(PM2.5−10) fraction of atmospheric particulate matter was measured at an hourly time resolution by the use of a streaker sampler during a winter
period at a Central European urban background site in Warsaw, Poland. A
combination of multivariate (Positive Matrix Factorization) and wind-
(Conditional Probability Function) and trajectory-based (Cluster Analysis)
receptor models was applied for source apportionment. It allowed for the
identification of five similar sources in both fractions, including sulfates, soil dust, road salt, and traffic- and industry-related sources. Another two sources, i.e., Cl-rich and wood and coal combustion, were solely identified in the fine fraction. In the fine fraction, aged sulfate aerosol related to emissions from domestic solid fuel combustion in the outskirts of the city was the largest contributing source to fine elemental mass (44 %), while traffic-related sources, including soil dust mixed with road dust, road dust, and traffic emissions, had the biggest contribution to the coarse elemental mass (together accounting for 83 %). Regional transport of aged aerosols and more local impact of the rest of the identified sources played a crucial role in aerosol formation over the city. In addition, two intensive Saharan dust outbreaks were registered on 18 February and 8 March 2016. Both episodes were characterized by the long-range transport of dust at 1500 and 3000 m over Warsaw and the concentrations of the soil component being 7 (up to 3.5 µg m−3) and 6 (up to 6.1 µg m−3) times higher than the mean concentrations observed during non-episodes days (0.5 and 1.1 µg m−3) in the fine and coarse fractions, respectively. The set of receptor models applied to the high time resolution data allowed us to follow, in detail, the daily evolution of the aerosol elemental composition and to identify distinct sources contributing to the concentrations of the different PM fractions, and it revealed the multi-faceted nature of some elements with diverse origins in the fine and coarse fractions. The hourly resolution of meteorological conditions and air mass back trajectories allowed us to follow the transport pathways of the aerosol as well. |
doi_str_mv | 10.5194/acp-21-14471-2021 |
format | article |
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(PM2.5−10) fraction of atmospheric particulate matter was measured at an hourly time resolution by the use of a streaker sampler during a winter
period at a Central European urban background site in Warsaw, Poland. A
combination of multivariate (Positive Matrix Factorization) and wind-
(Conditional Probability Function) and trajectory-based (Cluster Analysis)
receptor models was applied for source apportionment. It allowed for the
identification of five similar sources in both fractions, including sulfates, soil dust, road salt, and traffic- and industry-related sources. Another two sources, i.e., Cl-rich and wood and coal combustion, were solely identified in the fine fraction. In the fine fraction, aged sulfate aerosol related to emissions from domestic solid fuel combustion in the outskirts of the city was the largest contributing source to fine elemental mass (44 %), while traffic-related sources, including soil dust mixed with road dust, road dust, and traffic emissions, had the biggest contribution to the coarse elemental mass (together accounting for 83 %). Regional transport of aged aerosols and more local impact of the rest of the identified sources played a crucial role in aerosol formation over the city. In addition, two intensive Saharan dust outbreaks were registered on 18 February and 8 March 2016. Both episodes were characterized by the long-range transport of dust at 1500 and 3000 m over Warsaw and the concentrations of the soil component being 7 (up to 3.5 µg m−3) and 6 (up to 6.1 µg m−3) times higher than the mean concentrations observed during non-episodes days (0.5 and 1.1 µg m−3) in the fine and coarse fractions, respectively. The set of receptor models applied to the high time resolution data allowed us to follow, in detail, the daily evolution of the aerosol elemental composition and to identify distinct sources contributing to the concentrations of the different PM fractions, and it revealed the multi-faceted nature of some elements with diverse origins in the fine and coarse fractions. The hourly resolution of meteorological conditions and air mass back trajectories allowed us to follow the transport pathways of the aerosol as well.</description><identifier>ISSN: 1680-7324</identifier><identifier>ISSN: 1680-7316</identifier><identifier>EISSN: 1680-7324</identifier><identifier>DOI: 10.5194/acp-21-14471-2021</identifier><language>eng</language><publisher>Katlenburg-Lindau: Copernicus GmbH</publisher><subject>Aerosol formation ; Aerosols ; Air masses ; Air pollution ; Analysis ; Apportionment ; Atmospheric models ; Atmospheric particulate matter ; Atmospheric particulates ; Chemical composition ; Cluster analysis ; Coal combustion ; Combustion ; Conditional probability ; Dust ; Dust storms ; Emissions ; Fuel combustion ; Identification ; Indoor air quality ; Long-range transport ; Measurement ; Meteorological conditions ; Particulate emissions ; Particulate matter ; Precipitation ; Probability theory ; Receptors ; Resolution ; Road salt ; Roads ; Saharan dust ; Saharan dust outbreaks ; Soil ; Soils ; Solid fuels ; Sulfates ; Suspended particulate matter ; Traffic ; Trajectory analysis ; Transport ; Vehicle emissions</subject><ispartof>Atmospheric chemistry and physics, 2021-09, Vol.21 (19), p.14471-14492</ispartof><rights>COPYRIGHT 2021 Copernicus GmbH</rights><rights>2021. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c483t-ff65942354992b27a0e23b8ee003004364e904edb168f424148c05712298d3623</citedby><cites>FETCH-LOGICAL-c483t-ff65942354992b27a0e23b8ee003004364e904edb168f424148c05712298d3623</cites><orcidid>0000-0002-9476-1470 ; 0000-0002-7362-1874</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2577610793/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2577610793?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,25753,27924,27925,37012,44590,74998</link.rule.ids></links><search><creatorcontrib>Reizer, Magdalena</creatorcontrib><creatorcontrib>Calzolai, Giulia</creatorcontrib><creatorcontrib>Maciejewska, Katarzyna</creatorcontrib><creatorcontrib>Orza, José A. G</creatorcontrib><creatorcontrib>Carraresi, Luca</creatorcontrib><creatorcontrib>Lucarelli, Franco</creatorcontrib><creatorcontrib>Juda-Rezler, Katarzyna</creatorcontrib><title>Measurement report: Receptor modeling for source identification of urban fine and coarse particulate matter using hourly elemental composition</title><title>Atmospheric chemistry and physics</title><description>The elemental composition of the fine (PM2.5) and coarse
(PM2.5−10) fraction of atmospheric particulate matter was measured at an hourly time resolution by the use of a streaker sampler during a winter
period at a Central European urban background site in Warsaw, Poland. A
combination of multivariate (Positive Matrix Factorization) and wind-
(Conditional Probability Function) and trajectory-based (Cluster Analysis)
receptor models was applied for source apportionment. It allowed for the
identification of five similar sources in both fractions, including sulfates, soil dust, road salt, and traffic- and industry-related sources. Another two sources, i.e., Cl-rich and wood and coal combustion, were solely identified in the fine fraction. In the fine fraction, aged sulfate aerosol related to emissions from domestic solid fuel combustion in the outskirts of the city was the largest contributing source to fine elemental mass (44 %), while traffic-related sources, including soil dust mixed with road dust, road dust, and traffic emissions, had the biggest contribution to the coarse elemental mass (together accounting for 83 %). Regional transport of aged aerosols and more local impact of the rest of the identified sources played a crucial role in aerosol formation over the city. In addition, two intensive Saharan dust outbreaks were registered on 18 February and 8 March 2016. Both episodes were characterized by the long-range transport of dust at 1500 and 3000 m over Warsaw and the concentrations of the soil component being 7 (up to 3.5 µg m−3) and 6 (up to 6.1 µg m−3) times higher than the mean concentrations observed during non-episodes days (0.5 and 1.1 µg m−3) in the fine and coarse fractions, respectively. The set of receptor models applied to the high time resolution data allowed us to follow, in detail, the daily evolution of the aerosol elemental composition and to identify distinct sources contributing to the concentrations of the different PM fractions, and it revealed the multi-faceted nature of some elements with diverse origins in the fine and coarse fractions. The hourly resolution of meteorological conditions and air mass back trajectories allowed us to follow the transport pathways of the aerosol as well.</description><subject>Aerosol formation</subject><subject>Aerosols</subject><subject>Air masses</subject><subject>Air pollution</subject><subject>Analysis</subject><subject>Apportionment</subject><subject>Atmospheric models</subject><subject>Atmospheric particulate matter</subject><subject>Atmospheric particulates</subject><subject>Chemical composition</subject><subject>Cluster analysis</subject><subject>Coal combustion</subject><subject>Combustion</subject><subject>Conditional probability</subject><subject>Dust</subject><subject>Dust storms</subject><subject>Emissions</subject><subject>Fuel combustion</subject><subject>Identification</subject><subject>Indoor air quality</subject><subject>Long-range transport</subject><subject>Measurement</subject><subject>Meteorological conditions</subject><subject>Particulate emissions</subject><subject>Particulate matter</subject><subject>Precipitation</subject><subject>Probability theory</subject><subject>Receptors</subject><subject>Resolution</subject><subject>Road salt</subject><subject>Roads</subject><subject>Saharan dust</subject><subject>Saharan dust outbreaks</subject><subject>Soil</subject><subject>Soils</subject><subject>Solid fuels</subject><subject>Sulfates</subject><subject>Suspended particulate matter</subject><subject>Traffic</subject><subject>Trajectory analysis</subject><subject>Transport</subject><subject>Vehicle emissions</subject><issn>1680-7324</issn><issn>1680-7316</issn><issn>1680-7324</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptkt2KFDEQhRtRcF19AO8CXnnRa3473d4tiz8DK8Kq16EmqYwZujttkobdl_CZzcyIOiC5SFKc-qoOnKZ5yeiVYoN8A3ZpOWuZlJq1nHL2qLlgXU9bLbh8_M_7afMs5z2lXFEmL5qfnxDymnDCuZCES0zlLblDi0uJiUzR4RjmHfH1k-OaLJLgqjT4YKGEOJPoyZq2MBMfZiQwO2IjpIxkgVSCXUcoSCYoBRNZ84H1vXLGB4LjcSiMtWFaYg4H3PPmiYcx44vf92Xz7f27rzcf29vPHzY317etlb0orfedGiQXSg4D33INFLnY9oiUCkql6CQOVKLbVttecslkb6nSjPOhd6Lj4rLZnLguwt4sKUyQHkyEYI6FmHbmuP6IxiquFAB3ylLZKwe8HzrY1glOKK1YZb06sZYUf6yYi9lXh3Nd33CldceoHsRf1Q4qNMw-lgR2Ctma605roQemdFVd_UdVj8Mp2DijD7V-1vD6rKFqCt6XHaw5m82Xu3MtO2ltijkn9H-MM2oOKTI1RYYzc0yROaRI_AL8q7nb</recordid><startdate>20210930</startdate><enddate>20210930</enddate><creator>Reizer, Magdalena</creator><creator>Calzolai, Giulia</creator><creator>Maciejewska, Katarzyna</creator><creator>Orza, José A. 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G</au><au>Carraresi, Luca</au><au>Lucarelli, Franco</au><au>Juda-Rezler, Katarzyna</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Measurement report: Receptor modeling for source identification of urban fine and coarse particulate matter using hourly elemental composition</atitle><jtitle>Atmospheric chemistry and physics</jtitle><date>2021-09-30</date><risdate>2021</risdate><volume>21</volume><issue>19</issue><spage>14471</spage><epage>14492</epage><pages>14471-14492</pages><issn>1680-7324</issn><issn>1680-7316</issn><eissn>1680-7324</eissn><abstract>The elemental composition of the fine (PM2.5) and coarse
(PM2.5−10) fraction of atmospheric particulate matter was measured at an hourly time resolution by the use of a streaker sampler during a winter
period at a Central European urban background site in Warsaw, Poland. A
combination of multivariate (Positive Matrix Factorization) and wind-
(Conditional Probability Function) and trajectory-based (Cluster Analysis)
receptor models was applied for source apportionment. It allowed for the
identification of five similar sources in both fractions, including sulfates, soil dust, road salt, and traffic- and industry-related sources. Another two sources, i.e., Cl-rich and wood and coal combustion, were solely identified in the fine fraction. In the fine fraction, aged sulfate aerosol related to emissions from domestic solid fuel combustion in the outskirts of the city was the largest contributing source to fine elemental mass (44 %), while traffic-related sources, including soil dust mixed with road dust, road dust, and traffic emissions, had the biggest contribution to the coarse elemental mass (together accounting for 83 %). Regional transport of aged aerosols and more local impact of the rest of the identified sources played a crucial role in aerosol formation over the city. In addition, two intensive Saharan dust outbreaks were registered on 18 February and 8 March 2016. Both episodes were characterized by the long-range transport of dust at 1500 and 3000 m over Warsaw and the concentrations of the soil component being 7 (up to 3.5 µg m−3) and 6 (up to 6.1 µg m−3) times higher than the mean concentrations observed during non-episodes days (0.5 and 1.1 µg m−3) in the fine and coarse fractions, respectively. The set of receptor models applied to the high time resolution data allowed us to follow, in detail, the daily evolution of the aerosol elemental composition and to identify distinct sources contributing to the concentrations of the different PM fractions, and it revealed the multi-faceted nature of some elements with diverse origins in the fine and coarse fractions. The hourly resolution of meteorological conditions and air mass back trajectories allowed us to follow the transport pathways of the aerosol as well.</abstract><cop>Katlenburg-Lindau</cop><pub>Copernicus GmbH</pub><doi>10.5194/acp-21-14471-2021</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0002-9476-1470</orcidid><orcidid>https://orcid.org/0000-0002-7362-1874</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Aerosol formation Aerosols Air masses Air pollution Analysis Apportionment Atmospheric models Atmospheric particulate matter Atmospheric particulates Chemical composition Cluster analysis Coal combustion Combustion Conditional probability Dust Dust storms Emissions Fuel combustion Identification Indoor air quality Long-range transport Measurement Meteorological conditions Particulate emissions Particulate matter Precipitation Probability theory Receptors Resolution Road salt Roads Saharan dust Saharan dust outbreaks Soil Soils Solid fuels Sulfates Suspended particulate matter Traffic Trajectory analysis Transport Vehicle emissions |
title | Measurement report: Receptor modeling for source identification of urban fine and coarse particulate matter using hourly elemental composition |
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