Source apportionment of PM2.5 in Seoul, South Korea and Beijing, China using dispersion normalized PMF
East Asian countries experience severe air pollution owing to their rapid development and urbanization induced by substantial economic activities. South Korea and China are among the most polluted East Asian countries with high mass concentrations of PM2.5. Although the occurrence of transboundary a...
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description | East Asian countries experience severe air pollution owing to their rapid development and urbanization induced by substantial economic activities. South Korea and China are among the most polluted East Asian countries with high mass concentrations of PM2.5. Although the occurrence of transboundary air pollution among neighboring countries has been recognized for a long time, studies involving simultaneous ground-based PM2.5 monitoring and source apportionment in South Korea and China have not been conducted to date. This study performed simultaneous daily ground-based monitoring of PM2.5 in Seoul and Beijing from January to December 2019. The mass concentrations of PM2.5 and its major chemical components were analyzed simultaneously during 2019. Positive matrix factorization (PMF) as well as dispersion normalized PMF (DN-PMF) were utilized for the source apportionment of ambient PM2.5 at the two sites. 23 h average ventilation coefficients were applied for daily PM2.5 chemical constituents' data. Nine sources were identified at both sites. While secondary nitrate, secondary sulfate, mobile, oil combustion, biomass burning, soil, and aged sea salt were commonly found at both sites, industry/coal combustion and incinerator were identified only at Seoul and incinerator/industry and coal combustion were identified only at Beijing. Reduction of the meteorological influences were found in DN-PMF compare to C-PMF but the effects of DN on mobile source were reduced by averaging over the 23 h sampling period. The DN-PMF results showed that Secondary nitrate (Seoul: 25.5%; Beijing: 31.7%) and secondary sulfate (Seoul: 20.5%; Beijing: 17.6%) were most dominant contributors to PM2.5 at both sites. Decreasing secondary sulfate contributions and increasing secondary nitrate contributions were observed at both sites.
[Display omitted]
•PM2.5 measurements in Beijing and Seoul were made using comparable methods.•Conventional PMF and dispersion normalized PMF were applied for both sites.•Decreasing sulfate and increasing nitrate contributions were observed at both sites•Both sites were affected by regional and long-range transboundary air pollutants. |
doi_str_mv | 10.1016/j.scitotenv.2022.155056 |
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[Display omitted]
•PM2.5 measurements in Beijing and Seoul were made using comparable methods.•Conventional PMF and dispersion normalized PMF were applied for both sites.•Decreasing sulfate and increasing nitrate contributions were observed at both sites•Both sites were affected by regional and long-range transboundary air pollutants.</description><identifier>ISSN: 0048-9697</identifier><identifier>EISSN: 1879-1026</identifier><identifier>DOI: 10.1016/j.scitotenv.2022.155056</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>air pollution ; biomass ; China ; coal ; combustion ; Conditional bivariate probability function (CBPF) ; Dispersion normalized PMF (DN-PMF) ; environment ; incinerators ; industry ; nitrates ; oils ; PM2.5 ; Positive matrix factorization (PMF) ; Potential source contribution function (PSCF) ; soil ; Source apportionment ; South Korea ; sulfates ; urbanization</subject><ispartof>The Science of the total environment, 2022-08, Vol.833, p.155056-155056, Article 155056</ispartof><rights>2022 The Authors</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3456-fc1f6ad95162d1093c918b0e1795a3dc8ca2e071ed35a1044bdf78239c024dd53</citedby><cites>FETCH-LOGICAL-c3456-fc1f6ad95162d1093c918b0e1795a3dc8ca2e071ed35a1044bdf78239c024dd53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0048969722021490$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Park, Jieun</creatorcontrib><creatorcontrib>Kim, Hyewon</creatorcontrib><creatorcontrib>Kim, Youngkwon</creatorcontrib><creatorcontrib>Heo, Jongbae</creatorcontrib><creatorcontrib>Kim, Sang-Woo</creatorcontrib><creatorcontrib>Jeon, Kwonho</creatorcontrib><creatorcontrib>Yi, Seung-Muk</creatorcontrib><creatorcontrib>Hopke, Philip K.</creatorcontrib><title>Source apportionment of PM2.5 in Seoul, South Korea and Beijing, China using dispersion normalized PMF</title><title>The Science of the total environment</title><description>East Asian countries experience severe air pollution owing to their rapid development and urbanization induced by substantial economic activities. South Korea and China are among the most polluted East Asian countries with high mass concentrations of PM2.5. Although the occurrence of transboundary air pollution among neighboring countries has been recognized for a long time, studies involving simultaneous ground-based PM2.5 monitoring and source apportionment in South Korea and China have not been conducted to date. This study performed simultaneous daily ground-based monitoring of PM2.5 in Seoul and Beijing from January to December 2019. The mass concentrations of PM2.5 and its major chemical components were analyzed simultaneously during 2019. Positive matrix factorization (PMF) as well as dispersion normalized PMF (DN-PMF) were utilized for the source apportionment of ambient PM2.5 at the two sites. 23 h average ventilation coefficients were applied for daily PM2.5 chemical constituents' data. Nine sources were identified at both sites. While secondary nitrate, secondary sulfate, mobile, oil combustion, biomass burning, soil, and aged sea salt were commonly found at both sites, industry/coal combustion and incinerator were identified only at Seoul and incinerator/industry and coal combustion were identified only at Beijing. Reduction of the meteorological influences were found in DN-PMF compare to C-PMF but the effects of DN on mobile source were reduced by averaging over the 23 h sampling period. The DN-PMF results showed that Secondary nitrate (Seoul: 25.5%; Beijing: 31.7%) and secondary sulfate (Seoul: 20.5%; Beijing: 17.6%) were most dominant contributors to PM2.5 at both sites. Decreasing secondary sulfate contributions and increasing secondary nitrate contributions were observed at both sites.
[Display omitted]
•PM2.5 measurements in Beijing and Seoul were made using comparable methods.•Conventional PMF and dispersion normalized PMF were applied for both sites.•Decreasing sulfate and increasing nitrate contributions were observed at both sites•Both sites were affected by regional and long-range transboundary air pollutants.</description><subject>air pollution</subject><subject>biomass</subject><subject>China</subject><subject>coal</subject><subject>combustion</subject><subject>Conditional bivariate probability function (CBPF)</subject><subject>Dispersion normalized PMF (DN-PMF)</subject><subject>environment</subject><subject>incinerators</subject><subject>industry</subject><subject>nitrates</subject><subject>oils</subject><subject>PM2.5</subject><subject>Positive matrix factorization (PMF)</subject><subject>Potential source contribution function (PSCF)</subject><subject>soil</subject><subject>Source apportionment</subject><subject>South Korea</subject><subject>sulfates</subject><subject>urbanization</subject><issn>0048-9697</issn><issn>1879-1026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNqFkU1LxDAQhoMouH78BnP0YGuSNmly1MUvVBTUc4jJVLN0k5p0Bf31Zlnx6lyGged9YHgROqKkpoSK00WdrZ_iBOGzZoSxmnJOuNhCMyo7VVHCxDaaEdLKSgnV7aK9nBekTCfpDPVPcZUsYDOOMU0-hiWECcceP96zmmMf8BPE1XCCCze949uYwGATHD4Hv_Dh7QTP330weJXLgZ3PI6RcNDjEtDSD_wZXVJcHaKc3Q4bD372PXi4vnufX1d3D1c387K6yTctF1VvaC-MUp4I5SlRjFZWvBGinuGmcldYwIB0F13BDSdu-ur6TrFGWsNY53uyj4413TPFjBXnSS58tDIMJ5Y2smRBEqaYp6v_RVkrVSkEL2m1Qm2LOCXo9Jr806UtTotcl6IX-K0GvS9CbEkrybJOE8vSnh7TmIFhwPoGdtIv-X8cPRJSTjA</recordid><startdate>20220810</startdate><enddate>20220810</enddate><creator>Park, Jieun</creator><creator>Kim, Hyewon</creator><creator>Kim, Youngkwon</creator><creator>Heo, Jongbae</creator><creator>Kim, Sang-Woo</creator><creator>Jeon, Kwonho</creator><creator>Yi, Seung-Muk</creator><creator>Hopke, Philip K.</creator><general>Elsevier B.V</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope></search><sort><creationdate>20220810</creationdate><title>Source apportionment of PM2.5 in Seoul, South Korea and Beijing, China using dispersion normalized PMF</title><author>Park, Jieun ; Kim, Hyewon ; Kim, Youngkwon ; Heo, Jongbae ; Kim, Sang-Woo ; Jeon, Kwonho ; Yi, Seung-Muk ; Hopke, Philip K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3456-fc1f6ad95162d1093c918b0e1795a3dc8ca2e071ed35a1044bdf78239c024dd53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>air pollution</topic><topic>biomass</topic><topic>China</topic><topic>coal</topic><topic>combustion</topic><topic>Conditional bivariate probability function (CBPF)</topic><topic>Dispersion normalized PMF (DN-PMF)</topic><topic>environment</topic><topic>incinerators</topic><topic>industry</topic><topic>nitrates</topic><topic>oils</topic><topic>PM2.5</topic><topic>Positive matrix factorization (PMF)</topic><topic>Potential source contribution function (PSCF)</topic><topic>soil</topic><topic>Source apportionment</topic><topic>South Korea</topic><topic>sulfates</topic><topic>urbanization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Park, Jieun</creatorcontrib><creatorcontrib>Kim, Hyewon</creatorcontrib><creatorcontrib>Kim, Youngkwon</creatorcontrib><creatorcontrib>Heo, Jongbae</creatorcontrib><creatorcontrib>Kim, Sang-Woo</creatorcontrib><creatorcontrib>Jeon, Kwonho</creatorcontrib><creatorcontrib>Yi, Seung-Muk</creatorcontrib><creatorcontrib>Hopke, Philip K.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>The Science of the total environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Park, Jieun</au><au>Kim, Hyewon</au><au>Kim, Youngkwon</au><au>Heo, Jongbae</au><au>Kim, Sang-Woo</au><au>Jeon, Kwonho</au><au>Yi, Seung-Muk</au><au>Hopke, Philip K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Source apportionment of PM2.5 in Seoul, South Korea and Beijing, China using dispersion normalized PMF</atitle><jtitle>The Science of the total environment</jtitle><date>2022-08-10</date><risdate>2022</risdate><volume>833</volume><spage>155056</spage><epage>155056</epage><pages>155056-155056</pages><artnum>155056</artnum><issn>0048-9697</issn><eissn>1879-1026</eissn><abstract>East Asian countries experience severe air pollution owing to their rapid development and urbanization induced by substantial economic activities. South Korea and China are among the most polluted East Asian countries with high mass concentrations of PM2.5. Although the occurrence of transboundary air pollution among neighboring countries has been recognized for a long time, studies involving simultaneous ground-based PM2.5 monitoring and source apportionment in South Korea and China have not been conducted to date. This study performed simultaneous daily ground-based monitoring of PM2.5 in Seoul and Beijing from January to December 2019. The mass concentrations of PM2.5 and its major chemical components were analyzed simultaneously during 2019. Positive matrix factorization (PMF) as well as dispersion normalized PMF (DN-PMF) were utilized for the source apportionment of ambient PM2.5 at the two sites. 23 h average ventilation coefficients were applied for daily PM2.5 chemical constituents' data. Nine sources were identified at both sites. While secondary nitrate, secondary sulfate, mobile, oil combustion, biomass burning, soil, and aged sea salt were commonly found at both sites, industry/coal combustion and incinerator were identified only at Seoul and incinerator/industry and coal combustion were identified only at Beijing. Reduction of the meteorological influences were found in DN-PMF compare to C-PMF but the effects of DN on mobile source were reduced by averaging over the 23 h sampling period. The DN-PMF results showed that Secondary nitrate (Seoul: 25.5%; Beijing: 31.7%) and secondary sulfate (Seoul: 20.5%; Beijing: 17.6%) were most dominant contributors to PM2.5 at both sites. Decreasing secondary sulfate contributions and increasing secondary nitrate contributions were observed at both sites.
[Display omitted]
•PM2.5 measurements in Beijing and Seoul were made using comparable methods.•Conventional PMF and dispersion normalized PMF were applied for both sites.•Decreasing sulfate and increasing nitrate contributions were observed at both sites•Both sites were affected by regional and long-range transboundary air pollutants.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.scitotenv.2022.155056</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | air pollution biomass China coal combustion Conditional bivariate probability function (CBPF) Dispersion normalized PMF (DN-PMF) environment incinerators industry nitrates oils PM2.5 Positive matrix factorization (PMF) Potential source contribution function (PSCF) soil Source apportionment South Korea sulfates urbanization |
title | Source apportionment of PM2.5 in Seoul, South Korea and Beijing, China using dispersion normalized PMF |
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