Potential source contributions and risk assessment of PAHs in sediments from Taihu Lake, China: Comparison of three receptor models
In this work, three receptor models (Principal Component Analysis–Multiple Linear Regression (PCA–MLR) model, Unmix model and Positive Matrix Factorization (PMF) model) were employed to investigate potential source apportionment of PAHs in sediments from Taihu Lake, China. A total of 15 priority PAH...
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Veröffentlicht in: | Water research (Oxford) 2012-06, Vol.46 (9), p.3065-3073 |
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creator | Zhang, Yuan Guo, Chang-Sheng Xu, Jian Tian, Ying-Ze Shi, Guo-Liang Feng, Yin-Chang |
description | In this work, three receptor models (Principal Component Analysis–Multiple Linear Regression (PCA–MLR) model, Unmix model and Positive Matrix Factorization (PMF) model) were employed to investigate potential source apportionment of PAHs in sediments from Taihu Lake, China. A total of 15 priority PAHs in 29 sediments from Taihu Lake were measured, with ∑PAHs (sum of 15 PAHs) concentrations ranging from 209 to 1003 ng g−1 dw. Source apportionment results derived from three different models were similar, indicating that the highest contribution to ∑PAHs was from vehicular emission (53.6–54.3%), followed by coal combustion (23.8–28.8%) and wood combustion (11.9–16.0%). The contribution of mixed wood and coal combustion source identified by PCA–MLR was 41.3%. For the first time the risk assessment for each identified source category was quantitatively calculated by combining the BaP equivalents (BaPE) values with estimated source contributions. The results showed that vehicular emission posed the highest toxic risk, with BaPE values of 26.9–31.5 ng g−1 dw, and the BaPE values for coal combustion and wood combustion were 6.56–15.6 ng g−1 dw and 2.94–6.11 ng g−1 dw, respectively. The distributions of contribution and BaPE for each identified source category were studied as well, and showed similar trends among the sampling sites, for each source category.
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► Fifteen PAHs concentration were measured in sediments. ► Source apportionment was study by three receptor models. ► Vehicular emission got the highest contributions: 53.6–54.3%. ► Vehicular emission got the highest toxic risk: 26.9–31.5 ng g−1 dw for BaPE. |
doi_str_mv | 10.1016/j.watres.2012.03.006 |
format | Article |
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[Display omitted]
► Fifteen PAHs concentration were measured in sediments. ► Source apportionment was study by three receptor models. ► Vehicular emission got the highest contributions: 53.6–54.3%. ► Vehicular emission got the highest toxic risk: 26.9–31.5 ng g−1 dw for BaPE.</description><identifier>ISSN: 0043-1354</identifier><identifier>EISSN: 1879-2448</identifier><identifier>DOI: 10.1016/j.watres.2012.03.006</identifier><identifier>PMID: 22459329</identifier><identifier>CODEN: WATRAG</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Applied sciences ; Categories ; China ; Coal ; Combustion ; Exact sciences and technology ; Geologic Sediments - chemistry ; Lakes ; linear models ; Models, Theoretical ; PAHs ; Pollution ; Polyallylamine hydrochloride ; polycyclic aromatic hydrocarbons ; Polycyclic Compounds - analysis ; Receptor models ; risk ; Risk Assessment ; Sediments ; Source apportionment ; toxicity ; Water Pollutants, Chemical - analysis ; Water treatment and pollution ; Wood</subject><ispartof>Water research (Oxford), 2012-06, Vol.46 (9), p.3065-3073</ispartof><rights>2012 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><rights>Copyright © 2012 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c449t-22be8fcecb0bfae5ac76e9dcc6a9f614ea588961214824baa0c15c3ea5792fdc3</citedby><cites>FETCH-LOGICAL-c449t-22be8fcecb0bfae5ac76e9dcc6a9f614ea588961214824baa0c15c3ea5792fdc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.watres.2012.03.006$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3548,27923,27924,45994</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25867020$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22459329$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Yuan</creatorcontrib><creatorcontrib>Guo, Chang-Sheng</creatorcontrib><creatorcontrib>Xu, Jian</creatorcontrib><creatorcontrib>Tian, Ying-Ze</creatorcontrib><creatorcontrib>Shi, Guo-Liang</creatorcontrib><creatorcontrib>Feng, Yin-Chang</creatorcontrib><title>Potential source contributions and risk assessment of PAHs in sediments from Taihu Lake, China: Comparison of three receptor models</title><title>Water research (Oxford)</title><addtitle>Water Res</addtitle><description>In this work, three receptor models (Principal Component Analysis–Multiple Linear Regression (PCA–MLR) model, Unmix model and Positive Matrix Factorization (PMF) model) were employed to investigate potential source apportionment of PAHs in sediments from Taihu Lake, China. A total of 15 priority PAHs in 29 sediments from Taihu Lake were measured, with ∑PAHs (sum of 15 PAHs) concentrations ranging from 209 to 1003 ng g−1 dw. Source apportionment results derived from three different models were similar, indicating that the highest contribution to ∑PAHs was from vehicular emission (53.6–54.3%), followed by coal combustion (23.8–28.8%) and wood combustion (11.9–16.0%). The contribution of mixed wood and coal combustion source identified by PCA–MLR was 41.3%. For the first time the risk assessment for each identified source category was quantitatively calculated by combining the BaP equivalents (BaPE) values with estimated source contributions. The results showed that vehicular emission posed the highest toxic risk, with BaPE values of 26.9–31.5 ng g−1 dw, and the BaPE values for coal combustion and wood combustion were 6.56–15.6 ng g−1 dw and 2.94–6.11 ng g−1 dw, respectively. The distributions of contribution and BaPE for each identified source category were studied as well, and showed similar trends among the sampling sites, for each source category.
[Display omitted]
► Fifteen PAHs concentration were measured in sediments. ► Source apportionment was study by three receptor models. ► Vehicular emission got the highest contributions: 53.6–54.3%. ► Vehicular emission got the highest toxic risk: 26.9–31.5 ng g−1 dw for BaPE.</description><subject>Applied sciences</subject><subject>Categories</subject><subject>China</subject><subject>Coal</subject><subject>Combustion</subject><subject>Exact sciences and technology</subject><subject>Geologic Sediments - chemistry</subject><subject>Lakes</subject><subject>linear models</subject><subject>Models, Theoretical</subject><subject>PAHs</subject><subject>Pollution</subject><subject>Polyallylamine hydrochloride</subject><subject>polycyclic aromatic hydrocarbons</subject><subject>Polycyclic Compounds - analysis</subject><subject>Receptor models</subject><subject>risk</subject><subject>Risk Assessment</subject><subject>Sediments</subject><subject>Source apportionment</subject><subject>toxicity</subject><subject>Water Pollutants, Chemical - analysis</subject><subject>Water treatment and pollution</subject><subject>Wood</subject><issn>0043-1354</issn><issn>1879-2448</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kU1v1DAQhiMEokvhHyDwBYkDWWzH-TAHpGoFFGklKtGerYkzYb1N7K0noeqZP45XWeDGaaTR886Mnsmyl4KvBRfV-_36HqaItJZcyDUv1pxXj7KVaGqdS6Wax9mKc1XkoijVWfaMaM85l7LQT7MzKVWpC6lX2a-rMKGfHAyMwhwtMhv8FF07Ty54YuA7Fh3dMiBCojGxLPTs6uKSmPOMsHPHHrE-hpFdg9vNbAu3-I5tds7DB7YJ4wHShOCPuWkXEVlEi4cpRDaGDgd6nj3pYSB8carn2c3nT9eby3z77cvXzcU2t0rpKZeyxaa3aFve9oAl2LpC3Vlbge4roRDKptGVkEI1UrUA3IrSFqlda9l3tjjP3i5zDzHczUiTGR1ZHAbwGGYySWuta13oOqFqQW0MRBF7c4huhPiQoCNXmb1Z9JujfsMLk_Sn2KvThrkdsfsb-uM7AW9OAJCFoY_graN_XNlUNZc8ca8Xrodg4EfyZ26-p01l-qEQSpaJ-LgQSSD-dBgNWYfepockvZPpgvv_rb8BPjuxGg</recordid><startdate>20120601</startdate><enddate>20120601</enddate><creator>Zhang, Yuan</creator><creator>Guo, Chang-Sheng</creator><creator>Xu, Jian</creator><creator>Tian, Ying-Ze</creator><creator>Shi, Guo-Liang</creator><creator>Feng, Yin-Chang</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>FBQ</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>20120601</creationdate><title>Potential source contributions and risk assessment of PAHs in sediments from Taihu Lake, China: Comparison of three receptor models</title><author>Zhang, Yuan ; Guo, Chang-Sheng ; Xu, Jian ; Tian, Ying-Ze ; Shi, Guo-Liang ; Feng, Yin-Chang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c449t-22be8fcecb0bfae5ac76e9dcc6a9f614ea588961214824baa0c15c3ea5792fdc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Applied sciences</topic><topic>Categories</topic><topic>China</topic><topic>Coal</topic><topic>Combustion</topic><topic>Exact sciences and technology</topic><topic>Geologic Sediments - chemistry</topic><topic>Lakes</topic><topic>linear models</topic><topic>Models, Theoretical</topic><topic>PAHs</topic><topic>Pollution</topic><topic>Polyallylamine hydrochloride</topic><topic>polycyclic aromatic hydrocarbons</topic><topic>Polycyclic Compounds - analysis</topic><topic>Receptor models</topic><topic>risk</topic><topic>Risk Assessment</topic><topic>Sediments</topic><topic>Source apportionment</topic><topic>toxicity</topic><topic>Water Pollutants, Chemical - analysis</topic><topic>Water treatment and pollution</topic><topic>Wood</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Yuan</creatorcontrib><creatorcontrib>Guo, Chang-Sheng</creatorcontrib><creatorcontrib>Xu, Jian</creatorcontrib><creatorcontrib>Tian, Ying-Ze</creatorcontrib><creatorcontrib>Shi, Guo-Liang</creatorcontrib><creatorcontrib>Feng, Yin-Chang</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Water research (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Yuan</au><au>Guo, Chang-Sheng</au><au>Xu, Jian</au><au>Tian, Ying-Ze</au><au>Shi, Guo-Liang</au><au>Feng, Yin-Chang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Potential source contributions and risk assessment of PAHs in sediments from Taihu Lake, China: Comparison of three receptor models</atitle><jtitle>Water research (Oxford)</jtitle><addtitle>Water Res</addtitle><date>2012-06-01</date><risdate>2012</risdate><volume>46</volume><issue>9</issue><spage>3065</spage><epage>3073</epage><pages>3065-3073</pages><issn>0043-1354</issn><eissn>1879-2448</eissn><coden>WATRAG</coden><abstract>In this work, three receptor models (Principal Component Analysis–Multiple Linear Regression (PCA–MLR) model, Unmix model and Positive Matrix Factorization (PMF) model) were employed to investigate potential source apportionment of PAHs in sediments from Taihu Lake, China. A total of 15 priority PAHs in 29 sediments from Taihu Lake were measured, with ∑PAHs (sum of 15 PAHs) concentrations ranging from 209 to 1003 ng g−1 dw. Source apportionment results derived from three different models were similar, indicating that the highest contribution to ∑PAHs was from vehicular emission (53.6–54.3%), followed by coal combustion (23.8–28.8%) and wood combustion (11.9–16.0%). The contribution of mixed wood and coal combustion source identified by PCA–MLR was 41.3%. For the first time the risk assessment for each identified source category was quantitatively calculated by combining the BaP equivalents (BaPE) values with estimated source contributions. The results showed that vehicular emission posed the highest toxic risk, with BaPE values of 26.9–31.5 ng g−1 dw, and the BaPE values for coal combustion and wood combustion were 6.56–15.6 ng g−1 dw and 2.94–6.11 ng g−1 dw, respectively. The distributions of contribution and BaPE for each identified source category were studied as well, and showed similar trends among the sampling sites, for each source category.
[Display omitted]
► Fifteen PAHs concentration were measured in sediments. ► Source apportionment was study by three receptor models. ► Vehicular emission got the highest contributions: 53.6–54.3%. ► Vehicular emission got the highest toxic risk: 26.9–31.5 ng g−1 dw for BaPE.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><pmid>22459329</pmid><doi>10.1016/j.watres.2012.03.006</doi><tpages>9</tpages></addata></record> |
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subjects | Applied sciences Categories China Coal Combustion Exact sciences and technology Geologic Sediments - chemistry Lakes linear models Models, Theoretical PAHs Pollution Polyallylamine hydrochloride polycyclic aromatic hydrocarbons Polycyclic Compounds - analysis Receptor models risk Risk Assessment Sediments Source apportionment toxicity Water Pollutants, Chemical - analysis Water treatment and pollution Wood |
title | Potential source contributions and risk assessment of PAHs in sediments from Taihu Lake, China: Comparison of three receptor models |
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