Spatial distribution and sources of heavy metals in the sediment and soils of the Yancheng coastal ecosystem and associated ecological risks
Abstract Studies of heavy metal pollution are essential for the protection of coastal environments. In this study, positive matrix factorization (PMF) and a GeoDetector model were used to evaluate the sources of heavy metal contamination and associated ecological risks along the Yancheng Coastal Wet...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ni, Xin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Environmental science and pollution research - Berlin : Springer, 1994, 30(2022), 7 vom: 11. Okt., Seite 18843-18860 |
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| Übergeordnetes Werk: |
volume:30 ; year:2022 ; number:7 ; day:11 ; month:10 ; pages:18843-18860 |
| Links: |
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| DOI / URN: |
10.1007/s11356-022-23295-z |
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SPR049332457 |
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| 520 | |a Abstract Studies of heavy metal pollution are essential for the protection of coastal environments. In this study, positive matrix factorization (PMF) and a GeoDetector model were used to evaluate the sources of heavy metal contamination and associated ecological risks along the Yancheng Coastal Wetland. The distribution of heavy metals was shown to be greatly affected by clay content, except for Cr in shoal. Components from 6.5 to 9φ have the strongest ability to absorb heavy metals, where the effects of Cd and Zn sequestration in the wetlands were most apparent. The abilities of various wetland environments to sequester heavy metals were shown to be Spartina alterniflora wetland > woodland > Phragmites australis wetland > aquaculture pond > shoal > paddy > meadow > dry land. The sources of the heavy metals included parent soil material (59%), agriculture (15%), and industrial pollutants (26%). According to the single-factor pollution index, there was no evidence of pollution except Cr and Pb. In general, the heavy metal pollution was insignificant. The order of pollution loading index was shoal > paddy field > dry land > Spartina Alterniflora wetland > aquaculture ponds > woodland > meadow > Phragmites australis wetland. The ecological harm of heavy metal exposure was slight except for Cd and Hg, where vehicle emissions appeared to be the main cause of heavy metal pollution. | ||
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| 650 | 4 | |a Spatial distribution |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Ecological risk |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Zhao, Guangming |4 aut | |
| 700 | 1 | |a Ye, Siyuan |4 aut | |
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| 700 | 1 | |a Xie, Liujuan |4 aut | |
| 700 | 1 | |a Pei, Shaofeng |4 aut | |
| 700 | 1 | |a Laws, Edward A. |4 aut | |
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10.1007/s11356-022-23295-z doi (DE-627)SPR049332457 (SPR)s11356-022-23295-z-e DE-627 ger DE-627 rakwb eng Ni, Xin verfasserin aut Spatial distribution and sources of heavy metals in the sediment and soils of the Yancheng coastal ecosystem and associated ecological risks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Studies of heavy metal pollution are essential for the protection of coastal environments. In this study, positive matrix factorization (PMF) and a GeoDetector model were used to evaluate the sources of heavy metal contamination and associated ecological risks along the Yancheng Coastal Wetland. The distribution of heavy metals was shown to be greatly affected by clay content, except for Cr in shoal. Components from 6.5 to 9φ have the strongest ability to absorb heavy metals, where the effects of Cd and Zn sequestration in the wetlands were most apparent. The abilities of various wetland environments to sequester heavy metals were shown to be Spartina alterniflora wetland > woodland > Phragmites australis wetland > aquaculture pond > shoal > paddy > meadow > dry land. The sources of the heavy metals included parent soil material (59%), agriculture (15%), and industrial pollutants (26%). According to the single-factor pollution index, there was no evidence of pollution except Cr and Pb. In general, the heavy metal pollution was insignificant. The order of pollution loading index was shoal > paddy field > dry land > Spartina Alterniflora wetland > aquaculture ponds > woodland > meadow > Phragmites australis wetland. The ecological harm of heavy metal exposure was slight except for Cd and Hg, where vehicle emissions appeared to be the main cause of heavy metal pollution. Heavy metals (dpeaa)DE-He213 Spatial distribution (dpeaa)DE-He213 Source identification (dpeaa)DE-He213 Ecological risk (dpeaa)DE-He213 Yancheng wetland (dpeaa)DE-He213 Zhao, Guangming aut Ye, Siyuan aut Li, Guangxue aut Yuan, Hongming aut He, Lei aut Su, Dapeng aut Ding, Xigui aut Xie, Liujuan aut Pei, Shaofeng aut Laws, Edward A. aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 7 vom: 11. Okt., Seite 18843-18860 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:7 day:11 month:10 pages:18843-18860 https://dx.doi.org/10.1007/s11356-022-23295-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2022 7 11 10 18843-18860 |
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10.1007/s11356-022-23295-z doi (DE-627)SPR049332457 (SPR)s11356-022-23295-z-e DE-627 ger DE-627 rakwb eng Ni, Xin verfasserin aut Spatial distribution and sources of heavy metals in the sediment and soils of the Yancheng coastal ecosystem and associated ecological risks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Studies of heavy metal pollution are essential for the protection of coastal environments. In this study, positive matrix factorization (PMF) and a GeoDetector model were used to evaluate the sources of heavy metal contamination and associated ecological risks along the Yancheng Coastal Wetland. The distribution of heavy metals was shown to be greatly affected by clay content, except for Cr in shoal. Components from 6.5 to 9φ have the strongest ability to absorb heavy metals, where the effects of Cd and Zn sequestration in the wetlands were most apparent. The abilities of various wetland environments to sequester heavy metals were shown to be Spartina alterniflora wetland > woodland > Phragmites australis wetland > aquaculture pond > shoal > paddy > meadow > dry land. The sources of the heavy metals included parent soil material (59%), agriculture (15%), and industrial pollutants (26%). According to the single-factor pollution index, there was no evidence of pollution except Cr and Pb. In general, the heavy metal pollution was insignificant. The order of pollution loading index was shoal > paddy field > dry land > Spartina Alterniflora wetland > aquaculture ponds > woodland > meadow > Phragmites australis wetland. The ecological harm of heavy metal exposure was slight except for Cd and Hg, where vehicle emissions appeared to be the main cause of heavy metal pollution. Heavy metals (dpeaa)DE-He213 Spatial distribution (dpeaa)DE-He213 Source identification (dpeaa)DE-He213 Ecological risk (dpeaa)DE-He213 Yancheng wetland (dpeaa)DE-He213 Zhao, Guangming aut Ye, Siyuan aut Li, Guangxue aut Yuan, Hongming aut He, Lei aut Su, Dapeng aut Ding, Xigui aut Xie, Liujuan aut Pei, Shaofeng aut Laws, Edward A. aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 7 vom: 11. Okt., Seite 18843-18860 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:7 day:11 month:10 pages:18843-18860 https://dx.doi.org/10.1007/s11356-022-23295-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2022 7 11 10 18843-18860 |
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10.1007/s11356-022-23295-z doi (DE-627)SPR049332457 (SPR)s11356-022-23295-z-e DE-627 ger DE-627 rakwb eng Ni, Xin verfasserin aut Spatial distribution and sources of heavy metals in the sediment and soils of the Yancheng coastal ecosystem and associated ecological risks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Studies of heavy metal pollution are essential for the protection of coastal environments. In this study, positive matrix factorization (PMF) and a GeoDetector model were used to evaluate the sources of heavy metal contamination and associated ecological risks along the Yancheng Coastal Wetland. The distribution of heavy metals was shown to be greatly affected by clay content, except for Cr in shoal. Components from 6.5 to 9φ have the strongest ability to absorb heavy metals, where the effects of Cd and Zn sequestration in the wetlands were most apparent. The abilities of various wetland environments to sequester heavy metals were shown to be Spartina alterniflora wetland > woodland > Phragmites australis wetland > aquaculture pond > shoal > paddy > meadow > dry land. The sources of the heavy metals included parent soil material (59%), agriculture (15%), and industrial pollutants (26%). According to the single-factor pollution index, there was no evidence of pollution except Cr and Pb. In general, the heavy metal pollution was insignificant. The order of pollution loading index was shoal > paddy field > dry land > Spartina Alterniflora wetland > aquaculture ponds > woodland > meadow > Phragmites australis wetland. The ecological harm of heavy metal exposure was slight except for Cd and Hg, where vehicle emissions appeared to be the main cause of heavy metal pollution. Heavy metals (dpeaa)DE-He213 Spatial distribution (dpeaa)DE-He213 Source identification (dpeaa)DE-He213 Ecological risk (dpeaa)DE-He213 Yancheng wetland (dpeaa)DE-He213 Zhao, Guangming aut Ye, Siyuan aut Li, Guangxue aut Yuan, Hongming aut He, Lei aut Su, Dapeng aut Ding, Xigui aut Xie, Liujuan aut Pei, Shaofeng aut Laws, Edward A. aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 7 vom: 11. Okt., Seite 18843-18860 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:7 day:11 month:10 pages:18843-18860 https://dx.doi.org/10.1007/s11356-022-23295-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2022 7 11 10 18843-18860 |
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10.1007/s11356-022-23295-z doi (DE-627)SPR049332457 (SPR)s11356-022-23295-z-e DE-627 ger DE-627 rakwb eng Ni, Xin verfasserin aut Spatial distribution and sources of heavy metals in the sediment and soils of the Yancheng coastal ecosystem and associated ecological risks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Studies of heavy metal pollution are essential for the protection of coastal environments. In this study, positive matrix factorization (PMF) and a GeoDetector model were used to evaluate the sources of heavy metal contamination and associated ecological risks along the Yancheng Coastal Wetland. The distribution of heavy metals was shown to be greatly affected by clay content, except for Cr in shoal. Components from 6.5 to 9φ have the strongest ability to absorb heavy metals, where the effects of Cd and Zn sequestration in the wetlands were most apparent. The abilities of various wetland environments to sequester heavy metals were shown to be Spartina alterniflora wetland > woodland > Phragmites australis wetland > aquaculture pond > shoal > paddy > meadow > dry land. The sources of the heavy metals included parent soil material (59%), agriculture (15%), and industrial pollutants (26%). According to the single-factor pollution index, there was no evidence of pollution except Cr and Pb. In general, the heavy metal pollution was insignificant. The order of pollution loading index was shoal > paddy field > dry land > Spartina Alterniflora wetland > aquaculture ponds > woodland > meadow > Phragmites australis wetland. The ecological harm of heavy metal exposure was slight except for Cd and Hg, where vehicle emissions appeared to be the main cause of heavy metal pollution. Heavy metals (dpeaa)DE-He213 Spatial distribution (dpeaa)DE-He213 Source identification (dpeaa)DE-He213 Ecological risk (dpeaa)DE-He213 Yancheng wetland (dpeaa)DE-He213 Zhao, Guangming aut Ye, Siyuan aut Li, Guangxue aut Yuan, Hongming aut He, Lei aut Su, Dapeng aut Ding, Xigui aut Xie, Liujuan aut Pei, Shaofeng aut Laws, Edward A. aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 7 vom: 11. Okt., Seite 18843-18860 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:7 day:11 month:10 pages:18843-18860 https://dx.doi.org/10.1007/s11356-022-23295-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2022 7 11 10 18843-18860 |
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10.1007/s11356-022-23295-z doi (DE-627)SPR049332457 (SPR)s11356-022-23295-z-e DE-627 ger DE-627 rakwb eng Ni, Xin verfasserin aut Spatial distribution and sources of heavy metals in the sediment and soils of the Yancheng coastal ecosystem and associated ecological risks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Studies of heavy metal pollution are essential for the protection of coastal environments. In this study, positive matrix factorization (PMF) and a GeoDetector model were used to evaluate the sources of heavy metal contamination and associated ecological risks along the Yancheng Coastal Wetland. The distribution of heavy metals was shown to be greatly affected by clay content, except for Cr in shoal. Components from 6.5 to 9φ have the strongest ability to absorb heavy metals, where the effects of Cd and Zn sequestration in the wetlands were most apparent. The abilities of various wetland environments to sequester heavy metals were shown to be Spartina alterniflora wetland > woodland > Phragmites australis wetland > aquaculture pond > shoal > paddy > meadow > dry land. The sources of the heavy metals included parent soil material (59%), agriculture (15%), and industrial pollutants (26%). According to the single-factor pollution index, there was no evidence of pollution except Cr and Pb. In general, the heavy metal pollution was insignificant. The order of pollution loading index was shoal > paddy field > dry land > Spartina Alterniflora wetland > aquaculture ponds > woodland > meadow > Phragmites australis wetland. The ecological harm of heavy metal exposure was slight except for Cd and Hg, where vehicle emissions appeared to be the main cause of heavy metal pollution. Heavy metals (dpeaa)DE-He213 Spatial distribution (dpeaa)DE-He213 Source identification (dpeaa)DE-He213 Ecological risk (dpeaa)DE-He213 Yancheng wetland (dpeaa)DE-He213 Zhao, Guangming aut Ye, Siyuan aut Li, Guangxue aut Yuan, Hongming aut He, Lei aut Su, Dapeng aut Ding, Xigui aut Xie, Liujuan aut Pei, Shaofeng aut Laws, Edward A. aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 7 vom: 11. Okt., Seite 18843-18860 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:7 day:11 month:10 pages:18843-18860 https://dx.doi.org/10.1007/s11356-022-23295-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2022 7 11 10 18843-18860 |
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Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Studies of heavy metal pollution are essential for the protection of coastal environments. In this study, positive matrix factorization (PMF) and a GeoDetector model were used to evaluate the sources of heavy metal contamination and associated ecological risks along the Yancheng Coastal Wetland. The distribution of heavy metals was shown to be greatly affected by clay content, except for Cr in shoal. Components from 6.5 to 9φ have the strongest ability to absorb heavy metals, where the effects of Cd and Zn sequestration in the wetlands were most apparent. The abilities of various wetland environments to sequester heavy metals were shown to be Spartina alterniflora wetland > woodland > Phragmites australis wetland > aquaculture pond > shoal > paddy > meadow > dry land. The sources of the heavy metals included parent soil material (59%), agriculture (15%), and industrial pollutants (26%). According to the single-factor pollution index, there was no evidence of pollution except Cr and Pb. In general, the heavy metal pollution was insignificant. The order of pollution loading index was shoal > paddy field > dry land > Spartina Alterniflora wetland > aquaculture ponds > woodland > meadow > Phragmites australis wetland. 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Ni, Xin |
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Ni, Xin misc Heavy metals misc Spatial distribution misc Source identification misc Ecological risk misc Yancheng wetland Spatial distribution and sources of heavy metals in the sediment and soils of the Yancheng coastal ecosystem and associated ecological risks |
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Spatial distribution and sources of heavy metals in the sediment and soils of the Yancheng coastal ecosystem and associated ecological risks Heavy metals (dpeaa)DE-He213 Spatial distribution (dpeaa)DE-He213 Source identification (dpeaa)DE-He213 Ecological risk (dpeaa)DE-He213 Yancheng wetland (dpeaa)DE-He213 |
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Spatial distribution and sources of heavy metals in the sediment and soils of the Yancheng coastal ecosystem and associated ecological risks |
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Ni, Xin Zhao, Guangming Ye, Siyuan Li, Guangxue Yuan, Hongming He, Lei Su, Dapeng Ding, Xigui Xie, Liujuan Pei, Shaofeng Laws, Edward A. |
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spatial distribution and sources of heavy metals in the sediment and soils of the yancheng coastal ecosystem and associated ecological risks |
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Spatial distribution and sources of heavy metals in the sediment and soils of the Yancheng coastal ecosystem and associated ecological risks |
| abstract |
Abstract Studies of heavy metal pollution are essential for the protection of coastal environments. In this study, positive matrix factorization (PMF) and a GeoDetector model were used to evaluate the sources of heavy metal contamination and associated ecological risks along the Yancheng Coastal Wetland. The distribution of heavy metals was shown to be greatly affected by clay content, except for Cr in shoal. Components from 6.5 to 9φ have the strongest ability to absorb heavy metals, where the effects of Cd and Zn sequestration in the wetlands were most apparent. The abilities of various wetland environments to sequester heavy metals were shown to be Spartina alterniflora wetland > woodland > Phragmites australis wetland > aquaculture pond > shoal > paddy > meadow > dry land. The sources of the heavy metals included parent soil material (59%), agriculture (15%), and industrial pollutants (26%). According to the single-factor pollution index, there was no evidence of pollution except Cr and Pb. In general, the heavy metal pollution was insignificant. The order of pollution loading index was shoal > paddy field > dry land > Spartina Alterniflora wetland > aquaculture ponds > woodland > meadow > Phragmites australis wetland. The ecological harm of heavy metal exposure was slight except for Cd and Hg, where vehicle emissions appeared to be the main cause of heavy metal pollution. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Studies of heavy metal pollution are essential for the protection of coastal environments. In this study, positive matrix factorization (PMF) and a GeoDetector model were used to evaluate the sources of heavy metal contamination and associated ecological risks along the Yancheng Coastal Wetland. The distribution of heavy metals was shown to be greatly affected by clay content, except for Cr in shoal. Components from 6.5 to 9φ have the strongest ability to absorb heavy metals, where the effects of Cd and Zn sequestration in the wetlands were most apparent. The abilities of various wetland environments to sequester heavy metals were shown to be Spartina alterniflora wetland > woodland > Phragmites australis wetland > aquaculture pond > shoal > paddy > meadow > dry land. The sources of the heavy metals included parent soil material (59%), agriculture (15%), and industrial pollutants (26%). According to the single-factor pollution index, there was no evidence of pollution except Cr and Pb. In general, the heavy metal pollution was insignificant. The order of pollution loading index was shoal > paddy field > dry land > Spartina Alterniflora wetland > aquaculture ponds > woodland > meadow > Phragmites australis wetland. The ecological harm of heavy metal exposure was slight except for Cd and Hg, where vehicle emissions appeared to be the main cause of heavy metal pollution. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Studies of heavy metal pollution are essential for the protection of coastal environments. In this study, positive matrix factorization (PMF) and a GeoDetector model were used to evaluate the sources of heavy metal contamination and associated ecological risks along the Yancheng Coastal Wetland. The distribution of heavy metals was shown to be greatly affected by clay content, except for Cr in shoal. Components from 6.5 to 9φ have the strongest ability to absorb heavy metals, where the effects of Cd and Zn sequestration in the wetlands were most apparent. The abilities of various wetland environments to sequester heavy metals were shown to be Spartina alterniflora wetland > woodland > Phragmites australis wetland > aquaculture pond > shoal > paddy > meadow > dry land. The sources of the heavy metals included parent soil material (59%), agriculture (15%), and industrial pollutants (26%). According to the single-factor pollution index, there was no evidence of pollution except Cr and Pb. In general, the heavy metal pollution was insignificant. The order of pollution loading index was shoal > paddy field > dry land > Spartina Alterniflora wetland > aquaculture ponds > woodland > meadow > Phragmites australis wetland. The ecological harm of heavy metal exposure was slight except for Cd and Hg, where vehicle emissions appeared to be the main cause of heavy metal pollution. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Spatial distribution and sources of heavy metals in the sediment and soils of the Yancheng coastal ecosystem and associated ecological risks |
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Zhao, Guangming Ye, Siyuan Li, Guangxue Yuan, Hongming He, Lei Su, Dapeng Ding, Xigui Xie, Liujuan Pei, Shaofeng Laws, Edward A. |
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Zhao, Guangming Ye, Siyuan Li, Guangxue Yuan, Hongming He, Lei Su, Dapeng Ding, Xigui Xie, Liujuan Pei, Shaofeng Laws, Edward A. |
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| score |
7.4010954 |