Ecological risk assessment and source identification of heavy metals in the sediments of the Danjiang River Basin: A quantitative method combining multivariate analysis and the APCS-MLR model
The heavy metal content in river sediment is a sensitive indicator of pollution in aquatic ecosystems and plays a key role in understanding the risks, characteristics, and sources of heavy metal pollution in a region. This study combined traditional assessment methods with the Nemerow integrated ris...
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Elsevier
2025-06-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25004480 |
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| author | Zhiming Cao Hui Qian Yanyan Gao Kang Li Yixin Liu Xiaoxin Shi Siqi Li Weijie Zhao Shuhan Yang Panpan Tian Puxia Wu Yandong Ma |
| author_facet | Zhiming Cao Hui Qian Yanyan Gao Kang Li Yixin Liu Xiaoxin Shi Siqi Li Weijie Zhao Shuhan Yang Panpan Tian Puxia Wu Yandong Ma |
| author_sort | Zhiming Cao |
| collection | DOAJ |
| description | The heavy metal content in river sediment is a sensitive indicator of pollution in aquatic ecosystems and plays a key role in understanding the risks, characteristics, and sources of heavy metal pollution in a region. This study combined traditional assessment methods with the Nemerow integrated risk index (NIRI), which is improved based on the potential ecological risk index (RI) and the Nemerow integrated pollution index (NIPI), to evaluate the pollution level of sediment in the Danjiang River. Based on principal component analysis (PCA), the absolute principal component score-multiple linear regression (APCS-MLR) model was employed to analyze the contribution of pollution sources. The study results showed that the average concentrations of most heavy metals exceeded their corresponding background values, and the distribution of heavy metal content was significantly influenced by human activities. The degree of pollution varied among the sampling sites, and the results of NIRI on the spatial distribution and severity of contamination are generally consistent with other assessment indicators, providing a more detailed and comprehensive delineation. The results of the multivariate statistical analysis indicate that Cu, Zn, Pb, and As mainly originated from natural sources, Cd and Ni primarily came from mixed sources such as agriculture and mining, while Cr was mainly associated with industrial activities. The APCS-MLR model results further confirm with high confidence that the sources of heavy metals in the sediments of the study area are complex, predominantly influenced by natural processes such as weathering and erosion. As the water source for the Middle Route of the South-to-North Water Diversion Project, the safety of the Danjiang River’s aquatic ecosystem is crucial for the health of nearly 100 million people in China. These findings provide an important foundation for Danjiang River water resource protection and offer a reference for ecological security and pollution prevention in other rivers. |
| format | Article |
| id | doaj-art-cd7bbc2d2973482eb0fd78e2df99be05 |
| institution | OA Journals |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-cd7bbc2d2973482eb0fd78e2df99be052025-08-20T01:57:12ZengElsevierEcological Indicators1470-160X2025-06-0117511351810.1016/j.ecolind.2025.113518Ecological risk assessment and source identification of heavy metals in the sediments of the Danjiang River Basin: A quantitative method combining multivariate analysis and the APCS-MLR modelZhiming Cao0Hui Qian1Yanyan Gao2Kang Li3Yixin Liu4Xiaoxin Shi5Siqi Li6Weijie Zhao7Shuhan Yang8Panpan Tian9Puxia Wu10Yandong Ma11School of Water and Environment, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of Ministry of Water Resources, Chang’an University, Xi’an 710054, Shaanxi, PR ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of Ministry of Water Resources, Chang’an University, Xi’an 710054, Shaanxi, PR China; Corresponding authors at: School of Water and Environment, Chang’an University, Xi’an 710054, Shaanxi, PR China.School of Water and Environment, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of Ministry of Water Resources, Chang’an University, Xi’an 710054, Shaanxi, PR China; Corresponding authors at: School of Water and Environment, Chang’an University, Xi’an 710054, Shaanxi, PR China.School of Water and Environment, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of Ministry of Water Resources, Chang’an University, Xi’an 710054, Shaanxi, PR ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of Ministry of Water Resources, Chang’an University, Xi’an 710054, Shaanxi, PR ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of Ministry of Water Resources, Chang’an University, Xi’an 710054, Shaanxi, PR ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of Ministry of Water Resources, Chang’an University, Xi’an 710054, Shaanxi, PR ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of Ministry of Water Resources, Chang’an University, Xi’an 710054, Shaanxi, PR ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of Ministry of Water Resources, Chang’an University, Xi’an 710054, Shaanxi, PR ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, Shaanxi, PR China; Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of Ministry of Water Resources, Chang’an University, Xi’an 710054, Shaanxi, PR ChinaKey Laboratory of State Forest Administration on Soil and Water Conservation & Ecological Restoration of Loess Plateau, Shaanxi Academy of Forestry, Xi’an 710016, PR ChinaKey Laboratory of State Forest Administration on Soil and Water Conservation & Ecological Restoration of Loess Plateau, Shaanxi Academy of Forestry, Xi’an 710016, PR ChinaThe heavy metal content in river sediment is a sensitive indicator of pollution in aquatic ecosystems and plays a key role in understanding the risks, characteristics, and sources of heavy metal pollution in a region. This study combined traditional assessment methods with the Nemerow integrated risk index (NIRI), which is improved based on the potential ecological risk index (RI) and the Nemerow integrated pollution index (NIPI), to evaluate the pollution level of sediment in the Danjiang River. Based on principal component analysis (PCA), the absolute principal component score-multiple linear regression (APCS-MLR) model was employed to analyze the contribution of pollution sources. The study results showed that the average concentrations of most heavy metals exceeded their corresponding background values, and the distribution of heavy metal content was significantly influenced by human activities. The degree of pollution varied among the sampling sites, and the results of NIRI on the spatial distribution and severity of contamination are generally consistent with other assessment indicators, providing a more detailed and comprehensive delineation. The results of the multivariate statistical analysis indicate that Cu, Zn, Pb, and As mainly originated from natural sources, Cd and Ni primarily came from mixed sources such as agriculture and mining, while Cr was mainly associated with industrial activities. The APCS-MLR model results further confirm with high confidence that the sources of heavy metals in the sediments of the study area are complex, predominantly influenced by natural processes such as weathering and erosion. As the water source for the Middle Route of the South-to-North Water Diversion Project, the safety of the Danjiang River’s aquatic ecosystem is crucial for the health of nearly 100 million people in China. These findings provide an important foundation for Danjiang River water resource protection and offer a reference for ecological security and pollution prevention in other rivers.http://www.sciencedirect.com/science/article/pii/S1470160X25004480Sediments pollution indicesMultivariate statistical analysesPCA-APCS-MLRThe Danjiang River Basin |
| spellingShingle | Zhiming Cao Hui Qian Yanyan Gao Kang Li Yixin Liu Xiaoxin Shi Siqi Li Weijie Zhao Shuhan Yang Panpan Tian Puxia Wu Yandong Ma Ecological risk assessment and source identification of heavy metals in the sediments of the Danjiang River Basin: A quantitative method combining multivariate analysis and the APCS-MLR model Ecological Indicators Sediments pollution indices Multivariate statistical analyses PCA-APCS-MLR The Danjiang River Basin |
| title | Ecological risk assessment and source identification of heavy metals in the sediments of the Danjiang River Basin: A quantitative method combining multivariate analysis and the APCS-MLR model |
| title_full | Ecological risk assessment and source identification of heavy metals in the sediments of the Danjiang River Basin: A quantitative method combining multivariate analysis and the APCS-MLR model |
| title_fullStr | Ecological risk assessment and source identification of heavy metals in the sediments of the Danjiang River Basin: A quantitative method combining multivariate analysis and the APCS-MLR model |
| title_full_unstemmed | Ecological risk assessment and source identification of heavy metals in the sediments of the Danjiang River Basin: A quantitative method combining multivariate analysis and the APCS-MLR model |
| title_short | Ecological risk assessment and source identification of heavy metals in the sediments of the Danjiang River Basin: A quantitative method combining multivariate analysis and the APCS-MLR model |
| title_sort | ecological risk assessment and source identification of heavy metals in the sediments of the danjiang river basin a quantitative method combining multivariate analysis and the apcs mlr model |
| topic | Sediments pollution indices Multivariate statistical analyses PCA-APCS-MLR The Danjiang River Basin |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25004480 |
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