Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learning
Phosphorus contamination in rivers has received widespread attention. However, in areas with extensive sources of phosphorus and complex hydrogeology conditions, it is difficult to accurately evaluate the main influencing factors of phosphorus. In present study, the spatiotemporal variations of phos...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25001463 |
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| author | Gege Cai Jiamei Zhang Wanlu Li Jiejun Zhang Yun Liu Shanshan Xi Guolian Li Haibin Li Xing Chen Fanhao Song Fazhi Xie |
| author_facet | Gege Cai Jiamei Zhang Wanlu Li Jiejun Zhang Yun Liu Shanshan Xi Guolian Li Haibin Li Xing Chen Fanhao Song Fazhi Xie |
| author_sort | Gege Cai |
| collection | DOAJ |
| description | Phosphorus contamination in rivers has received widespread attention. However, in areas with extensive sources of phosphorus and complex hydrogeology conditions, it is difficult to accurately evaluate the main influencing factors of phosphorus. In present study, the spatiotemporal variations of phosphorus were analyzed at wet and dry seasons in the Yangtze River during 2020–2023. Phosphorus concentrations in the Yangtze River decreased by 9.15 % in four years, reaching a peak in summer. In addition, absolute principal component score‐multiple linear regression (APCS‐MLR) model was proved to be suitable for exploring the contribution of main phosphorus-influencing factors in Yangtze River. The contribution rate in wet season was ranked as point source pollution (40.13 %) > agricultural pollution (32.74 %) > organic pollutants (3.78 %), while the contribution rate in dry season was ranked as point source pollution (44.88 %) > organic pollutants (13.13 %) > seasonal factor (7.60 %). Machine learning models (e.g., RidgeCV, Random Forest, XGBoost) were used to establish a connection between total phosphorus concentrations and explanatory variables defining influencing factors, aiming to predict total phosphorus concentrations in the Yangtze River. The anthropogenic and natural variables, such as domestic sewage, GDP, agricultural area, livestock, rainfall, wind speed, temperature and population were selected as predictors. The Random Forest model performed well in predicting total phosphorus concentrations, with R2 value of 0.76. This study provides useful information for optimizing phosphorus pollution management and strategies for eutrophication control in the Yangtze River as well as in other large watersheds. |
| format | Article |
| id | doaj-art-e8af411fb0ce4df2b1c35611dd48deed |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-e8af411fb0ce4df2b1c35611dd48deed2025-02-12T05:30:47ZengElsevierEcological Indicators1470-160X2025-02-01171113217Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learningGege Cai0Jiamei Zhang1Wanlu Li2Jiejun Zhang3Yun Liu4Shanshan Xi5Guolian Li6Haibin Li7Xing Chen8Fanhao Song9Fazhi Xie10School of Environmental and Energy Engineering, Anhui Jianzhu University, Hefei 230031 Anhui, ChinaSchool of Environmental and Energy Engineering, Anhui Jianzhu University, Hefei 230031 Anhui, ChinaSchool of Materials and Chemical Engineering, Anhui Jianzhu University, Hefei 230031 Anhui, ChinaSchool of Environmental and Energy Engineering, Anhui Jianzhu University, Hefei 230031 Anhui, ChinaSchool of Environmental and Energy Engineering, Anhui Jianzhu University, Hefei 230031 Anhui, ChinaSchool of Environmental and Energy Engineering, Anhui Jianzhu University, Hefei 230031 Anhui, ChinaSchool of Environmental and Energy Engineering, Anhui Jianzhu University, Hefei 230031 Anhui, ChinaSchool of Materials and Chemical Engineering, Anhui Jianzhu University, Hefei 230031 Anhui, ChinaSchool of Environmental and Energy Engineering, Anhui Jianzhu University, Hefei 230031 Anhui, ChinaChinese Research Academy of Environmental Sciences, Beijing 100020, ChinaSchool of Environmental and Energy Engineering, Anhui Jianzhu University, Hefei 230031 Anhui, China; Corresponding author.Phosphorus contamination in rivers has received widespread attention. However, in areas with extensive sources of phosphorus and complex hydrogeology conditions, it is difficult to accurately evaluate the main influencing factors of phosphorus. In present study, the spatiotemporal variations of phosphorus were analyzed at wet and dry seasons in the Yangtze River during 2020–2023. Phosphorus concentrations in the Yangtze River decreased by 9.15 % in four years, reaching a peak in summer. In addition, absolute principal component score‐multiple linear regression (APCS‐MLR) model was proved to be suitable for exploring the contribution of main phosphorus-influencing factors in Yangtze River. The contribution rate in wet season was ranked as point source pollution (40.13 %) > agricultural pollution (32.74 %) > organic pollutants (3.78 %), while the contribution rate in dry season was ranked as point source pollution (44.88 %) > organic pollutants (13.13 %) > seasonal factor (7.60 %). Machine learning models (e.g., RidgeCV, Random Forest, XGBoost) were used to establish a connection between total phosphorus concentrations and explanatory variables defining influencing factors, aiming to predict total phosphorus concentrations in the Yangtze River. The anthropogenic and natural variables, such as domestic sewage, GDP, agricultural area, livestock, rainfall, wind speed, temperature and population were selected as predictors. The Random Forest model performed well in predicting total phosphorus concentrations, with R2 value of 0.76. This study provides useful information for optimizing phosphorus pollution management and strategies for eutrophication control in the Yangtze River as well as in other large watersheds.http://www.sciencedirect.com/science/article/pii/S1470160X25001463Yangtze RiverPhosphorusSpatiotemporal variationSource apportionmentAbsolute principal component score-multiple linear regressionMachine learning |
| spellingShingle | Gege Cai Jiamei Zhang Wanlu Li Jiejun Zhang Yun Liu Shanshan Xi Guolian Li Haibin Li Xing Chen Fanhao Song Fazhi Xie Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learning Ecological Indicators Yangtze River Phosphorus Spatiotemporal variation Source apportionment Absolute principal component score-multiple linear regression Machine learning |
| title | Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learning |
| title_full | Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learning |
| title_fullStr | Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learning |
| title_full_unstemmed | Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learning |
| title_short | Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learning |
| title_sort | spatiotemporal variation and influencing factors of phosphorus in asia s longest river based on receptor model and machine learning |
| topic | Yangtze River Phosphorus Spatiotemporal variation Source apportionment Absolute principal component score-multiple linear regression Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25001463 |
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