Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learning

• Annual average of TP contents in Yangtze River decreased by 9.15 % from 2020 to 2023. • APCS-MLR model was used to research the sources of phosphorus in Yangtze River. • Phosphorus in Yangtze River is mainly derived from point source pollution. • Random Forest model can identify the distribution of phosphorus well in Yangtze River. 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 R 2 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.

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Publication Details

Journal
Ecological Indicators
Published
2025-02-01
DOI
https://doi.org/10.1016/j.ecolind.2025.113217
Citations
9
Primary Topic
Water Quality and Pollution Assessment
Type
article
Field-Weighted Citation Impact
7.35

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article

Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learning

Jiejun Zhang, Wan‐Lu Li, Fanhao Song, Shanshan Xi et al.
9 citations
Ecological Indicators
Water Quality and Pollution Assessment
7.35
article

Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learning

Jiejun Zhang, Wan‐Lu Li, Fanhao Song, Shanshan Xi, Guoyin Cai, Guolian Li, Fazhi Xie, Yan Liu, Xing Chen, Haibin Li, Jiamei Zhang
article en
9 citations

Abstract

• Annual average of TP contents in Yangtze River decreased by 9.15 % from 2020 to 2023. • APCS-MLR model was used to research the sources of phosphorus in Yangtze River. • Phosphorus in Yangtze River is mainly derived from point source pollution. • Random Forest model can identify the distribution of phosphorus well in Yangtze River. 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 R 2 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.

Ecological IndicatorsVol. 171
Anhui Jianzhu University (CN), Chinese Research Academy of Environmental Sciences (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 3%
Water Quality and Pollution Assessment
7.35
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