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Machine learning reveals disruptive nutrient pollution shifts in Chinese rivers to 2100
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  • Published: 26 March 2026

Machine learning reveals disruptive nutrient pollution shifts in Chinese rivers to 2100

  • Xiaoyue Zhang1,2,
  • Hong Zhang1,2,
  • Dingkun Yin3,
  • Baojing Gu4 &
  • …
  • Lei Chen5 

npj Clean Water , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Climate sciences
  • Environmental sciences
  • Environmental studies

Abstract

Anticipating nutrient pollution under changing conditions is urgent for water security. Nonetheless, high-resolution predictive frameworks capturing nonlinear driver responses remain limited. Here, we present a nationwide assessment of China’s water-quality evolution from 2023 to 2100, integrating over 3 million daily records with 41 climatic, landscape, and socioeconomic drivers via regionally tailored Random Forest models (R2 of 0.88–0.92). Our results reveal a disruptive spatiotemporal shift, with projected NPI ranges from –50.9% to +218.1% under SSP5-8.5. Seasonal patterns restructure toward unimodal peaks, with pollution increases in spring/autumn (up to 28.3%) but decreases in summer (up to 27.0%). Spatial homogenization emerges via a westward/southward shift of pollution centers, with localized increases exceeding 200% from coldspots with low baselines (<0.4 vs >1.0 in hotspots). Landscape configuration dominates (64.5% feature importance) over climatic forcing (7.2%–35.5%), reinforced by minimal climatic projection uncertainty. Strategic land-use planning could be a cornerstone of future water security.

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Data availability

All data used in our synthesis are publicly available. Temperature data is available at https://doi.org/10.11888/Meteoro.tpdc.270961. Precipitation data can be accessed via https://doi.org/10.11888/Atmos.tpdc.300523. Evaporation and runoff from the ERA5-Land dataset can be accessed via https://doi.org/10.24381/cds.68d2bb30. Soil_SOC, Soil_pH, Soil_N, and Soil_caly can be accessed via https://doi.org/10.5194/soil-7-217-2021. The DEM data can be found at https://doi.org/10.5281/zenodo.14511570. The NDVI can be found at https://doi.org/10.11888/Terre.tpdc.300330. The GDP and fertilizer data were obtained from the Statistical Yearbook via https://www.stats.gov.cn/. Population data can be accessed via https://doi.org/10.48690/1531770. Land-use/land-cover data can be freely accessed at https://doi.org/10.5281/zenodo.12779975. Data for future projections: Temperature is available at https://doi.org/10.11866/db.loess.2021.003; Precipitation is available at https://doi.org/10.11866/db.loess.2021.002; Evaporation can be found at and https://doi.org/10.11888/Atmos.tpdc.300558; GDP is available at https://doi.org/10.5281/zenodo.7898409; Population can be accessed via https://doi.org/10.1038/s41597-022-01675-x; Land-use/land-cover data can be freely accessed at https://doi.org/10.25584/data.2020-07.1357/1644253.

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Acknowledgements

This research was funded by the Basic Science Center Project of the Natural Science Foundation of China (NSFC) (52388101), National Natural Science Foundation of China (No. 42407068), National Science and Technology Major Project of China (No. 2025ZD1204701), Innovative Research Group of the National Natural Science Foundation of China (No. 52221003), and Joint Funds of the National Natural Science Foundation of China (U2340219).

Author information

Authors and Affiliations

  1. Key Laboratory of Environmental Aquatic Chemistry, State Key Laboratory of Regional Environment and Sustainability, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Xiaoyue Zhang & Hong Zhang

  2. University of Chinese Academy of Sciences, Beijing, China

    Xiaoyue Zhang & Hong Zhang

  3. Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing, China

    Dingkun Yin

  4. State Key Laboratory of Soil Pollution Control and Safety, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China

    Baojing Gu

  5. State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, China

    Lei Chen

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  1. Xiaoyue Zhang
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  2. Hong Zhang
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  3. Dingkun Yin
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  5. Lei Chen
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Contributions

X.Z. led data acquisition, conceptualization, methodology, data analysis, and the writing of the first draft. D.Y. contributed equally to data acquisition and assisted in data analysis. B.G. guided the overall project scientifically and logistically. L.C. guided the overall project scientifically and logistically, including contributions in writing. H.Z. guided the editing of multiple versions and assisted in data analysis. All authors read and approved the final manuscript.

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Correspondence to Hong Zhang or Lei Chen.

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Zhang, X., Zhang, H., Yin, D. et al. Machine learning reveals disruptive nutrient pollution shifts in Chinese rivers to 2100. npj Clean Water (2026). https://doi.org/10.1038/s41545-026-00571-w

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  • Received: 20 January 2026

  • Accepted: 11 March 2026

  • Published: 26 March 2026

  • DOI: https://doi.org/10.1038/s41545-026-00571-w

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