{
  "abstract": "River water quality degradation poses a critical challenge to water security, yet one-size-fits-all management approaches often overlook region-specific pollution sources and drivers, thereby constraining effective water quality improvement. To address this issue, we conducted a multi-scale analysis of river water quality drivers and management priorities under different regional contexts, using observations from 725 monitoring stations across China and a framework integrating Bayesian hierarchical modeling, machine learning, pathway analysis, and exploratory change-point analysis. Results revealed that eutrophication remained the primary constraint on nationwide water quality improvement. Total nitrogen (TN), total phosphorus (TP), and permanganate index (COD Mn ) were the principal indicators of water quality degradation. Water quality exhibited marked spatial heterogeneity, with poorer conditions in northern rivers than in southern rivers. Indicator responses varied by scale and region: TN, TP, and COD Mn were best explained at riparian buffer scales, whereas the eutrophication index and water quality index were better explained at the sub-basin scale. Landscape composition dominated in both regions (41.40–43.40%), while natural factors contributed more in the north (14.05%) and socioeconomic factors in the south (17.80%). Pathway analysis suggested that riparian-scale drivers were more directly linked to pollutant indicators, whereas sub-basin conditions were more strongly associated with integrated water quality status. Exploratory change-point analysis identified distinct regional priorities, including reducing farmland dominance and atmospheric nitrogen pressure in northern China and limiting construction land expansion and population pressure in southern China. These findings support differentiated water quality management in China, while offering a transferable framework for water quality governance in other spatially heterogeneous regions.",
  "abstract_shown": true,
  "affiliations": [
    "Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China; University of Chinese Academy of Sciences, Beijing 100049, China",
    "Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China; Corresponding authors at: Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China."
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yang Gu"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaolong Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Cai Li"
    },
    {
      "name": "Xijun Lai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4ggptjrq9wmkj.bib",
  "cited_by": 1,
  "doi": "10.1016/j.ecolind.2026.115494",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S1470160X26008952",
  "id": "4ggptjrq9wmkj",
  "issns": [
    "1470-160X",
    "1872-7034"
  ],
  "landing_url": "https://doi.org/10.1016/j.ecolind.2026.115494",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QH540-549.5"
    ],
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      "openalex",
      "2312|Water Quality and Pollution Assessment"
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  "openalex": "W7212425798",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/4ggptjrq9wmkj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:43:18Z",
      "native_id": "29d019e1de3b472f96c06f4a02a31fa5",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.ecolind.2026.115494"
    }
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  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2312|Water Quality and Pollution Assessment",
      "subfield": "hydrology-soils"
    }
  ],
  "title": "Scale-sensitive river water quality indicators reveal spatially heterogeneous drivers across China",
  "title_text": "Scale-sensitive river water quality indicators reveal spatially heterogeneous drivers across China",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:18Z",
  "url": "https://research.draconitic.com/paper/4ggptjrq9wmkj",
  "venue": "Ecological Indicators",
  "versions": []
}