{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T01:56:33Z","timestamp":1790560593910,"version":"4.1.0"},"reference-count":37,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"vor","delay-in-days":25,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Water Environment Research"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Accurate identification of groundwater contamination factors is critical yet challenging at industrial sites with complex pollution sources and heterogeneous aquifers. This study developed a multi\u2010method integrated framework combining pollution index evaluation, statistical analysis, and machine learning to achieve stratified identification of characteristic pollutants across different aquifers. Applied to a chemical plant site, shallow groundwater pollutants were identified through pollution index, correlation analysis, and random forest\/XGBoost models. Deep groundwater pollutants were discriminated based on pollution grade distributions and exceedance characteristics, followed by spatial analysis. The results showed clear aquifer\u2010dependent differences in groundwater contamination. In shallow groundwater, total hardness showed the highest correlation with pollution levels (\n                    <jats:italic>\u03c1<\/jats:italic>\n                    \u2009=\u20090.62), followed by SO\n                    <jats:sub>4<\/jats:sub>\n                    <jats:sup>2\u2212<\/jats:sup>\n                    (\n                    <jats:italic>\u03c1<\/jats:italic>\n                    \u2009=\u20090.56), COD (\n                    <jats:italic>\u03c1<\/jats:italic>\n                    \u2009=\u20090.55), and NH\n                    <jats:sub>4<\/jats:sub>\n                    <jats:sup>+<\/jats:sup>\n                    \u2010N (\n                    <jats:italic>\u03c1<\/jats:italic>\n                    \u2009=\u20090.45). Machine learning results further indicated that nitrogen\u2010 and sulfur\u2010related indicators, especially NH\n                    <jats:sub>4<\/jats:sub>\n                    <jats:sup>+<\/jats:sup>\n                    \u2010N and SO\n                    <jats:sub>4<\/jats:sub>\n                    <jats:sup>2\u2212<\/jats:sup>\n                    , were the dominant factors controlling shallow groundwater pollution. Spatially, NH\n                    <jats:sub>4<\/jats:sub>\n                    <jats:sup>+<\/jats:sup>\n                    \u2010N mainly exhibited point\u2010source pollution characteristics, whereas SO\n                    <jats:sub>4<\/jats:sub>\n                    <jats:sup>2\u2212<\/jats:sup>\n                    showed a multi\u2010source distribution pattern. In contrast, deep groundwater showed lower contamination levels and a more limited spatial extent, suggesting restricted downward migration of pollutants. The proposed framework effectively reveals aquifer\u2010dependent characteristic pollutants and provides practical support for groundwater pollution identification and management in chemical plant areas.\n                  <\/jats:p>","DOI":"10.1002\/wer.70548","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T10:17:25Z","timestamp":1787739445000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi\u2010Method Identification of Characteristic Groundwater Pollutants Under Aquifer Structural Constraints and Their Indication of Environmental Processes"],"prefix":"10.1002","volume":"98","author":[{"given":"Lin","family":"Sun","sequence":"first","affiliation":[{"name":"Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological Sciences  Shijiazhuang China"},{"name":"Key Laboratory of Groundwater Remediation of Hebei Province and China Geological Survey  Zhengding China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Xu","sequence":"additional","affiliation":[{"name":"Shandong Haimeinong Project Consulting Co. Ltd.  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hefeng","family":"Lu","sequence":"additional","affiliation":[{"name":"Xingtai Ecological Environment Comprehensive Law Enforcement Detachment  Xingtai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8140-4473","authenticated-orcid":false,"given":"Jinlong","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Hydrology and Water Resources Hohai University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1489-7782","authenticated-orcid":false,"given":"Zhuo","family":"Ning","sequence":"additional","affiliation":[{"name":"Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological Sciences  Shijiazhuang China"},{"name":"Key Laboratory of Groundwater Remediation of Hebei Province and China Geological Survey  Zhengding 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