Application of machine learning in soil heavy metals pollution assessment in the southeastern Tibetan plateau
Abstract The Tibetan Plateau, a globally significant ecological region, is experiencing escalating pollution from heavy metals (HMs). This study applies a machine learning approach based on the self-organizing map hyper-clustering, alongside advanced methodologies such as Positive Matrix Factorizati...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97006-2 |
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| author | Yan Li Yilong Yu Shiyuan Ding Wenjing Dai Rongguang Shi Gaoyang Cui Xiaodong Li |
| author_facet | Yan Li Yilong Yu Shiyuan Ding Wenjing Dai Rongguang Shi Gaoyang Cui Xiaodong Li |
| author_sort | Yan Li |
| collection | DOAJ |
| description | Abstract The Tibetan Plateau, a globally significant ecological region, is experiencing escalating pollution from heavy metals (HMs). This study applies a machine learning approach based on the self-organizing map hyper-clustering, alongside advanced methodologies such as Positive Matrix Factorization (PMF), Incremental Spatial Autocorrelation, and Bivariate Local Indicators of Spatial Association (BiLISA), to analyze the ecological risk of soil HMs in representative watersheds of the southeastern Tibetan Plateau, focusing on spatial pattern clustering, pollutant source identification, and interaction risk assessment. The results indicated higher HMs concentrations in the middle and downstream areas. A comprehensive ecological risk assessment integrating the Improved Potential Ecological Risk Index, Enrichment Factor, Contamination Factor, and Geo-accumulation Index identified Cd, Pb, and As as the primary pollutants of concern. By combining PMF with Mantel analysis, pollution was attributed to geological background, agricultural activities, traffic emissions, and atmospheric deposition. The BiLISA method revealed significant spatial interactions among HMs, with the composite pollution of As and Cd occupying the largest proportion in High (As)-High (Cd) aggregation zones, underscoring the need for integrated management strategies. This study offers novel insights into the spatial pollution patterns and source apportionment of soil HMs, providing an advanced analytical framework for their precise control and ecological restoration. |
| format | Article |
| id | doaj-art-532b11b1cd504a67947a7a126980d0ff |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-532b11b1cd504a67947a7a126980d0ff2025-08-20T03:18:32ZengNature PortfolioScientific Reports2045-23222025-04-0115111510.1038/s41598-025-97006-2Application of machine learning in soil heavy metals pollution assessment in the southeastern Tibetan plateauYan Li0Yilong Yu1Shiyuan Ding2Wenjing Dai3Rongguang Shi4Gaoyang Cui5Xiaodong Li6Institute of Surface-Earth System Science, School of Earth System Science, Tianjin UniversityAgro-Environmental Protection Institute, Ministry of Agriculture and Rural AffairsInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin UniversityInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin UniversityAgro-Environmental Protection Institute, Ministry of Agriculture and Rural AffairsThe College of Geography and Environmental Science, Henan UniversityInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin UniversityAbstract The Tibetan Plateau, a globally significant ecological region, is experiencing escalating pollution from heavy metals (HMs). This study applies a machine learning approach based on the self-organizing map hyper-clustering, alongside advanced methodologies such as Positive Matrix Factorization (PMF), Incremental Spatial Autocorrelation, and Bivariate Local Indicators of Spatial Association (BiLISA), to analyze the ecological risk of soil HMs in representative watersheds of the southeastern Tibetan Plateau, focusing on spatial pattern clustering, pollutant source identification, and interaction risk assessment. The results indicated higher HMs concentrations in the middle and downstream areas. A comprehensive ecological risk assessment integrating the Improved Potential Ecological Risk Index, Enrichment Factor, Contamination Factor, and Geo-accumulation Index identified Cd, Pb, and As as the primary pollutants of concern. By combining PMF with Mantel analysis, pollution was attributed to geological background, agricultural activities, traffic emissions, and atmospheric deposition. The BiLISA method revealed significant spatial interactions among HMs, with the composite pollution of As and Cd occupying the largest proportion in High (As)-High (Cd) aggregation zones, underscoring the need for integrated management strategies. This study offers novel insights into the spatial pollution patterns and source apportionment of soil HMs, providing an advanced analytical framework for their precise control and ecological restoration.https://doi.org/10.1038/s41598-025-97006-2Self-Organizing mapHMs sourceImproved Potential Ecological Risk IndexBiLISA analysisInteraction risk |
| spellingShingle | Yan Li Yilong Yu Shiyuan Ding Wenjing Dai Rongguang Shi Gaoyang Cui Xiaodong Li Application of machine learning in soil heavy metals pollution assessment in the southeastern Tibetan plateau Scientific Reports Self-Organizing map HMs source Improved Potential Ecological Risk Index BiLISA analysis Interaction risk |
| title | Application of machine learning in soil heavy metals pollution assessment in the southeastern Tibetan plateau |
| title_full | Application of machine learning in soil heavy metals pollution assessment in the southeastern Tibetan plateau |
| title_fullStr | Application of machine learning in soil heavy metals pollution assessment in the southeastern Tibetan plateau |
| title_full_unstemmed | Application of machine learning in soil heavy metals pollution assessment in the southeastern Tibetan plateau |
| title_short | Application of machine learning in soil heavy metals pollution assessment in the southeastern Tibetan plateau |
| title_sort | application of machine learning in soil heavy metals pollution assessment in the southeastern tibetan plateau |
| topic | Self-Organizing map HMs source Improved Potential Ecological Risk Index BiLISA analysis Interaction risk |
| url | https://doi.org/10.1038/s41598-025-97006-2 |
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