ArticleScientific reports2025
Application of machine learning in soil heavy metals pollution assessment in the southeastern Tibetan plateau.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- Applications of Machine Learning in the Research of Heavy Metal(loid)s-Related Risk: A Scoping Review of Methodology.Toxics · 2026Review
- Integrating multi-spectral remote sensing and machine learning for quantifying and mapping heavy metal contamination in the Tarim River Basin, China.Scientific reports · 2026Article
- A statistical approach to model soil microbiota versus heavy metals: a case study on soil samples from Foggia, Southern Italy.Scientific reports · 2025Article
- Study on the Source and Microbial Mechanisms Influencing Heavy Metals and Nutrients in a Subtropical Deep-Water Reservoir.Microorganisms · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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.
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Registered trials
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