A synthesis framework using machine learning and spatial bivariate analysis to identify drivers and hotspots of heavy metal pollution of agricultural soils. (15th October 2021)

• Record Type: Journal Article
• Authors: Yang, Shiyan; Taylor, David; Yang, Dong; He, Mingjiang; Liu, Xingmei; Xu, Jianming
• Is Part Of: Environmental pollution. Volume 287(2021)
• Publication Date: 2021-10-15
• Subjects: Heavy metal pollution -- Source-sink theory -- Random forest model -- Spatial bivariate cluster -- Soil pollution control; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2021.117611
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Source apportionment can be an effective tool in mitigating soil pollution but its efficacy is often limited by a lack of information on the factors that influence the accumulation of pollutants at a site. In response to this limitation and focusing on a suite of heavy metals identified as priorities for pollution control, the study established a comprehensive pollution control framework using factor identification coupled with spatial agglomeration for agricultural soils in an industrialized part of Zhejiang Province, China. In addition to elucidating the key role of industrial and traffic activities on heavy metal accumulation through implementing a receptor model, specific influencing factors were identified using a random forest model. The distance from the soil sample location to the nearest likely industrial source was the most important factor in determining cadmium and copper concentrations, while distance to the nearest road was more important for lead and zinc pollution. Soil parent materials, pH, organic matter, and clay particle size were the key factors influencing accumulation of arsenic, chromium, and nickel. Spatial auto-correlation between levels of soil metal pollution and industrial agglomeration can enable a more targeted approach to pollution control measures. Overall, the approach and results provide a basis for improved accuracy in source apportionment, and thus improved soil pollution control, at the regional scale. Graphical abstract: Image 1 Highlights: Provision of a framework that links heavy metals in soils with their source. Identification of environmental influence over heavy metal contamination of soils. Spatially integrated industrial agglomerations and heavy metal-contaminated soils. Basis for targeted measures aimed at mitigating pollution and health risks.