A methodological framework for identifying potential sources of soil heavy metal pollution based on machine learning: A case study in the Yangtze Delta, China

• Published in: Environmental pollution (1987) Vol. 250; pp. 601 - 609
• Main Authors: Jia, Xiaolin, Hu, Bifeng, Marchant, Ben P., Zhou, Lianqing, Shi, Zhou, Zhu, Youwei
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.07.2019; Elsevier
• Subjects: Bivariate local Moran's I analysis; Computer Science; Environment and Society; Environmental Sciences; Heavy metal pollution; Modeling and Simulation; Multinomial naive bayesian methods; Potentially polluting enterprises; Source identification; Bivariate local Moran's I analysis; Source identification; Heavy metal pollution; Potentially polluting enterprises; Multinomial naive bayesian methods

It is a great challenge to identify the many and varied sources of soil heavy metal pollution. Often little information is available regarding the anthropogenic factors and enterprises that could potentially pollute soils. In this study we use freely available geographical data from a search engine...