Machine learning can identify the sources of heavy metals in agricultural soil: A case study in northern Guangdong Province, China

Abstract

Source tracing of heavy metals in agricultural soils is of critical importance for effective pollution control and targeting policies. It is a great challenge to identify and apportion the complex sources of soil heavy metal pollution. In this study, a traditional analysis method, positive matrix fraction (PMF), and three machine learning methodologies, including self-organizing map (SOM), conditional inference tree (CIT) and random forest (RF), were used to identify and apportion the sources of heavy metals in agricultural soils from Lianzhou, Guangdong Province, China. Based on PMF, the contribution of the total loadings of heavy metals in soil were 19.3% for atmospheric deposition, 65.5% for anthropogenic and geogenic sources, and 15.2% for soil parent materials. Based on SOM model, As, Cd, Hg, Pb and Zn were attributed to mining and geogenic sources; Cr, Cu and Ni were derived from geogenic sources. Based on CIT results, the influence of altitude on soil Cr, Cu, Hg, Ni and Zn, as well as soil pH on Cd indicated their primary origin from natural processes. Whereas As and Pb were related to agricultural practices and traffic emissions, respectively. RF model further quantified the importance of variables and identified potential control factors (altitude, soil pH, soil organic carbon) in heavy metal accumulation in soil. This study provides an integrated approach for heavy metals source apportionment with a clear potential for future application in other similar regions, as well as to provide the theoretical basis for undertaking management and assessment of soil heavy metal pollution.