Delineating and identifying risk zones of soil heavy metal pollution in an industrialized region using machine...

The ability to control the risk of soil heavy metal pollution is limited by the inability to accurately depict their spatial distributions and to reasonably delineate the risk zones. To overcome this limitation and develop machine learning methods, a hybrid data-driven method supported by random forest (RF) and fuzzy c-means with the aid of inverse distance weighted interpolation was proposed to delineate and further identify risk zones of soil heavy metal pollution on the basis of 577 soil samples and 12 environmental covariates. The results indicated that, compared to multiple linear regression, RF had a better prediction performance for As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, with the corresponding R² values of 0.86, 0.85, 0.78, 0.85, 0.84, 0.78, 0.79 and 0.76, respectively. The relative concentrations (predicted concentrations divided by risk screening values) of Cd (17.69), Cr (1.38), Hg (0.31), Pb (6.52), and Zn (8.24) were relatively high in the north central part of the study area. There were large differences in the key influencing factors and their contributions among the eight heavy metals. Overall, industrial enterprises (21.60% for As), soil pH (31.60% for Cd), and population (15.50% for Cr) were the key influencing factors for the heavy metals in soil. Four risk zones, including one high risk zone, one medium risk zone, and two low risk zones were delineated and identified based on the characteristics of the eight heavy metals and their influencing factors, and accordingly discriminated risk control strategies were developed. In the high risk zone, it will be necessary to strictly control the discharge of heavy metals from the various industrial enterprises and mines by the adoption of cleaner production practices, centralizedly treat the domestic wastes from residents, substantially reduce the irrigation of polluted river water, and positively remediate the Cd, Cr, and Ni-polluted soil.