A hybrid framework for delineating the migration route of soil heavy metal pollution by heavy metal similarity calculation and machine learning method.

Soil heavy metal contamination was a global environmental issue that posed adverse impacts on ecological and human health risks. The controlling of soil heavy metal is mainly focused on the emission source and pipe-end treatment, less is known about the intermediate controlling process. The migration route of heavy metals exhibited the spatial evolution of pollutants from the sources to the pipe-end, which provided the more reasonable location for the target-oriented treatment of soil heavy metal. Here, we proposed a new view of heavy metal similarity, which quantitatively expressed how closely of the contaminations between the study area and the test areas. We found that the similarity of different heavy metals was unequally distributed across locations that were related with five main sources, namely agricultural activities, natural sources, traffic emissions, industrial activities, and other sources. Based on the similarity, a state-of-the-art machine learning method was applied to delineate the migration route of soil heavy metals. Thereinto, As was concentrated around livestock farms, and its migration route was close to the water system. Cd migration route was over-dispersed in the areas where located mine fields and chemical plants. Migration routes of Hg and Pb were along rivers, which were related to agricultural activities and natural sources. Overall, the perspective on similarity and migration routes provided theoretical basis and method to alleviate soil heavy metal pollution at regional scale and can be extended across largescale regions.