Improving spatial prediction of health risk assessment for Hg, As, Cu, and Pb in soil based on land-use regression

Abstract

© 2019, Springer Nature B.V. Heavy-metal pollution is a significant health and environmental concern in areas of rapid industrialization in China. The accuracy of spatial mapping of pollutant is the main constraint on spatial prediction of health risks. Our study addressed the possibility of improving spatial prediction accuracy of risk assessment. We developed land-use regression (LUR) models for Hg, As, Cu, and Pb based on surface soil sampling, land-use data, and soil properties. The regression results suggested that LUR was more accurate than ordinary kriging method. Spatial prediction accuracy of Hg, As, Cu, and Pb were improved by 15%, 59%, 36%, and 20%, respectively. Then, spatial distribution of health risk was assessed by using distributions of heavy metal and exposure parameters. Chronic risk of children was controlled by distribution of Pb and carcinogenic controlled by As. The result indicated that Pb and As were the main sources of health risk for children in Kunshan. Chronic and carcinogenic risk maps could clearly show where we should pay attention to and control the risk. This study provided a simple approach to draw spatially explicit maps of health risk which were useful for pollution control and public health risk management.