Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models

Deyu Duan; Peng Wang; Xin Rao; Junhong Zhong; Meihong Xiao; Fei Huang; Rongbo Xiao

doi:10.1016/j.scitotenv.2024.173284

Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models

Sci Total Environ. 2024 Jul 15:934:173284. doi: 10.1016/j.scitotenv.2024.173284. Epub 2024 May 18.

Authors

Deyu Duan¹, Peng Wang², Xin Rao³, Junhong Zhong⁴, Meihong Xiao¹, Fei Huang¹, Rongbo Xiao⁵

Affiliations

¹ School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China.
² School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: wangpengdili@163.com.
³ School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou 510420, China.
⁴ School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, China.
⁵ School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: ecoxiaorb@163.com.

PMID: 38768726
DOI: 10.1016/j.scitotenv.2024.173284

Abstract

The accurate identification of spatial drivers is crucial for effectively managing soil heavy metals (SHM). However, understanding the complex and diverse spatial drivers of SHM and their interactive effects remains a significant challenge. In this study, we present a comprehensive analysis framework that integrates Geodetector, CatBoost, and SHapley Additive exPlanations (SHAP) techniques to identify and elucidate the interactive effects of spatial drivers in SHM within the Pearl River Delta (PRD) region of China. Our investigation incorporated fourteen environmental factors and focused on the pollution levels of three prominent heavy metals: Hg, Cd, and Zn. These findings provide several key insights: (1) The distribution of SHM is influenced by the combined effects of various individual factors and interactions within the source-flow-sink process. (2) Compared with the spatial interpretation of individual factors, the interaction between Hg and Cd exhibited enhanced spatial explanatory power. Similarly, interactions involving Zn mainly demonstrated increased spatial explanatory power, but there was one exception in which a weakening was observed. (3) Spatial heterogeneity plays a crucial role in determining the contributions of environmental factors to soil heavy metal concentrations. Although individual factors generally promote metal accumulation, their effects fluctuate when interactions are considered. (4) The SHAP interpretable method effectively addresses the limitations associated with machine-learning models by providing understandable insights into heavy metal pollution. This enables a comparison of the importance of environmental factors and elucidates their directional impacts, thereby aiding in the understanding of interaction mechanisms. The methods and findings presented in this study offer valuable insights into the spatial heterogeneity of heavy metal pollution in soil. By focusing on the effects of interactive factors, we aimed to develop more accurate strategies for managing SHM pollution.

Keywords: Geodetector; Interaction effects; Machine learning (ML); Shapley Additive exPlanations (SHAP); Soil heavy metals.