Modeling risk assessment of soil heavy metal pollution using partial least squares and fuzzy logic: A case study of a gully type coal-based solid waste dumpsite

Xiaofei Wang; Chaoli Zhao; Ziao Li; Jiu Huang

doi:10.1016/j.envpol.2024.124147

Modeling risk assessment of soil heavy metal pollution using partial least squares and fuzzy logic: A case study of a gully type coal-based solid waste dumpsite

Environ Pollut. 2024 May 10:352:124147. doi: 10.1016/j.envpol.2024.124147. Online ahead of print.

Authors

Xiaofei Wang¹, Chaoli Zhao¹, Ziao Li¹, Jiu Huang²

Affiliations

¹ School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou City, Jiangsu, 221116, China.
² School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou City, Jiangsu, 221116, China. Electronic address: jhuang@cumt.edu.cn.

PMID: 38735463
DOI: 10.1016/j.envpol.2024.124147

Abstract

Continuous release and migration of heavy metals from coal-based solid waste (CSW) dumpsites often results in significant encroachment on ecological lands and pollution of natural environments. As a result, there is an urgent need for long-term and rapid monitoring, analysis, and assessment to control environmental risks associated with large CSW dumpsites. We constructed a new composite model (PLS-FL) that uses partial least squares regression (PLSR) and fuzzy logic inference (FLI) to accurately predict heavy metal concentrations in soils and assess pollution risk levels. The potential application of the PLS-FL was tested through a gully type CSW case study. We compared 20 modeling strategies using the PLS-FL: five types heavy metals (Cd, Zn, Pb, Cr and As) * four spectral transformation methods (first derivative (FD), second derivative (SD), reverse logarithm (RL), and continuum removal (CR)) * one variable selection method (competitive adaptive reweighted sampling (CARS)). The results showed that the combination of derivative transformation and CARS was recommended for estimation, with R²_C > 0.80 and R²_P > 0.50. When comparing the PLSR model with four traditional machine learning methods (Support Vector Machines (SVM), Random Forests (RF), Extreme Learning Machines (ELM), and KNN), the PLSR model demonstrated the highest average prediction accuracy. Additionally, the FLI process no longer relies on human perception and expert opinion, enhancing the model's objectivity and reliability. The evaluation results revealed that the heavy metal contamination areas of the CSW dumpsite are concentrated at the bottom of the gully, with more severe contamination in the north. Furthermore, a high-risk zone exists in the interim storage area for CSW to the east of the dump. These findings align with the initial detections at the sampling sites and highlight the need for targeted monitoring and control in these areas. The application of the model will empower regulators to quickly assess the overall situation of large-scale heavy metal pollution and provide scientific program and data support for continuous large-scale pollution risk monitoring and sustainable risk management.

Keywords: Fuzzy logic (FL); Hyperspectral remote sensing; Partial least squares (PLS); Risk assessment; Soil heavy metal estimation.