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Article

Uncertainty Evaluation of Soil Heavy Metal(loid) Pollution and Health Risk in Hunan Province: A Geographic Detector with Monte Carlo Simulation

1
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Ministry of Education), School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
2
Department of Surveying and Mapping Geography, Hunan Vocational College of Engineering, Changsha 410151, China
*
Author to whom correspondence should be addressed.
Toxics 2023, 11(12), 1006; https://doi.org/10.3390/toxics11121006
Submission received: 14 November 2023 / Revised: 4 December 2023 / Accepted: 6 December 2023 / Published: 8 December 2023
(This article belongs to the Section Metals and Radioactive Substances)

Abstract

Research on soil heavy metal(loid) pollution and health risk assessment is extensive, but a notable gap exists in systematically examining uncertainty in this process. We employ the Nemerow index, the health risk assessment model, and the geographic detector model (GDM) to analyze soil heavy metal(loid) pollution, assess health risks, and identify driving factors in Hunan Province, China. Furthermore, the Monte Carlo simulation (MCS) method is utilized to quantitatively evaluate the uncertainties associated with the sampling point positions, model parameters, and classification boundaries of the driving factors in these processes. The experimental findings reveal the following key insights: (1) Regions with high levels of heavy metal(loid) pollution, accompanied by low uncertainty, are identified in Chenzhou and Hengyang Cities in Hunan Province. (2) Arsenic (As) and chromium (Cr) are identified as the primary contributors to health risks. (3) The GDM results highlight strong nonlinear enhanced interactions among lithology and other factors. (4) The input GDM factors, such as temperature, river distance, and gross domestic product (GDP), show high uncertainty on the influencing degree of soil heavy metal(loid) pollution. This study thoroughly assesses high heavy metal(loid) pollution in Hunan Province, China, emphasizing uncertainty and offering a scientific foundation for land management and pollution remediation.
Keywords: nemerow index; non-carcinogenic risk; carcinogenic risk; factor detector; interaction detector; uncertainty propagation nemerow index; non-carcinogenic risk; carcinogenic risk; factor detector; interaction detector; uncertainty propagation

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MDPI and ACS Style

Zhang, B.; Su, Y.; Shah, S.Y.A.; Wang, L. Uncertainty Evaluation of Soil Heavy Metal(loid) Pollution and Health Risk in Hunan Province: A Geographic Detector with Monte Carlo Simulation. Toxics 2023, 11, 1006. https://doi.org/10.3390/toxics11121006

AMA Style

Zhang B, Su Y, Shah SYA, Wang L. Uncertainty Evaluation of Soil Heavy Metal(loid) Pollution and Health Risk in Hunan Province: A Geographic Detector with Monte Carlo Simulation. Toxics. 2023; 11(12):1006. https://doi.org/10.3390/toxics11121006

Chicago/Turabian Style

Zhang, Baoyi, Yingcai Su, Syed Yasir Ali Shah, and Lifang Wang. 2023. "Uncertainty Evaluation of Soil Heavy Metal(loid) Pollution and Health Risk in Hunan Province: A Geographic Detector with Monte Carlo Simulation" Toxics 11, no. 12: 1006. https://doi.org/10.3390/toxics11121006

APA Style

Zhang, B., Su, Y., Shah, S. Y. A., & Wang, L. (2023). Uncertainty Evaluation of Soil Heavy Metal(loid) Pollution and Health Risk in Hunan Province: A Geographic Detector with Monte Carlo Simulation. Toxics, 11(12), 1006. https://doi.org/10.3390/toxics11121006

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