Pollution threshold assessment and risk area delineation of heavy metals in soils through the finite mixture distribution model and Bayesian maximum entropy theory

Xufeng Fei; Zhaohan Lou; Xiaonan Lv; Zhouqiao Ren; Rui Xiao

doi:10.1016/j.jhazmat.2023.131231

Pollution threshold assessment and risk area delineation of heavy metals in soils through the finite mixture distribution model and Bayesian maximum entropy theory

J Hazard Mater. 2023 Jun 15:452:131231. doi: 10.1016/j.jhazmat.2023.131231. Epub 2023 Mar 16.

Authors

Xufeng Fei¹, Zhaohan Lou², Xiaonan Lv¹, Zhouqiao Ren³, Rui Xiao⁴

Affiliations

¹ Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China.
² Zhejiang Academy of Agricultural Sciences, Hangzhou, China.
³ Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China. Electronic address: renzq@mail.zaas.ac.cn.
⁴ School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China.

PMID: 36934631
DOI: 10.1016/j.jhazmat.2023.131231

Abstract

Pollution threshold and high-risk area determination for heavy metals is important for effectively developing pollution control strategies. Based on heavy metal contents in 3627 dense samples, an integrated framework combining the finite mixture distribution model and Bayesian maximum entropy (BME) theory was proposed to assess pollution thresholds, contamination levels and risk areas in an uncertain environment for soil heavy metals. The results showed that the average heavy metal contents were in the order Zn > Cr > Pb > Cu > Ni > As > Cd > Hg, with strong/moderate variation, and the corresponding pollution thresholds were 158.39, 84.29, 47.84, 49.75, 28.95, 18.01, 0.49 and 0.16 mg/kg, respectively. The thresholds were consistently greater than the Zhejiang Province backgrounds but lower than the national risk screening values, except for Cd. Approximately 27.9% of the samples were classified as contaminated at various levels, and they were distributed in the northern, northwestern and eastern regions of the study area. Additionally, 3.73%, 5.34% and 8.22% of the total area were classified as at-risk areas under confidence levels of 95%, 90% and 75%, respectively, through BME theory. The findings provide a reasonable classification system and suggestions for heavy metal pollution management and control.

Keywords: Contamination assessment; Heavy metal; Pollution thresholds; Risk area detection; Spatial analysis.