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Predicting future contents of soil heavy metals and related health risks by combining the models of source apportionment, soil metal accumulation and industrial economic theory

分摊 环境科学 农业 健康风险评估 污染 人口 人类健康 土壤污染 土壤水分 环境工程 自然资源经济学 土壤科学 环境卫生 生物 生态学 经济 医学 政治学 法学
作者
Yimei Zhang,Shuai Li,Yuxian Lai,Liqun Wang,Fei Wang,Zhuang Chen
出处
期刊:Ecotoxicology and Environmental Safety [Elsevier]
卷期号:171: 211-221 被引量:22
标识
DOI:10.1016/j.ecoenv.2018.12.023
摘要

Enriched and bio-refractory soil heavy metals (SHMs) originate from the underground mineral, which supplies energy and materials for the development of economy and industry. Investigating soil metal contents and their adverse health impacts is the principal concern associated metal contaminated industrial areas, including both current assessments and future projections. In this research, we create a novel spatiotemporal model of SHMs prediction and risk characterization for future by citing a rigorous theory of industrial economics, and time series of activity intensity changes of various pollution sources are forecasted. The dynamic change of source contributions is quantitatively resolved and the mean SHMs concentrations are estimated by classical formulas for heavy metal accumulation. Human health risk in the future is described in a manner of time series. The results of the case study show that contribution rates of the five sources of the six metals change continuously over time. Pb, Cd and As assume the highest growth rates (400%, 500% and 165%), while Zn, Ni, Cr possesses relatively lower growth (< 130%), compared to their corresponding background values. Health risk of local sensitive population (children) is estimated at exceeding threshold in 2022 (non-carcinogenic) and 2012 (carcinogenic), and the upward trend will continue. Traffic emission, agriculture and household garbage are identified as major risky sources in the coming decades at the studied area, and improvement measures are recommended. Although a degree of uncertainties exists, the overall tendency is a conservative bias for chemical risk. Additionally, this paper is the first to explore a methodology of predicting future SHMs and associated human health risk, based on industrial economics and temporal source apportionment.
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