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A New Analytic Model to Identify Lead Pollution Sources in Soil Based on Lead Fingerprint

机译:基于铅指纹的土壤铅污染源识别新模型

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摘要

Gobeil’s model is one of the most widely used models to identify lead (Pb) pollution sources in the environment. It is based on a set of equations involving Pb isotope fractions. Although a well-established numerical method, Gobeil’s model is often unable to provide an accurate estimation of each pollution sources’ contribution. This paper comprehensively examines the drawbacks of Gobeil’s model based on a numerical analysis and proposes a revised numerical method that provides a more accurate estimation of Pb pollution sources. Briefly, the mathematical inaccuracy of Gobeil’s model mainly lies in the misinterpretation of “lead fingerprint ratio balance.” To address this problem, the new analytic model relies on the mass balance of total lead in the contaminated sites, and uses a set of linear equations to obtain the contribution of each pollution source based on the lead fingerprint. A subsequent case study from an industrial park in Guanzhong area of Shaanxi Province in China shows that we can calculate the lead contribution rates accurately with the new model.
机译:Gobeil的模型是识别环境中铅(Pb)污染源的最广泛使用的模型之一。它基于一组涉及Pb同位素分数的方程式。尽管建立了很好的数值方法,但Gobeil的模型通常无法准确估算每个污染源的贡献。本文在数值分析的基础上全面研究了Gobeil模型的弊端,并提出了一种修正的数值方法,该方法可以更准确地估算铅污染源。简而言之,Gobeil模型的数学错误主要在于对“铅指纹比率平衡”的误解。为了解决这个问题,新的分析模型依赖于受污染场地中总铅的质量平衡,并使用一组线性方程式基于铅指纹来获得每种污染源的贡献。随后从中国陕西省关中地区的一个工业园区进行的案例研究表明,我们可以使用新模型准确计算潜在客户贡献率。

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