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利用软计算 OSPRC 风险框架分析来自多个污染源的多种污染物;来自伊朗西北部胡伊平原的案例研究。

Using a soft computing OSPRC risk framework to analyze multiple contaminants from multiple sources; a case study from Khoy Plain, NW Iran.

机构信息

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran; Institute of Environment, University of Tabriz, Tabriz, Iran; Traditional Medicine and Hydrotherapy Research Center, Ardabil University of Medical Sciences, Ardabil, Iran; Department of Geography & Environmental Studies, Wilfrid Laurier University, Waterloo, Canada.

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran.

出版信息

Chemosphere. 2022 Dec;308(Pt 3):136527. doi: 10.1016/j.chemosphere.2022.136527. Epub 2022 Sep 20.

Abstract

Water shortage in arid and semi-arid areas like Iran makes groundwater contamination a crucial issue. In the Khoy aquifer, NW Iran, contaminants (e.g., arsenic (As), nitrate (NO), lead (Pb), and zinc (Zn)) may originate from both geological and anthropogenic sources. The objectives of the study are to (1) employ soft modeling framework to abstract available hydrogeochemical information into a perceptual model and (2) build a conceptual model using the risk cells (RCs) by applying the following two steps: (i) study Origin-Source-Pathways-Receptor-Consequence (OSPRC) as a risk system; and (ii) apply "soft modeling" as a set of diverse and classical tools including graphical representations, geological surveys, and multivariate statistical analysis to validate the information by evaluating their convergence or divergence behaviors among different tools used for investigating the groundwater contaminants. According to the perceptual model, the Khoy aquifer contains four RCs. RC4 (southern of plain) and RC2 (northern of the plain) contain high levels of As, while RC2 contains high amounts of Zn. In RC1 (northern of plain) and RC3 (middle of plain), a high concentration of Pb is detected, while in RC3 and RC4, there is a high concentration of NO. It was found that a soft modeling approach can only identify the dominant hydrogeochemical processes for each RC as a descriptive model, rather than the use of quantitative models if sufficient data are available.

摘要

伊朗等干旱和半干旱地区缺水,导致地下水污染成为一个关键问题。在伊朗西北部的霍伊含水层,污染物(如砷 (As)、硝酸盐 (NO)、铅 (Pb) 和锌 (Zn)) 可能来自地质和人为来源。本研究的目的是:(1) 采用软建模框架将可用的水文地球化学信息抽象为感知模型;(2) 通过应用以下两个步骤,使用风险单元 (RC) 构建概念模型:(i) 将源-途径-受体-后果 (OSPRC) 作为风险系统进行研究;(ii) 应用“软建模”作为一组不同的经典工具,包括图形表示、地质调查和多元统计分析,通过评估不同工具在调查地下水污染物时的收敛或发散行为来验证信息。根据感知模型,霍伊含水层包含四个 RC。RC4(平原南部)和 RC2(平原北部)含有高水平的 As,而 RC2 含有高水平的 Zn。在 RC1(平原北部)和 RC3(平原中部)中,检测到高浓度的 Pb,而在 RC3 和 RC4 中,NO 的浓度较高。结果表明,软建模方法只能识别每个 RC 的主要水文地球化学过程,作为描述性模型,而不是在有足够数据的情况下使用定量模型。

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