Groundwater in most urban areas around the globe is often contaminated by toxic substances. Among the various sources of contamination, industries cause the heaviest impact when toxic compounds are released underground, mainly through leaking tanks or pipelines. Some contaminants (typically chlorinated hydrocarbons) tend to persist within the underground and are hard to biodegrade. As a result, substances that leaked decades ago are still impacting groundwater. Milano and its surroundings (Functional Urban Area) is a good example of an area that has been hosting industries of all dimensions for over a century, many of them contributing to groundwater contamination from chlorinated hydrocarbons. While the position of the biggest industrial facilities is well-known, many smaller sources are hard to identify in many cases where direct surveys have not been undertaken. Furthermore, the overlapping effects of big, small, known, and unknown sources of groundwater contamination make it challenging to identify the contribution of each. In order to identify the contribution of several point sources responsible for tetrachloroethylene contamination in public water supply wells, a numerical model (MODFLOW-2005) has been implemented and calibrated using PEST in the northwestern portion of the Milano Functional Urban Area. In contaminant transport modeling, the deterministic approach is still favored over the stochastic approach because of the simplicity of its application. Nevertheless, the latter is considered by the authors as the most suitable for dealing with problems characterized by high uncertainty, such as hydrogeological parameter distributions. Adopting a Null-Space Monte Carlo analysis, 400 different sets of hydraulic conductivity fields were randomly generated of which only 336 were selected using an objective function threshold. Subsequently, particle backtracking was performed for each of the accepted hydraulic conductivity fields, by placing particles in a contaminated well. The number of particle passages is considered as being proportional to the contribution of each unknown point source to the tetrachloroethylene contamination identified in the target well. The study provides a methodology to help public authorities to locate the “more probable than not” area responsible for the tetrachloroethylene contamination detected in groundwater and to focus environmental investigations in specific sectors of Milano.

中文翻译：

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用于识别地下水四氯乙烯源的空空间蒙特卡罗粒子回溯

全球大多数城市地区的地下水经常受到有毒物质的污染。在各种污染源中，当有毒化合物主要通过泄漏的储罐或管道释放到地下时，工业造成的影响最大。一些污染物（通常是氯化碳氢化合物）往往会留在地下并且难以生物降解。因此，几十年前泄漏的物质仍在影响地下水。米兰及其周边地区（功能性城市区）是一个很好的例子，该地区一个多世纪以来一直承载着各种规模的工业，其中许多工业导致了氯化烃对地下水的污染。虽然最大的工业设施的位置是众所周知的，在许多未进行直接调查的情况下，很难确定许多较小的来源。此外，地下水污染的大、小、已知和未知来源的重叠影响使得确定每个来源的贡献具有挑战性。为了确定造成公共供水井中四氯乙烯污染的几个点源的贡献，在米兰功能城区的西北部使用 PEST 实施和校准了一个数值模型 (MODFLOW-2005)。在污染物迁移建模中，确定性方法仍然优于随机方法，因为其应用简单。尽管如此，作者认为后者最适合处理具有高不确定性特征的问题，如水文地质参数分布。采用零空间蒙特卡罗分析，随机生成了 400 组不同的水力传导率场，其中使用目标函数阈值仅选择了 336 组。随后，通过将颗粒放置在受污染的井中，对每个可接受的水力传导场进行颗粒回溯。粒子通过的次数被认为与每个未知点源对目标井中确定的四氯乙烯污染的贡献成正比。该研究提供了一种方法，可帮助公共当局确定对地下水中检测到的四氯乙烯污染负有“可能性”的区域，并将环境调查的重点放在米兰的特定部门。