Politecnico di Milano, Dipartimento di Ingegneria Civile e Ambientale, Piazza L. Da Vinci 32, 20133 Milano, Italy.
Università della Calabria, Dipartimento di Ingegneria per l'Ambiente e il Territorio e Ingegneria Chimica, via P Bucci 42B, 87036 Rende (CS), Italy.
Sci Total Environ. 2014 Apr 1;476-477:38-48. doi: 10.1016/j.scitotenv.2013.12.125. Epub 2014 Jan 19.
The estimation of natural background levels (NBLs) of dissolved concentrations of target chemical species in subsurface reservoirs relies on a proper assessment of the effects of forcing terms driving flow and transport processes taking place within the system and whose dynamics drive background concentration values. We propose coupling methodologies based on (a) global statistical analyses and (b) numerical modeling of system dynamics to distinguish between the impacts of different types of external forcing components influencing background concentration values. We focus on the joint application of a statistical methodology based on Component Separation and experimental/numerical modeling studies of groundwater flow and transport for the NBL estimation of selected chemical species in potentially contaminated coastal aquifers. We consider a site which is located in Calabria, Italy, and constitutes a typical example of a Mediterranean coastal aquifer which has been subject to intense industrial development. Our study is keyed to the characterization of NBLs of manganese and sulfate and is geared to the proper identification of the importance of a natural external forcing (i.e., seawater intrusion) on NBL assessment. Results from the Component Separation statistical approach are complemented by numerical simulations of the advective-dispersive processes that could influence the distribution of chemical species (i.e., sulfate) within the system. Estimated NBLs for manganese are consistent with the geochemical composition of soil samples. While Component Separation ascribes the largest detected sulfate concentrations to anthropogenic sources, our numerical modeling analysis suggests that they are mainly related to the natural process of seawater intrusion. Our results indicate that the use of statistical methodologies in complex groundwater systems should be assisted by a detailed characterization of the dynamics of natural (and/or induced) processes to distinguish effective anthropogenic contamination from natural conditions and to define realistic environmental clean-up goals.
地下储层中目标化学物质溶解浓度的天然背景水平 (NBL) 的估计依赖于对驱动系统内流动和传输过程的强制项的影响的适当评估，而这些过程的动态则驱动背景浓度值。我们提出了基于（a）全局统计分析和（b）系统动力学数值模拟的耦合方法，以区分影响背景浓度值的不同类型外部强制组件的影响。我们专注于联合应用基于成分分离的统计方法和地下水流动和传输的实验/数值模拟研究，以估算潜在污染沿海含水层中选定化学物质的 NBL。我们考虑了一个位于意大利卡拉布里亚的地点，该地点是一个典型的地中海沿海含水层，该含水层经历了强烈的工业发展。我们的研究是针对锰和硫酸盐的 NBL 特征描述，旨在正确确定自然外部强制因素（即海水入侵）对 NBL 评估的重要性。成分分离统计方法的结果通过对流扩散过程的数值模拟得到补充，这些过程可能会影响系统内化学物质（例如硫酸盐）的分布。估计的锰 NBL 与土壤样品的地球化学组成一致。虽然成分分离将最大的检测到的硫酸盐浓度归因于人为来源，但我们的数值模拟分析表明，它们主要与海水入侵的自然过程有关。我们的结果表明，在复杂的地下水系统中使用统计方法应该通过对自然（和/或诱导）过程的动态进行详细描述来辅助，以区分有效人为污染与自然条件，并定义现实的环境清理目标。
