Statistical methods and transport modeling to assess PCE hotspots and diffuse pollution in groundwater (Milan FUA)

• Main Author: Loris Colombo
• Format: Article
• Language: English
• Published: PAGEPress Publications 2017-12-01
• Series: Acque Sotterranee
• Subjects: urban groundwater, numerical modeling, solute transport, diffuse contamination, inverse iterative modeling
• Online Access: http://www.acquesotterranee.net/index.php/acque/article/view/301

The Italian law 152/2006 adopted the EU Water Framework Directive principles and delegated to the Regions the task of identifying areas subject to groundwater diffuse pollution. In the Lombardy Plain, the qualitative groundwater conditions are affected mainly by the presence of industries and anthropic activities. The aim of this work was to assess tetrachloroethylene (PCE) diffuse pollution in the Milan Functional Urban Area (FUA), where chlorinated solvents are the main groundwater contaminants and the results of monitoring campaigns for the years 2003-2014 were collected in a dataset. For this purpose, a new methodology was implemented both in a deterministic and stochastic process. At first, hotspots were identified through Cluster Analysis (CA) applied to concentration values collected in unconfined/confined aquifers (2003-14). Then, a numerical transport model was implemented to study the hotspot plume extension in reason to identify monitoring wells not affected by diffuse pollution but related to specific hotspot sources. Consequently, it was possible to erase these data from the whole initial dataset in order to have a new one containing only diffuse concentrations. Interpolating them through ordinary kriging, PCE iso-concentrations maps identified areas where values are over the Maximum Contaminant Level (1.1 μg/l, Italian Law 152/06). Considering descriptive statistics and iso-PCE concentration maps, a median PCE value estimation (10 μg/l) was find as representative of PCE diffuse contamination Milan city. Moreover, a stochastic methodology was used in order to consider uncertainties due to unknown multiple-sources and environmental heterogeneity. The innovative approach gave some interesting solutions to point out areas where the contaminant mass release is higher and where high probability unknown sources can be found.