The Milano Metropolitan Area [named FUA (functional urban area)] has a history of heavy industrialization causing a large portion of area being affected by significant diffuse contaminations of soil and groundwater. Among the various contaminants, chlorinated solvents (e.g., tetrachloroethylene and trichloroethylene) are the most used in industrial processes and represent the major cause of groundwater pollution within the FUA. The background diffuse contamination generated by these pollutants is so persistent and widely spread that makes it extremely challenging to identify the sources responsible for their release. Such background contamination originates from the overlapping of both known sources (point sources), associated to specific high release of contamination, and unknown small sources (multiple point sources), clustered within a large area, whose release is low but persistent. The aim of this article is to present the methodology, developed within the framework of the AMIIGA Project (Interreg Central Europe Grant N CE32), which combines multivariate statistical analysis and groundwater numerical modeling in order to separate the point sources contribution from the background diffuse contamination, and supporting public authorities in the management of groundwater remediation. A methodological workflow is proposed guiding local and regional institutions to use the methodology (i.e., exploratory analysis of big dataset, simulation of groundwater flow and transport, multivariate and geostatistical analysis) to assess diffuse pollution background levels in large urbanized areas.

Multi-Methodological Integrated Approach for the Assessment of Diffuse Pollution Background Levels (DPBLs) in Functional Urban Areas: The PCE Case in Milano NW Sector

Loris Colombo;Luca Alberti;Arianna Azzellino;
2020

Abstract

The Milano Metropolitan Area [named FUA (functional urban area)] has a history of heavy industrialization causing a large portion of area being affected by significant diffuse contaminations of soil and groundwater. Among the various contaminants, chlorinated solvents (e.g., tetrachloroethylene and trichloroethylene) are the most used in industrial processes and represent the major cause of groundwater pollution within the FUA. The background diffuse contamination generated by these pollutants is so persistent and widely spread that makes it extremely challenging to identify the sources responsible for their release. Such background contamination originates from the overlapping of both known sources (point sources), associated to specific high release of contamination, and unknown small sources (multiple point sources), clustered within a large area, whose release is low but persistent. The aim of this article is to present the methodology, developed within the framework of the AMIIGA Project (Interreg Central Europe Grant N CE32), which combines multivariate statistical analysis and groundwater numerical modeling in order to separate the point sources contribution from the background diffuse contamination, and supporting public authorities in the management of groundwater remediation. A methodological workflow is proposed guiding local and regional institutions to use the methodology (i.e., exploratory analysis of big dataset, simulation of groundwater flow and transport, multivariate and geostatistical analysis) to assess diffuse pollution background levels in large urbanized areas.
transport model, FUA, cluster analysis (CA), multivariate analysis, DPBLs, diffuse contamination, Milan
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1151717
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