Risk-based prioritization methodology for the classification of groundwater pollution sources

Water management is one of the EU environmental priorities and it is one of the most serious challenges that today's major cities are facing. The main European regulation for the protection of water resources is represented by the Water Framework Directive (WFD) and the Groundwater Directive (2006/118/EC) which require the identification, risk-based ranking and management of sources of pollution and the identification of those contamination sources that threaten the achievement of groundwater's good quality status. The aim of this paper is to present a new risk-based prioritization methodology to support the determination of a management strategy for the achievement of the good quality status of groundwater. The proposed methodology encompasses the following steps: 1) hazard analysis, 2) pathway analysis, 3) receptor vulnerability analysis and 4) relative risk estimation. Moreover, by integrating GIS functionalities and Multi Criteria Decision Analysis (MCDA) techniques, it allows to: i) deal with several sources and multiple impacted receptors within the area of concern; ii) identify different receptors' vulnerability levels according to specific groundwater uses; iii) assess the risks posed by all contamination sources in the area; and iv) provide a risk-based ranking of the contamination sources that can threaten the achievement of the groundwater good quality status. The application of the proposed framework to a well-known industrialized area located in the surroundings of Milan (Italy) is illustrated in order to demonstrate the effectiveness of the proposed framework in supporting the identification of intervention priorities. Among the 32 sources analyzed in the case study, three sources received the highest relevance score, due to the medium-high relative risks estimated for Chromium (VI) and Perchloroethylene. The case study application showed that the developed methodology is flexible and easy to adapt to different contexts, thanks to the possibility to introduce specific relevant parameters identified according to expert judgment and data availability.