Characterization of Large-Scale Spatial Patterns of Emerging Contaminants Supported by a Geographic Information System Platform

New potential contaminants of emerging concern are discovered with increasing frequency, and new products are released into the environment with limited knowledge of their potential harmful effects. In this con-text, characterization of temporal and spatial patterns of emerging contaminants (ECs) within large-scale groundwater systems is a relevant scientific and practical issue. At such a scale, it is possible to characterize patterns in the (space-time) distribution of contaminants in a way that is typically shadowed when considering a single aquifer system. Despite groundwater contaminants can be studied and monitored at local scales, there are still open challenges regarding three-dimensional modeling of reactive transport in large-scale heterogeneous media. Our study aims at: (i) presenting a general overview of the most common categories of ECs, summarizing main sources and potential harmful effects, (ii) providing a description of the processes involved in the integrated watershed-scale groundwater management (such as infiltration, groundwater flow, and contaminant transport) and of the commonly adopted modeling approaches, (iii) reviewing the input data required to populate and parametrize large-scale groundwater models, and (iv) showing preliminary results related to groundwater recharge estimation and hydrogeological system characterization. For illustration purposes, we select the Po River basin (Northern Italy, about 72,000 km2) as our study area. The Po basin has been the subject of several groundwater related studies and datasets sampled at various scales are available (ranging from in situ sampling to satellite earth observation systems). In this work, all available in-formation is collected, properly scaled, and geolocated through a Geographic Information System platform. We also present some preliminary results about groundwater recharge estimation and distribution of geo-materials in the heterogeneous subsurface upon relying in an artificial neural network (ANN) approach.