The concept of natural background level (NBL) aims at distinguishing the natural and anthropogenic contributions to concentrations of specific contaminants, as groundwater management and protection tools. This is usually defined as a unique value at a regional scale, even when the hydrogeological and geochemical features of a certain territory are far from homogeneous. The concentration of target contaminants is affected by multiple hydrogeochemical processes. This is the case of arsenic in the Calabria region, where concentrations are definitely variable in groundwater. To overcome the limitation of a traditional approach and to include the intrinsic hydrogeological and geochemical heterogeneity into the definition of the natural contribution to As content in groundwater, an integrated probabilistic approach to the NBL assessment combining aquifer-based preselection criteria and multivariate non-parametric geostatistics was proposed. In detail, different NBL values were selected, based on the aquifer type and/or hydrogeochemical features. Then, these aquifer-based NBL values of arsenic were used in the Probability Kriging method to map the probability of exceedance and to provide contamination risk management tools. This multivariate geostatistical approach that takes advantage of the physico-chemical variables used in the aquifer-based NBL values definition allowed mapping the probability of exceedance of As in a physically-based way. The hydrogeochemical diversity of the study area and all the processes affecting As concentrations in the aquifers have been considered too. As a result, the obtained map was characterized by a short-range and long-range variability due to local hydrogeochemical anomalies and water-rock interaction and/or atmospheric precipitation. By this approach, the NBL exceedance probability maps proved to be less “noisy”, because the local hydrogeochemical conditions were filtered, and more capable of pointing out anthropogenic inputs or very anomalous natural contributions, which need to be investigated more in detail and properly managed.

The Cone Penetration Test (CPT) measures enable to recognize vertical lithological sequence at each investigated point. From the tip resistance q_c and sleeve resistance f_s profiles, the Soil Behavior Type index I_SBT has been calculated, in order to identify the lithotypes alongside depth. The present study focuses on the combination of different variables to provide a lithological and mechanical subsoil characterization. The main objectives of the paper are: (1) to model the 3D spatial variability structure of the soil lithotypes and mechanical properties using q_c and f_s profiles; 2) to evaluate the uncertainties of the estimates for designing purposes. 182 CPTs were collected in a 900 km² area (corresponding to a subsoil volume of about 12 km³) located in the study site in the Bologna province (Italy). The study area is made up of fine-grained soils, silt and clay mixtures that are intercalated at different depths by sandy and gravelly soils. These variations of each soil fraction affect the engineering properties of these alluvial deposits. For 3D modeling, two geostatistical methods, Ordinary Kriging (stationary method), and Intrinsic Random Function theory (non-stationary method) have been used. The results show that the non-stationary method allows to obtain more reliable q_c and f_s predicted values. The final stochastic mechanical and lithological model enables engineers and geologists to detect the emerging of fan and paleochannels bodies where mean resistance values can abruptly change in terms of bearing capacity, liquefaction potential and static and dynamic settlement occurrence.