Probabilistic assessment of spatial heterogeneity of natural background concentrations in large-scale groundwater bodies through Functional Geostatistics.

We propose and exemplify a framework to assess Natural Background Levels (NBLs) of target chemical species in large-scale groundwater bodies based on the context of Object Oriented Spatial Statistics. The approach enables one to fully exploit the richness of the information content embedded in the probability density function (PDF) of the variables of interest, as estimated from historical records of chemical observations. As such, the population of the entire distribution functions of NBL concentrations monitored across a network of monitoring boreholes across a given aquifer is considered as the object of the spatial analysis.

Geostatistical multimodel approach for the assessment of the spatial distribution of natural background concentrations in large-scale groundwater bodies.

Quantification of the (spatially distributed) natural contributions to the chemical signature of groundwater resources is an emerging issue in the context of competitive groundwater uses as well as water regulation and management frameworks. Here, we illustrate a geostatistically-based approach for the characterization of spatially variable Natural Background Levels (NBLs) of target chemical species in large-scale groundwater bodies yielding evaluations of local probabilities of exceedance of a given threshold concentration. The approach is exemplified by considering three selected groundwater bodies and focusing on the evaluation of NBLs of ammonium and arsenic, as detected from extensive time series of concentrations collected at monitoring boreholes.

Geochemical modeling of arsenic release from a deep natural solid matrix under alternated redox conditions.

Dissolved arsenic (As) concentrations detected in groundwater bodies of the Emilia-Romagna Region (Italy) exhibit values which are above the regulation limit and could be related to the natural composition of the host porous matrix. To support this hypothesis, we present the results of a geochemical modeling study reproducing the main trends of the dynamics of As, Fe, and Mn concentrations as well as redox potential and pH observed during batch tests performed under alternating redox conditions. The tests were performed on a natural matrix extracted from a deep aquifer located in the Emilia-Romagna Region (Italy).

Arsenic release from deep natural solid matrices under experimentally controlled redox conditions.

We investigated the role of iron (Fe) on arsenic (As) release from two samples of a natural deep soil collected in an aquifer body in the Emilia-Romagna Region, Italy. Each sample is representative of a different solid matrix, i.e.

Natural background levels and threshold values of chemical species in three large-scale groundwater bodies in Northern Italy.

We analyze natural background levels (NBLs) and threshold values (TVs) of spatially distributed chemical species (NH(4), B and As) which may be a potential pressure and concern in three large scale alluvial and fluvio-deltaic aquifers at different depths of the Apennines and Po river plains in Emilia-Romagna, Northern Italy. Our results are based on statistical methodologies designed to separate the natural and anthropogenic contributions in monitored concentrations by modeling the empirical distribution of the detected concentration with a mixture of probability density functions. Available chemical observations are taken over a 20 years period and are associated with different depths and cover planar investigation scales of the order of hundreds of kilometers.