Assessment of natural background levels in potentially contaminated coastal aquifers.
The estimation of natural background levels (NBLs) of dissolved concentrations of target chemical species in subsurface reservoirs relies on a proper assessment of the effects of forcing terms driving flow and transport processes taking place within the system and whose dynamics drive background concentration values. We propose coupling methodologies based on (a) global statistical analyses and (b) numerical modeling of system dynamics to distinguish between the impacts of different types of external forcing components influencing background concentration values. We focus on the joint application of a statistical methodology based on Component Separation and experimental/numerical modeling studies of groundwater flow and transport for the NBL estimation of selected chemical species in potentially contaminated coastal aquifers. We consider a site which is located in Calabria, Italy, and constitutes a typical example of a Mediterranean coastal aquifer which has been subject to intense industrial development. Our study is keyed to the characterization of NBLs of manganese and sulfate and is geared to the proper identification of the importance of a natural external forcing (i.e., seawater intrusion) on NBL assessment. Results from the Component Separation statistical approach are complemented by numerical simulations of the advective-dispersive processes that could influence the distribution of chemical species (i.e., sulfate) within the system. Estimated NBLs for manganese are consistent with the geochemical composition of soil samples. While Component Separation ascribes the largest detected sulfate concentrations to anthropogenic sources, our numerical modeling analysis suggests that they are mainly related to the natural process of seawater intrusion. Our results indicate that the use of statistical methodologies in complex groundwater systems should be assisted by a detailed characterization of the dynamics of natural (and/or induced) processes to distinguish effective anthropogenic contamination from natural conditions and to define realistic environmental clean-up goals.
Politecnico di Milano, Dipartimento di Ingegneria Civile e Ambientale, Piazza L. Da Vinci 32, 20133 Milano, Italy. Electronic address: ant.molinari2002@libero.it.
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Molinari
Università della Calabria, Dipartimento di Ingegneria per l'Ambiente e il Territorio e Ingegneria Chimica, via P Bucci 42B, 87036 Rende (CS), Italy.
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Chidichimo
Università della Calabria, Dipartimento di Ingegneria per l'Ambiente e il Territorio e Ingegneria Chimica, via P Bucci 42B, 87036 Rende (CS), Italy.
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Straface
Politecnico di Milano, Dipartimento di Ingegneria Civile e Ambientale, Piazza L. Da Vinci 32, 20133 Milano, Italy; University of Arizona, Department of Hydrology and Water Resources, Tucson, AZ, USA.
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Guadagnini
D004784:Environmental Monitoring; D060587:Groundwater / Q000737:chemistry*; D007558:Italy; D054712:Salinity; D012623:Seawater / Q000032:analysis*; D014874:Water Pollutants, Chemical / Q000032:analysis*
D014874:Water Pollutants, Chemical
Chemical status; Component Separation; Contaminated aquifers; Natural background level; Seawater intrusion; Threshold values