The Forensic Hydrogeology project studied the identification of point sources that release contaminants into groundwater systems. Although aquifers possess a natural shield for pollutant activities on the surface, it is estimate that 2,500 million of potentially contaminated exist in Europe. When aquifers become contaminated the characterization of the new status and the source identification requires: (i) effective observation networks; (ii) and complex modelling methods.
The research idea was to identify the source parameters by analysing the aquifer's response predicted by a numerical model with observations. It required the application of the Ensemble Kalman Filter (EnKF), a sound and precise data assimilation method for linear systems, but with constraints to overcome when model parameters and state variables relate nonlinearly. We aimed to generate synthetic scenarios that mimic flow, transport, and reactive systems that account for the complexity found in nature, i.e. the spatial and temporal variability of parameters and nonlinear processes. This had been poorly explored in biogeochemical models and might reduce uncertainty in reactive parameter estimation in several types of reactive systems.
In the absence of information, the risk of evading environmental liability increases. Better knowledge on this topic provides a more reliable characterization of the natural processes and their site-specific parameters, improving the efficiency of remediation techniques and mitigating the effects of contaminant events, which may compromise human and ecosystem health conditions.
The scientific objectives of the project Forensic Hydrogeology were: (i) to develop a novel, flexible, and reliable ensemble Kalman filter data assimilation method for the optimal identification of contaminant sources of reactive pollutants in near-actual conditions by using synthetic scenarios, sandbox experiments, and demonstration sites; (ii) to transfer this novel technology in well-reported, practical, and universal open-source packages. Additionally, the MSCA-IF aimed to provide the opportunity for the Principal Investigator to gain experience as an independent scientist, in this case, in developing and applying advanced algorithms to study biogeochemical processes in the subsurface with stochastic inverse approaches.
