Optimal identification of unknown groundwater contaminant sources in conjunction with designed monitoring networks

Abstract

Human activities and improper management practices have resulted in widespread deterioration of groundwater quality worldwide. Groundwater contamination has seriously threatened its beneficial use in recent decades. Remediation processes are necessary for groundwater management. In the remediation of contaminated aquifer sites, identification of unknown groundwater contaminant sources has a crucial role. In other words, an effective groundwater remediation process needs an accurate identification of contaminant sources in terms of contaminant source locations, magnitudes and time-release. On the other hand, the efficiency and reliability of contaminant source identification depend on the availability, adequacy, and accuracy of hydrogeologic information and contaminant concentration measurements data. Whereas, generally when groundwater contaminations are detected, only limited and sparse measured contaminant concentration values are available. Usually, groundwater contaminations are detected after a long time, years or even decades after the starting of contaminant source activities or even after their extinction. Therefore, usually, there is not enough information regarding the number of contaminant sources, the duration of sources' activities and the contaminant magnitudes, as well as the hydrogeologic parameters of the contaminated aquifers. Simulations of groundwater flow and solute transport involve intrinsic uncertainties due to this sparse information or lack of enough hydrogeologic information of the porous medium. Therefore, for groundwater management, developing and applying an efficient procedure for identification of unknown contaminant sources is essential. Moreover, available observed contaminant concentration values are usually erroneous and this erroneous data could cause instability in the solution results. Various combinations of source characteristics can result in similar effects at observation locations and cause non-uniqueness in the solution. Due to these instabilities and non-uniqueness in solution (Datta, 2002), the source identification is known as an problem (Yeh, 1986). The non-uniqueness and uncertainties involved in this ill-posed make this a difficult and complex task. Suggested methodologies to tackle this task are not completely efficient. For instance, the crux of previous approaches is highly vulnerable to the accuracy and adequacy of contaminant concentration measurements and hydrogeologic data. As a result, many of the previously suggested approaches are not applicable to real-world cases and application of relevant approaches to real-world contaminant aquifer sites is usually tedious and time-consuming. The suggested methodologies involve enormous computational time and cost due to repeated runs of the numerical simulation models within the optimisation algorithms. Therefore, to identify the unknown characteristics of contaminant sources, different surrogate models were developed. Three different algorithms were utilized for developing the…