Identification of Unknown Number of Clandestine Groundwater Contamination Source Locations and Their Release Flux History

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AbstractAs a method of disposal, large amounts of untreated toxic waste are frequently buried underground. The extensive contamination of groundwater has arisen from such indiscrete methods of clandestine disposal. This means of disposal is often used to escape the expense of adequate waste management and the untraceability of such unauthorized disposals. However, the effect of such disposals manifests in form of groundwater contamination, which if left unchecked would potentially pollute the entire aquifer. Such contaminated aquifers must be recovered using the appropriate remediation method. Effective remediation strategy only depends on the exact estimation of contamination source characteristics, i.e., number of sources, locations, and release histories of such pollutant sources. In case of groundwater contamination, the number and location of the sources cannot be deciphered. Thus the challenge is to identify the number of contamination sources, their locations, and their flux release history from sparsely available contamination measurement concentration data. Methodologies developed so far rely on prior information about the number of contaminant sources present in the study area or the potential source locations that are known with certainty. However, such assumptions seldom hold true in real-world scenarios of groundwater contamination originating from clandestine sources. In this study, a noble technique for simultaneously estimating the unknown number of clandestine contamination sources, locations along, and release flux history is demonstrated. Simulated Annealing (SA) is used as the optimization algorithm in a linked simulation optimization (LSO)-based framework. Number of contaminant sources and locations are considered as unknown decision variables in the proposed method. The developed methodology is applied to a hypothetical study area. The results demonstrate the applicability of the methodology in estimating the number of groundwater contamination sources, locations, and release history, even though there is no prior information about the number of clandestine contamination sources and locations available.KeywordsGroundwater contaminationSource identificationClandestine sourcesLinked simulation–optimizationSimulated Annealing