Integrated Sequential Groundwater Contaminant Source Characterization and Pareto-Optimal Monitoring Network Design Application for a Contaminated Aquifer Site

Accurate and reliable groundwater contaminant source characterization with limited contaminant concentration monitoring measurement data remains a challenging problem. This study presents an illustrative application of developed methodologies to a real-life contaminated aquifer. The source characterization and optimal monitoring network design methodologies are used sequentially for a contaminated aquifer site located in New South Wales, Australia. Performance of the integrated optimal source characterization methodology combining linked simulation-optimization, fractal singularity mapping technique (FSMT) and Pareto optimal solutions is evaluated. This study presents an integrated application of optimal source characterization with spatiotemporal concentration measurement data obtained from sequentially designed monitoring networks. The proposed sequential source characterization and monitoring network design methodology shows efficiency in identifying the unknown source characteristics. The designed monitoring network achieves comparable efficiency and accuracy utilizing much smaller number of monitoring locations as compared to a more ideal scenario where concentration measurements from a very large number of widespread monitoring wells are available. The proposed methodology is potentially useful for efficient characterization of unknown contaminant sources in a complex contaminated aquifer site, where very little initial concentration measurement data are available. The illustrative application of the methodology to a real-life contaminated aquifer site demonstrates the capability and efficiency of the proposed methodology.

Identifying porphyry-Cu geochemical footprints using local neighborhood statistics in Baft area, Iran

Identifying geochemical anomalies related to ore deposition processes facilitates the practice of vectoring toward undiscovered mineral deposit sites.In districtscale exploration studies,analysis of dispersion patterns of ore-forming elements results in more-reliable targets.Therefore,deriving significant geochemical footprints and mapping the ensuing geochemical anomalies are of important issues that lead exploration geologists toward anomaly sources,e.g.,mineralization.This paper aims to examine the effectiveness of local relative enrichment index and singularity mapping technique,as two methods of local neighborhood statistics,in the delineation of anomalous areas for further exploration.A data set of element contents obtained from stream sediment samples in Baft area,Iran,therefore was applied to illustrate the procedure proposed.The close relationship between anomalous patterns recognized and known Cu-occurrences demonstrated that the procedures proposed can efficiently model complex dispersion patterns of geochemical anomalies in the study area.The results showed that singularity mapping method is a better technique,compared to local relative enrichment index,to delineate targets for follow-up exploration in the area.We made this comparison because,as pointed out by exploration geochemists,dispersion patterns of geochemical indicators in stream sediments vary in different areas even for the same deposit type.The variety in the dispersion patterns is due to the operation of post-mineralization subsystems,which are affected by local factors such as landscape of the areas under study.Therefore,the effectiveness of the methods should be evaluated in every area for every targeted deposit.