Optimization of Well Placement in Groundwater Long Term Monitoring Networksby Xiaoli Liu,
Amy B. Chan Hilton,
Document Type: Proceeding Paper
Part of: World Environmental and Water Resources Congress 2007: Restoring Our Natural Habitat
Abstract: Long Term Monitoring (LTM) networks play critical roles in tracking the migration of contaminants in groundwater, evaluating the efficiency of the existing remediation techniques, and determining further remediation activities on contamination sites. Monitoring of sites with contaminated groundwater is dictated by the Resource Conservation and Recovery Act (RCRA), Comprehensive Environmental Response, Compensation and Liability Act (CERCLA), and Underground Storage Tank (UST) programs. The initial LTM networks are designed and established after the active remediation processes are identified and adopted on contamination sites, and the LTM network usually is used for up to 30 years after remediation is terminated. During remediation, several hundred of samples may be collected and analyzed at as frequent as quarterly for a typical site to satisfy data sufficiency. In some sites, especially small sites, however, there are usually not adequate amount of data to evaluate whether remediation goals are met or not. The primary concern of LTM is the substantial cost of monitoring at private or/and government sites, given the large number of remediation sites, number of monitoring wells, numerous constituents analyzed at each well, and extensive LTM period required. For example, in 2005, the U.S. Air Force spent 32% (or $24.8 M) of its remediation budget on LTM systems. Furthermore, after the active remediation process is completed, a lot of data collected with the existing sampling network provide redundant information. It is imperative to remove the redundant sampling locations in order to improve the cost.efficiency of LTM networks while keeping the data loss at an acceptable level. The second problem of LTM is that data collected at existing monitoring locations may not accurately reflect the changing site situation. The existing LTM networks were designed according to the preliminary site assessment or initial stages of groundwater remediation, and may not capture the changes in the contaminant plume size and shape and concentrations. At small sites, existing monitoring wells may not provide sufficient data for site remediation status assessment. At certain groundwater remediation sites, more sampling wells need to be added to existing LTM networks to augment the accuracy of plume interpolation and remediation evaluation. The ant colony optimization algorithm (ACO) was the first applied by Li and Chan Hilton (2005, 2007) to LTM network improvement and was demonstrated to be an effective method for LTM network optimization. The ACO-LTM algorithm is based on the ant colony optimization (ACO) paradigm to remove spatial redundancy. This study is an extension of the work of Li and Chan Hilton. The ACO-LTM is modified and applied to 1) reduce LTM sampling costs by removing redundancy of data at sites with numerous sampling locations while minimizing data los and 2) improve the monitoring network by adding sampling points to minimize network estimation variance. This paper focuses on the first objective. Specifically, improved data interpolation methods will be incorporated into the ACO-LTM algorithm to improve the quantification of data loss.
Subject Headings: Remediation | Site investigation | Groundwater pollution | Data collection | Data processing | Algorithms | Pollutants | Underground storage
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