Incorporating Site Characterization Data into Contaminant Concentration Interpolations for Long-Term Monitoring Optimization

Liu, Xiaoli; Chan-Hilton, Amy B.; ,

Incorporating Site Characterization Data into Contaminant Concentration Interpolations for Long-Term Monitoring Optimization

by Xiaoli Liu,
Amy B. Chan-Hilton,

Document Type: Proceeding Paper

Part of: World Environmental and Water Resources Congress 2008: Ahupua'A

Abstract:

Long ? term monitoring (LTM) networks are installed for groundwater remediation systems to evaluate the performance of various remediation processes and to track the variation of contaminant concentration and contaminant plumes. In addition, the results of LTM can be used to determine whether remediation goals have been achieved, to select the techniques for further remediation, and eventually to minimize the risk of environmental and human health. LTM involves collecting subsurface soil samples, groundwater samples or/and vapor samples at selected depths from monitoring wells periodically, analyzing samples and data interpretation. LTM networks are costly at certain remediation sites due to the operation and maintenance of large number of monitoring wells, multiple sampling depths and multiple-parameters analyses, some of which are not necessary once the remediation goals have been achieved. This cost can be reduced by optimizing existing LTM networks,e.g., removing redundant monitoring wells or reducing sampling frequency while maintaining acceptable accuracy of the LTM networks. Data interpolation and site characterization are two important components in LTM network optimization. Inverse Distance Weighting (IDW) and Kriging (e.g., ordinary kriging, quantile kriging, etc.) are the most commonly used data interpolation techniques for predicting pollutant concentration values from neighborhood sampling locations at contaminated sites. The basic assumption of IDW is that the concentration at locations near each other in space are more likely to be similar than those at locations far from each other. The interpolated value is a weighted average of the concentration values at sampled points and the weight assigned to each point decreases as the distance from the interpolation point to the sampled point increases. The advantages of IDW include fast computing time, no assumptions required, and the method is very simple and basic. The IDW method, however, does not consider the trend of contaminant distribution due to the movement of groundwater.

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