[1] Contaminant mass discharge across a control plane downstream of a dense nonaqueous phase liquid (DNAPL) source zone has great potential to serve as a metric for the assessment of the effectiveness of source zone treatment technologies and for the development of risk-based source-plume remediation strategies. However, too often the uncertainty of mass discharge estimated in the field is not accounted for in the analysis. In this paper, a geostatistical approach is proposed to estimate mass discharge and to quantify its associated uncertainty using multilevel transect measurements of contaminant concentration (C) and hydraulic conductivity (K). The approach adapts the p-field simulation algorithm to propagate and upscale the uncertainty of mass discharge from the local uncertainty models of C and K. Application of this methodology to numerically simulated transects shows that, with a regular sampling pattern, geostatistics can provide an accurate model of uncertainty for the transects that are associated with low levels of source mass removal (i.e., transects that have a large percentage of contaminated area). For high levels of mass removal (i.e., transects with a few hot spots and large areas of near-zero concentration), a total sampling area equivalent to 6$7% of the transect is required to achieve accurate uncertainty modeling. A comparison of the results for different measurement supports indicates that samples taken with longer screen lengths may lead to less accurate models of mass discharge uncertainty. The quantification of mass discharge uncertainty, in the form of a probability distribution, will facilitate risk assessment associated with various remediation strategies.Citation: Li, K. B., P. Goovaerts, and L. M. Abriola (2007), A geostatistical approach for quantification of contaminant mass discharge uncertainty using multilevel sampler measurements, Water Resour. Res., 43, W06436,
[1] Mass discharge across a control plane has great potential to serve as a metric for the assessment of the impact of partial source zone depletion and for the development of risk-based source plume remediation strategies. However, field-estimated mass discharge is always subject to uncertainty, arising from nonexclusive sampling. The accuracy of the estimated discharge and the magnitude of its quantifiable uncertainty depend upon the amount of information provided by the sample data. A multistage spatial sampling strategy is proposed to select optimal sampling locations and determine minimal sampling density for accurate quantification of mass discharge uncertainty. Two sampling criteria are incorporated to ensure coverage of the control plane and delineation of highly concentrated areas (hot spots). Multiple criteria decision making theory is adapted to objectively weight the two sampling criteria, on the basis of the information importance of each criterion, with additional observations located according to the weighted average of the two criteria. Application of this methodology to numerically simulated plume transects shows that in comparison to one stage sampling design (regular pattern), this sampling strategy yields a 50% reduction in required sampling density for accurate uncertainty modeling. The developed sampling algorithm can be used in real time to guide staged field sampling.
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