Increasing Confidence in Mass Discharge Estimates Using Geostatistical Methods

Abstract

Mass discharge is one metric rapidly gaining acceptance for assessing the performance of in situ groundwater remediation systems. Multilevel sampling transects provide the data necessary to make such estimates, often using the Thiessen Polygon method. This method, however, does not provide a direct estimate of uncertainty. We introduce a geostatistical mass discharge estimation approach that involves a rigorous analysis of data spatial variability and selection of an appropriate variogram model. High-resolution interpolation was applied to create a map of measurements across a transect, and the magnitude and uncertainty of mass discharge were quantified by conditional simulation. An important benefit of the approach is quantified uncertainty of the mass discharge estimate. We tested the approach on data from two sites monitored using multilevel transects. We also used the approach to explore the effect of lower spatial monitoring resolution on the accuracy and uncertainty of mass discharge estimates. This process revealed two important findings: (1) appropriate monitoring resolution is that which yielded an estimate comparable with the full dataset value, and (2) high-resolution sampling yields a more representative spatial data structure descriptor, which can then be used via conditional simulation to make subsequent mass discharge estimates from lower resolution sampling of the same transect. The implication of the latter is that a high-resolution multilevel transect needs to be sampled only once to obtain the necessary spatial data descriptor for a contaminant plume exhibiting minor temporal variability, and thereafter less spatially intensely to reduce costs.

Mass Discharge in a Tracer Plume: Evaluation of the Theissen Polygon Method

“…The most commonly compared methods are based on: (1) sampling of transects of single or multilevel wells, generally interpreted using the Theissen Polygon Method (TPM), (2) integral pump tests conducted in wells across the plume, and (3) passive flux meters, that is, devices installed in wells across the plume which allow estimation of the groundwater flowing through the well and the amount of mass conveyed by the groundwater. Most of the studies have been based on monitoring of plumes emanating from accidental releases (“real” plumes) in various hydrogeological settings (Béland‐Pelletier et al 2011; Cai et al 2011; Dietze and Dietrich 2011). While of enormous value, particularly in comparing different M d estimation methods applied to the plumes, these studies are hampered by lack of knowledge of the true value of mass discharge at the time and location of sampling.…”

Screening-level estimates of mass discharge uncertainty from point measurement methods

“…A number of studies have been completed on pointmeasurement method uncertainty (Béland-Pelletier et al, 2011;Cai et al, 2011Cai et al, , 2012Chen et al, 2014;Klammler et al, 2012;Kübert and Finkel, 2006;Li and Abriola, 2009;Li et al, 2007;MacKay et al, 2012;Schwede and Cirpka, 2010;Troldborg et al, 2010Troldborg et al, , 2012. Two of these studies were based on field trials (Béland-Pelletier et al, 2011;MacKay et al, 2012), two of the studies used flow and transport simulations within Monte Carlo frameworks (Chen et al, 2014;Kübert and Finkel, 2006); two more studies likewise used flow and transport simulations within Monte Carlo frameworks, but simulations were conditioned to field data (Schwede and Cirpka, 2010;Troldborg et al, 2010); and the remaining studies employed various conditional, geostatistical techniques, wherein one or more parameters across the control plane were treated as spatial random variables.…”

“…Other studies that likewise presented methods to estimate ṁ uncertainty using geostatistical simulations of random spatial variables conditioned to field measurements include Cai et al (2011), Cai et al (2012, Klammler et al (2012), and Troldborg et al (2012). In each case, uncertainty was quantified by generating empirical CDFs of ṁ.…”

“…In each case, uncertainty was quantified by generating empirical CDFs of ṁ. Cai et al (2011) assumed uniform flow, such that uncertainty stemmed only from the spatial C distribution. This approach was extended in Cai et al (2012) to include K as a second, independent spatial random variable.…”

Simulating an exclusion zone for vapour intrusion of TCE from groundwater into indoor air

“…,King et al (1999),Rivett et al (2001),Hess et al (2002),Bockelmann et al (2003),Mackay et al (2006),Cai et al (2011) andBéland-Pelletier et al (2011), and further utilized in the body of work related to partial source zone depletion(Blum and Annable, 2008;Brooks et al, 2008;Sale et al, 2008) Yu et al (2009) andYao et al (2011). both discuss this idea of the soil gas flux as a contribution to the indoor air concentration of TCE.…”