Groundwater monitoring plans at small-scale sites—an innovative spatial and temporal methodology

Abstract

An innovative methodology for improving existing groundwater monitoring plans at small-scale sites is presented. The methodology consists of three stand-alone methods: a spatial redundancy reduction method, a well-siting method for adding new sampling locations, and a sampling frequency determination method. The spatial redundancy reduction method eliminates redundant wells through an optimization process that minimizes the errors in plume delineation and the average plume concentration estimation. The well-siting method locates possible new sampling points for an inadequately delineated plume via regression analysis of plume centerline concentrations and estimation of plume dispersivity values. The sampling frequency determination method recommends the future frequency of sampling for each sampling location based on the direction, magnitude, and uncertainty of the concentration trend derived from representative historical concentration data. Although the methodology is designed for small-scale sites, it can be easily adopted for large-scale site applications. The proposed methodology is applied to a small petroleum hydrocarbon-contaminated site with a network of 12 monitoring wells to demonstrate its effectiveness and validity.

Spatial–temporal assessment and redesign of groundwater quality monitoring network: a case study

“…This approach was selected instead of the ordinary least squares method to overcome the difficulties associated with data with outliers. Other methods such as the high-breakdown and high-efficiency robust regression do not dampen the influence of outliers as much as IRLS robust regression (Ling et al 2003). The magnitude and the direction become available from the slope that is estimated from the IRLS robust regression of the concentration trend.…”

“…The third approach is statistical trend analysis. Furthermore, Ling et al (2003) concluded that most of the mentioned approaches are statistically sound and improve the existing monitoring plans. However, one should consider that these techniques are designed for large-scale sites and are limited by several problems when applied to the sites with smaller scales.…”

Multiple Lines Of Evidence Supporting Natural Attenuation: Lines Of Inquiry Supporting Monitored Natural Attenuation And Enhanced Attenuatin Of Chlorinated Solvents

“…A suggested approach to dealing with the issue of a non-significant result for the MannKendall test is to use the coefficient of variation as an indication, or "test," of stability (Wiedemeier et al, 1999;GSI, 1998;Ling et al, 2003). The coefficient of variation (CV) measures the spread of a set of data as a proportion of its mean and the suggested approach concludes that a Mann-Kendall test that is not significant at the 90% confidence level where CV < 1 indicates stability.…”

Optimizing groundwater long‐term monitoring networks using Delaunay triangulation spatial analysis techniques

“…Herrera (1998) and Herrera et al (2000) combined a stochastic flow-and-transport model and a linear Kalman filter-a technique that is theoretically equivalent to geostatistical methods using advanced forms of kriging-to choose the optimal sampling locations for an existing monitoring network. Ling et al (2003) used an innovative enumeration-based optimization algorithm to identify redundant monitoring locations for small networks with less than 15 wells. Other methods have also appeared recently that could be helpful in reducing a groundwater monitoring network or obtaining the optimal locations of monitoring wells (e.g.…”

“…A genetic algorithm was used by Wagner (1995) to find optimal locations by minimizing a combination of model prediction and parameter estimation covariance. Ling et al (2003) developed a well-siting method for plumes that are inadequately delineated at their leading edge by using regression analysis of centerline concentrations and estimation of plume dispersivity values. Methods using the Kalman filter to augment a monitoring network have been discussed by Rizzo et al (2000).…”