A Critical Review of Discrete Soil Sample Data Reliability: Part 2—Implications

Roger Brewer,John Peard,Marvin Heskett

doi:10.1080/15320383.2017.1244172

Roger Brewer, John Peard + Show 1 more

Open Access

https://doi.org/10.1080/15320383.2017.1244172

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Abstract

ABSTRACTPart 2 of this study investigates the implications of random, small-scale contaminant concentration variability in soil for reliance on discrete soil sample data to guide environmental investigations. Random variability around an individual point limits direct comparison of discrete sample data to risk-based screening levels. “False negatives” can lead to premature termination of an investigation or remedial action. Small-scale distributional heterogeneity of contaminants in soil is expressed as artificial, seemingly isolated “hot spots” and “cold spots” in isoconcentration maps. Surgical removal of hot spots can lead to erroneous conclusions regarding the magnitude of remaining contamination. The field precision of an individual discrete sample data set for estimation of means for a contaminant in a risk assessment is not directly testable. Omission of “outlier” data in order to force data to fit a geostatistical model distorts estimates of mean concentrations and introduces error into a risk assessment. The potential for such errors was pointed out in early USEPA guidance but largely ignored or misunderstood. Decision Unit and Multi Increment sample investigation methods, long known to the agricultural and mining industries, were specifically developed to overcome these inherent shortcomings of discrete sampling methods and provide more reliable and defensible data for environmental investigations.

Highlights

The field study presented in Part 1 of this paper (Brewer et al, 2016) was designed to address a basic question: What is the variability of contaminant concentrations in soil around a fixed point at the scale of a typical, discrete soil sample? A significant variability in data for “colocated” or “split” samples as well as data for replicate analyses by the laboratory samples is often simplistically blamed on “laboratory error.”
The effect of field error in estimation of the mean contaminant concentration for a targeted area is highlighted by estimations of means and 95% Upper Confidence Limit (UCL) values for random non-stratified groupings of ten discrete sample data points at Study Site C (HDOH, 2015b)
The results of this field study highlight the need to transition from traditional discrete soil sample investigation methods to more science-based and reproducible Decision Unit and Multi Increment sampling methodologies