American Society of Civil Engineers

Worldwide Residential Soil Regulatory Guidance Values for Chlorinated Ethenes

by Aaron A. Jennings, Ph.D., P.E., M.ASCE, (Professor of Civil Engineering, Dept. of Civil Engineering, Case Western Reserve Univ., 10900 Euclid Ave., Cleveland, OH 44106. E-mail:

Journal of Environmental Engineering, Vol. 137, No. 8, August 2011, pp. 651-668, (doi:

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Document type: Journal Paper
Abstract: Surface soil contamination is often regulated by using guidance values that specify the maximum amount of pollutant that can be present without prompting a regulatory response. In the United States, there are at least 88 value sets, and another 35 worldwide, that provide guidance for at least one chlorinated ethene. Trichloroethene is the most commonly regulated chlorinated ethene (118 values) and may be the most commonly regulated synthetic organic surface soil contaminant. Cis- and trans-1,2-dichloroethene are the least regulated chlorinated ethenes. Overall, there are 617 guidance values for specific chlorinated ethenes plus another 32 for mixed isomers of dichlorethene. This analysis explores the origin, magnitude, and form of the variability of these values. Results indicate that values span from 4.9 to 6.6 orders of magnitude and follow distributions similar to lognormal random variables. However, distributions include value clusters similar to values advocated by the U.S. Environmental Protection Agency (USEPA) or the Canadian Council of Ministers of the Environment (CCME). Although only 9.5% of the regulatory guidance values (RGVs) are identical to USEPA or CCME values, 55% of these fall within the uncertainty bounds estimated for USEPA risk models. Results suggest that stronger national leadership and reduced risk model uncertainty could be effective in reducing the RGV variability of chlorinated ethenes.

ASCE Subject Headings:
Soil pollution
Monte Carlo method
Uncertainty principles

Author Keywords:
Surface soil contamination
Chlorinated organics
Statistical analysis
Monte Carlo simulation
Uncertainty analysis