Probabilistic Risk Assessment for Six Vapour Intrusion Algorithms

Abstract

A probabilistic assessment with sensitivity analysis using Monte Carlo simulation for six vapour intrusion algorithms, used in various regulatory frameworks for contaminated land management, is presented here. In addition a deterministic approach with default parameter sets is evaluated against observed concentrations for benzene, ethylbenzene and trichloroethylene. The screening-level algorithms are ranked according to accuracy and conservatism in predicting observed soil air and indoor air concentrations at two contaminated sites to determine their suitability for regulatory purposes and the possible occurrence of false-negative errors. Dominant parameters that drive the predictions are grouped by either physico-chemical, soil or building parameters, and also by parameters that are either uncertain or variable, to determine the prioritisation for further research actions such as additional measurements. The findings from this study suggest that the screening-level algorithms that have a higher degree of conservatism for their default parameter set are the Johnson and Ettinger model, Dilution Factor algorithm from Sweden, Vlier-Humaan and VolaSoil. From these four algorithms the Johnson and Ettinger model and VolaSoil have a relative high accuracy (discriminative power). For the latter two algorithms different parameters, that are variable and uncertain, contribute to the variation in indoor air concentration, and differences were observed between the aromatic and chlorinated hydrocarbons. For the chlorinated hydrocarbon trichloroethylene, the default parameter set of Vlier-Humaan, CSoil and Dilution Factor algorithm of Sweden might be adapted to arrive at a higher deterministically predicted indoor air concentration if more conservatism is required. The deterministically predicted air concentrations for aromatic hydrocarbons seem to be sufficiently conservative. It is shown that the probabilistic approach allows for an improved insight into the relative importance of parameters in the risk estimates.