Updating
Exposure Models of Indoor Air Pollution Due
to Vapor Intrusion: Bayesian Calibration of the Johnson-Ettinger Model
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Abstract
The migration of
chlorinated volatile organic compounds from groundwater
to indoor airknown as vapor intrusionis an important
exposure pathway at sites with contaminated groundwater. High-quality
screening methods to prioritize homes for monitoring and remediation
are needed, because measuring indoor air quality in privately owned
buildings is often logistically and financially infeasible. We demonstrate
an approach for improving the accuracy of the Johnson-Ettinger model
(JEM), which the Environmental Protection Agency (EPA) recommends
as a screening tool in assessing vapor intrusion risks. We use Bayesian
statistical techniques to update key Johnson-Ettinger input parameters,
and we compare the performance of the prior and updated models in
predicting indoor air concentrations measured in 20 homes. Overall,
the updated model reduces the root mean squared error in the predicted
concentration by 66%, in comparison to the prior model. Further, in
18 of the 20 homes, the mean measured concentration is within the
90% confidence interval of the concentration predicted by the updated
model. The resulting calibrated model accounts for model uncertainty
and variability and decreases the false negatives rate; hence, it
may offer an improved screening approach, compared to the current
EPA deterministic approach