Screening Houses for Vapor Intrusion Risks: A Multiple Regression Analysis Approach

The migration of chlorinated volatile organic compounds from groundwater to indoor airknown as vapor intrusioncan be an important exposure pathway at hazardous waste sites. Because sampling indoor air at every potentially affected home is often logistically infeasible, screening tools are needed to help identify at-risk homes. Currently, the U.S. Environmental Protection Agency (EPA) uses a simple screening approach that employs a generic vapor “attenuation factor,” the ratio of the indoor air pollutant concentration to the pollutant concentration in the soil gas directly above the groundwater table. At every potentially affected home above contaminated groundwater, the EPA assumes the vapor attenuation factor is less than 1/1000 – that is, that the indoor air concentration will not exceed 1/1000 times the soil–gas concentration immediately above groundwater. This paper reports on a screening-level model that improves on the EPA approach by considering environmental, contaminant, and household characteristics. The model is based on an analysis of the EPA’s vapor intrusion database, which contains almost 2,400 indoor air and corresponding subsurface concentration samples collected in 15 states. We use the site data to develop a multilevel regression model for predicting the vapor attenuation factor. We find that the attenuation factor varies significantly with soil type, depth to groundwater, season, household foundation type, and contaminant molecular weight. The resulting model decreases the rate of false negatives compared to EPA’s screening approach

Updating Exposure Models of Indoor Air Pollution Due to Vapor Intrusion: Bayesian Calibration of the Johnson-Ettinger Model

The migration of chlorinated volatile organic compounds from groundwater to indoor airknown as vapor intrusionis 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

Cross-tabulation of birth outcomes and maternal blood cadmium and cotinine levels.

<p>Note: The percentages in the total column are reported column-wise, all other percentages in this table are row-wise. Blood cadmium levels: low – ≤0.28 µg/L, medium – 0.29–0.49 µg/L, high – ≥0.50 µg/l.</p><p>Cross-tabulation of birth outcomes and maternal blood cadmium and cotinine levels.</p

Adjusted estimates and standard errors for continuous birth outcomes.

<p>Note: estimates are adjusted for the other model covariates; infant's sex was excluded as a covariate from the birth weight percentile for gestational age model since the birth weight percentiles are sex-adjusted. Blood cadmium levels: low – ≤0.28 µg/L, medium – 0.29–0.49 µg/L, high – ≥0.50 µg/l.</p><p>* p<0.05, ** p<0.01, ***p<0.001.</p><p>Adjusted estimates and standard errors for continuous birth outcomes.</p

Study population characteristics.

<p>Note: Percentages are reported as column-wise. Blood cadmium levels: low – ≤0.28 µg/L, medium – 0.29–0.49 µg/L, high – ≥0.50 µg/l.</p><p><i>*</i> p-values represent results from chi-square tests.</p><p>Study population characteristics.</p

Adjusted odds ratios and 95% confidence intervals for dichotomous birth outcomes.

<p>Note: estimates are adjusted for the other model covariates; LBW – low birth weight, SGA – small for gestational age, PTB – preterm birth; Blood cadmium levels: low – ≤0.28 µg/L, medium – 0.29–0.49 µg/L, high – ≥0.50 µg/l.</p><p>* p<0.05, ***p<0.001.</p><p>Adjusted odds ratios and 95% confidence intervals for dichotomous birth outcomes.</p