Field data and numerical modeling: A multiple lines of evidence approach for assessing vapor intrusion exposure risks

Sci Total Environ. 2016 Jun 15:556:291-301. doi: 10.1016/j.scitotenv.2016.02.185. Epub 2016 Mar 12.

Abstract

USEPA recommends a multiple lines of evidence approach to make informed decisions at vapor intrusion sites because the vapor intrusion pathway is notoriously difficult to characterize. Our study uses this approach by incorporating groundwater, soil gas, indoor air field measurements and numerical models to evaluate vapor intrusion exposure risks in a Metro-Boston neighborhood known to exhibit lower than anticipated indoor air concentrations based on groundwater concentrations. We collected and evaluated five rounds of field sampling data over the period of one year. Field data results show a steep gradient in soil gas concentrations near the groundwater surface; however as the depth decreases, soil gas concentration gradients also decrease. Together, the field data and the numerical model results suggest that a subsurface feature is limiting vapor transport into indoor air spaces at the study site and that groundwater concentrations are not appropriate indicators of vapor intrusion exposure risks in this neighborhood. This research also reveals the importance of including relevant physical models when evaluating vapor intrusion exposure risks using the multiple lines of evidence approach. Overall, the findings provide insight about how the multiple lines of evidence approach can be used to inform decisions by using field data collected using regulatory-relevant sampling techniques, and a well-established 3-D vapor intrusion model.

Keywords: Hazardous waste; Indoor air; Numerical modeling; Soil gas; Vapor intrusion.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Air Pollutants / analysis*
  • Air Pollution / statistics & numerical data*
  • Air Pollution, Indoor / statistics & numerical data
  • Boston
  • Environmental Exposure / statistics & numerical data*
  • Gases / analysis
  • Groundwater / chemistry
  • Humans
  • Models, Chemical
  • Volatilization

Substances

  • Air Pollutants
  • Gases