Simultaneous monitoring of volatile organic contaminant concentration and controlling factors for vapor intrusion risk evaluations—Two select cases

Abstract

The US Environmental Protection Agency (USEPA) recommends basing vapor intrusion risk-related decisions on reasonable maximum exposure (RME). The RME can occur during conditions and factors that control advective vapor transport. The most common vapor intrusion assessment approaches consist of randomly timed sample collection efforts without regard to vapor transport controlling factors. As such, they often do not accurately yield RME estimates and are, therefore, inconsistent with USEPA recommended risk decision criteria. To address these challenges, continuous high-frequency monitoring platforms have been deployed to concurrently track indoor concentrations of key volatile constituents, climatic conditions, and nominal pressure differential conditions that can result in toxic vapor transport and entry into buildings. The objective of this article is to demonstrate how vapor intrusion RME-based risks can be successfully and efficiently characterized by documenting concentrations during advective chemical transport into the building. Time series analyses of data from selected sites and time increments were performed and compared to results expected from the most commonly employed sampling methods. These analyses indicate that time-weighted analyses and resulting conclusions and risk-based decisions can vary depending upon the sample timing. More specifically, these findings demonstrate that RME estimates will only be representative with a sufficient level of confidence when samples are collected at appropriate times. Highfrequency monitoring of dynamic concentration and controlling factors, and determination of a time-weighted concentration average over a selected duration concurrent with advective flux conditions allows for the derivation of a representative RME-based risk estimate. Furthermore, these variable temporal data patterns can prove insightful regarding cause-and-effect relationships.