Chlorinated vapor intrusion indicators, tracers, and surrogates (ITS): Supplemental measurements for minimizing the number of chemical indoor air samples—Part 1: Vapor intrusion driving forces and related environmental factors

Abstract

Vapor intrusion (VI) assessment is complicated by spatial and temporal variability, largely due to compounded interactions among the many individual factors that influence the vapor migration pathway from subsurface sources to indoor air. Past research on highly variable indoor air datasets demonstrates that conventional sampling schemes can result in false negative determinations of potential risk corresponding to reasonable maximum exposures (RME). While high‐frequency chemical analysis of individual chlorinated volatile organic compounds (CVOCs) in indoor air is conceptually appealing, it remains largely impractical when numerous buildings are involved and particularly for long‐term monitoring. As more is learned about the challenges with indoor air sampling for VI assessment, it has become clear that alternative approaches are needed to help guide discrete sampling efforts and reduce sampling requirements while maintaining acceptable confidence in exposure characterization. Indicators, tracers, and surrogates (ITS), which include a collection of quantifiable metrics and tools, have been suggested as a potential solution for making VI pathway assessment and long‐term monitoring more informative, efficient, and cost‐effective. This review, compilation, and evaluation of ITS demonstrates how even low numbers of indoor air CVOC samples can provide high levels of confidence for representing the RME levels (e.g., 95th percentile) often sought by regulatory agencies for less than chronic effects. A two‐part compilation of available evidence for select low‐cost ITS is presented, with Part 1 focused on introducing the concepts of ITS, meteorologically based ITS, and the evidence from data‐rich studies to support lower cost CVOC VI assessments. Part 1 includes the results of quantitative analyses on two robust residential building VI datasets, where numerous supplemental metrics were collected concurrently with indoor air concentration data. These are supplemented with additional less‐intensive studies in different circumstances. These analyses show that certain ITS metrics and tools, including differential temperature, differential pressure, and radon (in Part 2), can provide benefits to VI assessment and long‐term monitoring. This includes indicators that narrow the assessment period needed to capture RME conditions, tracers that enhance understanding of the conceptual site model, and aid in the identification of preferential pathways and surrogates that support or substitute for CVOC sampling results. The results of this review provide insight into the scientifically supportable uses of ITS.

Observation of Conditions Preceding Peak Indoor Air Volatile Organic Compound Concentrations in Vapor Intrusion Studies

“…Select indicators and tracers (I&T) of VI processes, including differential temperature, differential pressure, and indoor air radon concentration, have been proposed as low-cost lines of evidence to support sample collection scheduling and indoor air sampling results interpretation. VI indicators are metrics that can identify an elevated potential for VI exposures and VI tracers are chemical substances that migrate along a similar path as subsurface VOCs (Schuver et al 2018).…”

Vapor intrusion risk evaluation using automated continuous chemical and physical parameter monitoring

“…Since dynamic concentration controlling factors are not typically considered, most sampling efforts are classified as randomly timed (Schuver, Lutes, Kurtz, Holton, & Truesdale, 2018). Schuver et al (2018) further maintain that 58 randomly timed time‐integrated samples would be required to achieve a 95% level of confidence in an RME estimate, which is not practical. Even so, beyond specifying seasonal events, randomly timed time‐integrated samples continue to serve as the most common sampling option for evaluating vapor intrusion risks.…”

“…In fact, many states even require their use (e.g., California DTSC, 2011; Massachusetts Department of Environmental Protection, 2016; New Jersey Department of Environmental Protection, 2018). Schuver et al (2018) also argue that due to spatial and temporal concentration dynamics, conventional random timing can result in incorrect risk assessments. More specifically, they state (Schuver et al, 2018, p.7):

Past research on highly variable indoor air datasets demonstrates that conventional sampling schemes can result in false negative determinations of potential risk corresponding to reasonable maximum exposures (RME).

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“…Practitioners have demonstrated that changes in temperature, barometric pressure, ventilation, and differential pressure can impact and control dynamic indoor concentration patterns (Hosangadi et al, 2017; Schuver et al, 2018; USEPA, 2015a). Regardless of whether the controlling factor is natural (e.g., barometric pressure trend) or anthropogenically induced (e.g., ventilation), the resulting differential pressure across the building foundation due to pressure induced advective flux often dictates whether and when vapor intrusion is occurring.…”

“…This is critical because these factors can dictate the most appropriate time to sample to estimate the RME with the a high level of confidence. Therefore, if practitioners are to accept the RME‐based risk criteria as correct (e.g., accept decision criteria recommended in USEPA, 2015a), since random sampling without consideration of potential controlling factors represents status quo, Schuver et al (2018) findings imply that traditional randomly‐timed samples not only fail to meet the required RME criteria, but they have an elevated probability of yielding flawed risk conclusions. This is consistent with Holton et al (2013) when they concluded the following (Holton et al, 2013, p.13354):

There can be relatively high probabilities of false‐negative decisions and poor characterization of long‐term mean concentrations with sparse data sets typical of current practice.

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