Examining the Use of USEPA's Generic Attenuation Factor in Determining Groundwater Screening Levels for Vapor Intrusion

A value of 0.001 is recommended by the United States Environmental Protection Agency (USEPA) for its groundwater‐to‐indoor air Generic Attenuation Factor (GAFG), used in assessing potential vapor intrusion (VI) impacts to indoor air, given measured groundwater concentrations of volatile chemicals of concern (e.g., chlorinated solvents). The GAFG can, in turn, be used for developing groundwater screening levels for VI given target indoor air quality screening levels. In this study, we examine the validity and applicability of the GAFG both for predicting indoor air impacts and for determining groundwater screening levels. This is done using both analysis of published data and screening model calculations. Among the 774 total paired groundwater‐indoor air measurements in the USEPA's VI database (which were used by that agency to generate the GAFG) we found that there are 427 pairs for which a single groundwater measurement or interpolated value was applied to multiple buildings. In one case, up to 73 buildings were associated with a single interpolated groundwater value and in another case up to 15 buildings were associated with a single groundwater measurement (i.e., that the indoor air contaminant concentrations in all of the associated buildings were influenced by the concentration determined at a single point). In more than 70% of the cases (390 of 536 paired measurements in which horizontal building‐monitoring well distance was recorded) the monitoring wells were located more than 30 m (and one up to over 200 m) from the associated buildings. In a few cases, the measurements in the database even improbably implied that soil gas contaminant concentrations increased, rather than decreased, in an upward direction from a contaminant source to a foundation slab. Such observations indicate problematic source characterization within the data set used to generate the GAFG, and some indicate the possibility of a significant influence of a preferential contaminant pathway. While the inherent value of the USEPA database itself is not being questioned here, the above facts raise the very real possibility that the recommended groundwater attenuation factors are being influenced by variables or conditions that have not thus far been fully accounted for. In addition, the predicted groundwater attenuation factors often fall far beyond the upper limits of predictions from mathematical models of VI, ranging from screening models to detailed computational fluid dynamic models. All these models are based on the same fundamental conceptual site model, involving a vadose zone vapor transport pathway starting at an underlying uniform groundwater source and leading to the foundation of a building of concern. According to the analysis presented here, we believe that for scenarios for which such a “traditional” VI pathway is appropriate, 10^?4 is a more appropriately conservative generic groundwater to indoor air attenuation factor than is the EPA‐recommended 10^?3. This is based both on the statistical analysis of USEPA's VI database, as well as the traditional mathematical models of VI. This result has been validated by comparison with results from some well‐documented field studies.     

Learning soil parameters and updating geotechnical reliability estimates under spatial variability – theory and application to shallow foundations

ABSTRACT

Field data is commonly used to determine soil parameters for geotechnical analysis. Bayesian analysis allows combining field data with other information on soil parameters in a consistent manner. We show that the spatial variability of the soil properties and the associated measurements can be captured through two different modelling approaches. In the first approach, a single random variable (RV) represents the soil property within the area of interest, while the second approach models the spatial variability explicitly with a random field (RF). We apply the Bayesian concept exemplarily to the reliability assessment of a shallow foundation in a silty soil with spatially variable data. We show that the simpler RV approach is applicable in cases where the measurements do not influence the correlation structure of the soil property at the vicinity of the foundation. In other cases, it is expected to underestimate the reliability, and a RF model is required to obtain accurate results.     

Probabilistic failure analysis of infinite slopes under random rainfall processes and spatially variable soil

Rainfall-induced landslides occur during or immediately after rainfall events in which the pore water pressure builds up, leading to shallow slope failure. Thereby, low permeability layers result in high gradients in pore water pressure. The spatial variability of the soil permeability influences the probability such low permeability layers, and hence the probability of slope failure. In this paper, we investigate the influence of the vertical variability of soil permeability on the slope reliability, accounting for the randomness of rainfall processes. We model the saturated hydraulic conductivity of the soil with a one-dimensional random field. The random rainfall events are characterised by their duration and intensity and are modelled through self-similar random processes. The transient infiltration process is represented by Richards equation, which is evaluated numerically. The reliability analysis of the infinite slope is based on the factor of safety concept for evaluating slope stability. To cope with the large number of random variables arising from the discretization of the random field and the rainfall process, we evaluate the slope reliability through Subset Simulation, which is an adaptive Monte Carlo method known to be especially efficient for reliability analysis of such high-dimensional problems. Numerical investigations show higher probability of slope failure with increased spatial variability of the saturated hydraulic conductivity and with more uniform rainfall patterns.     

Seismic performance of buildings with structural and foundation rocking in centrifuge testing

Rocking motion, established in either the superstructure in the form of a 2‐point stepping mechanism (structural rocking) or resulting from rotational motion of the foundation on the soil (foundation rocking), is considered an effective, low‐cost base isolation technique. This paper unifies for the first time the 2 types of rocking motion under a common experimental campaign, so that on the one hand, structural rocking can be examined under the influence of soil and on the other, foundation rocking can be examined under the influence of a linear elastic superstructure. Two building models, designed to rock above or below their foundation level so that they can reproduce structural and foundation rocking respectively, were tested side by side in a centrifuge. The models were placed on a dry sandbed and subjected to a sequence of earthquake motions. The range of rocking amplitude that is required for base isolation was quantified. Overall, it is shown that the relative density of sand does not influence structural rocking, while for foundation rocking, the change from dense to loose sand can affect the time‐frequency response significantly and lead to a more predictable behaviour.     

An Excel®‐Based Visualization Tool of Two‐Dimensional Soil Gas Concentration Profiles in Petroleum Vapor Intrusion

In this study, we present a petroleum vapor intrusion (PVI) tool implemented in Microsoft^® Excel^® using Visual Basic for Applications and integrated within a graphical interface. The latter helps users easily visualize two‐dimensional soil gas concentration profiles and indoor concentrations as a function of site‐specific conditions such as source strength and depth, biodegradation reaction rate constant, soil characteristics and building features. This tool is based on a two‐dimensional explicit analytical model that combines steady‐state diffusion‐dominated vapor transport in a homogeneous soil with a piecewise first‐order aerobic biodegradation model, in which rate is limited by oxygen availability. As recommended in the recently released United States Environmental Protection Agency's final PVI guidance, a sensitivity analysis and a simplified Monte Carlo uncertainty analysis are also included in the spreadsheet.