This book introduces key concepts in modeling and risk assessments of vapor intrusion, a process by which the subsurface volatile contaminants migrate into the building of concern. Soil vapor intrusion is the major exposure pathway for building occupants to chemicals from the subsurface, and its risk assessments determine the criteria of volatile contaminants in soil/groundwater in brownfield redevelopment. The chapters feature the recent advances in vapor intrusion studies and practices, including analytical and numerical modeling of vapor intrusion, statistical findings of United States Environmental Protection Agency’s Vapor Intrusion Database and Petroleum Vapor Intrusion Databases, the challenges of preferential pathways, and the application of building pressure cycling methods, and field practices of vapor intrusion risk assessments at developed contaminated sites and in brownfield redevelopment. This volume also summarizes the advantages and limits of current applications in vapor intrusion risk assessment, laying the groundwork for future research of better understanding in risk characterization of soil vapor intrusion using models. Written by experts in this field, Vapor Intrusion Simulations and Risk Assessments will serve as an invaluable reference for researchers, regulators, and practitioners, who are interested in perceiving the basic knowledge and current advances in risk assessments of soil vapor intrusion.

The primary objective of this demonstration study was to identify a cost effective and accurate protocol for investigation of vapor intrusion into buildings overlying contaminated groundwater. Intensively monitored sites, such as the Borden Landfill in Canada, have greatly contributed to our understanding of the physical and chemical processes that control the transport of chemicals in groundwater. For this project, we have used a similar approach (i.e., intensively monitored sites with specially-designed monitoring networks) to address the critical groundwater-to-indoor air vapor intrusion pathway. For this demonstration, vapor intrusion site investigations have been completed at a total of three buildings located at two demonstrations sites: two single-family residences near Hill AFB and a small office building at Altus AFB. For each site, the investigation program consisted of an initial sample point installation and sampling event and one (Hill AFB) or two (Altus AFB) follow-up sampling events.

Improving weather and climate prediction with better representation of fast processes in atmospheric models Many atmospheric processes that influence Earth’s weather and climate occur at spatiotemporal scales that are too small to be resolved in large scale models. They must be parameterized, which means approximately representing them by variables that can be resolved by model grids. Fast Processes in Large Scale Atmospheric Models: Progress, Challenges and Opportunities explores ways to better investigate and represent multiple parameterized processes in models and thus improve their ability to make accurate climate and weather predictions. Volume highlights include: Historical development of the parameterization of fast processes in numerical models Different types of major sub-grid processes and their parameterizations Efforts to unify the treatment of individual processes and their interactions Top-down versus bottom-up approaches across multiple scales Measurement techniques, observational studies, and frameworks for model evaluation Emerging challenges, new opportunities, and future research directions The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.

Vapor intrusion is the migration of volatile organic compounds (VOCs) from a subsurface source into the indoor air of an overlying building. Vapor intrusion models, including the Johnson and Ettinger (J&E) model, can be used to predict the concentration of VOCs in the indoor air of a building based on a measured subsurface soil gas concentration or contaminant source concentrations, either in non-aqueous phase liquid (NAPL), groundwater, or soil. An analysis of two of the EPA-implemented J&E spreadsheet models, one that considers subsurface soil gas data and one that considers groundwater data, was conducted. The governing equations, assumptions, and limitations of these spreadsheet models were investigated. A value of information (Vol) worksheet was developed that can assist practitioners in deciding what additional data to collect as part of a remedial investigation. The Vol worksheet calculates how varying values of model input parameters affect the model-predicted indoor air carcinogenic risk. The worksheet then compares the user-defined target risk to the range of potential risk values for different combinations of varying parameters. The results of this analysis allow the user to determine which groups of parameters have the most impact on the model results. This information can assist the practitioner in deciding whether or not to collect additional data to reduce the uncertainty in the input parameters. The EPA J&E soil gas and groundwater spreadsheet models, as well as the Vol worksheet developed for each model, were applied to case study data for a trichloroethylene-impacted site in Rhode Island. The results of the J&E model and Vol worksheet analyses for this case study predicted incremental carcinogenic risk values for trichloroethylene (TCE) below the risk value calculated based on measured indoor air data. This comparison suggests the potential for other sources of TCE within the building. Groups of parameters were identified for each model that impacted the model-predicted carcinogenic risk. The development of a cost-benefit analysis, which would be used to quantify the value of obtaining additional data for these critical parameters, is recommended for future research.

Vapor intrusion (VI) pathway assessment often involves the collection and analysis of groundwater, soil gas, and indoor air data. There is temporal variability in these data, but little is understood about the characteristics of that variability and how it influences pathway assessment decision-making. This research included the first-ever collection of a long-term high-frequency indoor air data set at a house with VI impacts overlying a dilute chlorinated solvent groundwater plume. It also included periodic synoptic snapshots of groundwater and soil gas data and high-frequency monitoring of building conditions and environmental factors. Indoor air trichloroethylene (TCE) concentrations varied over three orders-of-magnitude under natural conditions, with the highest daily VI activity during fall, winter, and spring months. These data were used to simulate outcomes from common sampling strategies, with the result being that there was a high probability (up to 100%) of false-negative decisions and poor characterization of long-term exposure. Temporal and spatial variability in subsurface data were shown to increase as the sampling point moves from source depth to ground surface, with variability of an order-of-magnitude or more for sub-slab soil gas. It was observed that indoor vapor sources can cause subsurface vapor clouds and that it can take days to weeks for soil gas plumes created by indoor sources to dissipate following indoor source removal. A long-term controlled pressure method (CPM) test was conducted to assess its utility as an alternate approach for VI pathway assessment. Indoor air concentrations were similar to maximum concentrations under natural conditions (9.3 [micro]g/m3 average vs. 13 [micro]g/m3 for 24 h TCE data) with little temporal variability. A key outcome was that there were no occurrences of false-negative results. Results suggest that CPM tests can produce worst-case exposure conditions at any time of the year. The results of these studies highlight the limitations of current VI pathway assessment approaches and demonstrate the need for robust alternate diagnostic tools, such as CPM, that lead to greater confidence in data interpretation and decision-making.