This software estimates uncontrolled air emissions from soil vapor extraction (SVE) systems. These estimates may be used to assess potential air pathway risks or to determine the need for controls. Both a Microsoft Excel and Lotus l,2,3 spreadsheet is included. The accompanying documentation references several public domain and commercially available emission models. The U.S. Environmental Protection Agency's (EPA) Office of Air Quality Planning and Standards (OAQPS) and the Regional Air Offices have been given the responsibility to evaluate air impacts from Superfund sites. An important part of this program is the analysis of air impacts from various alternatives for cleaning up Superfund sites. Since these analyses are frequently required for planning purposes prior to actual cleanup they depend on estimated emissions and ambient concentrations rather than on field measurements. SVE is a widely used technique for removing volatile organic compound (VOC) vapors from contaminated soil. The documentation provides several predictive emission models ranging from first-order gross estimates requiring little site-specific data, to refined models which calculate air flow through a porous medium as a result of the pressure gradient created by an extraction well and the vapor-phase concentration of the extracted air. When evaluating the array of predictive models, a primary concern is the ability of each model to estimate time-dependent removal rates. This is especially important for SVE in that initial removal rates may be quite high (e.g., 500 to 600 kg/day). Typically, removal rates decrease with time as initial soil concentrations are depleted. Time-dependent removal rates may be critical for estimating other than long-term exposures, and thus short-term risks to the community and to site workers. The documentation also provides guidance on air dispersion model selection and use to help develop different modeling scenarios and estimate ambient air impacts. Model
inputs, including source and receptor data, can vary greatly. These data can have a direct bearing on ambient air concentrations, which are important both in risk assessments and compliance with air applicable or relevant and appropriate requirements (ARARs). Modeling several scenarios can allow for the adjustment of equipment size, design, control equipment, and location in a cost-effective manner.