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Weekly UpdatesABSTRACT
Air quality models are typically used to predict the fate and transport of air emissions from industrial sources to comply with federal and state regulatory requirements and environmental standards, as well as to determine pollution control requirements. For many years, the U.S. Environmental Protection Agency (EPA) widely used the Industrial Source Complex (ISC) model because of its broad applicability to multiple source types. Recently, EPA adopted a new rule that replaces ISC with AERMOD, a state-of-the-practice air dispersion model, in many air quality impact assessments. This study compared the two models as well as their enhanced versions that incorporate the Plume Rise Model Enhancements (PRIME) algorithm. PRIME takes into account the effects of building downwash on plume dispersion. The comparison used actual point, area, and volume sources located on two separate facilities in conjunction with site-specific terrain and meteorological data. The modeled maximum total period average ground-level air concentrations were used to calculate potential health effects for human receptors. The results show that the switch from ISC to AERMOD and the incorporation of the PRIME algorithm tend to generate lower concentration estimates at the point of maximum ground-level concentration. However, the magnitude of difference varies from insignificant to significant depending on the types of the sources and the site-specific conditions. The differences in human health effects, predicted using results from the two models, mirror the concentrations predicted by the models.
INTRODUCTION
Air dispersion models are designed to predict the fate and transport of emissions of pollutants into the atmosphere. Pollutants once emitted will mix with the ambient air, where physical processes, such as turbulence and chemical reactions, cause the primary pollutants to disperse and their concentration to decrease. In some cases, chemical reactions may cause the primary pollutants to produce secondary pollutants such as ozone. Air dispersion models predict the ambient air concentrations of a compound at specific spatial locations (called receptors) using mathematical equations that represent the numerous and complex meteorological processes responsible for dispersion. Inputs to an air dispersion model typically include meteorological data, source emission data in the form of a mass emission rate, dimensions of nearby structures, and local terrain information. The U.S. Environmental Protection Agency (EPA) and state environmental regulatory agencies have used air dispersion models to implement many regulatory programs.
Generally, EPA regulatory air dispersion modeling is conducted in accordance with the procedures outlined in 40 CFR 51 Appendix W Guideline on Air Quality Models. On November 9, 2005, EPA issued the final rule to replace the widely used Industrial Source Complex (ISC) air dispersion model with a new state-of-practice air dispersion model AERMOD in many air quality impact assessments. In accordance with EPA (2005), AERMOD is fully promulgated as a replacement to ISC. (1) The potential impact of these changes is of interest to regulatory agencies and regulated industries. Air dispersion modeling is used to predict the fate and transport of air emissions from industrial sources to comply with regulatory requirements, environmental and health standards, and facility design criteria. Modeling air concentrations at receptors in a community is a crucial first step in assessing any potential risk to human health or the environment. For example, in assessing human health effects due to inhalation of air toxics, the health outcome is directly related to the air concentration of the chemical predicted by the air dispersion model.
Because different air dispersion models are likely to produce different results under various conditions, it would be interesting to evaluate the nature and magnitude of these differences and their implications on the human health risk assessment of air toxics. Several studies have compared ISC and AERMOD and their PRIME versions. (2-10) The PRIME versions of the models include the Plume Rise Model Enhancements (PRIME) algorithm developed to correct some shortcomings discovered in the building downwash algorithm. Perry et al. (11) compared several existing air dispersion models in terms of modeled and observed concentration distributions and concluded that with few exceptions the performance of AERMOD is superior to that of the other applied models.
This study compared the EPA preferred models (AERMOD and AERMOD-PRIME) to two widely used alternative models (ISC and ISC-PRIME). Moreover, this study assessed the impact of the model changes on the calculation of both carcinogenic and noncarcinogenic inhalation risk to human health. The risk impacts are important because the model results directly influence decisions under the current risk-based air toxics programs of the 1990 Clean Air Act Amendments (CAA). For example, the Maximum Achievable Control Technology (MACT) program was designed to significantly reduce emissions from major sources through pollution-control technologies. Once the control technologies have been implemented, the CAA requires that risk assessments be performed to evaluate any residual human health risk. The results of these risk assessments will determine if major sources will need to implement further controls to reduce pollution, which are usually very expensive to implement. (12) Therefore, changes to the regulatory accepted air dispersion model could have important economic consequences to regulated industries.
In this application, the air dispersion models were tested using a point source and two nonpoint sources (an area and a volume source) that are located on two actual industrial sites. The point source represents a typical stack from a pollution control device. The volume source represents the fugitive emissions associated with a typical process building. The area source represents the fugitive emissions associated with large storage tanks. The modeled maximum ground-level air concentrations were used to evaluate the human health risk from exposure to air toxics using the exposure factors, toxicity factors, and risk equations typically used for the calculation of residual risk.
METHODS
Model Descriptions
The ISC model is a Gaussian dispersion model that assumes any release from a source disperses in a steady-state manner from the time of release until the time it reaches a receptor. Gaussian dispersion models assume that a normal distribution can characterize the horizontal and vertical spread of a plume. (13) On-site structures can affect wind flow and contribute to building downwash, which can have important ramifications in air quality modeling. The building downwash algorithms in ISC are designed to evaluate the extent of building downwash. These algorithms require additional input and therefore, the EPA Building Profile Input Program (BPIP) is run for all point sources (stacks) to generate necessary inputs required for execution of ISC. BPIP determines whether a stack is potentially subject to wake effects due to the surrounding structures and this information is supplied as an input to ISC. (15) In addition, ISC requires input data on source characteristics, receptor location, meteorological parameters, and topography. AERMOD incorporates the same down-wash algorithms as ISC but contains advanced algorithms for dispersion, plume rise, buoyancy, and the handling of complex terrain. AERMOD, like ISC, is a steady-state model and is most useful for analyzing short-range pollutant transport within 20 km of the source. (16) The main justification for replacing ISC with AERMOD was that AERMOD incorporates many of the scientific advances made in the 1970s and 1980s in understanding turbulence and dispersion in the planetary boundary layer (PBL). The PBL is the lowest portion of the atmosphere (1-2 km deep) where pollutants are emitted, transported, mixed, and dispersed. (17) The AERMOD meteorological preprocessor makes use of the surface characteristics of the land surrounding the site along with the hourly surface meteorological data to produce more realistic estimates of parameters that affect dispersion, such as albedo, bowen ratio, and surface roughness. (18)
The PRIME algorithm was developed to correct some shortcomings discovered in the building downwash algorithm used in the ISC model. (19) Using ISC with the PRIME algorithm (ISC-PRIME) should result in more realistic predictions of building downwash effects. The PRIME model algorithm was also added to AERMOD (AERMOD-PRIME). The PRIME models are better at handling the turbulent wake and reduced plume rise caused by the descending flow seen on the leeward side of the building. (19-21)
Air Dispersion Modeling Inputs
The two industrial facilities modeled are both manufacturing facilities located in the eastern United States. The terrain within 3 km of Site 1 is relatively flat. The terrain within 3 km of Site 2 is relatively flat to the west but the terrain to the east is variable and hilly with increasing elevations as distance from the site increases. Site 1 is surrounded by forest to the west and by a combination of grassland and deciduous trees to the east. Site 2 is surrounded by grassland to the west and dense forest with increasing elevation to the east. Both sites are considered rural.
Terrain elevation data representative for each site were obtained from various U.S. Geological Survey (USGS) data sources. Because 7.5-min terrain data at a scale of 1:24,000 were not readily available for both sites, 1[degrees] USGS digital elevation models (DEMs) at a scale of 1:250,000 were used instead. To determine surface roughness, a circle with a radius of 3 km was drawn around the center of each site and the circular area was divided into 12 sectors of 30[degrees] each, starting with sector 1, which was centered on 0[degrees](i.e., due north). The land use within each sector was classified as either water, urban, deciduous forest, coniferous forest, or grassland, consistent with EPA guidance. (18) The areal extent of each land use classification, in square meters and as a percentage of the total sector area, was determined using the Geographical Information System (GIS) software package, ArcView (ESRI).
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