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Weekly UpdatesFor the area sources on the two sites, ISC predicted higher maximum air concentrations for Site 1 whereas AERMOD predicted higher maximum air concentrations for Site 2 for both averaging periods. The differences in model performance could be due to the terrain differences between the two sites and/or the enhanced treatment of plume dispersion and growth in AERMOD.
For the volume source on Site 1, AERMOD predicted the higher maximum total period average concentration whereas ISC predicted the higher maximum 1-hr average concentration. However, the magnitude of the observed differences is very small. For the volume source on Site 2, ISC predicted higher maximum air concentration levels than AERMOD for both averaging periods. In the case of the 1-hr averaging period, the maximum air concentration level predicted by ISC is five times higher than by AERMOD.
Overall, the results in Table 3 suggest that the magnitude of the variability between the models is greater for Site 2 than Site 1. We therefore decided to further compare the predicted air concentration levels for Site 2 using GIS mapping. For the point source located on Site 2, ISC-PRIME predicts higher total period air concentrations than AERMOD-PRIME in the receptors closer to the site as shown in Figure 1. As the receptor distance from the site increases, the predicted air concentrations and the shapes of the predicted concentration isopleths from the models become more similar. The same trend is observed for the 1-hr averaging period. The predicted maximum air concentrations, for both averaging periods, are higher for ISC-PRIME. For the 1-hr averaging period, ISC-PRIME predicted higher concentrations close to the source. As the distance from the source increases, the modeled concentrations for ISC-PRIME shift and become lower than those predicted by AERMOD-PRIME. For the total averaging period, the top 10% of the modeled air concentrations from ISC-PRIME are greater than those predicted by AERMOD-PRIME.
[FIGURE 1 OMITTED]
For the area source and volume source on Site 2, ISC and AERMOD predict similar concentrations for the total averaging period for receptors in close proximity to the site. For the 1-hr averaging period for the area source, ISC and AERMOD predict similar concentrations in proximity to the site, but ISC predicts higher air concentrations than AERMOD for the receptors that are away from the site. For the volume source, ISC consistently predicts higher concentrations for the 1-hr averaging period at all receptors.
Human Health Risk Assessment
The HQ and LICR were calculated at the receptor with the maximum total period average air concentration. Table 4 presents the HQs and LICRs calculated using the air concentrations predicted by the different air dispersion models for the point, area, and volume sources on the two sites. All calculated HQs and LICRs were below accepted thresholds of concern at the emission levels modeled. As stated previously, EPA considers a HQ less than 1 to be safe and cites an acceptable range of 1 x [10.sup.-4] to 1 x [10.sup.-6] for potential cancer risk, with 1 x [10.sup.-6] being considered the de minimus value.
There is a linear relationship between the predicted maximum average concentration and the HQ and LICR as indicated by eqs 1 and 2. Therefore, the predicted HQ and LICR values calculated using the results from the different air dispersion models simply mirror the results of the total period maximum average concentrations estimated by the models. For point sources, AERMOD predicts slightly higher air concentrations than ISC for Site 1 and lower air concentrations than ISC for Site 2. For Site 1, this results in a negligible difference in the calculated HI and LICR using both models. For Site 2, the calculated HI and LICR values using the air concentrations predicted by AERMOD are approximately one-third less than the values calculated using the air concentrations predicted by ISC. When the PRIME algorithm is considered, AERMOD-PRIME generates lower air concentration values (and subsequently lower HI and LICR values) than ISC-PRIME for both sites. For Site 1, this results in a negligible difference in the calculated HI and LICR using both models. For Site 2, the calculated HI and LICR values using the air concentrations predicted by AERMOD-PRIME are approximately one-third less than the values calculated using the air concentrations predicted by ISC-PRIME. For area sources, AERMOD generates lower air concentration values for Site 1 and higher air concentration values for Site 2 when compared with ISC. In the case of volume sources, the results are opposite: ISC generates slightly lower air concentration values for Site 1 and higher air concentration values for Site 2 when compared with AERMOD.
DISCUSSION
ISC and AERMOD generate different results because they embed different algorithms for dealing with plume dispersion, plume rise, and underlying surface conditions at the receptors. AERMOD takes into account wind and temperature changes above the stack top in stable meteorological conditions (i.e., little turbulence due to convection or buoyancy) and convective updrafts and downdrafts in unstable meteorological conditions (i.e., increased convective turbulence). However, ISC does not account for convective turbulence. Downdrafts can potentially bring pollutants down to the surface early on and with minimal dilution, thereby creating higher ground-level concentrations closer to the source. Updrafts can carry pollutants further downwind and in different directions. In unstable atmospheres, convective mixing causes an elevated plume to descend over distance. (15,21,24)
AERMOD also handles plume dispersion and plume growth rates differently than ISC. As a plume moves downwind from the release point, it grows in both the vertical and horizontal directions. ISC uses Gaussian models to calculate atmospheric dispersion in both the horizontal and vertical directions. However, AERMOD uses Gaussian models in both the horizontal and vertical directions only under stable conditions. Under unstable conditions, AERMOD uses a Gaussian model in the horizontal direction and a non-Gaussian probability density function in the vertical direction to account for the effects of vertical variations in wind and turbulence on air dispersion. AERMOD's treatment of the vertical air dispersion during unstable conditions is a more accurate portrayal of actual air movement. When the atmosphere is unstable, a surface release encounters turbulence at the ground and is rapidly diluted so that the maximum ground-level concentrations occur close to the source. In stable atmospheres, convective turbulence is minimal and plume dispersion is mainly effected by wind speed. Wind speed changes with height, with lower wind speeds occurring closer to the ground level. In regards to the plume growth rates, ISC uses either rural or urban plume dispersion curves that are a function of distance and one of six possible discrete stability classes. AERMOD uses profiles of vertical and horizontal turbulence that can be either measured or calculated from the meteorological dataset. The vertical profiles vary with height and use continuous growth functions rather than discrete stability classes. Use of turbulence-based plume growth with height gives AERMOD a substantial advancement over ISC. The greatest enhancement would most likely be seen during stable conditions when plume dispersion is minimal because of low turbulence. (15,21,24)
In the meteorological dataset used for Site 2, approximately 75% of the hours were classified as stable or neutral and 25% of the hours were classified as unstable. In this case study, ISC predicted higher maximum ground-level concentrations close to the sources for both the 1-hr and total period averaging time frames. AERMOD predicted lower maximum ground-level concentrations than ISC possibly because of its ability to better handle the stable periods. Because a majority of the meteorological hours were stable, AERMOD should allow for increased dispersion, which would result in lower maximum ground-level concentrations. The enhanced handling of ground-level releases in AERMOD may help explain why AERMOD predicts higher maximum ground-level concentrations for releases from the area sources.
The underlying surface conditions at the receptors were handled differently by the two models. ISC does not consider the underlying surface conditions at the receptors. However, AERMOD uses data on the underlying surface conditions at the receptors as well as data on seasonal variation in its meteorological preprocessor to calculate the values of albedo, bowen ratio, and surface roughness for all four seasons. AERMOD's consideration of the surface characteristics at the receptor are potentially important because the turbulence caused by surface friction will certainly affect the modeling results, especially in windy conditions. Wind speed is retarded because of the frictional effects caused by surface roughness. The more obstacles in the path of the wind, the greater the turbulence and frictional effects; therefore, the wind could blow much stronger several meters above a plowed field than it would above an urban area. (24) In the case of Site 2, the surface roughness in the forest surrounding the site was greater than the surface roughness in the grassland. The predicted maximum ground-level concentrations occurred in a sector predominated by forest. In this case, the increased surface roughness leads to increased turbulence, faster dispersion of the plume, and therefore, the maximum air concentration occurs closer to the emission source. In this case study, ISC, using rural dispersion coefficients, predicted higher air concentrations. The surface frictional effects may explain why AERMOD predicted higher 1-hr average concentrations for the receptors farther away from the sources.
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