Comparison of the Industrial Source Complex and AERMOD dispersion models: case study for human health risk assessment.

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.

One fundamental enhancement in air dispersion modeling was the addition of the PRIME algorithm for calculating building downwash effects into both ISC and AERMOD models. Buildings in the path of a plume create downwash effects, which essentially bring the contaminants to ground level more quickly than in a wide-open area. As discussed above, the modeling results presented in Table 3 tend to confirm that theoretical expectation that the PRIME versions of the air dispersion models predict lower air concentrations than their standard models. Another observation from Table 3 is that the variations in the predicted air concentrations between AERMOD and AERMOD-PRIME tend to be smaller than between ISC and ISC-PRIME especially for the 1-hr average air concentrations. Such an observation is reasonable because AERMOD embeds the more updated and realistic air dispersion processes that lead to lower ground-level air concentrations as discussed above.

CONCLUSIONS

Numerous federal and state environmental regulations in the United States incorporate risk assessment into the policy-making process. In most cases, these decisions are made based on a quantitative human health risk assessment in which the estimated risks are compared with a set of acceptable criteria. A quantitative risk assessment dealing with industrial emissions to the atmosphere requires adequate site-specific data and realistic physical air dispersion modeling. An air dispersion model that tends to overpredict or underpredict air concentrations of pollutants can directly affect the outcome of a risk assessment, which can have a profound impact on decision-making. Because AERMOD has replaced ISC, it would be necessary to compare AERMOD and ISC to evaluate the potential impacts of the replacement on human health risk assessments.

This study evaluates the performances of AERMOD, ISC, and their PRIME versions for point, area, and volume sources on two realistic sites. In general, the switch from ISC to AERMOD and the incorporation of the PRIME algorithms tends to generate lower air concentration estimates for point sources at the point of maximum air concentration. However, the magnitude of difference varies from insignificant to significant depending on the types of the sources and the site-specific conditions. The spatial distribution of the predicted maximum air concentration for the point source shows ISC predicted higher air concentrations nearer the site than AERMOD for both averaging periods. As the distance from the site increased, the predicted air concentrations and the shapes of the concentration isopleths become similar. Therefore, the impact of the proposed change on resultant risk to human health when considering chronic exposure can be significant due to the decreased air concentration maximums predicted by AERMOD. The biggest differences between the models appear to occur for the 1-hr averaging period. This could have implications for acute risk assessments and odor evaluations where decisions are often made based on the predicted maximum 1-hr average air concentrations.

In all modeled scenarios, the predicted points of the maximum average air concentrations were separated by no more than 300 ft. In reality, the point of maximum exposure may not have an actual receptor present and it potentially can be located in the middle of a road or in a body of water. It is therefore essential to assess the spatial distribution of air concentrations of pollutants when determining the magnitude and extent of the health impact of air emissions from industrial sources. The spatial distribution will allow for a more accurate assessment of impact or risk to human health and the environment. GIS packages are especially suited to handling the large concentration datasets generated by air dispersion models. The GIS allows for viewing and interpretation of these large spatial and temporal datasets and is a valuable tool for assessing risk. Spatial analysis of risk will allow industry and policy makers to assess site-specific exposures, evaluate the extent of risk to exposed populations, and determine the potential risk to environmental systems such as specific water bodies. Spatial issues may therefore become a key component of environmental and health decision-making in the future.

Because there is a relationship between the modeled air concentration, the HI, and the LICR, the predicted values using different air dispersion models will vary. In this case study, AERMOD and AERMOD-PRIME tended to estimate lower air concentrations. In one case, the LICR based on the air concentration predicted by AERMOD is about one-third of the LICR based on the air concentration predicted by ISC. In this study, all the calculated HIs and LICRs are below accepted thresholds of concern. This case study was performed to gain some practical experience with the models. However, it only examined a limited number of sources on a limited number of sites. Future studies should address sites with multiple sources and sites located in areas of extremely complex terrain.

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