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Space Observations and Global Climatic Data Reanalysis in AERMOD Modeling Package to Enhance the Industrial Air Pollution and Health Risk Assessment

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Document pages: 19 pages

Abstract: We try to enhance the AERMOD industrial pollution dispersion model withremote sensing observations and climatic models based on them. In this paper,we focus on surface parameters (albedo, roughness, Bowen ratio) and land useclassification on which they depend. We model maximum hourly concentrations andthe resulting acute health risk and assess the effect on them produced by usingremote sensing data for local areas around industrial plants instead of globalstandard AERMOD parameters. We consider five real multi-source plants for theeffect of classification and two of them for the effect of surface parameters.The effect on the critical pollutant is measured in three ways: a) asdifference between the yearly maxima of hourly concentrations of a criticalpollutant (“absolute”); b) the same limited to daytime workhours and 95 quantile instead of absolute maximum (“regulatory”); c) as maximum hourlydifference over a year (“instant”). The measure of effect is divided either bythe reference concentration of the pollutant, which yields the impact on healthrisk, or by the concentration obtained with AERMOD standards, which yieldsrelative measure of impact. For a), the impact of roughness dominates, that ofalbedo is small and that of the Bowen ratio is almost zero. For b), the impactof roughness is less prominent, and that of albedo and Bowen ratio isnoticeable. For c), the impact is considerable for all three parameters. Theeffect of land use classification is considerable in all three cases a) - c). We provide thefigures for different measures of remote sensing data effect and discuss theperspective of using remote sensing data in regulatory context.

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