Abstract
We try to enhance the AERMOD industrial pollution dispersion model with remote sensing observations and climatic models based on them. In this paper, we focus on surface parameters (albedo, roughness, Bowen ratio) and land use classification on which they depend. We model maximum hourly concentrations and the resulting acute health risk and assess the effect on them produced by using remote sensing data for local areas around industrial plants instead of global standard AERMOD parameters. We consider five real multi-source plants for the effect of classification and two of them for the effect of surface parameters. The effect on the critical pollutant is measured in three ways: a) as difference between the yearly maxima of hourly concentrations of a critical pollutant (“absolute”); b) the same limited to daytime workhours and 95% quantile instead of absolute maximum (“regulatory”); c) as maximum hourly difference over a year (“instant”). The measure of effect is divided either by the reference concentration of the pollutant, which yields the impact on health risk, or by the concentration obtained with AERMOD standards, which yields relative measure of impact. For a), the impact of roughness dominates, that of albedo is small and that of the Bowen ratio is almost zero. For b), the impact of roughness is less prominent, and that of albedo and Bowen ratio is noticeable. For c), the impact is considerable for all three parameters. The effect of land use classification is considerable in all three cases a) - c). We provide the figures for different measures of remote sensing data effect and discuss the perspective of using remote sensing data in regulatory context.
Highlights
AERMOD is one of the most widely used pollutant dispersion modeling tools for assessing the health risk from industrial air pollution
We focus on surface parameters and land use classification on which they depend
This paper focuses on two parameters directly reconstructed from space observations and one (Bowen parameter), which requires the in-depth modeling of heat flux balance
Summary
AERMOD is one of the most widely used pollutant dispersion modeling tools for assessing the health risk from industrial air pollution. It contains a micrometeorological model based on the description of surface by albedo, Bowen parameter, and roughness using global standards for land cover categories. Since the introduction of AERMOD, many potentially useful free data have become available through space observations of surface and atmosphere and through reanalysis databases oriented mainly to climatic research. We study the potential application of these new data sources to AERMOD pollution modeling. This paper focuses on two parameters directly reconstructed from space observations (albedo and roughness) and one (Bowen parameter), which requires the in-depth modeling of heat flux balance
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