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Space observations of surface parameters and reanalysis data for AERMOD modeling of industrial air pollution. Part 2. Albedo, surface roughness and Bowen parameter

B.M. Balter¹ , D.B. Balter¹ , V.V. Egorov¹ , M.V. Stalnaya¹ , M.V. Faminskaya²

¹ Space Research Institute RAS, Moscow, Russia
² Russian State Social University, Moscow, Russia

Accepted: 10.12.2020
DOI: 10.21046/2070-7401-2021-18-3-121-137

We try to enhance the AERMOD industrial pollution dispersion model with remote sensing observations and climatic models based on them. In this paper, the focus is on three surface parameters (albedo, roughness and Bowen ratio). Roughness and albedo are reconstructed directly from remote observations, and Bowen parameter requires the thermal flux estimates from climatic models. 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 three real multi-source industries 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, and that of albedo and Bowen ratio is small. 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. We provide the figures for different measures of remote sensing data effect and discuss the perspective of using remote sensing data in regulatory context.

Keywords: AERMOD, pollutant dispersion model, albedo, roughness, Bowen parameter, land use classification, maximal hourly concentrations, Landsat, ALOS, GLASS, ERA5

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