TITLE:
Space Observations and Global Climatic Data Reanalysis in AERMOD Modeling Package to Enhance the Industrial Air Pollution and Health Risk Assessment
AUTHORS:
Marina Faminskaya
KEYWORDS:
Pollutant Dispersion Model, AERMOD, Albedo, Bowen Parameter, Roughness, Landsat, ALOS, GLASS, ERA5
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.8 No.7,
July
18,
2020
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.