The article is concerned with statistical analysis of the relationship between the ground level fine aerosol concentrations and the aerosol optical depth of the atmosphere. The aerosol characteristics measurements at two monitoring sites in the Middle Urals (Yekaterinburg city and the background region) combined with meteorological parameters and vegetation indices were used. Several linear models to estimate fine particulate matter concentrations using aerosol optical depth measurements and meteorological parameters are presented.
A machine learning approach to solve a multiple regression problem is considered. Mass concentration of aerosol particles in the surface layer of the atmosphere was used as a dependent variable. The aerosol optical depth of the atmosphere and a number of meteorological parameters from the ECMWF ERA5 reanalysis database were chosen as predictors. The problem was solved using an ensemble machine learning algorithm - a random forest.
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