In this paper the results of the study of diurnal and annual behavior of concentrations CO2 and CH4 are presented. This study is based on monitoring data using gas concentration analyzers in the surface layer of the atmosphere in urban and background observation sites in the Middle Urals in 2021 – 2022. Two maxima and two minima in the annual behavior of CH4 concentration are observed for urban area (maxima: December – January and August; minima: May-June and October- November). One maximum in January and one minimum in May and summer months in the annual behavior of CH4 concentration are detected for background region. The annual variations of CO2 are differing in two areas: the highest values were recorded in December to January for urban site and in July for background monitoring point. The diurnal variation CH4 concentration is maximal in July and August in the urban area. The changes from minimal to maximal CH4 concentration are ∼20% The highest diurnal variability of CO2 is observed in July at the background area (∼47%).
The long-term variability (2004-2021) of the aerosol parameters in the atmospheric column, were obtained at a location near the city of Yekaterinburg in the Middle Urals using solar photometer CIMEL CE-318 of the ground-based aerosol robotic network (AERONET). In this study, we present the results of analysis of trend of long-term changes of the aerosol optical depth (AOD) and the characteristics of the aerosol microstructure in the atmospheric column. The AERONET measurements show gradual decrease in annual values of AOT at a wavelength of 0.5 μm, fine and coarse components of atmospheric aerosol.
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.
Based on data of 3-year (2016-2018) measurements in the Middle Urals, we investigated the diurnal variability in aerosol characteristics: atmospheric aerosol optical depth (τ0.5) and concentrations of fine aerosol (PM2.5) in the surface layer of the atmosphere at urban and background sites in the Middle Urals. Also, as a part of this study, we estimated the relation between τ0.5 and PM2.5. The linear regression functions of the hourly PM2.5 concentration and τ0.5 were PM2.5 = 2.83 × τ0.5 + 20.35 (R=0.46) and PM2.5 = 1.8 × τ0.5 + 15.87 (R=0.31) for urban and background sites, respectively. In addition, the results also demonstrated significant differences in correlation for different time window during the day.
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