The results of the research are numerical estimates of the average fields of black carbon mass concentration in the surface layer of the atmosphere of the Arctic region obtained using the numeric technology referred to as fluid location of the atmosphere (FLA). The modelling has been based on measurements of the black carbon concentrations in the near-surface atmosphere obtained during the two cruises of the Professor Multanovskiy (28 July–7 September 2019) and Akademik Mstislav Keldysh (31 July–24 August 2020) research vessels. These measurements have been supplemented by measurements at stationary monitoring points located on the Spitsbergen and the Severnaya Zemlya archipelagoes. The simulation in the summertime demonstrates that areas of increased black carbon concentrations were observed over Northern Europe and, in 2019, also over the Laptev Sea basin. The obtained spatial distribution of mass concentrations of black carbon qualitatively agreed with the same data derived from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) but showed quantitative differences. The average values of mass concentrations of black carbon in the modelling zones are as follows: 85.3 ng/m³ (2019) and 53.6 ng/m³ (2020) for fields reconstructed by the FLA technology; and 261.69 ng/m³ (2019) and 131.8 ng/m³ (2020) for the MERRA-2 data.

The well-known method of the back trajectory statistics (BTS) is used to develop a new approach to estimating atmospheric pollution fields according to local measurements. On the basis of instrumental measurements at one or a few monitoring points and information on atmospheric dynamics (in this work, back trajectories of the motion of air particles), the BTS method makes it possible to estimate the spatial structure of fields of a measured quantity. Results from solving a simplified demonstration of estimating the spatial distribution of the volume concentration of a fine aerosol fraction are presented; these results were obtained on the basis of an analysis of photometric measurements in the period of 2004–2010 at six AERO-NET monitoring sites on the territory of Russia: Zvenigorod, Moscow, Yekaterinburg, Tomsk, Yakutsk, and Ussuriisk.

We analyzed the measurements of aerosol parameters in the surface air layer with sets of Panasonic PM_2.5 optical sensors and in throughout the atmospheric column by the photometric method at urban and background observation sites in the Middle Urals for 2016–2019. The features of the intra-annual and daily variations in the aerosol parameters in the surface air layer and in the atmospheric column are compared; also, the relationships between the PM_2.5 concentration, AOD, and meteorological parameters in two regions are studied. For the first time for the Middle Urals, we constructed the statistical models for estimating the PM_2.5 concentration. Multivariate regression models for estimating the logarithm of PM_2.5 concentration are far superior to the single-factor models. The significant predictors are found to be: boundary layer height (blh, m), ln AOD, normalized difference vegetation index (NDVI), relative air humidity (Hu, %), and pressure (P, Pa).

A variant of determining errors in the statistical estimate of the average effective concentration field calculated in the quasi-two-dimensional approximation by the method of fluid location of the atmosphere (FLA) is proposed. Results of the FLA verification by three methods are presented. In spite of the fact that the quasi-two-dimensional approximation has some restrictions, all the considered ways of verification point to the possibility to use the FLA method for estimating average concentration fields of fine aerosol.

We have proposed a fundamentally new formulation of the problem of estimating the spatial structure of the average concentration field of atmospheric contaminant on the base of measurements at one or several monitoring points, without involving information about the sources of the contaminant — the problem of passive location (remote sensing) of the atmosphere by wind or Fluid Location of the Atmosphere. Two new functions are defined: t he a verage effective c oncentration field an d th e average effective sources field. The equations for finding these functions are written in the differential and integral forms. In the quasi-two-dimensional approximation for the case of a passive pollutant, the estimates of the average effective methane concentration in the Kara and Barents Seas region are presented based on measurements on Bely Island in July and August 2016.

The present work aims to develop a regional Middle Urals Aerosol model (MUrA model) based on the joint analysis of long-term ground-based photometric measurements of the Aerosol Robotic NETwork (AERONET) and the results of lidar measurements of the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite relying on information on the air trajectories at different altitudes calculated using the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory model) software package. The MUrA model contains parameters of normalized volume size distributions (NVSDs) characterizing the tropospheric aerosol subtypes detected by the CALIPSO satellite. When comparing the MUrA model with the global CALIPSO Aerosol Model (CAMel), we found significant differences in NVSDs for elevated smoke and clean continental aerosol types. NVSDs for dust and polluted continental/smoke aerosol types in the global and regional models differ much less. The total volumes of aerosol particles along the atmospheric column reconstructed from satellite measurements of the attenuation coefficient at a wavelength of 532 nm based on the regional MUrA model and global CAMel are compared with the AERONET inversion data. The mean bias error for the regional model is 0.016 μm³/μm², and 0.043 μm³/μm² for the global model.