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Article

Assessing the Impact of Natural and Anthropogenic Pollution on Air Quality in the Russian Far East

by
Georgii Nerobelov
1,2,3,
Vladislav Urmanov
1,
Andrei Tronin
1,
Andrey Kiselev
1,
Mihail Vasiliev
1,
Margarita Sedeeva
2 and
Alexander Baklanov
4,*
1
St. Petersburg Federal Research Center of the Russian Academy of Sciences, 199178 St. Petersburg, Russia
2
Laboratory of Modeling of Middle and Upper Atmosphere (LIMA), Russian State Hydrometeorological University, 195196 St. Petersburg, Russia
3
O3Lab, Saint Petersburg University, 199034 St. Petersburg, Russia
4
Niels Bohr Institute, University of Copenhagen, 2200 Copenhagen, Denmark
*
Author to whom correspondence should be addressed.
Climate 2025, 13(12), 252; https://doi.org/10.3390/cli13120252
Submission received: 14 October 2025 / Revised: 2 December 2025 / Accepted: 10 December 2025 / Published: 16 December 2025
(This article belongs to the Section Weather, Events and Impacts)
Browse Figures
Review Reports Versions Notes

Abstract

The Russian Far East is one of the regions of the country with the least investigated processes affecting the air quality and related climate changes of the region. In the current study 3D numerical modeling (WRF-Chem) together with the ground- and satellite-based observation data of the particular atmospheric pollutants (NO2, CO, SO2, O3, aerosols) were applied to demonstrate how wildfires and transboundary pollution from China could influence air quality in the Far East of Russia (with focus on the Amur region) in July 2015 and January 2023. The WRF-Chem modeling system represents a near-surface air temperature with bias (compared to observations) of 0.5–2 °C and standard deviation, or STD, of 2–5 °C. In general the model overestimates near-surface wind speed—the bias varies in the range 0.8–1.9 m/s with STD of ~2 m/s. This fact should affect the model performance of near-surface gaseous and aerosol composition. Robust Pearson correlation coefficient (from ~0.5) in both periods was found only between modeled and observed near-surface NO2 and CO. Significant correlation for O3 (0.73) was found only in January. By using WRF-Chem regional modeling it was demonstrated that seasonal wildfires in the northern Amur region, Zabaykalsky Krai, and the Republic of Yakutia (July 2015) and transboundary pollution from northeastern China (January 2023) could cause the degradation of air quality in the Amur region. Additionally, the possible effect on air quality from the domestic anthropogenic emissions of the Amur region was found in January 2023. According to the modeling, in July 2015 monthly mean NO2 concentration higher than state standards was found in the territory of the Amur region. The highest monthly mean near-surface NO2 concentrations exceeding state standards were modeled in northeastern China (~0.05 ppm). The modeled concentrations of other pollutants in the Russian Far East fit the state norms in both periods. The effect of wildfires and transboundary pollution episodes on air quality in the Russian Far East can be considered for the evaluation in the future state air quality reports.

1. Introduction

The territory of the Russian Far East has a unique geographical position that significantly influences air quality in certain regions of this part of the country. First, the vast forest coverage [1] combined with high summer air temperatures and low air humidity increases the likelihood of uncontrolled wildfires [2]. Second, its proximity to China—a country recognized as one of the world’s leading economies—also plays a role. According to [3], China hosts numerous anthropogenic sources of air pollutants (e.g., NO2). Despite the implementation of a national air quality improvement program in 2013 [4] and the subsequent positive outcomes achieved [5], air quality in the northern regions of China has improved more slowly than in the central and southern parts of the country [6]. For example, Heilongjiang province, which is on the Russian–Chinese border, concentrates relatively large sources of anthropogenic pollutants, in particular NO2, which are observed from the satellite-based data [7]. According to [8] in 2024, 55% of the population of the Far Eastern Federal District of Russia was exposed to high or very high levels of air pollution. A lot of different sources of air pollutants are located in this district. Those are, at first, natural emissions due to seasonal wildfires (see Figure 2 in [9]). According to [10], three regions of the Far Eastern Federal District of Russia—the Republic of Sakha (Yakutia), Zabaykalsky Krai, and Amur region—together with Heilongjiang province of China were mentioned as the regions with the largest burned areas due to wildfires in corresponding countries during 2024–2025. The fire activity was related to the heat and drought season accompanied by dry thunderstorms and accidental ignition by the local population. Secondly, among all the sources of anthropogenic emissions of gases and aerosols in the district, the following can be mentioned due to their significance for air quality changes: power plants, transport, mining, and different industries (i.e., shipbuilding, oil and gas production, fishing industry) [11].
The Amur region is one of the areas in the Russian Far East where air quality is potentially most affected by both natural emissions from forest fires and the transboundary transport of pollutants originating from anthropogenic emissions in China. In addition, local anthropogenic sources of air pollution are present within the region and contribute to overall air quality levels as well [12]. According to [13], air quality in Blagoveshchensk, the administrative center of the Amur region, was assessed as poor in 2021. This is further supported by [14], which reported that in 2021, 53% of the region’s population lived under conditions of high or very high air pollution. However, data from reviews in 2023 and 2024 [15,16] indicate that only 4% of the population lived under similar conditions in those years. The main contributors to air pollution in the city are identified as local sources such as machinery manufacturing, energy production, transportation, and others. Nevertheless, reports [8,13,14,15,16,17] do not provide information (including quantitative assessments) regarding the potential contribution of forest fires and transboundary transport from China to the air quality of the Amur region.
A combination of atmospheric gas composition measurement techniques and three-dimensional numerical modeling represents an essential tool for investigating the spatiotemporal variability of air quality and pollution sources. Unfortunately there are not too many studies dedicated to the problem of air pollution in the Russian Far East or Siberia. For example, Thorp et al., 2021 [18] demonstrated, using observational data together with three-dimensional numerical modeling of the lower atmosphere, that tropospheric ozone levels in Western Siberia (Russia) during spring and summer are influenced more strongly by anthropogenic precursor emissions than by forest fire emissions. As far as we are aware, no studies have examined the factors affecting air quality changes in the Russian Far East using regional numerical modeling.
The aim of this study is to assess the potential contribution of forest fires and transboundary transport of pollutants to ground-level air pollution in selected regions of the Russian Far East, using three-dimensional numerical modeling in conjunction with measurement data.

2. Materials and Methods

Based on the analysis conducted (Figure A1) of satellite- and ground-based measurement data, two periods were selected for further investigation of air pollution in the Amur region: July 2015, corresponding to forest fires in the Russian Far East confirmed in Dynamics of Forest Fires in the Russian Far East [19] and January 2023, representing anthropogenic air pollution. Additionally, the correctness of the investigation periods (i.e., high wildfire probabilities in July 2015) was also partially verified by the analysis of synoptic conditions in the territory of the Russian Far East and northeastern China (Figure A2).

2.1. WRF-Chem

The study employs the three-dimensional regional chemical transport model WRF-Chem [20] to simulate the state and composition of the lower atmosphere over the Russian Far East. The modeling domain is shown in Figure 1 and primarily covers the Far Eastern Federal District, northeastern China, eastern Mongolia, Japan, and part of the Pacific Ocean, with a horizontal resolution of 24 km. Vertically, the model extends from the surface to approximately 20 km, represented by 35 vertical levels.
The following parameterization schemes were employed to represent physical processes: the RRTMG shortwave and longwave radiation schemes [21] (radiative transfer of shortwave and longwave fluxes in the atmosphere), the Unified Noah Land Surface Model [22] (land surface), the Revised MM5 scheme [23] (surface layer), the Bougeault–Lacarrere scheme (BouLac) [24] (planetary boundary layer), the Grell 3D ensemble scheme [25] (convective transport), and the Thompson scheme [26] (cloud microphysics).
For the representation of chemical transformations, the MOZART (Model for Ozone and Related Chemical Tracers) chemical mechanism [27] was employed. This scheme includes 157 gas-phase, 12 heterogeneous, and 39 photolytic reactions, describing the evolution of 85 gaseous species in the lower atmosphere. To simulate the dynamics of aerosol particles, the GOCART (Goddard Chemistry Aerosol Radiation and Transport) scheme [28] was used.
The parameterization schemes for physical processes and chemical reactions were adopted following [29]. Table 1 presents the main parameters of the numerical experiment. Numerical experiments were carried out over two periods with 15 days of a spinup, covering 15 June–31 July 2015 and 15 December 2022–31 January 2023.
For the meteorological boundary and initial conditions, ERA5 reanalysis data were used [30]. The concentration of gases and aerosols at the boundaries of the modeling domain were prescribed from the results of the global Earth system model CAM-Chem [31] and the Whole Atmosphere Community Climate Model (WACCM) [32].
The global emission database EDGARv8.1 [33] was applied to represent anthropogenic sources of trace gases and aerosols, such as NOx, CO, SO2, PM2.5, PM10, and others. This database covers the entire Earth’s surface at a horizontal resolution of 0.1° for the respective study years and accounts only for seasonal variability.
Biogenic fluxes associated with vegetation activity were represented using the online MEGAN (Model of Emissions of Gases and Aerosols from Nature) module embedded in WRF-Chem [34]. Emissions of gases and aerosols resulting from biomass burning (e.g., forest fires) were prescribed from the satellite-based Fire INventory from NCAR (FINN), versions 2.4 and 2.5 [35] (Wiedinmyer et al., 2023).

2.2. Spatial Distribution of Gas and Aerosol Sources

Figure 2 shows the spatial distribution of anthropogenic emissions of NO2 (a), CO (b), and PM2.5 (c) used for the July 2015 simulation period by the EDGARv8.1 emission inventory. On average, the spatial patterns and relative contributions of anthropogenic sources are similar across all three components. The highest values are observed in the southern part of the domain (northern China), whereas the northern territory (eastern Russia) is almost free of anthropogenic sources according to the EDGARv8.1 inventory. Within this region, the major urban centers include Blagoveshchensk (the Amur region, central part of the modeling domain), Yakutsk (Republic of Sakha (Yakutia), north of the Amur region), and the southern part of Zabaykalsky Krai (west of the Amur region).
Figure 3 presents the spatial distribution of total July 2015 emissions of NO2 (a), CO (b), and BC (c) from biomass burning, based on the FINNv2.5 database. Two distinct source types can be identified: the first dominates the central part of the study domain (Zabaykalsky Krai, Amur region), while the second is located in the southern part of the domain (northern China, Japan). Biomass-burning emissions in the central part of the domain are 2–3 orders of magnitude higher than those in the south and are likely associated with seasonal forest fires during summer. The second source type is probably related to controlled agricultural burning, which is widespread in China [35].

3. Results and Discussion

3.1. Evaluation of Model Results

Figure 4 demonstrates the mean differences (bias—observations minus modeled data), standard deviation (STD) of the differences, and Pearson correlation coefficient (CC) between modeled and observed meteorological parameters (2 m air temperature, 10 m wind speed, and direction) at meteorological stations in Yakutsk, Blagoveshchensk (Russia), and Harbin (China) (see locations of the stations as blue circles in Figure 1). The observational data are a time series of corresponding parameters covering July 2015 and January 2023 periods with 3 h frequency. The observation data are freely available at the rp5.ru website. For the comparison, modeled data were retrieved from the closest cells to the observation stations’ model cells. The retrieved model time series with 1 h frequency were averaged then to fit the observation 3 h frequency.
In general there is high correlation between modeled and observed 2 m air temperature (~0.9 and higher) for all three stations and periods except for January 2023 at the station in Yakuts in January 2023 (~0.5) (Figure 4a). Bias varies in a range from less than 0.5 °C (July 2015 in Yakutsk and Blagoveshchensk) to ~2 °C, with the highest values predominantly during a cold period. The STD of bias at all three stations changes from 2–3 °C in July to ~4–5 °C in January. The results indicate that with the current model setup it represents near-surface air temperature in the study territory in July 2015 better than in January 2023. Taking into account that in the territory of interest near-surface air temperature amplitude during both months is up to 15–20 °C, the modeled air temperature bias (0.5–2 °C) and STD (2–5 °C) still provide the possibility to reproduce daily and monthly air temperature variation.
The model overestimates wind speed at the three stations in both periods by 0.8–1.9 m/s with an STD of ~2 m/s (Figure 4b). The highest correlation is observed in January 2023 for Blagoveshchensk and Harbin (above 0.6), while the lowest values occur in Yakutsk in January 2023 and Harbin in July 2015 (0–0.1). At the same time, measurements in Yakutsk in January show the lowest mean air temperature (−42.6 °C, compared to no colder than −24 °C in Blagoveshchensk and Harbin) and the lowest wind speed (1.1 m/s compared to more than 2.2 m/s in Blagoveshchensk and Harbin). In the same month, as it was mentioned above, there is the lowest correlation between simulated and observed near-surface wind speed in Yakutsk (Figure 4a, about −0.1). This suggests that the model tends to reproduce atmospheric states less reliably under conditions of extremely low air temperature (below 40 °C) and low wind speed (at ~1 m/s and below).
The wind direction bias (Figure 4c) generally ranges from 15° to 30°, with an STD of differences of 60–100°. Based on the correlation coefficient, the model reproduces near-surface wind better in winter in Blagoveshchensk and Harbin. This may be related to a simpler wind regime near the surface in winter compared to summer (due to the absence of intensive vertical mixing).
It should be noted that the observational wind speed and direction data represent 10 min averages, each of which includes several hundred individual measurements. In contrast, the results of the numerical experiment represent values corresponding to the model time step, which in our case is 2 min. This factor may influence the comparison between modeled and observed data, since averaging hundreds of measurements over 10 min is expected to smooth out wind gusts that can still appear in the two-minute model outputs. Therefore, in future studies, simulated data should be used in the form of an average rather than instantaneous values.
Another factor influencing the correspondence between the modeled and observed data is spatial resolution of the regional modeling. With 24 km spatial resolution of the numerical experiment, local processes (i.e., water–energy exchange with small inland lakes and rivers, landscape features) could be smoothed, possibly reflected in the relatively high bias of wind speed and direction.

Comparison with ERA5

A brief description of the ERA5 data is provided in Section 2.1. The analysis of the agreement between model results and ERA5 meteorological reanalysis indicates complex spatial inhomogeneities in January 2023 (Figure 5b) and greater homogeneity in July 2015 (Figure 5a). In July, the model underestimates near-surface air temperature by about 2 °C across most of the domain, with a uniform STD of differences of about 2 °C, increasing to 3 °C in the central region. In January, the model both underestimates and overestimates near-surface temperature relative to reanalysis data, with differences reaching 5 °C and showing a spatially inhomogeneous pattern. In January, STD values reach 6 °C across much of the domain, indicating temporal variability in the differences between the two datasets over the course of the month. The correlation coefficient is generally around 0.9 over land in both months. Only in January 2023 does the correlation coefficient drop to 0.7 in the central part of the domain and to 0.4 in the northern part of the study area, where, according to measurements, mean temperatures reached about −42 °C.
In summer (Figure 5a), particular attention should be paid to the central part of the study domain, where both the mean bias and its STD are elevated, along with a slightly lower correlation coefficient. According to Figure 5a, the model shows a pronounced underestimation of air temperature relative to the reanalysis in the central part of the domain. This phenomenon may be related to forest fires, which, as discussed above, occurred in Yakutia and Zabaykalsky Krai and were accounted for in the WRF-Chem simulations. The mechanism of air temperature reduction during biomass burning may be associated with the interaction of biomass-burning aerosols with incoming shortwave solar radiation, which is scattered or absorbed by particles and therefore does not reach the Earth’s surface. This mechanism is parameterized in WRF-Chem and was included in the simulations.
Figure 6 presents the July 2015 mean spatial distribution of downward solar radiation at the Earth’s surface from WRF-Chem simulations. A relative decrease in incoming solar radiation (by 10–15 W m−2) is observed, particularly in the northern part of the Amur region. This area, as will be shown below, coincides with the region of elevated PM2.5 concentrations. According to [36,37] in the current setup of the IFS model used to produce ERA5 reanalysis (CY41R2), climatological aerosol data were used in the radiation scheme which definitely cannot reproduce local time-specific aerosol radiative effects due to wildfires. However, the aerosol effect should be taken into account to ERA5 via the assimilation of observation data, such as the radiance measured by satellites [38]. Therefore, this local modeled phenomenon (the impact of time and region specific biomass-burning aerosols on near-surface air temperature) is either not represented in the ERA5 reanalysis data or it is represented differently in WRF-Chem. The last could be related to the inaccuracies in biogenic emission data used or modeling of direct aerosol effect or aerosol field which is prognostically estimated in the case of WRF-Chem.
Figure 7 shows the same as Figure 5 but for near-surface wind speed. On average, the model overestimates near-surface wind speed relative to the reanalysis, consistent with the validation results. Smaller differences with the reanalysis are obtained in July—up to 1–1.5 m/s—compared to January, when differences reach up to 5 m/s. The largest discrepancies are observed in hilly and mountainous areas.

3.2. Numerical Simulation of Lower-Atmosphere Composition

  • July 2015
Figure 8 presents the spatial distribution of the mean and STD of near-surface NO2, CO, O3, and PM2.5 concentrations in July 2015 from WRF-Chem simulations. A logarithmic scale is applied in all panels except Figure 8c. The highest concentrations of all four components occur over northern China, where, as shown in Figure 2, the largest number of anthropogenic sources is located. The lowest concentrations are simulated over the northern part of the study domain, particularly in the Republic of Sakha (Yakutia).
Thus, in the figures, elevated near-surface concentrations of gases and aerosols are simulated in the central part of the study domain, particularly in northern Amur region, Zabaykalsky Krai, and the Republic of Buryatia. This increase in near-surface concentrations of all five species corresponds to the locations of wildfire outbreaks during this period (see Figure 3). The spatial distribution of near-surface ozone appears more uniform and differs from that of the other components considered. For example, the maxima of nitrogen dioxide and sulfur dioxide clearly correspond to the locations of anthropogenic and natural sources. At the same time, the concentrations of these two gases near their sources are 2–3 orders of magnitude lower than over the rest of the domain. In contrast, near-surface ozone concentrations vary spatially by no more than about 50%. Model simulations also indicate that wildfires contributed to increased near-surface ozone levels in the affected regions mentioned above.
According to [39], the maximum allowable daily average concentrations (MACda) of NO2, CO, and O3 under standard atmospheric conditions are 0.05, 2.6, and 0.15 ppm, respectively, while the maximum allowable annual average concentrations (MACaa) are 0.02, 2.6, and 0.02 ppm, respectively. Based on the modeling results, monthly mean concentrations of NO2 and O3 exceed the MACaa values by a factor of 2–4. The MACda is exceeded only for NO2, by a factor of 1.5–2. The exceedances are mainly observed in the southern part of the domain and correspond to the locations of anthropogenic sources in northeastern China. According to spatial distribution of the standard deviation (STD) of the mean (Figure 8a right), the exceedance of MACda for NO2 could also be possible in the northern part of the Amur region. In addition, in the central part of the study domain (the Amur region and Zabaykalsky Krai), exceedances of the annual average MAC for ozone (by a factor of 2–3) are observed, associated with wildfires.
Modeled daily mean concentrations (sample size 31) of gases and aerosols near the Chinese city of Heihe (located on the border with Russia, at a distance ~700 m from Blagoveshchensk, see Figure 1) were compared with ground-based measurements. The data were obtained from the resource aqicn.org (last access: 11 August 2025) which collects open ground-based observation data of the main atmospheric pollutants from different services. The detailed analysis of the observation data is provided in the Appendix A. Since the measurement data are presented as Air Quality Index or AQI rather than concentrations, only a Pearson correlation analysis between the two datasets was performed.
Statistically significant correlations at the 95% confidence level were identified for NO2 (0.59), CO (0.67), and PM2.5 (0.40). For O3 and SO2, the correlation estimates were not significant, with coefficients below 0.2. Figure 9 illustrates, as an example, the time series of the mixing ratio of all five pollutants based on simulations (in ppmv) and measurements (AQI) for July 2015. The best fit in temporal variations in the observed and modeled data can be seen for NO2 and CO (Figure 9a,b). Both modeled time series reproduce main peaks of the pollutant’s mixing ratio and their temporal duration (10 and ~20–22 July) relative to the measurements. Despite the statistically insignificant correlation coefficient for near-surface O3 (Figure 9c), both the modeled and observed data show an increase in ozone concentrations approximately on the same dates of NO2 and CO maximum concentrations. There is poor correlation between observed and modeled near-surface SO2 mixing ratio in Jul 2015 (Figure 9d). In [40], SO2 near-surface mixing ratio annual variation in some European cities fits the observation data worse (in particular during a summer period) when compared with NO2 and O3. Finally, the time series of aerosol concentrations by observations and modeling (Figure 9e) match better in the first half of July 2015. The measured peak on 24–25 July was not captured correctly by the model. This could be attributed to the inaccuracies in the modeling of sulfate aerosol dynamics or to the incorrectly positioned sources of emissions.
Summarizing, there are several factors which could influence the match between modeled and observed mixing ratio of the considered pollutants, including the following: measurement error, which is, unfortunately, unknown for us; local factors such as city urban effect, which are smoothed in the modeled data; and inaccuracies in the emission inventory used.
According to Figure 9a, from 15 to 29 July 2015, near-surface CO concentrations in the Heihe area showed elevated values in both observations and simulations. For this period, we consider the spatial distribution of CO, one of the main indicators of wildfires, as well as ozone, for which CO serves as a precursor. Figure 10 (left) shows that, according to the simulations, CO concentrations began to increase around 9 July, reaching a maximum around 17 July, and then decreasing by the 20s. The numerical experiment demonstrates that the observed rise in CO at the Chinese measurement station (and also in the Amur region) was caused by wildfires in the northern part of the region as well as in the neighboring region to the west. Near-surface ozone concentrations coincide with the strong increases in CO mixing ratio on 17 and 21 July.
  • January 2023
Figure 11 shows the spatial distribution of mean near-surface mixing ratios of NO2 (a), CO (b), O3 (c), and PM2.5 (d) in January 2023 from WRF-Chem simulations (for convenience, the scales are the same as in Figure 9). Similarly to July 2015, the January results highlight elevated concentrations over northeastern China, spatially corresponding to anthropogenic sources. Compared to July 2015, near-surface CO, NO2, and PM2.5 concentrations in January 2023 are higher over northeastern China as well as in major Russian cities within the study domain (Blagoveshchensk, Yakutsk, Chita). This is likely attributable to increased emissions from energy production during winter. With respect to natural emissions, the absence of forest and agricultural fires is reflected in lower concentrations of pollutants and aerosols in the northern part of the study domain. The notable elevated mixing ratios of NO2 and CO (Figure 11a,b) were found on a border between the Amur region and Heilongjiang province (see Figure 1). It could be related to the emissions of the Russian cities which are concentrated in this region. This is also partially confirmed by the spatial distribution of NO2, CO, and aerosol emissions (Figure 2). According to the simulations, the monthly mean NO2 concentration exceeds MACda by a factor of 1.5–2 and MACaa by a factor of 3–4 over northeastern China and Japan.
Due to the increased concentration of the main ozone precursor (NO2) in the southern half of the study domain, ozone mixing ratios decrease in January. In contrast, in the northern part, near-surface ozone concentrations increase in winter (from ~0.03 ppm in July 2015 to 0.04–0.05 ppm in January 2023), according to the simulations. The decrease in ozone concentrations in January 2023 relative to July 2015 over northeastern China and the simultaneous increase over the Russian Far East (Figure 10 and Figure 11d) are associated with differences in the mean concentrations of ozone precursors in these regions, particularly CO and NO2 (Figure 8 and Figure 11a,b). Near-surface ozone exceeds the annual average MACaa by a factor of 2–3 over nearly the entire study domain (including over water surfaces), except in areas near major anthropogenic sources (northeastern China). As in July 2015, the simulations do not show exceedances of the relevant MAC values for carbon monoxide. Assuming that the daily average MAC (MACda) should be greater than the monthly average MAC, which is not specified in [39], hazardous exceedances of pollutant concentrations in July 2015 and January 2023 are identified only for NO2.
In contrast to the summer period of 2015, for January 2023 the correlation between the daily mean surface concentrations of the five considered components from WRF-Chem simulations and observations at the Chinese station is statistically significant at the 95% confidence level. The linear correlation coefficients are 0.73 (O3), 0.69 (PM2.5), 0.48 (NO2), 0.46 (CO), and 0.39 (SO2). The better agreement in winter compared to summer is likely associated with the reduced contribution of photochemical reactions (for O3), as well as with the decrease in natural pollutant emissions from biomass burning during the cold season.
Figure 12 shows the time series of daily mean concentrations from both measurements and model simulations for January 2023. According to the analysis, at the station in Heihe, China, elevated concentrations of all components (except ozone) were observed during the periods of 5–10, 15–17, and 27–30 January. Ozone, in contrast, exhibited a weak inverse correlation with variations in the other pollutants (correlation in the range −0.43 to −0.47).
Again, as in the case of July 2015, the worst fit was found for near-surface SO2 (Figure 12c). Since, in January anthropogenic sources are the main contributor to SO2 mixing ratio, the worst fit could be caused by spatiotemporal distribution of anthropogenic emissions in the territory of interest.
On Figure 13 a spatial distribution of daily mean surface PM2.5 (left) and ozone (right) concentrations on 3, 5, 7, and 9 January 2023 based on WRF-Chem simulations is presented. In this case, PM2.5 was chosen instead of CO because, according to the model results, CO has virtually no sources in the northern part of the domain (Figure 11b). Therefore, PM2.5 was considered in order to assess the possible contribution of transport from the northern region.
According to the analysis of Figure 13, during 3–7 January 2023, aerosols emitted from anthropogenic sources in northeastern China could have been transported northward, leading to increased surface PM2.5 concentrations both at the Chinese station and, according to the model results, in the southern part of the Amur region. Ground-based measurements at the Blagoveshchensk station indicate that on 6 January the wind direction shifted mainly from westerly and northwesterly to southwesterly and southeasterly. On 9 January (Figure 13d), surface PM2.5 concentrations in the southern Amur region decreased, which may be related either to changes in wind direction or to an increase in horizontal transport velocity. The latter is supported by the presence of a “plume” from emissions in Yakutsk, located in the northern part of the domain, extending southeastward. A similar spatiotemporal distribution pattern during the analyzed days is also characteristic of NO2 and CO. Meanwhile, an air mass with minimal ozone concentrations over northeastern China was transported northward, resulting in lower O3 levels in the southern Amur region by 7 January. At the same time, on 7 January (Figure 13c) the possible contribution to CO near-surface mixing ratio from the Russian anthropogenic sources was also visible in the border area between the Amur region and Heilongjiang province. Even though, according to Figure 2, there are significantly more anthropogenic sources of NO2, O3 and aerosols in northeastern China than in the Far East of Russia, the impact on the air quality in the Amur region from the domestic sources is also present. In further studies the domestic effect on air quality should be investigated in more detail.

4. Conclusions

Using numerical modeling together with satellite- and ground-based observations of atmospheric composition, this study investigated the possible effect of wildfires and transboundary transport on the air pollution of the Russian Far East, with a focus on the Amur region.
On average, the employed model adequately reproduced the temporal variability of major surface pollutants (NO2, CO, and PM2.5 aerosols) in July 2015 and January 2023. However, the agreement for SO2 and O3 surface concentrations in January 2023 (correlation coefficients of 0.39 and 0.73, respectively) was substantially better compared to July 2015, when correlations were statistically insignificant. The model lacks the capability to reproduce temporal variation of SO2 in both months which could be due to the inaccuracies in sulfate aerosol modeling and incorrectly positioned emission sources. The model’s overestimation of surface wind speed relative to both observations and reanalysis in hilly and mountainous areas and under extremely low air temperature is likely one of the key factors limiting the accuracy of three-dimensional air quality simulations over the study domain.
The results indicate that seasonal wildfires in the northern Amur region, Zabaykalsky Krai, and the Republic of Yakutia are a major driver of air quality deterioration in this part of the Russian Far East. Another important factor is cross-border transport from northeastern China together with domestic pollution from anthropogenic sources.
Numerical experiments revealed the potential for surface NO2 concentrations to reach levels hazardous to human health in the Amur region both in July 2015 and in January 2023. In July, such concentrations were found in the northern part of the region and were associated with wildfire emissions, whereas in January they occurred in the south of the region and were linked to anthropogenic pollution.
Beyond the ecological impacts, the study also demonstrated the effect of enhanced aerosol loading from wildfires in the Amur region on reducing incoming solar radiation and, consequently, lowering near-surface air temperature (by more than 3 °C). This feature was not captured in the ERA5 meteorological reanalysis for the Amur region. The local aerosol effect on incoming solar radiation could be not represented in the ERA5 reanalysis. Also it is possible that the aerosol radiative effect was overestimated by the WRF-Chem model. This highlights the importance of further investigations and independent assessments of air quality and its impacts on the atmosphere in regions of Russia regularly affected by wildfire emissions.

Author Contributions

Conceptualization, G.N., V.U. and A.T.; methodology, G.N., V.U. and A.T.; software, G.N. and A.K.; validation, G.N. and V.U.; formal analysis, G.N., V.U., A.T., A.K., M.V., M.S. and A.B.; investigation, G.N., V.U., A.T. and A.K.; resources, G.N., V.U. and A.K.; data curation, G.N., V.U. and A.K.; writing—original draft preparation, G.N., V.U. and A.T.; writing—review and editing, G.N., V.U., A.T., A.K., M.V., M.S. and A.B.; visualization, G.N., A.K., M.V. and M.S.; supervision, A.T. and G.N.; project administration, A.T.; funding acquisition, A.T. and A.B. All authors have read and agreed to the published version of the manuscript.

Funding

The analysis of satellite and ground-based observations of atmospheric pollutants was funded by the Russian Science Foundation project 24-27-00156 (https://rscf.ru/project/24-27-00156/ (accessed on 1 December 2025)). Numerical modeling using the WRF-Chem model was funded by Russian Science Foundation project 23-77-30008 (https://rscf.ru/project/23-77-30008/ (accessed on 1 December 2025)).

Data Availability Statement

The modeling data and processed satellite observations are available on the request to akulishe95@gmail.com.

Acknowledgments

The authors would like to thank all the contributors to the NASA data platform GES DISC (disc.gsfc.nasa.gov), NASA Worldview (https://worldview.earthdata.nasa.gov/ (accessed on 1 December 2025)), IASI data portal (iasi.aeris-data.fr), weather forecast web service (rp5.ru), and Real Time Air Pollution service (aqicn.org).

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Appendix A.1. Selection of the Study Period

  • Satellite data analysis
To determine the study period, satellite observations of NO2, aerosols, and CO were used. The NO2 and aerosol data were obtained from the OMI instrument onboard the Aura satellite, and the CO data from the IASI instrument onboard Metop.
The OMI spectrometer has been operating in Earth orbit since 2004, measuring reflected and scattered solar radiation in the spectral range 270–500 nm with a spectral resolution of ~0.5 nm. Its maximum spatial resolution is 13 × 24 km [41]. Due to its wide field of view of 2600 km, OMI provides daily global coverage. The main principles of the retrieval algorithms for trace gases are described in [42]. The data were obtained from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) [43] with a spatial resolution of 0.25° for each day in the period from 2004 to 2023.
The IASI instrument measures outgoing longwave radiation in the spectral range 645–2760 cm−1 with a spectral resolution of 0.25 cm−1, providing global coverage twice per day. The data were obtained from the IASI database [44] with a spatial resolution of ~12 km for each day in the period 2008–2024.
  • Results of long-term analysis
Figure A1 shows the spatial distribution of the multi-year mean concentrations of NO2, aerosol optical depth (AOD), and CO, as well as their trends. Trends were calculated for each pixel of input satellite data using a linear regression of monthly mean concentrations over the time. The slope value of the resulting equation (i.e., a rate of concentration change) was projected on a map. The analysis reveals that the spatial distribution of mean NO2 concentrations differs from that of CO and AOD, while the latter two show similar patterns. The highest atmospheric NO2 concentrations are observed mostly in the southern part of the domain, corresponding to northeastern China. In contrast, maximum CO and AOD concentrations are observed in the northern part of the study area, over Russian territory. These patterns are most likely associated with recurring summer wildfires, as was noted earlier [45].
The evaluation of the linear trends indicates a pronounced decrease in NO2 over central China and an increase in certain northern regions of the country. For AOD and CO, a clear increase is observed in the northern part of the domain (eastern Russia). This increase is likely associated with the increasing frequency of wildfires in recent years in this part of the northern hemisphere, which is also confirmed in the study by [45].
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Figure A1. Spatial distribution of multi-year mean atmospheric content of NO2 (a), AOD (b), and CO (c) (left) and linear trends (right) for 2004–2024 (NO2, AOD) and 2008–2024 (CO) by satellite observations (OMI and IASI).
Figure A1. Spatial distribution of multi-year mean atmospheric content of NO2 (a), AOD (b), and CO (c) (left) and linear trends (right) for 2004–2024 (NO2, AOD) and 2008–2024 (CO) by satellite observations (OMI and IASI).
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To analyze the temporal evolution of pollutants, three locations were chosen: in the north (56° N, 127° E), in the center (50.26° N, 127.53° E, corresponding to the city of Blagoveshchensk), and in the south of the study domain (44° N, 127° E). The selected data are shown in Figure A2 and Figure A3.
Moving from the north to the south of the domain, the monthly mean NO2 concentration increases by more than an order of magnitude (Figure A2a–c). A smoothed seasonal cycle of NO2 is evident in the north (Figure A2a, Republic of Yakutia) and resembles the background seasonal variability at similar latitudes, as also reported by [46]. By contrast, the seasonal cycle of NO2 in the south (Figure A2c, northern China) is noticeably different, with maxima occurring in autumn and winter, most likely due to the heating season. Notably, in the central part of the domain (Figure A2b), the seasonal cycle exhibits transitional features: an increase in NO2 toward summer, as in the north, but also winter and spring maxima in certain years, which coincide with the southern maxima and may be linked to atmospheric transport. Such episodes include winter 2009–2010 (absolute maximum), as well as 2014, 2018, 2019, 2021, and 2022.
The increase in AOD during winter is most likely associated with heating activities, which is also characteristic of northeastern China [47]. In the southern part of the domain, no pronounced peaks are observed compared to the central or northern regions. Instead, peak values tend to occur in summer and are most likely related to wildfires. Such summer peaks are clearly seen in 2014, 2017, and 2023 (see Figure A2a,b).
Finally, the seasonal variability of CO, with maxima in autumn, may be partly related to wildfires in the Republic of Yakutia and in northeastern China [48]. Analysis of the spatiotemporal distribution of CO from satellite observations (figures are not given in the text) indicates that monthly mean CO concentrations reach their maximum in southern Yakutia around October. CO concentrations decrease from north to south (Figure A3). In the south (Figure A3c), CO is more frequently elevated in autumn, whereas in the north (Figure A3a), maxima tends to shift toward summer. The strongest CO peaks occur simultaneously in the north, center, and south of the domain, in particular in 2010, 2015, 2020, and 2024.
According to annual air quality reports, the summer of 2015 was marked by severe wildfires (fire danger class 4) in Zabaykalsky Krai and the Republic of Sakha (Yakutia). In July 2015, Zabaykalsky Krai (west of the Amur region) was under an officially declared state of emergency due to wildfires.
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Figure A2. Time series of monthly mean NO2 and AOD for 2004–2024 by satellite observations (OMI) in three regions: northern—55.875° N, 126.875° E (a); central—49.875° N, 126.875° E (b); and southern—43.875° N, 126.875° E (c).
Figure A2. Time series of monthly mean NO2 and AOD for 2004–2024 by satellite observations (OMI) in three regions: northern—55.875° N, 126.875° E (a); central—49.875° N, 126.875° E (b); and southern—43.875° N, 126.875° E (c).
Climate 13 00252 g0a2aClimate 13 00252 g0a2b
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Figure A3. Time series of monthly mean NO2 and CO for 2004–2024 (from 2008 for CO) by satellite observations (OMI) in three regions: northern—55.875° N, 126.875° E (a); central—49.875° N, 126.875° E (b); and southern—43.875° N, 126.875° E (c).
Figure A3. Time series of monthly mean NO2 and CO for 2004–2024 (from 2008 for CO) by satellite observations (OMI) in three regions: northern—55.875° N, 126.875° E (a); central—49.875° N, 126.875° E (b); and southern—43.875° N, 126.875° E (c).
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  • Analysis of ground-based measurements in China (Heihe station)
To analyze the temporal variability of pollutant concentrations (PM2.5, PM10, NO2, and CO), as well as to validate the modeling results, data from the measurement station in the Chinese city of Heihe, located on the Russian–Chinese border near the Blagoveshchensk city, were used (Figure A4).
The analysis of daily mean surface concentrations of PM2.5, PM10, NO2, and CO at the Heihe station (located a few hundred meters south of Blagoveshchensk) for the period 2015–2024 shows that all considered pollutants exhibit a broadly similar seasonal cycle. The cycle is characterized by maximum concentrations during the winter months. The differences compared to the previously identified seasonal cycles of these components are likely due to the fact that satellite data represent a much deeper and more extensive atmospheric column compared to surface measurements. Nevertheless, during the periods 2015–2019 and 2024, pronounced increases in PM2.5 and PM10 concentrations were observed in spring and summer, which may be associated with wildfires and agricultural burning both in China and Russia.
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Figure A4. Time series of daily near-surface mean PM2.5, PM10 (a), NO2 (b), and CO (c) for 2015–2024 at measurement station in Heihe, China (50.248° N, 127.503° E).
Figure A4. Time series of daily near-surface mean PM2.5, PM10 (a), NO2 (b), and CO (c) for 2015–2024 at measurement station in Heihe, China (50.248° N, 127.503° E).
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Appendix A.2. Synoptic Analysis

The synoptic conditions were analyzed using ERA5 reanalysis data [30], synoptic charts from the Russian Hydrometeorological Center archived at RSHU [49], as well as satellite imagery [50] and meteorological station data. Since open-access meteorological station data are not available, information published on the rp5.ru website was used, which is based on KN-01 SYNOP code telegrams [51].
  • July 2015
According to the Russian Hydrometeorological Center [52], precipitation in Zabaykalsky Krai and the Amur region was significantly below the 1961–1990 climatological normal. In southern Eastern Siberia and Zabaykalsky Krai, anomalously high air temperatures were observed (in Chita, nine days with maximum temperatures of 30–35 °C, with an absolute maximum of 37 °C above zero), which also exceeded the long-term average for the same reference period. The Hydrometeorological Center identified two main factors contributing to the positive monthly temperature anomalies in July 2015: (1) the influence of southern advections in the warm sectors of western and southern cyclones, and (2) prevailing anticyclonic weather during the second ten-day period of July over southern Krasnoyarsk Krai.
During the first ten-day period of July, with the exception of 1–2 and 6–8 July, the Amur region was affected by high-pressure anticyclones. Zabaykalsky Krai was influenced by cyclones only on 6–8 July during this period. After the passage of a cyclone on 12–13 July, anticyclonic circulation was established over Zabaykalsky Krai and the Amur region. No significant precipitation was recorded by meteorological stations until 20–21 July. In the third ten-day period of the month, cyclones prevailed. On 22 July, a cyclone developed over Zabaykalsky Krai and persisted until 30 July. The precipitation associated with this cyclone was insufficient to extinguish the fires. Figure A5 presents satellite images overlaid with thermal anomaly data related to wildfires, derived from MODIS sensors onboard the Terra and Aqua satellites [50].
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Figure A5. Thermal anomalies associated with wildfires on (a) 17 July 2015 and (b) 31 July 2015, based on NASA data [50].
Figure A5. Thermal anomalies associated with wildfires on (a) 17 July 2015 and (b) 31 July 2015, based on NASA data [50].
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According to the wind direction analysis at the 850 hPa and 700 hPa isobaric levels during the considered period, significant transport of atmospheric pollutants from Zabaykalsky Krai (westerly winds) was unlikely. Most of the observed pollution was most likely associated with forest fires within the Amur region itself.
  • January 2023
According to the Amur Center for Hydrometeorology and Environmental Monitoring (Amur CGMS) [53], January 2023 was notably cold due to extremely low air temperatures in the second half of the month, when the region was influenced by a cold upper-level cyclone that descended southward from central Yakutia, while at the surface the Siberian anticyclone prevailed. Mean monthly temperatures in the northern part of the region ranged from −28.7 °C (M-2 Yerofey Pavlovich) to −33.6 °C (M-2 Bomnak, Ekimchan, Stoyba), which was 4–6 °C below the climatological normal. In the southern and central districts, mean monthly temperatures deviated from the normal by 3–4 °C, ranging from −24 °C (OGMS Blagoveshchensk) to −31 °C (M-2 Mazanovo). January 2023 was the coldest January in the Amur region since 2013. During the same period, the temperature regime also reached the criteria of another hazardous phenomenon—abnormally cold weather—when mean daily temperatures remained 7–17 °C below the climatological normal for five or more consecutive days. From 16 to 30 January, severe frosts of hazardous intensity were observed across most of the region.
At the 850 hPa level, reanalysis data show that northwesterly and westerly winds prevailed during the first and second ten-day periods, while northerly and northwesterly winds dominated in the third ten-day period. These wind directions favored advection from the western and northwestern parts of Siberia, and during the third ten-day period, from northern Siberia.

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  51. Weather Forecast Web-Service. Available online: https://rp5.ru (accessed on 1 December 2025).
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  53. State Report on the Protection of the Population and Territories of the Russian Federation from Natural and Technogenic Emergencies in 2023. Amur Center for Hydrometeorology and Environmental Monitoring (Amur CGMS). Available online: https://amurmeteo.ru/news/416-obzor-pogodnyh-uslovij-v-2023-g.html (accessed on 1 December 2025).
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Figure 1. WRF-Chem modeling domain and locations of measurement stations (blue circles, from North to South—Yakutsk, Blagoveshchensk/Heihe, and Harbin); numbers depict regions in focus: 1—Republic of Sakha (Yakutia), 2—Amur region, 3—Zabaykalsky Krai (Russia), and 4—Heilongjiang Province (China).
Figure 1. WRF-Chem modeling domain and locations of measurement stations (blue circles, from North to South—Yakutsk, Blagoveshchensk/Heihe, and Harbin); numbers depict regions in focus: 1—Republic of Sakha (Yakutia), 2—Amur region, 3—Zabaykalsky Krai (Russia), and 4—Heilongjiang Province (China).
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Figure 2. Spatial distribution of anthropogenic emissions of NO2 (a), CO (b), and PM2.5 (c) in July 2015 based on the EDGARv8.1 inventory.
Figure 2. Spatial distribution of anthropogenic emissions of NO2 (a), CO (b), and PM2.5 (c) in July 2015 based on the EDGARv8.1 inventory.
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Figure 3. Spatial distribution of sources and total emissions of NO2 (a), CO (b), and BC (c) from biomass burning in July 2015 based on the FINNv2.5 database.
Figure 3. Spatial distribution of sources and total emissions of NO2 (a), CO (b), and BC (c) from biomass burning in July 2015 based on the FINNv2.5 database.
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Figure 4. Comparison statistics between observed and simulated near-surface air temperature in degrees of Celsius (a), wind speed in m/s (b), and wind direction in degrees (c) in July 2015 and January 2023 at three measurement stations in Yakutsk, Blagoveshchensk (Russia), and Harbin (China). STD—standard deviation; CC—Pearson correlation coefficient, Blag—Blagoveshchensk.
Figure 4. Comparison statistics between observed and simulated near-surface air temperature in degrees of Celsius (a), wind speed in m/s (b), and wind direction in degrees (c) in July 2015 and January 2023 at three measurement stations in Yakutsk, Blagoveshchensk (Russia), and Harbin (China). STD—standard deviation; CC—Pearson correlation coefficient, Blag—Blagoveshchensk.
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Figure 5. Spatial distribution of the mean differences (left), STD (middle), and correlation coefficient (right) between 2 m air temperature by ERA5 and WRF-Chem simulations in Jul 2015 (a) and Jan 2023 (b).
Figure 5. Spatial distribution of the mean differences (left), STD (middle), and correlation coefficient (right) between 2 m air temperature by ERA5 and WRF-Chem simulations in Jul 2015 (a) and Jan 2023 (b).
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Figure 6. Spatial distribution of monthly mean downward shortwave radiation by WRF-Chem in Jul 2015.
Figure 6. Spatial distribution of monthly mean downward shortwave radiation by WRF-Chem in Jul 2015.
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Figure 7. Spatial distribution of the mean differences (left), STD (middle), and correlation coefficient (right) between 10 m wind speed by ERA5 and WRF-Chem simulations in Jul 2015 (a) and Jan 2023 (b).
Figure 7. Spatial distribution of the mean differences (left), STD (middle), and correlation coefficient (right) between 10 m wind speed by ERA5 and WRF-Chem simulations in Jul 2015 (a) and Jan 2023 (b).
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Figure 8. Spatial distribution of the mean and standard deviation (STD) of the mean of near-surface NO2 (a), CO (b), O3 (c), and PM2.5 (d) concentrations in July 2015 from WRF-Chem simulations.
Figure 8. Spatial distribution of the mean and standard deviation (STD) of the mean of near-surface NO2 (a), CO (b), O3 (c), and PM2.5 (d) concentrations in July 2015 from WRF-Chem simulations.
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Figure 9. Time series of NO2 (a), CO (b), O3 (c), SO2 (d), and PM2.5 (e) near-surface content in Heihe, China, for Jul 2015 by observations (AQI, unitless, right scale) and WRF-Chem modeling.
Figure 9. Time series of NO2 (a), CO (b), O3 (c), SO2 (d), and PM2.5 (e) near-surface content in Heihe, China, for Jul 2015 by observations (AQI, unitless, right scale) and WRF-Chem modeling.
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Figure 10. Spatial distribution of daily mean near-surface CO (left) and O3 (right) mixing ration on 9, 13, 17, and 21 (a–d) July 2015 from WRF-Chem simulations.
Figure 10. Spatial distribution of daily mean near-surface CO (left) and O3 (right) mixing ration on 9, 13, 17, and 21 (a–d) July 2015 from WRF-Chem simulations.
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Figure 11. Spatial distribution of the mean and standard deviation (STD) of the mean of near-surface NO2 (a), CO (b), O3 (c), and PM2.5 (d) concentrations in January 2023 from WRF-Chem simulations.
Figure 11. Spatial distribution of the mean and standard deviation (STD) of the mean of near-surface NO2 (a), CO (b), O3 (c), and PM2.5 (d) concentrations in January 2023 from WRF-Chem simulations.
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Figure 12. Time series of NO2 (a), CO (b), O3 (c), SO2 (d), and PM2.5 (e) near-surface content in Heihe, China, for Jan 2023 by observations (AQI, unitless, right scale) and WRF-Chem modeling.
Figure 12. Time series of NO2 (a), CO (b), O3 (c), SO2 (d), and PM2.5 (e) near-surface content in Heihe, China, for Jan 2023 by observations (AQI, unitless, right scale) and WRF-Chem modeling.
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Figure 13. Spatial distribution of daily mean near-surface PM2.5 (left) and O3 (right) mixing ration on 3 (a), 5 (b), 7 (c), and 9 (d) Jan 2023 by WRF-Chem modeling.
Figure 13. Spatial distribution of daily mean near-surface PM2.5 (left) and O3 (right) mixing ration on 3 (a), 5 (b), 7 (c), and 9 (d) Jan 2023 by WRF-Chem modeling.
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Table 1. Main parameters of the WRF-Chem numerical experiment.
Table 1. Main parameters of the WRF-Chem numerical experiment.
ParameterDescription
Horizontal domain and resolution3096 × 3768 km2, 24 km
Time steps (dynamics/chemistry)2 and 15 min
Vertical distribution35 hybrid levels, from the surface up to 50 hPa
Initial and boundary conditionsMeteorologyERA5 reanalysis, horizontal resolution 0.25°, up to ~80 km on 137 hybrid levels
ChemistryCAM-chem and WACCM simulations, horizontal resolution 0.9 × 1.25°, up to ~45 km on 56 hybrid levels
Emission sourcesAnthropogenic emissionsEDGARv8.1, 0.1° resolution, monthly mean data
Biogenic fluxesOnline biogenic model MEGAN, ~1 km resolution
Biomass burningFINN v2.4 and v2.5, ~1 km resolution
Dust and sea saltOnline dust and sea salt emission preprocessors
Chemical mechanismMOZART
Aerosol dynamics schemeGOCART
Simulation periods15 June–31 July 2015
15 December 2022–31 January 2023
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Nerobelov, G.; Urmanov, V.; Tronin, A.; Kiselev, A.; Vasiliev, M.; Sedeeva, M.; Baklanov, A. Assessing the Impact of Natural and Anthropogenic Pollution on Air Quality in the Russian Far East. Climate 2025, 13, 252. https://doi.org/10.3390/cli13120252

AMA Style

Nerobelov G, Urmanov V, Tronin A, Kiselev A, Vasiliev M, Sedeeva M, Baklanov A. Assessing the Impact of Natural and Anthropogenic Pollution on Air Quality in the Russian Far East. Climate. 2025; 13(12):252. https://doi.org/10.3390/cli13120252

Chicago/Turabian Style

Nerobelov, Georgii, Vladislav Urmanov, Andrei Tronin, Andrey Kiselev, Mihail Vasiliev, Margarita Sedeeva, and Alexander Baklanov. 2025. "Assessing the Impact of Natural and Anthropogenic Pollution on Air Quality in the Russian Far East" Climate 13, no. 12: 252. https://doi.org/10.3390/cli13120252

APA Style

Nerobelov, G., Urmanov, V., Tronin, A., Kiselev, A., Vasiliev, M., Sedeeva, M., & Baklanov, A. (2025). Assessing the Impact of Natural and Anthropogenic Pollution on Air Quality in the Russian Far East. Climate, 13(12), 252. https://doi.org/10.3390/cli13120252

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