Assessment of smoke aerosol contribution to atmosphere of the south-eastern coast of Lake Baikal in 2021 ("Boyarsky" station)

Assessment of smoke aerosol contribution to atmosphere of the south-eastern coast of Lake Baikal in 2021 ("Boyarsky" station) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of smoke aerosol contribution to atmosphere of the south-eastern coast of Lake Baikal in 2021 ("Boyarsky" station) Ayuna Dementeva, Galina Zhamsueva, Alexander Zayakhanov, Tamara Khodzher, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7203689/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Smoke aerosols emitted by wildfires can ascend into the free troposphere, be transported over long distances, and subsequently descend, thereby affecting local air quality in remote areas. This article presents a study of aerosol pollution in the atmosphere over the southeastern shore of Lake Baikal during the summer of 2021, utilizing an automated system for real-time air quality monitoring. Analysis of mass concentrations of fine particulate matter fractions PM 10 and PM 2.5 , aerosol optical depth (AOD) measurements, aerosol sampling, and backward air mass trajectory analysis using the HYSPLIT model demonstrated that the prolonged atmospheric turbidity over Lake Baikal was caused by the transport of smoke aerosol from large-scale wildfire centers in the Republic of Sakha (Yakutia) and Siberia. Two prolonged smoke aerosol intrusion events, transported from Yakutia between July 26 and 29 and August 7 to 14, 2021, were analyzed. During these periods, smoke aerosols were entrained into the planetary boundary layer and accumulated near the surface due to temperature inversion conditions. Wildfires mass concentration of aerosol РМ10 и РМ2.5 aerosol optical depth Lake Baikal sun photometer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. INTRODUCTION The Lake Baikal depression constitutes a unique natural object. Lake Baikal possesses a significant north-south extent, concentrates an enormous mass of water, and is surrounded by mountains (reaching elevations of up to 2500 m above sea level), which impede air exchange with surrounding regions. This leads to the formation of specific natural and climatic conditions within the Baikal depression, substantially differing from other continental areas. Along the Baikal coast, favorable conditions for the accumulation of pollutants develop in both summer and winter due to low-gradient fields of high and low pressure (Shimaraev et al., 2002 ). The ecosystem of Lake Baikal is affected by the Irkutsk-Cheremkhovo industrial hub due to the transport of pollutants along the Angara River valley into the lake’s water area, predominantly impacting its southern part (Arguchintseva and Vologzhina, 2011 ; Obolkin et al., 2017 ; Izrael et al., 1991; Latysheva et al., 2004 ; Potemkin and Makukhin, 2007 ; Golobokova et al., 2011 ). In addition, the air quality of Lake Baikal is influenced by tourism and recreational activities associated with the expansion of infrastructure in the lake’s coastal zone (Golobokova et al., 2021 ; Zhamsueva et al., 2022 ). Therefore, environmental monitoring at Baikal is of critical importance for preserving the natural environment and biodiversity of the region. Over the past decade, wildfires have had a strong impact on air quality at Baikal region (Zhamsueva et al., 2021 ; Dementeva et al., 2022 ). During the warm season, from April to October, wildfires represent major natural sources of pollutant emissions into the atmosphere at Baikal natural territory. Due to dry weather conditions, wildfires episodically occur in the boreal forests of Eastern Siberia as a result of thunderstorms and human activity. The average annual burned area in Buryatia increased significantly from 11,984.6 ha in 1966–1995 to 100,721.7 ha in 1996–2016 (Sidorov and Sanzhieva, 2018 ). Wildfires emit large quantities of trace gases (both chemically active and greenhouse gases), non-methane hydrocarbons, and aerosols (Crutzen and Andreae, 1990 ). These aerosols and pollutants substantially affect atmospheric chemistry, cloud properties, Earth’s radiation balance, climate change, and more (Crutzen and Andreae, 1990 ; Ramanathan et al., 2001 ; IPCC, 2013). During catastrophic wildfires, smoke saturates the atmosphere up to altitudes of 7–10 km, where over 50% of the air mass is concentrated. Within the formed fire center, additional heat and smoke create a high-pressure region that influences synoptic conditions in adjacent territories until the end of summer. Cyclones avoid fire centers, causing heavy rainfall in those areas (Sokolova, 2006 ; Sokolova and Teteryatnikova, 2003 ). Studying the distribution and transport of aerosol particles in the Baikal region is great importance for understanding the mechanisms of atmospheric composition formation and controlling air pollution over Lake Baikal. Ground-based monitoring plays a key role in assessing the impact of smoke aerosols from forest and peat fires on the Earth’s radiation balance and, consequently, on local and regional climate, especially for Lake Baikal, which is recognized as a UNESCO World Natural Heritage site (Badarinath et al., 2009 ; Mielonen et al., 2013 ; Dementeva et al., 2022 ; Khodzher et al., 2024 ). To investigate the scale of smoke gas and aerosol impacts and long-range transport, satellite monitoring and modeling tools such as MODIS, CALIPSO, NAAPS, HYSPLIT, and others are required, providing essential information on fire locations and intensity, transport pathways, smoke plume distribution, and aerosol type identification (Lin et al., 2021 ; Johnson et al., 2021). The primary aim of this paper was to investigate the influence of smoke aerosols from wildfires on the chemical composition of the atmosphere and air quality along the south-eastern coast of Lake Baikal. 2. METHODS AND MATERIALS In this study, an integrated approach combining ground-based measurements, satellite data, and numerical modeling was employed to analyze atmospheric smoke pollution over Lake Baikal during the summer period (July–August) of 2021. “Boyarsky” scientific station (51°50′47″ N; 106°04′01″ E) is situated in the coastal zone 160 km from Ulan-Ude on the southeastern coast of Lake Baikal in 500 m from the shoreline. Station is position in the forest zone and a rather large distance (> 100 km) from large cities and the sources of industrial emissions. The mass concentration of aerosol fractions PM 2.5 and PM 10 in the air were measured using a Diffusion Aerosol Spectrometer Model DAS 2702M (Aeronanotech, https://aeronanotechnology.com/das_2702-m ). The DAS consists of a set of different filter meshes that forma stage on which particles are deposited as they pass through the battery. The DAS includes an aerosol particle counter and a condensation particle counter, which are installed to measure the number concentration and size distribution of aerosol particles. The particle size distribution is measured. Its principle is based on the condensation enlargement of particles. In order to obtain information about nanometer-sized particles, the particles are passed through a diffusion cell to obtain information about their size. These particles are deposited on the grid of the diffusion cell. Such deposition is selective. In other words, the smaller the particles, the smaller the particles, the greater the diffusion coefficient and the easier it is for them to be deposited on the grid of the battery. The spectrometer has 2 operating modes: parameter measurement mode aerosol particles in the size range from 0.005 to 0.2 microns (40 ranges with a step of 5 nm); the mode of measuring the parameters of aerosol particles in the size range from 0.2 to 10 µm (12 channels). Aerosol sampling for Whatman-41 filters using Andersen Instruments Inc. PM 10 high-volume sampler (USA) were held in the summer 2021. The chemical composition of the soluble aerosol substance fraction was determined using modern analytical methods of atomic adsorption and ion chromatography, recommended to ensure comparability with data from other regions of the world. The samples were analyzed by gas chromatography mass spectrometry, Agilent Technologies 7890B GC System 7000C GC-MS Triple Quad chromatograph mass spectrometer, Santa Clara, CA, USA (Marinaite et al., 2022 ). To analyze the transport of smoke aerosol and its spatial distribution on a regional scale data from MODIS (MAIC) satellite ( https://worldview.earthdata.nasa.gov/ ) and the trajectory model NCEP/NCAR HYSPLIT ( http://www.arl.noaa.gov ) and archival meteorological data of the NOAA were used. Experimental measurements of aerosol optical depth (AOD) in atmosphere at the Boyarsky station were conducted using the multi-wavelength sun photometer SP-9 across the wavelength range of 0.34–2.2 µm (Tomsk, Russia), is designed for year-round atmospheric transparency measurements aimed at subsequent determination of AOD and atmospheric water vapor content. The measurement uncertainty of AOD is between 0.01 and 0.02. The process is fully automated and operates without operator intervention (Sakerin, 2012 ). Measurements of atmospheric meteorological parameters at the Boyarsky station were conducted using the acoustic meteorological complex AMK-03 (Tomsk, Russia). The AMK-03 meteorological complex provides instantaneous values of wind speed (along three mutually perpendicular directions) and air temperature, with a resolution of no more than 0.01 m s -1 for wind speed and no more than 0.01°C for air temperature, at a measurement frequency ranging from 10 to 160 Hz (Azbukin et al., 2006 ). This capability allows for the analysis of atmospheric turbulent parameters. Analysis of the vertical aerosol distribution and determination of its type were performed using data from the space-based CALIOP lidar on the CALIPSO satellite ( https://www-calipso.larc.nasa.gov/ ) and MODIS Blue Aerosol Type ( https://worldview.earthdata.nasa.gov/ ). The study of atmospheric aerosol transport and the distribution of dominant aerosol components (sulfate aerosol, dust, smoke) was carried out using the global aerosol model Navy Aerosol Analysis and Prediction System (NAAPS, https://www.nrlmry.navy.mil/aerosol/ ). NAAPS generates operational six-day forecasts for sulfates, dust, smoke, sea salt, and SO 2 (Hogan and Rosmond, 1991 ). 3. RESULTS AND DISCUSSION 3.1 Monitoring wildfires in Siberia In recent years, an increase has been observed in both the duration of fire hazard season and frequency of wildfire occurrences. Figure 1 a presents the dynamics of wildfires area and number from 2013 to 2024 within the territory of Russian Federation and Baikal natural territory (BPT) - Republic of Buryatia and Irkutsk region. During the research, highest number of wild and peat fires was recorded in 2014, 2015, and 2017. The maximum burned forest area in Russia was observed from 2019 to 2021, reaching up to 10 million hectares annually. In 2019, the Republic of Sakha and Krasnoyarsk region contributed the most to the total burned forest area, which amounted to 6.426 million hectares. In 2020, the burned area in Republic of Sakha comprised 6.343 million hectares, while in 2021 it reached nearly 8 million hectares. In 2023, more than half of the burned area was concentrated in two Far Eastern federal subjects—approximately 2.3 million hectares in the Republic of Sakha and Khabarovsk region. In 2024, wildfires were registered in Russia over a total burned area of 7.5 million hectares ( https://rosleshoz.gov.ru/rates/forest-fires/area/ ). During the large-scale wildfires in the summer months of 2019–2021 in Yakutia, a strong anticyclone was established that contributed to the deterioration of the fire hazard situation. The transport of smoke aerosol from the wildfire sources in Yakutia led to the formation of a persistent haze over Lake Baikal and, as a consequence, to significant aerosol pollution throughout the atmospheric column. Figure 1 b illustrates, as an example, the synoptic map showing the establishment of the anticyclone over Yakutia in July 2019 and the backward air mass trajectories calculated using the HYSPLIT model, which demonstrated the transport of smoke aerosol from the wildfire centers to the Baikal region. . Numerous active wildfires were detected by MODIS in 2021 to the north, northwest, and northeast of Lake Baikal in the Northeastern Siberian Lowland region (Krasnoyarsk and Irkutsk regions, Yakutia). Biomass burning emissions in Eastern Siberia peaked between August 7 and 14, with maximum total emissions of PM 2.5 and PM 10 . Satellite image analysis from MODIS revealed the transport of smoke plumes from extensive wildfires into the atmosphere of Baikal region. Wildfires ignited in the North Siberian Lowland region generated extensive smoke plumes identifiable as gray streaks on true-color imagery, spreading over Lake Baikal. Using MODIS Deep Blue Aerosol Type data from satellite imagery, the aerosol type was identified, showing the planetary boundary layer was entirely filled with smoke aerosol. To determine the transport pathways of air masses over Baikal region, backward trajectories of smoke aerosol transport were calculated with the HYSPLIT model based on the timing, location, and altitude of the smoke plumes. Air mass trajectories were computed for durations of 5 days (120 hours) with 6-hour intervals at altitudes of 100, 500, and 1500 meters, effectively representing regional and interregional pollutant transport. The backward trajectories indicated that smoke aerosol transport occurred at heights of 1500 to 3000 meters from wildfire sources in Yakutia (Fig. 2 a). Figure 2 b presents a vertical cross-section of the atmospheric column with a qualitative classification of aerosol distribution types, as measured by the spaceborne CALIOP lidar on August 9, 2021. In the upper troposphere above Lake Baikal, at altitudes ranging from 5 to 10 km, the atmosphere was densely populated with smoke aerosol. Simultaneously, aerosol typing in the lower tropospheric layer was not possible due to dense cloud cover obstructing the measurements. To identify the aerosol type in the lower tropospheric layer, forecast maps from NAAPS ( http://www.nrlmry.navy.mil/aerosol/ ) were analyzed. The analysis results demonstrated high concentrations of smoke aerosol in the near-surface atmospheric layer from August 7 to 14, reaching up to 512 µg m - ³ (Fig. 3 ). 3.2 Interannual variability of AOD at Boyarsky station To assess the regional characteristics of interannual variability of the spectral properties of atmospheric aerosol optical depth, data from the Boyarsky station for the period 2019–2024 were analyzed. Experimental measurements of AOD at the Boyarsky site were performed using the multi-wavelength sun photometer SP-9 in the wavelength range of 0.34–1.24 µm. Figure 4 a depicts the interannual variability of AOD at a wavelength of 0.5 µm at the Boyarsky station during the summer. The highest atmospheric turbidity was observed from 2019 to 2021, with AOD values ranging from 0.23 to 0.27. The main contribution to aerosol pollution at Boyarsky station is attributed to large-scale wildfires. Consequently, this resulted in a high level of aerosol loading and an increase in AOD. A long-term increasing trend and high variability in the fine mode component of AOD have been observed due to wildfire smoke in Baikal region (Dementeva et al., 2022 ). To identify the main features of the spatiotemporal variability of aerosol characteristics in Baikal region atmosphere, average spectral characteristics of atmospheric aerosol optical depth were examined under "smoke/clean atmosphere" conditions. The largest changes in AOD during smoke events occur in the visible spectral range, driven by contribution of fine aerosol fraction. It was shown that in the UV range (0.34 µm) AOD under smoke conditions increased by a factor of 4.2, in the visible range (0.5 µm) by 6 times, and in the IR range (1.24 µm) by 3.6 times (Fig. 4 b). Studies were conducted on the mass concentration of aerosol fractions PM 2.5 and PM 10 in atmosphere of coastal zone of Lake Baikal using the diffusion aerosol spectrometer DAS 2702M from July to September 2021. Two cases of prolonged smoke aerosol transport from Yakutia from July 26 to 29 and from August 7 to 14, 2021 were analyzed. During the intense smoke pollution period from August 7 to 14 (highlighted separately), average concentrations reached 146 µg m - ³ for PM 2.5 and 314 µg m - ³ for PM 10 , with maximum PM 10 concentrations reaching 920 µg m - ³ and PM 2.5 up to 404 µg m - ³ (Fig. 5 ). The smoke plumes increased the mass concentration of PM 10 and PM 2.5 at Boyarsky, exceeding the single allowable maximum concentrations by factors of 3 and 2.5, respectively. During the same period, high PM 2.5 concentrations—up to 240 µg m - ³—and CO concentrations up to 2 µg m - ³ were recorded at the southwestern shore of Lake Baikal, at Listvyanka monitoring station. Joint analysis of satellite data and trajectory calculations showed that air pollution episodes near the Listvyanka monitoring stations were caused by the transport of smoke plumes from intense wildfires located 1500–2000 km away (Molozhnikova et al., 2022 ). For further quantitative assessment of the contribution of smoke aerosols to PM 10 and PM 2.5 concentrations, average concentrations of these fractions were calculated for smoke and non-smoke cases. Average PM 10 and PM 2.5 concentrations for smoke conditions were 21 and 45 µg m - ³, respectively, compared to 4 and 9 µg m - ³ for non-smoke conditions. Based on measurement data during of smoke haze in the atmosphere at Boyarsky station, a strong correlation was identified between AOD and the concentrations of aerosol fractions PM 10 and PM 2.5 (Fig. 6 ). Daily average values of atmosphere AOD, τ 0,5 at a wavelength of 0.5 µm and daily average concentrations of PM were used as the basis of analysis. The correlation coefficients between τ 0,5 and PM 10 (PM 2.5 ) were 0.86 and 0.84, respectively. From July 26 to 29, 2021, a sharp increase of AOD (τ 0,5 = 0.6) at Boyarsky station was observed, reaching an absolute maximum on July 27 (τ 0,5 = 0.8). At the same time, maximum mass concentrations of PM 2.5 increased up to 36 µg m - ³ and PM 10 up to 78 µg m - ³, from baseline levels of 23 µg m - ³. The average aerosol concentrations of PM 10 and PM 2.5 during the entire smoke period were 20 µg m - ³ and 40 µg m - ³, respectively, caused by smoke advection from Yakutia. To investigate the ways of smoke plumes transfer, 5-day backwards trajectories of air masses were constructed using the NOAA HYSPLIT model. The smoke aerosol transport trajectories from July 26 to July 29 were carried out from the northeastern direction (Yakutia). Smoke plumes from Yakutia moved at altitudes of 2000–3500 m from the eastern side of Lake Baikal, and on July 27, they reached Middle Baikal with a decrease in altitude to the near-water layer and the speed of air mass transport. 3.3. Meteorological and synoptic conditions Overall, the two smoking events differ in terms of smoke plume intensity, transport pathways, and weather conditions. Synoptic weather conditions during the smoke episodes can be one of the possible reasons for spatial variability of aerosol fractions PM 10 and PM 2.5 . Meteorological conditions can influence not only the transport path but also the process of downward mixing of smoke aerosols in atmospheric boundary layer. Meteorological and turbulent characteristics have a significant impact on advective and convective processes of transport and transformation of pollutants in Lake Baikal region. Measurements of meteorological and turbulent parameters of atmosphere were carried out using the automated meteorological complex AMK-03 at heights of 2 m and 20 m. Starting from August 5, Lake Baikal was under the influence of a cyclone, characterized by a drop in pressure followed by a decrease in temperature. On August 7, the cyclone was replaced by an anticyclone, which brought air enriched with smoke aerosol. During this period, intensive turbulent exchange between the upper and lower layers was observed until August 9. The total turbulent kinetic energy (Ev) was 2.5 m² s - ², and the turbulent exchange coefficient (Kh) was 0.8 (Fig. 7 a). It is assumed that the smoke aerosols aloft were entrained into the planetary boundary layer and mixed turbulently during the passage of the front. Subsequently, the development of planetary boundary layer was suppressed, leading to the accumulation of smoke aerosols near the surface and resulting in maximum PM concentrations. Long-range transported smoke aerosols at altitude can be entrained into the planetary boundary layer as it grows during the morning and early daytime hours, mixed downward by turbulence, and thereby affect surface air quality (Colarco et al., 2004 ; Hung et al., 2020 ). With the change of air masses on August 9 from 06 p.m. to 08 a.m. on August 10, under the influence of cyclonic activity, a temperature inversion was formed and a further decrease of total turbulent kinetic energy and turbulent exchange coefficient, which led to the maximum accumulation of smoke aerosol in the surface layer (Fig. 7 b). Thus, the combination of dynamic transport of smoke masses, turbulent mixing, temperature inversion and a weak level of particle deposition led to a high mass concentration of PM during the transport of smoke aerosol from wildfires under cyclonic conditions. 3.4 Impact of smoke pollution on air quality in Lake Baikal atmosphere To investigate the impact of wildfires on air quality in atmosphere of Lake Baikal, an analysis of polycyclic aromatic hydrocarbons (PAHs) content in PM 10 aerosol was conducted. Polycyclic aromatic hydrocarbons are typical carcinogenic and mutagenic compounds (Křůmal and Mikuška, 2020 ). The source of PAHs is incomplete combustion of organic fuels such as coal, wood, and petroleum fuels (Kulshrestha et al., 2019 ). PAHs are classified into low molecular weight hydrocarbons: 2–3 aromatic rings and high molecular weight hydrocarbons: 4 or more rings (Boente et al., 2020 ). As a result of aerosol sample analysis, 21 PAHs were identified: naphthalene, 2-methylnaphthalene, 1-methylnaphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, retene, benz[a]anthracene, chrysene, benz[b]fluoranthene, benz[k]fluoranthene, benz[e]pyrene, benz[a]pyrene, perylene, indeno[1,2,3-cd]pyrene, benzo[g,h,i]perylene, dibenz[a,h]anthracene. PAHs were extracted using hexane in an ultrasonic bath at room temperature. The extract was concentrated to 0.1–0.2 ml and analyzed using an Agilent GC 6890 gas chromatograph coupled with an MSD 5973 Network mass spectrometer. PAH concentrations were calculated using internal standards phenanthrene–d10, chrysene–d12, and perylene–d12 from Supelco (USA). The measurement error does not exceed 10% (Zhamsueva et al., 2015 ). The study of PAH content in aerosol at Boyarsky station in 2021 was conducted from July 25 to August 27, during which 23 samples were collected (Fig. 8 ). The concentration of polycyclic aromatic hydrocarbons ranged from 0.226 to 5.095 ng m - ³, with an average of 1.608 ng m - ³. The main components were benzo[b]fluoranthene (0.013–0.912 ng m - ³, average 0.219 ng m - ³), fluoranthene (0.018–0.962 ng m - ³, average 0.194 ng m - ³), benzo[k]fluoranthene (0.017–0.402 ng m - ³, average 0.118 ng m - ³), pyrene (0.014–0.703 ng m - ³, average 0.139 ng m - ³), indeno[1,2,3-cd]pyrene (0.018–0.429 ng m - ³, average 0.130 ng m - ³), benzo[g,h,i]perylene (0.018–0.409 ng m - ³, average 0.117 ng m - ³), and 2-methylnaphthalene (0.027–0.777 ng m - ³, average 0.105 ng m - ³). In 2021, air mass transport at Boyarsky station was predominantly from wildfire sources in Yakutia, Evenkia, and the northern Irkutsk region. This resulted in high levels of smoke aerosol over Lake Baikal. The fraction of light PAHs accounted for 19.5%, or 0.314 ng m - ³ (2-ring PAHs constituted 11.4% or 0.184 ng m - ³, and 3-ring PAHs 8.1% or 0.130 ng m - ³). PAHs with 4 rings made up 33.6% (0.541 ng m - ³), 5-ring PAHs accounted for 31.4% (0.505 ng m - ³), and 6-ring PAHs represented 15.4% (0.247 ng m - ³) (Fig. 9 ). Retene is a characteristic product of coniferous wood combustion (Cecinato et al., 2014 ) and is therefore used as a marker to determine the influence of wildfires on the chemical composition of air (Gorshkov et al., 2021 ). Retene concentrations in 2021 ranged from 0.020 to 0.467 ng m - ³, with an average of 0.090 ng m - ³, accounting for 5.6% of the total analyzed PAHs. To investigate the origin of polycyclic aromatic hydrocarbons in PM 10 aerosol, diagnostic ratios were examined. The fluoranthene / (fluoranthene + pyrene) ratio in the 2021 samples ranged from 0.5 to 0.9, indicating coal and biomass combustion as the primary sources of PAHs in the aerosol during this period. The benzo[a]pyrene / (benzo[a]pyrene + benzo[b]fluoranthene) ratio was generally below 0.5, suggesting aged aerosol and, consequently, long-range transport of PAHs from distant particulate matter sources (Tobiszewski and Namiesnik, 2012 ). A correlation analysis was performed among different PAHs to identify the sources of polycyclic aromatic hydrocarbons in suspended particles. The correlation analysis showed that retene exhibited strong correlations with light PAHs and 6-ring hydrocarbons (r = 0.51–0.84) at the Boyarsky station, indicating their predominant origin from biomass combustion. Overall, components associated with smoke aerosol transport accounted for 40.5% of PAH mass, demonstrating the significant role of wildfires in shaping the aerosol composition on the southeastern shore of Lake Baikal during summer 2021. Under the influence of smoke aerosol transport, significant changes occur in the content and composition of PAHs in aerosol. We conducted a comparison of aerosol samples taken on August 1 during transport from regions weakly affected by wildfires (Central Siberia, northern Buryatia) and on July 28, when there was transport from Yakutia, where many wildfire sources were present. Under the influence of transport from wildfire sources, the total PAHs on July 28 increased to 5.10 ng m - ³ compared to 1.02 ng m - ³ on August 1. The concentration of retene during smoke aerosol transport was 21 times higher than on August 1 (0.467 ng m - ³ and 0.022 ng m - ³, respectively). Exposure to biomass combustion products resulted in a strong increase in the share of light PAHs in the sample (38.3% and 9.4%, respectively). In 2021, air mass transport to Boyarsky predominantly originated from wildfire sources in Yakutia, Evenkia, and northern Irkutsk region. This led to high smoke aerosol content over Lake Baikal. Observed PAH measurements and modeling data demonstrate that interregional transport of smoke aerosol from Siberian wildfires significantly influenced the atmospheric composition on the southeastern shore of Lake Baikal in summer 2021. 4. CONCLUSIONS In this paper investigated two cases of severe atmospheric smoke pollution using ground-based and satellite observations, along with backward trajectory calculations of smoke aerosol transport via the NOAA HYSPLIT model, to characterize the contribution of transported smoke aerosols to air quality over Lake Baikal in summer and analyze the influence of meteorological conditions on smoke events. Analysis of PM 10 and PM 2.5 measurements showed that 13 to 26% of polluted events in summer at Boyarsky station were associated with smoke aerosol transport. Detection of MODIS fire hotspots combined with backward trajectories indicated that smoke aerosol transport originated from wildfire sources in Yakutia and northern Irkutsk region. In the case from August 7 to 14, smoke presence was linked to the passage of a cold front. The analysis suggests that upper-level smoke aerosols were entrained into the planetary boundary layer and mixed down to the surface through turbulent mixing during the front passage. Subsequent suppression of planetary boundary layer development resulted in accumulation of smoke aerosols near the surface and a peak in PM mass concentration at Boyarsky station at 10:00 p.m. local time on August 9. For the episode from July 26 to 29, transport occurred from Yakutia but moved along the eastern shore of Lake Baikal, where wildfires activity was low compared to the western shore. During aerosol loading events, correlations between spectral characteristics of aerosol optical depth and mass concentrations of PM 2.5 and PM 10 were observed. During dense smoke aerosol episodes, simultaneous increases in PM 2.5 (19–24 µg m - ³), PM 10 (42–55 µg m - ³) and AOD (0.8) were recorded. The mutual correlation coefficients between PM 10 (PM 2.5 ) mass concentrations and AOD were 0.86 (0.84), respectively. Overall, this study of two smoke pollution cases demonstrates that meteorological conditions influence the downward mixing of upper-level smoke aerosols and photochemical reactions. The research showed that smoke aerosol transport played a major role in shaping the composition and concentration of PAHs in PM 10 aerosols at Boyarsky station in 2021. Analysis of PAH composition and content revealed that wildfires are a significant source of PAHs—especially retene, light PAHs, and 6-ring PAHs—which led to their increase in PM 10 in 2021 at Boyarsky station. Declarations A CKNOWLEDGMENTS This research was supported by the project of the Russian Science Foundation (RSF) No. 19-77-20058 - P in terms of data analysis and processing and partly supported by budget funds the State Assignment № 124041500027-2 in the organization of expedition researches and scientific equipment. CONTRIBUTIONS Conceptual and design: A.D., G.Z., A.Z. and A.S. Data collection: A.D., G.Z., A.Z., A.S., T.K., V.T. and T.B. Data and result analysis: A.D., G.Z., A.Z., A.S., T.K. and V.T. Writing and editing: A.D., G.Z., A.Z. and A.S. Literature review: V.T. and T.B. Visualization: AS. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Ethics declarations Ethics approval and consent to participate Not applicable. Consent to Participate Not applicable. Consent for publication Not applicable. 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Transport of smoke from Canadian forest fires to the surface near Washington, D.C.: injection height, entrainment, and optical properties. J. Geophys. Res. Atmos. 109, https://doi.org/10.1029/2003JD004248. Crutzen P.J., Andreae M.O. (1990). Biomass burning in the tropics: impact on atmospheric chemistry and biogeochemical cycles. Science. 250, 1669-1678. https://doi.org/10.1126/science.250.4988.1669. Dementeva A., Zhamsueva G., Zayakhanov A., Tcydypov V. (2022). Interannual and Seasonal Variation of Optical and Microphysical Properties of Aerosol in the Baikal Region. Atmosphere. 13, 211. https://doi.org/10.3390/atmos13020211. Draxler, R.R. (1999). HYSPLIT4 user’s guide. NOAA Tech. Memo. ERL ARL-230, NOAA Air Resources Laboratory, Silver Spring, MD. https://www.arl.noaa.gov/documents/reports/hysplit_user_guide.pdf. Accessed 26 June 2025. Federal Forestry Agency (Rosleskhoz). Available online: https://rosleshoz.gov.ru/rates/forest-fires/area/. Accessed 26 June 2025. Golobokova, L.P., Fillipova, U.G., Marinaite, I.I., Belozerova, O.Yu., Gorshkov, A.G., Obolkin, V.A., Potemkin, V.L., Khodzher, T.V. (2011). The chemical composition of atmospheric aerosols over Lake Baikal. Atmos. Ocean. Opt. 24, 236-241. (in Russian) Gorshkov A.G., Izosimova O.N., Kustova O.V., Marinaite I.I., Galachyants Y.P., Sinyukovich V.N., Khodzher T.V. (2021). Wildfires as a Source of PAHs in Surface Waters of Background Areas (Lake Baikal, Russia). Water. 13, 1-16. https://doi.org/10.3390/w13192636. Hogan T.F., Rosmond T.E. (1991). The description of the Navy operational global atmospheric prediction system's spectral forecast model. Mon. Wea. Rev., 119, 1786-1815. Hung W.T., Lu, C.H. (Sarah), Shrestha B., Lin H.C., Lin C.A., Grogan D., Hong J., Ahmadov R., James E., Joseph E. (2020). The impacts of transported wildfire smoke aerosols on surface air quality in New York State: a case study in summer 2018. Atmos. Environ. 227, 117415. https://doi.org/10.1016/j.atmosenv.2020.117415. Marinaite I., Penner I., Molozhnokova E., Shikhovtsev M., Khodzher T. (2022). Polycyclic Aromatic Hydrocarbons in the Atmosphere of the Southern Baikal Region (Russia): Sources and Relationship with Meteorological Conditions. Atmosphere. 13(3), 420. https://doi.org/10.3390/atmos13030420. Izrael Yu.A., Anokhin Yu.A., Kokorin O.A. (1991). Monitoring the state of Lake Baikal. Leningrad: Gidrometeoizdat, 264 p. Khodzher T.V., Yausheva E.P., Shikhovtsev M.Y., Zhamsueva G.S., Zayakhanov A.S., Golobokova L.P. (2024). Black Carbon in the Air of the Baikal Region, (Russia): Sources and Spatiotemporal Variations. Applied Sci. 14(16), 6996. https://doi.org/10.3390/app14166996. Křůmal K., Mikuška P. (2020). Mass concentrations and lung cancer risk assessment of PAHs bound to PM1 aerosol in six industrial, urban and rural areas in the Czech Republic, Central Europe. Atmos. Pollut. 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Environ. 252, 118241. https://doi.org/10.1016/j.atmosenv.2021.118241. Mielonen T., Aaltonen V., Lihavainen H., Hyvärinen A., Arola A., Komppula M., Kivi R. (2013). Biomass burning aerosols observed in northern Finland during the 2010 wildfires in Russia. Atmosphere-Basel. 4, 17-34. https://doi.org/10.3390/atmos4010017. Molozhnikova E.V., Shikhovtsev M.Yu., Popova A.K., Obolkin V.A., Khodzher T.V. (2022). Comparative Analysis of Satellite and Continuous Ground-Based Measurements of Gaseous Impurities at the Listvyanka Station, Baikal. https://symp.iao.ru/files/symp/aoo/28/D.pdf. Accessed 26 June 2025. Diffusion Aerosol Spectrometer Model DAS 2702M. Aeronanotech. Available online: https://aeronanotechnology.com/schetchik-chastits-das-2702-m. Accessed 26 June 2025. Navy Aerosol Analysis and Prediction System (NAAPS). Available online: https://www.nrlmry.navy.mil/aerosol. Accessed 26 June 2025. NCEP/NCAR HYSPLIT. Available online: http://www.arl.noaa.gov. Accessed 26 June 2025. Obolkin V.A., Potemkin V.L., Makukhin V.L., Khodzher T.V., Chepanina E.V. (2017). Long-Range Transport of Plumes from Regional Thermal Power Plants to the Southern Baikal Water Area. Atmos. Ocean Opt. 30, 60-65. (in Russian). Potemkin V.L., Makukhin, V.L. (2007). Investigation of the spatio-temporal distribution of concentrations of small gas impurities regularity in the Baikal region atmosphere. Ecological chemistry 3, 160-165. (in Russian). Ramanathan V., Crutzen P.J., Kiehl J.T., Rosenfeld D. (2001). Aerosols, climate, and the hydrological cycle. Science. 294, 2119-2124. https://doi.org/10.1126/science.1064034. Reid J.S., Koppmann R., Eck T.F., Eleuterio D.P. (2005). A review of biomass burning emissions part II: intensive physical properties of biomass burning particles. Atmos. Chem. Phys. 5, 799-825. https://doi.org/10.5194/acp-5-799-2005. Shimaraev M.N., Kuimova L.N., Sinyukovich V.N., Tsekhanovskii V.V. (2002). Climate and hydrological processes in lake Baikal in the 20th century. Russian Meteorology and Hydrology. 3, 52-58. (in Russian). Sidorov A.A., Sanzhieva S.E. (2018). Chronology of wildfires at Republic of Buryatia. Bulletin of Krasnoyarsk State Agrarian University. 4, 204-208. (in Russian). Sokolova G.V. (2006). Influence of wildfires on weather. Lesnoy Zhurnal (Forestry Journal). 6, 129-132. (in Russian). Sokolova G.V., Teteryatnikova E.P. (2003). Atmospheric disturbances in the impact zone of large wildfires and the possibility of long-term forecasting of environmental consequences. Proceedings of FSBI DalNIILKH. 36, 144–150. (in Russian). Stein A.F., Draxler R.R., Rolph G.D., Stunder B.J.B., Cohen M.D. and Ngan F. (2015). NOAA’s HYSPLIT atmospheric transport and dispersion modelling system. Bull. Amer. Meteor. Soc. 96, 2059-2077, https://doi.org/10.1175/BAMS-D-14-00110.1. Sakerin S.M. (2012). Study of radiation of aerosol characteristics in the Asian part of Russia. Tomsk. Russia. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). Available online: https://www-calipso.larc.nasa.gov. Accessed 26 June 2025. Tobiszewski M., Namiesnik J. (2012). PAH diagnostic ratios for the identification of pollution emission sources. Environ. Pollut. 162, 110-119. https://doi.org/10.1016/j.envpol.2011.10.025. Zhamsueva G., Zayakhanov A., Starikov A., Tsydypov V., Khodzher T., Golobokova L., Marinayte I., Onichyk N., Azzaya D., Oyunchimeg D. (2015). Water-soluble inorganic ions and PAHs of summer PM10 samples in Mongolia during 2005-2010. Atmos. Pollut. Res. 6, 120-128. https://doi.org/10.5094/APR.2015.014. Zhamsueva, G., Zayakhanov, A., Khodzher, T., Tcydypov, V., Balzhanov, T., Dementeva, A. (2022). Studies of the dispersed composition of atmospheric aerosol and its relationship with small gas impurities in the near-water layer of Lake Baikal based on the results of ship measurements in the summer of 2020. Atmosphere. 13. https://doi.org/10.3390/atmos13010139. Zhamsueva, G., Zayakhanov, A., Tcydypov, V., Dementeva, A., Balzhanov, T. (2021). Spatial-Temporal Variability of Small Gas Impurities over Lake Baikal during the Forest Fires in the Summer of 2019. Atmosphere. 12. https://doi.org/10.3390/atmos12010020. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7203689","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504698634,"identity":"e3db58d8-459f-4bd1-87a9-99ab39212d42","order_by":0,"name":"Ayuna Dementeva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYNACNgYGfhCdUECKFskGkBYDUrQYHAAxiNHCPyPH7MGPMpto4/OrEz88MGCQ5xc7gF+LxI0cc8Oec2m522683SwBdJjhzNkJBKy5kWMmwdt2GKjl7AaQlgSD2wS0yAO1SP5t+5+7ecbZzT+I0mIA1CLN23YgdwN/7zbibDE886xMWuZccu6MG7zbLBIMJAj7Re548jbJN2V2uf39Zzff/FFhI88vTUALgwBMgQSYIUFAOQjwH0BnjIJRMApGwShAAwAGn0cn/INo9gAAAABJRU5ErkJggg==","orcid":"","institution":"Institute of Physical Materials Science Siberian Branch of the Russian Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Ayuna","middleName":"","lastName":"Dementeva","suffix":""},{"id":504698635,"identity":"949f65c5-644c-4bfc-bbf4-3d3b81189679","order_by":1,"name":"Galina Zhamsueva","email":"","orcid":"","institution":"Institute of Physical Materials Science Siberian Branch of the Russian Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Galina","middleName":"","lastName":"Zhamsueva","suffix":""},{"id":504698636,"identity":"97a0ba77-430c-4b49-9491-0532cd60f1ce","order_by":2,"name":"Alexander Zayakhanov","email":"","orcid":"","institution":"Institute of Physical Materials Science Siberian Branch of the Russian Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Zayakhanov","suffix":""},{"id":504698637,"identity":"01e412da-c084-4c15-93a3-802bfc967ef8","order_by":3,"name":"Tamara Khodzher","email":"","orcid":"","institution":"Limnological Institute Siberian Branch of the Russian Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tamara","middleName":"","lastName":"Khodzher","suffix":""},{"id":504698638,"identity":"027fb019-8eac-49e8-a66c-786caf62793f","order_by":4,"name":"Alexey Starikov","email":"","orcid":"","institution":"Institute of Physical Materials Science Siberian Branch of the Russian Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Alexey","middleName":"","lastName":"Starikov","suffix":""},{"id":504698639,"identity":"42be61cd-a717-43bd-b793-a71af5a809ca","order_by":5,"name":"Vadim Tsydypov","email":"","orcid":"","institution":"Institute of Physical Materials Science Siberian Branch of the Russian Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Vadim","middleName":"","lastName":"Tsydypov","suffix":""},{"id":504698640,"identity":"3cbbbe6d-c453-47aa-9dd9-aac866623f1e","order_by":6,"name":"Tumen Balzhanov","email":"","orcid":"","institution":"Institute of Physical Materials Science Siberian Branch of the Russian Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tumen","middleName":"","lastName":"Balzhanov","suffix":""}],"badges":[],"createdAt":"2025-07-24 09:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7203689/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7203689/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90045367,"identity":"933b0878-d7f4-4882-82f7-a24781894f2d","added_by":"auto","created_at":"2025-08-27 18:06:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210796,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of wildfires in Irkutsk region and Republic of Buryatia, and the burned area in the Russian Federation and Baikal region (Irkutsk region and Republic of Buryatia) (a); Synoptic map showing the position of the anticyclone over the territory of Yakutia and backward air mass trajectories computed using the HYSPLIT model, July 2019 (b)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7203689/v1/8883e42bbe7230a530fb1c5d.png"},{"id":90045369,"identity":"3a29cef9-9bca-479b-a979-52474ac329db","added_by":"auto","created_at":"2025-08-27 18:06:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230680,"visible":true,"origin":"","legend":"\u003cp\u003ebackward air mass trajectories computed using the HYSPLIT model for August 9, 2021 (a); Vertical cross-section of the atmospheric column with a qualitative classification of aerosol distribution types on August 9, 2021 (b).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7203689/v1/4a7c31f0265d196a69732e28.png"},{"id":90045825,"identity":"32bc3780-b429-4213-809b-ee55116974de","added_by":"auto","created_at":"2025-08-27 18:14:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":280041,"visible":true,"origin":"","legend":"\u003cp\u003eForecast maps of smoke aerosol distribution for August 2021 based on data from the global aerosol model NAAPS (Navy Aerosol Analysis and Prediction System).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7203689/v1/f32882345ee78935e8c225d4.png"},{"id":90044899,"identity":"34011a99-faa6-4a36-ad7e-c80af3514eff","added_by":"auto","created_at":"2025-08-27 17:58:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":48047,"visible":true,"origin":"","legend":"\u003cp\u003eInterannual variation of aerosol optical depth at a wavelength of 0.5 µm (\u003cem\u003eτ\u003c/em\u003e\u003csub\u003e\u003cem\u003e0,5\u003c/em\u003e\u003c/sub\u003e) at Boyarsky station (2019–2024 years) (a); average spectral characteristics of AOD under conditions of \"smoke / clear atmosphere\" (b).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7203689/v1/86db1bc525ed87cfe3b32d02.png"},{"id":90044901,"identity":"6580802f-4170-48be-a73d-e5fb75d75130","added_by":"auto","created_at":"2025-08-27 17:58:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":114364,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of mass concentration of aerosol fractions PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e over the entire observation period (July–September 2021).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7203689/v1/49d8da79ace56eea68691353.png"},{"id":90044892,"identity":"bdb4430e-135e-4638-9b61-2b9c51ab241a","added_by":"auto","created_at":"2025-08-27 17:58:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":40955,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal dynamics of atmosphere AOD and mass concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e in July-August 2021.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7203689/v1/4eeb2e51353a77a61e19ce66.png"},{"id":90044906,"identity":"8a904bf5-98dd-48f6-b303-17db4e458b6c","added_by":"auto","created_at":"2025-08-27 17:58:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":93978,"visible":true,"origin":"","legend":"\u003cp\u003eMass concentration of PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e and vertical temperature gradient (a); total turbulent kinetic energy and turbulent exchange coefficient, Boyarsky station (August 2021) (b).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7203689/v1/f5794af476cf6bd04d2d3344.png"},{"id":90044911,"identity":"f681c409-6ee0-4ad5-9032-45faafb581c2","added_by":"auto","created_at":"2025-08-27 17:58:33","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":85446,"visible":true,"origin":"","legend":"\u003cp\u003eConcentration of PAHs in PM\u003csub\u003e10\u003c/sub\u003e aerosol at Boyarsky station, July-August 2021.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7203689/v1/cc54723574fde74158fd1233.png"},{"id":90045826,"identity":"84699165-2aff-43dd-a0ff-250905c6df9b","added_by":"auto","created_at":"2025-08-27 18:14:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":84080,"visible":true,"origin":"","legend":"\u003cp\u003eAverage component composition of PAHs in PM\u003csub\u003e10\u003c/sub\u003e aerosol at Boyarsky station, July-August 2021.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7203689/v1/85e1ec30ebc05e2c78092de2.png"},{"id":105033937,"identity":"f33c1f00-0cf6-4853-a31f-8154e511ee9f","added_by":"auto","created_at":"2026-03-20 07:22:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1647829,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7203689/v1/7c2a904c-4bb9-40d0-97a0-d9bbdada886e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of smoke aerosol contribution to atmosphere of the south-eastern coast of Lake Baikal in 2021 (\"Boyarsky\" station)","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe Lake Baikal depression constitutes a unique natural object. Lake Baikal possesses a significant north-south extent, concentrates an enormous mass of water, and is surrounded by mountains (reaching elevations of up to 2500 m above sea level), which impede air exchange with surrounding regions. This leads to the formation of specific natural and climatic conditions within the Baikal depression, substantially differing from other continental areas. Along the Baikal coast, favorable conditions for the accumulation of pollutants develop in both summer and winter due to low-gradient fields of high and low pressure (Shimaraev et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe ecosystem of Lake Baikal is affected by the Irkutsk-Cheremkhovo industrial hub due to the transport of pollutants along the Angara River valley into the lake\u0026rsquo;s water area, predominantly impacting its southern part (Arguchintseva and Vologzhina, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Obolkin et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Izrael et al., 1991; Latysheva et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Potemkin and Makukhin, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Golobokova et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition, the air quality of Lake Baikal is influenced by tourism and recreational activities associated with the expansion of infrastructure in the lake\u0026rsquo;s coastal zone (Golobokova et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhamsueva et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, environmental monitoring at Baikal is of critical importance for preserving the natural environment and biodiversity of the region. Over the past decade, wildfires have had a strong impact on air quality at Baikal region (Zhamsueva et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dementeva et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). During the warm season, from April to October, wildfires represent major natural sources of pollutant emissions into the atmosphere at Baikal natural territory. Due to dry weather conditions, wildfires episodically occur in the boreal forests of Eastern Siberia as a result of thunderstorms and human activity. The average annual burned area in Buryatia increased significantly from 11,984.6 ha in 1966\u0026ndash;1995 to 100,721.7 ha in 1996\u0026ndash;2016 (Sidorov and Sanzhieva, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWildfires emit large quantities of trace gases (both chemically active and greenhouse gases), non-methane hydrocarbons, and aerosols (Crutzen and Andreae, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). These aerosols and pollutants substantially affect atmospheric chemistry, cloud properties, Earth\u0026rsquo;s radiation balance, climate change, and more (Crutzen and Andreae, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Ramanathan et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; IPCC, 2013).\u003c/p\u003e\u003cp\u003eDuring catastrophic wildfires, smoke saturates the atmosphere up to altitudes of 7\u0026ndash;10 km, where over 50% of the air mass is concentrated. Within the formed fire center, additional heat and smoke create a high-pressure region that influences synoptic conditions in adjacent territories until the end of summer. Cyclones avoid fire centers, causing heavy rainfall in those areas (Sokolova, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Sokolova and Teteryatnikova, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudying the distribution and transport of aerosol particles in the Baikal region is great importance for understanding the mechanisms of atmospheric composition formation and controlling air pollution over Lake Baikal. Ground-based monitoring plays a key role in assessing the impact of smoke aerosols from forest and peat fires on the Earth\u0026rsquo;s radiation balance and, consequently, on local and regional climate, especially for Lake Baikal, which is recognized as a UNESCO World Natural Heritage site (Badarinath et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mielonen et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dementeva et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Khodzher et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To investigate the scale of smoke gas and aerosol impacts and long-range transport, satellite monitoring and modeling tools such as MODIS, CALIPSO, NAAPS, HYSPLIT, and others are required, providing essential information on fire locations and intensity, transport pathways, smoke plume distribution, and aerosol type identification (Lin et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Johnson et al., 2021).\u003c/p\u003e\u003cp\u003eThe primary aim of this paper was to investigate the influence of smoke aerosols from wildfires on the chemical composition of the atmosphere and air quality along the south-eastern coast of Lake Baikal.\u003c/p\u003e"},{"header":"2. METHODS AND MATERIALS","content":"\u003cp\u003eIn this study, an integrated approach combining ground-based measurements, satellite data, and numerical modeling was employed to analyze atmospheric smoke pollution over Lake Baikal during the summer period (July\u0026ndash;August) of 2021.\u003c/p\u003e\u003cp\u003e\u0026ldquo;Boyarsky\u0026rdquo; scientific station (51\u0026deg;50\u0026prime;47\u0026Prime; N; 106\u0026deg;04\u0026prime;01\u0026Prime; E) is situated in the coastal zone 160 km from Ulan-Ude on the southeastern coast of Lake Baikal in 500 m from the shoreline. Station is position in the forest zone and a rather large distance (\u0026gt;\u0026thinsp;100 km) from large cities and the sources of industrial emissions.\u003c/p\u003e\u003cp\u003eThe mass concentration of aerosol fractions PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e in the air were measured using a Diffusion Aerosol Spectrometer Model DAS 2702M (Aeronanotech, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://aeronanotechnology.com/das_2702-m\u003c/span\u003e\u003cspan address=\"https://aeronanotechnology.com/das_2702-m\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The DAS consists of a set of different filter meshes that forma stage on which particles are deposited as they pass through the battery. The DAS includes an aerosol particle counter and a condensation particle counter, which are installed to measure the number concentration and size distribution of aerosol particles. The particle size distribution is measured. Its principle is based on the condensation enlargement of particles. In order to obtain information about nanometer-sized particles, the particles are passed through a diffusion cell to obtain information about their size. These particles are deposited on the grid of the diffusion cell. Such deposition is selective. In other words, the smaller the particles, the smaller the particles, the greater the diffusion coefficient and the easier it is for them to be deposited on the grid of the battery. The spectrometer has 2 operating modes: parameter measurement mode aerosol particles in the size range from 0.005 to 0.2 microns (40 ranges with a step of 5 nm); the mode of measuring the parameters of aerosol particles in the size range from 0.2 to 10 \u0026micro;m (12 channels).\u003c/p\u003e\u003cp\u003eAerosol sampling for Whatman-41 filters using Andersen Instruments Inc. PM\u003csub\u003e10\u003c/sub\u003e high-volume sampler (USA) were held in the summer 2021.\u003c/p\u003e\u003cp\u003eThe chemical composition of the soluble aerosol substance fraction was determined using modern analytical methods of atomic adsorption and ion chromatography, recommended to ensure comparability with data from other regions of the world. The samples were analyzed by gas chromatography mass spectrometry, Agilent Technologies 7890B GC System 7000C GC-MS Triple Quad chromatograph mass spectrometer, Santa Clara, CA, USA (Marinaite et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo analyze the transport of smoke aerosol and its spatial distribution on a regional scale data from MODIS (MAIC) satellite (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://worldview.earthdata.nasa.gov/\u003c/span\u003e\u003cspan address=\"https://worldview.earthdata.nasa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the trajectory model NCEP/NCAR HYSPLIT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.arl.noaa.gov\u003c/span\u003e\u003cspan address=\"http://www.arl.noaa.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and archival meteorological data of the NOAA were used.\u003c/p\u003e\u003cp\u003eExperimental measurements of aerosol optical depth (AOD) in atmosphere at the Boyarsky station were conducted using the multi-wavelength sun photometer SP-9 across the wavelength range of 0.34\u0026ndash;2.2 \u0026micro;m (Tomsk, Russia), is designed for year-round atmospheric transparency measurements aimed at subsequent determination of AOD and atmospheric water vapor content. The measurement uncertainty of AOD is between 0.01 and 0.02. The process is fully automated and operates without operator intervention (Sakerin, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMeasurements of atmospheric meteorological parameters at the Boyarsky station were conducted using the acoustic meteorological complex AMK-03 (Tomsk, Russia). The AMK-03 meteorological complex provides instantaneous values of wind speed (along three mutually perpendicular directions) and air temperature, with a resolution of no more than 0.01 m s\u003csup\u003e-1\u003c/sup\u003e for wind speed and no more than 0.01\u0026deg;C for air temperature, at a measurement frequency ranging from 10 to 160 Hz (Azbukin et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This capability allows for the analysis of atmospheric turbulent parameters.\u003c/p\u003e\u003cp\u003eAnalysis of the vertical aerosol distribution and determination of its type were performed using data from the space-based CALIOP lidar on the CALIPSO satellite (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www-calipso.larc.nasa.gov/\u003c/span\u003e\u003cspan address=\"https://www-calipso.larc.nasa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and MODIS Blue Aerosol Type (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://worldview.earthdata.nasa.gov/\u003c/span\u003e\u003cspan address=\"https://worldview.earthdata.nasa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study of atmospheric aerosol transport and the distribution of dominant aerosol components (sulfate aerosol, dust, smoke) was carried out using the global aerosol model Navy Aerosol Analysis and Prediction System (NAAPS, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nrlmry.navy.mil/aerosol/\u003c/span\u003e\u003cspan address=\"https://www.nrlmry.navy.mil/aerosol/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). NAAPS generates operational six-day forecasts for sulfates, dust, smoke, sea salt, and SO\u003csub\u003e2\u003c/sub\u003e (Hogan and Rosmond, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Monitoring wildfires in Siberia\u003c/h2\u003e\u003cp\u003eIn recent years, an increase has been observed in both the duration of fire hazard season and frequency of wildfire occurrences. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea presents the dynamics of wildfires area and number from 2013 to 2024 within the territory of Russian Federation and Baikal natural territory (BPT) - Republic of Buryatia and Irkutsk region. During the research, highest number of wild and peat fires was recorded in 2014, 2015, and 2017. The maximum burned forest area in Russia was observed from 2019 to 2021, reaching up to 10\u0026nbsp;million hectares annually. In 2019, the Republic of Sakha and Krasnoyarsk region contributed the most to the total burned forest area, which amounted to 6.426\u0026nbsp;million hectares. In 2020, the burned area in Republic of Sakha comprised 6.343\u0026nbsp;million hectares, while in 2021 it reached nearly 8\u0026nbsp;million hectares. In 2023, more than half of the burned area was concentrated in two Far Eastern federal subjects\u0026mdash;approximately 2.3\u0026nbsp;million hectares in the Republic of Sakha and Khabarovsk region. In 2024, wildfires were registered in Russia over a total burned area of 7.5\u0026nbsp;million hectares (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rosleshoz.gov.ru/rates/forest-fires/area/\u003c/span\u003e\u003cspan address=\"https://rosleshoz.gov.ru/rates/forest-fires/area/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDuring the large-scale wildfires in the summer months of 2019\u0026ndash;2021 in Yakutia, a strong anticyclone was established that contributed to the deterioration of the fire hazard situation. The transport of smoke aerosol from the wildfire sources in Yakutia led to the formation of a persistent haze over Lake Baikal and, as a consequence, to significant aerosol pollution throughout the atmospheric column. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb illustrates, as an example, the synoptic map showing the establishment of the anticyclone over Yakutia in July 2019 and the backward air mass trajectories calculated using the HYSPLIT model, which demonstrated the transport of smoke aerosol from the wildfire centers to the Baikal region.\u003c/p\u003e\u003cp\u003e. Numerous active wildfires were detected by MODIS in 2021 to the north, northwest, and northeast of Lake Baikal in the Northeastern Siberian Lowland region (Krasnoyarsk and Irkutsk regions, Yakutia). Biomass burning emissions in Eastern Siberia peaked between August 7 and 14, with maximum total emissions of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e. Satellite image analysis from MODIS revealed the transport of smoke plumes from extensive wildfires into the atmosphere of Baikal region. Wildfires ignited in the North Siberian Lowland region generated extensive smoke plumes identifiable as gray streaks on true-color imagery, spreading over Lake Baikal.\u003c/p\u003e\u003cp\u003eUsing MODIS Deep Blue Aerosol Type data from satellite imagery, the aerosol type was identified, showing the planetary boundary layer was entirely filled with smoke aerosol. To determine the transport pathways of air masses over Baikal region, backward trajectories of smoke aerosol transport were calculated with the HYSPLIT model based on the timing, location, and altitude of the smoke plumes. Air mass trajectories were computed for durations of 5 days (120 hours) with 6-hour intervals at altitudes of 100, 500, and 1500 meters, effectively representing regional and interregional pollutant transport. The backward trajectories indicated that smoke aerosol transport occurred at heights of 1500 to 3000 meters from wildfire sources in Yakutia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb presents a vertical cross-section of the atmospheric column with a qualitative classification of aerosol distribution types, as measured by the spaceborne CALIOP lidar on August 9, 2021. In the upper troposphere above Lake Baikal, at altitudes ranging from 5 to 10 km, the atmosphere was densely populated with smoke aerosol. Simultaneously, aerosol typing in the lower tropospheric layer was not possible due to dense cloud cover obstructing the measurements.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo identify the aerosol type in the lower tropospheric layer, forecast maps from NAAPS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nrlmry.navy.mil/aerosol/\u003c/span\u003e\u003cspan address=\"http://www.nrlmry.navy.mil/aerosol/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were analyzed. The analysis results demonstrated high concentrations of smoke aerosol in the near-surface atmospheric layer from August 7 to 14, reaching up to 512 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Interannual variability of AOD at Boyarsky station\u003c/h2\u003e\u003cp\u003eTo assess the regional characteristics of interannual variability of the spectral properties of atmospheric aerosol optical depth, data from the Boyarsky station for the period 2019\u0026ndash;2024 were analyzed. Experimental measurements of AOD at the Boyarsky site were performed using the multi-wavelength sun photometer SP-9 in the wavelength range of 0.34\u0026ndash;1.24 \u0026micro;m. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea depicts the interannual variability of AOD at a wavelength of 0.5 \u0026micro;m at the Boyarsky station during the summer. The highest atmospheric turbidity was observed from 2019 to 2021, with AOD values ranging from 0.23 to 0.27. The main contribution to aerosol pollution at Boyarsky station is attributed to large-scale wildfires. Consequently, this resulted in a high level of aerosol loading and an increase in AOD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA long-term increasing trend and high variability in the fine mode component of AOD have been observed due to wildfire smoke in Baikal region (Dementeva et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To identify the main features of the spatiotemporal variability of aerosol characteristics in Baikal region atmosphere, average spectral characteristics of atmospheric aerosol optical depth were examined under \"smoke/clean atmosphere\" conditions. The largest changes in AOD during smoke events occur in the visible spectral range, driven by contribution of fine aerosol fraction. It was shown that in the UV range (0.34 \u0026micro;m) AOD under smoke conditions increased by a factor of 4.2, in the visible range (0.5 \u0026micro;m) by 6 times, and in the IR range (1.24 \u0026micro;m) by 3.6 times (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eStudies were conducted on the mass concentration of aerosol fractions PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e in atmosphere of coastal zone of Lake Baikal using the diffusion aerosol spectrometer DAS 2702M from July to September 2021.\u003c/p\u003e\u003cp\u003eTwo cases of prolonged smoke aerosol transport from Yakutia from July 26 to 29 and from August 7 to 14, 2021 were analyzed. During the intense smoke pollution period from August 7 to 14 (highlighted separately), average concentrations reached 146 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3; for PM\u003csub\u003e2.5\u003c/sub\u003e and 314 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3; for PM\u003csub\u003e10\u003c/sub\u003e, with maximum PM\u003csub\u003e10\u003c/sub\u003e concentrations reaching 920 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3; and PM\u003csub\u003e2.5\u003c/sub\u003e up to 404 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3; (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The smoke plumes increased the mass concentration of PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e at Boyarsky, exceeding the single allowable maximum concentrations by factors of 3 and 2.5, respectively.\u003c/p\u003e\u003cp\u003eDuring the same period, high PM\u003csub\u003e2.5\u003c/sub\u003e concentrations\u0026mdash;up to 240 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;\u0026mdash;and CO concentrations up to 2 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3; were recorded at the southwestern shore of Lake Baikal, at Listvyanka monitoring station. Joint analysis of satellite data and trajectory calculations showed that air pollution episodes near the Listvyanka monitoring stations were caused by the transport of smoke plumes from intense wildfires located 1500\u0026ndash;2000 km away (Molozhnikova et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor further quantitative assessment of the contribution of smoke aerosols to PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, average concentrations of these fractions were calculated for smoke and non-smoke cases. Average PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e concentrations for smoke conditions were 21 and 45 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, respectively, compared to 4 and 9 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3; for non-smoke conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on measurement data during of smoke haze in the atmosphere at Boyarsky station, a strong correlation was identified between AOD and the concentrations of aerosol fractions PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Daily average values of atmosphere AOD, τ\u003csub\u003e0,5\u003c/sub\u003e at a wavelength of 0.5 \u0026micro;m and daily average concentrations of PM were used as the basis of analysis. The correlation coefficients between τ\u003csub\u003e0,5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e (PM\u003csub\u003e2.5\u003c/sub\u003e) were 0.86 and 0.84, respectively.\u003c/p\u003e\u003cp\u003eFrom July 26 to 29, 2021, a sharp increase of AOD (τ\u003csub\u003e0,5\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.6) at Boyarsky station was observed, reaching an absolute maximum on July 27 (τ\u003csub\u003e0,5\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.8). At the same time, maximum mass concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e increased up to 36 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3; and PM\u003csub\u003e10\u003c/sub\u003e up to 78 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, from baseline levels of 23 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;. The average aerosol concentrations of PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e during the entire smoke period were 20 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3; and 40 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, respectively, caused by smoke advection from Yakutia.\u003c/p\u003e\u003cp\u003eTo investigate the ways of smoke plumes transfer, 5-day backwards trajectories of air masses were constructed using the NOAA HYSPLIT model. The smoke aerosol transport trajectories from July 26 to July 29 were carried out from the northeastern direction (Yakutia). Smoke plumes from Yakutia moved at altitudes of 2000\u0026ndash;3500 m from the eastern side of Lake Baikal, and on July 27, they reached Middle Baikal with a decrease in altitude to the near-water layer and the speed of air mass transport.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Meteorological and synoptic conditions\u003c/h2\u003e\u003cp\u003eOverall, the two smoking events differ in terms of smoke plume intensity, transport pathways, and weather conditions. Synoptic weather conditions during the smoke episodes can be one of the possible reasons for spatial variability of aerosol fractions PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e. Meteorological conditions can influence not only the transport path but also the process of downward mixing of smoke aerosols in atmospheric boundary layer.\u003c/p\u003e\u003cp\u003eMeteorological and turbulent characteristics have a significant impact on advective and convective processes of transport and transformation of pollutants in Lake Baikal region. Measurements of meteorological and turbulent parameters of atmosphere were carried out using the automated meteorological complex AMK-03 at heights of 2 m and 20 m. Starting from August 5, Lake Baikal was under the influence of a cyclone, characterized by a drop in pressure followed by a decrease in temperature. On August 7, the cyclone was replaced by an anticyclone, which brought air enriched with smoke aerosol. During this period, intensive turbulent exchange between the upper and lower layers was observed until August 9. The total turbulent kinetic energy (Ev) was 2.5 m\u0026sup2; s\u003csup\u003e-\u003c/sup\u003e\u0026sup2;, and the turbulent exchange coefficient (Kh) was 0.8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). It is assumed that the smoke aerosols aloft were entrained into the planetary boundary layer and mixed turbulently during the passage of the front. Subsequently, the development of planetary boundary layer was suppressed, leading to the accumulation of smoke aerosols near the surface and resulting in maximum PM concentrations. Long-range transported smoke aerosols at altitude can be entrained into the planetary boundary layer as it grows during the morning and early daytime hours, mixed downward by turbulence, and thereby affect surface air quality (Colarco et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Hung et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWith the change of air masses on August 9 from 06 p.m. to 08 a.m. on August 10, under the influence of cyclonic activity, a temperature inversion was formed and a further decrease of total turbulent kinetic energy and turbulent exchange coefficient, which led to the maximum accumulation of smoke aerosol in the surface layer (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eThus, the combination of dynamic transport of smoke masses, turbulent mixing, temperature inversion and a weak level of particle deposition led to a high mass concentration of PM during the transport of smoke aerosol from wildfires under cyclonic conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Impact of smoke pollution on air quality in Lake Baikal atmosphere\u003c/h2\u003e\u003cp\u003eTo investigate the impact of wildfires on air quality in atmosphere of Lake Baikal, an analysis of polycyclic aromatic hydrocarbons (PAHs) content in PM\u003csub\u003e10\u003c/sub\u003e aerosol was conducted. Polycyclic aromatic hydrocarbons are typical carcinogenic and mutagenic compounds (Křůmal and Mikuška, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The source of PAHs is incomplete combustion of organic fuels such as coal, wood, and petroleum fuels (Kulshrestha et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). PAHs are classified into low molecular weight hydrocarbons: 2\u0026ndash;3 aromatic rings and high molecular weight hydrocarbons: 4 or more rings (Boente et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a result of aerosol sample analysis, 21 PAHs were identified: naphthalene, 2-methylnaphthalene, 1-methylnaphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, retene, benz[a]anthracene, chrysene, benz[b]fluoranthene, benz[k]fluoranthene, benz[e]pyrene, benz[a]pyrene, perylene, indeno[1,2,3-cd]pyrene, benzo[g,h,i]perylene, dibenz[a,h]anthracene. PAHs were extracted using hexane in an ultrasonic bath at room temperature. The extract was concentrated to 0.1\u0026ndash;0.2 ml and analyzed using an Agilent GC 6890 gas chromatograph coupled with an MSD 5973 Network mass spectrometer. PAH concentrations were calculated using internal standards phenanthrene\u0026ndash;d10, chrysene\u0026ndash;d12, and perylene\u0026ndash;d12 from Supelco (USA). The measurement error does not exceed 10% (Zhamsueva et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study of PAH content in aerosol at Boyarsky station in 2021 was conducted from July 25 to August 27, during which 23 samples were collected (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The concentration of polycyclic aromatic hydrocarbons ranged from 0.226 to 5.095 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, with an average of 1.608 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;. The main components were benzo[b]fluoranthene (0.013\u0026ndash;0.912 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, average 0.219 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;), fluoranthene (0.018\u0026ndash;0.962 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, average 0.194 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;), benzo[k]fluoranthene (0.017\u0026ndash;0.402 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, average 0.118 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;), pyrene (0.014\u0026ndash;0.703 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, average 0.139 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;), indeno[1,2,3-cd]pyrene (0.018\u0026ndash;0.429 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, average 0.130 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;), benzo[g,h,i]perylene (0.018\u0026ndash;0.409 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, average 0.117 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;), and 2-methylnaphthalene (0.027\u0026ndash;0.777 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, average 0.105 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn 2021, air mass transport at Boyarsky station was predominantly from wildfire sources in Yakutia, Evenkia, and the northern Irkutsk region. This resulted in high levels of smoke aerosol over Lake Baikal.\u003c/p\u003e\u003cp\u003eThe fraction of light PAHs accounted for 19.5%, or 0.314 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3; (2-ring PAHs constituted 11.4% or 0.184 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, and 3-ring PAHs 8.1% or 0.130 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;). PAHs with 4 rings made up 33.6% (0.541 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;), 5-ring PAHs accounted for 31.4% (0.505 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;), and 6-ring PAHs represented 15.4% (0.247 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRetene is a characteristic product of coniferous wood combustion (Cecinato et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and is therefore used as a marker to determine the influence of wildfires on the chemical composition of air (Gorshkov et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Retene concentrations in 2021 ranged from 0.020 to 0.467 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, with an average of 0.090 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, accounting for 5.6% of the total analyzed PAHs. To investigate the origin of polycyclic aromatic hydrocarbons in PM\u003csub\u003e10\u003c/sub\u003e aerosol, diagnostic ratios were examined. The fluoranthene / (fluoranthene\u0026thinsp;+\u0026thinsp;pyrene) ratio in the 2021 samples ranged from 0.5 to 0.9, indicating coal and biomass combustion as the primary sources of PAHs in the aerosol during this period. The benzo[a]pyrene / (benzo[a]pyrene\u0026thinsp;+\u0026thinsp;benzo[b]fluoranthene) ratio was generally below 0.5, suggesting aged aerosol and, consequently, long-range transport of PAHs from distant particulate matter sources (Tobiszewski and Namiesnik, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA correlation analysis was performed among different PAHs to identify the sources of polycyclic aromatic hydrocarbons in suspended particles. The correlation analysis showed that retene exhibited strong correlations with light PAHs and 6-ring hydrocarbons (r\u0026thinsp;=\u0026thinsp;0.51\u0026ndash;0.84) at the Boyarsky station, indicating their predominant origin from biomass combustion. Overall, components associated with smoke aerosol transport accounted for 40.5% of PAH mass, demonstrating the significant role of wildfires in shaping the aerosol composition on the southeastern shore of Lake Baikal during summer 2021.\u003c/p\u003e\u003cp\u003eUnder the influence of smoke aerosol transport, significant changes occur in the content and composition of PAHs in aerosol. We conducted a comparison of aerosol samples taken on August 1 during transport from regions weakly affected by wildfires (Central Siberia, northern Buryatia) and on July 28, when there was transport from Yakutia, where many wildfire sources were present. Under the influence of transport from wildfire sources, the total PAHs on July 28 increased to 5.10 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3; compared to 1.02 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3; on August 1. The concentration of retene during smoke aerosol transport was 21 times higher than on August 1 (0.467 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3; and 0.022 ng m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, respectively). Exposure to biomass combustion products resulted in a strong increase in the share of light PAHs in the sample (38.3% and 9.4%, respectively).\u003c/p\u003e\u003cp\u003eIn 2021, air mass transport to Boyarsky predominantly originated from wildfire sources in Yakutia, Evenkia, and northern Irkutsk region. This led to high smoke aerosol content over Lake Baikal. Observed PAH measurements and modeling data demonstrate that interregional transport of smoke aerosol from Siberian wildfires significantly influenced the atmospheric composition on the southeastern shore of Lake Baikal in summer 2021.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. CONCLUSIONS","content":"\u003cp\u003eIn this paper investigated two cases of severe atmospheric smoke pollution using ground-based and satellite observations, along with backward trajectory calculations of smoke aerosol transport via the NOAA HYSPLIT model, to characterize the contribution of transported smoke aerosols to air quality over Lake Baikal in summer and analyze the influence of meteorological conditions on smoke events. Analysis of PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e measurements showed that 13 to 26% of polluted events in summer at Boyarsky station were associated with smoke aerosol transport. Detection of MODIS fire hotspots combined with backward trajectories indicated that smoke aerosol transport originated from wildfire sources in Yakutia and northern Irkutsk region.\u003c/p\u003e\u003cp\u003eIn the case from August 7 to 14, smoke presence was linked to the passage of a cold front. The analysis suggests that upper-level smoke aerosols were entrained into the planetary boundary layer and mixed down to the surface through turbulent mixing during the front passage. Subsequent suppression of planetary boundary layer development resulted in accumulation of smoke aerosols near the surface and a peak in PM mass concentration at Boyarsky station at 10:00 p.m. local time on August 9.\u003c/p\u003e\u003cp\u003eFor the episode from July 26 to 29, transport occurred from Yakutia but moved along the eastern shore of Lake Baikal, where wildfires activity was low compared to the western shore. During aerosol loading events, correlations between spectral characteristics of aerosol optical depth and mass concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e were observed. During dense smoke aerosol episodes, simultaneous increases in PM\u003csub\u003e2.5\u003c/sub\u003e (19\u0026ndash;24 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;), PM\u003csub\u003e10\u003c/sub\u003e (42\u0026ndash;55 \u0026micro;g m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;) and AOD (0.8) were recorded. The mutual correlation coefficients between PM\u003csub\u003e10\u003c/sub\u003e (PM\u003csub\u003e2.5\u003c/sub\u003e) mass concentrations and AOD were 0.86 (0.84), respectively.\u003c/p\u003e\u003cp\u003eOverall, this study of two smoke pollution cases demonstrates that meteorological conditions influence the downward mixing of upper-level smoke aerosols and photochemical reactions. The research showed that smoke aerosol transport played a major role in shaping the composition and concentration of PAHs in PM\u003csub\u003e10\u003c/sub\u003e aerosols at Boyarsky station in 2021. Analysis of PAH composition and content revealed that wildfires are a significant source of PAHs\u0026mdash;especially retene, light PAHs, and 6-ring PAHs\u0026mdash;which led to their increase in PM\u003csub\u003e10\u003c/sub\u003e in 2021 at Boyarsky station.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003eCKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the project of the Russian Science Foundation (RSF) No. 19-77-20058 - P in terms of data analysis and processing and partly supported by budget funds the State Assignment № 124041500027-2 in the organization of expedition researches and scientific equipment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptual and design: A.D., G.Z., A.Z. and A.S. Data collection: A.D., G.Z., A.Z., A.S., T.K., V.T. and T.B. Data and result analysis: A.D., G.Z., A.Z., A.S., T.K. and V.T. Writing and editing: A.D., G.Z., A.Z. and A.S. Literature review: V.T. and T.B. Visualization: AS.\u003c/p\u003e\n\u003cp\u003eAll authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnthropogenic and Natural Radiative Forcing, in Climate Change (2013): The Physical Science Basis Cambridge Univ. 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Res. 6, 120-128. https://doi.org/10.5094/APR.2015.014.\u003c/li\u003e\n\u003cli\u003eZhamsueva, G., Zayakhanov, A., Khodzher, T., Tcydypov, V., Balzhanov, T., Dementeva, A. (2022). Studies of the dispersed composition of atmospheric aerosol and its relationship with small gas impurities in the near-water layer of Lake Baikal based on the results of ship measurements in the summer of 2020. Atmosphere. 13. https://doi.org/10.3390/atmos13010139.\u003c/li\u003e\n\u003cli\u003eZhamsueva, G., Zayakhanov, A., Tcydypov, V., Dementeva, A., Balzhanov, T. (2021). Spatial-Temporal Variability of Small Gas Impurities over Lake Baikal during the Forest Fires in the Summer of 2019. Atmosphere. 12. https://doi.org/10.3390/atmos12010020.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wildfires, mass concentration of aerosol РМ10 и РМ2.5, aerosol optical depth, Lake Baikal, sun photometer","lastPublishedDoi":"10.21203/rs.3.rs-7203689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7203689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmoke aerosols emitted by wildfires can ascend into the free troposphere, be transported over long distances, and subsequently descend, thereby affecting local air quality in remote areas. This article presents a study of aerosol pollution in the atmosphere over the southeastern shore of Lake Baikal during the summer of 2021, utilizing an automated system for real-time air quality monitoring. Analysis of mass concentrations of fine particulate matter fractions PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e, aerosol optical depth (AOD) measurements, aerosol sampling, and backward air mass trajectory analysis using the HYSPLIT model demonstrated that the prolonged atmospheric turbidity over Lake Baikal was caused by the transport of smoke aerosol from large-scale wildfire centers in the Republic of Sakha (Yakutia) and Siberia. Two prolonged smoke aerosol intrusion events, transported from Yakutia between July 26 and 29 and August 7 to 14, 2021, were analyzed. During these periods, smoke aerosols were entrained into the planetary boundary layer and accumulated near the surface due to temperature inversion conditions.\u003c/p\u003e","manuscriptTitle":"Assessment of smoke aerosol contribution to atmosphere of the south-eastern coast of Lake Baikal in 2021 (\"Boyarsky\" station)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 17:58:28","doi":"10.21203/rs.3.rs-7203689/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f718bfdb-8b6e-42cb-bc95-7ec64a5ecceb","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T01:24:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 17:58:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7203689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7203689","identity":"rs-7203689","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}