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Article

Application of Modern Low-Cost Sensors for Monitoring of Particle Matter in Temperate Latitudes: An Example from the Southern Baikal Region

by
Maxim Yu. Shikhovtsev
*,
Mikhail M. Makarov
,
Ilya A. Aslamov
,
Ivan N. Tyurnev
and
Yelena V. Molozhnikova
*
Limnological Institute, Siberian Branch, Russian Academy of Sciences, Ulan-Batorskaya Street 3, Irkutsk 664033, Russia
*
Authors to whom correspondence should be addressed.
Sustainability 2025, 17(8), 3585; https://doi.org/10.3390/su17083585
Submission received: 6 March 2025 / Revised: 10 April 2025 / Accepted: 15 April 2025 / Published: 16 April 2025
(This article belongs to the Special Issue Air Pollution Control and Sustainable Urban Climate Resilience)
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Abstract

:
The aim of this study was to expand the monitoring network and evaluate the accuracy of inexpensive WoMaster ES-104 sensors for monitoring particulate matter (PM) in temperate latitudes, using the example of the Southern Baikal region. The research methods included continuous measurements of PM2.5 and PM10 concentrations, temperature, and humidity at three stations (Listvyanka, Patrony, and Tankhoy) from October 2023 to October 2024, using the LCS WoMaster ES-104. ERA5-Land reanalysis data and the HYSPLIT model were used to analyze meteorological conditions and air mass trajectories. The results of this study showed a high correlation between the WoMaster ES-104 and the DustTrak 8533; the correlation coefficient was 0.94 (R2 = 0.85) for both fractions. The seasonal dynamics of PM2.5 and PM10 were characterized by a dual-mode distribution with maxima in summer (secondary aerosols, high humidity) and winter (anthropogenic emissions, inversions). The diurnal cycles showed morning/evening peaks associated with transport activity and atmospheric stratification. Extreme concentrations were recorded in anticyclonal weather (weak north-westerly winds, stable atmosphere). This study confirms the suitability of the LCS WoMaster ES-104 for real-time monitoring of PM2.5 and PM10, which contributes to sustainable development by increasing the availability of air quality data for ecologically significant regions such as Lake Baikal.

1. Introduction

Particulate matter (PMx) comprises microscopic particles of solid or liquid matter of various shapes suspended in air. Traditionally, PMx is categorized by size (PM1, PM2.5, PM10), with different health effects [1,2] and physicochemical properties [3,4]. In the context of rapid urbanization and climate change, research aimed at combating atmospheric pollution and adapting urban spaces to climate challenges is becoming particularly important. Air pollution from aerosols is directly related to an increase in cases of respiratory and, cardiovascular diseases and birth defects. In addition, the effects of urban heat islands exacerbate the risks associated with extreme temperatures. In this regard, air quality control and climate mitigation are becoming key aspects of the sustainable development of urban and natural ecosystems. Depending on the mode of formation, PMx can be categorized into primary PMx, which enters the atmosphere from various sources, and secondary PMx, which is formed by the conversion of gaseous combustion by-products such as volatile organic compounds (VOCs), ammonia (NH3), sulfur oxides (SOx), and nitrogen oxides (NOx) [5,6]. Atmospheric particulate matter has traditionally been monitored using ground-based monitoring networks owned by government [7,8] and scientific [9,10,11] organizations. Although reference-level monitoring stations can capture temporal variations in PMx concentrations with a high degree of accuracy and precision, their high cost, the need for specialized personnel to maintain them, and their size make them difficult to use for obtaining data with the high spatial detail required to investigate heterogeneities in the distribution of PMx concentrations over large areas [12,13].
Low-cost sensors (LCSs), can fill gaps in existing monitoring networks, and the data obtained from them can be useful for analyzing PMx distribution fields, identifying sources of atmospheric pollution, verifying mathematical models, evaluating trends, and planning regulatory action to improve air quality. The use of LCSs is also extremely effective for studying spatial differences in the dust content in cities with very complex morphologies of air pollution [14,15]. Increasing the density of monitoring networks using LCSs is a promising approach that has already been applied in many regions of the world [16,17]. The first reason for this success is the low cost of the devices, which creates the possibility of simultaneous installation of a large number of LCSs, including in remote, hard-to-reach areas [18,19]. The second reason is the high frequency of measurements, which provides a reliable picture of PMx variability in the atmosphere [20,21]. The third reason that can be considered as contributing to the success of LCSs is the ease of installation and operation of the instruments. Compared with professional equipment, LCSs require much less power and are better suited for temporary or mobile installation, including in hard-to-reach areas [22].
Lake Baikal, located in Eastern Siberia (Russia), is the largest reservoir of pure fresh water on the planet (containing about 19% of the world’s reserves). The importance of the lake as a natural world heritage site is defined in UNESCO documents, as well as by the Federal Law of 1 May 1999 N 94-FZ “On the Protection of Lake Baikal”. Atmospheric air in the region is subject to constant pollution by various impurities caused by multiple industrial enterprises. In 2023, the volume of emissions from stationary sources located in the Irkutsk region and the Republic of Buryatia amounted to 831.6 thousand tons/year (Figure 1). The leading role in this industrial structure is played by enterprises operating in the fuel and energy sectors, chemical and petrochemical industries, metallurgical production, woodworking, and pulp and paper production. It was previously established that significant air pollution in South Baikal occurs due to the high-altitude transfer of gas and aerosol impurities from remote regional thermal power plants located to the north-west [23] and south-east of the lake [24,25]. As a rule, emissions from sources of air pollution located in the immediate vicinity of the lake spread locally. Their contribution to the total level of atmospheric pollution above the lake in the cold period is insignificant.
At the moment, in the Baikal region, the state atmosphere monitoring system, which meets modern requirements, includes 24 stations for automatic atmosphere monitoring (https://www.feerc.ru/baikal/ru/monitoring/air/ask_overall, (accessed on 20 February 2025)). The posts within this network are mainly located in large source cities (Irkutsk, Angarsk, Shelekhov, Baikalsk, Selenginsk, Ulan-Ude, Gusinoozersk, Cheremkhovo, Sayansk) [26]. Atmospheric research in remote rural and background areas affected by anthropogenic pollution is systematically conducted at only two stations located on the coast of South Baikal: “Listvyanka” (western coast) [27,28,29] and “Boyarsky” (eastern coast) [30,31,32]. Studies covering large spatial scales have been carried out in summer for a limited amount of time (up to two weeks) [33,34,35]. An analysis of research work devoted to the study of PMx in the atmosphere of the region [36,37] showed that mean monthly PM10 concentrations in the atmosphere at Listvyanka station varied in the range from 9 to 36 µg/m3. PM10 concentrations in the fall of 2021 ranged from 1 to 5 µg/m3, with the mean value equal to 3 µg/m3. This difference is explained by the location of the station on the path of the main air mass transport from large anthropogenic sources in the region. Analysis of the chemical composition of the snow cover [38] also showed the spatial distribution of aerosols in the atmosphere of the region to be extremely heterogeneous. The work carried out has shown that it is necessary to expand the existing network for monitoring the content of aerosols and gas impurities, in order to control the entry of PMx into the atmosphere of Southern Baikal.
Previous comparison of the results of measurements obtained with an LSC WoMaster ES-104 and a Lighthouse 3060 in the summer of 2023, from an atmosphere not subjected to obvious anthropogenic influence, showed that the PM2.5 concentrations recorded by LSC WoMaster ES-104 with a high degree of reliability agreed with the results of the Lighthouse 3060 measurements across a range of concentrations from 1 to 10 µg/m3. The correlation coefficient between the data amounted to 0.94, with a determination coefficient R2 = 0.88.
Within the framework of this study, we tried to achieve the desired result using LCS. For this purpose, we conducted an experiment in Southern Pribaikalie, comparing the readings from a WoMaster ES-104 particle counter with the readings from a DustTrak 8533.

2. Sampling and Analysis Methods

In this paper, we compare the results of measurements of PMx concentrations obtained using an LCS WoMaster ES-104 (Taoyuan, Taiwan) and a DustTrak 8533 (TSI Incorporated, Shoreview, Minnesota, USA). The measurements were carried out at three stations located in the Southern Baikal Region during the period from 1 October 2023 to 1 October 2024. The meteorological complex Sokol-M1 (Kazan, Russia) was used to measure meteorological parameters at Listvyanka station. A schematic map showing the location of the stations is presented below (Figure 2).
The Patrony station is located on the outskirts of the settlement of the same name in the valley of the Angara River, at an altitude of 462 m above sea level (52.15 N; 104.46 E). The main local sources of anthropogenic aerosols near the station are the stove stacks of houses. The station is located at a distance of 17 km to the south-east of Irkutsk city and is the closest measurement point to the large stationary sources of atmospheric pollution discussed in this paper.
Listvyanka station is located on the western coast of South Baikal (51.84 N; 104.89 E), at a distance of ~60 km southeast of Irkutsk. The station is located on the top of the coastal hill, at a height of 205 m from the water’s edge (656 m above sea level) and at a distance of ~1 km from Listvyanka settlement. This location minimizes the influence of local sources of atmospheric pollution located in the settlement on the obtained results, and allows the influence of regional sources to be monitored; plumes from these can rise to heights of more than 200 m and be recorded at the station.
Tankhoy station is located on the southeastern coast of Lake Baikal (51.33 N; 105.08 E), at an altitude of 455 m above sea level. The station is located at a distance of 300 m from the Trans-Siberian Railway and 500 m from the federal highway. According to previous studies at the Boyarsky station, under similar conditions, the impact of motor transport is clear, confirmed by NWR analysis of NOx concentrations [39]. Considering that gas impurities (including NOx) can be converted into PMx, it is necessary to understand that the contribution of vehicles to the dust load in the atmosphere at Tankhoy station is possible. According to model calculations demonstrated in previous research [40,41] impurities from enterprises in the Angara River valley can reach the south-eastern shore in a narrow band under atmospheric conditions associated with weak atmospheric turbulence. Together with high precipitation (>1500 mm/year) [42], this causes a high level of anthropogenic impact on this area and may lead to ecosystem changes [43,44,45,46]. However, as in the case of Patrony station, the current study represents the first time that an assessment of the frequency of polluted air mass transport has been carried out based on in situ observations with high temporal resolution.
The location of these stations allowed us to track the transport of pollutants in the lake’s air basin from industrial complexes of Pribaikalye along the Irkutsk–Listvyanka–Tankhoy transect. To fulfill this goal, we carried out synchronous measurements at three stations.
To determine PMx concentrations in the surface layer of the atmosphere at the Patrony, Listvyanka, and Tankhoy stations, we used an LCS WoMaster ES-104. The WoMaster ES-104 uses a light scattering method to determine particle concentrations in the range of 0.01 to 10 mg/m3, with a detection limit of ±0.001 mg/m3, a relative error of ±10%, and a time resolution of 1 min.
The DustTrak 8533, used in operation as a comparison device, uses a light scattering method to determine particle concentrations in the range from 0.01 to 150 mg/m3, with a detection limit of ±0.001 mg/m3, a relative error of ±20%, and a time resolution of 1 min. Due to its portability and the ability to monitor PMx concentrations in real time, the DustTrak 8533 monitor can provide high spatial and temporal resolution and is widely used to assess PMx content in the atmosphere. Previous estimates of the convergence of the DustTrak 8533 and gravimetric measurement results have shown that there is a very strong, almost functional relationship between the results (a correlation coefficient of more than 0.994) [47]. However, it has been shown that DustTrak analyzers overestimate PMx concentrations as 2–3 times higher than the actual value [48,49].
When using inexpensive sensors to monitor PMx concentrations, it is important to consider their limitations related to measurement accuracy. Unlike the DustTrak 8533, sensors such as the WoMaster ES-104 can exhibit systematic errors caused by various factors such as changes in relative humidity or air temperature and peculiarities of aerosol chemical composition. In order to minimize the influence of these factors and increase the reliability of the data, we applied the following approaches to LCS data correction: use of a correction factor, comparison with reference data, consideration of meteorological factors, spatial and temporal calibration, and assessment of statistical significance. The chosen correction method was consistent with the recommendations proposed in the works of other authors. For example, Giordano et al. emphasize the importance of applying correction factors to compensate for systematic LCS errors [50]. Similarly, Castell et al. demonstrate that even inexpensive sensors can provide reliable results when properly calibrated [51].
For trajectory analysis, the hybrid single-particle Lagrangian HYSPLIT model developed by the National Oceanic and Atmospheric Administration (NOAA) was used to model the trajectories of air masses [52]. For each hour, 24 h reverse trajectories were modeled based on archived GFS meteorological data with a horizontal resolution of 0.25°. Calculations were carried out for three high-altitude levels: 100, 250, and 500 m above ground level (AGL). The first of them, 100 m, was chosen as the level reflecting the behavior of the particles in the lower layer of the atmosphere. The second (250 m) and third (500 m) levels were selected as layers characterizing the transport of smoke plumes from large stationary atmospheric sources, taking into account winter temperature inversions. As previously demonstrated, mesoscale jet streams form in the atmosphere above Listvyanka station. The characteristic heights of their formation are 200–250 m [53]. Using this method allowed us to track the path of air masses arriving at the station, identify areas of location of sources of atmospheric pollution by solid particles, and assess the contribution of their influence.
To determine meteorological characteristics in the study area (wind fields at 10 m height, spatial distribution of temperature at 2 m height), we used ERA5-Land reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF), with a spatial resolution of 0.1° and a temporal resolution of 1 h [54]. The ERA5-Land reanalysis data are available as an assimilated globally harmonized dataset based on satellite observations and measurements from meteorological weather stations and atmospheric radiosonde stations, combined with weather models. Due to its large geographic and temporal resolution, this dataset is often used in studies of air pollution [55,56], cloudiness [57,58], and wind field analysis [59].

3. Results and Discussions

3.1. Comparison of WoMaster ES-104 and DustTrak 8533 Measurements

To compare the temporal variations in PMx concentrations measured with the LCS WoMaster ES-104 and the DustTrak 8533, Figure 3 shows an example from 25 December 2023 to 2 January 2024. Both devices were installed at the Listvyanka station, where simultaneous measurements were carried out with a time resolution of 1 min. Subsequently, the measurement results were averaged over time for 1 h. It should be noted that the uncalibrated sensors provided quite accurate measurements, especially when capturing hourly average variations in the concentrations. A strong linear relationship was observed between PM2.5 and PM10 concentrations measured with WoMaster ES-104 and DustTrak 8533, with a correlation coefficient of 0.92 and R2 = 0.85. A positive correlation coefficient of 0.92 with R2 = 0.85 was also observed between the temperature measured at the Sokol-M1 meteorological complex and by the WoMaster ES-104 (N = 216). When the number of observations increased (up to N = 1806), the correlation coefficient decreased to 0.72, but it remained at a fairly high level. The absolute values of PMx concentrations differed significantly from each other. Therefore, to obtain a more reliable hourly average PMx concentration using WoMaster ES-104, we applied a correction factor of 3.5, which was calculated based on the comparison of the LCS data with the DustTrak 8533. This factor was determined by regression analysis of the time series of PM2.5 and PM10 concentrations obtained simultaneously from both devices. The correction factor allowed the absolute values of the concentrations to be adjusted while maintaining a high level of consistency between temporal changes. The choice of this particular constant coefficient was based on the fact that under the conditions considered in this study (the temperate climate of the Southern Baikal Region), most of the errors were related to the physical characteristics of the sensor, such as its sensitivity to particle size and mass. The approach we used is justified if the purpose of the study is to identify daily and seasonal variations in concentrations rather than absolute values.

3.2. Spatial Calibration of Aerosol Particle Counters

To obtain a representation of the spatial and temporal distribution of particulate matter in the atmosphere of the region, we performed synchronous measurements at three stations.
Figure 4 shows the intra-annual variations of PM2.5 and PM10 concentrations in the atmosphere of Listvyanka station. The figure shows that the seasonal dynamics of PM2.5 are characterized by a dual-mode distribution with maxima in the summer (June–August) and winter periods (December–February) and minima in April and October. Monthly mean PM2.5 concentrations ranged from 11.7 µg/m3 to 71.6 µg/m3 (32.6 µg/m3 for the entire period). The medians of the PM2.5 distribution were lower and varied from 9.3 µg/m3 to 60.0 µg/m3 (22.3 µg/m3 for the entire observation period). Differences between mean and median values indicate the presence of outliers in the data series—atypical values exceeding the mean by several times and leading to overestimation of the median. Atmospheric PM10 concentrations at the station changed according to a similar pattern.
We observed the highest monthly mean concentrations in July 2024, amounting to 72.6 µg/m3. The lowest was in October (12.6 µg/m3). On average, PM10 concentrations over the observation period were 33.3 µg/m3. This was 2.7 times more than was recorded at the Listvyanka station during 2021–2022 [36].
The picture of intra-annual distribution of PMx in the atmosphere at Listvyanka station differed from the patterns characteristic of gas impurities, which have a well-defined U-shape [60]. This indicates that PMx was formed in the atmosphere not only during the transition from gas impurities, but also from natural sources. One of the supposed reasons for the increase in the growth of PMx concentrations may have been anomalously hot weather and increased humidity due to a large amount of precipitation, which contributed to the increase in the number of aerosol particles of submicron fraction in the atmosphere of the region. According to the data from the Irkutsk Department for Hydrometeorology and Environmental Monitoring (https://www.irmeteo.ru, (accessed on 10 February 2025)), the Baikal region experienced abnormally hot weather in the summer period of 2024, exceeding the long-term average values by 3 °C. This could have been an additional factor responsible for the formation of the summer maximum, as gaseous pollutants had difficulty remaining in the air for a long time due to the unstable atmosphere and the increased rate of gas-to-particle transformation caused by high temperature and relative humidity [61,62]. Another characteristic feature of the summer of 2024 was a powerful outflow of warm air from the territory of Kazakhstan and the south of Western Siberia, which resulted not only in anomalously hot weather but also in the growth of aerosol particles. In addition, July 2024 was marked by increased thunderstorm activity with heavy showers. On average, 266 mm of precipitation fell in the Irkutsk Oblast, which amounted to 135% of the climatic norm of the region.
The increase in PMx concentrations in the atmosphere of the study region in the winter months was associated with the increased intensity of seasonally dependent sources of atmospheric pollution, increased frequency of development of surface inversions, decreased thickness of the atmospheric boundary layer [63], low precipitation [64], and peculiarities of the wind regime in the region [65], described in more detail in previous publications, with the examples of gas and aerosol impurities [42,43,66].
To assess statistical significance, we performed a detailed analysis of the data, including calculation of confidence intervals and medians to exclude outliers (Figure 4, Figure 5 and Figure 6).
Figure 4 presents the daily variations in mass concentration of aerosol PM2.5 and PM10 at Listvyanka station. As can be seen, the characteristic features of the diurnal variations are the morning and evening maxima and the afternoon minimum. The night maximum occurred due to a daily decrease in the thickness of the atmospheric boundary layer and the development of temperature inversions, contributing to the accumulation of impurities in the surface layer of the atmosphere. In the morning hours at Listvyanka station, anomalous situations are often recorded in which the aerosol pollution exceeds the background level by several times. These anomalies are connected with smoke plumes from large heat power facilities located to the north-west of the station being introduced into the atmosphere [54,67]. At noon, the concentrations of particulate matter decrease as a result of the development of convective mixing promoting the inflow of clean air from the overlying layers.
Figure 5 shows the intra-annual variations of PM2.5 and PM10 concentrations in the atmosphere of Patrony station. It can be seen from the figure that the seasonal dynamics of PM2.5 are characterized by a dual-mode distribution, with maxima in July and December and a minimum in April. Monthly mean PM2.5 concentrations ranged from 17.6 µg/m3 to 85.6 µg/m3 (42.2 µg/m3 for the whole period). Median values were slightly lower and ranged from 13.0 µg/m3 to 80.9 µg/m3 (29.2 µg/m3 for the entire period). PM10 concentrations at the station changed according to a similar pattern. The highest mean monthly concentrations were 114.2 µg/m3 and the lowest 22.4 µg/m3. The daily cycles of PMx in the atmosphere at Patrony station are characterized by one broad peak, which falls in the range from 17 to 23 h with a subsequent daily decrease in concentrations. This peak occurs firstly as a result of activation of local anthropogenic sources of atmospheric pollution (stove heating). Secondly, deterioration of conditions for the dispersion of impurities contributes to the accumulation of impurities in the surface layer of the atmosphere (decrease in air temperature, decrease in boundary layer thickness, decrease in wind speed).
Figure 6 shows the results of measurements of PMx concentration in the surface atmosphere at Tankhoy station. As can be seen from Figure 6, the character of intra-annual variations of PMx concentrations is similar to what we observed at other stations in the region. During the observation period, the mean monthly PM2.5 concentrations varied from 9.2 to 65.3 µg/m3 (mean 22.8 µg/m3), and PM10 varied from 11.6 to 84.9 µg/m3 (mean 29.1 µg/m3). Because of the presence of short-term peaks distorting the general picture, we calculated medians of the distribution to more reliably reflect the average level of atmospheric pollution at the station, as these are less affected by atypical emissions. Median estimates of concentrations turned out to be around half of the mean and amounted to 11.4 µg/m3 for PM2.5 and 13.6 µg/m3 for PM10.
As can be seen, the diurnal variations of PMx in the atmosphere of Tankhoy station are clearly pronounced and similar to the rhythm at Listvyanka station. In the daytime and evening hours, the concentration of PM2.5 and PM10 increases. The highest values are recorded from 7 to 9 pm. The lowest values are recorded at nighttime.
By analyzing data from open governmental sources we found that particulate matter concentrations increased from 12–13 µg/m3 (in 2021–2023) to 75–80 µg/m3 (from May to October 2024) (https://www.feerc.ru/baikal/ru/monitoring/air, (accessed on 10 February 2025)). Thus, the prevailing weather conditions in mid-summer 2024 significantly influenced the increase in aerosol particle concentrations in the atmosphere of South Baikal.
Analyzing the spatial and temporal distribution of PMx in the atmosphere of the region, we can draw several intermediate conclusions: (1) in the Baikal region, there is a tendency for PMx concentrations in the surface layer of the atmosphere to increase; (2) the seasonal variability during 2024 had two extremes: summer and winter, different from the seasonal variability of PMx in the region described earlier. To confirm our measurements, we turned to the archival data of PMx measurements obtained by the state monitoring services. Since the mid-2010s, a network of automatic atmospheric monitoring stations consisting of 24 stations has been deployed in the Baikal region. The posts of this network are mainly located in large source cities (Irkutsk, Angarsk, Shelekhov, Selenginsk, Ulan-Ude, Gusinoozersk, Cheremkhovo, Sayansk). Pollution control in the atmosphere of Lake Baikal is carried out at only one station: Baikalsk (Figure 2).

3.3. Assessment of PMx Content Under Conditions of Extreme Atmospheric Pollution

Of particular interest are situations when concentrations of particulate matter in the atmosphere are several times higher than the annual mean values. In our study, a conditional boundary of 100 µg/m3 was chosen to distinguish such situations. Having carried out sampling, we attempted to estimate the recurrence of these cases (Table 1). The table shows that these episodes occurred most frequently at Patrony station. The recurrence of such situations amounted to 10% and 16% of the total number of observations. These episodes mainly occurred in the periods from October to December and in July.
Figure 7A presents an example of registration of high PM2.5 values at Patrony (blue), Tankhoy (green), and Listvyanka (red curve) stations on 27–28 January 2024. The meteorological situation during this period was characterized by anticyclonic weather: frosty and predominantly dry, with low cloud and low wind speeds. The air temperature varied from −15 °C to −30 °C during the night hours (https://rp5.ru, (accessed on 14 February 2025)). Additionally, we analyzed the atmospheric radiosonde data at the Angarsk station (https://weather.uwyo.edu/upperair/sounding.shtml, (accessed on 14 February 2025)). As a result, the heights of temperature inversions were determined for the period from 27 January 2024 to 28 January 2024, as follows:
(1)
27 January 2024 (00 UTC)—580 m AGL;
(2)
27 January 2024 (12 UTC)—401 m AGL;
(3)
28 January 2024 (00 UTC)—437 m AGL;
(4)
28 January 2024 (12 UTC)—175 m AGL.
According to the meteorological situation and concentration, the PM2.5 concentration varied across several separate periods.
On 27 January from 00:00 to 4:00 local time, low cloudy weather with weak wind flows was observed. The levels of PM2.5 at Patrony and Tankhoy stations, despite the presence of a temperature inversion in the area of stationary sources, were close to the regional background values (5–7 µg/m3). The PM2.5 levels at Listvyanka station were stable in the range of 25–30 µg/m3. Calculations of the reverse trajectories of air mass movement indicated that the air masses approached the stations from the southern and south-western directions, where there are no large anthropogenic sources of atmospheric pollution (Figure 7B). By 5:00 local time, the weather conditions changed and at Listvyanka station, an increase of PM2.5 concentrations up to 101 µg/m3 was recorded (Figure 7A). The results of the trajectory analysis indicated that this increase was caused by the transfer of air masses to the station, influenced by stationary sources at Shelekhov town (Figure 7C). Note that no increase in PM2.5 concentrations was observed at other stations, which was probably connected with the fact that the emission plumes from stationary sources were moving away from the measurement sites.
In the afternoon of 27.01 the meteorological situation in the region changed. According to the wind fields (Figure 8A), the prevailing direction in the study area became north-western. This resulted in an increase in PM2.5 concentrations at all observation stations: from 5–10 µg/m3 to 119 µg/m3 at Tankhoy station to 199 µg/m3 at Listvyanka station and 223 µg/m3 at Patrony station. An additional factor that led to an increase in PM2.5 concentrations was the formation of a temperature inversion in the vicinity of the Angarsk station (up to 401 m AGL). This pattern was preserved until the night of 28.01. After a change of the wind direction to the north and north-east, the concentrations at Listvyanka and Tankhoy stations decreased to their initial values.
The highest peak and average PM2.5 concentrations were recorded at Patrony station. This situation was caused by the combined action of a number of factors. Firstly, the station atmosphere was influenced by large stationary sources located 20–50 km to the north-west, in the Irkutsk agglomeration. This is evidenced by the results of the trajectory analysis (Figure 7D). Secondly, the station is located on the outskirts of the settlement, where more than 1100 people live. An important source of heat supply for the residents of this settlement is wood stove heating, which is an additional local source of PM2.5 air pollution [67]. Thirdly, the station is located in the low-wind zone (Figure 8 and Figure 9A). According to the ERA5-Land reanalysis, average wind speeds at 10 m height did not exceed 1.5 m/s. Although low wind speeds do not always lead to high PM2.5 concentrations in the surface layer of the atmosphere, this factor is a necessary condition for the formation of such serious pollution episodes, since it directly affects the conditions of dispersion of impurities.
After the wind direction changed to the north-east (Figure 7E and Figure 9A), PM2.5 concentrations at all stations began to decrease to their initial values. At the same time, the height of the inversion layer decreased significantly and amounted to 175 m AGL (28 January 2024 (12 UTC)). This led to a deterioration in the conditions of dispersion of impurities from local sources of atmospheric pollution and the formation of a local maximum concentration of PM2.5 at the stations of Patrony and Tankhoy but did not affect the Listvyanka station.

4. Conclusions

During this study, synchronous measurements of PM2.5 and PM10 particulate matter content in the atmosphere of the Southern Baikal region were carried out using a low-cost WoMaster ES-104 sensor and a DustTrak 8533. The results showed a high degree of consistency between the data obtained with both types of devices. Analysis of the temporal changes in PMx concentrations obtained with the WoMaster ES-104 and the DustTrak 8533 showed that the LCS sensors provided fairly accurate measurements. The chosen method of LCS sensor correction was reasonable and adequate for the study conditions. It enabled use of LCS data to analyze both long-term trends and short-term pollution episodes; such analysis is especially important for assessing the state of the atmosphere in the unique region of Lake Baikal.
The seasonal distribution of PM2.5 and PM10 concentrations can be characterized by dual-mode behavior with maxima in summer and winter. Summer peaks are associated with enhanced convection and increased humidity, favoring the formation of secondary organic aerosols and an increase in the number of particles of the PM10 size fraction. Winter maxima are due to the seasonal activity of anthropogenic pollution sources such as thermal power plants, the development of surface temperature inversions, and a decrease in the thickness of the atmospheric boundary layer, which limits the vertical dispersion of impurities. Extreme PMx concentrations were recorded at all monitoring stations under the anticyclone weather type characterized by low wind speeds from the north-west and stable atmosphere. After the change in wind direction, a significant decrease in PMx concentration was observed in all cases, indicating the influence of large industrial centers located to the north-west of the monitoring stations.
The diurnal variations in PM2.5 and PM10 concentrations showed clear regularities with morning and evening maxima, which can be explained by the intensity of motor transport emissions during these periods. The afternoon minimum was due to an increase in the atmospheric boundary layer thickness and improved conditions for aerosol particle dispersion. The nighttime maximum was due to the development of stable stratification of the atmosphere caused by the decrease in wind speed and the formation of surface inversions, leading to the accumulation of impurities in the lower atmospheric layers.
The study conducted in the current study framework revealed a significant potential for using inexpensive LCS sensors to monitor aerosol impurities not only in large industrial centers and small settlements but also in background areas. The use of low-cost sensors such as the WoMaster ES-104 opens up broad prospects for promoting sustainable development by providing accessible and accurate air quality data. This is necessary for conducting detailed analytical studies, modeling the spread of pollutants, and developing measures to reduce anthropogenic impact on the environment. The introduction of these devices into atmospheric air monitoring systems can contribute to the implementation of environmental initiatives aimed at reducing anthropogenic impact, improving public health and developing strategies for adaptation to climate change in various regions.
The work was carried out under the theme of the state assignment LIN SB RAS No 0279-2021-0014, “Investigation of the role of atmospheric deposition on aquatic and terrestrial ecosystems of the Lake Baikal basin, identification of sources of atmospheric pollution”.

Author Contributions

Conceptualization, methodology, validation, M.Y.S. and Y.V.M.; software, M.Y.S., M.M.M., I.A.A. and I.N.T.; formal analysis, M.Y.S. and I.N.T.; investigation, M.M.M. and I.A.A.; resources, M.Y.S.; writing—original draft preparation, M.Y.S. and Y.V.M.; writing—review and editing, M.Y.S., M.M.M., I.A.A. and Y.V.M. All authors have read and agreed to the published version of the manuscript.

Funding

The work was carried out under the theme of the state assignment LIN SB RAS No 0279-2021-0014, “Investigation of the role of atmospheric deposition on aquatic and terrestrial ecosystems of the Lake Baikal basin, identification of sources of atmospheric pollution”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data used are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Volume and structure of emissions from stationary sources of air pollution in the Irkutsk Oblast and the Republic of Buryatia in 2023 (https://rpn.gov.ru/open-service/analytic-data/statistic-reports/air-protect, (accessed on 1 March 2025)).
Figure 1. Volume and structure of emissions from stationary sources of air pollution in the Irkutsk Oblast and the Republic of Buryatia in 2023 (https://rpn.gov.ru/open-service/analytic-data/statistic-reports/air-protect, (accessed on 1 March 2025)).
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Figure 2. Location of monitoring stations and main sources of atmospheric pollution (red dots).
Figure 2. Location of monitoring stations and main sources of atmospheric pollution (red dots).
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Figure 3. Temporal variability and dispersion patterns of PM2.5 and PM10 concentrations measured with a WoMaster ES-104 and a DustTrak 8533 in the atmosphere at Listvyanka station from 25 December 2023 to 2 January 2024.
Figure 3. Temporal variability and dispersion patterns of PM2.5 and PM10 concentrations measured with a WoMaster ES-104 and a DustTrak 8533 in the atmosphere at Listvyanka station from 25 December 2023 to 2 January 2024.
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Figure 4. Temporal variability of PM2.5 and PM10 concentrations in the atmosphere at Listvyanka station measured by WoMaster ES-104 from 1 October 2023 to 1 October 2024. Shaded areas on the graph indicate 95% confidence intervals.
Figure 4. Temporal variability of PM2.5 and PM10 concentrations in the atmosphere at Listvyanka station measured by WoMaster ES-104 from 1 October 2023 to 1 October 2024. Shaded areas on the graph indicate 95% confidence intervals.
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Figure 5. Temporal variability of PM2.5 and PM10 concentrations in the atmosphere at Patrony station measured by WoMaster ES-104 from 1 October 2023 to 1 October 2024. Shaded areas on the graph indicate 95% confidence intervals.
Figure 5. Temporal variability of PM2.5 and PM10 concentrations in the atmosphere at Patrony station measured by WoMaster ES-104 from 1 October 2023 to 1 October 2024. Shaded areas on the graph indicate 95% confidence intervals.
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Figure 6. Temporal variability of PM2.5 and PM10 concentrations in the atmosphere of Tankhoy station measured by WoMaster ES-104 from 1 October 2023 to 1 October 2024. Shaded areas on the graph indicate 95% confidence intervals.
Figure 6. Temporal variability of PM2.5 and PM10 concentrations in the atmosphere of Tankhoy station measured by WoMaster ES-104 from 1 October 2023 to 1 October 2024. Shaded areas on the graph indicate 95% confidence intervals.
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Figure 7. (A) Temporal variability of hourly mean PM2.5 concentrations for the period from 27 January 2024 to 28 January 2024; reverse trajectories of air masses for: (B) 27 January 2024 2:00 LT; (C) 27 January 2024 6:00 LT; (D) 27 January 2024 22:00 LT; (E) 28 January 2024 12:00 LT. Red dots indicate the following cities. The numbers indicate the time episode for which the calculation was performed.
Figure 7. (A) Temporal variability of hourly mean PM2.5 concentrations for the period from 27 January 2024 to 28 January 2024; reverse trajectories of air masses for: (B) 27 January 2024 2:00 LT; (C) 27 January 2024 6:00 LT; (D) 27 January 2024 22:00 LT; (E) 28 January 2024 12:00 LT. Red dots indicate the following cities. The numbers indicate the time episode for which the calculation was performed.
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Figure 8. Spatial distribution of (A) wind speed and direction at a height of 10 m, (B) air temperature at a height of 2 m, according to ERA5-Land reanalysis data on 27 January 2024 at 10:00 UTC. Black dots on the map indicate cities of the Irkutsk agglomeration (Irkutsk, Angarsk, Shelekhov).
Figure 8. Spatial distribution of (A) wind speed and direction at a height of 10 m, (B) air temperature at a height of 2 m, according to ERA5-Land reanalysis data on 27 January 2024 at 10:00 UTC. Black dots on the map indicate cities of the Irkutsk agglomeration (Irkutsk, Angarsk, Shelekhov).
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Figure 9. Spatial distribution of (A) wind speed and direction at a height of 10 m; (B) air temperature at a height of 2 m, according to ERA5-Land reanalysis data on 28 January 2024 at 4:00 UTC. Black dots on the map indicate cities of Irkutsk agglomeration (Irkutsk, Angarsk, Shelekhov).
Figure 9. Spatial distribution of (A) wind speed and direction at a height of 10 m; (B) air temperature at a height of 2 m, according to ERA5-Land reanalysis data on 28 January 2024 at 4:00 UTC. Black dots on the map indicate cities of Irkutsk agglomeration (Irkutsk, Angarsk, Shelekhov).
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Table 1. Number of episodes with elevated PM2.5 and PM10 concentrations at Patrony, Listvyanka, and Tankhoy stations, n—number of cases with concentrations > µg/m3/%—share of such situations.
Table 1. Number of episodes with elevated PM2.5 and PM10 concentrations at Patrony, Listvyanka, and Tankhoy stations, n—number of cases with concentrations > µg/m3/%—share of such situations.
LocationPM2.5PM10
Patrony656/10%1063/16%
Listvyanka401/5%455/6%
Tankhoy291/4%514/7%
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Shikhovtsev, M.Y.; Makarov, M.M.; Aslamov, I.A.; Tyurnev, I.N.; Molozhnikova, Y.V. Application of Modern Low-Cost Sensors for Monitoring of Particle Matter in Temperate Latitudes: An Example from the Southern Baikal Region. Sustainability 2025, 17, 3585. https://doi.org/10.3390/su17083585

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Shikhovtsev MY, Makarov MM, Aslamov IA, Tyurnev IN, Molozhnikova YV. Application of Modern Low-Cost Sensors for Monitoring of Particle Matter in Temperate Latitudes: An Example from the Southern Baikal Region. Sustainability. 2025; 17(8):3585. https://doi.org/10.3390/su17083585

Chicago/Turabian Style

Shikhovtsev, Maxim Yu., Mikhail M. Makarov, Ilya A. Aslamov, Ivan N. Tyurnev, and Yelena V. Molozhnikova. 2025. "Application of Modern Low-Cost Sensors for Monitoring of Particle Matter in Temperate Latitudes: An Example from the Southern Baikal Region" Sustainability 17, no. 8: 3585. https://doi.org/10.3390/su17083585

APA Style

Shikhovtsev, M. Y., Makarov, M. M., Aslamov, I. A., Tyurnev, I. N., & Molozhnikova, Y. V. (2025). Application of Modern Low-Cost Sensors for Monitoring of Particle Matter in Temperate Latitudes: An Example from the Southern Baikal Region. Sustainability, 17(8), 3585. https://doi.org/10.3390/su17083585

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