Atmospheric Monitoring System Research Articles

ANALYSIS OF THE PROSPECTS OF APPLICATION OF NATURAL GAS LEAK DETECTION METHODS TO INCREASE THE EFFICIENCY OF ATMOSPHERIC AIR MONITORING AT HTS FACILITIES

The activities of the oil and gas industry enterprises, in particular the objects of gas transportation systems (GTS), are accompanied by a significant anthropogenic impact on the environment. The atmospheric air itself is significantly affected, since many technological processes are accompanied by emissions of natural gas (organized, unorganized and emergency) and, as a result, a significant amount of volatile substances, many of which are dangerous and harmful, enter the atmosphere. There is a wide range of methods for detecting gas leaks from elements of gas transportation systems. Ways of classifying methods directly depend on the features that form the basis of their distribution. However, the insufficient definition of the classification features, their "one-sidedness" do not allow a full assessment of the shortcomings and advantages of certain methods and means, and, therefore, the possibility of their application to increase the effectiveness of atmospheric air monitoring at HTS facilities in order to ensure environmental safety. The main tasks solved in this article are the formulation of the optimal classification of existing methods of controlling the presence of gas leaks in terms of applicability in environmental monitoring systems of atmospheric air; analysis of their advantages and disadvantages; substantiation of the expediency and perspective of using these means and methods to ensure constant monitoring of the state of atmospheric air at GTS facilities. A classification of methods and means of control is proposed based on the level of application of technical means, according to which all methods are divided into organoleptic, hardware and computational (software-mathematical). According to the results of the analysis of the data of the groups, it was determined that, from the point of view of the prospects of application in the atmospheric air quality monitoring systems at the GTS facilities, hardware means, namely gas analyzers, have the most advantages.

Near real-time interpolative algorithm for modelling air quality in underground mines

As real-time air quality monitoring becomes more prevalent in US underground mines, it is important to provide the highest data reliability with the fewest possible sensors. Real-time sensors remain costly, and these costs are not exclusively financial; the time required to install, calibrate, and maintain real-time sensors poses a large barrier to widespread implementation. Current atmospheric monitoring systems typically rely on displaying point-specific values. This requires operators to infer real-time airborne contamination distributions. Monitoring and control software utilizing mine ventilation network (MVN) solvers has been implemented in limited cases because of their ability to simulate ventilation systems quickly, but these solvers use a one-dimensional representation of the mine, limiting spatial resolution of estimated distributions. Computational fluid dynamics (CFD) has likewise been considered as a means to improve spatial resolution, but processing times prevent its use as a basis for monitoring and control. For the real-time monitoring of airborne contamination distributions, we propose a spatial interpolation method that can estimate the distribution of airborne contaminants in near-real time. This method provides a middle ground between fast processing times and increased spatial resolution. With the use of a pathfinding algorithm and optimization through absolute percentage error minimization, this method outperforms spatial interpolation with a Euclidean distance. By providing contamination distribution information to operators, this method and its derivatives stand to outperform current atmospheric monitoring systems.

Modeling Urban-Rural health disparities using a Taxi-Based mobile atmospheric monitoring system

The disparities of pollutant exposures and health burdens exists among regions. To capture the spatio-temporal variability of PM2.5 and analyze the inequity between urban and rural areas, this study developed a health impact assessment (HIA) model, which incorporates hyper-localized roadside PM2.5 concentration collected by a taxi-based mobile monitoring system. High-resolution pollutant concentrations, health risks, and PM2.5-related deaths were mapped to illustrate the spatio-temporal patterns among regions. The results showed that the average PM2.5 concentrations in some rural counties were higher than urban areas. 7.85%, 3.72%, 8.74%, and 0.97% of individuals were exposed to environment higher than the maximum concentration of central districts in winter, spring, summer, and autumn, respectively. The application of refined HIA model was discussed and grid cells with high risks of all-cause mortality, cardiovascular disease, respiratory disease, and lung cancer were identified. Establishing the comprehensive pollutant monitoring system facilitates targeted interventions and realization of health equity.

Evaluation of Machine Learning Models for Ozone Concentration Forecasting in the Metropolitan Valley of Mexico

In large and densely populated cities, the concentration of pollutants such as ozone and its dispersion is related to effects on people’s health; therefore, its forecast is of great importance to the government and the population. Given the increased computing capacity that allows for processing massive amounts of data, the use of machine learning (ML) as a tool for air quality analysis and forecasting has gotten a significant boost. This research focuses on evaluating different models, such as Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (GB), to forecast ozone (O3) concentration 24 h in advance, using data from the Mexico City Atmospheric Monitoring System using meteorological variables that influence the phenomenon of ozone dispersion and formation.

Monitoring biomass burning aerosol transport using CALIOP observations and reanalysis models: a Canadian wildfire event in 2019

Abstract. In May–June 2019, smoke plumes from wildfires in Alberta, Canada, were advected all the way to Europe. To analyze the evolution of the plumes and to estimate the amount of smoke aerosols transported to Europe, retrievals from the spaceborne lidar CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) were used. The plumes were located with the help of a trajectory analysis, and the masses of smoke aerosols were retrieved from the CALIOP observations. The accuracy of the CALIOP mass retrievals was compared with the accuracy of ground-based lidars/ceilometer near the source in North America and after the long-range transport in Europe. Overall, CALIOP and the ground-based lidars/ceilometer produced comparable results. Over North America the CALIOP layer mean mass was 30 % smaller than the ground-based estimates, whereas over southern Europe that difference varied between 12 % and 43 %. Finally, the CALIOP mass retrievals were compared with simulated aerosol concentrations from two reanalysis models: MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) and CAMS (Copernicus Atmospheric Monitoring System). The simulated total column aerosol optical depths (AODs) and the total column mass concentration of smoke agreed quite well with CALIOP observations, but the comparison of the layer mass concentration of smoke showed significant discrepancies. The amount of smoke aerosols in the model simulations was consistently smaller than in the CALIOP retrievals. These results highlight the limitations of such models and more specifically their limitation to reproduce properly the smoke vertical distribution. They indicate that CALIOP is a useful tool monitoring smoke plumes over secluded areas, whereas reanalysis models have difficulties in representing the aerosol mass in these plumes. This study shows the advantages of spaceborne aerosol lidars, e.g., being of paramount importance to monitor smoke plumes, and reveals the urgent need of future lidar missions in space.

Outline Design of an Atmospheric Corrosion Data Monitoring System

Atmospheric corrosion monitoring and evaluation are important means for studying the mechanism and behavior of atmospheric corrosion of metals. In view of the long cycle, cumbersome process, and poor data quality of the traditional outdoor hanging method, an innovative atmospheric corrosion data monitoring system has been designed to detect atmospheric environment data and metal corrosion data on site, and display the detection data of the equipment’s service location to users in real-time in the form of a “dynamic corrosion map”. This can provide a differentiated design for the anti-corrosion of important equipment such as power grid transmission and transformation engineering components Provide effective data support for engineering life prediction and operation and maintenance. At present, the system has been piloted and promoted in some network provinces, achieving good social and economic benefits.

ИНДИКАЦИЯ СОСТОЯНИЯ РАСТИТЕЛЬНОСТИ В Г. МАГНИТОГОРСКЕ ДЛЯ ОЦЕНКИ КАЧЕСТВА АТМОСФЕРНОГО ВОЗДУХА И СОВЕРШЕНСТВОВАНИЯ ЕГО МОНИТОРИНГА1

In 2016, specialists from the research institute UralNII Ecology conducted a scientific experiment in Magnitogorsk to assess the impact of atmospheric air pollution on vegetation in different parts of the city. The purpose of the study was to assess the condition of vegetation in different areas by measuring chlorophyll fluorescence and carotenoid content in ash maple leaves in order to develop proposals for modernizing the atmospheric air monitoring system in Magnitogorsk. Based on the modeling of pollutant dispersion in the atmospheric air, 13 points were identified in the territory of Magnitogorsk, where experimental samples of ash-leaved maple were collected for further research in the laboratory (five points directly bordering the main site of the city-forming enterprise and the territories of its large subsidiaries; five points located in the residential area in the left-bank industrial hub; two points located in the residential area in the right-bank part of the city; one point in the residential area of the city, most remote from the city-forming enterprise and characterizing the ‘background’ value of atmospheric air pollution). During the research, the relative index of delayed chlorophyll fluorescence (DCF) and the content of photosynthetic pigments (carotenoids) were determined in the leaves of the ash-leaved maple. As a result of the studies, the areas of the city of Magnitogorsk were identified whereatmospheric air pollution had the greatest and the least impact on vegetation. The purpose of the study was achieved. Theresults of the studies were subsequently used in the development of proposals for the modernization of the atmosphericair monitoring system in Magnitogorsk. It is concluded that it is possible to use the results of the study of vegetation, namely the DCF values and the content of carotenoids, as an indirect method for assessing the effectiveness of the existing network of POPs.

Improving the ozone retrieval accuracy by minimizing the asynchronous offset in differential absorption lidar systems

The differential absorption lidar (DIAL) has been proposed as an effective method for detecting ozone in the atmosphere. An important factor affecting its detecting precision is the asynchronous offset between backscattered signals at different wavelengths. Recently, a DIAL was built by our group, and the impact of the asynchronous offset was tested and studied. The simulation results show that the measurement error caused by the offset is negatively correlated with the altitude. Meanwhile, the offset has an additional effect in areas with sharp ozone concentration changes. Comparative experiments were carried out by our DIAL system and an airborne atmospheric monitoring system in the Nanjing test site. The results show that a 15 m offset in DIAL led to serious errors in retrieval results. These errors are inversely related to the detection altitude and reach up to 13 ppb, which is consistent with the results from simulations. After controlling the relative position of the backscattered signal to minimize the asynchronous offset, the maximum error was reduced to 2 ppb. Then the optimized DIAL was used for 48-hour continuous observation with a proven ozone analyzer. It shows that the optimized DIAL has high detecting accuracy and stability as point-fixed instruments.

Construction of Light-Activated Alarm System for Atmospheric Monitoring

The work aims to create a dependable, effective, and affordable alarm solution that makes use of the characteristics of light-dependent resistors (LDRs). The specific objectives are to design and construct an alarm system that responds to changes in light levels, provide a cost-effective and reliable monitoring solution for various atmospheric applications, and test the device for optimal utilization. The construction process involved assembling electronic components on a breadboard or Vero board. The system's voltage source was a 9-volt battery, and the key components included resistors, capacitors, a transistor, an LDR (Light Dependent Resistor), and a buzzer. LDR was used as the primary sensor to detect changes in light levels, triggering the alarm when the light level fell below a predetermined threshold. The system was tested to ensure its effectiveness and reliability. Different light conditions and intensities are simulated to check if the alarm triggers appropriately. In troubleshooting the constructed light-activated alarm, related to the power source, connections, sensor positioning, interference, or alarm settings to ensure the alarm functions reliably and accurately were identified and resolved. Considering availability, accessibility and affordability the design and constructed light-activated alarm system is a veritable tool that compliments monitoring atmospheric applications in equipment-scarce regions.

On the Kalman Smoother Interpolation Error Distribution in Collocation Comparison of Atmospheric Profiles

The intercomparison between different atmospheric monitoring systems is key for instrument calibration and validation. Common cases involve satellites, radiosonde and atmospheric model outputs. Since instruments and/or measures are not perfectly collocated, miss-collocation uncertainty must be considered in related intercomparison uncertainty budgets. This paper is motivated by the comparison of GNSS-RO, the Global Navigation Satellite System Radio Occultation, with ERA5, the version 5 Reanalysis of the European Centre for Medium-range Weather Forecasts. We consider temperature interpolation observed at GNSS-RO pressure levels to the ERA5 levels. We assess the interpolation uncertainty using as ‘truth’ high-resolution reference data obtained by GRUAN, the Reference Upper-Air Network of the Global Climate Observing System. In this paper, we propose a mathematical representation of the interpolation problem based on the well-known State-space model and the related Kalman filter and smoother. We show that it performs the same (sometimes better) than linear interpolation and, in addition, provides an estimate of the interpolation uncertainty. Moreover, with both techniques, the interpolation error is not Gaussian distributed, and a scaled Student’s t distribution with about 4.3 degrees of freedom is an appropriate approximation for various altitudes, latitudes, seasons and times of day. With our data, interpolation uncertainty results larger at the equator, the Mean Absolute Error being MAE≅0.32 K, and smaller at a high latitude, MAE≅0.21 K at −80° latitude. At lower altitudes, it is close to the measurement uncertainty, with MAE<0.2 K below the tropopause. Around 300 hPa, it starts increasing and reaches about 0.8 K above 100 hPa, except at the equator, where we observed MAE about 1 K.

The pregnancy research on inflammation, nutrition, & city environment: systematic analyses study (PRINCESA) cohort, 2009-2015.

The Pregnancy Research on Inflammation, Nutrition, & City Environment: Systematic Analyses Study (PRINCESA) cohort was set up to evaluate associations between air pollution and birth outcomes among pregnant persons in Mexico City. Specifically, the study was designed to improve air pollution exposure assessment and elucidate biological mechanisms underlying associations between maternal exposures and adverse pregnancy outcomes. Pregnant persons (all women) (N = 935) between ages 18-45 who lived and/or worked in metropolitan Mexico City, Mexico, from 2009 to 2015 and liveborn singleton infants (N = 815) of participants who completed follow-up were enrolled in the cohort. We followed participants monthly from enrollment to delivery and the following categories of data were obtained: demographic, medical and obstetric history, geo-referenced data, repeated measures on daily activity patterns, reported food intake, anthropometric, clinical and obstetric data, 20 serum and 20 cervicovaginal cytokines, and lower reproductive tract infection. Repeated ultrasound measures of fetal parameters and infant birth data are also included in the study's database. In addition, PRINCESA investigators calculated air pollution exposure measures for six pollutants measured by the Mexico City Atmospheric Monitoring System (SIMAT). These estimates utilize participants' addresses to account for spatial variation in exposure (nearest monitor, inverse distance weighting, and kriging) and are available daily during pregnancy for participants. To date, associations between environmental and nutritional impacts on maternal and child health outcomes have been evaluated. PRINCESA has a comprehensive database of maternal and infant data and biological samples and offers collaboration opportunities to study associations between environmental and other factors, including nutrition and pregnancy outcomes.

Methodological provision of improving the system of location of monitoring points for atmospheric air pollution in agglomerations

Goal. The evaluation of current atmospheric air monitoring system effectiveness in the territory of the Dnipro agglomerations and improvement methodological approaches to the placement of observation points in accordance with the current requirements of the legislation of Ukraine and the countries of the European Union in the field of environmental protection. Research methodology. A comprehensive analysis of the effectiveness of the current system of assessing the level of pollution of the atmosphere of inhabited cities was carried out; statistical analysis – to evaluate the dynamics of pollutants from the emissions of industrial enterprises and vehicles; geospatial analysis – to justify the locations of additional observation posts based on cartographic data. Research results. The dynamics of pollutants from the emissions of industrial enterprises and vehicles were evaluated. A critical analysis of the current atmospheric air monitoring system has been conducted. The location of different types of observation points for the agglomeration of Dnipro is proposed. Scientific novelty. For the first time for the agglomeration of the Dnieper, the location of additional observation points, taking into account the location of the platforms of industrial enterprises and the volume of gross emissions of priority pollutants from the main sources of atmospheric pollution, as well as data on the number and density of the population in administrative areas of the city and characteristics etc. Practical meaning. The approach proposed in the work will provide information on the quality of the atmospheric air both by current domestic standards and regulations and international, which will facilitate the expansion of the panel network of atmospheric air monitoring on the territory of Ukraine.

The formation of atmospheric air pollution emissions from separate explosions as a result of military operations

As is well known, military operations in urbanized areas lead to deterioration of atmospheric air quality. The article examines the model of the formation of atmospheric polluted air from individual explosions as a result of military operations. A technique based on the theory of the convective jet is proposed for estimating the concentrations of hazardous substances formed above the burning area as a result of the explosion. The proposed approach makes it possible to obtain the concentration of pollutants in the conditions of emission into the atmosphere open to solar radiation at a height of three or more diameters of the explosion funnel (or the conventional diameter of the warm surface), the temperature and speed of hot air jets over the warm surface, the consumption of the desired substance. The obtained data make it possible to calculate the emission of hazardous substances into the atmosphere according to the formulas for a high unshaded source of pollution depending on the meteorological conditions of the area, to determine the concentration of the main acid-forming substances when assessing the probability of acid precipitation, and to serve as a basis for modern innovative atmospheric air monitoring systems.An assessment of the existing approach to determining atmospheric air pollution due to the impact of X-101 missile fragments in Kyiv on December 16, 2022 was made, which is a confirmation of the validity of this approach when calculating the impact of a separate explosion on atmospheric air quality. Calculated emissions of pollutants and their concentrations in the air in the upper part of the convective polluted jet in the case of separate explosions of the Kalibr cruise missile, the Kh-101 missile and the BUK-M1 projectile. The error from the comparison of these calculation results with the atmospheric air monitoring system depends on the nearest active observation points. This approach to assessing and forecasting concentrations of pollutants in atmospheric air can be used under neutral atmospheric conditions that determine the emission of pollutants and transformations of chemical substances in the air.

РАЗРАБОТКА АВТОМАТИЗИРОВАННОЙ СИСТЕМЫ ЭКОЛОГИЧЕСКОГО МОНИТОРИНГА С ПРИМЕНЕНИЕМ ТЕХНОЛОГИЙ IOT

The widespread use of the Internet of Things (IoT) in the modern world allows you to connect anything. In combination with the characteristics and requirements of the Russian atmospheric monitoring system, the development of a monitoring system in which users can directly access monitoring equipment through the system website to view data and set up operations is considered. The developed atmospheric monitoring system should continuously collect data on the concentration of air pollutants and provide data-driven support for environmental management and decision-making.

Analysis of the air quality in upper atmospheric boundary layer in a high-density city in Asia using 3-year vertical profiles measured by the 3-Dimensional Real-Time Atmospheric Monitoring System (3DREAMS).

Past studies focused on ground-level air quality, whereas air quality in upper atmospheric boundary layer (ABL) remains unclear due to lack of long-term and high time-resolution profile data. This study utilized the 3-Dimensional Real-Time Atmospheric Monitoring System (3DREAMS) to provide vertical profiles of aerosol backscatter coefficients and wind for three years (2019-2021), along with DustTrak to describe and analyze the characteristics of aerosols in the upper ABL in a high-density city in Asia (Hong Kong, China). It is the first study to assess the long-term record and spatial variations of upper-level aerosol in a high-density city using a LiDAR network. Results show an opposite diurnal profile of aerosol comparing between ground-level and upper ABL, which is different with the diurnal pattern observed in ground measurements (higher air pollutant concentration level in daytime and lower in nighttime). The co-location vertical wind measurements provided the explanation of the opposite diurnal patterns. The 3-year vertical profiles also show the significant spatial variation of vertical distribution of aerosol at different locations, whereas the temporal variations can be affected by various factors such as emissions and transboundary air pollution. Our episode analysis clearly demonstrated the capability of 3DREAMS to monitor transboundary air pollution with detailed information of horizontal transport and vertical convection. 3DREAMS is therefore proved as a suitable system for transboundary air pollution monitoring. Our findings provide a critical reference for atmospheric scientists and decisionmakers to understand transboundary air pollution.

Towards monitoring the CO2 source–sink distribution over India via inverse modelling: quantifying the fine-scale spatiotemporal variability in the atmospheric CO2 mole fraction

Abstract. Improving the estimates of CO2 sources and sinks over India through inverse methods calls for a comprehensive atmospheric monitoring system involving atmospheric transport models that make a realistic accounting of atmospheric CO2 variability along with a good coverage of ground-based monitoring stations. This study investigates the importance of representing fine-scale variability in atmospheric CO2 in models for the optimal use of observations through inverse modelling. The unresolved variability in atmospheric CO2 in coarse models is quantified by using WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) simulations at a spatial resolution of 10 km × 10 km. We show that the representation errors due to unresolved variability in the coarse model with a horizontal resolution of 1∘ (∼ 100 km) are considerable (median values of 1.5 and 0.4 ppm, parts per million, for the surface and column CO2, respectively) compared to the measurement errors. The monthly averaged surface representation error reaches up to ∼ 5 ppm, which is even comparable to half of the magnitude of the seasonal variability or concentration enhancement due to hotspot emissions. Representation error shows a strong dependence on multiple factors such as time of the day, season, terrain heterogeneity, and changes in meteorology and surface fluxes. By employing a first-order inverse modelling scheme using pseudo-observations from nine tall-tower sites over India, we show that the net ecosystem exchange (NEE) flux uncertainty solely due to unresolved variability is in the range of 3.1 % to 10.3 % of the total NEE of the region. By estimating the representation error and its impact on flux estimations during different seasons, we emphasize the need to take account of fine-scale CO2 variability in models over the Indian subcontinent to better understand processes regulating CO2 sources and sinks. The efficacy of a simple parameterization scheme is further demonstrated to capture these unresolved variations in coarse models.

Development and application of an environment monitoring system based on IPv6

The widespread use of the Internet of Things (Iot) makes it possible to connect everything but having enough IP addresses is a fundamental requirement of this paradigm. All previous environmental monitoring systems in China are based on IPv4. In combination with the characteristics and requirements of China's atmospheric environment monitoring system, this paper develops a monitoring system based on IPv6 technology. Users can directly access the monitoring equipment through the IPv6 website to view data and configure operations. This paper first introduces the design and implementation of the software and hardware of the system, then introduces the simplification of IPv6 protocol, the transplantation of IPv6 protocol on ARM and the design and implementation of embedded Web server system. The experimental results show that the developed atmospheric environment monitoring system can realize continuous data acquisition based on IPv6 and provide data-driven support for environmental protection management and decision-making.

Spatiotemporal variability of formaldehyde content in atmospheric air according to satellite measurements

Formaldehyde (HCHO) is a key ingredient in atmospheric chemistry, a precursor to ozone (O3) and an important indicator of atmospheric photochemical activity. It is also known to be a human and animal carcinogen. To assess the spatial and temporal variability of the formaldehyde content, the article analyzes long-term data (2003–2016) of measurements of the OMI spectrometer located on the Aura satellite. The results of the analysis showed that the tropospheric content of formaldehyde over most of the territory of Belarus for 2003–2016 had an uneven spatial distribution: there is a decrease in concentrations in the direction from the southwest to the northeast. Large cities and industrial centers form zones of increased troposphere pollution. The content of formaldehyde in the cells over the territory of Belarus during the study period was in the range of up to 30.8×1015 mol/cm2 and averaged 6.5×1015 molec./cm2. In most of the obtained values (84.1 %), the formaldehyde concentrations were fixed within the range of 2.0–10.0×1015 molec./cm2, and only 0.6 % exceeded 15.0×1015 molec./cm2. The obtained data are compared with the data of the ground-based atmospheric air monitoring system. The content of formaldehyde in the troposphere over the territory of Belarus, as well as in the surface layer of the atmosphere, has a pronounced seasonal dynamics with a maximum in the warm season with the highest concentrations in July and August. An analysis of the intra-annual dynamics of formaldehyde content in the surface layer and in the troposphere column indicates the presence of general trends, both for Belarus as a whole and for individual cities.1

Real-Time Methane Prediction in Underground Longwall Coal Mining Using AI

Detecting the formation of explosive methane–air mixtures in a longwall face is still a challenging task. Even though atmospheric monitoring systems and computational fluid dynamics modeling are utilized to inspect methane concentrations, they are not sufficient as a warning system in critical regions, such as near cutting drums, in real-time. The long short-term memory algorithm has been established to predict and manage explosive gas zones in longwall mining operations before explosions happen. This paper introduces a novel methodology with an artificial intelligence algorithm, namely, modified long short-term memory, to detect the formation of explosive methane–air mixtures in the longwall face and identify possible explosive gas accumulations prior to them becoming hazards. The algorithm was trained and tested based on CFD model outputs for six locations of the shearer for similar locations and operational conditions of the cutting machine. Results show that the algorithm can predict explosive gas zones in 3D with overall accuracies ranging from 87.9% to 92.4% for different settings; output predictions took two minutes after measurement data were fed into the algorithm. It was found that faster and more prominent coverage of accurate real-time explosive gas accumulation predictions are possible using the proposed algorithm compared to computational fluid dynamics and atmospheric monitoring systems.

Monitoring the state of snow cover in the Khabarovsk Territory

In the article, the authors considered the issues of snow cover monitoring in the conditions of the Khabarovsk Territory. An analysis was made of the main pollutants in the period from 2014 to 2020, the impact of snow cover on the environment was studied, and the ways in which pollutants entered were analyzed. The presented work is of an analytical and research nature — to trace the accumulation of pollutants in the snow cover, for further development of solutions to improve the atmospheric air monitoring system in the cities of the region. The work was carried out within the framework of research work 3.21-TOGU. The state of protection of the environment and vital human interests from the possible negative impact of economic and other activities on the territory of the Khabarovsk Territory at the present time cannot be considered satisfactory. Environmental problems are global in nature and do not have territorial, state borders, but to date, a unified system for monitoring and assessing the ecological state of the ecosystem with the prolongation of the dynamics of changes in the future has not been created in the region. And the known set of methods and tools for assessing the environmental situation does not provide the required level of reliability. Snow cover is a natural accumulator of a large amount of aerosol substances, in which atmospheric precipitation is accumulated and preserved; in addition, it can be considered a natural indicator of both the state of the atmosphere and pollution of soils, water, and biosphere objects. At present, under the conditions of an ever-increasing anthropogenic load on the environment, high-quality, timely information about the state of the environment is needed. Although interest in research on the chemical composition of the snow cover has recently grown, it should be noted that in the territory of the Khabarovsk Territory, little is done in depth research in this area. The territory of this region is located in the form of a narrow strip on the eastern outskirts of Asia, the climate of the region is monsoon. It is characterized by cold, snowy winters and wet, hot summers. Climatic conditions change significantly both from north to south, and depending on the proximity to the sea, as well as on the nature of the relief. The period of snow occurrence is from 4 months, mainly in the southern, southwestern regions, to 6 months in the northern regions, which, according to the authors, is a good help for a detailed study of accumulated pollutants. In addition, in the region, more than 83.14 % of the population lives in cities with high and very high levels of air pollution, which primarily affects health (among the common types of diseases, respiratory diseases are in first place, circulatory system diseases are in second, third disease of the musculoskeletal system). To reduce the technogenic load on the atmosphere, it is necessary to install more efficient cleaning systems at thermal power plants and introduce effective technical control of the transport composition in the region, review and improve the atmospheric air monitoring system.