Network Of Monitoring Stations Research Articles

Ақылды қалалардағы ауа сапасын бақылау мониторинг станциялары желісін оңтайландыру жүйесінің концептуалды архитектурасы

A modular architecture of an Intelligent Information System (IIS) is proposed for optimizing the network of air quality monitoring stations in smart cities. The architecture employs IoT and Data Science technologies to implement environmental monitoring functions, illustrating key elements and their interconnections. The concept highlights the importance of strategic planning and technology selection to build a scalable and efficient system. The system enables the collection, analysis, and interpretation of air quality data, facilitating informed decisions to improve environmental conditions and enhance citizens' quality of life. Furthermore, the architecture integrates analytical modules aimed at data analysis and forecasting. Scalability and flexibility ensure the system adapts to new requirements and technological trends. These solutions provide a foundation for long-term strategic planning to improve air quality.

Exploring Machine Learning's Potential for Estimating High Resolution Daily Snow Depth in Western Himalaya Using Passive Microwave Remote Sensing Data Sets

AbstractSnow depth (SD) exhibits high spatiotemporal heterogeneity in Western Himalaya (WH), and its knowledge is essential for applications related to water resources, disaster management, climate, etc. However, due to inclement weather and rugged topographical conditions, only a sparse network of SD monitoring stations exists in WH. Spaceborne passive microwave (PMW) remote sensing data sets provides valuable information about SD; however, only a limited PMW SD studies that cover subregions of WH are available. Different machine learning (ML) methods viz. support vector machine, random forest, and Extremely Randomized Trees (ERT) were tested for estimating SD. Based on our preliminary assessment of these ML approaches, the current study utilizes ERT approach to estimate daily SD over the entire WH region. The ERT SD model is developed using PMW brightness temperature data sets from Advanced Microwave Scanning Radiometer‐2 (AMSR‐2), snow cover duration (SCD), and other auxiliary parameters (i.e., location, elevation, vegetation cover, etc.) during the winter period between 2012–2013 and 2019–2020. The data between 2012–2013 and 2017–2018 is used for training the model, whereas the data between 2018–2019 and 2019–2020 is used for testing the model. The results demonstrate: (a) The ERT SD model has shown improved SD estimates compared to the available PMW remote sensing‐based operational SD products and empirical PMW SD models. (b) In general, with an increase in SD, the mean absolute error of SD retrievals has increased in all SD products/models. (c) Unlike the operational AMSR2 SD product, and Northern Hemisphere Machine Learning SD product, the ERT SD model retrievals have shown better consistency with MODIS snow cover. (d) The developed model has shown a wider range in SD retrievals as compared to other products considered in this study.

The integrated monitoring of karst systems (MIKS) model, applied in karst studies in Bulgaria

Karst is a widespread natural phenomenon that is highly sensitive and very vulnerable to both natural and various anthropogenic impacts. This is due to the complex structure of karst territories, which comprise both surface forms and complex systems of underground cavities. Therefore, the study of karst requires a geosystem approach and integrated monitoring, which gives a real idea of the state of karst geosystems and the impacts of global changes on karstogenesis. The article presents a model of this approach, which is applied in karst studies in Bulgaria. The methodological basis is the original ProKARSTerra paradigm, based on the geosystem approach. Its main part is the Integrated Monitoring of Karst Systems (MIKS). It combines six types of monitoring, and it is carried out in three forms: expeditionary, remote and stationary with continuous instrumental measurements. They are concentrated in cave systems representative of karst in Bulgaria, in two of which networks of underground monitoring stations (Speleo-MIKS) have been built. The most important problems in the organization and maintenance of MIKS are summarized based on many years of experience. Examples of interpretation of results from the conducted long-term monitoring using the geosystem approach are presented in Supplementary information to the article.

Comparative analysis of Ball Mapper and conventional Mapper in investigating air pollutants' behavior.

This study investigates the effectiveness and efficiency of two topological data analysis (TDA) techniques, the conventional Mapper (CM) and its variant version, the Ball Mapper (BM), in analyzing the behavior of six major air pollutants (NO2, PM10, PM2.5, O3, CO, and SO2) across 60 air quality monitoring stations in Malaysia. Topological graphs produced by CM and BM reveal redundant monitoring stations and geographical relationships corresponding to air pollutant behavior, providing better visualization than traditional hierarchical clustering. Additionally, a comparative analysis of topological graph structures was conducted using node degree distribution, topological graph indices, and Dynamic Time Warping (DTW) to evaluate the sensitivity and performance of these TDA techniques. Both approaches yielded valuable insights in representing the air quality monitoring stations network; however, the complexity of CM, which requires multiple parameters, poses a challenge in graph construction. In contrast, the simplicity of BM, requiring only a single parameter, is preferable for representing air pollutant behavior. The findings suggest an alternative approach for assessing and identifying redundancies in air quality monitoring stations, which could contribute to improved air quality monitoring management and more effective regulatory policies.

Enhancing air quality predictions in Chile: Integrating ARIMA and Artificial Neural Network models for Quintero and Coyhaique cities.

In this comprehensive analysis of Chile's air quality dynamics spanning 2016 to 2021, the utilization of data from the National Air Quality Information System (SINCA) and its network of monitoring stations was undertaken. Quintero, Puchuncaví, and Coyhaique were the focal points of this study, with the primary objective being the construction of predictive models for sulfur dioxide (SO2), fine particulate matter (PM2.5), and coarse particulate matter (PM10). A hybrid forecasting strategy was employed, integrating Autoregressive Integrated Moving Average (ARIMA) models with Artificial Neural Networks (ANN), incorporating external covariates such as wind speed and direction to enhance prediction accuracy. Vital monitoring stations, including Quintero, Ventanas, Coyhaique I, and Coyhaique II, played a pivotal role in data collection and model development. Emphasis on industrial and residential zones highlighted the significance of discerning pollutant origins and the influence of wind direction on concentration measurements. Geographical and climatic factors, notably in Coyhaique, revealed a seasonal stagnation effect due to topography and low winter temperatures, contributing to heightened pollution levels. Model performance underwent meticulous evaluation, utilizing metrics such as the Akaike Information Criterion (AIC), Ljung-Box statistical tests, and diverse statistical indicators. The hybrid ARIMA-ANN models demonstrated strong predictive capabilities, boasting an R2 exceeding 0.90. The outcomes underscored the imperative for tailored strategies in air quality management, recognizing the intricate interplay of environmental factors. Additionally, the adaptability and precision of neural network models were highlighted, showcasing the potential of advanced technologies in refining air quality forecasts. The findings reveal that geographical and climatic factors, especially in Coyhaique, contribute to elevated pollution levels due to seasonal stagnation and low winter temperatures. These results underscore the need for tailored air quality management strategies and highlight the potential of advanced modeling techniques to improve future air quality forecasts and deepen the understanding of environmental challenges in Chile.

ASSESSMENT OF THE EVAPOTRANSPIRATION COMPONENTS DYNAMICS IN DIFFERENT AGRO-CLIMATIC ZONES OF UKRAINE USING THE PENMAN-MONTEITH-LEUNING MODEL

The results of the assessment of evapotranspiration (ET) and its components based on remote sensing data are presented in the paper. To obtain this assessment, a special software was developed, namely the scripts using the JavaScript programming language for remote data processing in the Google Earth Engine (GEE) cloud software environment. This software allows to adjust the Penman-Monteith-Leuning (PML) model for the conditions of Ukraine and visualize the spatial distribution of ET. The usage of the cloud capabilities of the service made it possible to access the collection of images and carry out their remote processing using the Penman-Monteith-Leuning algorithm calibrated according to the data of the global network of eddy covariance monitoring stations. The result of such an assessment was a composite mosaic - a spatially distributed generalized image of evapotranspiration and its three main components: transpiration from vegetation (Ec), evaporation from the soil (Es) and evaporation of precipitation intercepted by vegetation cover (Ei) on the territory of Ukraine for the 2020 growing season. Understanding the dynamics of these components helps to optimize the water resources use and develop effective irrigation schemes, especially in the climate change conditions. As a result of the analysis of the evapotranspiration components’ dynamics during the growing season, the most important component of evapotranspiration for different agro-climatic zones was determined. However, the models, which are using remote data to estimate evapotranspiration dynamics, require additional validation and comparison with field measurements to improve their accuracy. Quantitative indicators obtained through modeling should be consistent with the data from ground-based greenhouse gas flows monitoring stations, which will contribute to the improvement of the methodology and its adaptation to the conditions of different agricultural regions. In addition, the use of the maps of geospatial distributed evapotranspiration allows to identify regions with increased transpiration and potential shortage of water resources. Such maps become a valuable tool for planning and forecasting water resources, which is critically important for the agricultural sector.

The role of hydrological factors in the die-back of the European spruce in the territory of the Southeast Carpathians

Formulation of the problem. The article is dedicated to studying the processes of European spruce stand dieback, a phenomenon that is currently relevant not only in Ukraine but also in most European countries, including the Czech Republic, Slovakia, Germany, Austria, and Poland. The focus is on an in-depth investigation of the relationships between one of the abiotic components – the hydrological factor – and the locations where European spruce dieback occurs on the southeastern slopes of the Carpathians. Geographically, the research area is outlined by the boundaries of the first and one of the largest nature parks in Ukraine, the Carpathian National Nature Park (NNP). Purpose. The aim of the study is to identify the relationships between the processes of European spruce dieback and dynamic and quasi-static (relatively constant) hydrological descriptors in the southeastern Carpathians. Methods. The informational basis for the study includes the results of forest pathology research conducted by specialists of the Carpathian NNP in 2022, along with long-term hydrometeorological observation data from the Carpathian Flood Monitoring Station Network (Ukrainian Hydrometeorological Center of the State Emergency Service of Ukraine). The study also utilized various remote sensing, field, dendrochronological, laboratory, analytical, comparative, cartographic, mathematical, and instrumental methods. Research results. The research established that zones of spruce stand dieback are linked to stream source systems, associated with "meanders" in river channels, and are localized near water springs, particularly those with unique chemical water composition. These features may serve as indirect indicators of connections with regional and local tectonic disturbances. The study found a significant relationship between the annual growth of European spruce trunks and the average annual water discharge of the Prut River (r = 0.6) and the annual precipitation in the area (r = 0.68). In areas where spruce dieback is active, a distinctive evaporation mist formation regime and soil moisture patterns were observed. Temporally, the timing of spruce dieback appears to be connected to abrupt changes in annual water discharge and precipitation levels, likely indicating the impact of "water stress" on the biotic resilience of Picea abies [L.] Karst. Conclusions. The results of this research can contribute to local, regional, or global models for spatiotemporal forecasting of spruce stand dieback processes, not only in the Carpathian region but also in other areas where this issue is relevant. The conclusions and recommendations presented in the article are important for the development of the local tourism and recreation sector, for planning forestry management activities, and for scientific and educational purposes (including conducting scientific excursions and field practices for students in natural sciences, among others).

An alternative low-cost proposal for environmental radiation monitoring in Brazil

Nowadays, it is increasingly necessary to environmental radiation monitoring to ensure the protection of the population from nuclear or radiological accidents. In this sense, some countries have already implemented environmental monitoring networks. Despite this, Brazil not only has a radiological survey of the subsoil of its territory, but also only a small fraction of the urban environment has been monitored, not yet having a network of monitoring stations. Therefore, the objective of this paper is to present a system, already under development, which consists of an alternative form of monitoring environmental radiation, based on a device that can be installed in fleet vehicles, which travel routes according to their own operational needs, presenting itself as a low-cost system related to citizen science activities. In this case, the device itself is self-sufficient to take all the readings and send them via the mobile telephone network, depending only on energy provided by a battery. Each device is a mobile station composed of a Geiger counter, a temperature, humidity and atmospheric pressure sensor, in addition to a microcontroller associated with a telephony chip and Global Positioning System (GPS). Today the system is in the calibration and adjustments phase, already having results of a preliminary study, in which more than 25,000 readings were collected accumulated in a period of about 15 days. The results, although preliminary and in need of adjustments in the filters and visualization on the map, demonstrate the applicability and full potential of the proposed system.

Improving PM10 sensor accuracy in urban areas through calibration in Timișoara

Low-cost particulate matter sensors (LCS) are vital for improving the spatial and temporal resolution of air quality data, supplementing sparsely placed official monitoring stations. Despite their benefits, LCS readings can be biased due to the physical properties of aerosol particles and device limitations. An optimization model is essential to enhance LCS data accuracy. This paper presents a calibration study of the LCS network of Timișoara, Romania. The calibration began by selecting LCS devices near National Air Quality Monitoring Network (NAQMN) stations and developing parametric models, choosing the best for broader application. Plantower, Sensirion, and Honeywell sensors showed comparable accuracy. Calibration involved clusters within a 750 m radius around NAQMN stations. Models incorporating RH corrections and multiple linear regression (MLR) were fitted. The best model was validated against data from unseen sensors, leading to mean bias errors (MBE) within 9-17% and RMSEs of 33-35%, within sensor uncertainty margins. Applied to the city-wide LCS network, the model identified several stations regularly exceeding the EU daily PM10 threshold, unnoticed by NAQMN stations due to their limited coverage. The study highlights the necessity of granular monitoring to accurately capture urban air quality variations.

Deriving water quality index using site-specific water quality parameter guidelines for real-time water quality stations

ABSTRACT The Newfoundland and Labrador Real-Time Water Quality (RTWQ) monitoring program operates a network of surface water monitoring stations, measuring physical water quality parameters in near-real-time. The network generates vast amounts of data which are too complex for many stakeholders to interpret. While the CCME Water Quality Index (WQI) has been a valuable tool to simplify and summarize ambient quality of water, its use in RTWQ is virtually non-existent. This is largely due to the lack of alignment between the limited number of parameters from real-time networks, and availability of aquatic Water Quality Guidelines. This paper is the first attempt to outline a method of utilizing real-time water quality data to generate site specific water quality guidelines using Background Concentration method. Using these guidelines, we compute weekly WQI scores for select monitoring stations and examine how score is influenced by stage and precipitation. The influence of each parameter on the RTWQ WQI score is also plotted. Comparison is made between the Canadian Environmental Sustainability Indicator (CESI) WQI and RTWQ WQI yearly scores. The paper also discusses the challenges and benefits of using this methodology as well as the sensitivity of WQI scoring at stations with various anthropogenic influences.

Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales

Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales

Case studies of the winds in the urban area of Hong Kong – Microclimate station observations and high resolution numerical simulations

Case studies of the winds in the urban area of Hong Kong – Microclimate station observations and high resolution numerical simulations

Recent Expansion of the Cascades Volcano Observatory Geophysical Network at Mount Rainier for Improved Volcano and Lahar Monitoring

Abstract The U.S. Geological Survey Cascades Volcano Observatory (CVO) recently expanded its continuous geophysical monitoring at Mount Rainier, an active stratovolcano in Washington state. CVO monitors volcanoes in Oregon, Washington, and Idaho to characterize volcanic systems and detect unrest. Mount Rainier has a history of large lahar occurrences in the Holocene, including at least one that may not have been associated with volcanic activity. Pierce County, Washington, is one of the areas most at risk from large lahars. In the 1990s, CVO collaborated with Pierce County to install the Rainier lahar detection system (RLDS), an automated system designed to detect large lahars in high-risk drainages and mitigate hazards to heavily populated areas. The system was designed to detect lahars within 5–10 min of their occurrence and alert authorities of the need to evacuate populated low-lying areas before lahar arrival. In addition, CVO and the Pacific Northwest Seismic Network (PNSN) maintained and expanded a network of seismic and geodetic monitoring stations on and near the edifice to provide adequate volcano monitoring capabilities. Since 2016, CVO has worked to upgrade the existing RLDS and to expand its capabilities into other drainages around Mount Rainier. This expansion includes installation of 25 new broadband seismic stations with many including infrasound along high-risk drainages, as well as support for equipment upgrades at existing PNSN and CVO volcano monitoring sites. All stations transmit continuous, near-real-time data with dramatically improved spatial coverage for volcano monitoring and lahar hazard mitigation compared to the previous system.

Long-term Exposure to Ambient Air Pollution and its Association with Mental Well-Being, Depression and Anxiety: A Nationally Representative Study.

Exposure to ambient air pollution may play a role in the onset of common mental disorders like depressive and anxiety disorders. The association of long-term exposure to particles smaller than 10 μm (PM10) with these diseases remains unclear. This study aimed to estimate the association of long-term exposure to PM10 with mental well-being and the frequency of probable diagnosis of common mental disorders. A nationally representative cross-sectional study was done in mainland Portugal. Long-term exposure was estimated through one-year average concentrations of PM10, calculated with data from the Portuguese Environment Agency, attributed individually considering individuals' postal codes of residence. The mental well-being and the probable diagnosis of common mental disorders were ascertained through the five-item Mental Health Inventory scale. Linear and Robust Poisson regression models were computed to estimate change percentages, prevalence ratios (PR), and their 95% confidence intervals (95% CI). The median (interquartile range) concentration of PM10 was 18.6 (15.3 - 19.3) μg/m3. The mental well-being score was 72 (56 - 84) points, on a scale from 0 to 100. A probable diagnosis of common mental disorders was found in 22.7% (95% CI: 20.0 to 25.6). Long-term exposure to PM10 was associated with a non-statistically significant decrease in the mental well-being score [for each 10 μg/m³ increment in one-year average PM10 concentrations, there was a 2% (95% CI: -8 to 4) decrease], and with a non-statistically significant increase in the common mental health frequency (PR = 1.012, 95% CI: 0.979 to 1.045). We did not find statistically significant associations between long-term exposure to PM10 and mental well-being or the frequency of probable diagnosis of common mental disorders. These results may be explained by the reduced variability in the exposure values, given the geographical distribution and functioning of the network of air quality monitoring stations. This study contributes with evidence for low levels of air pollutants, being one of the first to adjust for individual and aggregate-level variables. Moreover, to the best of our knowledge, this was the first nationally representative, population-based study conducted on the Portuguese population using real-life data. Maintaining a robust and nationwide air quality monitoring network is essential for obtaining quality exposure data.

Optimising the network of air quality monitoring stations

This study explores the application of genetic algorithms (GAs) for optimizing the network of air quality monitoring stations. Recognizing the complexity of accurately assessing air pollution levels across diverse urban and rural landscapes, the research focuses on finding the most effective station placements to maximize coverage and data fidelity while minimizing costs. The methodology entails simulating natural selection processes, including selection, crossover, and mutation, to evolve a population of potential solutions. Each candidate solution in the population represents a unique configuration of monitoring stations. A fitness function evaluates the efficiency of each configuration based on criteria such as population coverage, proximity to pollution sources, and installation and operational expenses. The research employs a genetic algorithm developed in Python, which iteratively refines the population of solutions over thousands of generations. The algorithm's performance is assessed through experimental validation, with an emphasis on the adaptability of the approach to accommodate various environmental, economic, and regulatory constraints. Results indicate that GAs can effectively balance multiple optimization objectives, leading to a network design that is both cost-efficient and comprehensive in its monitoring capabilities. The outcome of the study is an optimized network that significantly improves upon the initial state in terms of coverage and cost-effectiveness. The study concludes that genetic algorithms offer a promising avenue for addressing the challenges of air quality monitoring network design. The flexibility and global search capabilities of GAs make them suitable for the complex, multi-objective nature of the task. Moreover, the findings suggest potential for further improvements and applications of GAs in environmental monitoring and other complex systems optimization scenarios.

Exact optimisation of spatiotemporal monitoring networks by p‐splines with applications in groundwater assessment

AbstractThis paper develops methods to optimise the sampling strategy for monitoring networks which have fixed locations with regular sampling but with only a proportion of these locations to be used on each sampling occasion. This creates the need for a dynamic spatiotemporal sampling design which makes optimal choices of the locations to be sampled on each occasion. This is a commonly occurring scenario in many environmental settings where there is an existing network of monitoring stations and sampling can be expensive. The particular context of optimisation of an existing groundwater monitoring network is discussed in the paper. The standard design criteria of integrated variance (IV) and variance of the integral (VI) are adapted to the spatiotemporal setting. p‐spline models are shown to allow exact computation of IV and VI, in the case of the additive errors, and a very good approximation of IV in the case of multiplicative errors. The speed of these exact computations allows the globally optimal sampling design to be identified efficiently. In the standard case of additive errors, the design criteria are able to exploit information across time. The only information needed is the location of sampling points, not the values sampled. This contrasts with the case of multiplicative errors where the design criteria are also influenced by the observed response data. Simulated and real examples are used to illustrate the results throughout.

Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods

Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned for its computational efficacy in forecasting streamflow events with a forecast horizon of 7 days. The novel integration of the groundwater level, precipitation, and river discharge as predictive variables offers a holistic view of the hydrological cycle, enhancing the model’s accuracy. Our findings reveal that for a 7-day forecasting period, the STA-GRU model demonstrates superior performance, with a notable improvement in mean absolute percentage error (MAPE) values and R-square (R2) alongside reductions in the root mean squared error (RMSE) and mean absolute error (MAE) metrics, underscoring the model’s generalizability and reliability. Comparative analysis with seven conventional deep learning models, including the Long Short-Term Memory (LSTM), the Convolutional Neural Network LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the Spatio-Temporal Attention LSTM (STA-LSTM), the Gated Recurrent Unit (GRU), the Convolutional Neural Network GRU (CNNGRU), and the STA-GRU, confirms the superior predictive power of the STA-LSTM and STA-GRU models when faced with long-term prediction. This research marks a significant shift towards an integrated network of real-time monitoring stations with advanced deep-learning algorithms for streamflow forecasting, emphasizing the importance of spatially and temporally encompassing streamflow variability within an urban watershed’s stream network.

Vegetation dieback in the Mississippi River Delta triggered by acute drought and chronic relative sea-level rise

Vegetation dieback and recovery may be dependent on the interplay between infrequent acute disturbances and underlying chronic stresses. Coastal wetlands are vulnerable to the chronic stress of sea-level rise, which may affect their susceptibility to acute disturbance events. Here, we show that a large-scale vegetation dieback in the Mississippi River Delta was precipitated by salt-water incursion during an extreme drought in the summer of 2012 and was most severe in areas exposed to greater flooding. Using 16 years of data (2007–2022) from a coastwide network of monitoring stations, we show that the impacts of the dieback lasted five years and that recovery was only partial in areas exposed to greater inundation. Dieback marshes experienced an increase in percent time flooded from 43% in 2007 to 75% in 2022 and a decline in vegetation cover and species richness over the same period. Thus, while drought-induced high salinities and soil saturation triggered a significant dieback event, the chronic increase in inundation is causing a longer-term decline in cover, more widespread losses, and reduced capacity to recover from acute stressors. Overall, our findings point to the importance of mitigating the underlying stresses to foster resilience to both acute and persistent causes of vegetation loss.

In the nuclear explosion monitoring context, what is an anomaly?

In the early years of nuclear explosion monitoring, experts used downwind detections with meaningful ratios of radioactive species to identify an explosion. Today’s reality is sparse networks of radionuclide monitoring stations looking for weak signals. Analysts need to discriminate between industrial background radioactivity and nuclear explosion signals, even using the detection of one isotope. Aerosol and xenon measurements potentially related to nuclear tests in 2006 and 2013 announced by the Democratic People’s Republic of Korea and from worldwide civilian background radioactivity are considered when defining radionuclide detection anomalies to objectively guide the use of limited analyst resources and reduce the possibility of not detecting nuclear explosions.

МОНИТОРИНГ ХИМИЧЕСКОГО СОСТАВА СНЕЖНОГО ПОКРОВА И ФАКТОРЫ ЕГО ФОРМИРОВАНИЯ В ПЕЧОРО-ИЛЫЧСКОМ БИОСФЕРНОМ ЗАПОВЕДНИКЕ

The chemical composition of the snow cover in the Pechora-Ilych Biosphere Reserve in the winters during 2014-2021 is analyzed based on the data of the network of monitoring stations. It is similar to the one in European Russia, although there is an increase in the inflow of substances with precipitation associated with the west-to-east transport due to the condensation in the Ural foothills. It has been found that there is a difference in the chemical composition of snow in the lowland and foothill parts of the reserve. The peculiarities of atmospheric circulation and the regions from which the air mass are transported largely determine the saturation of precipitation with various chemical components. The computation of the air mass back trajectories has made it possible to identify the regions where the air masses coming to the research area, containing the substances, and potentially forming the chemical composition of precipitation can be formed.