Air Pollution Monitoring Network Research Articles

PM<sub>2.5</sub> air pollution trends and patterns in Kandy, Sri Lanka

Air pollution is the leading environmental hazard that has devastating health effects on human health. Identification of trends and patterns of air pollution is crucial in a specific geographic area to inform and implement effective control and mitigation measurements. Standard air pollution monitoring networks are costly for most low-and middle-income countries. The aim of this study was to investigate the variations in the mean concentrations of air pollutants that occur at various times of the day, days of the week, months of the year, and seasons during the period of January 2019 to December 2019 in Kandy, Sri Lanka using low-cost sensors. A regression analysis with dummy variables was used to model the relationship between the concentrations of air pollutants and categorical independent variables, the time of day and day of the week. Considering temporal variations, the study found that pollution is highest in the early morning and evening, and lowest in the afternoon. The worst days for the air quality were Wednesdays and Thursdays, while Sundays had the lowest PM2.5 pollution. The analysis of air quality variations by month showed that December had the lowest air pollution of the examined months. The variation in PM2.5 was greatly influenced by wind speed, wind direction, and rainfall patterns. The study emphasized the importance of long-term consistently management and monitoring of air pollution levels.

Influence of Air Pollution and Climate on Daily Health of Gabonese Students in the Capital (Libreville): A Pilot Study

Air pollution is a global issue affecting billions of people, especially urban populations in low- and middle-income countries. Since air pollution is a health problem that also involves the climate, this study assessed the relationship between the individual health of 50 college students living in Libreville (Gabon) and air quality, temperature, and humidity using low-cost equipment. The study utilised 81 days of data from the air pollution monitoring network in Libreville. These data were paired with measurements of heart rate, breathing, and stress measured using 5 Garmin vívosmart® smartwatches. In addition to the environmental and health data, participants were also asked about their state of health and their lifestyle during the study. We found average concentrations of PM2.5 (25.25 µg/m3) and PM10 (29.50 µg/m3) that exceed the 24-h WHO air quality standards. The daily average of PM1 was around 17.84 µg/m3. Temperatures observed during the study period varied between 27 and 35 °C (mean = 30.15 °C), and humidity was around 51-77% (mean = 59.5%). Overall, the relationships between the environmental conditions and the health observations were negligible, with correlation coefficients R ≤ 0.36. Despite their weakness, coefficients between 0.31 and 0.36 showed that stress levels are associated with temperature, PM2.5 and PM10. The average stress level was associated with PM10 (R = 0.34) and PM2.5 (R = 0.36). Finally, the study reveals that smokers, mosquito repellent users, and fan users have reduced breathing capacities compared to non-smokers, non-users of mosquito repellent products, non-users of fans, users and non-users of air conditioning. The study recommends the use of portable sensors to measure individual exposure to environmental parameters for similar studies, which will resolve the problem of spatial representativeness highlighted in this study. It is also recommended that Gabon's health policies incorporate early warnings of forecast high temperatures or pollution peaks, in order to limit health risks

The impact of restriction measures implementation due to COVID-19 on particulate atmospheric pollution in Greek urban areas

In this work, the variation on the levels of suspended particulates, in Greek urban areas, was examined, for the period of the restrictive measures implementation (period 1: March to May 2020 and period 2: November 2020 to January 2021) due to the pandemic caused by the COVID-19 virus. The PM10 and PM2.5 concentrations are measured by the National Air Pollution Monitoring Network of Greece monitored by the Energy/Environment Ministry. In particular, a comparison of the PM10 and PM2.5 concentration levels was made, concerning the above time periods, with the corresponding atmospheric pollution levels during the periods 2001-2009 (period before the Greek economic crisis), 2010-2019 (period during the Greek economic crisis), as well as with the entire time period 2001-2019. The assessment showed a significant reduction in the levels of both PM10 concentrations, which ranged from 8% to 45%, and PM2.5 concentrations, which ranged from 13% to 48%, thus demonstrating the effectiveness of the control measures in improving air quality in Greek urban areas. The above findings show that with appropriate emission control strategies, vital improvements in the air environment of urban areas can be achieved.

Network Optimization of Carbon Monoxide Sensor Nodes in the Metropolitan Region of São Paulo

Air pollution is one of the biggest problems affecting large urban areas. Better monitoring of regions suffering from this type of pollution is in the interest of public health. Although many cities employ sensors to monitor air pollution, a current concern is how to establish the ideal number of sensors to monitor a given geographical region. To address this concern, this research proposes a method to optimize the number of sensors in an air pollution monitoring network to cover a given region efficiently and precisely and uses the metropolitan region of São Paulo, Brazil, and CO sensors as an example. The model of Fragmentation into Groups via Routes is proposed to distribute sensors within micro-regions that display similar air pollution characteristics. A network of virtual sensors is created, and the output of each sensor is established using a method of spatial interpolation called IDW. To identify the optimum sensor configuration, a genetic algorithm is used to assess the topology with the lowest variance of data spread. A lesser number of sensor stations to be treated leads to faster responses to sudden changes in urban conditions. Therefore, municipality authorities can take quick measures to improve the population’s wellness.

A systematic comparison of different machine learning models for the spatial estimation of air pollution

Air pollutants harm human health and the environment. Nowadays, deploying an air pollution monitoring network in many urban areas could provide real-time air quality assessment. However, these networks are usually sparsely distributed and the sensor calibration problems that may appear over time lead to missing and wrong measurements. There is an increasing interest in developing air quality modelling methods to minimize measurement errors, predict spatial and temporal air quality, and support more spatially-resolved health effect analysis. This research aims to evaluate the ability of three feed-forward neural network architectures for the spatial prediction of air pollutant concentrations using the measures of an air quality monitoring network. In addition to these architectures, Support Vector Machines and geostatistical methods (Inverse Distance Weighting and Ordinary Kriging) were also implemented to compare the performance of neural network models. The evaluation of the methods was performed using the historical values of seven air pollutants (Nitrogen monoxide, Nitrogen dioxide, Sulphur dioxide, Carbon monoxide, Ozone, and particulate matters with size less than or equal to 2.5 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu $$\\end{document}m and to 10 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu $$\\end{document}m) from an urban air quality monitoring network located at the metropolitan area of Madrid (Spain). To assess and compare the predictive ability of the models, three estimation accuracy indicators were calculated: the Root Mean Squared Error, the Mean Absolute Error, and the coefficient of determination. FFNN-based models are superior to geostatistical methods and slightly better than Support Vector Machines for fitting the spatial correlation of air pollutant measurements.Graphical abstract

A PM10 chemically characterized nation-wide dataset for Italy. Geographical influence on urban air pollution and source apportionment

Urban textures of the Italian cities are peculiarly shaped by the local geography generating similarities among cities placed in different regions but comparable topographical districts. This suggested the following scientific question: can different topographies generate significant differences on the PM10 chemical composition at Italian urban sites that share similar geography despite being in different regions? To investigate whether such communalities can be found and are applicable at Country-scale, we propose here a novel methodological approach. A dataset comprising season-averages of PM10 mass concentration and chemical composition data was built, covering the decade 2005–2016 and referring to urban sites only (21 cities). Statistical analyses, estimation of missing data, identification of latent clusters and source apportionment modeling by Positive Matrix Factorization (PMF) were performed on this unique dataset. The first original result is the demonstration that a dataset with atypical time resolution can be successfully exploited as an input matrix for PMF obtaining Country-scale representative chemical profiles, whose physical consistency has been assessed by different tests of modeling performance.Secondly, this dataset can be considered a reference repository of season averages of chemical species over the Italian territory and the chemical profiles obtained by PMF for urban Italian agglomerations could contribute to emission repositories.These findings indicate that our approach is powerful, and it could be further employed with datasets typically available in the air pollution monitoring networks.

Job Exposure Matrix, a Solution for Retrospective Assessment of Particle Exposure in a Subway Network and Their Long-Term Effects.

Health effects after long-term exposure to subway particulate matter (PM) remain unknown due to the lack of individual PM exposure data. This study aimed to apply the job exposure matrix (JEM) approach to retrospectively assess occupational exposure to PM in the Parisian subway. Job, the line and sector of the transport network, as well as calendar period were four JEM dimensions. For each combination of these dimensions, we generated statistical models to estimate the annual average PM10 concentration using data from an exhaustive inventory of the PM measurement campaigns conducted between 2004 and 2020 in the Parisian subway and historical data from the Parisian air pollution monitoring network. The resulting JEM and its exposure estimates were critically examined by experts using the uncertainty analysis framework. The resulting JEM allows for the assignment of the estimated annual PM10 concentration to three types of professionals working in the subway: locomotive operators, station agents, and security guards. The estimates' precision and validity depend on the amount and quality of PM10 measurement data used in the job-, line-, and sector-specific models. Models using large amounts of personal exposure measurement data produced rather robust exposure estimates compared to models with lacunary data (i.e., in security guards). The analysis of uncertainty around the exposure estimates allows for the identification of the sources of uncertainty and parameters to be addressed in the future in order to refine and/or improve the JEM. The JEM approach seems relevant for the retrospective exposure assessment of subway workers. When applied to available data on PM10, it allows for the estimation of this exposure in locomotive operators and station agents with an acceptable validity. Conversely, for security guards, the current estimates have insufficient validity to recommend their use in an epidemiological study. Therefore, the current JEM should be considered as a valid prototype, which shall be further improved using more robust measurements for some jobs. This JEM can also be further refined by considering additional exposure determinants.

Nitrogen dioxide spatiotemporal variations in the complex urban environment of Athens, Greece

In this study, five years of total, tropospheric and near-surface nitrogen dioxide, ΝΟ2, observations from a Pandora spectrometer system routinely operating in the city center of Athens, Greece, are presented and compared with space-borne observations from the S5P/TROPOMI sensor and in-situ air quality monitoring station measurements from the Greek National Air Pollution Monitoring Network. Each of the three different systems exhibits its own monitoring capabilities and restrictions, based on the spatial representativeness and temporal resolution of its measurements. However, they all reveal a clear weekly pattern with low NO2 concentrations on Sundays while a diurnal cycle with higher levels of NO2 in the morning is observed from the ground due to the high NOx emissions from heavy traffic in the urban environment. A seasonal pattern is further demonstrated by both space- and ground-based remote sensing instruments with enhanced NO2 loadings in wintertime and decreased levels in summertime. Even though, as expected, S5P/TROPOMI appears to underestimate the tropospheric NO2 load, the correlation between the ground-based and space-borne data is relatively high, with corresponding correlation coefficients ranging between 0.73 in spring and 0.80 in winter. High correlations are also found between the relatively new near-surface NO2 product of Pandora and the in situ observations, ranging from 0.91 to 0.99. However, an underestimation is observed by the Pandora instrument compared to those in situ monitoring stations which are highly affected by the increased NOx emissions in the city center. For the Pandora near-surface NO2 at 0° (north) azimuthal viewing angle, the best agreement is found with the urban background in situ stations, with a negative bias at 0.99 ± 1.60 × 1011 molecules cm−3, whereas the Pandora retrievals at 39° (north-east) are closer to the urban in situ NO2 levels, with a bias of −1.10 ± 2.02 × 1011 molecules cm−3 on average.

A Two-Stage Hybrid Model for Determining the Scopes and Priorities of Joint Air Pollution Control

Due to the spillover nature of air pollution, the territorial separate governance mode is ineffective in combating pollution, making Joint Prevention and Control of Air Pollution (JPCAP) among multiple regions the only viable option. However, determining the appropriate scopes and priorities for JPCAP is known to be a challenging and significant issue. To address this, we propose a new two-stage hybrid model. In the first stage, making use of long-term, wide area monitoring data provided by the air pollution monitoring network, we propose a new method for subdividing large regions into sub-regions by using data mining techniques. In the second stage, we propose a comprehensive decision-making framework to evaluate the priorities of JPCAP sub-regions from three different perspectives, namely, the impact of a sub-region on the pollution level of the entire target region, as well as the urgency and elasticity of sub-regional air pollution control. A case study is conducted on 27 cities of the Yangtze River Delta region of China. The case study demonstrates the validity and practicality of the proposed two-stage hybrid model. This work provides a viable tool for the effective implementation of air pollution control in China and other regions of the world.

Automatic air pollution monitoring and corporate environmental disclosure: a quasi-natural experiment from China

PurposeThis study aims to investigate the role of regional environmental transparency on corporate environmental disclosure.Design/methodology/approachThis study uses the introduction of a nationwide automated air pollution monitoring network in China as a quasi-natural experiment and employs regression analysis. Robustness checks, including parallel trend test and placebo test, are performed to test the robustness of the results.FindingsSharing air pollution data with the public can improve corporate environmental disclosure. Firms with poorer environmental, social and governance (ESG) performance prefer to disclose less informative information after the automated network is implemented compared with firms with better ESG performance. The relationship between information sharing and corporate environmental transparency is more pronounced when local air pollution is severer, firms face stronger investor scrutiny and firms are from heavily polluting industries. The mechanism tests suggest the automated system can draw public environmental attention and improve governments’ aspiration for environmental governance. Finally, corporate environmental disclosure can reduce stock price crash risk and cost of equity.Practical implicationsReal-time pollution data reporting is an important solution to raising public environmental awareness and then enhancing the effectiveness of pollution control.Social implicationsThis study has implications for policy-making regarding environmental governance and environmental disclosure.Originality/valueThis study confirms that pollution information transparency can motivate firms to increase environmental disclosure.

Expansion of Roadside Air Pollution Monitoring Network in Seoul using Learning-based Optimization Method

Objectives : In this study, a learning-based optimization method is proposed and implemented for determining new monitoring sites when expanding the roadside air pollution monitoring network. Utilizing the bigdata available in Seoul, this decision-making tool is developed that takes into account the objectives of selecting new monitoring sites and incorporates social, economic, and environmental characteristics. The optimized results can suggest potential locations for new roadside air pollution monitoring sites. Additionally, the capability of this tool to facilitate objective decision-making processes is evaluated by determining the influence range providing reliable air pollution information with the addition of the new monitoring sites.Methods : The proposed learning-based optimization algorithm is a new approach for selecting the new optimal monitoring sites by comprehensively considering social, economic, and environmental factors aligned with the installation purpose of the monitoring system in Seoul. The algorithm starts with genetic algorithms to select candidate locations for new monitoring sites that maximize the influence area of the expanded monitoring network compared to the existing monitoring network, capture a high overall level of air pollution, and do not overlap with the existing monitoring network. After that, PROMETHEE method is applied to evaluate the solutions generated by the genetic algorithm and choose the final solution that best fits six evaluation factors (Information entropy, number of new monitoring sites, distance from point sources, wind speed, traffic volume, and population) to be considered when installing new monitoring sites.Results and Discussion : The learning-based optimization algorithm selects 10 potential new monitoring sites adding to the existing roadside air pollution monitoring network having 15 monitoring sites. The explainable spatiotemporal range of the air pollution information that can be expected after the installation of the new monitoring sites is quantified to cover 84.33% of Seoul, reducing the uncertainty of the air pollution information of existing monitoring network by 26.15%. The final solution, selected from several solutions, can get new optimal roadside air pollution monitoring sites reflecting the regional characteristics of Seoul and the installation purpose of the monitoring system by having a small number of newly established monitoring locations, being close to air pollution emissions facilities, and having a high population and traffic volume.Conclusion : The proposed learning-based optimization method, using relevant variables for the installation purpose of the monitoring system, can derive the objective solution for deciding new monitoring locations of the roadside air pollution monitoring network, considering additional social factors as opposed to urban air pollution monitoring network. The final solution obtained through the optimization algorithm has great potential for future use, as it can guide to determine practical and feasible new monitoring sites with additional on-site verification. Furthermore, this optimized approach can be applied widely during the decision-making process for the expansion of other environmental monitoring networks.

Assessing the Spatial Distribution of NO2 and Influencing Factors in Urban Areas—Passive Sampling in a Citizen Science Project in Berlin, Germany

Nitrogen dioxide (NO2) is a major air pollutant with diverse impacts on human health and the environment. In urban areas, road traffic is the main emission source for NO2. In Berlin, Germany, a network of measurement stations is operated by the state, fulfilling the monitoring requirements set by the European Union. To get a more detailed overview of the spatial distribution of NO2 concentrations in Berlin, a citizen science project allowed for collection of additional data and an increase in the number of sampling sites. Passive samplers (modified Palmes tubes) were distributed to participants to collect NO2 at a site of their choice. When returned, the samplers were analyzed based on the Griess–Ilosvay reaction and spectrophotometric detection. The results confirmed a seasonal trend of higher NO2 concentrations in winter and lower concentrations during the summer period. Furthermore, the spatially and monthly averaged NO2 concentrations observed in the study period from March 2019 to October 2020 were in good agreement with the average urban background concentration. At small spatial scales, a tendency of decreasing NO2 concentrations with increasing distance from roads was observed. Overall, this study shows the added benefit of extensive low-cost measurements of NO2 concentrations across urban environments in a citizen science project to complement stationary air pollution monitoring networks.

Mapping Health Fragility and Vulnerability in Air Pollution-Monitoring Networks in Dallas-Fort Worth.

Environmental air pollution remains a major contributor to negative health outcomes and mortality, but the relationship between socially vulnerable populations and air pollution is not well understood. Although air pollution potentially affects everyone, the combination of underlying health, socioeconomic, and demographic factors exacerbate the impact for socially vulnerable population groups, and the United States Clean Air Act (CAA) describes an obligation to protect these populations. This paper seeks to understand how air pollution monitor placement strategies and policy may neglect social vulnerabilities and therefore potentially underestimate exposure burdens in vulnerable populations. Multivariate logistic regression models were used to assess the association between being in an ozone-monitored area or not on 15 vulnerability indicators. It was found that the odds of not being in an ozone-monitored area (not covered, outside) increased for the predictor mobile homes (OR = 4.831, 95% CI [2.500-9.338] and OR = 8.066, 95% CI [4.390-14.820] for the 10 and 20 km spatial units, respectively) and decreased for the predictor multiunit structures (OR = 0.281, 95% CI [0.281-0.548] and OR = 0.130, 95% CI [0.037, 0.457] for the 10 and 20 km spatial units, respectively) and the predictor speaks English "less than well" (OR = 0.521, 95% CI [0.292-0.931] for 10 km). These results indicate that existing pollution sensor coverage may neglect areas with concentrations of highly vulnerable populations in mobile homes, and future monitoring placement policy decisions must work to address this imbalance.

Vector autoregressive clustering for redundancy analysis in air pollution monitoring networks at Türkiye

Vector autoregressive clustering for redundancy analysis in air pollution monitoring networks at Türkiye

Graph Signal Reconstruction Techniques for IoT Air Pollution Monitoring Platforms

Air pollution monitoring platforms play a very important role in preventing and mitigating the effects of pollution. Recent advances in the field of graph signal processing have made it possible to describe and analyze air pollution monitoring networks using graphs. One of the main applications is the reconstruction of the measured signal in a graph using a subset of sensors. Reconstructing the signal using information from sensor neighbors can help improve the quality of network data, examples are filling in missing data with correlated neighboring nodes, or correcting a drifting sensor with neighboring sensors that are more accurate. This paper compares the use of various types of graph signal reconstruction methods applied to real data sets of Spanish air pollution reference stations. The methods considered are Laplacian interpolation, graph signal processing low-pass based graph signal reconstruction, and kernel-based graph signal reconstruction, and are compared on actual air pollution data sets measuring O3, NO2, and PM10. The ability of the methods to reconstruct the signal of a pollutant is shown, as well as the computational cost of this reconstruction. The results indicate the superiority of methods based on kernel-based graph signal reconstruction, as well as the difficulties of the methods to scale in an air pollution monitoring network with a large number of low-cost sensors. However, we show that scalability can be overcome with simple methods, such as partitioning the network using a clustering algorithm.

Estimation of fine particulate matter in an arid area from visibility based on machine learning.

The absence of air pollution monitoring networks makes it difficult to assess historical fine particulate matter (PM2.5) exposures for countries in the areas, such as Kuwait, which are severe impacted by desert dust and anthropogenic pollution. We constructed an ensemble machine learning model to predict daily PM2.5 concentrations for regions lack of PM2.5 observations. The model was constructed based on daily PM2.5, visibility, and other meteorological data collected at two sites in Kuwait. Then, our model was applied to predict the daily level of PM2.5 concentrations for eight airports located in Kuwait and Iraq from 2013 to 2020. As compared to traditional statistic models, the proposed machine learning methods improved the accuracy in using visibility to predict daily PM2.5 concentrations with a cross-validation R2 of 0.68. The predicted level of daily PM2.5 concentrations were consistent with previous measurements. The predicted average yearly PM2.5 concentration for the eight stations is 50.65 µg/m3. For all stations, the monthly average PM2.5 concentrations reached their maximum in July and their minimum in November. These findings make it possible to retrospectively estimate daily PM2.5 exposures using the large-scale databases of historical visibility in regions with few particulate matter monitoring stations. The scarcity of air pollution ground monitoring networks makes it difficult to assess historical fine particulate matter exposures for countries in arid areas such as Kuwait. Visibility is closely related to atmospheric particulate matter concentrations and historical airport visibility records are commonly available in most countries. Our model make it possible to retrospectively estimate daily PM2.5 exposures using the large-scale databases of historical visibility in arid regions with few particulate matter ground monitoring stations. The product of such models can be critical for environmental risk assessments and population health studies.

Data reconstruction applications for IoT air pollution sensor networks using graph signal processing

The analysis of sensor networks for air pollution monitoring is challenging. Recent studies have demonstrated the ability to reconstruct the network measurements with graphs derived from the acquired data, thus describing the complex relationships between the sensors that compose the network. In this work, we propose a graph-based data reconstruction framework that can be used to carry out different post-processing applications that arise in real-world low-cost sensor deployments for air pollution monitoring. This data reconstruction framework first describes the relationships between the different network sensors by means of a graph learned from the measured data, and then a signal reconstruction model is superimposed to reconstruct sensor data. This methodology allows reconstructing sensor data to carry out missing value imputation, signal reconstruction at points where there are no physical sensors (virtual sensing), or data fusion. The results, using real data taken with an air pollution monitoring network including reference stations and low-cost sensors, show how this framework performs well for these applications compared to estimates obtained from individual sensors and task-specific state-of-the-art methods that are not able to deal with this necessary range of applications. In short, the results show the potential of using graphs, whose topology is based on the measurements taken, to calibrate and reconstruct signals in heterogeneous networks of low-cost air pollution sensors for a wide variety of applications.

Community-based participatory research for low-cost air pollution monitoring in the wake of unconventional oil and gas development in the Ohio River Valley: Empowering impacted residents through community science

Belmont County, Ohio is heavily dominated by unconventional oil and gas development that results in high levels of ambient air pollution. Residents here chose to work with a national volunteer network to develop a method of participatory science to answer questions about the association between impact on the health of their community and pollution exposure from the many industrial point sources in the county and surrounding area and river valley. After first directing their questions to the government agencies responsible for permitting and protecting public health, residents noted the lack of detailed data and understanding of the impact of these industries. These residents and environmental advocates are using the resulting science to open a dialogue with the EPA in hopes to ultimately collaboratively develop air quality standards that better protect public health. Results from comparing measurements from a citizen-led participatory low-cost, high-density air pollution sensor network of 35 particulate matter and 25 volatile organic compound sensors against regulatory monitors show low correlations (consistently R2 < 0.55). This network analysis combined with complementary models of emission plumes are revealing the inadequacy of the sparse regulatory air pollution monitoring network in the area, and opening many avenues for public health officials to further verify people’s experiences and act in the interest of residents’ health with enforcement and informed permitting practices. Further, the collaborative best practices developed by this study serve as a launchpad for other community science efforts looking to monitor local air quality in response to industrial growth.

완주군 농촌마을 지역의 미세먼지 측정 농도와 대기오염측정망과의 비교 평가

This study aimed to evaluate the concentration of particulate matter less than 10 micrometers in diameter or less than 2.5micrometres in diameter (PM<SUB>10</SUB> and PM<SUB>2.5</SUB>) in rural villages, and thus protect the health of elderly farmers, by using the air pollution monitoring network data based on the particulate matter forecast grade. The sampling sites selected included a farm-work environment and the village entrance in the Wanju-gun village. PM<SUB>10</SUB> and PM<SUB>2.5</SUB> were measured using a portable aerosol spectrometer (PAS). When the particulate matter forecast grade was good and normal, PM<SUB>10</SUB> and PM<SUB>2.5</SUB> monitoring were performed once every two weeks. When the forecast grades were poor and very poor, PM<SUB>10</SUB> and PM<SUB>2.5</SUB> monitoring was performed daily. To compare the air pollution monitoring network data to the PM<SUB>10</SUB> and PM<SUB>2.5</SUB> levels measured at the sampling sites, the data were extracted from Air Korea. When the particulate matter forecast grade was poor, the concentration of PM<SUB>10</SUB> and PM<SUB>2.5</SUB> in the farm-work environment (80.1 ㎍/㎥, 54.1 ㎍/㎥) was the highest among all the sites (air pollution monitoring network: 78.8 ㎍/㎥, 41.4 ㎍/㎥, village entrance: 68.1 ㎍/㎥, 53.3 ㎍/㎥). Also, the concentration of PM<SUB>2.5</SUB> was significantly different between the sampling sites and the air pollution monitoring network during the sampling period (p=0.002). When the particulate matter forecast grade was poor, the sampling sites showed a higher PM<SUB>2.5</SUB>/PM<SUB>10</SUB> ratio (farm-work environment: 0.68, village entrance: 0.78) compared to the air pollution monitoring network (0.52). Based on these results, when the fine dust forecast is bad, farmers living in rural areas need to reconsider leaving their homes for farming or for any other activity. Also, it is essential to carry out particulate matter management in rural areas, including farm-work environments and residential areas.

Monitoring-based assessment of environmental pollution in regions of the Russian Federation

Abstract The article assesses the environmental pollution level in urban areas of the Russian Federation regions. Environmental monitoring data were used as data sources. For each Russian region the environmental pollution level was considered as the sum of pollution indices for the basic environmental media: air, surface water and soil. It allowed ranking regions and grouped them into categories (from extremely high polluted to extremely low ones). The air pollution monitoring network in cities and industrial centres does not cover all regions of the country, leading to undetermined air pollution level in 12 of 85 regions and thus to underestimated environmental pollution level there. The paper proposes to use the monitoring network data of the snow cover chemistry for the air pollution assessment in problematic regions and more accurate calculation of the environmental pollution level in them. Based on the air monitoring data for 2018 we revealed regions with high and extremely high levels of air pollution, their list was added after analyzing the snow cover chemistry data. As a result, the total assessment of environmental pollution was recalculated upward.