Atmospheric Pollution Monitoring Research Articles

Defect-Induced Sensitivity Improvement in HfNBr Monolayers for Ammonia Detection.

Two-dimensional materials, owing to their unique physical properties and high surface area, play a crucial role in intelligent sensing, particularly in the domain of atmospheric pollutant monitoring. In this work, we have extensively investigated the gas-sensing capabilities of the HfNBr monolayer for ammonia detection by introducing point defects, utilizing density functional theory and nonequilibrium Green's function calculations. Upon the introduction of point defects, the adsorption energy of HfNBr monolayers for ammonia significantly increased (from -0.162 to -1.257 eV), indicating a markedly strengthened affinity. To further elucidate the sensing mechanism, we conducted an in-depth investigation into the charge transfer dynamics, the density of states, and the charge density difference between the adsorbent and the adsorbate. Besides, we employed the NEGF method to evaluate the changes in the current-voltage characteristics of the HfNBr monolayer before and after adsorption, which revealed a remarkable change in the apparent resistance, thereby demonstrating excellent sensitivity. The exceptional performance of the HfNBr monolayer toward NH3 demonstrates its significant value in practical applications for ammonia detection.

Monitoring of atmospheric air pollution in the samarkand region under the influence of vehicles

This article examines the influence of road transport on air pollution in the city of Samarkand, Republic of Uzbekistan. Based on the results of an analysis of the city map, five streets were identified with the largest number of vehicles. As part of the study, the volumes of emissions of harmful gases emitted by vehicles were measured, for this purpose 2 stationary and 2 mobile environmental control posts were used. Based on the results of measurements, it was revealed that road transport accounts for about 30.7 percent of the volume of emissions of pollutants into the air from all emissions in the city of Samarkand. Based on the results of the studies, appropriate conclusions were drawn and recommendations were made to reduce the impact of road transport on the city’s environment.

Optimisation method for atmospheric environment pollution monitoring site selection based on improved genetic algorithm

Optimisation method for atmospheric environment pollution monitoring site selection based on improved genetic algorithm

Development of the method of remote sensing of low-concentration gases of the atmosphere taking into account the humidity fluctuations of the air

The emergence of strong desert vortices covering large areas, including the areas of large cities with dense population, the need to quickly obtain sufficient information on the state of desert ecosystems in large areas makes the application of remote sensing methods an urgent issue. At present, the method of measuring the desert aerosol using scattered solar radiation has been proposed, and the method of measuring the optical thickness of the dust aerosol has been substantiated. A new method of optimization of remote sensing systems and information-measurement systems in general is proposed in the article. A method of remote determination of aerosol optical thickness increase, which involves measuring the scattered radiation of the sun, has been proposed. The main goal of the study is to develop a remote sensing method that allows determining the dynamics and cause of the increase in aerosol optical thickness. The scientific novelty of the work is a new method that is invariant to the variations of the aerosol optical thickness under the influence of hydration and dehydration processes. By using the proposed three-wave biparametric Sun photometer, the effect of aerosol hygroscopic growth on the measurement results is eliminated.The given critical assessment of the state of atmospheric pollution monitoring and the consideration of the main shortcomings of the aerosol pollution monitoring carried out in the country will allow further improvement of such monitoring in the future. The proposed remote sensing method for determining the dynamics of aerosol optical thickness growth allows to determine the origin of aerosol optical thickness growth by measuring scattered solar radiation at two different wavelengths and in two optical air masses.

The development of a meta-learning calibration network for low-cost sensors across domains

Low-cost sensor arrays are an economical and efficient solution for large-scale networked monitoring of atmospheric pollutants. These sensors need to be calibrated in situ before use, and existing data-driven calibration models have been widely used, but require large amounts of co-location data with reference stations for training, while performing poorly across domains. To address this problem, a meta-learningbased calibration network for air sensors is proposed, which has been tested on ozone datasets. The tests have proved that it outperforms five other conventional methods in important metrics such as mean absolute error, root mean square error and correlation coefficient. Taking Manlleu and Tona as the source domain and Vic as the target domain, the proposed method reduces MAE and RMSE by 17.06% and 6.71% on average, and improves R2 by an average of 4.21%, compared with the suboptimal pre-trained multi-source transfer calibration. The method can provide a new idea and direction to solve the problem of cross-domain and reliance on a large amount of co-location data in the calibration of sensors.

Quality assessment of aerosol lidars at 1064 nm in the framework of the MEMO campaign

Abstract. Aerosol lidar networks can play an important role in revealing structural characteristics of the atmospheric boundary layer, the urban heat island effect, and the spatial distribution of aerosols, especially in relation to the monitoring of atmospheric pollution in megacities. To fulfill the need of the monitoring and numerical forecasting of atmospheric pollution, an aerosol lidar network is proposed by the China Meteorological Administration which serves as an important part of the “MegaCity Experiment on Integrated Meteorological Observation in China” (MEMO). To ensure a high standard of data quality and traceability of measurement error, an inter-comparison campaign, dedicated to the quality assessment of lidar systems from different institutes and manufacturers, was designed and performed at Beijing Southern Suburb Observatory in September 2021. Six Mie–Rayleigh lidar systems at 1064 nm were involved in this campaign. The strategies for lidar self-evaluations and inter-comparisons were predefined. A lidar system at 1064 nm, which was developed by the Atmospheric Remote Sensing group at Wuhan University, was selected as the reference lidar system after passing all strict self-evaluation quality checks. The reference lidar system serves as the cornerstone for evaluating the performance of other lidar systems. After using the Rayleigh fit and signal-to-noise evaluation self-tests for each individual lidar system as a fast check of the data quality, the range-corrected signal and backscatter coefficient obtained from all the lidar systems were inter-compared with a reference lidar system. In the end, the lidar systems passed the quality control/assurance, ensuring that the standard deviation of range-corrected signal could be controlled within 5 % at 500–2000 m and 10 % at 2000–5000 m. For the derived aerosol backscatter coefficients, standard deviations can be controlled within 10 % at 500–2000 and 2000–5000 m. The quality assurance strategy lays down a solid basis for atmospheric lidar at near-infrared wavelengths and will be applied in Chinese lidar network development.

The development and application of a novel helicopter-based airborne platform for near-surface monitoring and sampling of atmospheric pollutants

This study presents a novel approach utilising helicopters as platforms for real-time monitoring of air pollutants and routine canister sampling to enhance our understanding of air pollution in the Greater Bay Area (GBA). By employing this method, local spatial and vertical distribution of various air pollutants (including CO, NO, NO2, O3, PM2.5 and VOCs) in Hong Kong could be obtained within hours for fast snapshots of air pollution.The investigation involved hovering ascent and descent operations above Tap Mun Island, revealing no indication of self-contamination from engine exhaust during the hovering process. The analysis of air pollutant spatial distribution in and around Hong Kong through multiple trips showed higher concentrations of O3 and PM2.5 in the northern region on days with episodic pollution, while urban areas exhibited elevated levels of NO2 and CO concentration. In contrast, Tap Mun Island as the background area demonstrated relatively low pollutant concentrations. The vertical distribution of concentrations of VOC species with high ozone formation potential revealed minor variations at sampling altitudes of 500 ft and 1000 ft over the waters around Hong Kong. The novel monitoring protocol using helicopters as platforms proves to be an effective approach for pollution episode investigations, offering valuable insights in the formation mechanisms and transport pathways of O3. The findings of the heterogenous spatial distribution of O3 and its precursors underscore the significance of ongoing monitoring efforts and policy development to combat air pollution in the dynamic GBA and similar regions worldwide.

Retrieval of the aerosol extinction coefficient from scanning Scheimpflug lidar measurements for atmospheric pollution monitoring

In this work, an 808-nm Scheimpflug lidar (SLidar) system, employing a laser diode as the light source and an image sensor as the detector, has been utilized for practical applications of air pollution monitoring in urban area. A comprehensive retrieval method has been proposed and developed for reliable and automatic retrieval of the aerosol extinction coefficient (AEC) from near-horizontal scanning SLidar measurements. The characteristics of the pixel-intensity curves measured by the SLidar system have been statistically examined, and a quality control process is developed to eliminate abnormal lidar profiles. An improved Douglas-Pucker algorithm has been utilized for the determination of the boundary value of the AEC. The AEC profile is thus obtained according to the Klett method with the boundary value as the input. Meanwhile, an angular iterative retrieval (AIR) algorithm has been developed with the AECs at neighboring scanning angles as references, which is able to significantly improve the retrieval reliability of the AEC for each scanning angle. Comparison studies with a local pollution monitoring station have also been carried out to verify the present retrieval method. Besides, the Scheimpflug lidar technique in combination with the retrieval method has shown promising performance in tracking pollution sources in urban area.

Koopman-inspired approach for identification of exogenous anomalies in nonstationary time-series data

In many scenarios, it is necessary to monitor a complex system via a time-series of observations and determine when anomalous exogenous events have occurred so that relevant actions can be taken. Determining whether current observations are abnormal is challenging. It requires learning an extrapolative probabilistic model of the dynamics from historical data, and using a limited number of current observations to make a classification. We leverage recent advances in long-term probabilistic forecasting, namely Deep Probabilistic Koopman, to build a general method for classifying anomalies in multi-dimensional time-series data. We also show how to utilize models with domain knowledge of the dynamics to reduce type I and type II error. We demonstrate our proposed method on the important real-world task of global atmospheric pollution monitoring, integrating it with NASA’s Global Earth Observing System Model. The system successfully detects localized anomalies in air quality due to events such as COVID-19 lockdowns and wildfires.

Mechanism and Model for Data Evaluation of the System of Automated Monitoring of Atmospheric Air Pollution in Industrially Developed Regions

The mechanism of environmental monitoring, a model and a system for evaluating data obtained using an automated environmental monitoring system for atmospheric air pollution, which describes the evolution of a variable measured over the same sampling period as a non-linear function of past evolutions are considered. The object of research is the system of automated monitoring of atmospheric air pollution in industrialized regions. The subject of the study is the mechanisms for conducting environmental monitoring and models for evaluating automated monitoring data.

Estimating Daily NO2 Ground Level Concentrations Using Sentinel-5P and Ground Sensor Meteorological Measurements

Environmental and health deterioration due to the increasing presence of air pollutants is a pressing topic for governments and organizations. Institutions such as the European Environment Agency have determined that more than 350,000 premature deaths can be attributed to atmospheric pollutants. The measurement of trace gas atmospheric concentrations is key for environmental agencies to fight against the decreased deterioration of air quality. NO2, which is one of the most harmful pollutants, has the potential to cause diseases such as Chronic Obstructive Pulmonary Disease (COPD). Unfortunately, not all countries have local atmospheric pollutant monitoring networks to perform ground measurements (especially Low- and Middle-Income Countries). Although some alternatives, such as satellite technologies, provide a good approximation for tropospheric NO2, these do not measure concentrations at the ground level. In this work, we aim to provide an alternative to ground sensor measurements. We used a combination of ground meteorological measurements with satellite Sentinel-5P observations to estimate ground NO2. For this task, we used state-of-the-art Machine Learning models, linear regression models, and feature selection algorithms. From the results obtained, we found that a Multi-layer Perceptron Regressor and Kriging in combination with a Random Forest feature selection algorithm achieved the lowest RMSE (2.89 µg/m3). This result, in comparison with the real data standard deviation and the models using only satellite data, represented an RMSE decrease of 55%. Future work will focus on replacing the use of meteorological ground sensors with only satellite-based data.

High-performance MTJ-based sensors for monitoring of atmospheric pollution

Solid and liquid particles in the atmosphere, referred to as airborne particulate matter (PM), have been rising significantly over the past two decades. Exposure to PM carries significant health risks such as lungs damage, heart disease, cancer, and death. PM2.5 is a subgroup of PM particles that are smaller than 2.5 µm and is a major concern as it is more harmful to health and more difficult to detect. One problematic component of PM2.5 is magnetite nanoparticles (<200 nm), which are readily absorbed into the bloodstream through the respiratory system. Eventually, magnetite nanoparticles deposit inside the brain causing neurodegenerative diseases such as Alzheimer’s or cancerous tumors by inducing oxidative stress. Additionally, Magnetite nanoparticles are often surrounded by heavy metal nanoparticles such as Cadmium and lead which are a great concern to the environment and health. Traditional PM detection methods such as laser scattering are bulky, expensive, and incapable of detecting particles smaller than 200 nm such as magnetite nanoparticles. Therefore, developing a low-cost highly sensitive sensor for monitoring magnetite nanoparticles is vital. Tunneling Magneto-Resistance (TMR) sensors are an attractive option due to their low-cost and high sensitivity toward magnetic nanoparticle detection. Moreover, developing a cheap, portable, and precise remote monitoring technique will allow for the creation of high spatial resolution highly sensitive monitoring networks for magnetic PM2.5. This work focuses on developing, modeling, and simulation of low-cost highly sensitive TMR sensor based on Magnetic Tunnel Junction (MTJ) that can detect and count magnetite nanoparticles.

Public monitoring as a tool for assessing the state of the air basin in the Ukrainian Regions

Over the recent years automated observation systems that work continuously and allow obtaining information at any time have been widely implemented. Such systems include public monitoring systems in order to receive more complete information about the quality characteristics of the air basin over Ukraine. Examples of public monitoring organizations include SaveEcoBot, EcoCity, SaveDnipro.
 The information presented on the websites of the public monitoring system contains large amount of data that are difficult to process and analyze. In order to optimize data processing, a program called PublicMonitoringParser was developed. It transforms large volumes of data presented on the Internet resource SaveEcoBot.com into compact, convenient, small files containing average monthly concentrations of pollutants and indicators of meteorological parameters. The program is written using the C# programming language and the .NET platform. The structure of the program and the principles of its operation are presented in this research.
 According to the data of the website SavEecoBot.com the developed program was applied for assessment of the air basin state of Lviv Region in 2021. In general, for most substances, monitoring data were available only at the station in the city of Lviv at the following address: 13 Plastova Str. Monitoring of PM2.5 and PM10 content was carried out across the whole region.
 It was detected that the content of NO2, SO2, NH3 exceeded the limit value. Significant excesses were also noted for the NH3 content (up to 9 – 16 MPC). It was established that the location of the public monitoring station was surrounded by industrial buildings, numerous transport industry enterprises, an oil depot for OKKO gas stations and city sewage treatment facilities. A comparison of the data obtained at stationary and automated points that monitor individual pollutants showed that the results are comparable only for the NO2 content.
 Data from automated observation points complement the network of stationary points. Currently, the existing stationary network of atmospheric pollution monitoring across most regions of Ukraine is outdated and needs to be re-equipped. In general, it is necessary to further implement automated systems that are able to characterize the state of the air basin across individual populated places in a more complete and detailed manner.

Short-term atmospheric pollution monitoring in a selection of European capitals

Air pollution is increasing in the context of uncontrolled emissions from human activities, mostly from industry, transportation and agriculture. Urban agglomerations concentrate most environmental pollution sources, given the high population density and their needs of transportation and energy, both of electrical and thermal types, along with huge quantities of wastes, whose improper management leads to heavy pollutant emissions in atmosphere, but also to pollutant leakage in waters and soil. In general, in each country, at least in Europe, the capital is the most populated city, which also concentrates important industrial sites, leading to poor air quality. This affects the population both on short and long term. Studies have shown that air pollution even induces chronic diseases, not only of respiratory-type, but also of the heart, up till reducing life expectancy. When dealing with urban air pollution, one must also consider the physical-geographical conditions, which may accentuate or, on the contrary, may diminish pollution through an intense air circulation, thus dispersing pollutants.A selection of European capitals was chosen for the study presented here, as related to their air quality which was monitored for short term within the peak of the recent pandemic, in order to have a view and even a reference of the expected reduced pollution status as compared to the normal urban life conditions. The chosen capitals were: Paris, Berlin, Bucharest and Skopje, which are well-spread geographically across Europe and also have different economic statuses, along with different landscape conditions and climate influences, making them appropriate for comparison, differences being expected.

Drone-Assisted Monitoring of Atmospheric Pollution—A Comprehensive Review

Ambient air quality is a complex issue that depends on multiple interacting factors related to emissions coming from energy production and use, transportation, industrial processes, agriculture, and waste and wastewater treatment sectors. It is also impacted by adverse meteorological conditions, pollutants concentrations, their transport and dispersion in the atmosphere, and topographic constraints. Therefore, air pollutants distribution is not uniform and their monitoring at proper temporal and spatial resolution is necessary. Drone-borne analytical instrumentation can fulfill these requirements. Thanks to the rapid development in the drone manufacturing sector as well as in the field of portable detectors construction, applications of unmanned aerial vehicles (UAVs) for atmospheric pollution monitoring are growing. The purpose of this work is to give an overview of this matter. Therefore, this paper contains basic information on UAVs (i.e., description of different types of drones with their advantages and disadvantages) and analytical instrumentation (i.e., low-cost gas sensors, multi-sensor systems, electronic noses, high-accuracy optical analyzers, optical particle counters, radiation detectors) used for the monitoring of airborne pollution. Different ways of payload integration are addressed and examples of commercially available solutions are given. Examples of applications of drone-borne analytical systems for pollution monitoring coming from natural (i.e., volcanoes, thawing permafrost, wildfires) and anthropological (i.e., urbanization and industrialization; extraction, transport and storage of fossil fuels; exploitation of radioactive materials; waste and wastewater treatment; agriculture) sources are also described. Finally, the current limitations and future perspectives are discussed. Although there is a great potential for drones applications in the field of atmospheric pollution monitoring, several limitations should be addressed in the coming years. Future research should focus on improving performances of available analytical instrumentation and solving problems related to insufficient payload capacity and limited flight time of commonly used drones. We predict that applications of drone-assisted measurements will grow in the following years, especially in the field of odor pollution monitoring.

Development of low-cost RFID sensors dedicated to air pollution monitoring for preventive conservation

Monitoring of atmospheric pollutants is a key point for preventive conservation since these species are known to impact the integrity of many artifacts including metals, papers, pigment and textiles. The problem can be amplified in closed environments, like in exhibition rooms and showcases, where temperature and relative humidity gradients and levels can introduce additional micro-climatic problems. One objective of the EU-SensMat project concerns the development of low cost and low visual nuisance sensors sensitive to these pollutants, making them affordable for all museums including small ones. For this purpose, the Radio-Frequency Identification (RFID) technology was considered to produce air-quality sensors based on reactive metallic dosimeter. Besides the low cost, they can be easily integrated to other existing RFID applications such as identification tasks since the interrogation is made with a commercial UHF-RFID reader. The description and the main features of these sensors are discussed in this paper. Moreover, as it will be shown, the subsequent chemical analysis of the sensitive part of the sensors provide useful information to determine the origin of the pollutants.

Atmospheric pollution, soil nutrients and climate effects on Mucoromycota arbuscular mycorrhizal fungi.

SummaryFine root endophyte mycorrhizal fungi in the Endogonales (Mucoromycota arbuscular mycorrhizal fungi, M‐AMF) are now recognized as at least as important globally as Glomeromycota AMF (G‐AMF), yet little is known about the environmental factors which influence M‐AMF diversity and colonization, partly because they typically only co‐colonize plants with G‐AMF. Wild populations of Lycopodiella inundata predominantly form mycorrhizas with M‐AMF and therefore allow focussed study of M‐AMF environmental drivers. Using microscopic examination and DNA sequencing we measured M‐AMF colonization and diversity over three consecutive seasons and modelled interactions between these response variables and environmental data. Significant relationships were found between M‐AMF colonization and soil S, P, C:N ratio, electrical conductivity, and the previously overlooked micronutrient Mn. Estimated N deposition was negatively related to M‐AMF colonization. Thirty‐nine Endogonales Operational Taxonomic Units (OTUs) were identified in L. inundata roots, a greater diversity than previously recognized in this plant. Endogonales OTU richness correlated negatively with soil C:N while community composition was mostly influenced by soil P. This study provides first evidence that M‐AMF have distinct ecological preferences in response to edaphic variables also related to air pollution. Future studies require site‐level atmospheric pollution monitoring to guide critical load policy for mycorrhizal fungi in heathlands and grasslands.

Зонирование территории г. Севастополя для организации мониторинга загрязнения атмосферы

The article evaluates the features of the terrain to determine the zone of influence of large sources of pollution. The list of the largest stationary sources of pollution and transport highways of Sevastopol with the most intensive traffic is given. A map of the location of emission sources is presented. According to a number of criteria developed, zoning of the territory of the city of Sevastopol according to an integral criterion is proposed. That will make possible to reasonably plan a network of atmospheric pollution monitoring posts for collecting representative data.

Long-term monitoring of atmospheric pollution in the Maritime Antarctic with the lichen Usnea aurantiaco-atra (Jacq.) Bory: a magnetic and elemental study

AbstractAntarctica is a natural research laboratory thanks to its unique climate, geography, flora and fauna. The conservation of Antarctica's environment is monitored through the Madrid Protocol; however, there are local pollution problems associated with human activities such as research and tourism; in particular, there are negative impacts on air quality from the use of fossil fuels. In this work, we studied for the first time the magnetic and elemental characteristics of the lichen Usnea aurantiaco-atra (Jacq.) Bory collected during different years and from various sites in King George Island, Antarctic Peninsula, as well as some samples of its supporting substrate, for long-term monitoring of atmospheric pollution. Several anthropogenic elements (Ni, Pb, Mo, Cd and Zn) have been identified on sites close to human activities, but also on sites far from them. We found that magnetic proxies from U. aurantiaco-atra samples show a spatial correlation with human influence (scientific bases or airstrips). We observed a correlation between magnetic parameters and Ni and, to a lesser extent, with Cr, Co, V and Ag. The results suggest that by using these magnetic and elemental techniques it is possible to implement monitoring with the lichen U. aurantiaco-atra as a bioindicator for some elements of anthropogenic origin.

Air Quality during New Year’s Eve: A Biomonitoring Study with Moss

Mosses are one of the best bioindicators in the assessment of atmospheric aerosol pollution by heavy metals. Studies using mosses allow both short- and long-term air quality monitoring. The increasing contamination of the environment (including air) is causing a search for new, cheap and effective methods of monitoring its condition. Once such method is the use of mosses in active biomonitoring. The aim of the study was to assess the atmospheric aerosol pollution with selected heavy metals (Ni, Cu, Zn, Cd, Hg and Pb) from the smoke of fireworks used during New Year’s Eve in the years 2019/2020 and 2020/2021. In studies a biomonitoring moss-bag method with moss Pleurozium schreberi (Willd. ex Brid.) Mitt. genus Pleurozium was used. The research was conducted in the town Prószków (5 km in south direction from Opole, opolskie voivodship, Poland). The moss was exposed 14 days before 31 December (from 17 to 30 of December), on New Year’s Eve (31 December and 1 January) and 2 weeks after the New Year (from 2–15 January). Higher concentrations of analysed elements were determined in samples exposed during New Year’s Eve. Increases in concentrations were demonstrated by analysis of the Relative Accumulation Factor (RAF). The results indicate that the use of fireworks during New Year’s Eve causes an increase in air pollution with heavy metals. In addition, it was shown that the COVID-19 induced restrictions during New Year’s Eve 2020 resulted in a reduction of heavy metal content in moss samples and thus in lower atmospheric aerosol pollution with these analytes. The study confirmed moss usefulness in monitoring of atmospheric aerosol pollution from point sources.