Air Pollution Monitoring Research Articles

Analysis of Primary Air Pollutants’ Spatiotemporal Distributions Based on Satellite Imagery and Machine-Learning Techniques

Accurate monitoring of air pollution is crucial to human health and the global environment. In this research, the various multispectral satellite data, including MODIS AOD/SR, Landsat 8 OLI, and Sentinel-2, together with the two most commonly used machine-learning models, viz. multi-layer backpropagation neural network (MLBPN) and random forest (RF), have been employed to analyze the spatiotemporal distributions of the primary air pollutant from 2019 to 2022 in Guanzhong Region, China. In the conducted experiments, the RF-based model, using the MODIS AOD data, has generally demonstrated the “optimal” estimation performance for the ground-surface concentrations of the primary air-pollutants. Then, the “optimal” estimation model has been employed to analyze the spatiotemporal distribution of the various air pollutants—in terms of temporal distribution, the annual average concentrations of PM2.5, PM10, NO2, and SO2 in the research area showed a decreasing trend from 2019 to 2022, while the annual average concentration of CO remained relatively stable and the annual average concentration of O3 slightly increased; in terms of the spatial distribution, the air pollution presents a gradual increase from west to east in the research area, with the distribution of higher concentrations in the center of the built-up areas and lower in the surrounding rural areas. The proposed estimation model and spatiotemporal analysis can provide reliable methodologies and data support for the further study of the air pollution characteristics in the research area.

The impacts of earthquakes on air pollution and strategies for mitigation: a case study of Turkey

This study delves into the repercussions of the 2023 earthquake in Turkey, particularity its impact on air pollution. A year post-event, it is evident that scientific literature has paid limited attention to monitoring the situation. However, the release of hazardous substances, such as asbestos, lead, and other toxins, from damaged structures poses a significant threat by contaminating nearby air, soil, and water sources, thereby jeopardizing ecosystems and public well-being. The improper disposal of waste post-earthquake and the presence of mining and oil refinery sites in the region contribute to potential air pollutants. These circumstances create challenging environments conducive to the spread of respiratory diseases, with potential long-term health and social consequences. Unfortunately, existing data gaps hinder a comprehensive understanding of the situation. This paper pioneers the reporting and analysis of data regarding potential sources of air pollution resulting from the earthquake in Turkey. It also pinpoints gaps in knowledge, outlining areas that demand further investigation. To effectively prevent and mitigate air pollution risks and associated health concerns linked to earthquakes, strategic recommendations are proposed. A key suggestion is the establishment of post-disaster air pollution monitoring systems capable of swiftly identifying emerging health issues, facilitating efficient responses, and curtailing potential long-term effects of the disaster. The paper underscores the necessity for continuous health monitoring of the affected population to mitigate possible adverse impacts on human health. These strategies play a pivotal role in reducing the likelihood of air pollution, supporting emergency response and recovery initiatives, and fostering new dedicated scientific studies.

Estimating annual ambient air pollution using structural properties of road networks

In recent years, the world has become increasingly concerned about air pollution. Particularly, High-Income Countries (HIC) and Upper Middle-Income Countries (UMIC) are implementing systems to monitor air pollution on a large scale to aid decision-making. Such efforts are essential, but they have at least three shortcomings: (1) they are costly; (2) they are slow to deploy; and (3) they focus on urban areas, which leads to urban-rural inequalities. Here, we show that we can estimate annual air pollution using open-source information about the structural properties of roads; we focus on England and Wales in the United Kingdom (UK) in this paper, although we argue that our methods are independent of specific country features. Our approach is an inexpensive method of estimating annual air pollution concentrations to an accuracy level that can underpin policymakers’ decisions while providing an estimate in all districts, not just urban areas. Furthermore, we contend that our process is interpretable and explainable.

Indoor air pollutants and their seasonal monitoring in European museums

The monitoring of indoor air pollutants is an important part of the management of heritage collections. In this work, acetic acid, formic acid, formaldehyde, acetaldehyde and NO2 were measured with passive samplers along with temperature and relative humidity once per season in seven European institutions of different sizes with different types of objects. The measurements were carried out in a variety of locations, from modern and old display cases in exhibition rooms of different sizes to storage rooms and their enclosures for different types of objects. The results were evaluated based on the characteristics of the sampling locations and the extent to which the changing seasons affect pollutant concentrations were estimated. The dataset obtained from this study can be a valuable asset as a snapshot of the current state of the environment in European heritage collections.

A Survey on-Advancing Urban Sustainability through Smart City Integration

The three essential elements of smart city development that are highlighted in this project are air pollution monitoring, video surveillance, and optimised street lighting. Energy economy and safety are improved when intelligent sensors and controls are integrated into street lights. Proactive security measures are guaranteed by advanced video surveillance combined with AI analytics. IoT-enabled air pollution monitoring allows for the real-time collection of data for focused interventions. Savvy streetlamps outfitted with sensors and versatile controls offer energy investment funds and upgraded wellbeing by changing lighting levels in view of constant circumstances. Video reconnaissance frameworks, enabled by man-made intelligence calculations, give proactive observing and reaction abilities, guaranteeing public wellbeing and security. Moreover, air contamination observing utilizes IoT sensors to gather constant information on air quality boundaries, empowering designated intercessions to alleviate contamination levels. When these systems are integrated, sustainable urban growth is promoted, resource utilisation is optimised, and quality of life is enhanced.

Improved A-Star Search Algorithm for Probabilistic Air Pollution Detection Using UAVs.

Recently, air pollution problems in urban areas have become serious, and unmanned aerial vehicles (UAVs) can be used to monitor air pollution because they can perform spatial movement. However, because air pollution sources are fluid, probabilistic search methods are required to identify a target through the probability of its existence. This study proposes an efficient algorithm to detect air pollution in urban areas using UAVs. An improved A-star algorithm that can efficiently perform searches based on a probabilistic search model using a UAV is designed. In particular, in the proposed improved A-star algorithm, several special weights are used to calculate the probability of target existence. For example, a heuristic weight based on the expected target, a weight based on data collected from the drone sensor, and a weight based on the prior information of obstacles presence are determined. The method and procedure for applying the proposed algorithm to the stochastic search environment of a drone are described. Finally, the superiority of the proposed improved A-star algorithm is demonstrated by comparing it with existing stochastic search algorithms through various practical simulations. The proposed method exhibited more than 45% better performance in terms of successful search rounds compared with existing methods.

First estimation of hourly full-coverage ground-level ozone from Fengyun-4A satellite using machine learning

Ground-level ozone (O3), renowned for its adverse impacts on human health and crop production, has garnered significant attention from governmental and public sectors. To address the limitations posed by sparse and uneven ground-level O3 observations, this study proposes an innovative method for hourly full-coverage ground-level O3 estimation using machine learning. Meteorological data from National Centers for Environmental Prediction global forecasting system, satellite data from Fengyun-4 A(FY-4 A) and Ozone Monitoring Instrument, emission inventory from Multi-resolution Emission Inventory for China, and other auxiliary data are utilized as input variables, while ground-based O3 observations serve as the response variable. The method is applied on a monthly basis across China for the year 2022, resulting in the generation of an hourly full-coverage high-resolution (4 km) ground-level O3 estimation, termed ML-derived-O3. Cross-validation results demonstrate the robustness of ML-derived-O3 yielding a coefficient of determination (R 2) of 0.96 (0.91) for sample-based (site-based) evaluations and a root-mean-square error (RMSE) of 9.22 (13.65) µg m−3. However, the date-based evaluation is less satisfactory due to the imbalanced training data, resulting from the pronounced daily variations in ground-level O3 concentrations. Nevertheless, the seasonal and hourly ML-derived-O3 exhibits high prediction accuracy, with R 2 values surpassing 0.95 and RMSE remaining below 7.5 µg m−3. This study marks a significant milestone as the first successful attempt to obtain hourly full-coverage ground-level O3 data across China. The diurnal variation of ML-derived-O3 demonstrates high consistency with ground-based observations, irrespective of clear or cloudy days, effectively capturing ground-level O3 pollution exposure events. This novel estimation method will be employed to establish a long-term high spatial-temporal resolution ground-level O3 dataset, which holds valuable applications for air pollution monitoring and environmental health research in future endeavors.

Design and development of a CanSat for air pollution monitoring with RSSI based position retrieval system

A CanSat is a miniature satellite system designed to be launched and operate in a manner similar to a satellite but on a much smaller scale. The term “CanSat” is derived from the combination of “Can” (referring to the soda can-sized container often used for the prototype) and “Satellite.” These small and cost-effective systems are typically used for educational purposes and scientific experiments. The main objective of this study is to present the design of CanSat, which is intended to track air pollution. The total weight of the CanSat includes the weight of the sensors, communication equipment, and other subsystems, which makes up the CanSat's total weight of 150 g. The data transmission rate can vary depending on the capabilities of the communication equipment used, but typically ranges between 1 and 10 transmissions per second. The Xbee communication protocol is commonly used for telemetry, typically at either 2.4 GHz or 900 MHz, but not both simultaneously. The major components of the CanSat are the power and sensor subsystems. The appropriate sensors are chosen by considering the main design elements, and typically include sensors to measure temperature, humidity and gases that can identify various pollutants to collect data on local air quality. CanSat is also equipped with a location retrieval system to ensure it can be recovered after launch. However, the type of system used can vary depending on the specific design. Power conservation techniques are employed to ensure that the CanSat can operate for as long as possible. Component selection is also carefully considered to make the CanSat more portable, affordable, and able to gather data on air pollution effectively.

Bioindicators and human biomarkers as alternative approaches for cost-effective assessment of air pollution exposure

Traditional methods of air pollution monitoring require substantial investment in equipment and infrastructure. However, efficient and cost-effective alternatives offer promising solutions for region-specific pollution assessments and understanding their impact on local populations. This review explores examples of low-cost monitoring methods, focusing on natural bioindicators, human interaction-based techniques, and the outcomes associated with air pollution exposure. Bioindicators such as spider webs, lichens, mosses, and Tradescantia pallida (T. pallida) are discussed as potential tools for air pollution monitoring. Human biomonitoring techniques, including the micronucleus assay and the assessment of pulmonary anthracosis, are examined for their ability to provide valuable insights into genotoxic effects and long-term exposure. The advantages and limitations of each method are highlighted. The review advocates for continued research and development to refine these approaches, with the aim of mitigating the adverse health impacts of air pollution on both individuals and communities.

Taking Flight for a Greener Planet: How Swarming Could Help Monitor Air Pollution Sources

As the world grapples with the pressing challenge of environmental sustainability, the need for innovative solutions to combat air pollution has become paramount. Air pollution is a complex issue that necessitates real-time monitoring of pollution sources for effective mitigation. This paper explores the potential of swarm algorithms applied as a novel and efficient approach to address this critical environmental concern. Swarm algorithms offer a promising framework for coordinating fleets of drones to collaboratively monitor and analyze air pollution sources. The unique capabilities of drones, including their agility, accessibility, and versatility, make them ideal candidates for aerial data collection. When harnessed in a swarm, these drones can create a dynamic and adaptable network that provides a more comprehensive and fine-grained understanding of air pollution dynamics. This paper delves into the conceptual foundations of using swarm algorithms in drone-based air pollution monitoring.

AI-driven environmental sensor networks and digital platforms for urban air pollution monitoring and modelling

Recent advances in Artificial Intelligence (AI) research have opened up new opportunities for leveraging AI research for societal impacts. AI research offers novel ways of tackling societal problems including environmental, health, and education challenges. Despite the potential, there are limited documented use cases and methodologies for translating AI research to societal impact at a large scale. This paper presents AirQo, an AI and advanced technology-driven use case for urban environmental pollution monitoring and modelling and the resulting societal impacts that have been realised. The research outputs include a set of digital solutions for the environmental air pollution challenges including (1) custom-designed low-cost air quality monitors that are premised on IoT technology (2) a methodology for deploying a high-resolution and citizen-driven air quality monitoring (3) AI-powered digital tools for air quality information modelling and analysis for citizens and city leaders, and (4) a framework for engagement for citizens and leaders. The AirQo project has been deployed and scaled out in cities in Eastern, Western, and Central African countries. The societal impacts resulting from the implementation of the AirQo research project include policy and regulations, education and awareness, and research around air quality issues.

Levels of Dry Deposition Submicron Black Carbon on Plant Leaves and the Associated Oxidative Potential

There is a need for monitoring air pollution associated with black carbon (BC) using a passive monitor is required in remote areas where the measurements are absent. In this pilot study, we developed a quantitative method to determine dry deposition submicron BC using dual-wavelength ultraviolet–visible spectroscopy. Furthermore, we measured the levels of dry deposition BC on plant leaves from 30 plant species located in urban Nanjing using the established method. The oxidative potential of BC on plant leaves as passive bio-monitoring samplers was assessed. The concentrations of black carbon (BC) on tree leaves varied from 0.01 to 1.6 mg m−2. Significant differences in levels of BC across leaves from different tree types were observed. The values of oxidative potential in deposited particles of leaf samples were observed to be in the range of 33–46 nmol min−1 mg−1 using the dithiothreitol (DTT) assay and 18–32 nmol min−1 mg−1 using the ascorbic acid (AA) assay, respectively. In comparison, the oxidative potential of BC-dominated mass in water extracts of leaf samples was in the range of 5–35 nmol min−1 mg−1 measured using the DTT assay and 2 to 12 nmol min−1 mg−1 using the AA assay, respectively. We found variations in the levels of OP across the leaves of different tree types were not large, while the levels of OP in terms of BC-dominated mass varied greatly. These results indicate that the established method with dual-wavelength ultraviolet–visible spectroscopy could provide a simple tool to determine submicron BC in plant leaves of the passive monitor.

International and Constitutional Efforts to Protect the Environment Through the Use of Artificial Intelligence Techniques

The purpose of the study is to demonstrate the role of artificial intelligence in the ability to accelerate global efforts to protect the environment and conserve natural resources by monitoring air pollution and energy emissions. Monitoring attacks on forest areas, as well as describing the role of the constitutions of the world's countries, including the Jordanian Constitution, by adding constitutional texts addressing environmental protection using artificial intelligence technologies. The importance of this research lies in demonstrating international and constitutional efforts to protect the environment using artificial intelligence technologies by accelerating global and local efforts to protect the environment, conserve natural resources, and use digital tools to monitor air pollution and energy emissions. And monitoring attacks on forest areas, as well as describing the role of countries' constitutions, including the Jordanian Constitution, by adding constitutional texts addressing environmental protection using artificial intelligence technologies.

Ambient Air Pollution Monitoring and Health Studies Using Low-Cost Internet of Things Monitor within KNUST Community

Urban environments with high industrialization are infested with hazardous chemicals and airborne pollutants. These pollutants CO, O3, SO2, NO2, and PM can have devastating effects on human health, causing both acute and chronic diseases such as respiratory infections, lung cancer, and heart disease. Air pollution monitoring is vital to warn citizens of the health risks associated with exposure to high concentrations of these criteria pollutants. This study designed a low-cost IoT monitor to measure concentration levels of criteria pollutants emitted from transportation sources within Kwame Nkrumah University of Science and Technology environs. Three monitoring sites, KNUST Tech junction, Ayeduase gate junction and KNUST campus junction, were identified as the locations within the proximity of the University for the Deployment of the monitor. Hourly and mean daily CO, NO2, O3 and SO2 concentrations at each of the three sites were measured for a week using the IoT monitor, when students were in school and when students were on vacation. The average daily CO, NO2 and O3 concentrations measured at the selected locations when school was in session and during vacation were presented on histogram. The mean weekly concentrations of CO, NO2 and O3 were also estimated as 13.2ppm, 0.277ppm and 0.106ppb respectively at KNUST Tech junction; 10.1ppm, 0.254ppm and 0.110ppb respectively at Ayeduase gate junction; and 8.0ppm, 0.415ppm and 0.100ppb respectively at the KNUST campus junction when school was in session. The results show that the concentrations of all the pollutants were higher and exceeded the EPA standards except for CO at KNUST Campus junction monitoring site. These high levels of emissions is an indication of a health concern for the students at the university and university authorities can device means of curbing it.

Modeling a Smart IoT Device for Monitoring Indoor and Outdoor Atmospheric Pollution

Air pollution is caused by chemical, physical, or biological components alters the fundamental properties of the atmosphere, and contaminates the interior or outdoor settings. With the rapid industrialization activity in recent years, there is an urgent need to monitor air quality. The proposed research provides a mechanism for monitoring air pollution in indoor and outdoor environments. The system consists of an IoT-based Arduino device. The Arduino IDE connects the Temperature and Humidity sensor, Grove light sensor, and air quality sensor to measure the air pollutants such as Carbon dioxide CO2, Nitrogen oxide (NOx), and Particulate Matter PM2.5. The sensors work efficiently and provide qualitative findings from the environment when they are exposed to CO2, gasoline, solvents, thinner, formaldehyde, and other harmful chemicals. The Wi-Fi module of the Blues Wireless Notehub is used for secure data routing to the IoT cloud. The Air Quality Index (AQI) measures provide information on whether there is something unsafe in indoor and outdoor environments. For collecting and analyzing the device data, the Notecard is intended to be used with a cloud storage service. Also, the indoor fire detector identifies the incident of fire intimates the users through the alarm, and measures the indoor pollutant at that time. The proposed smart IoT product model would be an excellent device for air quality monitoring because of its long-term consistency and low electricity consumption.

Enhancing Air Pollution Monitoring and Prediction using African Vulture Optimization Algorithm with Machine Learning Model on Internet of Things Environment

An optimal solution for monitoring air pollution, the Internet of Things (IoT)-enabled system delivers real-time data and insights on the air quality within a specific location. Air pollution poses a substantial risk to human health worldwide, with pollutants like nitrogen dioxide, particulate matter, ozone, and sulfur dioxide contributing to a range of cardiovascular and respiratory ailments. Monitoring air pollution levels is critical to understand the effect on public health and the environment. Air Pollution Monitoring includes the systematic analysis and measurement of pollutant concentration in the air, through a network of monitoring stations equipped with instruments and sensors. This station provides real-time data on air quality, allowing authorities to evaluate issue warnings, and pollution levels, and implement strategies to alleviate its negative impact. Machine learning (ML) approaches are becoming more integrated into air pollution monitoring systems for enhancing efficiency and accuracy. By analyzing vast quantities of information gathered from satellite imagery, monitoring stations, and other sources, ML approaches could detect patterns, forecast pollution levels, and pinpoint sources of pollution. This study introduces Air Pollution Monitoring and Prediction using African Vulture Optimization Algorithm with Machine Learning (APMP-AVOAML) model in IoT environment. The drive of the APMP-AVOAML methodology is to recognize and classify the air quality levels in the IoT environment. In the APMP-AVOAML technique, a four stage process is encompassed. Firstly, min-max normalization is applied for scaling the input data. Secondly, a harmony search algorithm (HSA) based feature selection process is executed. Thirdly, the extreme gradient boosting (XGBoost) model is utilized for air pollution prediction. Finally, AVOA based parameter selection process is exploited for the XGBoost model. To illustrate the performance of the APMP-AVOAML algorithm, a brief experimental study is made. The resultant outcomes inferred that the APMP-AVOAML methodology has resulted in effectual outcome.

SELECTION OF BIOINDICATORS FOR AIR POLLUTION BASED ON PEROXIDASE ACTIVITY OF PLANT SPECIES FOUND IN THE CHEMBUR INDUSTRIAL AREA OF MUMBAI, INDIA -A PRIMARY STEP TO GREEN ZONE ENRICHMENT

Industrialization and population growth are the important factors for the deterioration of air quality in the current scenario. Air pollution monitoring using plants as bioindicators is the best and cheapest way. Oxidative stress induced on plants due to air pollution can be studied using antioxidant peroxidase enzyme activity. The present study aims at the comparative assessment of peroxidase activity of twelve plant species from the Chembur industrial area and less polluted zone of similar ecological conditions for two years. The peroxidase activity was determined using the spectrophotometric method. The observations based on the current investigation revealed a rise in the peroxidase activity of twelve plant species of the industrial zone as compared to the less polluted zone. It may indicate an increase in the resistant capacity of all experimental samples toward the oxidative stress caused due to air pollution. Plant species like Pithecellobium dulce (Roxb.) Benth, Ricinus communis L., Leucaena leucocephala (Lam.) de Wit., Albizia saman (Jacq.) Merr, Thespesia populnea (L.) Sol. ex. Correa, Polyalthia longifolia (Sonn.) Thwaites, Prosopis juliflora (SW.) DC., Terminalia catappa L. showed higher peroxidase activity values and hence may be used as bioindicators. Thespesia populnea (L.) Sol. ex. Correa, Polyalthia longifolia (Sonn.) Thwaites and Terminalia catappa L. being evergreen plants may be planted in good numbers in the Chembur industrial area. It may act as a primary step to ameliorate the industrial air pollution in the Chembur industrial zone by enhancing the green zone. This kind of investigation with other factors may help us to sustain our mother nature for future generations.

Enhancing water and air pollution monitoring and control through ChatGPT and similar generative artificial intelligence implementation

Enhancing water and air pollution monitoring and control through ChatGPT and similar generative artificial intelligence implementation

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.

A mathematical model to restrain pneumonia spread in children under five years considering nutritional status, household air pollution, vaccination, and health monitoring

Currently, pneumonia remains the leading cause of mortality among children under the age of five worldwide. According to UNICEF data, children continue to lose their lives due to pneumonia. In addition, malnutrition and air pollution have been identified as major factors contributing to the spread of pneumonia among children. This study aims to develop a mathematical model to analyze the spread of pneumonia among toddlers, considering the impacts of nutrition and air pollution. The study’s outcome includes a theorem concerning the stability of fixed points, which can serve as a foundation for controlling the transmission of pneumonia among children under five years of age. Furthermore, simulations demonstrate the potential impact of vaccination, treatment, and health monitoring in mitigating the spread of pneumonia among this age group.