Air Pollution Detection Research Articles

Metastable CoCu2O3 Nanocrystals from Combustion-Aerosols for Molecular Sensing and Catalysis.

Metastable nanostructures are kinetically trapped in local energy minima featuring intriguing surface and material properties. To unleash their potential, there is a need for non-equilibrium processes capable of stabilizing a large range of crystal phases outside thermodynamic equilibrium conditions by closely and flexibly controlling atomic reactant composition, spatial temperature distribution and residence time. Here, the capture of metastable pseudo-binary metal oxides at room temperature is demonstrated with scalable combustion-aerosol processes. By a combination of X-ray diffraction, electron microscopy and on-line flame characterization, the occurrence of metastable CoCu2O3 is investigated with controlled crystal size (4-16nm) over thermodynamically stable CuO and Co3O4. Immediate practical impact is demonstrated by exceptional sensing and stable catalytic performance for air pollutant detection (e.g., 15 parts-per-billion benzene) shown for, at least, 21 days. This approach can be extended to various binary, ternary and high entropy oxides with even more components. Also, secondary phases can be loaded on such metastable nanocrystals to access novel materials promising for actuators, energy storage or solar cells.

A Pilot Study for the Detection of Air Pollution of Puducherry, India by Machine Learning Algorithms

A Pilot Study for the Detection of Air Pollution of Puducherry, India by Machine Learning Algorithms

Perancangan Sistem Pendeteksi Polusi Udara Di Dalam Kamar Mesin Secara Wireless Menggunakan Sensor MQ135 Dan TGS2106 Berbasis Lora Ra-02

Air pollution is a problem that needs attention because it can threaten human life. Many human activities cause air pollution. Especially in closed rooms such as ship engine rooms. Therefore, an air pollution detection tool is needed to determine the air pollution index in that place in order to maintain pollution levels below the threshold value. Therefore, it is necessary to have a tool to detect air pollution using the MQ-135 gas sensor to detect carbon dioxide (CO2) gas and TGS2106 to detect nitrogen dioxide (NO2) gas. The type of research method used is the experimental research method. After designing the tool, testing the tool, the air pollution detection system in the engine room wirelessly using the MQ135 and TGS2106 sensors based on LoRa Ra-02 had an average error rate of 1.3% for the MQ135 sensor reading while the TGS2106 sensor reading had an average level. -average error of 6%. Therefore, the Air Pollution Detection System in the Engine Room Wirelessly Using the MQ135 and TGS2106 Sensors Based on LoRa Ra-02 can work well.

IoT Based Air Pollution Monitoring System

Abstract: In light of the growing concern for air quality and its impact on public health, there is an increasing demand for air pollution monitoring systems that are both effective and efficient. This article introduces an IoT-based Air Pollution Detection System that utilizes the power of Internet of Things (IoT) technology to provide real-time monitoring and analysis of air quality parameters. The system incorporates a network of advanced air quality sensors that can measure particulate matter (PM) concentrations, as well as various gas levels such as carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). These sensors are integrated with a microcontroller that enables data processing and communication capabilities.

Statement on Impact of Air Pollution on Human Health; Africa Region

The impacts of air pollution on human health are enormous and in African region, morbidity and mortality from air pollution causes rose since 1990. The significant sources of air pollution have been observed from human activities such as Industrialization, agricultural burning, transport and traffic, combustion of wood, coal, paraffin and animal dung for household energy needs, dusty roads and burning of household solid waste in areas without regular residential waste collection services. Africa with a poor health system faces a severe lack of air pollution detection metrics, with only 7 out of 54 countries having air quality detection monitors. Long- and short-term exposure to air pollution is associated with increasing rate of morbidity and mortality from acute and chronic respiratory diseases, cardiovascular diseases and cancers. The progression of asthma, lung cancer, cardiovascular diseases, Alzheimer's and Parkinson's diseases is linked to air pollution exposure. Measures to reduce and control air pollution in Africa Region have been suggested in this paper.

Advancements in Mobile-Based Air Pollution Detection: From Literature Review to Practical Implementation

The aim of this paper is to investigate the effectiveness of combining mobile technologies and sensors to detect harmful particles in the air and address the problem of air pollution caused by traffic and exhaust emissions. The paper contains a systematic literature review of information technology research related to pollution detection in order to point out the main obstacles in the field and propose solutions. Furthermore, the paper presents the development of an Android smartphone-based real-time monitoring system that utilizes an external analog sensor board to acquire and evaluate physical measurements. The proposed system is calibrated for CO gas measurements, and it is compared with a commercial gas analyzer instrument. Results imply that the developed system is capable of detecting concentration levels in the air and that the accuracy is within the range of the industrial device’s accuracy.

Annealing-induced enhancement of TiO2-ZnO nanocomposites for high-performance room-temperature air pollutant detection in fiber optic sensors

Fiber optic sensors, promising for their compactness and remote sensing abilities, are explored in this study as a platform for detecting volatile organic compounds (VOCs) using nanocrystalline metal oxide composites like TiO2-doped ZnO. Hydrothermally synthesized (S1 sample)and annealed composites (500 °C (S2 sample) and 1200 °C (S3 sample) annealing) were investigated for their gas-sensing properties towards inorganic gas like ammonia and VOCs like acetone, and isopropyl alcohol. Remarkably, annealing at 1200 °C yielded S3 sample with higher response (7.5 %) towards ammonia at room temperature for 500 ppm concentration. This superior performance is attributed to increased surface area, narrowed bandgap, and faster response/recovery times (18/15 s) compared to other samples. The proposed sensing mechanism involves the adsorption and desorption of oxygen molecules on the sensor surface, altering refractive index and light scattering, ultimately modulating the transmitted light intensity. X-ray diffraction, SEM imaging, and UV–Vis absorption techniques confirmed the structure and morphology of the nanocomposites.

METHOD FOR AGENT-ORIENTED TRAFFIC PREDICTION UNDER DATA AND RESOURCE CONSTRAINTS

Context. Problem of traffic prediction in a city is closely connected to the tasks of transportations in a city as well as air pollution detection in a city. Modern prediction models have redundant complexity when used for separate stations, require large number of measuring stations, long measurement period when predictions are made hourly. Therefore, there is a lack of method to overcome these constraints. The object of the study is a city traffic.
 Objective. The objective of the study is to develop a method for traffic prediction, providing models for traffic quantification at measuring stations in the future under data and resource constraints.
 Method. The method for agent-oriented traffic prediction under data and resource constraints was proposed in the paper. This method uses biLSTM models with input features, including traffic data obtained from agent, representing target station, and other agents, representing informative city stations. These agents are selected by ensembles of decision trees using Random Forest method. Input time period length is proposed to set using autocorrelation data.
 Results. Experimental investigation was conducted on traffic data taken in Madrid from 59 measuring stations. Models created by the proposed method had higher prediction accuracy with lower values of MSE, MAE, RMSE and higher informativeness compared to base LSTM models.
 Conclusions. Obtained models as study results have optimal number of input features compared to the known models, do not require complete system of city stations for all roads. It enables to apply these models under city traffic data and resource constraints. The proposed solutions provide high informativeness of obtained models with practically applicable accuracy level.

Air Pollution Detection and Control: A Review of Methods and Technologies

Air Pollution Detection and Control: A Review of Methods and Technologies

Comparison Between Machine Learning Techniques for Early Air-Pollution Detection: A Case of Bojanala Platinum District Municipality (BPDM), South Africa

Comparison Between Machine Learning Techniques for Early Air-Pollution Detection: A Case of Bojanala Platinum District Municipality (BPDM), South Africa

Weather Pollution Prediction Based on XGBoost and ARIMA: Evidence from Shanghai

As a matter of fact, the weather pollution prediction is a pretty important issue in environment analysis. With the rapid development of machine learning techniques and bigdata analysis, it is available to realize accurate prediction based on the state-of-art models. In recent years, the weather condition is pretty worse in Shanghai. With this in mind, the general situation of air quality in Shanghai was statistically analyzed using air pollution detection data such as API in Shanghai from 2014 to 2023, and the relationship between air quality influencing factors and the air quality index API was investigated based on the construction of the machine learning model and scenarios. With the analysis and the usage of XGBoost and ARIMA, one can get the future trends and prediction. According to the analysis, the impact factors are discussed as well as the current limitations are demonstrated. At the same time, the future prospects are discussed.

Monitoring Emissions over Coastal Regions of Pakistan: Current Status and Applicable Techniques

Shipping operations have an environmental impact both in ports, as well as in the immediate vicinity of the coast. These impacts are due to the fuel burning exhaust from the ship’s main and auxiliary engines which contain gases like Carbon Dioxide (CO2), Carbon Monoxide (CO), Nitrogen Dioxide (NO2) and Sulphur Dioxide (SO2) and machinery used for loading and unloading, in addition to particulate matter and dust from the handling of materials such as grain, coal and sand. Remote Sensing (RS) and Geographic Information System (GIS) is an efficient tool being used worldwide for the monitoring of environmental problems. Response of Earth’s atmosphere or surface to electromagnetic radiation can be mapped through RS for the status of changes in the natural environment. Smart sensors developed for detection of air pollutants is an emerging technique to monitor emissions for real time monitoring and management of air pollution in distant regions. In the current study, satellite data is used to investigated concentrations of key emissions from shipping operations at ports to analyse the status of air pollutants over coastal regions of Pakistan. The pollutants investigated in this study through RS and GIS technique include CO2, SO2, NO2, CO and Particulate Matter (PM). These emission trends can be used to determine the efficacy of mitigatory measures to reduce emissions at ports and monitor the air quality of coastal areas. Furthermore, use of scientific technology and current trends in the measurement and monitoring of shipping emission have been studied for their applicability in Pakistan.

Prototype of Monitoring Transportation Pollution Spikes through the Internet of Things Edge Networks

Air pollution is a critical problem in densely populated urban areas, with traffic significantly contributing. To mitigate the adverse effects of air pollution on public health and the environment, there is a growing need for the real-time monitoring and detection of pollution spikes in transportation. This paper presents a novel approach to using Internet of Things (IoT) edge networks for the real-time detection of air pollution peaks in transportation, specifically designed for innovative city applications. The proposed system uses IoT sensors in buses, cabs, and private cars. These sensors are equipped with air quality monitoring capabilities, including the measurement of pollutants such as particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and carbon dioxide (CO2). The sensors continuously collect air quality data and transmit them to edge devices within the transportation infrastructure. The data collected by these sensors are analyzed, and alerts are generated when pollution levels exceed predefined thresholds. By deploying this system within IoT edge networks, transportation authorities can promptly respond to pollution spikes, improving air quality, public health, and environmental sustainability. This paper details the sensor technology, data analysis methods, and the practical implementation of this innovative system, shedding light on its potential for addressing the pressing issue of transportation-related pollution. The proposed IoT edge network for real-time air pollution spike detection in transportation offers significant advantages, including low-latency data processing, scalability, and cost-effectiveness. By leveraging the power of edge computing and IoT technologies, smart cities can proactively monitor and manage air pollution, leading to healthier and more sustainable urban environments.

Analytical Gas Sensing in the Terahertz Spectral Range.

Exploiting the terahertz (THz) part of the electromagnetic spectrum is attracting attention in various scientific and applied disciplines worldwide. THz technology has also revealed its potential as an effective tool for gas analysis in astronomy, biomedicine and chemical analysis. Recently, it has also become important in environmental applications for monitoring hazardous and toxic gases in the atmosphere. This paper gives an overview of THz gas detection analytical methods for environmental and biomedical applications, starting with a brief introduction to THz technology and an explanation of the interaction of THz radiation with gaseous species and the atmosphere. The review focuses on several gaseous species and groups of air pollutants that have been or can be analysed by THz spectrometry. The review concludes that different but complementary THz detection methods allow unique detection, identification and quantification of gaseous and particulate air pollutants with high selectivity, specificity and sensitivity. THz detection methods also allow further technological improvements and open new application possibilities.

Development of public lighting system with smart lighting control systems and internet of thing (IoT) technologies for smart city

This study proposes the design and development of public light systems integrated with Internet of Things (IoT) applications for smart cities. Smart public lighting systems are designed using LED light sources in combination with a controller based on light intensity and motion sensors to control the brightness in working areas as per standard requirements. The control of smart public lighting systems is designed to operate in three modes: manual, scheduled, and auto modes, which are tested and compared in terms of lighting and power quality. Other functions of IoT, such as air pollution detection, security system-based video surveillance, and warning systems for flood disasters, are integrated into smart public lighting systems. A prototype of the designed smart public lighting system is presented in this paper. The parameters of average illuminance, overall uniformity, and threshold increment were implemented using DIALux software to optimise the luminaire spacing. The results showed that the goal of energy saving could be achieved using the auto mode. Although the automatic mode caused current harmonic distortion to the electrical grid, the harmonic value was less than the specified standard. Furthermore, the precision of IoT system operation was dependent on environmental variations and the reliability of wireless network technology.

Condensation particle counters: Exploring the limits of miniaturisation

The measurement of airborne particles continues to attract increased interest for the detection and characterisation of air pollution, emissions, fire detection, occupational and climate impacts. Concerted efforts have reduced the size and cost of optical counters that have enabled widespread sensor deployment. Currently the World Health Organisation (WHO) enforces particle regulations that are mass-based which has resulted in an influx of low-cost portable optical particle counters (OPCs) calibrated to mass standards and widely utilised for research and personal monitoring. However, OPCs are incapable of measuring particles with diameters dp≲ 300 nm which misses ultrafine particles (≤ 100 nm) that often represent the majority of particles by number concentration. Condensation particle counters (CPCs) capable of measuring ultrafine particle number concentration with high instrument sensitivity, accuracy and precision have recently been targeted for miniaturisation.Herein, we explore the limitations of the miniaturisation of a CPC growth chamber (CPC-GC). Results presented in this work use water, but equivalent expressions can be obtained for other working fluids or design criteria using the same methods. Scaling analysis considers phenomenological constraints of supersaturation, heat transfer, particle penetration and droplet growth to establish limits during miniaturisation.The results showed that optimal thermal and saturation conditions are similar where the optimum device aspect ratio is L∗≥Lmin,s∗= 1.68 Re + 7.98 and L∗≥Lmin,th∗= 1.78 Re + 8.02 respectively. L∗ is the non-dimensional aspect ratio and Lmin∗ is the minimum non-dimensional device length required as a function of the flow Reynolds number Re. Higher Reynolds numbers lead to a convection-dominated regime, where the saturation and temperature profiles take a parabolic shape. As the Reynolds number approaches unity, the reduced mass and thermal Péclet numbers result in vapour and heat diffusing upstream of the condenser, creating ellipsoidal saturation and temperature profiles. To achieve a satisfactory performance, the heat and mass transport within the CPC-GC must be convection-dominated, leading to a minimum value of Re ≳2. Desired penetration P≥0.9 for particles with an initial diameter dpinit=10nm is achieved by designs with an aspect ratio, L∗≤Lmax,p∗= 2.5 Re. Together, these constraints define an allowable range of aspect ratios for a CPC-GC as a function of the Reynolds number.The final size of the grown droplets can be controlled by varying the interaction residence time τint, defined as the time a particle spends in the supersaturation profile. Having defined a target final droplet size for optical detection, and hence having fixed τint, the aspect ratio necessary to achieve the desired growth is a function of the Reynolds number and of the dimensional radius of the CPC-GC (Ricpc) according to: Lgrowth∗=γRe/(Ricpc)2 with γ= 2τintν, where ν is the kinematic viscosity of air. Selecting a value of Ricpc such that the line for Lgrowth∗ falls in the centre of the allowable operating region defined above will optimise the dimensional CPC-GC for the selected design criteria.Limits from this model were used to realising a prototype successfully tested experimentally against a reference commercial CPC within a laboratory environment. The outcome of the present work enables designers and researchers with a toolkit to optimise the next generation of high performance miniaturised CPC-GC designs.

Time series prediction of the chemical components of PM2.5 based on a deep learning model

Modeling-based prediction methods enable rapid, reagent-free air pollution detection based on inexpensive multi-source data than traditional chemical reaction-based detection methods in order to quickly understand the air pollution situation. In this study, a convolutional neural network (CNN) and long and short-term memory (LSTM) neural networks are integrated to create a CNN-LSTM time series prediction model to predict the concentration of PM2.5 and its chemical components (i.e., heavy metals, carbon component, and water-soluble ions) using meteorological data and air pollutants (PM2.5, SO2, NO2, CO, and O3). In the integrated CNN-LSTM model, the CNN uses convolutional and pooling layers to extract features from the data, whereas the powerful nonlinear mapping and learning capabilities of LSTM enable the time series prediction of air pollution. The experimental results showed that the CNN-LSTM exhibited good generalization ability in the prediction of As, Cd, Cr, Cu, Ni, and Zn, with a mean R2 above 0.9. Mean R2 predicted for PM2.5, Pb, Ti, EC, OC, SO42−, and NO3− ranged from 0.85 to 0.9. Shapley value showed that PM2.5, NO2, SO2, and CO had a greater influence on the predicted heavy metal results of the model. Regarding water-soluble ions, the predicted results were dominantly influenced by PM2.5, CO, and humidity. The prediction of the carbon fraction was affected mainly by the PM2.5 concentration. Additionally, several input variables for various components were eliminated without affecting the prediction accuracy of the model, with R2 between 0.70 and 0.84, thereby maximizing modeling efficiency and lowering operational costs. The fully trained model prediction results showed that most predicted components of PM2.5 were lower during January to March 2020 than those in 2018 and 2019. This study provides insight into improving the accuracy of modeling-based detection methods and promotes the development of integrated air pollution monitoring toward a more sustainable direction.

Metal‐Organic Frameworks for Air Pollution Purification and Detection

AbstractMetal‐organic frameworks (MOFs), an emerging class of porous crystalline material, are identified as a promising candidate for tackling the most formidable global challenges due to their unique and intriguing properties including well‐defined porous crystal structure, large specific surface area, and vast tunable chemical and physical property. Air pollution resulting from various toxic gases, volatile organic compounds, and fine particulate matters has posed a big threat to human health and ecological sustainability. In recent years, various MOFs are studied and shown great potential as active materials for air pollution control in both pollutant detection and air quality remediation. In this review, the most recent research progress for MOFs‐based materials for air pollution control is summarized. The discussion is categorized based on the different approaches for air quality control with a focus on the performance correlations to the structure and functionality of MOFs. In addition, the technical merits and downsides of each approach are discussed based on their application scenarios. Finally, the remaining challenges and future research directions in this field are proposed.

Hidden in snow: Selected aspects of chemical composition of an urban snow cover (Kielce, SE Poland)

Abstract Snow cover is a valuable source of information about air quality. It enables detection of dust and other air pollutants which have been accumulated throughout the period since the snow cover was formed. Research conducted in Kielce confirms multidirectional human pressure from local and regional emission sources. Combustion of fuels resulted in lower pH and increased EC, SO4 and NO3 concentrations in the southern and northern parts of the city. Elevated concentrations of Cl and Na indicate the effect of transportation and winter road maintenance. Apart from local emitters, air quality in Kielce is affected by the regional sources, including the nearby center of the lime and cement industries. Climate change is reflected in the number of days with snow and thickness of snow cover, declining since the 1990s.

Satellite and In Situ Detection of Air Pollution in Urban Area – Case Study of El Eulma Town (North East of Algeria)

Satellite and In Situ Detection of Air Pollution in Urban Area – Case Study of El Eulma Town (North East of Algeria)