Real-time Air Pollution Monitoring Research Articles

Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

The Lack of Real-Time Air Pollution Monitoring in Africa Supports Environmental Injustice

The Lack of Real-Time Air Pollution Monitoring in Africa Supports Environmental Injustice

Cost-Efficient measurement platform and machine-learning-based sensor calibration for precise NO2 pollution monitoring

Air quality significantly impacts human health, the environment, and the economy. Precise real-time monitoring of air pollution is crucial for managing associated risks and developing appropriate short- and long-term measures. Nitrogen dioxide (NO2) stands as a common pollutant, with elevated levels posing risks to the human respiratory tract, exacerbating respiratory infections and asthma, and potentially leading to chronic lung diseases. Notwithstanding, precise NO2 detection typically demands complex and costly equipment. This paper explores NO2 monitoring using low-cost platforms, meticulously calibrated for reliability. An integrated measurement unit is first presented that contains primary and supplementary nitrogen dioxide sensors, as well as auxiliary detectors for evaluating outside and inside temperature and humidity. The calibration process utilizes data acquired over the period of five months from various reference stations. Employing machine learning with an artificial neural network (ANN)-based and kriging interpolation surrogate models, the correction strategy integrates additive and multiplicative enhancement, predicted by the ANN through auxiliary sensor data such as temperature, humidity, and the sensor-detected NO2 levels. Extensive verification studies showcase that this calibration approach notably enhances monitoring precision (coefficient of determination surpassing 0.85 concerning reference data, and RMSE of less than four μg/m3), rendering low-cost NO2 detection practical and dependable.

Performance Evaluation on Applied Low-Cost Multi-Sensor Technology in Air Pollution Monitoring

This research aims to discuss the application of multi-sensor network technology for the monitoring of indoor air pollution. Indoor air pollution has become a severe problem that affects public health, especially indoor parking. The indoor air pollution monitoring system will provide information about vehicle exhaust emission levels. We have improved the system to identify six parameters of the vehicles' gas emissions within a different location at once. This research aimed to measure the parameter of Carbon Monoxide (CO), Carbon Dioxide (CO2), Hydro Carbon (HC), temperature and humidity, and levels of particulates in the air (PM10). The performance of this system shows good ability to compare the results of measurements of air quality measuring professionals. In this study, we investigated the performance of a custom-built prototype developed under the android-based application to detect air pollution levels in the parking area. Our objective was to evaluate the suitability of a low-cost multi-sensor network for monitoring air pollution in parking and the other area. The benefit of our approach is that its time and space complexity make it valuable and efficient for real-time monitoring of air pollution.

3D AQI Mapping Data Assessment of Low-Altitude Drone Real-Time Air Pollution Monitoring

Air pollution primarily originates from substances that are directly emitted from natural or anthropogenic processes, such as carbon monoxide (CO) gas emitted in vehicle exhaust or sulfur dioxide (SO2) released from factories. However, a major air pollution problem is particulate matter (PM), which is an adverse effect of wildfires and open burning. Application tools for air pollution monitoring in risk areas using real-time monitoring with drones have emerged. A new air quality index (AQI) for monitoring and display, such as three-dimensional (3D) mapping based on data assessment, is essential for timely environmental surveying. The objective of this paper is to present a 3D AQI mapping data assessment using a hybrid model based on a machine-learning method for drone real-time air pollution monitoring (Dr-TAPM). Dr-TAPM was designed by equipping drones with multi-environmental sensors for carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), particulate matter (PM2.5,10), and sulfur dioxide (SO2), with data pre- and post-processing with the hybrid model. The hybrid model for data assessment was proposed using backpropagation neural network (BPNN) and convolutional neural network (CNN) algorithms. Experimentally, we considered a case study detecting smoke emissions from an open burning scenario. As a result, PM2.5,10 and CO were detected as air pollutants from open burning. 3D AQI map locations were shown and the validation learning rates were apparent, as the accuracy of predicted AQI data assessment was 98%.

Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

Air pollution is one of the major concerns considering detriments to human health. This type of pollution leads to several health problems for humans, such as asthma, heart issues, skin diseases, bronchitis, lung cancer, and throat and eye infections. Air pollution also poses serious issues to the planet. Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions. Thus, real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions. The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks. Localization is the main issue in WSNs; if the sensor node location is unknown, then coverage and power and routing are not optimal. This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities. These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants, such as PM2.5 particulate matter, PM10, nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and sulfur dioxide (SO2). The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization. The dataset is divided into training and testing parts based on 10 cross-validations. The evaluation on predicting the air pollutant for localization is performed with the training dataset. Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.

AI powered IoT based Real-Time Air Pollution Monitoring and Forecasting

Air is one of the most fundamental constituents for the sustenance of life on earth. The consumption of non-renewable energy sources and industrial parameters steadily increases air pollution. These factors affect the welfare and prosperity of life on earth; therefore, the nature of Air Quality in our environment needs to be monitored continuously. This paper presents the execution and plan of Internet-of-Things (IoT) based Air Pollution Monitoring and Forecasting utilising Artificial Intelligent (AI) methods. Also, Online Dashboard was created for real-time monitoring of Air pollutants (both live and forecasted data) through ‘firebase’ from the Google cloud server. The air pollutants like Carbon Mono Oxide (CO), Ammonia (NH3), and Ozone (O3) layer information are collected from IoT-based sensor nodes in Vijayawada Region. Time Series modelling techniques like the Naive Bayes Model, Auto Regression Model (AR), Auto Regression Moving Average Model (ARMA), and Auto-Regression Integrating Moving Average Model (ARIMA) used to forecast the individual air pollutants aforementioned. The data collected from the IoT sensor node with a time frame is fed as input features for training the model, and optimised model parameters are obtained. The obtained model parameters are again verified with new unseen data for time. The performances of various Time Series models are validated with the help of performance indices like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The machine learning algorithm flashed in Raspberry Pi-3. It acts as an edge computing device. The current air pollutants data and forecasted data are monitored for the next 4 hours through an online dashboard created in an open-source firebase from Google cloud service.

A Proposal for Architectural Framework Using Internet of Things with Fog Computing for an Air Quality Monitoring System

Air Monitoring becomes a systematic approach for sensitivity and finding out the circumstances of the atmosphere. The major concern of air quality monitoring is to measure the concentration of pollution and other important parameter related to the contamination and provides information in real-time to make decisions at right time to cure lives and save the environment. This paper proposes an Architectural Framework for the air quality monitoring system based on Internet-of-Things (IoT) and via Fog computing techniques with novel methods to obtain real-time and accurate measurements of conventional air quality monitoring. IoT-based real-time air pollution monitoring system is projected to at any location and stores the measured value of various pollutants over a web server with the Internet. It can facilitate the process and filter data near the end of the IoT nodes in a concurrent manner and improve the Latency issue with the quality of services.

Design and implementation of IoT based environment pollution monitoring system: A case study of Iraq

Researches confirm the existence of widespread pollution in Iraq, where a large number of citizens suffer from symptoms related to environmental pollutants. This is accompanied by the lack of an integrated monitoring system that provides wide-scale, real-time monitoring of air pollution for pollutants (Particulate Matter (PM), Sulfur Dioxide (SO2), Ground-Level Ozone (O3), Carbon Monoxide (CO), and Nitrogen Dioxide (NO2), ionizing radiation). In this paper, an integrated system was designed and implemented to meet the requirements of air pollution monitoring in Iraq on a wide-scale and provide its results in real-time to the end-users in community service as a significant step towards smart cities. Distributed computing concepts and design patterns were used to realize the system in the form of web services along with the embedded sensing units. Where the readings from the former are sent in real-time to the later to be aggregated and analysed in database systems. Reporting services are run on the data in web-services to inform the citizens with the air quality, radioactively polluted areas, and provide real-time alerts and recommendations to those with respiratory diseases.

A new type of electron microscope rapid collection device foratmospheric particles and its application cases.

A device for collecting atmospheric particulates matter for on-line electron microscopy was designed and manufactured by using 3D modeling and printing technology. The device was used to collect atmospheric particulates matter in Xining city of Qinghai province for seven consecutive days and used to collect atmospheric particulates matter in Jinan city of Shandong province for 30 consecutive days. And the samples were analyzed by electron microscopy. Through it can obtain ultra structure and size distribution information of atmospheric particulates matter. The experimental results reflected the distribution of atmospheric particulate matter in real time. The on-line acquisition device for electron microscopy is practical, fast, and convenient, which greatly shortens the sampling period and can be carried to any place outdoors for real-time sampling. The morphological structure and particle size distribution of atmospheric particles in sampling area was observed by electron microscope. The device has reference value to the study of PM2.5 and PM10 in atmosphere and air pollution. This acquisition device is convenient for the detection of sample by electron microscopy. It can further expand and simplify the sampling technology of large-scale instruments and equipment. This new method of real-time monitoring of air pollution is worth popularizing and applying.

Green Home: New Application for Monitoring Indoor Air Quality Using Arduino Platform

Air pollution is one of the most serious problems facing the atmosphere on the planet. Air pollution is defined as a collection of harmful chemicals and an organic material from factories are emitted in the atmosphere layer and causes many different diseases such as cough, eye irritation and even death. According to the World Health Organization (WHO), the number of deaths per year due to pollution from gases is about 3.5 million. The main objective of this research is to develop a real time air pollution monitoring web application able to detect indoor toxic gases titled Aircom. The proposed application has a special feature in which IoT technology is embedded in one of its units. The main purpose of using such technology is to help individual to check and get real time information about air’s parameters such as Methane, Ethanol, Toluene, CO2, CO, Alcohol, Acetone, LPG, NH4, Benzene and Hexane along with the temperature, humidity and dust. Aircome will be implemented as an integrated pollution monitoring application which consist of MQ-2, MQ-3, MQ-135, MQ-9, GP2Y1010AU0F, GPS,DHT11, ESP8266 Wi-Fi, Arduino Uno board and web server. All the collected data form the suggested sensors are transmitting using Wifi technology to IoT module and in an online database. Moreover, the collected data later can be viewed using web browser which is installed in any of electronic media. The retrieved data will be displayed in the form of tables and graphs. An alert will be send by Aircom instantly in case the level of air‘s parameters reach above normal level. Generally speaking, Aircom will be developed by using different languages such as C++, Arduino, Java, Java script, PHP, html and MySQL. For further verification of our proposal, we employed a quantitative study to check if what we proposed will have positive impact among different samples in the society. The outcome of the survey indicates that using such application helps to protect individuals from the bad air quality and decreases the potential health problems.

Development of Drone Real-time Air Pollution Monitoring for Mobile Smart Sensing in Areas with Poor Accessibility

Development of Drone Real-time Air Pollution Monitoring for Mobile Smart Sensing in Areas with Poor Accessibility

An Analysis on the Implementation of Air Pollution Monitoring System

Air quality is a major concern in cities because of the effect of air pollution on public health and the environment. Air pollution kills several million humans every year. WHO says about 9 out of 10 people breathe polluted air. Accurate air quality monitoring helps in assessing the pollutant levels with respect to the accepted ambient air quality standards. Monitoring is done in order to keep track of the quality of air and to collect pollutant information and to purify the air. Real-time air monitoring system with high-speed ratio-temporal resolution is essential because of the limited data availability and non-scalability of conventional air pollution monitoring systems. Currently, researchers are focusing on the concept of the next generation air pollution monitoring systems for smart cities and have achieved a significant breakthrough by utilizing advanced sensing technologies, micro-electro-mechanical systems (MEMS), wireless sensor networks (WSN), low power wide area networks (LPWAN), internet of things (IoT) and cloud computing. Implementation of a real time monitoring system requires suitable sensors to collect ambient air quality data, an efficient wide area network for communication, and a system to store, process, and visualize the data. This paper is an insight to design and develop a low cost, low power and accurate real-time air pollution monitoring system by incorporating advanced technologies.

Low cost air pollution monitoring systems: A review of protocols and enabling technologies

Abstract Air pollution is the foremost concern especially in modern cities, because of its noteworthy influences on the community health, and the global economy. The significance of the air quality statistics makes the highly accurate real-time monitoring systems vital. The partial data access, high cost and the non-scalability of conventional air monitoring system enforce the researchers to develop future air pollution monitoring system employing advance technologies such as internet of things (IoT), wireless sensor network (WSN) and the low-cost ambient sensors. This paper presents a short but comprehensive review of these air pollution monitoring systems (APMS), their enabling technologies and protocols. We categorize the recent work into two main classes as static and mobile air monitoring systems based on the carriers of the sensing node and the deployment strategies. Sub categories include, portable monitoring devices, community supported approaches, WSN based systems, and IoT supported approaches. Broad performance assessments and the comparison among these categories were performed with respect to their architecture and the incorporated tools. Furthermore, the present study explored the issues, groundwork, and the methodology of designing a real-time air pollution monitoring system. Finally, the review deliberated the limits of the existing works and summaries the objectives that need to attain in the future air monitoring systems to make them more accurate and realistic.

IoT-Based Real Time Air Pollution Monitoring System

Humans can be adversely affected by exposure to air pollutants in ambient air. Hence, health-based standards and objectives for a number of pollutants in the air are set by each country. Detection and measurement of contents of the atmosphere are becoming increasingly important. Careful planning of measurements is essential. One of the major factors that influence the representativeness of data collected is the location of monitoring stations. The planning and setting up of a monitoring station are complex and incurs a huge expenditure. An IoT-based real time air pollution monitoring system is proposed to monitor the pollution levels of various pollutants in Coimbatore city. The geographical area is classified as industrial, residential and traffic zones. This article proposes an IoT system that could be deployed at any location and store the measured value in a cloud database, perform pollution analysis, and display the pollution level at any given location.

A temporal-spatial interpolation and extrapolation method based on geographic Long Short-Term Memory neural network for PM2.5

Abstract Nowadays, real-time air pollution monitoring has been an important approach for supporting pollution control and reduction. However, due to the high construction cost and limited detection range of monitoring stations, not all the air pollutant concentrations in every corner can be monitored, and a whole picture of the spatial distribution of air pollution is usually lacked for comprehensive spatial analysis and air quality control. To address this problem, satellite remote sensing and spatial interpolation/extrapolation technologies have been commonly used in past research. However, the spatial distribution calculated by remote sensing techniques could be less accurate due to the limited amount of recorded data for testing and adjustments. Performance of traditional spatial interpolation/extrapolation techniques, such as Kriging and IDW, was limited by several subjective assumptions and pre-set formulations that are less suitable for non-linear real-world situations. As an alternative, machine learning and neural network-based methods have been proposed recently. However, most of these methods failed to well consider the long short temporal trend and spatial associations of air pollution simultaneously. To overcome these limitations, this paper proposed a newly designed spatial interpolation/extrapolation methodology namely Geo-LSTM to generate the spatial distribution of air pollutant concentrations. The model was developed based on the Long Short-Term Memory (LSTM) neural network to capture the long-term dependencies of air quality. A geo-layer was designed to integrate the spatial-temporal correlation from other monitoring stations. To evaluate the effectiveness of the proposed methodology, a case study in Washington state was conducted. The experimental results show that Geo-LSTM has a RMSE of 0.0437, and is almost 60.13% better than traditional methods like IDW.

Real-time air pollution monitoring with sensors on city bus

This paper presents an experimental study on real-time air pollution monitoring using wireless sensors on public transport vehicles. The study is part of the GreenIoT project in Sweden, which utilizes Internet-of-Things to measure air pollution level in the city center of Uppsala. Through deploying low-cost wireless sensors, it is possible to obtain more fine-grained and real-time air pollution levels at different locations. The sensors on public transport vehicles complement the readings from stationary sensors and the only ground level monitoring station in Uppsala. The paper describes the deployment of wireless sensors on Uppsala buses and the integration of the mobile sensor network with the GreenIoT testbed. Extensive experiments have been conducted to evaluate the communication quality and data quality of the system.

On Featured IoT for Smart Public Transportation with Cloud-based Machine Learning

With rapid development of science and technology and the accelerated pace of urbanization, smart city has been also widely spread across our country. By Internet of Things, we put forward a kind innovative way of real-time air pollution monitoring into public city transportation vehicle. We take information about the surrounding environment through sensors and upload it directly to the internet. This paper fosters the advantages of the cloud-based computing frameworks (such as Microsoft Azure Machine Learning Studio) and presents a practical work-flow well-suited for the standard machine learning tasks.

Development of IoT-Based Sensor Tag for Smart Factory

To reduce the rate of defective products and ensure the health of employees, it has become increasingly necessary to improve air quality in factories. To do so, it is important to accurately determine the air quality in the factory first. However, currently available industry-grade sensors require complex and often separate construction processes, making them difficult to use in complicated industrial sites. In order to solve such an issue, this study developed an IoT-based sensor that can collect data such as factory temperature, humidity, CO2 levels, dust, and stench. This study has also developed software to manage the IoT-based sensors, which can transmit the data via RF communication. Sensor tags were tested for basic environmental durability, including waterproof, dustproof, and vibration standards. The sensors can be connected via USB to a computer to configure its settings and access the stored data within the sensors. Unlike other existing environmental sensors, these can be easily installed anywhere as they transmit data via wireless communication. These sensors can also be installed in the factory to check the factory’s internal pollution level in real time. The application of this sensor will enable the real-time monitoring of air pollution in factories. By locating the polluted areas accurately, it will be possible to exercise distributed control over ventilation devices to prevent further spread of pollutants, while also pushing out the polluted air to maintain an optimal working environment. Future studies should develop a simulation model for determining the ideal installation location for the sensors. There also needs to be further studies in developing a distributed-controlled air-conditioning system to run empirical tests.

An integrated low-cost road traffic and air pollution monitoring platform for next citizen observatories

Abstract An integrated monitoring platform was developed for real-time monitoring of air pollution and traffic flows in urban areas. The air quality monitoring unit, integrating the “Arduino” open-source technology with low-cost and high-resolution sensors, collects concentrations of CO, NO 2 and CO 2 . The traffic monitoring device, equipped with a camera sensor and a video analysis software, collects vehicles’ counts, speed and size. Air pollution and traffic readings are archived on a spatial data infrastructure composed of a central GeoDatabase, a GIS engine, and a web interface. A platform's description and the results of its installation in Florence (Italy) are presented.