Air Pollution Monitoring System Research Articles

A Systematic Review for Indoor and Outdoor Air Pollution Monitoring Systems Based on Internet of Things

A Systematic Review for Indoor and Outdoor Air Pollution Monitoring Systems Based on Internet of Things

An intelligent air quality monitoring system using quality indicators and Transfer learning based Lightweight recurrent network with skip connection

<p>Rapid industrialization and urbanization have resulted in poor air quality, which poses a risk to human health by causing a variety of lung diseases. The precise forecast of air quality is of practical importance. Consequently, the development of an automated air pollution monitoring system based on environmental toxicology is required. Although advanced machine learning approaches can yield reasonable results in air quality prediction, they require more historical data collection. In order to address this problem, a lightweight recurrent network based on transfer learning with skip connection (LRN-SC) is proposed for air quality prediction. LRN-SC pretrains the model using data from an available station. The features that were learned from the previous station are retained, and the pre-trained model is then adjusted to fit the new one. After that, Transfer learning-based light weight recurrent network with skip connection (TL2RN-SC) is trained, and the model is tested using data from the new station. The proposed model reduces the decoding burden by adding skip contacts between the decoder and the linear forecasting layer. The simulation results show that the proposed model outperforms the existing models by attaining average RMSE and MAE of 0.974 and 2.63 respectively.</p>

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.

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.

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.

In-cabin and outdoor environmental monitoring in vehicular scenarios with distributed computing

Accurate environmental monitoring is becoming the basis for assuring sustainable development in administrations at different levels, including cities and industry as key actors. However, current techniques rely on static stations that may not be representative of larger areas, for the case of outdoor scenarios, or even not considering indoor spaces where people can remain for long periods. This is the case of vehicles. The COVID-19 pandemic has remarked the importance of measuring air quality indoors, for instance. With the aim of solving this two-fold issue, this work proposes an in-cabin and outdoor air pollution monitoring system to assure healthy conditions when travelling, driving and operating vehicles, and to analyse the evolution of environmental parameters in cities. This effort is carried out exploiting distributed computing with micro-services, betting for an on-board hardware solution provided with sensors for measuring particulate matter, CO, CO2, NO2, O3, temperature and humidity. While basic data pre-processing is carried out in this acquisition unit, edge processing is performed on a single board computer aboard and intermediary communication nodes in the network path from the vehicle to the cloud. Vehicle connectivity is provided by 4G cellular and Low-Power Wide-Area (LPWAN) networks. Global environmental perception is acquired by cloud-based software powered by machine learning and time series analysis. The whole solution has been validated and tested in the city of Cartagena (Spain), with good performance in terms of data collection, communication links and service offered.

An IoT Implemented Dynamic Air Pollution Monitoring System

In recent years, pollution of air has become a critical concern in many urban areas, causing serious health problems and environmental damage. To address this issue, an Internet-of-Things (IoT)-based air pollution monitoring system was proposed. The mechanism was designed to measure various air quality parameters such as temperature, humidity, various gases, microbes, and light intensity in real time. The proposed sys-tem consists of sensor nodes, a gateway, WIFI module, an LCD display, and a cloud server. The sensor nodes were placed at different locations to measure air quality parameters, and the data were transmitted to the gateway via wireless communication. The gateway aggregates the data from the sensor nodes and sends them to the cloud server for further analysis and processing. The cloud server processes the data, and the system also includes a web interface that displays data on the pollution levels of the air in real time. The sys-tem can also send alerts to users when the air quality is poor, allowing them to take the necessary precautions. This system could assist decision-makers in taking appropriate measures to alleviate air pollution and safeguard the health of the community by providing real-time information about air quality. The system was evaluated in a real-world environment and the results demonstrated its effectiveness in providing accurate and reliable air quality information. The proposed system has the potential to be used in various applications, including public health and environmental monitoring, and can be integrated with other IoT devices to enhance their functionality.

Air-Pollution Monitoring using IoT

Abstract: The level of pollution has increased due to a lot of factors like the increase in population, commercial vehicles, industrialization and stubble burning which has caused harmful effects on human wellbeing by directly affecting health of population exposed to it. The worst affected by this are the individuals, like traffic police officers, who are required to stay outside breathing the toxic gases and bearing the strong heat to maintain a constant traffic flow and order in society. Studies and surveys show that they suffer from respiratory diseases, photo sensitivity, skin infections, nausea, high blood pressure and many other problems. This research paper presents an innovative way to monitor real-time air quality and collect data to provide insights to local authorities. The ‘Air Pollution Monitoring System’ using Internet of Things will monitor the air quality over a web server using cloud technology and will trigger a alarm when the air quality goes beyond a certain level, meaning there are dangerous amount of harmful gases present in the air like CO2, CO, particulate matter, alcohol, benzene and NH3.Governments and citizens are looking for scientificintellect to challenge the common threat of pollution in its many procedures. Currently mobile apps are capable of accomplishing functions like reporting status of air quality, air quality forecasts, air quality monitoring in a particular area, and risks highlighting. But these applications do not provide the user with data of a local area, where the user is currently located, which will be more beneficial for an individual.

Insight into the novel low cost green air pollution monitoring and control systems: A technological solution from concept to market

Nitrogen oxide emissions, also referred to as NOx including NO and NO2 are hazardous to human life and have negative effects on the environment. Wet and dry treatments have both been explored as methods to significantly minimize these emissions. The wet process offers several benefits, the most notable of which are the flue gas adaptability, low operating temperatures, and the lack of poisoning and catalyst inactivation. In the research work, a NOX filter is being developed, with composite material for better transparency and non-reactive nature. Mechanical and electronic components have been employed to develop a stable and rigid experimental setup that effectively handles environmental challenges. Different tests are performed during performance analysis with known quantities of NOX through cylinders and varied the flow rate of ambient air. The concentration of NOX, H2O2, nitric acid, and other parameters were captured to get the optimum configuration that will provide maximum efficiency. The envisaged cost of implementing a solar tree-based air pollution monitoring and control system is estimated at $2796. This cost projection serves to exemplify the techno-economic analysis of the system, showcasing its potential to contribute to a cleaner environment and facilitate renewable energy utilization. The different financial model payback periods of the system are from 3.12 to 5.21 years. The developed system benefits people with respiratory health issues, elderly people, children, and infants, sports enthusiasts, traffic police persons, street hawkers or vendors, and people living in areas where the pollution level is high.

IoT based System for Air Pollution Monitoring in Banda Aceh

IoT based System for Air Pollution Monitoring in Banda Aceh

IoT Based Air Pollution Monitoring System

This paper proposes an IOT based air pollution monitoring system that utilizes a multitude of sensors to measure air pollution levels and provide real-time data to users. Specifically, a gas sensor, dust sensor, humidity sensor, sound sensor, temperature sensor, Arduino controller, and LCD are used to measure and display the various levels of air pollution. Furthermore, the system is powered by the IOT, allowing for the data to be streamed online, providing users with a better understanding of their local air pollution levels. This system can be used to inform users of the air quality in their area and help them take necessary steps to protect themselves from the dangers of air pollution.

Air Quality Monitoring and Controlling Using IOT

Smart cities are growing as a crucial area of research on an international level; they improve infrastructure to address issues brought on by the high rate of development. With Arduino's assistance, this system is cheap and simple to construct, and it operates relatively efficiently in locations with medium traffic. The public plays a significant role in deciding on policies to fight air pollution. To encourage public recognition of the duty and some form of action is a crucial role in any society. The studies determined the pollution level; if the level exceeds the permissible level, a decision is made to notify the authoñties, who can use this information to plan preventive actions and take steps to halt tile ongoing negative effects of air pollution and prevent the evolution of developments in technology are beneficial to us; in our current surroundings, air pollution monitoring systems play an important role since they directly affect our daily lives. The government determines the acceptable emission limits in accordance with Bharat Stage standards. The level of a vehicle's emissions rises as a result of incorrect maintenance. The owner of the car will be warned of the dangerous levels of pollution when the emission levels of the vehicle grow.There will be emissions from every car. Emissions cannot be totally avoided, but they may be tracked and managed with the use of an embedded pollution detection system. In order to provide some insightful ideas for the sustainable growth of urban traffic, it focuses on learning some achievable strategies for managing and effectively reducing urban traffic pollution

Assessment Of Air Pollution And Its Association With Population Health: Geo-Statistical Evidence From Pakistan

Human health is harmed by air pollution. The objective of this research was to show that air pollution in Pakistan is getting worse and is negatively impacting people’s health. IQ Air and the Institute for Health Metrics and Evaluation provided the data for this descriptive research. Monthly data of PM2.5 μg/m³ from ten different localities across Pakistan are used to show spatial distribution through the geospatial technique of interpolation. The findings show that two third of the country has high PM2.5 concentration, with Lahore as the most polluted city. In Pakistan, solid fuel use has decreased, leading to a decline in associated mortality and morbidity. However, there have been significant increases in PM2.5 and ozone levels, resulting in a rise in the country’s overall health burden caused by air pollution. Furthermore, the number of deaths attributed to air pollution has also increased since 1990. A total of 57% of chronic obstructive pulmonary disease, 40% of lower respiratory infections, 36% of ischemic stroke, 35% of ischemic heart diseases, 32% of lung cancer, 25% of diabetes, and 20% of neonatal outcomes are directly attributed to air pollution in Pakistan. The main contributors to air pollution are population growth, growing motorization, and unsustainable energy usage. The main challenges due to air pollution control and monitoring in Pakistan include a lack of awareness, poor policy creation and implementation, the use of improper fuel, rising energy demands, and an absence of pollution monitoring stations in most cities. Therefore, there is a need for a robust air pollution monitoring system, increased public awareness, and the implementation of clean and sustainable policies to regulate this environmental health issue.

Prototype design of urban monitoring on several gaseous pollutants using low-cost electrochemical sensors

Monitoring atmospheric gaseous pollutants in urban areas is increasingly essential to anticipate poor urban air quality and the increasing number of acute respiratory infections caused by air pollution. Usually, the monitoring of air pollution is conducted by the government and scientists by using accurate and expensive instruments. However, this instrument’s high-cost installation and maintenance limited the number of monitoring stations that can be placed. It makes it hard to identify the pollutant’s local source, especially on an urban scale. Hence, this study designed alternative techniques to monitor several gaseous pollutants using the Low-cost Air Quality System (LAQS). LAQS has enormous potential to be a reference station’s supporting tool and increase the resolution of air pollution monitoring systems. A custom-made low-cost electrochemical sensor was used in this study to detect several gaseous pollutants, including ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO), together with temperature and relative humidity measurements. The system was also equipped with a data logger and 4G module for data storage and transmission, respectively. In the first stage, two scenarios were employed, including its examination inside and outside the room before co-location.

Development of an IoT-Enabled Air Pollution Monitoring and Air Purifier System

Air Pollution is one of the severe hazards which affect the health quality of all living beings and the environment itself, especially in urban areas. It creates significant health issues in many countries and harms human health if its concentration exceeds tolerable levels. Monitoring these pollutants and their concentrations is a crucial preventative measure to alert the public to the air quality in the area. The primary objective of this paper is to design a smart air pollution prototype to purify the air with the help of a PM sensor, gas sensors, analog to digital converter (ADC), Raspberry Pi 3 B+ microcontroller, and multiple filters (Step-by-step filtering of particles from sizes 200 to 0.3 µm will be done by different filters). The gas sensors, MQ2 and MQ135, measure the presence/concentration of the LPG, smoke, CO, CO2, and NH4 before and after the filtering process. The PMSA003 sensor detects Particulate Matter (PM) in the range of 0.3 microns to 10 microns. ADC and Raspberry Pi 3 B+ convert the outputs of gas sensors and PM sensors into concentrations of particles. These results are transmitted to the Thingspeak Cloud platform through the WiFi Module. The sensor outputs can be viewed in any system/mobile in the Thingspeak Cloud platform. The concentration of different gases and the measured size concentration of various particles are plotted before and after the filtration, and the results are compared. It is found that the designed filter effectively filters all the particles above 0.3 µm. The performance and efficiency test of both air quality monitoring and air purifier system is done.

IOT Based Air Pollution Monitoring System

Today it’s a very serious issue of air pollution in our environment. Day by Day its increasing because of industries, vehicles and constructions sites. So, this IOT based Air Pollution Monitoring System works to monitor the current quality of air over a web server using the internet. Air Pollution effects mainly human lungs and by this they face many serious disease as well. The particle which is generated from these sectors are categorized in two types and first PM 2.5 which is generated from vehicle, bricks chimneys and industries and second one is PM 10 which is generated from construction sites, mines etc. Keywords: Air Pollution, Internet of Things, Arduino Uno, MQ135 Sensor.

Development of IOT Based Air Pollution Monitoring system

Development of IOT Based Air Pollution Monitoring system

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 the economic value of ultrafine particle information: a contingent valuation method

Global concern regarding ultrafine particles (UFPs), which are particulate matter (PM) with a diameter of less than 100nm, is increasing. These particles are difficult to measure using the current methods because their characteristics are different from those of other air pollutants. Therefore, a new monitoring system is required to obtain accurate UFP information, which will raise the financial burden of the government and people. In this study, we estimated the economic value of UFP information by evaluating the willingness-to-pay (WTP) for the UFP monitoring and reporting system. We used the contingent valuation method (CVM) and the one-and-one-half-bounded dichotomous choice (OOHBDC) spike model. We analyzed how the respondents' socio-economic variables, as well as their cognition level of PM, affected their WTP. Therefore, we collected WTP data of 1040 Korean respondents through an online survey. The estimated mean WTP for building a UFP monitoring and reporting system is KRW 6958.55-7222.55 (USD 6.22-6.45) per household per year. We found that people satisfied with the current air pollutant information, and generally possessing relatively greater knowledge of UFPs, have higher WTP for a UFP monitoring and reporting system. We found that people are willing to pay more than the actual installation and operating costs of current air pollution monitoring systems. If the collected UFP data is disclosed in an easily accessible manner, as is current air pollutant data, it will be possible to secure more public acceptance for expanding the UFP monitoring and reporting system nationwide.

A Detailed Analysis of Air Pollution Monitoring System and Prediction Using Machine Learning Methods

Predicting air quality is a complex task due to the dynamic nature, volatility, and high variability in the time and space of pollutants and particulates.Due to the presence of governing factors, varying land uses, and many sources for the elaboration of pollution, the forecast and analysis of air pollution is a difficult procedure. At the same time, being able to model, predict, and monitor air quality is becoming more and more relevant, especially in urban areas, due to the observed critical impact of air pollution on citizens’ health and the environment. In this paper, various air pollution monitoring and prediction models with respect to hardware interfacing modules and various classification approaches. The Air Quality Index (AQI) parameter is used in this paper to monitor the quality of air pollution in various regions of the world. The drawbacks of the conventional air pollution monitoring and prediction models have been stated in this paper with the methodologies used for air pollution prediction.