Air Quality Monitoring System Research Articles

Design of a WiFi sensor network to identify transmission zones of respiratory diseases through air quality indicators

infants and postoperative patients. While air extraction and droplet filtration systems are effective in containing these viruses, interconnected ventilation ducts can still facilitate virus transmission in areas with lower viral concentration. This study introduces a Wireless Sensor Network based on WiFi technology, designed to monitor crucial environmental variables such as CO2, TVOC, temperature, and humidity, collectively influencing indoor air quality. Moreover, this network connects to an online server, collaborating with subscribers to store historical data. The stored information aids in identifying patterns and high-risk zones associated with the transmission of respiratory diseases. The outcomes demonstrate a comprehensive, adaptable, and scalable WiFi-based Indoor Air Quality sensor network. Employing the publish-subscribe pattern, the MQTT message transport protocol, and open-access platforms like Streamlit, Supabase, and HiveMQ, along with affordable boards and sensors, this solution offers an innovative, scalable, and cost-effective approach to identifying areas at risk of airborne disease transmission. The system's design prioritizes data precision and reliability, making it a valuable addition to indoor air quality monitoring systems.

GAMMA: A universal model for calibrating sensory data of multiple low-cost air monitoring devices

Due to global air pollution, there is a growing demand for accurate and large-scale air quality monitoring systems. Consequently, low-cost air monitoring devices have emerged as a potential alternative to expensive conventional ones. However, the low-cost devices’ major drawback is their insufficient level of accuracy. This work investigates the problem of calibrating the sensory data, especially PM2.5 concentration, collected by low-cost sensor-based air quality monitoring devices. Recently, deep learning has emerged as a potential solution for data calibration instead of using traditional methods, whose accuracy is relatively low. Nevertheless, it generally incurs significant costs. Moreover, it is necessary to employ a dedicated calibration model for each device to increase precision, resulting in additional expenditures. To address the issue, this study provides a novel approach named GAMMA, which entails the development of a deep learning-based model capable of accurately calibrating data for multiple devices simultaneously. The proposed method leverages the multitask learning paradigm to solve the challenge of concurrently processing several devices’ data. This involves capturing common features across all devices’ data while also distinguishing the device-specific characteristics. Furthermore, GAMMA also employs the adversarial training approach to augment the accuracy of predictions. This method has been implemented and integrated into an air quality monitoring system in Hanoi, Vietnam. Comprehensive experiments are conducted on real-world data to demonstrate the superiority of GAMMA against the comparison benchmarks in terms of various metrics. Notably, GAMMA reduces MAE from 60.19% to 74.09% compared to the best comparison baseline. The source code is available at https://github.com/anhduy0911/FimiCalibIdea/tree/multi_attention.

Intelligent, low-cost, high-performance system for environmental air quality monitoring through integrated gas, temperature, and humidity analysis

Intelligent, low-cost, high-performance system for environmental air quality monitoring through integrated gas, temperature, and humidity analysis

IOT-based solution for detection of air quality using ESP32

Air pollution, a growing public health crisis worldwide, has become one of the most imperative concerns challenging human health, the environment, and the economy. The detrimental effects of air pollution on individuals' well-being and the ecological system necessitate implementing advanced solutions to accurately monitor and manage air quality. In response to this global challenge, a comprehensive, real-time IoT-based air quality monitoring system is proposed using ESP32 and multiple pollutant sensors. This versatile system endeavors to measure and report various air quality parameters and pollutants, such as temperature, humidity, carbon dioxide (CO2), particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), and other noxious gases, including nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2). The innovative solution showcased in this proposal combines the functionalities of an ESP32 microcontroller, a highly integrated and low-power device, with diverse pollutant sensors chosen for their high sensitivity and ability to offer precise and reliable readings. The various sensors integrated into the system include the MQ135, CCS811, PMS5003, and BME280, each responsible for detecting specific pollutants and air quality parameters. As a leading component of the air quality monitoring system, the ESP32 microcontroller warrants seamless wireless data transmission to cloud servers for real-time monitoring, allowing for remote access and analysis of the air quality data through secure networks. The modular design and ease of deployment of the IoT-based air quality monitoring systems make them installable in various scenarios, including residential, commercial, and industrial settings. Users can make informed decisions about their environment by analyzing extensive real-time data, promoting timely exposure warnings, proactive pollution management, and environmental sustainability. This portability and versatility of deployment ensure substantial coverage of areas that would benefit from real-time air quality monitoring. The air quality monitoring system is further enhanced by incorporating a user-friendly mobile application or web interface, empowering users to access real-time sensor data directly, configure threshold-based alerts and notifications, and visualize trends in air quality parameters over time. This hands-on approach to accessing real-time air quality data encourages individuals to adopt environmentally conscious habits and fosters a better understanding of the impact of pollution on their health and surroundings. To ensure the optimum performance and efficacy of the air quality monitoring system, regular maintenance and calibration services are advocated. By undertaking timely sensor calibration, replacing worn-out components, and updating software when required, the life span, accuracy, and reliability of the system are enhanced, promising continued effectiveness in the real-time monitoring and management of air quality

Machine Learning Models for Predicting Air Pollution

Abstract: The air quality monitoring system collects data of pollutants from different location to maintain optimum air quality. In the current situation, it is the critical concern,. The introduction of hazardous gases into the atmosphere from industrial sources, vehicle emissions, etc. pollutes the air. Today, the amount of air pollution in large cities has surpassed the governmentset air quality index value and reached dangerous levels. It has a significant effect on a human health. The prediction of air pollution can be done by the Machine Learning (ML) algorithms. Machine Learning (ML) combines statistics and computer science to maximize the prediction power. ML is used in order to calculate the Air Quality Index. Various sensors and an Arduino Uno microcontroller are utilized to collect the dataset. Then by using K- Nearest Neighbor (KNN) algorithm, the air quality is predicted.

Harnessing Machine Learning for Enhanced Air Quality Prediction

Air pollution poses significant health risks and necessitates the need for accurate air quality monitoring systems. This research paper focuses on predicting the Air Quality Index (AQI) using both regression and classification models. Various machine learning algorithms such as Linear Regression, Decision Tree Regressor, Random Forest Regressor, Logistic Regression, Decision Tree Classifier, Random Forest Classifier, and K-Nearest Neighbors Classifier were employed to estimate AQI based on pollutant concentrations of SO₂, NO₂, RSPM, and SPM. The performance of these models was evaluated using metrics like RMSE, R² score, accuracy, and Cohen's Kappa score. The results demonstrate the efficacy of the models in predicting AQI, with Random Forest models exhibiting superior performance in both regression and classification tasks. This study underscores the potential of machine learning techniques in enhancing the accuracy and reliability of air quality predictions, thereby contributing to better environmental management and public health policies.

Determination of the Spatial Distribution of Air Pollutants in Bucheon, Republic of Korea, in Winter Using a GIS-Based Mobile Laboratory.

Driven by industrialization and urbanization, urban air pollution can increase respiratory, heart, and cerebrovascular diseases, and thus mortality rates; as such, it is necessary to improve air quality through the consideration of individual pollutants and emission sources. In Republic of Korea, national and local governments have installed urban and roadside air quality monitoring systems. However, stations are lacking outside metropolitan regions, and roadside stations are sparsely distributed, limiting comparisons of pollutant concentrations with vehicle traffic and floating population levels. Local governments have begun using mobile laboratories (MLs) to supplement the fixed measurement network and investigate road pollution source characteristics based on their spatiotemporal distribution; however, the collected data cannot be used effectively if they are not visualized. Here, we propose a method to collect and visualize global information system (GIS)-based air quality data overlayed with environmental variables to support air quality management measures. Spatiotemporal analyses of ML-derived data from Bucheon, Korea, confirmed that particulate and gaseous pollutant concentrations were high during typical commuting hours, at intersections, and at a specially managed road. During commuting hours, the maximum PM10 concentration reached 200.7 µg/m3 in the Nae-dong, Gyeongin-ro, and Ojeong-dong ready-mix concrete complex areas, and the maximum PM2.5 concentration was 161.7 µg/m3. The maximum NOx, NO2, and NO levels of 1.34 ppm, 0.18 ppm, and 1.18 ppm, respectively, were also detected during commuting hours. These findings support the need for targeted management of air pollution in this region, and highlight the benefit of comprehensively comparing road levels, driving speed, and traffic levels when identifying hotspots of air pollution. Such analyses will contribute to the development of air quality management measures customized to regional characteristics.

Design cognitive IoT architecture framework for immersive visual technologies of air quality monitoring systems

Design cognitive IoT architecture framework for immersive visual technologies of air quality monitoring systems

AIR QUALITY TESTING- A DESIGN THINKING APPROACH

This paper introduces a groundbreaking and holistic approach to revolutionize air quality testing by integrating design thinking principles, sensor technology, and the Internet of Things (IoT). Focused on addressing the critical challenge of air pollution, this research advocates for the development of user-centric, cost-effective, and scalable solutions for air quality monitoring. The design thinking process is comprehensively examined, covering crucial stages such as problem identification, ideation, prototype development, and user feedback. A central theme of this approach is the active involvement of stakeholders and cross- disciplinary collaboration, which ensures the creation of innovative and practical solutions. Real-world case studies underscore the tangible implications of this approach, emphasizing successful outcomes, including the development of affordable air quality monitoring devices and systems. Sensors and IoT technologies play pivotal roles in enabling real-time data collection, remote monitoring, and seamless integration with urban management systems, thereby contributing to enhanced environmental sustainability and public health. Additionally, this paper discusses the potential for adaptive air quality management strategies, wherein the data generated by these innovative monitoring systems can inform immediate response measures and long-term policy decisions. The adaptability and scalability of the proposed approach make it a valuable tool for addressing not only the challenges of today but also those of the future, such as climate change and evolving pollution sources.

Low‐cost air quality monitoring networks for long‐term field campaigns: A review

AbstractThe application of low‐cost air quality monitoring networks has substantially grown over the last few years, following the technological advances in the production of cheap and portable air pollution sensors, thus potentially greatly increasing the limited spatial information on air quality conditions provided by traditional stations. However, the use of low‐cost air quality sensors still presents many limitations, mostly related to the reliability of their measurements. Despite the increasing number of papers focusing on these issues, some of the challenges connected to the use of low‐cost air quality sensors are still poorly investigated and understood, considering in particular those related to long‐term applications of low‐cost air quality networks and their integration within the reference air quality monitoring system. The present review aims at filling this gap, by analysing the characteristics of low‐cost air quality monitoring networks that were run across long‐term field campaigns, including their geographical location, the pollutants monitored, the type and number of stations employed, and the length of the field campaign, with a particular attention on assessing the aims for their deployment and on the evaluation of their integration within official air quality monitoring networks. Moreover, a critical analysis of the most insightful suggestions and recommendations delivered in the literature, as well as of the relevant critical issues, is presented, highlighting still open research areas and outlining future challenges.

Rancang Bangun Sistem Pemantauan dan Pengendali Kualitas Udara Pada Aplikasi Welding Habitat Proses Welding Pipeline Sistem Dengan Data Logger

Abstract
 Air Quality Monitoring and Control System is a method of monitoring and controlling air quality in an area or place where air contamination may occur in an activity or work. In this case, the Air Quality Monitoring and Control System is applied to the welding habitat or the welding house in the system pipe welding process. At this time, the welding habitat in the welding pipe pipeline system with the shielding gas process has not been monitored and controlled for air quality during the welding process, this condition causes the welder in the welding habitat to become uncomfortable which can affect the quality of the welding and endanger the welder's health. For this reason, an air quality monitoring and control system is needed that can unite and control the air quality in the welding habitat during the welding process. The purpose of the design of this Air Quality Monitoring and Control System is to make the welding process in the welding habitat of the system pipe welding process more comfortable and safer for the welder's health. So that the welder can concentrate fully on the welding process and produce good and quality welding production. With the application of this system, it can provide a level of weld repair that can reduce welding repair costs incurred by the Company.
 Keywords: Air Quality; Data Logger; ESP32; ISPU (Air Pollution Standards Index); Sensors

Statement of Retraction: An Intelligent and Secure Air Quality Monitoring System Using Neural Network Algorithm and Blockchain

Statement of Retraction: An Intelligent and Secure Air Quality Monitoring System Using Neural Network Algorithm and Blockchain

AUTOMATRIC AIR QUALITY MONITORING SYSTEM

A technology known as an automatic air quality monitoring system continuously measures and evaluates the air quality in a specific environment. It is made to find and look at different pollutants and give real-time data for monitoring. These frameworks are regularly utilized in modern settings, urban communities, and homes to guarantee the air we inhale is protected and sound. For measuring the air quality, we are using ESP32 NODEMCU (microcontroller) with humidity and temperature sensors and a display unit for displaying the value of air quality. We are using IOT (Internet of Things) technology. and we are interfacing with cloud technology by accessing the data from the display and sending the message or notification to the mobile phone or tv and give the alarm sound. our main motto to fulfil the needs of industry people. By ensuring the level of air quality monitoring in their workplace. If the level of any gases like CO2 or some other gases increased means automatically the signal will be sent to the display system. By using this display module in their workplace, they can be priorly notified and take safety measures like wearing masks etc. so no one gets harmed by any type of diseases like lung diseases and cancer etc. This also impacts their health and reduces the chances of occurring any health issues by maintaining a moderate ambiance or as required. Hence, IoT-powered solutions provide better services to the industrialists, which in turn, provides better services to their customers. It creates a positive impact on human health by eliminating unwanted air. Pollutants and particulate matter by allowing the authorities to take decisions according to the situation. Key Words: Internet of Things, ESP32 NODEMCU, Detect gas level, Alarm sound.

Low-cost mobile air quality monitoring based on internet of things for factory area

The most significant effect of industrialization is air pollution. Air pollution monitoring has become an important requirement in affected areas. With the advancement of electronic sensor technology and the Internet of Things (IoT), there are plenty of remote ambient air quality monitoring systems have been developed by measuring the levels of major air pollutants such as Sulfur Dioxide, (SO2), Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Ozone (O3), and Particulate Matter (PM). However, other hazardous gases such as Hydrogen Sulfide (H2S), Carbon Monoxide (CO), and Ammonia (NH3) also have the potential to appear in factory areas, especially factory areas located in rural areas. Therefore, this study proposes a low-cost mobile ambient air quality monitoring system by measuring CO, H2S, and NH3 gas levels that are easy to implement. The proposed system has been tested in factory areas and rural areas where factory pollutants can be mixed with agricultural pollutants. The test results show that the proposed system can detect CO, H2S, and NH3 gas levels. The levels of CO, H2S, and NH3 gases at the test site were still at a safe level. A mobile remote monitoring scheme by utilizing thinger.io as an IoT platform also works very well.

Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

Systems for monitoring air quality are essential for reducing the negative consequences of air pollution, but creating real-time systems encounters several challenges. The accuracy and effectiveness of these systems can be greatly improved by integrating federated learning and multi-access edge computing (MEC) technology. This paper critically reviews the state-of-the-art methodologies for federated learning and MEC-enabled air quality monitoring systems. It discusses the immense benefits of federated learning, including privacy-preserving model training, and MEC, such as reduced latency and improved response times, for air quality monitoring applications. Additionally, it highlights the challenges and requirements for developing and implementing real-time air quality monitoring systems, such as data quality, security, and privacy, as well as the need for interpretable and explainable AI-powered models. By leveraging such advanced techniques and technologies, air monitoring systems can overcome various challenges and deliver accurate, reliable, and timely air quality predictions. Moreover, this article provides an in-depth analysis and assessment of the state-of-the-art techniques and emphasizes the need for further research to develop more practical and affordable AI-powered decentralized systems with improved performance and data quality and security while ensuring the ethical and responsible use of the data to support informed decision making and promote sustainability.

Development of a Smart Air Quality Monitoring System Using Wireless Sensors

With the fast-growing urbanization and increased industrialization around cities in Nigeria, the quality of the air we breathe has become a paramount concern. There is a need for an accurate, real-time air quality monitoring system to monitor and evaluate the air quality we inhale. This article presents the development of a Smart Air Quality Monitoring System (SAQMS) utilizing wireless sensor technology. The SAQMS was designed to provide a comprehensive and intelligent solution for monitoring air quality index, including but not limited to particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), carbon monoxide (CO), temperature and humidity. Leveraging wireless sensor nodes and advanced IOT, the system offers real-time data collection and analysis, enabling immediate response to environmental changes. Key features of the SAQMS include a network of wireless sensors (MQ 135 and DHT11) strategically deployed across targeted areas, data transmission through secure communication protocols, cloud-based data storage and processing, and a user-friendly interface for data visualization and interpretation. As we confront the challenges of air pollution and its impact on human well-being, the development of the SAQMS presents a significant step towards informed decision-making and a more sustainable future as the experiment shows our developed system can detect air quality in parts per million (ppm), with other environmental factors such as temperature and relative humidity.

Measuring Indoor Air Quality Does Not Prevent COVID-19.

Boston Public Schools (BPS) closed for in-person learning in March 2020 due to COVID-19 and didn't fully reopen until the 2021-2022 school year. Due to the age of schools and absent ventilation systems, coupled with decades of disinvestment in the infrastructure, BPS entered the pandemic with serious challenges impacting the health of students and staff. These challenges were magnified by an infectious airborne virus. Instead of using this opportunity to improve ventilation systems, BPS opted to invest in an air quality monitoring system. This system only confirmed what was already known-there is poor ventilation in most school buildings. It did not lead to correction of new or long-standing problems. This failure has harmed the BPS community, which includes primarily low-income Black and Brown families. This article describes Boston's school system, its track record of inadequate attention to infrastructure, and explores pitfalls of focusing on evaluation instead of correction.

Air Quality Monitoring and Analysis for Sustainable Development of Solid Waste Dump Yards Using Smart Drones and Geospatial Technology

Air pollution has become a global health issue, affecting millions of people annually. It is essential to obtain accurate and up-to-date information on air quality levels to reduce the impact of air pollution on human health. Traditional air quality monitoring methods are limited by spatial coverage and the time required for sample collection and analysis. However, the Internet of Things (IoT), drone technology, and Geographic Information System (GIS) technology have enabled the development of real-time air quality monitoring systems that provide accurate and up-to-date information on air quality levels across large areas. The study found that gas concentration varied significantly at different heights, with the highest concentration at 3 m from the ground and the lowest at 9 m. The concentration of gases also varied by month, with the highest concentration of CO recorded in March at 45 ppm at 3 m, and the highest concentration of NH4 recorded in September at 66.7 ppm at 3 m. Kodungaiyur dump yard needs environmental monitoring due to the high levels of environmental pollution and health risks caused by unsegregated waste. In conclusion, developing real-time air quality monitoring systems using GIS technology is necessary to obtain accurate and up-to-date information on air quality levels. It is essential to monitor the Kodungaiyur dump yard and other, similar sites regularly to prevent the health risks associated with environmental pollution.

SPATIO-TEMPORAL VARIABILITY OF AIR QUALITY AND RELATIONSHIP WITH METEOROLOGICAL PARAMETERS IN CAPE TOWN, SOUTH AFRICA

Abstract. African cities are growing fast, and this rapid urbanisation has tremendously increased air pollution and greenhouse gas emissions. Despite this disturbing reality, the deleterious impacts of air pollution on livelihoods and the environment are often overlooked. Recently, the link between air quality and meteorological parameters has received attention from researchers and understanding this relationship could significantly improve our understanding of the spatial and temporal dynamics of air quality. This study focuses on analysing the spatiotemporal variation of three key air quality parameters, namely nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter (PM10), in Cape Town between 2020 and 2021. The study also aims to assess the relationship between air quality and meteorological parameters during this period, and the compliance with national and international air quality guidelines. Air quality data were collected from five monitoring stations in the City of Cape Town. A preliminary analysis of the data reveals high increases in the concentration of air pollutants from 2020 to 2021. For instance, the average monthly concentration of NO2 and SO2 at Bellville South station more than doubled during this period (from 6.7–14.8 µg/m3 and 3.4–8.1 µg/m3, respectively). This is worrisome as the air quality index (AQI) exceeded the safe limits at several sites. There is a need for urgent action by national and city governments in Africa to invest in air quality monitoring systems to enhance the well-being of citizens and promote the long-term sustainability of cities and infrastructure.

Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis.

High-quality indoor air is essential in open-kitchen restaurants for ensuring a healthy workplace and comfortable conditions for visitors. In this study, indoor air quality interdependence between the kitchen and the dining zones in open-kitchen restaurants was analyzed. The method was based on measurements of selected air parameters using a sensor technique and mutual information (MI) analysis. A long-term approach (based on a several-hour time series) and a short-term approach (based on a several-minute time series) were applied. This study involved four open-kitchen restaurants. The indoor conditions were represented by the temperature, relative humidity, CO2 concentration, and content of the total volatile organic compounds (TVOC) in the air. The MI analyses showed that the long-term co-dependence of the indoor conditions between the kitchen and the dining zones was smaller during business hours (MI = 0.12 ÷ 0.40) compared to night hours (MI = 0.24 ÷ 0.58). The ranking of the long-term MI values for the individual air parameters was MICO2 (0.34) ≅ MIT (0.34) > MIRH (0.28) > MITVOC (0.23). The short-term interdependencies were smaller during night hours (median MI = 0.01 ÷ 0.56) compared to business hours (MI = 0.23 ÷ 0.61). Additionally, the short-term MI was subject to high temporal variability. The ranking of the short-term MI values for the individual air parameters was MICO2 (0.48) > MIT (0.46) > MIRH (0.37) > MITVOC (0.26). Due to the weak and highly variable co-dependence of the air parameters between the kitchen and dining areas, multi-zone monitoring of air parameters with an emphasis on TVOC measurements is recommended to ensure proper indoor conditions in open-kitchen restaurants. The presented approach may be applied to design indoor air quality monitoring and ventilation systems not only in open-kitchen restaurants but also in other interiors with functionally different zones.