Real-time Air Quality Monitoring Research Articles

Near-real-time hourly PM2.5 prediction over East Asia using geostationary satellite products and machine learning

Airborne ground-level particulate matter of size <2.5 μm (PM2.5) is a potential health hazard, even with short-term exposure. To quantify the amount of ambient PM2.5, there have been many advances in the use of satellite observations in estimating PM2.5 concentrations over broader areas. Although hindcasts have successfully estimated PM2.5 concentrations at locations with no ground-based observations, nowcasts are crucial in preventing potential exposure to PM2.5 in real time. In this study, a near-real-time hourly PM2.5 prediction framework was designed using a geostationary earth orbit satellite, Geostationary Ocean Color Imager-II (GOCI-II) aerosol products, and random forest (RF) models to yield hourly PM2.5 predictions over East Asia 10 times daily at 2.5 km resolution. The framework includes an oversampling stage, which increases the number of infrequent PM2.5 occurrences. The algorithm was tested over a one-year period, during which predictions and observations were strongly correlated (R2 = 0.79) with a relative root mean square error of 41.99%. The results were comparable to or better than those of previous hindcast models. Uncertainties in prediction stemmed mainly from the number of valid predictions and the absolute volume of training samples. Due to the high temporal and spatial resolution, predicted PM2.5 concentrations successfully filled in regions with no ground-based stations. The products also identified concentration gradients within sub-districts, and their temporal changes during daytime. Prediction with a model with a training dataset from an hour ago still produced robust results. The products may be used in releasing health hazard alerts through real-time monitoring of air quality.

Air Quality Monitoring and the Safety of Farmworkers in Wildfire Mandatory Evacuation Zones.

The increasing frequency and severity of wildfires due to climate change pose health risks to migrant farm workers laboring in wildfire-prone regions. This study focuses on Sonoma County, California, investigating the effectiveness of air monitoring and safety protections for farmworkers. The analysis employs AirNow and PurpleAir PM2.5 data acquired during the 2020 wildfire season, comparing spatial variability in air pollution. Results show significant differences between the single Sonoma County AirNow station data and the PurpleAir data in the regions directly impacted by wildfire smoke. Three distinct wildfire pollution episodes with elevated PM2.5 levels are identified to examine the regional variations. This study also examines the system used to exempt farmworkers from wildfire mandatory evacuation orders, finding incomplete information, ad hoc decision-making, and scant enforcement. In response, we make policy recommendations that include stricter requirements for employers, real-time air quality monitoring, post-exposure health screenings, and hazard pay. Our findings underscore the need for significant consideration of localized air quality readings and the importance of equitable disaster policies for protecting the health of farmworkers (particularly those who are undocumented migrants) in the face of escalating wildfire risks.

Deep Smooth Random Sampling and Association Attention for Air Quality Anomaly Detection

Real-time monitoring and timely warning of air quality are vital components of building livable cities and implementing the “Healthy China” strategy. Real-time, efficient, and accurate detection of air quality anomalies holds great significance. However, almost all existing methods for air quality anomaly detection often overlook the imbalanced distribution of data. In addition, many traditional methods cannot learn both pointwise representation and pairwise association, so they cannot solve complex features. This study proposes an anomaly detection method for air quality monitoring based on Deep Smooth Random Sampling and Association Attention in Transformer (DSRS-AAT). Firstly, based on the third geographical law, the more similar the geographical environment, the closer the geographical target features are. We cluster sites according to the surrounding geographic features to fully explore latent feature associations. Then, we employ Deep Smooth Random Sampling to rebalance the air quality datasets. Meanwhile, the Transformer with association attention considers both prior associations and series associations to distinguish anomaly patterns. Experiments are carried out with real data from 95 monitoring stations in Haikou City, China. Final results demonstrate that the proposed DSRS-AAT improves the effectiveness of anomaly detection and provides interpretability analysis for traceability, owing to a significant improvement with the baselines (OmniAnomaly, THOC, etc.). The proposed method effectively enhances the effectiveness of air quality anomaly detection and provides a reference value for real-time monitoring and early warning of urban air quality.

Rule based complex event processing for an air quality monitoring system in smart city

Smart city development is accelerating, aiming for systematic urban living, but challenges like air quality and traffic management need addressing for sustainability in cities. The World Health Organization (WHO) links air pollution to severe respiratory diseases, emphasizing the need for real-time air quality monitoring and timely decisions based on varying pollutant concentrations (NH3, O3, SO2, NO2) across locations. This research work proposes a framework combining Siddhi Complex Event Processing (CEP) and SPARQL queries for air quality monitoring. Dataset collected from Central Pollution Control Board (CPCB) India, is preprocessed, passed through Apache Kafka, and transformed into a knowledge graph. The RDF data is integrated with a knowledge graph via Apache Jena to enhance CEP performance. Decision trees generate rules and enrich CEP engines with CPCB parameters to identify complex patterns. SPARQL queries on real-time RDF data categorize air quality conditions for all stakeholders. The model's effectiveness is validated by deploying RDF chunks to the CEP engine and evaluating performance through various query execution times. Our model efficiently processed numerous air pollutant events (200, 400, 600 etc.), filtering out about 80% and demonstrating robust event processing capabilities, which enhances air quality management and supports sustainable urban ecosystems and societies.

Implementation of a sustainable real-time air quality monitoring system using the Internet of Things for Kaduna metropolis, Nigeria

Implementation of a sustainable real-time air quality monitoring system using the Internet of Things for Kaduna metropolis, Nigeria

Distributed Embedded System for Air Quality Monitoring based on Long Range (LoRa) Technology

This study addresses the persistent challenge of urban air quality deterioration through the introduction of the Distributed Embedded System (DES). In response to limitations associated with conventional air quality measurement methods, the DES system offers a cost-effective and portable alternative for real-time monitoring. The study focuses on implementing a low-cost distributed system and developing effective communication protocols between sensor nodes. Equipped with Metal Oxide Semiconductor (MOS) sensors for pollutant gases, optical sensors for particulate matter, and meteorological sensors, the system strategically deploys multiple nodes within a 4 km range of urban areas. Real-time AQI and pollution severity have been measured for various locations. Implementation of Long Range (LoRa) communication technology allows the sensor nodes to efficiently transmit data to a central base station. Observations of received signal strength and signal quality indicate reliable and effective communication. To validate accuracy and reliability, acquired DES system data undergoes comparative analysis with data from a government-established meteorological station, revealing a strong correlation. This innovative approach presents a promising solution for widespread, continuous, and cost-effective real-time air quality monitoring in urban areas. The DES system addresses key challenges associated with air pollution, offering a portable and accessible tool that could revolutionize urban air quality management.

Real-time air quality monitoring based on locally developed unmanned aerial vehicle and low-cost smart electronic device

This article deals with the development of real-time air quality monitoring based on Unmanned Aerial Vehicles (UAVs) and low-cost Internet of Things (IoT) devices. This investigation aims to design and develop an UAV-based platform that can monitor a large number of air pollutants in real-time with high spatial and temporal resolution. The proposed environmental monitoring system consists of five main elements, namely the UAV, sensors, data storage module, programmable card, and IoT communication module. Estimated pollutants such as particulate matter (PM2.5) and toxic gases (carbon monoxide CO, nitrogen dioxide NO2, and carbon dioxide CO2) are detected by low-cost sensors. The ZigBee wireless protocol is used for communication between the PC and UAV. This work is carried out to assess the air quality in urban areas, given the heavy road traffic and the emissions of some companies. The data analyzed were collected from December 2, 2022 to January 3, 2023, in two major cities of Cameroon, Douala and Kribi. The periodic average values of the detected pollutants are 222 ± 22 μg/m3 and 85.7 ± 8.6 μg/m3 for PM2.5, 560.8 ± 1.0 ppm and 555.6 ± 1.0 ppm for CO2, 4.2 ± 0.2 ppm and 0.7 ± 0.1 ppm for NO2, and 27.6 ± 2.8 ppm and 4.5 ± 0.5 ppm for CO in Douala and Kribi respectively. This made it possible to have an air quality index (AQI) of 444.2 for Douala City and 171.3 for Kribi City. These high values indicate poor air quality during the measurement period.

Design and optimization of haze prediction model based on particle swarm optimization algorithm and graphics processor

With the rapid expansion of industrialization and urbanization, fine Particulate Matter (PM2.5) pollution has escalated into a major global environmental crisis. This pollution severely affects human health and ecosystem stability. Accurately predicting PM2.5 levels is essential. However, air quality forecasting currently faces challenges in processing vast data and enhancing model accuracy. Deep learning models are widely applied for their superior learning and fitting abilities in haze prediction. Yet, they are limited by optimization challenges, long training periods, high data quality needs, and a tendency towards overfitting. Furthermore, the complex internal structures and mechanisms of these models complicate the understanding of haze formation. In contrast, traditional Support Vector Regression (SVR) methods perform well with complex non-linear data but struggle with increased data volumes. To address this, we developed CUDA-based code to optimize SVR algorithm efficiency. We also combined SVR with Genetic Algorithms (GA), Sparrow Search Algorithm (SSA), and Particle Swarm Optimization (PSO) to identify the optimal haze prediction model. Our results demonstrate that the model combining intelligent algorithms with Central Processing Unit-raphics Processing Unit (CPU-GPU) heterogeneous parallel computing significantly outpaces the PSO-SVR model in training speed. It achieves a computation time that is 6.21–35.34 times faster. Compared to other models, the Particle Swarm Optimization-Central Processing Unit-Graphics Processing Unit-Support Vector Regression (PSO-CPU-GPU-SVR) model stands out in haze prediction, offering substantial speed improvements and enhanced stability and reliability while maintaining high accuracy. This breakthrough not only advances the efficiency and accuracy of haze prediction but also provides valuable insights for real-time air quality monitoring and decision-making.

Near real-time interpolative algorithm for modelling air quality in underground mines

As real-time air quality monitoring becomes more prevalent in US underground mines, it is important to provide the highest data reliability with the fewest possible sensors. Real-time sensors remain costly, and these costs are not exclusively financial; the time required to install, calibrate, and maintain real-time sensors poses a large barrier to widespread implementation. Current atmospheric monitoring systems typically rely on displaying point-specific values. This requires operators to infer real-time airborne contamination distributions. Monitoring and control software utilizing mine ventilation network (MVN) solvers has been implemented in limited cases because of their ability to simulate ventilation systems quickly, but these solvers use a one-dimensional representation of the mine, limiting spatial resolution of estimated distributions. Computational fluid dynamics (CFD) has likewise been considered as a means to improve spatial resolution, but processing times prevent its use as a basis for monitoring and control. For the real-time monitoring of airborne contamination distributions, we propose a spatial interpolation method that can estimate the distribution of airborne contaminants in near-real time. This method provides a middle ground between fast processing times and increased spatial resolution. With the use of a pathfinding algorithm and optimization through absolute percentage error minimization, this method outperforms spatial interpolation with a Euclidean distance. By providing contamination distribution information to operators, this method and its derivatives stand to outperform current atmospheric monitoring systems.

Sensorsync auto: Integrating IoT sensors for smart vehicle air quality monitoring

This project introduces an innovative Air Quality Monitoring System based on IoT technology, leveraging components such as NodeMCU, DHT11, MQ135, a buzzer, an LCD, and the Blynk 2.0 app. The primary objective is to provide real-time monitoring of environmental conditions, encompassing temperature, humidity, and air quality, while also offering audible alerts for immediate user notification. The NodeMCU functions as the central microcontroller, interfacing with the DHT11 sensor for temperature and humidity readings and the MQ135 sensor for air quality measurements. The collected data is visually displayed on a 16x2 LCD screen. Additionally, the system enables remote monitoring and control through the Blynk 2.0 app. The app displays comprehensive information using gauge widgets for temperature, humidity, and air quality, along with detailed statistics through a display widget. To enhance user awareness, an integrated buzzer delivers audible alerts when air quality exceeds predefined thresholds. The Arduino sketch incorporates essential libraries for sensor interfacing, data processing, and seamless integration with the Blynk app. The app is configured with widgets corresponding to virtual pins in the sketch, ensuring smooth communication between the hardware and the app. This IoT-based Air Quality Monitoring System serves as a valuable tool for individuals and communities concerned about indoor or outdoor air quality. The system's features, including real-time data visualization, remote monitoring capabilities, and an alert system, contribute to creating a more informed and healthier living environment.

IoT Based Air Quality Monitoring and Controlling System for Hospital

This project introduces an IoT based Air Quality Monitoring and Controlling System tailored for hospital environments. Utilizing a variety of sensors, including those for particulate matter, carbon dioxide, and volatile organic compounds, the system ensures continuous real-time monitoring of indoor air quality. NodeMCU facilitates wireless communication, enabling the transmission of data to a central server or cloud platform. Hospital staff can remotely monitor air quality conditions, receiving threshold alerts in the event of pollutant levels surpassing predefined limits. The system integrates a controlling mechanism, allowing for the activation of ventilation systems or air purifiers based on the collected data. Energy efficiency is prioritized through intelligent control of air quality devices. Historical data analysis enables administrators to identify patterns and improve overall air quality management. The system seamlessly integrates with existing hospital infrastructure and is designed for scalability, ensuring adaptability to evolving needs. Overall, this IoT-based solution offers a smart and automated approach to maintaining a healthy indoor environment in hospital settings.

Energy Management System of Luminosity Controlled Smart City Using IoT

INTRODUCTION: With the escalating rates of urbanization, there is a pressing need for enhanced urban services. The concept of smart cities, leveraging digital technologies, offers a promising solution to elevate urban living. The integration of Internet-of-Things (IoT) in urban infrastructure, particularly on highways, opens avenues for novel services and cross-domain applications through Information and Communication Technologies. However, the efficient functioning of an IoT-enabled smart city necessitates careful energy resource management.&#x0D; OBJECTIVES: Propose a Highway Lighting System (HWLS) integrating IoT technologies to enhance urban services, focusing on significant energy savings and real-time environmental parameter monitoring.&#x0D; METHODS: To achieve the objective of enhancing urban services through the proposed Highway Lighting System (HWLS), the system was designed and implemented by integrating cutting-edge sensors, communication links, and the Blynk IoT app. The deployment involved incorporating IoT technologies for real-time monitoring of air quality, air moisture, and soil moisture, alongside a fault identification system using GSM and GPS modules.&#x0D; RESULTS: The proposed HWLS demonstrates significant energy savings, consuming only 37.6% of the original power consumption. The incorporation of IoT technologies facilitates real-time monitoring of environmental parameters, enabling informed decision-making for urban service optimization. The fault-finding system, utilizing GSM and GPS modules, enhances the reliability of the lighting system.&#x0D; CONCLUSION: In conclusion, the Highway Lighting System (HWLS) represents a novel approach to smart city infrastructure, particularly in the context of urban lighting. The integration of IoT technologies not only contributes to energy savings but also enhances the overall efficiency of urban services. The proposed system's ability to monitor environmental parameters and identify faults demonstrates its potential for sustainable urban development and improved quality of life.

Wildfires exacerbate inequalities in indoor pollution exposure

Wildfires lead to dramatic increases in fine particulate matter pollution concentrations. Based on the premise that higher-income households purchase more defensive investments to reduce the degree to which outdoor pollution infiltrates indoors, in this study, we investigate how income contributes to outdoor–indoor pollution infiltration rates during wildfire events. Using crowd-sourced data from the PurpleAir Real-Time Air Quality Monitoring Network and econometric models that explore variations in monitor readings over time, we find increases in outdoor pollution lead to significant increases in indoor pollution, but disproportionately so in lower-income areas. The results highlight a new inequality in pollution exposure: not only are outdoor pollution levels higher for lower-income individuals, but indoor pollution levels are higher even for similar outdoor pollution levels.

Machine learning approaches for automatic classification of single-particle mass spectrometry data

Abstract. The chemical composition of aerosol particles is a key parameter for human health and climate effects. Single-particle mass spectrometry (SPMS) has evolved to a mature technology with unique chemical coverage and the capability to analyze the distribution of aerosol components in the particle ensemble in real time. With the fully automated characterization of the chemical profile of the aerosol particles, selective real-time monitoring of air quality could be performed, e.g., for urgent risk assessments due to particularly harmful pollutants. For aerosol particle classification, mostly unsupervised clustering algorithms (ART-2a, K-means and their derivatives) are used, which require manual postprocessing. In this work, we focus on supervised algorithms to tackle the problem of the automatic classification of large amounts of aerosol particle data. Supervised learning requires data with labels to train a predictive model. Therefore, we created a labeled benchmark dataset containing ∼ 24 000 particles with eight different coarse categories that were highly abundant at a measurement in summer in Central Europe: elemental carbon (EC), organic carbon and elemental carbon (OC-EC), potassium-rich (K-rich), calcium-rich (Ca-rich), iron-rich (Fe-rich), vanadium-rich (V-rich), magnesium-rich (Mg-rich) and sodium-rich (Na-rich). Using the chemical features of particles, the performance of the following classical supervised algorithms was tested: K-nearest neighbors, support vector machine, decision tree, random forest and multi-layer perceptron. This work shows that despite the entrenched position of unsupervised clustering algorithms in the field, the use of supervised algorithms has the potential to replace the manual step of clustering algorithms in many applications, where real-time data analysis is essential. For the classification of the eight classes, the prediction accuracy of several supervised algorithms exceeded 97 %. The trained model was used to classify ∼ 49 000 particles from a blind dataset in 0.2 s, taking into account also a class of “unclassified” particles. The predictions are highly consistent with the results obtained in a previous study using ART-2a.

A Portable ppb-Level Formaldehyde Sensor for Real-Time Air Quality Monitoring

A Portable ppb-Level Formaldehyde Sensor for Real-Time Air Quality Monitoring

Optimizing Transportation Safety with Realtime Air Quality Monitoring and Health Alert System

Abstract: The proposed system is a smart air quality monitoring and health alert system designed specifically for truck drivers. The system aims to address the issue of poor air quality within truck cabins, which can lead to a range of health problems for drivers who spend extended periods on the road. The system utilizes advanced sensors to monitor the air quality within the truck cabin, measuring a range of parameters including carbon monoxide, carbon dioxide, and particulate matter. In addition to air quality monitoring, it also includes a health alert system that can notify drivers of potential health risks based on their personal health data and environmental conditions. This feature helps drivers take proactive measures to prevent health problems and maintain their well-being while on the road. Proposed system is a cost-effective and user-friendly solution that can improve the health and safety of truck drivers, ultimately benefiting both the drivers and the trucking industry. It can also be easily integrated into existing fleet management systems, making it a scalable solution for trucking companies of all sizes.

Using Mobile and Fixed Internet of Things Sensing Networks, Real-Time Monitoring and Prediction of Air Quality in One\'s Immediate Vicinity

Abstract: A significant global problem has emerged in several locations as a result of air pollution and its adverse impact on human well-being. Recently, there has been a rise in the number of researchers that are keen on evaluating and forecasting the air quality in close proximity to individuals. The use of the Internet of Things (IoT) across several businesses has significantly enhanced people's quality of life by interconnecting multiple sensors in diverse locations. Moreover, the Internet of Things (IoT) has streamlined the task of monitoring air pollution. The conventional utilization of stationary sensors is inadequate for acquiring an accurate and all-encompassing representation of the air pollution levels in close proximity to people. This is due to the fact that the sensors in closest proximity to people may be located many kilometers apart from each other. The objective of our study is to construct a model that precisely depicts the air quality pattern within a certain geographic area. This objective will be accomplished by using a combination of stationary and portable Internet of Things sensors. The sensors are affixed to vehicles that are carrying out surveillance in the vicinity. Our methodology allows for a thorough examination of the whole spectrum of air quality fluctuations in neighboring regions. Through the use of many machine learning algorithms and realworld data, we showcase the efficacy of our methodology in accurately discerning and forecasting air quality without compromising on accuracy. The results of our research indicate that there is significant potential for efficiently monitoring and forecasting air quality in the context of a smart city.

Analysis of Water Pollution in The Musi River Due to Community and Industrial Activities Using Onlimo

The Musi River is a natural resource which is one of the main trade routes and the largest air supplier for the people of South Sumatra. However, the condition of the Mus River has undergone changes due to dense settlements and industry. Water quality is an important factor in the sustainability of an ecosystem. Ecosystem damage can start from declining air quality which results in industrial waste that pollutes the Musi River. Water quality measurement parameters include measurements of oxygen, TSS, COD, BOD. Observation station sampling was carried out using Onlimo telemetry technology. Onlimo is a remote, online and real-time air quality monitoring technology developed by BPPT. Sampling was carried out during the transition season on the Musi River which was divided into station 1 located in the Musi River (PDAM Tirta Randik area, Balui Air Unit, Sanga Desa District). On May 10, 2023 there was a pollution index of 7.32 with moderate contamination with the critical parameters Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD). There was an increase in the Pollution index of 7.51 due to an increase in Nitrate from 16 May 2023 to 17 May 2023 due to input from anthropogenic waste caused by differences in sampling time, besides that season and rainfall are reported to play an important role in dissolving a number of components in the waters. For this reason, to prevent and improve the function of the Musi River, namely the government must act decisively on violations committed by factories or companies or the public who dispose of waste carelessly

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

Enhanced IoT Solution System for Smart Agriculture in Indonesia

This innovative solution encompasses an IoT-based smart agricultural system. The system includes a solar panel power supply, a weather station (monitoring temperature, humidity, air pressure, wind speed and direction, raindrop), an air quality monitoring module (measuring NH4, CO2, and PM2.5 levels), a soil quality measurement module, a microcontroller, a GSM cellular module for internet connectivity, and an automated relay actuator for a water pump. The water pump's operation is contingent upon the soil moisture levels, ensuring efficient irrigation. The utilization of an IoT-driven smart agricultural system enables real-time monitoring of weather conditions, air quality, and agricultural soil conditions. Additionally, it facilitates the remote control of automated water pumps via smartphones—an aspect that remains unattainable within the confines of traditional Indonesian agriculture. Leveraging an Android application on smartphones, this system delivers detailed insights. To present the collected sensor data in accordance with prevailing environmental and soil states, a dedicated Android application has been developed. Moreover, this application facilitates the control of the water pump to irrigate arid soil as required. The data is transmitted via the internet to a cloud server, serving as the intermediary that receives data from the IoT system's sensors positioned at the farm.