Air Quality Monitoring System Research Articles

Detection and identification of shape, size, and concentration of particulate matter in ambient air using bright field microscopy-based system

Currently, air pollution monitoring for supplemental information is performed using optical sensors based on light scattering by particulate matter. These devices provide results in the form of particle counts and the concentration of particulate matter is calculated and demonstrated in real-time. We have developed a bright field (BF) microscopy-based system for air quality monitoring (AQM) in real-time, named SENSurAIR, which provides not only just counts but shape, size as well as concentration. The system comprises a mini vacuum pump, cyclone separator, LED light source (λ = 480 nm), and bright field microscopic unit. The pump drives the ambient air toward the sampling media so that PM2.5 is impacted and deposited. The light rays from the source are incident on the sampling media, and microscopic unit records images of particulate matter. The custom-developed algorithm then processes the recorded images to identify the shape, size distribution, and concentration of PM2.5. The size and shape of PM2.5 are the principal factors that help identify the sources contributing to PM2.5 in ambient air. The developed device is evaluated by collocating with the reference grade Beta Attenuation Monitoring (BAM) system. The system is fully automatic, compact, low-cost, and can be integrated with a wireless network for data communication. The simultaneous monitoring of shape, size, and concentration using the developed device is unique and which will be highly useful to alarm the health hazards due to the existing pollution.

Air Quality Detector and Analysis System

This paper proposes an air quality detector and analysis system with a web application. The massive development of the Industrial factories in urban and peri-urban areas is creating much chaos; such as urbanization, deforestation and mostly continuous greenhouse gas emission. Such development has much impact on Air quality, the Air quality monitoring, detection and analysis system can be one of the initial solutions that should be developed to help the stakeholders and decision makers such as National Environmental Affairs Department, Ministry of Health, National Planning Commissions or other environmental researchers and laboratories, both public and private institutions to monitor and measure the surrounding air quality and come up with the better solutions and planning. The data collected by device or system will help in research, decision and policy making in finding the best solutions to avoid health risks for citizens, industrial workers and other surrounding living species. To develop an effective solution, there is need to embed the system with Machine learning and AI Algorithm that will be able to detect the presence and identify the number of various gases, help in decision making and calculations by using just few numbers of sensors. The system will use Neural network model (Self organizing feature maps) algorithm. This algorithm can work with both low number of data sets and high hence it is robust enough to handle data with noise and errors. Developed models are applied to simulate and forecast based on the annual and daily data. In general, the models could predict air quality patterns with modest accuracy. The device will be used to alert the users on the presence of flammable gases as well in real-time

Smart Campus 4.0: Digitalization of University Campus with Assimilation of Industry 4.0 for Innovation and Sustainability

According to the United Nations, global sustainability in terms of social, economic, and environmental issues must be achieved by 2030. SDGs 4 and 9 are related to education and strengthen the attainment of quality education and infrastructure innovation. Resilient infrastructure plays a significant role in strengthening the campus in terms of education, management, placement and environment. These all aspects come under the smart campus. Smart campus 4.0 is the amalgamation of multitude industry 4.0 enabling technologies for delivering smart and innovative facilities with the aspect of sustainability. The previous studies have proved that the sustainable development goals (SDGs) can be achieved with the amalgamation of industry 4.0 enabling technologies in the campus such as cloud computing, artificial intelligence (AI), Internet of things (IoT), edge/fog computing, blockchain, robot process automation (RPA), drones, augmented reality (AR), virtual reality (VR), big data, digital twin, and metaverse. The main objective of this study to provide the detailed discussion of all industry 4.0 enabling technologies in single research related to smart campus. The findings observed are IoT-Based Drone system is intended to ground patrolling, and a cloud server to develop a smart campus energy monitoring system. AI for campus placement prediction model; cloud and Edge computing architecture to build an intelligent air-quality monitoring system. The novelty of the study, it has discussed all industry 4.0 enabling technologies for a smart campus with challenges, recommendations, and future directions.

Perceived air quality and satisfaction during implementation of an automated indoor air quality monitoring and control system

Cooking and cleaning are among the largest sources of home pollutant emissions. To reduce these emissions, we developed an automated indoor air quality (IAQ) control intervention that operated based on real-time sensor readings of particulate matter (PM2.5) and evaluated the perceptual differences between this intervention and a baseline condition. We employed a 14-participant crossover study design in a one-bedroom apartment module. Participants experienced one of two conditions: (1) Advanced Control–automated IAQ interventions including a stove hood, two portable air cleaners, and a bathroom exhaust powered on/off based on predefined PM2.5 thresholds measured by environmental sensors; and (2) Standard Control–participants controlling IAQ interventions (e.g., stove hood) manually. Each condition lasted two weeks. Participants followed standardized cooking and cleaning protocols and filled out surveys assessing psychosocial and perceptual outcomes. Observations indicated that weekly IAQ satisfaction, perception, and preferences were similar between the two conditions—despite lower PM2.5 concentrations during cooking and cleaning for the Advanced Control versus Standard Control Condition. When pairing IAQ complaints with PM2.5 concentrations during cooking, we observed participants made complaints when PM2.5 concentrations >∼80 μg/m3 but few complaints when <∼60 μg/m3. Lower PM2.5 concentrations were observed for the Advanced Control versus Standard Control Condition during cleaning, but these lower concentrations did not appear perceivable by participants due to far lower PM2.5 concentrations during cleaning overall. Our observations suggest a connection between PM2.5 concentration and IAQ complaints made by participants, thereby providing possible thresholds for perceivable IAQ changes.

Real-Time IoT-Based Air Quality Monitoring and Health Hazards Indicator System for Mines Regions: A Case Study of Bulyanhulu Gold Mine

Air quality in mining regions is a significant concern due to the release of pollutants from mining activities, posing health risks to nearby communities. However, limited information on air quality levels often leads to neglect of this issue. Inhaling pollutants like PM2.5/PM10, CO, CO2, SO2, and NO2 can result in chronic diseases such as respiratory issues, asthma, and cancer. To tackle this problem, a study suggests the implementation of a real-time Internet of Things (IoT)-based air quality monitoring and health hazards indicator system for mining regions. The proposed system utilizes a reliable wireless sensing system, incorporating sensors like MQ7, MQ135, MQ136, MiCS4514, PMS7003, and DHT22, along with ESP8266, STM32, ATmega328 microcontroller, LoRa shields, and the ThingSpeak IoT server. It ensures continuous operation with a self-contained design, including a solar charger shield connected to a photovoltaic solar panel and rechargeable battery. The smart sensing device continuously monitors air quality and uploads real-time data to the cloud through a coordinator node. The collected data is processed to calculate the Air Quality Index (AQI), which is analyzed to generate early warnings and indicate potential health hazards. The results are accessible through a web-based dashboard for easy visualization. This system simplifies monitoring and provides accurate pollutant data. It supports environmental stakeholders by aggregating and analyzing air quality data, generating reports, and facilitating public access to air quality information. Additionally, it helps identify health hazards, enabling informed decision-making, policy formulation, and mitigation strategies.

DEVELOPMENT OF AN INTERNET OF THINGS BASED AIR QUALITY MONITORING SYSTEM USING MACHINE LEARNING

Air pollution and its negative impacts on human health have become serious concerns in many places throughout the world. The traditional methods of monitoring air quality, such as manual sampling and laboratory analysis, are time-consuming, expensive, and may not provide real-time information. In this study, an IoT-based Air Quality Monitoring System that uses Machine Learning to provide accurate and timely analysis of air quality data is presented. The system collects data from a network of sensors measuring various air quality parameters, processes the data using ML algorithms to identify patterns and predict future conditions, and provides insights into the current state of the environment. The findings showed that the emissions had an inversely proportional impact on air quality in the study region and the system achieved an accuracy of 0.978. This study has the potential to provide accurate and timely analysis of air quality data and regulate air quality in real-time.

Highly Sensitive Wireless NO2 Gas Sensing System

There is a need for reliable and accurate air quality monitoring systems in order to secure human health from the air pollutants such as nitrogen dioxide (NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ). The development of low-cost and modern technology-compatible sensing systems is essential. In this work, a highly sensitive and selective Indium Gallium Zinc oxide (IGZO) thin film transistor has been used to design an efficient and wireless NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sensing system. The sensing system uses the IGZO TFT as a current source for the current-starved ring oscillator where exposed NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> concentration determines the oscillation frequency. The presented NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> gas sensing system exhibited excellent sensitivity of 5.39 MHz/ppm with the lowest detection limit (LOD) of 50 ppb at room temperature. To the best of the authors’ knowledge, the achieved sensitivity is the best-reported performance for frequency-based NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sensors. The sinusoidal output of the sensor obviates the need for costly peripheral signal conditioning circuits and allows direct integration with wireless systems. The 13.56 MHz RFID antenna connected to the output of the sensor is used to show wireless sensing compatibility and highlights the excellent candidacy of the proposed work for the growing Internet of Things (IoT) and untethered sensor applications.

Webar Application For An Air Quality System In IOT-Based Smart Environement

Augmented Reality (AR) and the Internet of Things (IOT) are trending technologies that have gained popularity in smart cities. In this paper, we propose a non-expansive and uncomplicated prototype that combines the two technologies, namely, the Internet of Things and Augmented Reality, to build an air quality monitoring system for a smart environment. The webAR application developed can be used on any device, and it control measurements of temperature, humidity, and air quality data collected from multiple IOT devices. In addition to the ease of use and speed of loading compared to other applications, our prototype offers an application that does not require installation, a major factor that limits user use. Finally, the paper indicates the prospects of these technologies and the challenges their development is facing.

Analysis of Spatio-temporal PM2.5 and CO2 Concentrations Distribution with PSCF in the Greater Bandung Air Basin

The limitations of expensive main monitoring stations can be addressed by using low-cost sensor-based measuring stations. A low-cost sensor-based air quality monitoring system has been implemented in Telkom University, BRIN Pasteur, and BRIN Taman Sari areas to measure PM2.5 and CO2 concentration in Bandung vertically and horizontally. Vertically, the CO2 concentration at the highest measuring station is indirectly affected by local activities. However, PM2.5 concentration is still influenced by local actions. Horizontally, using the independent T-test and ANOVA, PM2.5 concentrations tended to be homogeneous regarding the significance values in the four periods, namely 0.916, 0.03, 0.727, and 0.047. Meanwhile, the concentration of CO2 at each station tends to be heterogeneous along significance values of 0.646, 0.03, 0.02, and 0.01. The vertical and horizontal analysis shows that CO2 concentrations tend to be heterogeneous due to differences in altitude and spatial characteristics of the measurement sites. Meanwhile, PM2.5 tends to be more homogeneous by having the same pattern at different heights and spatial locations. The long-distance potential sources of PM2.5 are estimated to come from the Indian Ocean and Cirebon region because they have the highest PSCF and CWT values, 0.5-0.7 and 55-65 µg/m3.

Якість повітря житлових приміщень як важлива детермінанта здоров’я та пріоритетне завдання у галузі гігієни середовища життєдіяльності людини

According to WHO definition, indoor air quality is an important determinant of human health. Emissions from building materials, furniture, and consumer goods, combustion processes, and infiltration of polluted atmospheric air are considered the main sources of hazardous chemicals and solid pollutants in indoor air. Aim. To conduct an analysis of the results of comprehensive sanitary and hygienic studies of air quality in residential buildings and apartments in order to determine priority chemical pollutants and products of their transformation; justify the need to improve the air quality monitoring system of residential premises taking into account modern WHO recommendations. Materials and Methods. The study of air samples of residential premises was carried out on the basis of the L.I. Medved’s Research Center of Preventive Toxicology, Food and Chemical Safety of the Ministry of Health of Ukraine (2000-2015) and Kyiv Center for Disease Control and Prevention of the Ministry of Health of Ukraine (2015-2019). A comprehensive toxicological and hygienic assessment of the air in residential premises was carried out based on the appeals of citizens who complained about the unpleasant smell of the air. A total of 123 residential objects (856 samples) were investigated. Applied methods of systematic and comparative analysis, cross-sectional survey method (survey of residents of residential buildings and apartments), sanitary and hygienic methods; sanitary and chemical methods (29 methods according to the institution's accreditation). Statistical data processing was performed using the IBM SPSS Statistics 29.0.0.0 program (Spearman correlation analysis), with p≤0.05. Results. A high level of chemical air pollution in residential premises, carried out based on citizen complaints, was established. Various external and internal sources of chemical air pollution were determined, in particular by marker pollutants: formaldehyde, phenol, and acetone. It has been established that the cause of their emission into the air is household items (furniture), various construction and finishing materials (polymers), which are used mainly during repair work or in new buildings. A direct positive connection was found between the level of exceeding the maximum permissible limit of marker chemical pollutants and the well-being and health of residents of settlements. Symptoms such as fatigue, cough, diseases of the upper respiratory tract, headaches and dizziness, allergic conditions were most often observed, which determines the urgency of improving the preventive work of primary care doctors ‒ family medicine in the direction of detecting pre-pathological conditions among healthy individuals and especially sensitive contingents (children, teenagers, pregnant women) living in the conditions of a "sick building". Conclusions. The unsatisfactory state of air inside residential premises determines the need to improve the existing system of monitoring the quality of air inside residential premises, in particular, improving methods of sampling and analysis of priority chemical indoor air pollutants to assess the risk of their combined action. Keywords: indoor air, "sick building syndrome", phenol, formaldehyde.

A Case Study of Air Quality and a Health Index over a Port, an Urban and a High-Traffic Location in Rhodes City

One of people’s greatest concerns about air quality degradation is its impact on human health. This work is a case study that aims to investigate the air quality and the related impact on people’s health in a coastal city over the eastern Mediterranean. The analysis proceeded during a low-tourist density period, covering the days from 17 to 27 November 2022. Hourly PM2.5, NO2 and O3 concentration records from three, mobile, Air Quality Monitoring Systems (AQMS), established in an urban location, port and central area of Rhodes city, are analyzed. To investigate the impact of pollution levels on human health, the Air Quality Health Index (AQHI) is calculated. The daily and diurnal variation of pollutants’ concentration and AQHI among the different areas, as well as the relation among the ambient air pollutants and AQHI, are studied. Additionally, to investigate the impact of wind regime on the variation of pollution and AQHI levels, the hourly zonal and meridional wind-speed components, as well as the temperature at 2 m, the dew point temperature at 2 m, and the height of the boundary layer from ERA5 reanalysis, are retrieved for the region of the southeastern Mediterranean. Results show that the highest pollution level occurs in the city center of Rhodes, compared to the rest of the studied locations. In general, the findings do not show exceedances of the pollutants’ concentration according to the European Directive 2008/50/EC. Moreover, findings show that in some cases, the health risk is classified from Low to Moderate in terms of AQHI. The analysis indicates that the climate conditions affect the pollutants’ concentration due to dispersion, and likely, the atmospheric transport of pollutants. Finally, this work aims to improve the knowledge regarding the air quality of southeastern Greece, promoting the framework for the green and sustainable development of the South Aegean Sea.

Air Watch: An Ample Design of Indoor Air Quality Monitoring System

The environment is getting contaminated drastically by introducing harmful materials into the atmosphere through the excessive activities of human in adding comfort and luxurious style in their living. The pollutant level in air has high impact of healthiness of the person inhaling it. Air not outside as well indoor is infected by various hazardous particles and gases. To assess the air quality in the particular environment, it stimulates the need to monitor the hazardous elements listed. The internet of things (IoT) and artificial intelligence (AI) became the part of human life by adding smartness in their daily routines from facilitating control over appliances to own health factor as well automate operations. The primary objective of the effort is to identify the gases that cause air pollution, measure the air quality, and assess the level of pollution so that we can determine which gases cause pollution and at what place is the air being impacted. An IoT based indoor air quality monitoring system is built through incorporating carbon monoxide (CO), carbon dioxide (CO2), Ozone (O3), particulate matters (PM) and volatile organic components (VOC) sensors into Arduino board. The design ensures a complete air monitor, extends reliable service at low cost. A rule based system is developed to automate events upon the estimated air quality index (AQI) out of the sensory circuit.

Design of a Low-Cost System for the Measurement of Variables Associated With Air Quality

Poor air quality is a global problem that affects human health and ecosystems all over the world. For that reason, many countries invest in networks of reference monitoring stations that measure air quality in real-time. However, the high installation and maintenance costs of these stations mean that smaller cities or underdeveloped regions are less covered by such stations. In these regions, alternative detection technologies such as low-cost monitoring systems can be used instead. Unfortunately, the results of such monitoring devices are often questioned by academics. This contribution proposes the design of an air quality monitoring system where cost and reliability have been optimized from a hardware point of view. In addition, the device has been coupled to an IoT platform so that inexperienced users can view and share the collected information The design of affordable systems increases the ability to perform ad hoc analysis at the level of neighborhoods or cities by citizen science organizations or governmental entities.

In-Car Air Quality Notification Using Internet of Things Platform

In the development of modern society, transportation is essential to support daily activities. With the existence of vehicles, the activities carried out by the community will be much easier. The car is a means of transportation that is often used by the community to carry out activities related to their respective goals. In-car air quality is very crucial for society because most people spend their time in cars. Often, the air in the car contains not only good air but also bad air for humans. The impact of poor air quality can make people sleepy, as well as cause respiratory problems and several other diseases that can even affect a driver's ability to make decisions. Therefore, there must be a real-time monitoring and notification system for air quality in the car. The purpose of this research is to be able to develop an air quality monitoring system for cars and provide notifications if the air quality worsens in real-time. In this study, researchers developed an air quality monitoring and notification system for cars using the NodeMCU ESP8266 microcontroller, sensors MQ-7, MQ-135, PMS5003, and the IoT platform, namely Blynk and ThingSpeak. The result of this research is a system that can detect, measure, and monitor air quality levels of carbon dioxide (CO), carbon monoxide (CO2), particulate matter (PM10), and particulate matter (PM2.5) via the internet in real-time. and displays air quality data on the dashboard, then provides notifications using the Blynk application if the air quality is low and getting worse.

Development of Arduino Based Air Quality Monitoring Systems for Measuring Gas Sensor

There has been an increase in the number of hazardous gases emitted in the environment due to rapid industrialization. It is becoming very important to monitor the quality of air around so as to take adequate steps in protecting ourselves. This work proposes a mobile air quality monitoring system that is portable enough to be carried around. This system is designed to give real time update on the air quality around. The system contains an air quality sensor, which measures the air quality. The system also contains an LCD screen which displays alerts, warnings and the air quality, in parts per million (ppm), on the screen. A buzzer is also implemented in the system which is triggered when the air quality, is above a certain value. The air quality in different venues in Covenant University were obtained, analysed, and compared with one another. The gases tested in this study include CO2, ammonia, and alcohol. Another experiment was conducted to check the effect that distance has on how the sensor senses the gas. Due to the unavailability of another indoor air quality measuring device for comparison, several data from different locations were taken during the course of this project in order to make comparisons. A significant observation that was made was that increase in temperature is directly proportional to poor air quality. The values of the air quality in H213 in CST ranged from 250 ppm to 275 ppm which shows that the air quality in H213 is good and the environment is conducive. The values of the air quality CST Buttery ranged from 330 ppm to 380 ppm which shows that the air quality in the Buttery is not so good and the environment is not so conducive. When the gas was placed 0 cm to 1.2 cm away from the sensor, the air quality ranged from 1000 ppm to 4500 ppm and when the gas was placed more than 1.2 cm away from the sensor, the air quality ranged from 200 ppm to 500 ppm. This shows that the sensor is more sensitive when the gas is placed close to it.

Development and Verification of an IoT-enabled Air Quality Monitoring System Based on Petri Nets

An internet of things (IoT) system for monitoring environmental parameters on campus has been successfully developed in this study. Various sensors are adopted in front of the IoT system for detecting air pollutants. The architecture of LongRange in Class C uses low-power and long-distance transmission technologies, which has been set up on the large campus so that the terminal equipment can reach a balance between downlink latency and battery life, making it the best communication protocol. In addition, this monitoring system uses the Petri net software tool to build a correct IoT platform based on the fundamental design process and to demonstrate the feasibility of the IoT system through simulation-based verification. Finally, the experimental results indicate that the IoT system for monitoring the environmental parameters on campus achieves the goal of an acceptable data transmission success rate of more than 95%. Thus, it can facilitate the air quality trends for policy making as well as the hazardous prediction and prevention.

Air pollution in Iran: The current status and potential solutions

Air pollution has been integrated into global challenges over the last few years due to its negative impact on the health of human beings, increasing socio-economic risks, and its contribution to climate change. This study attempts to evaluate the current status of Iran’s air pollution with regard to the sources of emissions, control policies, and the health and climate consequences that have resulted through available data from monitoring stations reported in the literature, official documents, and previously published papers. Many large cities in Iran surpass the permissible concentration of air pollutants, particularly particulate matter, sulfur dioxide, black carbon, and ozone. Although regulations and policies are in place and enormous efforts are being made to address air pollution issues in the country, implementation and enforcement are not as effective as they could be. The significant challenges may be regarded as the inefficiency of regulation and supervision systems, the lack of air quality monitoring systems and technology, particularly in industrial cities rather than Tehran, and the lack of continual feedback and investigations on the efficiency of regulation. Providing such an up-to-date report can bring opportunities for international collaboration, which is essential in addressing air pollution worldwide. We suggest that a way forward could be more focused on conducting systematic reviews using scientometric methods to show an accurate picture and trend in air pollution and its association in Iran, implementing an integrated approach for both climate change and air pollution issues, collaborating with international counterparts to share knowledge, tools, and techniques.

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.

Physical Fitness and Inflammatory Bowel Disease Risk Among Children and Adolescents in Taiwan

The incidence of inflammatory bowel disease (IBD) is increasing in newly industrialized countries but disease etiologies remain unclear. To investigate the association between physical fitness and subsequent IBD risk among children and adolescents in Taiwan. This nationwide cohort study was conducted between January 1, 2010, and December 31, 2018. Data sources included the Taiwan National Health Insurance Research Database, the National Student Fitness Tests Database, and the Air Quality Monitoring System Database. This study included students who were aged 10 years, completed physical fitness tests between grades 4 and 13, and had at least 1 year of follow-up. Data analysis was last performed on January 15, 2023. Physical fitness tests included cardiorespiratory endurance (CE; number of minutes to complete an 800-m run), musculoskeletal endurance (ME; number of bent-leg curl-ups in 1 minute), musculoskeletal power (MP; standing broad jump distance), and flexibility fitness (FF; 2-leg sit-and-reach distance). Subsequent risk of IBD was compared among students based on physical fitness test results. Six-year cumulative incidences and hazard ratios (HRs) were calculated after adjusting for competing mortality. Performance was reported in quantiles, ranging from 1 (best) to 4 (poorest). There were 4 552 866 students who completed physical fitness tests between grades 4 and 13; among these students, 1 393 641 were aged 10 years and were included in the analysis. Six-year cumulative incidence of IBD risk was lowest among students in the best-performing quantile of CE (quantile 1, 0.74% [95% CI, 0.63%-0.86%]; P < .001), ME (0.77% [0.65%-0.90%]; P < .001), and MP (0.81% [0.68%-0.93%]; P = .005) compared with students in quantiles 2 through 4, respectively; however, no association was observed for quantiles of FF. After adjusting for competing HRs for mortality and other confounders, better CE was inversely associated with IBD risk (adjusted HR, 0.36 [95% CI, 0.17-0.75]; P = .007). Other measures of physical fitness were not independently associated with IBD risk. The results of this study suggest that CE was inversely associated with IBD risk among children and adolescents, but ME, MP, and FF were not independently associated with IBD risk. Future studies that explore the mechanisms are needed.

Evaluation of Air Quality Monitoring Networks in Korea: Focusing on Wind Direction and Speed

In this study, we suggested a methodology for evaluating the appropriateness of an air quality network using monthly wind direction and speed. First, the nearest weather station was identified using the latitude and longitude of the Air Quality Monitoring system (AQMs) in 2020. Next, the wind direction and wind speed data measured by the nearest weather station from 2010 to 2021 were applied to AQMs. Finally, a map with the range of air pollution influence was produced while varying air pollution influence distance and air pollution influence angle. On varying conditions, there were consistent areas in Gangwon-do that were without a range of air pollution influence. The percentage of areas without a range of air pollution influence in entire areas called percentage by non-measurement. The results derived are as follows: it showed that the percentage by non-measurement had an inverse relationship with the air pollution influence angle and distance, then it was from 0.01% to 48.86% for each condition. The method could propose additional AQMs locations based on the range of air pollution influence. The results indicate that the proportion of areas excluded from the impact range of air pollution should be considered when establishing regulations for additional installation of air quality monitoring systems.