Continuous Air Quality Research Articles

Spatial and temporal changes of air quality in Shandong Province from 2016 to 2022 and model prediction

This study investigates the spatial and temporal dynamics of air quality in Shandong Province from 2016 to 2022. The Air Quality Index (AQI) showed a seasonal pattern, with higher values in winter due to temperature inversions and heating emissions, and lower values in summer aided by favorable dispersion conditions. The AQI improved significantly, decreasing by approximately 39.4 % from 6.44 to 3.90. Coastal cities exhibited better air quality than inland areas, influenced by industrial activities and geographical features. For instance, Zibo's geography restricts pollutant dispersion, resulting in poor air quality. CO levels remained stable, while O3 increased seasonally due to photochemical reactions in summer, with correlation coefficients indicating a strong positive correlation with temperature (r = 0.65). Winter saw elevated NO2 levels linked to heating and vehicular emissions, with an observed increase in correlation with AQI (r = 0.78). PM2.5 and PM10 concentrations were higher in colder months due to heating and atmospheric dust, showing a significant decrease of 45 % and 40 %, respectively, over the study period. Predictive modeling forecasts continued air quality improvements, contingent on sustained policy enforcement and technological advancements. This approach provides a comprehensive framework for future air quality management and improvement.

Aerosols and black carbon variability using OMI and MERRA-2 and their relationship to near-surface air temperature.

An extinction of incoming solar radiation is taking place by absorption and scattering by dust, water droplets, and gaseous molecules. Such phenomena are responsible for altering meteorological variables. In the present study, temporal analysis of the aerosol optical thickness (AOT) and black carbon (BC) surface mass concentration was undertaken using an ozone monitoring instrument (OMI) and modern-era retrospective analysis for research and applications, version 2 (MERRA-2) satellite from the year 2018 to 2022. The study was mainly focused on the western states of India which are Rajasthan, Gujarat, and Maharashtra. The correlation of AOT and BC surface mass concentration with near-surface temperature (2m above ground level) was analyzed. BC and temperature shows strong negative correlation as BC is known for its absorption of radiation. It accumulates in the atmosphere and contributes to atmospheric warming while simultaneously bringing down the near-surface air temperature due to the reduced sunlight reaching the ground. Also, seasonal analysis was conducted for winter, summer, monsoon, and post-monsoon, which shows the higher values of AOT in monsoon; however, seasonal average BC surface mass concentration was found high in winter in each year for all three states. AERONET data from Jaipur, Rajasthan, and Pune, Maharashtra for the year 2021 was used to further evaluate the AOT generated from OMI. The results demonstrated a significant connection, with R2 values of 0.62 and 0.69, respectively. The temperature retrieved from MERRA-2 was also validated with ground truth data of the Continuous Ambient Air Quality Monitoring Station (CAAQMS) at both stations showing high agreement with R2 > 0.70.

Spatial Analysis of Intra-Urban Air Pollution Disparities through an Environmental Justice Lens: A Case Study of Philadelphia, PA

Urban air pollution has been long understood as a critical threat to human health worldwide. Worsening urban air quality can cause increased rates of asthma, respiratory illnesses, and mortality. Air pollution is also an important environmental justice issue as it disproportionately burdens populations made vulnerable by their socioeconomic and health status. Using spatially continuous fine-scale air quality data for the city of Philadelphia, this study analyzed the relationship between two air pollutants: particulate matter (PM2.5, black carbon (BC), and three dimensions of vulnerability: social (non-White population), economic (poverty), and health outcomes (asthma prevalence). Spatial autoregressive models outperformed Ordinary Least Squares (OLS) regression, indicating the importance of considering spatial autocorrelation in air pollution-related environmental-justice modeling efforts. Positive relationships were observed between PM2.5 concentrations and the socioeconomic variables and asthma prevalence. Percent non-White population was a significant predictor of BC for all models, while percent poverty was shown to not be a significant predictor of BC in the best fitting model. Our findings underscore the presence of distributive environmental injustices, where marginalized communities may bear a disproportionate burden of air pollution within Philadelphia.

Designing a low-cost wireless sensor network for particulate matter monitoring: Implementation, calibration, and field-test

Poor air quality can provoke severe impacts on health, necessitating environmental monitoring of atmospheric particulate matter (PM) to assess potential threats to human well-being. However, traditional continuous air quality monitoring systems are often costly and time-consuming in data treatment. Lately, there is a growing trend towards the use of low-cost wireless PM sensors, providing more detailed information than standard systems. This paper presents a system designed to measure air quality, specifically, a wireless sensor network composed of a distributed sensor network linked to a cloud system. The proposed system can efficiently measure air quality as it is cost-effective, small-sized, and consumes little power. Sensor nodes based on low-power long range (LoRa) motes transmit field measurement data to the cloud via a gateway, and a cloud computing system is implemented to store, monitor, process, and visualise the data. Advanced techniques were included in our cloud for data processing and analysis to optimise the detection of PM. Laboratory and field tests in the historic Riotinto mine validate the system's viability, offering real-time air quality information for nearby populations. Once calibrated, sensors demonstrate high accuracy, presenting mean error of −0.3% and low deviation (R2 = 0.96) when compared to regulatory systems for both low (<10 μgPM10/m3) and hazardous concentrations (300 μgPM10/m3), which makes them perfect as early warning systems for atmospheric pollution in mining.

Fine particulate matter air pollution and health implications for Nairobi, Kenya

Background: Continuous ambient air quality monitoring in Kenya has been limited, resulting in a sparse data base on the health impacts of air pollution for the country. We have operated a centrally located monitor in Nairobi for measuring fine particulate matter (PM2.5), the pollutant that has demonstrated impact on health. Here, we describe the temporal levels and trends in PM2.5 data for Nairobi and evaluate associated health implications. Methods: We used a centrally located reference sensor, the beta attenuation monitor (BAM-1022), to measure hourly PM2.5 concentrations over a 3-year period (21 August 2019 to 20 August 2022). We used, at minimum, 75% of the daily hourly concentration to represent the 24-hour concentrations for a given calendar day. To estimate the deaths attributable to air pollution, we used the World Health Organization (WHO) AirQ+ tool with input as PM2.5 concentration data, local mortality statistics, and population sizes. Results: The daily (24-hour) mean (±SEM) PM2.5 concentration was 19. 2 ± 0.6 (µg/m3). Pollutant levels were lowest at 03:00 and, peaked at 20:00. Sundays had the lowest daily concentrations, which increased on Mondays and remained high through Saturdays. By season, the pollutant concentrations were lowest in April and highest in August. The mean annual concentration was 18.4 ± 7.1 (µg/m3), which was estimated to lead to between 400 and 1,400 premature deaths of the city’s population in 2021 hence contributing 5%–8% of the 17,432 adult deaths excluding accidents when referenced to WHO recommended 2021 air quality guideline for annual thresholds of 5 µg/m3. Conclusion: Fine particulate matter air pollution in Nairobi showed daily, day-of-week, and seasonal fluctuations consistent with the anthropogenic source mix, particularly from motor vehicles. The long-term population exposure to PM2.5 was 3.7 times higher than the WHO annual guideline of 5 µg/m3 and estimated to lead to a substantial burden of attributable deaths. An updated regulation targeting measures to reduce vehicular emissions is recommended.

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.

Deciphering multi-temporal scale dynamics in the concentration, sources and processes of near surface ozone over different climatic regions of India.

This study aims to investigate the factors influencing seasonal and long-term (2003-2021) changes in the near surface ozone (850 hpa) concentrations over different climatic sub-regions of India. Detailed comparison of daily (2019-2021) near surface ozone values of ERA-5 and CAAQMS (Continuous Ambient Air Quality Monitoring Stations) ground-based measurements revealed that ERA-5 is temporally in phase with CAAQMS measurements falling indifferent climatic sub-regions of India. ERA-5 near surface ozone shows statistically significant long-term (2003-2021) positive trends [2-4 percent per decade (ppd)] over most of the climatic sub-regions, over Indo-Gangetic Planes (IGPs), Southern and Central India trends are particularly strong. Trends were also estimated for each season separately, which were largely positive (2-6 ppd) over Central and Southern India in the Autumn and Winter seasons. Extensive climatological analysis reveals that the reversal of winds in the Indian monsoonal system plays a vital role in such trend patterns across the Indian subcontinent. South-westerly winds from June through September presumably bring ozone deficit air of marine origin, thus causing a dilution effect while the North-easterly winds during late Autumn and early Winters plausibly bring ozone-rich air from the stratospheric-tropospheric efflux dominated Himalayan region. It allows near surface ozone enhancement over Central and Southern India. Seasonal Principal component analysis (PCA) revealed that precursor gases (CH4 and NO2) and climatic variables especially specific humidity (SH) are the primary drivers of near surface ozone variability in the Winter season, while in Spring, climatic variables like boundary layer height (BLH), temperature (T) and SH have a significant role. Principal component regression (PCR) reveals a long-term increase in near surface ozone levels mostly dominated by precursor concentration over IGPs and Southern sub-regions. Whereas, BLH, T and SH significantly explain near surface ozone trends over North-eastern and Coastal India.

IoT Based Smart Helmet for Industry Rutuja Kokate

The safety and well-being of industrial workers has become a primary concern for organizations worldwide. One of the critical aspects of industrial safety is the use of personal protective equipment (PPE), including helmets. This project proposes the development of an IoT-based smart helmet designed to enhance the safety and efficiency of industrial workers. The smart helmet integrates multiple sensors, such as temperature, humidity, motion, and gas sensors, to monitor the worker's environment in real-time. These sensors gather data on potential hazards like extreme temperatures, toxic gases, or sudden impacts. The helmet also includes a communication system that enables seamless interaction with control centers or other team members. The mining industry, a vital contributor to global economic growth and infrastructure development, grapples with inherent safety challenges posed by hazardous environments and unpredictable events. In response to these concerns, this abstract introduces the "Smart Helmet for Air Quality and Hazardous Event Detection" – a groundbreaking technology tailored specifically for mining. This advanced helmet integrates sensors like the MQ3 gas sensor, temperature and humidity sensors, LED lights with automatic controls, and a GPS module. Real-time data from these sensors are transmitted to an IoT platform, enabling continuous monitoring and immediate alerts. Traditional safety measures, including personal protective equipment and periodic monitoring, fall short in addressing the dynamic conditions of mining environments. The Smart Helmet offers a comprehensive solution by providing continuous air quality and environmental monitoring, coupled with automatic lighting and location tracking. This innovation marks a paradigm shift in mining safety, addressing the urgent need for proactive measures to safeguard the well-being of miners in the face of persistent safety challenges. Key Words: Safety, Sensors, IoT, Smart Helmet, Air quality monitoring

Tropospheric Ozone levels in and around southern city Bengaluru, India

The diurnal to seasonal ozone (O3) pre-cursors (NO, NO2, CO, VOCs) trends over local climatic zones (LCZs) with the changes in weather patterns with respect to land cover classes determines the O3 trends and useful for air quality management. In this work, diurnal to seasonal patterns of O3 concentrations are analysed across the different LCZs in Bengaluru region. The Central/State Pollution Control Boards (CPCB/SPCB) operate more than 500 continuous air quality and weather monitoring stations across the India. Hourly O3 concentration data were collected from CPCB for 5 different LCZs within the Bengaluru region over a period of four years (2019-2022). Time series analyses were conducted to examine the temporal variations in O3 levels. The results showed significant fluctuations in diurnal trends of O3 due to the influence of air temperature and relative humidity variations across the five LCZs within Bengaluru region. There were distinct seasonal trends in O3 concentrations with maximum (60-65 ppb) observed during the winter and summer seasons and that of minimum concentration in the range 20-25 ppb during the monsoon months. The observed maximum ozone levels (60 -65 ppb) found to be beyond the threshold national ambient air quality standard (NAAQ) and the accumulation exposure over threshold of 40 ppb (AOT 40) during the winter and summer. The O3 concentration trends are in line with the published research on premature mortality about 1200 annually for Karnataka state for chronic obstructive pulmonary disease owing to population exposure to O3 beyond its threshold level of NAAQ. Also, the annual rice yield loss is about 2% (~0.1 Metric Tonne) with the AOT 40 to paddy crop for Karnataka state. Thus, O3 diurnal to seasonal trends serve the first-hand information to local authorities for the action plan of air quality management taking into consideration O3 precursors, wind speed, and wind direction.

Assessing Real-Time Health Impacts of outdoor Air Pollution through IoT Integration

Air pollution constitutes a significant global challenge in both public health and the environment, particularly for countries undergoing industrialization and transitioning from low- to middle-income economies. This study aims to investigate the feasibility and effectiveness of a real-time air quality prediction system based on data collected from Internet of Things (IoT) sensors to help people and public institutions track and manage atmospheric pollution. The primary objective of this study was to investigate whether an IoT-based approach can provide accurate and continuous real-time air quality forecasting. The standard dataset provided by the Indian government was analyzed using regression, traditional Long-Short-Term Memory (LTSM), and bidirectional LSTM (BLSTM) models to evaluate their performance on multivariate air quality features. The results show that the proposed BLSTM model outperformed the other models in minimizing RMSE errors and avoiding overfitting.

CO2 synergistic emission reduction and health benefits of PM2.5 reaching WHO-III level in Pearl River Delta

Protecting human health is one of the fundamental goals of continuous air quality improvement. Considering the same sources of CO2 and air pollutants emissions, their emission control measures often have a certain degree of synergistic emission reduction effects To balance the costs and policy efficiency, policy makers should assess the synergistic benefits of CO2 emission reduction when formulating air quality improvement strategies. Aimed at the air quality improvement strategy targeting WHO-III level (i.e. 15 μg/m3) for PM2.5 in the Pearl River Delta (PRD) region, this study evaluates the synergistic CO2 reduction effects and the associated health benefits from this strategy which includes a range of air pollution prevention and control measures. The results of the study show that promoting the attainment of WHO-III for PM2.5 in the PRD region can bring about 74 Mt of synergistic CO2 emission reduction, resulting in an 18% reduction of CO2 emissions in the PRD compared to 2017. Among various measures considered, industrial restructuring, power supply transition and industrial energy consumption transformation exhibit the most pronounced synergistic effects. Therefore, these measures are recommended to be prioritized and promoted in the next stage of air pollution prevention and CO2 emission reduction. Furthermore, when the PM2.5 concentration in the PRD region reaches the WHO-III level, the number of PM2.5-related deaths will be estimated to reduce approximately 5.5 thousand compared to that in the current policy scenario. Through continuous structural transformation and emission reduction efforts, it not only facilitates the decline in regional PM2.5 concentration but also helps more regional residents to live in an environment with a relatively low PM2.5 concentration. In addition, in order to reduce the health impacts of PM2.5, it is recommended that the government should guide people to change their production and living styles in order to reduce pollutant emissions from anthropogenic activities.

Physio-chemical analysis of pollutants in residential micro-environment through continuous monitoring and settled dust characterization

The concern about indoor air quality in various micro-environments has been on the rise as buildings are vulnerable to an array of contaminants generated from various sources. The study aims to investigate and highlight the pollutant sources and composition in middle-income residential spaces through a case study approach. With large migration from small towns and saturation of megacities, the middle-income population in tier-2 cities in India is on the rise, raising concern about the well-being of the dense urban population. It involves a careful selection of typical MIG housing to identify major pollution sources from outdoor and indoor environments. The focus lies in the findings stemming from continuous air quality monitoring of PM10, PM2.5, SO2, NO2, HCHO, TVOC, CO and C6H6 and an in-depth physio-chemical analysis facilitated by Fourier transform infrared (FTIR), Analytical Scanning Electron Microscope (A-SEM) and energy-dispersive X-ray (EDX) spectroscopy of settled household dust. Analysis unveiled indoor PM2.5 concentrations of 21.93 µg/m3, exceeding WHO standards, whereas, TVOC concentrations were higher indoors than outdoors. Stark disparities between indoor and outdoor dust compositions and sources were also observed. Despite limitations, it provides a portrait of middle-income housing conditions, offering insights for interventions and future research on indoor air quality challenges.

Experimental Analysis on Continuous Ambient Air Quality Monitoring System (CAAQMS) at Vandalur, Chennai, India

Experimental Analysis on Continuous Ambient Air Quality Monitoring System (CAAQMS) at Vandalur, Chennai, India

Transition metals concentration within the PM10 fraction of atmospheric suspended particulate matter – case study, Craiova, 2021

The current case study had the main purpose of determining and reporting the concentrations of the most toxic transition metals present in the composition of the particles suspended in the atmosphere, namely: lead, nickel, arsenic and cadmium. In the collection of experimental data, the fraction of suspended particulate matter represented by inhalable particles with an aerodynamic diameter smaller than 10 microns (PM10) was studied, by taking, during two weeks within different seasons of the year 2021, two set of seven filters, each used for one day – from an area of heavy traffic, on one of the main arteries of the municipality of Craiova, near a continuous air quality monitoring station capable of monitoring PM10. Thus, it was possible to permanently compare the results obtained through this application with the official data – registered during the same time intervals – by the local division of the EPA, founding a remarkable data concordance.

Rancang Bangun Particle Counter untuk Monitoring Konsentrasi PM1, PM2.5 dan PM10 di Udara Berbasis IoT

Poor air quality is a serious health and environmental issue. Microscopic particles such as PM1, PM2.5, and PM10 cause respiratory disorders and other health problems. Therefore, accurate and continuous air quality monitoring is crucial to mitigate the impacts of air pollution. This research aims to design an Internet of Things (IoT)--based particle counter capable of real-time air quality monitoring and reporting via an online platform. The system utilizes a PMS5003 sensor to measure PM1, PM2.5, and PM10 concentrations precisely. Data from the sensor is processed by an ESP8266 microcontroller connected to the internet, enabling direct data transmission to an online platform for further analysis and visualization. Testing is done by creating a 1x1x1 meter testing chamber to simulate various environmental conditions and validate the device's performance. Results show that the particle counter provides accurate data, with an error rate of less than 10% compared to standard devices. The device demonstrates reliable operation across different environmental conditions, showcasing its robustness in practical applications. This IoT-based particle counter offers an innovative solution for effective and efficient air quality monitoring. It is expected to significantly contribute to human health protection efforts and minimize the adverse environmental impacts of air pollution.

Impact of industrial pollution on children's lungs: A comparative analysis of pulmonary function in critically polluted and non-polluted areas in western India

BackgroundAir pollution is a pressing global health concern, particularly in low- and middle-income countries like India. Children living near industrialized and critically polluted areas (CPAs) face heightened health risks due to their developing respiratory systems. This study aimed to assess the impact of industrial pollution on children's lung function in a CPA in western India. MethodologyThe study evaluated 362 children studying in 5th to 7th standard from both a CPA and a relatively non-polluted area (NPA) for comparison. Ambient and indoor air quality measurements were taken, and pulmonary function tests were conducted to assess lung health. Group difference and regression analysis were performed with SPSS 26.0. ResultsThe CPA group showed significantly higher air pollution levels and a higher prevalence of obstructive and restrictive patterns in pulmonary function compared to the NPA group. The findings also revealed that children residing in CPA exhibited a significant decline of 530 ml forced expiratory flow rate (FEF 25–75%), and a reduction of 3.9% FEV1/FVC ratio when compared to children residing in relatively NPA. ConclusionWhile this study's cross-sectional design has limitations, it provides valuable evidence of the impact of air pollution on children's lungs in a semi-urban industrialized area. To mitigate these health risks, integrating respiratory health check-ups, including pulmonary function tests, in existing health programs, like Rastriya Bal Swasthya Karyakram (RBSK), is recommended for early detection and intervention. Implementing initiatives such as enhancing green belts around schools and continuous air quality monitoring will be crucial.

COVID-19 infection risk assessment in a kindergarten utilizing continuous air quality monitoring data

COVID-19 infection risk assessment in a kindergarten utilizing continuous air quality monitoring data

Portable Air Quality Monitoring and Controlling System Using Vacuum Draw-Off

Air pollution is one of the most concerning facts of today’s world. Among all air pollution poses a serious threat for health. It affects human body through breathing and since this pollution is barely visible in eye, a monitoring system is necessary to stay cautious. This is the motivation behind our work so that in hospital all people can stay aware of the toxic gas (CO) they are breathing. Our objective is to provide a system to measure continuous Air Quality in hospital environment and AQI level using gas sensors and also an inbuilt fire detector is present to determine fire out broken. We proposed a portable model in which monitoring sensors are connected through IoT technology. From previous dataset we got high accuracy. In this model our aim to detect the toxic gas and fire outbreak by using the gas sensor MQ2, MQ7, MQ135 and flame sensor. When the toxic gas is detected, the vacuum can automatically run and suck the toxic gas. In addition to this, it provides alert to the main device which is connected through IOT and buzzer. we included fire detector, DHT11-temperature and moisture to maintain the environment in hospital. Whenever the temperature and moisture are crossed the normal range, it gives alert to the device (mobile) by Bluetooth. Also, we're going to use the air purifier model to reduce the problems.

Evaluating the impact of air purifiers and window operation upon indoor air quality - UK nurseries during Covid-19

Many indoor air pollutants have been demonstrated to have a negative impact on occupants and due to physiological and behavioural differences, young children are more vulnerable to these effects than adults. Millions of children in the UK spend large parts of the day in nurseries, where occupant density is high, and indoor air quality can be poor. Therefore, it is imperative to understand the quality of indoor air in nurseries and how to improve it. The aims of the research presented here were to explore the indoor air quality (IAQ) in nurseries and the impact of both the use of air purifiers and window operations on IAQ. Three nurseries in London were selected and monitored via both continuous air quality sensors and passive sampling covering a total of 21 pollutants. Key findings include that mean reduction rate of PM2.5 by using air purifier was 63% with window closed, and 46% with window open. The results also highlight the impacts of operational changes implemented during the Covid-19 pandemic. Windows were operated more frequently for ventilation needs rather than being driven by temperature alone. The increased ventilation in the monitored nurseries in London led to low levels of VOCs and aldehydes (except for formaldehyde and 2-ethylhexanol) but could bring thermal discomfort to occupants. Both temperature and noise levels were shown to be relevant factors impacting the operation of air purifiers. Air purifiers can be effective at reducing PM2.5 when combined with proper window operation and have potential to bring substantial health benefits.

Spatiotemporal analysis of fine particulate matter for India (1980–2021) from MERRA-2 using ensemble machine learning

Particle exposure affects more humans globally than any other air pollutant. However, due to expensive instruments and infrastructural deficiency, a high spatiotemporal network of monitoring stations is not possible, leading to data-scarce regions. Satellite and reanalysis datasets can be implemented to estimate particulate matter, but they do not provide surface concentration and needs to be reconstructed from the components. In this study, a machine learning (ML) framework is implemented to reconstruct PM2.5 from MERRA-2 data components, namely black carbon (BC), organic carbon (OC), dust (DUST), sea salt (SS), and sulfate (SO4) mass concentration. The ground-level data were collected from India's 335 continuous ambient air quality monitoring stations (CAAQMS) and respective MERRA-2 data for 2017–2021 at hourly resolution. Random forest (RF) performs better with train and test scores (R2) of 0.84 and 0.73, respectively, while the empirical equation provides an R2 of only 0.26 on test data. The estimated PM2.5 for Indian states from 1980 to 2021 indicates a significant increase in most cases. However, states in the Indo-Gangetic plain such as Delhi, Punjab, Haryana, and Uttar Pradesh, are the most polluted regions of India. The major shift in concentration is from 2000 onwards, which can be seen as a direct result of the economic liberalization policies implemented in 1991. The results provide evidence for the limitations of the broad application of the empirical equation and the feasibility of ML algorithms as a potential reconstruction technique for developing robust and accurate region-specific models from MERRA-2 data.