Ambient Air Quality Monitoring Research Articles

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.

Quantitative analysis of air pollution levels and its health implications in Delhi-NCR: A longitudinal study

Air pollution is a pressing global issue, particularly acute in rapidly urbanizing regions like Delhi-NCR. This research presents a longitudinal study to quantitatively analyze air pollution levels and their health implications in Delhi-NCR. The research objectives include assessing air quality trends, examining the relationships between various pollutants, and investigating the impact of air pollution on respiratory admissions. Data was sourced from the Ambient Air Quality Monitoring Network, covering the years 2020 to 2022, focusing on key pollutants, including PM2.5, PM10, NO2, SO2, CO, and O3. A range of data analysis techniques, including correlation analysis, regression analysis, longitudinal data analysis, spatial analysis, and compliance with WHO guidelines, were applied. Key findings highlight persistently high levels of air pollutants, strong interrelations among pollutants, and consistent respiratory admissions, especially in winter. Regression analysis confirms a significant link between PM2.5 and respiratory admissions. Longitudinal analysis demonstrates the long-term health consequences of air pollution, and spatial analysis reveals district-wise variations in air quality. The study also emphasizes seasonal strategies, compliance with international guidelines, and socioeconomic disparities in health outcomes. This research has broader implications for evidence-based policymaking, emphasizing the urgency of addressing air quality concerns. It calls for targeted interventions, district-level strategies, season-specific measures, and equitable access to healthcare. These findings offer a comprehensive basis for addressing the air quality crisis in Delhi-NCR, ultimately promoting the well-being of its residents.

Assessment of the Ambient Air Quality of Sangli City, Maharashtra, vis-à-vis National Ambient Air Quality Standards (NAAQS)

Assessment of ambient air quality is important to formulate short-term and long-term plans to mitigate air pollution. This study attempts to present an ambient air quality assessment for Sangli City, located at 16.85° N, and 74.58° E. The assessment is done through ambient air quality data measured over three ambient air quality monitoring stations located in residential, commercial, and industrial zones of Sangli City. The data was collected from 2009 to 2017 under the National Air Quality Monitoring Program of the Central Pollution Control Board (CPCB), India. The observed concentrations of gaseous pollutants, SO2 and NOx, and respirable particulate matter are compared with the concentration levels prescribed by the National Ambient Air Quality Standards (NAAQS) in 2009. The concentration levels of particulate matter are quite on the higher side of the prescribed standards. The levels of NOx are almost approaching the prescribed values, whereas SO2 levels are well within the margin. We have also studied seasonal variations in the pollutants during the study period. In general, the air quality in Sangli City is quite undesirable during the mid-winter to mid-summer period. The study is useful for common men to increase awareness. It also forms baseline data and is useful for local and central government to design appropriate strategies to apply corrective measures, protect health, and promote environmental protection.

Using Diverse Data Sources to Impute Missing Air Quality Data Collected in a Resource-Limited Setting

The sustainable operation of ambient air quality monitoring stations in developing countries is not always possible. Intermittent failures and breakdowns at air quality monitoring stations often affect the continuous measurement of data as required. These failures and breakdowns result in missing data. This study aimed to impute NO2, SO2, O3, and PM 10 to produce complete data sets of daily average exposures from 2010 to 2017. Models were built for (a) an individual pollutant at a monitoring station, (b) a combined model for the same pollutant from different stations, and (c) a data set with all the pollutants from all the monitoring stations. This study sought to evaluate the efficacy of the Multiple Imputation by Chain Equations (MICE) algorithm in successfully imputing air quality data that are missing at random. The application of classification and regression trees (CART) analysis using the MICE package in the R statistical programming language was compared with the predictive mean matching (PMM) method. The CART method performed better, with the pooled R-squared statistics of the imputed data ranging from 0.3 to 0.7, compared to a range of 0.02 to 0.25 for PMM. The MICE algorithm successfully resolved the incompleteness of the data. It was concluded that the CART method produced better reliable data than the PMM method. However, in this study, the pooled R2 values were accurate for NO2, but not so much for other pollutants.

Preliminary Indications on an Atmospheric Particulate Matter and Rainwater Chemistry in Colombo, Sri Lanka: A Study During Southwestern Monsoon

The atmospheric cleansing nature of rainfall, specifically in the removal of particulate matter, is a determinant of rainwater quality. This study investigated the relationship between atmospheric particulate matter (PM10) concentration and rainwater quality in Colombo during the southwest monsoon period in 2023. The rainwater samples were collected using a manually prepared high- density polyethylene collector and analysed for pH, electrical conductivity (EC), water-soluble cations (Na+, K+, Ca2+, Mg2+, and NH4+), and anions (Cl-, NO3–, and SO42–). The concentration of atmospheric PM10 was obtained from the ambient air quality monitoring station (AQMS) at Battaramulla. The measured data were analysed using multivariate statistical techniques, including principal component analysis and Pearson correlation analysis, to identify relationships between the concentration of atmospheric PM10 and rainwater quality. The atmospheric PM10 showed an insignificant positive correlation with southwest monsoon rainfall (r=0.14, p>0.05). The concentration of atmospheric PM10 increased the total concentration of water-soluble ions (r=0.30) and decreased the pH (r=-0.42) and electrical conductivity (r=0.07) of rainwater samples. The concentration of Na+, K+ and Ca2+ displayed moderate positive correlation, while Mg2+, NH4+, Cl-,
 NO3- and SO42- showed a weak positive correlation of rainwater samples in the atmospheric PM10.
 This study contributes valuable insights into the variations of PM10 in the atmosphere and its potential implications on rainwater quality in southwest monsoon, underscoring the importance of comprehensive analyses for a more nuanced understanding of the intricate relationships between atmospheric components and rainwater chemistry.
 
 Keywords: Atmospheric chemistry, Correlation analysis, Particulate matter, Rainwater quality, Southwest monsoon

Ambient air quality monitoring of Chandrapur District, Central India

Declining air quality is highly ignored and very common form of degradation of the environment in nations that are both developed and developing. There are several contaminants in the air that have been identified in various studies on air pollution. The crucial parameters of all air pollutants are gaseous and particle pollution. The present study was undertaken to estimate the quality of ambient air in Chandrapur district, Maharashtra state of India. In this paper, an effort has been made to study the standing and trend of Sulphur dioxide (SO2), Oxides of Nitrogen (NOx), Carbon Monoxide (CO), Ozone (O3), Ammonia (NH3), Respirable Suspended Particulate Matter i.e.; PM10, PM2.5, toxic pollutants i.e.; lead, arsenic, nickel, benzo [a] pyarene and benzene and hydrocarbons. The results clearly show that, all parameters of ambient air quality monitoring values were some shown slightly below permissible limit. The ambient air quality monitoring data show that the Chandrapur district has substantial air pollution concerns in terms of SO2, NOx, PM, and other air pollutants. However, after comparing these values with NAAQS levels, the yearly averages of these air pollutants have been found to be below than the NAAQS levels. However, without effective mitigation measures, the concentrations of these pollutants will rise at quite alarming rate.

Comprehensive Hygienic Assessment of Anthropogenic Load on the Territory of the Voronezh Region

Introduction: A comprehensive assessment of adverse human health effects of environmental pollutants found in residential areas has been carried out in the Voronezh Region. Objective: To conduct a comprehensive hygienic assessment of the anthropogenic load on the territory of the Voronezh Region. Materials and methods: We analyzed the results of ambient air, drinking water, and soil quality monitoring collected by the Center for Hygiene and Epidemiology in the Voronezh Region in 2018–2022 for compliance with the maximum permissible concentrations specified in Regulations SanPiN 1.2.3685–21. The complex load was estimated in accordance with method guidelines MR 01-19/17-17. Results: We established that pollution of ambient air in residential areas contributed the most (69.8 %) to the total anthropogenic load in the town Pavlovsk; drinking water – to that in the Semiluksky district (36.5 %), and soil – in the Rossoshansky district (10.5 %). In the city of Voronezh, the level of complex anthropogenic load exceeded both the regional and district averages. The problem of ambient air pollution dominates in all districts; it should be noted, however, that air sampling is carried out only in urban areas and urban-type settlements. In rural areas, effects of agriculture-associated nitrate pollution of drinking water is more pronounced. The impact of soil pollution is more pronounced in urban residential areas. At the same time, the excess of maximum permissible concentrations of soil pollutants were registered neither in rural nor in urban areas over the study period. Conclusion: In Voronezh, the level of complex anthropogenic load exceeds the average values estimated for the region and districts. Ambient air pollution with vehicle and industrial emissions make the greatest contributions to this load both on the territory of Voronezh and in the districts. Nitrate pollution of potable water associated with agriculture is a challenge in rural areas.

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

Modulatory effects of dietary saturated fatty acids on platelet mitochondrial function following short-term exposure to ambient Particulate Matter (PM2.5)

ABSTRACT Exposure to ambient fine particulate matter (PM2.5) was found to produce vascular injury, possibly by activating platelets within days after exposure. The aim of this study was to investigate the modulatory effects of dietary saturated fatty acids on platelet mitochondrial respiratory parameters following short-term inhalational exposure to PM2.5. A total of 22 healthy male volunteers were recruited from the Research Triangle area of North Carolina. Platelets were isolated from fresh whole blood samples and mitochondrial respiratory parameters were measured using an extracellular flux analyzer. Intake of saturated fat was averaged from multiple 24-hr dietary recalls. Daily ambient PM2.5 concentrations were obtained from ambient air quality monitoring stations. Correlation and ANOVA were used in data analyses, along with the pick-a-point method and the Johnson-Neyman technique for probing moderation. After controlling for age and omega-3 index, the intake of dietary saturated fatty acids after reaching 9.3% or higher of the total caloric intake significantly moderated the associations between PM2.5 exposure and several platelet mitochondrial respiratory parameters. In conclusion, dietary saturated fatty acids above 9.3% of total caloric intake influenced the relationship between short-term PM2.5 exposure and platelet mitochondrial respiration. Further research is needed to understand these associations and their implications for cardiovascular health.

Ambient Air Quality Monitoring with Reference to Particulate Matter (PM10) in Kolhapur City

Air is an important medium for all living beings and is essential for the well-being of all. Monitoring of air is important to know the quality of air. The air quality monitoring was carried out in Kolhapur City under the National Air Monitoring Program. The present study involves the assessment of PM10 as described in the National Ambient Air Quality Standards (NAAQS). The source apportionment study related to particulate matter was carried out in Kolhapur City. The study also determined the average PM10 concentration in the city as it will be useful for preparing an action plan to reduce PM10 concentration. PM10 concentration was calculated as per the standard method adopted by CPCB. Sampling was carried out for 8 hours in three shifts twice a week at each sampling site for three consecutive years. Mahadwar Road (MR) and Dabholkar Corner (DC) were selected per the surrounding residential area, population density, and traffic conjunction. The third site Shivaji University (SUK), was selected as a control site. The results indicated that the PM10 level has risen above the prescribed standards of NAAQS. The reason for the rise in PM10 may be due to fossil fuel burning, construction activity, vehicles, and unpaved roads. The Analysis of Variance (ANOVA) technique is used to check the equality of the mean concentration of PM10 at these three locations and found a significant difference between mean concentrations of PM10, suggesting increased particulate matter.

PROFOUND INVESTIGATION OF DUST CHEMICAL COMPONENT IN INDUSTRIAL EMISSIONS FOR CORRECT ESTIMATION OF GEOECOLOGICAL IMPACT AND MONITORING

Background. Effective management of dust emissions from industrial enterprises should be based on reliable data on emission sources and emitted components. Correct estimation of geoecological impacts exerted by dust emissions is a complicated process due to chemical diversity of the soil component in them.
 Aim. The aim was to test a practical approach to identifying actual ambient air pollution with solid particles in a zone influenced by an economic entity for correct estimation of impacts and monitoring of ambient air quality.
 Materials and methods. Samples of industrial dust emissions were analyzed with a scanning electronic high-resolution microscope (magnification was from x5 to x300,000; acceleration voltage varied between 0.3 to 30 kW) with S3400N HITACHI x-ray fluorescent device (the limit of detection was approximately 5–10 mass % and the minimum field of view was 100 µm). X-ray photographs were processed by using XRD 6000/7000 software, Version 5.21. The results obtained by calculating dispersion of the dust component were mapped with ArcView 3.2 and ArcGIS 9.3.1., GIS-platforms created by ESRI.
 Results. Complex investigations of dust emissions were performed at a selected enterprise and this made it possible to identify a profile of the dust component in emissions from major sources. Actual composition of dusts emitted by the analyzed enterprise was established to differ fundamentally from the data declared in the inventory lists of emissions issued by the enterprise. The results of dispersion calculations gave grounds for spotting out substances that were priority (marker) ones for the analyzed production and we also estimated actual effects produced by dust emissions on ambient air.
 Conclusions. The study results allow concluding that a dust emission profile is a reliable instrument for estimating actual impacts of an industrial enterprise on ambient air. It can be applied to solve tasks within setting standards, monitoring and estimating actual impacts exerted by anthropogenic dust emissions when environmental measures are being planned and developed.

Long-Term Assessment of PurpleAir Low-Cost Sensor for PM2.5 in California, USA

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment, while emerging low-cost sensors have the potential to fill in the gaps. Recent advances in air quality monitoring have produced portable, easy-to-use, low-cost, sensor-based monitors which have given a new dimension to air pollutant monitoring and have democratized the air quality monitoring process by making monitors and results directly available at the community level. This study used PurpleAir © sensors for PM2.5 assessment in California, USA. The evaluation of PM2.5 from sensors included Quality Assurance and quality control (QA/QC) procedures, assessment concerning reference-monitored PM2.5 concentrations, and the formulation of a decision support system integrating these observations using geostatistical techniques. The hourly and daily average observed PM2.5 concentrations from PurpleAir monitors followed the trends of observed PM2.5 at regulatory monitors. PurpleAir monitors also captured the peak PM2.5 concentrations due to incidents such as forest fires. In comparison with reference-monitored PM2.5 levels, it was found that PurpleAir PM2.5 concentrations were mostly higher. The most important reason for PurpleAir’s higher PM2.5 concentrations was the inclusion of moisture or water vapor as an aerosol in contrast to measurements of PM2.5 excluding water content in FEM/FRM and non-FEM/FRM monitors. Long-term assessment (2016–2023) revealed that R2 values were between 0.54 and 0.86 for selected collocated PurpleAir sensors and regulatory monitors for hourly PM2.5 concentrations. Past research studies that were conducted for mostly shorter periods resulted in higher R2 values between 0.80 and 0.98. This study aims to provide reasonable estimations of PM2.5 concentrations with high spatiotemporal resolutions based on statistical models using PurpleAir measurements. The methods of Kriging and IDW, geostatistical interpolation techniques, showed similar spatio-temporal patterns. Overall, this study revealed that low-cost, sensor-based PurpleAir sensors could be effective and reliable tools for episodic and long-term ambient air quality monitoring and developing mitigation strategies.

Long term trend analysis and Spatio-temporal variation of air pollutants over Uttar Pradesh, India

Air pollution poses a significant challenge for developing countries like India, particularly in cities where industrial growth and urban development have led to increased levels of air pollutants. Due to limited monitoring resources and a focus on specific variables, there is a lack of comprehensive understanding regarding air quality in Indian urban areas. This study aims to explore the spatial and temporal variations in air pollutant concentrations and identify long-term trends over a seven-year period (2016-2022) in the state of Uttar Pradesh. Data for PM10, SO2, and NO2 were collected from 78 ambient air quality monitoring stations. The study investigates how these pollutants behave in terms of their distribution on different temporal scales (seasonal, yearly) as well as across various locations. The findings reveal that the concentration of PM10 consistently exceeds national and international air quality standards. Using the Inverse Distance Weighting (IDW) technique, air pollutant distribution maps were created to identify specific hotspots. Additionally, the Mann-Kendall Test was applied to analyze trends within the available data. This research can serve as valuable information for local government authorities to formulate effective strategies for mitigating air pollution in the region.

Ambient air quality monitoring and assessment of an industrial area as a tool for sustainable environmental management

Different ambient air pollutants like PM10, PM2.5, SO2 and NO2 were studied for three years with respect to seasons starting from Mar’2017 to Feb’2020 at eight different monitoring locations (ML). It has been revealed from the study that, the levels of air pollution are worsening year by year. It is due to increased transportation of raw materials and finished products from industries and mining activities near to the study area. During the study period 2020–21, the maximum concentrations of PM10 was observed in winter season at ML-5, ML-3 i.e. 216.5 μg/m3, 212.3 μg/m3 and PM2.5 at ML-3, ML-2 i.e. 111.8 μg/m3, 100.4 μg/m3 respectively followed by summer season which exceeding the standard norms. However, it was observed from the study that, in general during all the three seasons and years the concentrations of SO2 & NO2 and found well within the norms. Wind rose graph during October to December 2019 shows the wind direction (predominant) was NW to SE and the wind class frequency distribution graph shows that the air is 26.5 % calm. Similarly the wind rose and wind class distribution pattern during March- May 2019 shows that the wind direction (predominant) was SW to NE and the wind class frequency distribution graph shows that the air is 30.2 % calm.

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

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

Air quality management status and needs of countries in South Asia and Southeast Asia

Countries in South Asia and Southeast Asia are experiencing severe air pollution problems. Most countries lack technical capabilities and adequate air quality management (AQM) infrastructure. A capacity building workshop on AQM was organised from 19-23 September 2022 for countries in South Asia and Southeast Asia. A total of 17 countries (eight in South Asia and nine in Southeast Asia) participated in the workshop. Each country was invited to present on available AQM infrastructure, including challenges and needs. This article synthesises information on available AQM facilities, challenges and needs of the countries. The information reveals that, except for a few low-population countries and Thailand, most countries lack enough ambient air quality monitoring stations (AAQMS) based on the population-weighted criteria. It is also found that only a few countries have started compiling emissions inventories (EI) and performing air quality modelling, including air pollution impact assessments. It is noted that all countries have enacted air pollution mitigation regulations, including the development of clean policies and action plans. However, policies and action plans lack scientific evidence based on local data. The findings of this article, including challenges and gaps, provide immense opportunities for countries to invest in strengthening various components of AQM, including mobilising financial resources from international funding agencies.

Effects of pre-natal and post-natal exposures to air pollution on onset and recurrence of childhood otitis media

BackgroundDespite mounting evidence linking outdoor air pollution with otitis media (OM), the role of air pollutant(s) exposure during which critical window(s) on childhood OM remains unknown. ObjectivesWe sought to identify the key air pollutant(s) and critical window(s) associated with the onset and recurrent attacks of OM in kindergarten children. MethodsA combined cross-sectional and retrospective cohort study involving 8689 preschoolers aged 3–6 years was performed in Changsha, China. From 2013–2020, data on air pollutants were collected from ambient air quality monitoring stations in Changsha, and the exposure concentration to each child at their home address was calculated using the inverse distance weighted (IDW) method. The relationship between air pollution and OM in kindergarten children was studied using multiple logistic regression models. ResultsChildhood lifetime OM was associated with PM2.5, SO2 and NO2, with ORs (95% CI) of 1.43 (1.19–1.71), 1.18 (1.01–1.37) and 1.18 (1.00–1.39) by per IQR increase in utero exposure and with PM2.5, PM2.5–10 and PM10, with ORs = 1.15 (1.00–1.32), 1.25 (1.13–1.40) and 1.49 (1.28–1.74) for entire post-natal exposure, respectively. The 2nd trimester in utero and the post-natal period, especially the 1st year, were key exposure time windows to PM2.5 and PM10 associated with lifetime OM and the onset of OM. Similarly, the 4th gestational month was a critical window for all pollutants except CO exposure in relation to lifetime OM and OM onset, but not recurrent OM attacks. PM2.5 exposure during the nine gestational months and PM10 exposure during the first three years had cumulative effects on OM development. Our subgroup analysis revealed that certain children were more susceptible to the OM risk posed by air pollution. ConclusionsEarly-life exposure to air pollution, particularly PM2.5 during the middle of gestation and PM10 during the early post-natal period, was associated with childhood OM.

Monitoring of Ambient Air Quality Patterns and Assessment of Air Pollutants’ Correlation and Effects on Ambient Air Quality of Lahore, Pakistan

Industrialization, explosive population growth, anthropogenic activities, and vehicular exhaust deteriorate ambient air quality across the world. The current study aims at assessing the impacts on ambient air quality patterns and their co-relations in one of the world’s most polluted cities, i.e., Lahore, Pakistan, during a strict, moderate, and post-COVID-19 period of 28 months (March 2020–June 2022). The purpose of this study is to monitor and analyze the relationship between criteria air pollutants (SO2, particulate matter (PM 10 and 2.5), CO, O3, and NO2) through a Haz-Scanner 6000 and mobile van (ambient air quality monitoring station) over nine towns in Lahore. The results showed significantly lower concentrations of pollutants during strict lockdown which increased during the moderate and post-COVID-19 lockdown periods. The post-COVID-19 period illustrates a significant increase in the concentrations of SO2, PM10, PM2.5, CO, O3, and NO2, in a range of 100%, 270%, 500%, 300%, 70%, and 115%, respectively. Major peaks (pollution concentration) for PM10, PM2.5, NO2, and SO2 were found during the winter season. Multi-linear regression models show a significant correlation between PM with NO2 and SO2. The ratio of increase in the PM concentration with the increasing NO2 concentration is nearly 2.5 times higher than SO2. A significant positive correlation between a mobile van and Haz-Scanner was observed for CO and NO2 data as well as ground-based observation and satellite data of SO2, NO2, and CO. During the strict COVID-19 lockdowns, the reduction in the vehicular and industrial exhaust significantly improved the air quality of nine towns in Lahore. This research sets the ground for further research on the quantification of total emissions and the impacts of vehicular/industrial emissions on human health.

Characteristics and mechanism of a persistent ozone pollution event in Pearl River Delta induced by typhoon and subtropical high

Ozone (O3) is a significant air pollutant in the Pearl River Delta (PRD) region, with severe pollution events primarily associated with the Western Pacific Subtropical High (WPSH) and typhoon periphery synoptic weather patterns. To investigate the continuity and differences in O3 pollution processes influenced by these patterns, we analyze a severe O3 pollution event in Zhuhai from September 10 to 13, 2022. The study collects vertical profiles of O3 from the differential absorption O3 lidar and wind fields observed by the Doppler wind profile lidar. We utilize ground-level monitoring data from national ambient air quality monitoring network and meteorological stations to analyze diurnal variations of air pollutant concentrations and ground-level meteorological conditions. The study reveals that both the WPSH and the typhoon periphery create favorable meteorological conditions for severe O3 pollution events. The highest temperatures during the WPSH and typhoon periphery stages are 32.1 °C and 37.5 °C, respectively. During the WPSH control stage, the combined effects of the inversion layer and light static wind hinder O3 dispersion. The ground-level O3 concentration reaches a maximum of 106 ppb. Then the typhoon periphery control stage, the ground-level O3 peak increases to 170 ppb, showing a 60% increase compared to the WPSH stage. O3 horizontal and vertical transport processes play a crucial role in the O3 episode during this stage. The northerly winds predominantly transport pollutants from upstream urban clusters to the local area, and intense downdrafts facilitate the downward transport of O3 from the residual layer. The study investigates the mechanisms of three-dimensional O3 transport within the boundary layer influenced by the WPSH and typhoon periphery in the PRD region. The results contribute valuable insights to pollution events in the PRD region under typical synoptic weather patterns.

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.