Ambient Air Quality Monitoring Stations Research Articles

Prediction of land surface temperature using spectral indices, air pollutants, and urbanization parameters for Hyderabad city of India using six machine learning approaches

Urban areas face rising land surface temperatures (LST) and increased air pollution due to urbanization and industrialization. The consistent rise in LST impacts lives of the residents. Thus, LST forecasts can help manage activities more comfortably. The present study aims to predict LST for Hyderabad city, India using five-year (2018–2022) data on air pollution and meteorological parameters (from ambient air quality monitoring stations) and MODIS LST data. Six machine learning models i.e., Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Networks (ANN), XGBoost, and Long Short-Term Memory (LSTM), were used for LST prediction. Considerable influence of PM2.5 and CO (during summer) and SO2 (during winter) on LST was observed which demonstrated high sensitivity of these parameters on LST. LST exhibited a weak correlation with individual air pollutants while strong relationship of LST with all the study variables, when considered simultaneously, was observed. ANN method demonstrated better accuracy with lower error metrics, comprising of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE), compared to the other approaches with ranking in the order ANN > RF > SVR > XGBoost > LSTM > MLR. ANN model for Hyderabad city was validated by using it for prediction of LST of another geographical area i.e. Bengaluru city, India. The result of this study can provide insights for policymakers, urban planners, and environmental agencies for targeted interventions for temperature regulations and to mitigate urbanization's impact.

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

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.

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.

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.

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.

Air-quality networks collect environmental DNA with the potential to measure biodiversity at continental scales

One of the biggest planetary challenges is the accelerating loss of biodiversity threatening ecosystem functioning on a global scale. The WWF Living Planet Report (https://livingplanet.panda.org/) estimates a 69% decline in populations since 1970. The Convention on Biological Diversity and related international treaties ask countries to monitor shifts in community composition and assess rates of species decline to quantify extant biodiversity relative to global targets1. However, quantifying biodiversity is a challenge, and monitoring continual change is impossible at almost any scale due to a lack of standardized data and indicators2,3. A common problem is that the required infrastructure for such global monitoring does not exist. Here, we challenge this notion by analysing environmental DNA (eDNA) captured along with particulate matter by routine ambient air quality monitoring stations in the UK. In our samples, we identified eDNA from >180 vertebrate, arthropod, plant and fungal taxa representative of local biodiversity. We contend that air monitoring networks are in fact gathering eDNA data reflecting local biodiversity on a continental scale, as a result of their routine function. In some regions, air quality samples are stored for decades, presenting the potential for high resolution biodiversity time series. With minimal modification of current protocols, this material provides the best opportunity to date for detailed monitoring of terrestrial biodiversity using an existing, replicated transnational design and it is already in operation.

Comparative assessment of ground-level air quality in the metropolitan area of Prague using local street canyon modelling

Metropolitan areas have recently been identified as hotspots in terms of air quality and pollution-related diseases caused by gaseous pollutants and particulate matter primarily from road emissions. This study was carried out to investigate the suitability of the air dispersion modelling for predicting the short-term concentration of selected pollutants within the asymmetric street canyon geometries. The geographic projection covered a modelling domain of 0.5 × 1.4 km, with a grid resolution of 2 m mapped to the receptor site of an operational ambient air quality monitoring station in Prague, Czech Republic. Basic street canyon simulation showed the best performance metric for the prediction of PM10 concentration. High spatiotemporal performance was found by the advanced street canyon simulation in predicting hourly averages of NOx and PM10. The underprediction of the modelled NOx concentration corresponded to those of previous studies with FB value of 0.11 and NMSE <4. The slightly overprediction of PM10 concentration with the obtained FB value of −0.19, the NMSE value of 0.04, and the FAC2 of 0.53 were in agreement with previous modelling work. The simulation results provided additional evidence for the underpredicted concentration of PM2.5 in different model configurations. Advanced street canyon simulation improves the accuracy of ADMS-Urban by a maximum of 34%. The underprediction of short-term emission was graphically analysed in time-series profiles. The validation results and significant remarks greatly contribute to the essential optimization of urban street canyon model to better represent observations in the street canyon geometries of historic European building architectures.

Impact of COVID-19 Lockdown on Ambient Air Quality in the Southwest Coastal Urban Regions of India

The rapid growth of urban areas and population as well as associated development over recent decades have been a major factor controlling ambient air quality of the urban environment in Kerala (India). Being located at the southwestern fringe of the Indian peninsula, Kerala is one of the regions that has been significantly influenced by the activities in the Indian Ocean. The present study focuses on the effect of the COVID-19 lockdown (in 2021) on ambient air quality in the selected coastal metropolitan areas of Kerala. Although previous research studies reported improvement in ambient air quality in Kerala during the lockdown period, this study demonstrates the potential of onshore transport of air pollutants in controlling the air quality of coastal urban regions during the lockdown period. Data from the ambient air quality monitoring stations of the Kerala State Pollution Control Board in the urban areas of Thiruvananthapuram (TM), Kollam (KL), Kozhikode (KZ), and Kannur (KN) are used for the analysis. Temporal variation in the concentration of air pollutants during the pre-lockdown (PRLD), lockdown (LD), and post-lockdown (PTLD) periods (i.e., 1 March to 31 July) of 2021 is examined to assess the effect of lockdown measures on the National Air Quality Index (AQI). Results indicate a significant decline in the levels of air pollutants and subsequent improvement in air quality in the coastal urban areas. All the effect of lockdown measures has been evident in the AQI, an increase in the concentration of different pollutants including CO, SO2, and NH3 during the LD period suggests contributions from multiple sources including onshore transport due to marine traffic and transboundary transport.

Air pollution and its effects on emergency room visits in tertiary respiratory care centers in Delhi, India.

Environmental pollution has harmful effects on human health, particularly the respiratory system. We aimed to study the impact of daily ambient air pollution on daily emergency room visits for acute respiratory symptoms. This study was conducted in two tertiary respiratory care centres in Delhi, India. Daily counts of emergency room visits were collected. All patients attending the emergency room were screened for acute onset (less than 2 weeks) of respiratory symptoms and were recruited if they were staying in Delhi continuously for at least 4 weeks and having onset (≤2 weeks) of respiratory symptoms. Daily average air pollution data for the study period was obtained from four continuous ambient air quality monitoring stations. A total of 61,285 patients were screened and 11,424 were enrolled from June 2017 to February 2019. Cough and difficulty in breathing were most common respiratory symptoms. Poor air quality was observed during the months of October to December. Emergency room visits with acute respiratory symptoms significantly increased per standard deviation increase in PM10 from lag days 2-7. Increase in wheezing was primarily seen with increase in NO2. Pollutant levels have effect on acute respiratory symptoms and thus influence emergency room visits. *************************************************************** *Appendix Authors list Kamal Singhal,1 Kana Ram Jat,2 Karan Madan,3 Mohan P. George,4 Kalaivani Mani,5 Randeep Guleria,3 Ravindra Mohan Pandey,5 Rupinder Singh Dhaliwal,6 Rakesh Lodha,2 Varinder Singh1 1Department of Paediatrics, Lady Hardinge Medical College and associated Kalawati Saran Children's Hospital, New Delhi, India 2Department of Paediatrics, All India Institute of Medical Sciences, New Delhi, India 3Department of Pulmonary Medicine, Critical Care and Sleep Disorders, All India Institute of Medical Sciences, New Delhi, India 4Department of Environment, Delhi Pollution Control Committee, Kashmere Gate, New Delhi, India 5Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India 6Department of Non-communicable Diseases, Indian Council of Medical Research, New Delhi, India.

Multivariate Urban Air Quality Assessment of Indoor and Outdoor Environments at Chennai Metropolis in South India

The present study examines indoor and outdoor environmental particulate matter and gaseous pollutants in order to evaluate the urban air quality, the sources and pathways of pollutants, and its impact on Chennai megacity, South India. A total number of 25 air conditioner filter particulate matter samples collected from residential buildings, schools, colleges, commercial shopping malls, and buildings near urban highways were studied for indoor air quality. Similarly, outdoor air quality assessments have been done in various parts of the Chennai metropolis, including the Manali-Industrial area, the Velachery-Residential site, and the Alandur Bus Depot, as well as collected air quality data sets from the Central Pollution Control Board at continuous ambient air quality monitoring stations. The suspended atmospheric particles where the highest concentration (47%) occurred were mostly located in the roadside environments followed by commercial areas (42%), which indicates the increase in air pollution in the roadside areas. Further, environmental magnetism and ecological risk indices were studied from the collected data set. The study predicts that the air pollutants were predominantly from anthropogenic sources, such as vehicle emissions, effluents from power plants, abrasion of tires, steelworks, burning of fossil fuels and construction materials, etc. As a result, the current study suggests 68% of indoor pollutants were from the anthropogenic input, 18% from the pedogenic origin, and 14% from high heavy metal pollution at the sampling sites. This indicates that raising the ventilation rate via mechanical components significantly enhances the indoor air quality. These findings might be valuable in improving urban air quality, reducing traffic-related pollutants, and improving environmental quality.

Non-uniform effect of COVID-19 lockdown on the air quality in different local climate zones of the urban region of Kochi, India

Deterioration of ambient air quality associated with urbanisation is a serious concern in many parts of the world. In India, air pollution, primarily due to particulate matter, has increased exponentially in the last few decades due to rapid urbanization, industrialization and population growth. This study investigates the non-uniform influence of COVID-19 lockdown on the ambient air quality of three distinct local climate zones (LCZs) within the urban region of Kochi (Kerala, India). The analysis of the air pollutant data of the ambient air quality monitoring stations during the pre-lockdown (PRLD), lockdown (LD) and post-lockdown (PTLD) periods of 2021 implies the significance of lockdown measures in the improvement of urban air quality. The air quality index (AQI) exhibits characteristic variability in different LCZs and contrasting behaviour between the LD period of 2020 and 2021, primarily due to the differences in the lockdown restrictions and strategies as well as the influence of local climatic factors. This study highlights the need for multiple monitoring stations in the urban regions with respect to different LCZs to identify the urban air quality hot spots.

Domain Adaptation-Based Deep Calibration of Low-Cost PM₂.₅ Sensors

Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> levels of LCSDs. The dataset used for the experimentation consists of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> values and other parameters (PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> , temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> , PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> , temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.

Emission level of air pollutants during 2019 pre-haze, haze, and post-haze episodes in Kuala Lumpur and Putrajaya

Nowadays, due to population growth and industrialisation, air quality in Malaysia is becoming a critical threat. Air pollution has become a serious issue due to its impacts on humans, animals, and the environment. Malaysia experienced air quality deterioration in 2019 when the episodes of haze happened from July to September. It was due to the local and transboundary sources such as vehicles, factories, power plants, and biomass burning from Sumatra. This study aims to differentiate the level of the potential air pollutants, examine the influence of meteorological factors on the potential air pollutants and determine the local and transboundary impact on the potential air pollutants during episodes of pre-haze, haze, and post-haze in Kuala Lumpur and Putrajaya in 2019. Secondary physical and data on meteorology were obtained from the continuous ambient air quality monitoring (CAQM) stations by the Malaysian Department of Environment (DOE). The data obtained from CAQM were physical: particulate matters (PM2.5 &amp; PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and level ozone (O3); as well as meteorological: temperature (T), relative humidity (RH), wind speed (WS) and wind direction (WDir). Overall, the particulate matter (PM2.5, PM10) and carbon monoxide which are the pollutants that involve the formation of haze in Kuala Lumpur and Putrajaya are higher during haze episodes compared to pre-haze and post-haze episodes while the other pollutants (NO2, SO2, O3) are fluctuated throughout the entire episode due to its sources and the influence of meteorological factors. The backward trajectory indicated that the air pollutants are influenced by wind direction from South West Malaysia (SWM) and North East Malaysia (NEM) throughout the entire year.

Understanding seasonal variation in ambient air quality and its relationship with crop residue burning activities in an agrarian state of India.

In India, Indo-Gangetic Plains (IGP) is becoming the hotspot of air pollution due to increasing anthropogenic activities such as rapid industrial growth, infrastructure development, transportation activities, and seasonal practice of crop residue burning. In the current study, seasonal variation in ambient air quality for 14 parameters, i.e., particulate matter (PM), trace gases, and volatile organic compounds (VOCs), along with meteorological parameters, was studied in 21 districts of the Haryana state for year 2019, situated in IGP. To analyze spatial variation of pollutants, ambient air quality data of 23 continuous ambient air quality monitoring stations were divided into three zones based on ecology and cropping pattern. All the zones showed annual mean PM10 and PM2.5 concentrations much higher than national ambient air quality standards. Annual mean PM10 concentration (±standard deviation) in Zones-1, 2, and 3 was 156±86, 174±93, and 143±74 μg m-3, whereas for PM2.5 was 71±44, 85±54, and 78±47 μg m-3. The results showed a considerable seasonal variation in the concentration of all pollutants. Most of the pollutants peak in the post-monsoon season, followed by winters in which crop residue burning predominates in many parts of the Haryana. PM10 concentrations increased by 65-112% and PM2.5 concentrations increased by 131-147% in the post-monsoon season compared to monsoons. The post-monsoon season showed the highest concentration of PM10, NO, and toluene (Zone-1); and PM2.5, NH3, CO, and benzene (Zone-2); whereas in winters, SO2 (Zone-1); ethylbenzene, m,p-xylene, and xylene (Zone-2); and NO2 and NOx (Zone-3) showed the maximum pollution levels. The O3 concentration was highest in the pre-monsoon season (Zone-1). The satellite-based fire counts and PCA results show a significant influence of crop residue burning in the post-monsoon season and solid biomass burning in winters on Haryana's air quality. The study could help to understand seasonal variation in ambient air quality and the influence of factors such as crop residue burning in the IGP region, which could help to formulate season-specific control measures to improve regional air quality.

Ambient Air Pollution Exposure and Risk of Developmental Delay in Children and Teenagers in Taiwan

(1) Background: Studies on the association between air pollution and developmental delay in children are limited. Therefore, we evaluated the risk of developmental delay in Taiwanese children exposed to air pollution. (2) Methods: We merged the two nationwide databases, and the annual average pollutant concentrations were grouped into tertiles to evaluate the risk of developmental delay (ICD-9 code 315.9). We identified the patients’ active residential locations based on the location of the clinic or hospital in which they sought treatment for acute upper respiratory infections (ICD 9 code 460). The two nationwide databases were linked for analysis based on the active residential locations of each participant and the locations of the 74 ambient air quality monitoring stations. (3) Results: We observed an increased risk of developmental delay in children and teenagers exposed to SO2, CO, and NO2. The adjusted odds ratios (ORs) of developmental delay for comparison among the tertiles with respect to SO2, CO, and NO2 exposures were 1.12 (95% confidence interval [CI], 1.01–1.24), 1.21 (95% CI, 1.09–1.34), and 1.40 (95% CI, 1.261.55), respectively. (4) Conclusions: The present findings suggest that air pollution exposure increases the risk of developmental delay in children and teenagers in Taiwan.