Exceedances and trends of particulate matter (PM2.5) in five Indian megacities

Examining trends and variability of PM2.5-associated organic and elemental carbon in the megacity of Beijing, China: Insight from decadal continuous in-situ hourly observations

Organic carbon (OC) and elemental carbon (EC) in fine particulate matter (PM2.5) play pivotal roles in impacting human health, air quality, and climate change dynamics. Long-term monitoring datasets of OC and EC in PM2.5 are indispensable for comprehending their temporal variations, spatial distribution, evolutionary patterns, and trends, as well as for assessing the effectiveness of clean air action plans. This study presents and scrutinizes a comprehensive 10-year hourly dataset of PM2.5-bound OC and EC in the megacity of Beijing, China, spanning from 2013 to 2022. Throughout the entire study period, the average concentrations of OC and EC were recorded at 8.8 ± 8.7 and 2.5 ± 3.0 μg/m3, respectively. Employing the seasonal and trend decomposition methodology, specifically the locally estimated scatter plot smoothing method combined with generalized least squares with the autoregressive moving average method, the study observed a significant decline in OC and EC concentrations, reducing by 5.8 % yr-1 and 9.9 % yr-1 at rates of 0.8 and 0.4 μg/m3 yr-1, respectively. These declining trends were consistently verified using Theil-Sen method. Notably, the winter months exhibited the most substantial declining trends, with rates of 9.3 % yr-1 for OC and 10.9 % yr-1 for EC, aligning with the positive impact of the implemented clean air action plan. Weekend spikes in OC and EC levels were attributed to factors such as traffic regulations and residential emissions. Diurnal variations showcased higher concentrations during nighttime and lower levels during daytime. Although meteorological factors demonstrated an overall positive impact with average reduction in OC and EC concentrations by 8.3 % and 8.7 %, clean air action plans including the Air Pollution Prevention and Control Action Plan (2013-2017) and the Three-Year Action Plan to Win the Blue Sky War (2018-2020) have more contributions in reducing the OC and EC concentrations with mass drop rates of 87.1 % and 89.2 % and 76.7 % and 96.7 %, respectively. Utilizing the non-parametric wind regression method, significant concentration hotspots were identified at wind speeds of ≤2 m/s, with diffuse signals recorded in the southwestern wind sectors at wind speeds of approximately 4-5 m/s. Interannual disparities in potential source regions of OC and EC were evident, with high potential source areas observed in the southern and northwestern provinces of Beijing from 2013 to 2018. In contrast, during 2019-2022, potential source areas with relatively high values of potential source contribution function were predominantly situated in the southern regions of Beijing. This analysis, grounded in observational data, provides insights into the decadal changes in the major atmospheric composition of PM2.5 and facilitates the evaluation of the efficacy of control policies, particularly relevant for developing countries.

Examining the relationship between atmospheric pollutants and meteorological factors in Asansol city, West Bengal, India, using statistical modelling

Meteorological conditions significantly impact ambient air quality in urban environments. This study focuses on Asansol, known as the "Coal City" and the "Industrial Heart of West Bengal," a notable hotspot for air pollution. Despite its significance, limited research has addressed the influence of meteorological factors on key air pollutants in this urban area. From January 2019 to December 2023, this investigation explores the relationships between meteorological parameters (including atmospheric temperature, relative humidity, rainfall, wind speed) and the concentrations of crucial air pollutants (PM2.5, PM10, NO2, SO2). Temporal trends in air pollutant concentrations are also analysed. The Spearman correlation method is used to establish associations between pollutant concentrations and meteorological variables, while multiple linear regression (MLR) models are employed to assess meteorological factors and potential impact on pollutant concentrations. The analysis reveals a decreasing trend in pollutant concentrations in Asansol. Temperature exhibits negative correlations with all pollutants in all seasons except for a positive correlation during the monsoon. Rainfall consistently displays significant negative correlations with pollutants in all seasons. Relative humidity is negatively correlated with pollutants in all seasons, and wind speed, except during the post-monsoon season, shows negative correlations with all pollutants. Linear models excel in predicting particulate matter concentrations but perform poorly in predicting gaseous contaminants. Accounting for seasonal fluctuations and meteorological parameters, this research enhances the accuracy of air pollution forecasting, contributing to a better understanding of air quality dynamics in Asansol and similar urban areas.

The cellular consequences of particulate matter pollutants in plants: Safeguarding the harmonious integration of structure and function

Particulate matter (PM) pollution is one of the pressing environmental concerns confronting human civilization in the face of the Anthropocene era. Plants are continuously exposed to an accelerating PM, threatening their growth and productivity. Although plants and plant-based infrastructures can potentially reduce ambient air pollutants, PM still affects them morphologically, anatomically, and physiologically. This review comprehensively summarizes an up-to-date review of plant-PM interaction among different functional plant groups, PM deposition and penetration through aboveground and belowground plant parts, and plants' cellular strategies. Upon exposure, PM represses lipid desaturases, eventually leading to modification of cell wall and membrane and altering cell fluidity; consequently, plants can sense the pollutants and, thus, adapt different cellular strategies. The PM also causes a reduction in the photosynthetically active radiation. The study demonstrated that plants reduce stomatal density to avoid PM uptake and increase stomatal index to compensate for decreased gaseous exchange efficiency and transpiration rates. Furthermore, genes and gene sets associated with photosynthesis, glycolysis, gluconeogenesis, and the TCA cycle were dramatically lowered by PM stress. Several transcription factors, including MYB, C2H2, C3H, G2-like, and WRKY were induced, and metabolites such as proline and soluble sugar were accumulated to increase resistance against stressors. In addition, enzymatic and non-enzymatic antioxidants were also accumulated to scavenge the PM-induced reactive oxygen species (ROS). Taken together, this review provides an insight into plants' underlying cellular mechanisms and gene regulatory networks in response to the PM to determine strategies to preserve their structural and functional blend in the face of particulate pollution. The study concludes by recommending that future research should precisely focus on plants' response to short- and long-term PM exposure.

Association between sea-land breeze and particulate matter in five coastal urban locations in India

Particulate matter less than 2.5 μm particle diameter (PM2.5) is the most significant environmental issue globally. PM2.5 is an integral component of air quality monitoring and management, human health, weather, climate, and epidemiological research. In this work, we investigate the seasonal variation in PM2.5 mass concentrations and the association between the sea-land breeze system and particulate matter in five coastal urban locations in India (Kolkata, Visakhapatnam, Chennai, Thiruvananthapuram, and Mumbai). The relative occurrence of high PM2.5 mass concentrations was the greatest during the winter season (December through February) while the relative occurrence of low PM2.5 mass concentrations was the greatest during the monsoon season (June through September). Amongst locations, Kolkata experiences the highest PM2.5 loading in winter while Thiruvananthapuram experiences the lowest PM2.5 loading in monsoon. Indo-Gangetic Plain (IGP) outflow onto the Bay of Bengal significantly impacts locations along the eastern coast of India with reduced impact from north (Kolkata) to south (Chennai). The sea-breeze component analysis revealed daily cycles of the sea-land breeze with varying magnitudes of the breeze between the different seasons. Overall, we found a negative association between the sea-land breeze magnitude and PM2.5 mass concentrations, implying that the weakened sea-land breeze may deteriorate air quality in coastal locations due to poor ventilation. The vertical profiles of aerosol extinction showed elevated aerosol layers within 1 km from the surface in almost all locations. The decreasing trend in the land-sea temperature contrast in coastal locations is expected to deteriorate air quality in coastal locations in the warming future. Nevertheless, critical analyses using ground-based remote sensing techniques are required for a better understanding the impact of sea-land breeze dynamics on air quality in coastal locations.

Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980-2021)

In this study, the spatial-temporal trends of PM2.5 pollution were analyzed for subregions in Africa and the entire continent from 1980 to 2021. The distributions and trends of PM2.5 were derived from the monthly concentrations of the aerosol species from MERRA-2 reanalysis datasets comprising of sulphates (SO4), organic carbon (OC), black carbon (BC), Dust2.5 and Sea Salt (SS2.5). The resulting PM2.5 trends were compared with the climate factors, socio-economic indicators, and terrain characteristics. Using the Mann-Kendall (M-K) test, the continent and its subregions showed positive trends in PM2.5 concentrations, except for western and central Africa which exhibited marginal negative trends. The M-K trends also determined Dust2.5 as the dominant contributing aerosol factor responsible for the high PM2.5 concentrations in the northern, western and central regions of Africa, while SO4 and OC were respectively the most significant contributors to PM2.5 in the eastern and southern Africa regions. For the climate factors, the PM2.5 trends were determined to be positively correlated with the wind speed trends, while precipitation and temperature trends exhibited low and sometimes negative correlations with PM2.5. Socio-economically, highly populated, and bare/sparse vegetated areas showed higher PM2.5 concentrations, while vegetated areas tended to have lower PM2.5 concentrations. Topographically, low laying regions were observed to retain the deposited PM2.5 especially in the northern and western regions of Africa. The Air Quality Index (AQI) results showed that 94 % of the continent had an average PM2.5 of 12-35 μg/m3 hence classified as "Moderate" AQI, and the rest of the continent's PM2.5 levels was between 35 and 55 μg/m3 implying AQI classification of "Unhealthy for Sensitive People". Northern and western Africa regions had the highest AQI, while southern Africa had the lowest AQI. The approach and findings in this study can be used to complement the evaluation and management of air quality in Africa.

Air pollution status and attributable health effects across the state of West Bengal, India, during 2016-2021

Air pollution is one of the most significant threats to human safety due to its detrimental health consequences worldwide. This study examines the air pollution levels in 22 districts of West Bengal from 2016 to 2021, using data from 81 stations operated by the West Bengal Pollution Control Board (WBPCB). The study assesses the short- and long-term impacts of particulate matter (PM) on human health. The highest annual variation of PM10 was noted in 2016 (106.99 ± 34.17 μg/m3), and the lowest was reported in 2020 (88.02 ± 13.61 μg/m3), whereas the highest annual variations of NO2 (μg/m3) were found in 2016 (35.17 ± 13.55 μg/m3), and lowest in 2019 (29.72 ± 13.08 μg/m3). Similarly, the SO2 level was lower (5.35 μg/m3) in 2017 and higher in 2020 (7.78 μg/m3). In the state, Bardhaman, Bankura, Kolkata, and Howrah recorded the highest PM10 concentrations. The monthly and seasonal variations of pollution showed higher in December, January, and February (winter season) and lowest observed in June, July, and August (rainy season). The southern part of West Bengal state has recorded higher pollution levels than the northern part. The short- and long-term health impact assessment due to particulate matter shows that the estimated number of attributable cases (ENACs) for incidence of chronic bronchitis in adults and prevalence of bronchitis in children were 305,234 and 14,652 respectively. The long-term impact of PM2.5 on human health ENACs for mortality due to chronic obstructive pulmonary disease for adults, acute lower respiratory infections in children aged 0-5, lung cancer, and stroke for adults were 21,303, 12,477, 25,064, 94,406, and 86,272 respectively. This outcome assists decision-makers and stakeholders in effectively addressing the air pollution and health risk concerns within the specified area.

Air pollution analysis in Northwestern South America: A new Lagrangian framework

This study examines the spatiotemporal variations of PM2.5, PM10, SO2, O3, NO, and NO2 concentrations in Northwestern South America (NWSA). We assess the efficacy of existing policies, identify underlying phenomena, and highlight areas for further research. Significant findings have emerged by analyzing reanalysis and in-situ data, employing the WRF-Chem model, and utilizing a new Lagrangian framework designed to overcome some drawbacks common to analysis of pollution Long-Range Transport. Wildfires in the first half of the year and volcanic activity (for SO2) in July-August contribute to over 90 % of the pollutant's advection, leading to high pollution levels in urban areas. SO2 volcanic emissions contribute to secondary PM, explaining the peak in PM concentrations in Cali in July. In the second half of the year, pollutant behavior varies based on factors such as city characteristics, vehicular-volume, air temperature, wind speed, and boundary layer height, and O3 is influenced by solar radiation and the NO/NO2 ratio. Diurnal variations of PM and NOx correlate with vehicular density, SO2 with industrial activity, and O3 depends on solar radiation. Trend analysis reveals decreasing PM10 levels except in three Cundinamarca cities and Cali suggesting the need to implement/evaluate control plans in those locations. Although data is limited, NO and NO2 levels show an increasing trend due to the rising number of vehicles. SO2 levels are decreasing, except in Cali, potentially influenced by the nearby industrial and polluted city of Yumbo. O3 displays a downward trend in most cities, except Bogotá, due to the NO/NO2 ratio favoring O3 increase. These findings provide a starting point for further research to deepen our understanding of NWSA air pollution. Such investigations are essential before modifying existing policies or enacting new ones. Collaborative efforts at the international, regional, and inter-city levels are crucial for effective air quality management.

The interplay between air pollution, built environment, and physical activity: Perceptions of children and youth in rural and urban India

The role of physical inactivity as a contributor to non-communicable disease risk in children and youth is widely recognized. Air pollution and the built environment can limit participation in physical activity and exacerbate non-communicable disease risk; however, the relationships between perceptions of air pollution, built environment, and health behaviours are not fully understood, particularly among children and youth in low and middle-income countries. Currently, there are no studies capturing how child and youth perceptions of air pollution and built environment are associated with physical activity in India, thus, this study investigated the association between perceived air pollution and built environment on moderate-to-vigorous physical activity (MVPA) levels of Indian children and youth. Online surveys captured MVPA, perception of air pollution as a problem, built environment factors, as well as relevant sociodemographic characteristics from parents and children aged 5-17 years in partnership with 41 schools across 28 urban and rural locations during the Coronavirus disease lockdowns in 2021. After adjusting for age, gender, and location, a significant association was found between the perception of air pollution as a problem and MVPA levels (β = -18.365, p < 0.001). Similarly, the perception of a high crime rate was associated with lower MVPA levels (β = -23.383, p = 0.002). Reporting the presence of zebra crossings, pedestrian signals, or attractive natural sightings were associated with higher MVPA levels; however, this association varied across sociodemographic groups. These findings emphasize the importance of addressing air pollution and improving the built environment to facilitate outdoor active living, including active transportation, among children and youth - solutions that are particularly relevant not only for preventing non-communicable disease risk but also for climate change mitigation.

Impact of air pollutants on climate change and prediction of air quality index using machine learning models

The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms.

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.

Tracking air quality trends and vehicle traffic dynamics at urban scale using satellite and ground data before and after the COVID-19 outbreak

The implications of the COVID-19 outbreak are subjected to an increasing number of studies. So far, air quality trends related to the lockdown due to the pandemic have been analysed in large cities or entire regions. In this work, the region studied is the metropolitan area of Cagliari, which is the main city on the island of Sardinia (Italy) and can be representative of a coastal city that includes industrial settlements. The purpose of the study is to evaluate the effect of restrictions related to the COVID-19 outbreak on air quality levels and the traffic dynamics in this type of urban area. Nitrogen Dioxide (NO₂) levels before, during and after COVID-19 lockdown have been investigated using data acquired from the Sentinel-5P/TROPOMI satellite combined with on-site measurements. Both TROPOMI detected and ground-based data have revealed higher levels of NO₂ before and after the lockdown, compared to those during the period of COVID-related restrictions, in particular in the urban area of Cagliari. On the other hand, NO2 registered in the oil refinery area did not show significant differences associated with lockdown. The correlation of TROPOMI NO₂ tropospheric column with ground data (surface NO2) on a monthly mean basis showed different values based on the background and the highest Pearson's coefficient was of about 0.78 near to the city centre, where traffic can be considered a significant source of emission. In addition, a comparison of the air pollution level with the dynamics of vehicle traffic was investigated. The study highlighted a remarkable correlation between the reduction of the number of vehicles and the corresponding tropospheric NO₂ values that decreased on a weekly mean basis.

Spatiotemporal heterogeneity of the relationships between PM2.5 concentrations and their drivers in China's coastal ports

PM2.5 is one of the primary air pollutants that affect air quality and threat human health in the port areas. To prevent and control air pollution, it is essential to understand the spatiotemporal distributions of PM2.5 concentrations and their key drivers in ports. 19 coastal ports of China are selected to examine the spatiotemporal distributions of PM2.5 concentrations during 2013-2020. The annual average PM2.5 concentration decreases from 61.03 μg/m3 to 30.17 μg/m3, with an average decrease rate of 51.57%. Significant spatial autocorrelation exists among PM2.5 concentrations of ports. The result of the geographically and temporally weighted regression (GTWR) model shows significant spatiotemporal heterogeneity in the effects of meteorological and socioeconomic factors on PM2.5 concentrations. The effects of boundary layer height on PM2.5 concentrations are found to be negative in most ports, with a stronger effect found in the Pearl River Delta, Yangtze River Delta and some ports of the Bohai Rim Area. The total precipitation shows negative effects on PM2.5 concentrations, with the strongest effect found in ports of the Southeast Coast. The effects of surface pressure on PM2.5 concentrations are positive, with stronger effects found in Beibu Gulf Port and Zhanjiang Port. The effects of wind speed on PM2.5 concentrations generally increase from south to north. Cargo throughput shows strong and positive effects on PM2.5 concentrations in ports of Bohai Rim Area; the positive effects found in Beibu Gulf Port increased from 2013 to 2018 and decreased since 2019. The positive effects of GDP and nighttime light on PM2.5 concentrations gradually decrease and turn negative from south to north. Understandings obtained from this study can potentially support the prevention and control of air pollution in China's coastal ports.

Environmental management: a country-level evaluation of atmospheric particulate matter removal by the forests of India

Particulate matter (PM) is a critical air pollutant, responsible for an array of ailments leading to premature mortality worldwide. Nature-based solutions for mitigation of PM and especially role of forests in mitigating PM from an ecosystem perspective are less explored. Forests provide a natural pollution abatement strategy by providing a surface area for the deposition of PM. Depending on their structure and composition, forests have varying capacities for PM adsorption, which is again less explored. Hence, in the present study, we evaluate the removal capacity of PM by the forest-type groups of India. Deposition flux and total PM removal across sixteen forest types were estimated based on the 2019 dataset of PM using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data. Externality values and PM removal costs by industrial equipment were used for associating an economic value to the air pollution abatement service by forests. The total PM2.5 removal by forests in 2019 was estimated to be 1361.28 tons and PM10 was estimated to be 303,658.27 tons. Deposition of PM was found to be high in littoral and swamp forests, tropical semi-evergreen forests, tropical moist deciduous forests, and sub-tropical pine forests. Tropical dry deciduous forests had the highest net weight % removal of PM with 39% removal for PM2.5 and 39% removal for PM10. The air pollution abatement service by forests for PM removal was 188 M US dollars (USD) with externality-based removal service by forests of 2009 M USD. The net PM removed by all forests of India was estimated to be approximately worth ₹ 470-648 Crore (59-81 million dollars) for PM2.5 and worth ₹56,746-1,22,617 Crore (7093-15,327 million dollars) for PM10 based on valuation using value transfer method. The study concludes that forests can be a significant contributor to PM reduction at a global level. Especially for India's National Clean Air Programme and further research and policy considerations, the findings would be extremely useful.

AQI prediction using layer recurrent neural network model: a new approach

The air quality index (AQI) prediction is important to evaluate the effects of air pollutants on human health. The airborne pollutants have been a major threat in Delhi both in the past and coming years. The air quality index is a figure, based on the cumulative effect of major air pollutant concentrations, used by Government agencies, for air quality assessment. Thus, the main aim of the present study is to predict the daily AQI one year in advance through three different neural network models (FF-NN, CF-NN and LR-NN) for the year 2020 and compare them. The models were trained using AQI values of previous year (2019). In addition to main air pollutants like PM10/PM2.5, O3, SO2, NOx, CO and NH3, the non-criteria pollutants and meteorological data were also included as input parameter in this study. The model performances were assessed using statistical analysis. The key air pollutants contributing to high level of daily AQI were found to be PM2.5/PM10, CO and NO2. The root mean square error (RMSE) values of 31.86 and 28.03 were obtained for the FF-NN and CF-NN models respectively whereas the LR-NN model has the minimum RMSE value of 26.79. LR-NN algorithm predicted the AQI values very closely to the actual values in almost all the seasons of the year. The LR-NN performance was also found to be the best in post-monsoon season i.e., October and November (maximum R2 = 0.94) with respect to other seasons. The study would aid air pollution control authorities to predict AQI more precisely and adopt suitable pollution control measures. Further research studies are recommended to compare the performance of LR-NN model with statistical, numerical and computational models for accurate air quality assessment.

Reversal of trends in global fine particulate matter air pollution

Ambient fine particulate matter (PM2.5) is the world's leading environmental health risk factor. Quantification is needed of regional contributions to changes in global PM2.5 exposure. Here we interpret satellite-derived PM2.5 estimates over 1998-2019 and find a reversal of previous growth in global PM2.5 air pollution, which is quantitatively attributed to contributions from 13 regions. Global population-weighted (PW) PM2.5 exposure, related to both pollution levels and population size, increased from 1998 (28.3 μg/m3) to a peak in 2011 (38.9 μg/m3) and decreased steadily afterwards (34.7 μg/m3 in 2019). Post-2011 change was related to exposure reduction in China and slowed exposure growth in other regions (especially South Asia, the Middle East and Africa). The post-2011 exposure reduction contributes to stagnation of growth in global PM2.5-attributable mortality and increasing health benefits per µg/m3 marginal reduction in exposure, implying increasing urgency and benefits of PM2.5 mitigation with aging population and cleaner air.

Very high particulate pollution over northwest India captured by a high-density in situ sensor network

Exposure to particulate matter less than 2.5 µm in diameter (PM2.5) is a cause of concern in cities and major emission regions of northern India. An intensive field campaign involving the states of Punjab, Haryana and Delhi national capital region (NCR) was conducted in 2022 using 29 Compact and Useful PM2.5 Instrument with Gas sensors (CUPI-Gs). Continuous observations show that the PM2.5 in the region increased gradually from < 60 µg m-3 in 6-10 October to up to 500 µg m-3 on 5-9 November, which subsequently decreased to about 100 µg m-3 in 20-30 November. Two distinct plumes of PM2.5 over 500 µg m-3 are tracked from crop residue burning in Punjab to Delhi NCR on 2-3 November and 10-11 November with delays of 1 and 3 days, respectively. Experimental campaign demonstrates the advantages of source region observations to link agricultural waste burning and air pollution at local to regional scales.

A 1-D model to retrieve the vertical profiles of minor atmospheric constituents for cloud microphysical modeling: I. Formulation and validation

Determining the number concentration of minor constituents in the atmosphere is very important as it determines the whole tropospheric chemistry process. These constituents may act as cloud condensation nuclei (CCN) and ice nuclei (IN), impacting heterogeneous nucleation inside the cloud. However, the estimations of the number concentration of CCN/IN in cloud microphysical parameters are associated with uncertainties. In the present work, a hybrid Monte Carlo Gear solver has been developed to retrieve profiles of CH₄, N₂O, and SO₂. The idealized experiments have been carried out using this solver for retrieving vertical profiles of these constituents over four megacities, viz., Delhi, Mumbai, Chennai, and Kolkata. Community Long-term Infrared Microwave Coupled Atmospheric Product System (CLIMCAPS) dataset around 0800 UTC (2000UTC) has been used for initializing the number concentration of CH₄, N₂O, and SO₂ for daytime (nighttime). The daytime (nighttime) retrieved profiles have been validated using 2000 UTC (next day 0800 UTC) CLIMCAPS products. ERA5 temperature dataset has been used to estimate the kinematic rate of reactions with 1000 perturbations determined using Maximum Likelihood Estimation (MLE). The retrieved profiles and CLIMCAPS products are in very good agreement, as evidenced by the percentage difference between them within the range of 1.3 × 10^-5-60.8 % and the coefficient of determination mainly within the range between 81 and 97 %. However, during the passage of tropical cyclone and western disturbance, its value became as low as 27 and 65 % over Chennai and Kolkata, respectively. The enactment of synoptic scale systems such as western disturbances, tropical cyclone Amphan, and easterly waves caused disturbed weather over these megacities-the retrieved profiles during disturbed weather cause large deviations of vertical profiles of N₂O. However, the profiles of CH₄ and SO₂ have less deviation. It is inferred that incorporating this methodology in the dynamical model will be useful to simulate the realistic vertical profiles of the minor constituents in the atmosphere.

Understanding the influence of summer biomass burning on air quality in North India: Eight cities field campaign study

Near real-time monitoring of major air pollutants, i.e., particulate matter (PM₁₀, PM_2.5, PM₁), trace gases (O₃, CO, NO, NO₂, NO_x, NH₃, CO₂, SO₂) and Volatile Organic Compounds (VOCs: benzene, ethylbenzene, m-, p-xylene, o-xylene and toluene) along with climatological parameters was done in eight-cities field campaigns during the rabi (wheat) crop residue burning period in the northwest of Indo-Gangetic Plain (IGP) region. The phase-wise monitoring was done at eight locations representing rural, semi-urban and urban backgrounds. During the whole campaign, the semi-urban site (Sirsa) observed the highest average concentration of PM₁₀ (226 ± 111 μg m^-3) and PM_2.5 (91 ± 67 μg m^-3). The urban site (Chandigarh) reported the minimum concentrations of all the three size fractions of particulate matter with PM₁₀ as 89 ± 54 μg m^-3, PM_2.5 as 42 ± 22 μg m^-3 and PM₁ as 20 ± 13 μg m^-3 where the monitoring was done in the early phase of the campaign. The highest VOC concentration was recorded at the semi-urban (Sirsa) site, whereas the lowest was at a rural location (Fatehgarh Sahib). NH₃ concentration was observed highest in rural sites (31.7 ± 29.8 ppbv), which can be due to the application of fertilizers in agricultural activities. Visible Infrared Imaging Radiometer Suite (VIIRS) based fire and thermal anomalies, along with HYSPLIT back trajectory analysis, show that major air masses over monitoring sites (22 %-70 %) were from the rabi crop residue burning regions. The characteristic ratios and Principal component analysis (PCA) results show that diverse sources, i.e., emissions from crop residue burning, solid biomass fuels, vehicles and industries, majorly degrade the regional air quality. This multi-city study observed that semi-urban regions have the most compromised air quality during the rabi crop residue burning and need attention to address the air quality issues in the IGP region.

Spatio-temporal analysis of PM2.5 and policies in Northwestern South America

This paper analyzes the spatio-temporal variations, and exceedances of the PM_2.5 concentrations in Northwestern South America at different scales to assess the implemented policies and identify the involved phenomena. Through reanalysis and ground-based data, we found that high PM_2.5 levels in most cities of the region are caused by wildfires and local emissions, including the capital cities of Venezuela, Ecuador, Colombia, and Panamá. In-situ measurements suggest that the majority of the cities comply with the local but not with the WHO guidelines, indicating that local annual limits should be more restrictive. Two peaks in the daily variations of PM_2.5 (related to vehicle emissions) and also a steeper decrease around noon (associated with an increase in wind speed and in the boundary layer height) were identified. The trend-analysis shows that Bogotá and Medellín have a decreasing PM_2.5 annual-trend (between -0.8μgm^-3 and -1.7μgm^-3) that corresponds to effective policies. In contrast, Cali has a positive annual-trend (0.8μgm^-3) most likely because of Short-Range Transport produced by a northerly-flow from a highly polluted neighboring city, which also affects Cali's PM_2.5 diurnal cycle, or by local-dynamics. The exceedances show that the policies are working on an annual but not at a daily time-scale. These results serve as a first input for additional studies, with the aim of gaining a better understanding of the contaminant before adapting current policies or implementing new policies and measures that need to include a joint international, regional, and inter-city efforts regarding pollution transport.

Frequency distribution of pollutant concentrations over Indian megacities impacted by the COVID-19 lockdown

The megacities experience poor air quality frequently due to stronger anthropogenic emissions. India had one of the longest lockdowns in 2020 to curb the spread of COVID-19, leading to reductions in the emissions from anthropogenic activities. In this article, the frequency distributions of different pollutants have been analysed over two densely populated megacities: Delhi (28.70° N; 77.10° E) and Kolkata (22.57° N; 88.36° E). In Delhi, the percentage of days with PM_2.5 levels exceeding the National Ambient Air Quality Standards (NAAQS) between 25 March and 17 June dropped from 98% in 2019 to 61% in 2020. The lockdown phase 1 brought down the PM₁₀ (particulate matter having an aerodynamic diameter ≤ 10 μm) levels below the daily NAAQS limit over Delhi and Kolkata. However, PM₁₀ exceeded the limit of 100 μgm^-3 during phases 2-5 of lockdown over Delhi due to lower temperature, weaker winds, increased relative humidity and commencement of limited traffic movement. The PM_2.5 levels exhibit a regressive trend in the highest range from the year 2019 to 2020 in Delhi. The daily mean value for PM_2.5 concentrations dropped from 85-90 μgm^-3 to 40-45 μgm^-3 bin, whereas the PM₁₀ levels witnessed a reduction from 160-180 μgm^-3 to 100-120 μgm^-3 bin due to the lockdown. Kolkata also experienced a shift in the peak of PM₁₀ distribution from 80-100 μgm^-3 in 2019 to 20-40 μgm^-3 during the lockdown. The PM_2.5 levels in peak frequency distribution were recorded in the 35-40 μgm^-3 bin in 2019 which dropped to 15-20 μgm^-3 in 2020. In line with particulate matter, other primary gaseous pollutants (NO_x, CO, SO₂, NH₃) also showed decline. However, changes in O₃ showed mixed trends with enhancements in some of the phases and reductions in other phases. In contrast to daily mean O₃, 8-h maximum O₃ showed a reduction over Delhi during lockdown phases except for phase 3. Interestingly, the time of daily maximum was observed to be delayed by ~ 2 h over Delhi (from 1300 to 1500 h) and ~ 1 h over Kolkata (from 1300 to 1400 h) almost coinciding with the time of maximum temperature, highlighting the role of meteorology versus precursors. Emission reductions weakened the chemical sink of O₃ leading to enhancement (120%; 11 ppbv) in night-time O₃ over Delhi during phases 1-3.

Data-driven predictive modeling of PM2.5 concentrations using machine learning and deep learning techniques: a case study of Delhi, India

The present study intends to use machine learning (ML) and deep learning (DL) models to forecast PM_2.5 concentration at a location in Delhi. For this purpose, multi-layer feed-forward neural network (MLFFNN), support vector machine (SVM), random forest (RF) and long short-term memory networks (LSTM) have been applied. The air pollutants, e.g., CO, Ozone, PM₁₀, NO, NO₂, NO_x, NH₃, SO₂, benzene, toluene, as well as meteorological parameters (temperature, wind speed, wind direction, rainfall, evaporation, humidity, pressure, etc.), have been used as inputs in the present study. Moreover, this is one of the first papers that employ aerodynamic roughness coefficient as an input parameter for the prediction of PM_2.5 concentration. The result of the study shows that the LSTM model with index of agreement (IA) 0.986, root mean square error (RMSE) 21.510, Nash-Sutcliffe efficiency index (NSE) 0.945, (coefficient of determination)R² 0.945, and (correlation coefficient)R 0.972 is the best performing technique for the prediction of PM_2.5 followed by MLFFNN, SVM, and RF models. The sensitivity analysis for the LSTM model reported that PM₁₀, wind speed, NH₃, and benzene are the key influencing parameters for the estimation of PM_2.5. The findings in this work suggest that the LSTM could advance in PM_2.5 forecasting and thus would be useful for developing fine-scale, state-of-the-art air pollution forecasting models.

Four year long simulation of carbonaceous aerosols in India: Seasonality, sources and associated health effects

India's air quality is in a dismal state, with many studies ascribing it to PM_2.5. Most of these corroborate that carbonaceous aerosol (CA) constitute significant fraction of PM_2.5. However, investigations on the effect of long-term meteorological or emission changes on PM_2.5 and its components, and their associated health effects are rare. In this work, WRF-Chem simulations for three seasons over four years (2016-2019) were carried out to cogitate the spatial and temporal changes in PM_2.5 and its components in India. Model predicted PM_2.5 concentrations were in good agreement with the ground-based observations for 25 cities. PM_2.5 was highest in winter and lowest in pre-monsoon. PM_2.5 reduced by ∼8% in Indo-Gangetic Plain (IGP) but increased by ∼38% and ∼130% in south and northeast India, respectively, from 2016 to 2019. IGP witnessed three times higher average PM_2.5 concentrations than south India. No significant interannual change in CA contributions was observed, however, it peaked in the winter season. Other inorganics (OIN) were the major component of PM_2.5, contributing more than 40%. Primary organic aerosol (POA) fractions were higher in north India, while secondary inorganic aerosol (SIA) dominated south India. Transport and residential sectors were the chief contributors to CA across India. Biomass burning contributed up to ∼23% of PM_2.5 in regions of IGP during post-monsoon, with CA fractions up to 50%. Associations between PM_2.5 and its components with daily inpatient admissions from a tertiary care centre in Delhi showed that PM_2.5 and OIN had lower associations with daily hospital admissions than CA. Every 10 μg/m³ increase in POA, black carbon (BC), and secondary organic aerosol (SOA) were associated with ∼1.09%, ∼3.07% and ∼4.93% increase in the risk of daily hospital admissions. This invigorates the need for more policies targeting CA rather than PM_2.5 to mitigate associated health risks, in India.

COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts

This paper proposes a novel grey spatiotemporal model and quantitatively analyzes the spillover and momentum effects of the COVID-19 lockdown policy on the concentration of PM2.5 (particulate matter of diameter less than 2.5 μm) in Wuhan during the COVID-19 pandemic lockdown from 23 January to 8 April 2020 inclusive, and the post-pandemic period from 9 April 2020 to 17 October 2020 inclusive. The results suggest that the stringent lockdowns lead to a reduction in PM2.5 emissions arising from a momentum effect (9.57-18.67%) and a spillover effect (7.07-27.60%).

Assessment of PM2.5 using satellite lidar observations: Effect of bio-mass burning emissions over India

The present study estimates the particulate matter with aerodynamic diameters less than 2.5 μm (PM_2.5) over the Indian sub-continent using near-surface retrieval of aerosol extinction coefficient (2007-2021) of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. Climatology of wintertime PM_2.5 during the last 15 years shows the highest concentration over the middle Indo-Gangetic Plain (IGP) and northwest India with a 3 to 4 fold increase in magnitude compared to the peninsular India. Surface-level PM_2.5 mass concentration during winter (December to February) shows statistically significant positive trends over the Indian subcontinent. It increases at a rate of ~3% over the IGP and arid regions of northwest India, and ~4% over peninsular India during the last fifteen years (2006-2020). Interannual variability of average near-surface PM_2.5 concentration over the Indian sub-continent during the fog occurring season (December to February) shows a statistically significant correlation with the post-harvest agro-residue burning over the western IGP (Punjab and Haryana) during November. The wintertime near-surface PM_2.5 concentration shows a higher correlation with anthropogenic agro-residue burning activity compared to meteorological parameters. The influence of agro-residue burning during November over northern India extends up to peninsular India and might contribute to continental pollution outflow and associated aerosol plumes persisting over the Northern Indian Ocean during the winter season. Sustainable energy recovery solutions to the agro-residue burning need to be implemented to effectively reduce the far-reaching implications of the post-monsoon burning activity over the western IGP.

Hesperidin Protects Human HaCaT Keratinocytes from Particulate Matter 2.5-Induced Apoptosis via the Inhibition of Oxidative Stress and Autophagy

Numerous epidemiological studies have reported that particulate matter 2.5 (PM2.5) causes skin aging and skin inflammation and impairs skin homeostasis. Hesperidin, a bioflavonoid that is abundant in citrus species, reportedly has anti-inflammatory properties. In this study, we evaluated the cytoprotective effect of hesperidin against PM2.5-mediated damage in a human skin cell line (HaCaT). Hesperidin reduced PM2.5-induced intracellular reactive oxygen species (ROS) generation and oxidative cellular/organelle damage. PM2.5 increased the proportion of acridine orange-positive cells, levels of autophagy-related proteins, beclin-1 and microtubule-associated protein light chain 3, and apoptosis-related proteins, B-cell lymphoma-2-associated X protein, cleaved caspase-3, and cleaved caspase-9. However, hesperidin ameliorated PM2.5-induced autophagy and apoptosis. PM2.5 promoted cellular apoptosis via mitogen-activated protein kinase (MAPK) activation by promoting the phosphorylation of extracellular signal-regulated kinase, c-Jun N-terminal kinase, and p38. The MAPK inhibitors U0126, SP600125, and SB203580 along with hesperidin exerted a protective effect against PM2.5-induced cellular apoptosis. Furthermore, hesperidin restored PM2.5-mediated reduction in cell viability via Akt activation; this was also confirmed using LY294002 (a phosphoinositide 3-kinase inhibitor). Overall, hesperidin shows therapeutic potential against PM2.5-induced skin damage by mitigating excessive ROS accumulation, autophagy, and apoptosis.

Impact of Circular, Waste-Heat Reuse Pathways on PM2.5-Air Quality, CO2 Emissions, and Human Health in India: Comparison with Material Exchange Potential

India is home to 1.3 billion people who are exposed to some of the highest levels of ambient air pollution in the world. In addition, India is one of the fastest-growing carbon-emitting countries. Here, we assess how two strategies to reuse waste-heat from coal-fired power plants and other large sources would impact PM_2.5-air quality, human health, and CO₂ emissions in 2015 and a future year, 2050, using varying levels of policy adoption (current regulations, proposed single-sector policies, and ambitious single-sector strategies). We find that power plant and industrial waste-heat reuse as input to district heating systems (DHSs), a novel, multisector strategy to reduce local biomass burning for heating emissions, can offset 71.3-85.2% of residential heating demand in communities near a power plant (9.3-12.4% of the nationwide heating demand) with the highest benefits observed during winter months in areas with collocated industrial activity and higher residential heating demands (e.g., New Delhi). Utilizing waste-heat to generate electricity via organic Rankine cycles (ORCs) can generate an additional 22 (11% of total coal-fired generating capacity), 41 (8%), 32 (13%), and 6 (5%) GW of electricity capacity in the 2015, 2050-current regulations, 2050-single-sector, and 2050-ambitious-single-sector scenarios, respectively. Emission estimates utilizing these strategies were input to the GEOS-Chem model, and population-weighted, simulated PM_2.5 showed small improvements in the DHS (0.2-0.4%) and ORC (0.3-3.4%) scenarios, where the minimal DHS PM_2.5-benefit is attributed to the small contribution of biomass burning for heating to nationwide PM_2.5 emissions (much of the biomass burning activity is for cooking). The PM_2.5 reductions lead to ∼130-36,000 mortalities per year avoided among the scenarios, with the largest health benefits observed in the ORC scenarios. Nationwide CO₂ emissions reduced <0.04% by DHSs but showed larger reductions using ORCs (1.9-7.4%). Coal fly-ash as material exchange in cement and brick production was assessed, and capacity exists to completely reutilize unused fly-ash toward cement and brick production in each of the scenarios.

p-Phenylenediamine Antioxidants in PM2.5: The Underestimated Urban Air Pollutants

The wide use and continuous abrasion of rubber-related products appears to be leading to an incredible release of p-phenylenediamine (PPD) antioxidants in the environment. However, no related research has been conducted on the pollution characteristics and potential health risks of PM_2.5-bound PPDs. We report for the first time the ubiquitous distributions of six emerging PPDs and a quinone derivative, N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6PPDQ), in PM_2.5 from urban areas of China. Atmospheric contamination levels of PM_2.5-bound PPDs were found to be mostly in pg m^-3 amounts between 2018 and 2019. Urban vehicle rubber tire abrasion was found to probably contribute to the PPDs in PM_2.5 and accounted for their significant spatiotemporal-dependent concentration variations. Furthermore, 6PPDQ, an emerging oxidation product of 6PPD in the environment, was first quantified (pg m^-3) with a total detection rate of 81% in the urban PM_2.5, demonstrating its broad existence. On the basis of the determined ambient concentrations, the annual intakes of PPDs and 6PPDQ for adults were not low, indicating their possible human health risks induced by long-term exposure. This study confirms the widespread occurrence of PPDs and 6PPDQ in PM_2.5, showing that the pollution of such compounds in urban air should not be underestimated.

Impact of COVID-19 Pandemic Lockdown in Ambient Concentrations of Aromatic Volatile Organic Compounds in a Metropolitan City of Western India

The real-time Benzene, Toluene, Ethylbenzene, and Xylenes (BTEX) concentrations were measured in a metropolitan city of India during January to May of 2020 and 2014-2015-2018 to assess the impact of emission reduction during the COVID-19 lockdown. The total BTEX (∑BTEX) concentrations were 11.5 ± 9.0, 15.7 ± 16, 5.3 ± 5.0, 2.9 ± 2.0, and 0.93 ± 1.2 ppbv in January-May 2020, respectively. The evening rush hour peaks of BTEX during lockdown decreased by 4-5 times from the same period of years 2014-2015-2018. A significant decline in background concentrations suggests a regional-scale reduction in anthropogenic emissions. The contributions of ∑TEX compounds to ∑BTEX increased from 42% to 59% in winter to 64%-75% during the lockdown under hot summer conditions. While emission reductions dominated during the lockdown period, the meteorological and photochemical factors may also have contributed. Meteorological influence on actual observed BTEX data was removed by normalizing with ventilation coefficient (VC). The actual ambient air reductions of 85%-90% and VC-normalized reductions of 54%-88% of the BTEX concentrations during lockdown were estimated compared to those during the same period of 2014-2015-2018. The estimated changes using nighttime data, which take into account BTEX photooxidation removal, are ∼8% lower than the VC-normalized estimates using all data. These significant reductions in BTEX concentrations are consistent with the change in people's movement as inferred from mobility data during the lockdown. Although enforced, the significant decline in ambient BTEX levels during lockdown was a good change for the air quality. The study suggests a need for more effective science-based policies that consider local and regional factors.

Long term influence of groundwater preservation policy on stubble burning and air pollution over North-West India

Stubble burning (SB) has been a major source of seasonal aerosol loading and pollution over northern India. The aftereffects of groundwater preservation act i.e., post 2010 era (2011-2020) has seen delay in crop harvesting thereby shifting the peak SB to May (Wheat SB) and to November (Paddy SB) by 8-10 and 10-12 days compared to pre-2010. Groundwater storage depletion rate of 29.2 mm yr^-1 was observed over the region. Post 2010 era shows an increase of 1.4% in wheat SB and 21% in Paddy SB fires over Punjab and Haryana with 70% of PM_2.5 air mass clusters (high probability > 0.8) advecting to the downwind regions leading to 23-26% increase in PM_2.5 and 4-6% in aerosol loading over National Capital Region (NCR). Although the objective of water conservation policy was supposed to preserve the groundwater by delaying the paddy transplantation and sowing, on the contrary the implementation of this policy has seen groundwater storage after 2013 depleting at a rate of 29.2 mmyr^-1 over these regions. Post policy implementation has led to shift and shrinking of harvest window with increased occurrences in SB fires which also increase associated particulate matter pollution over North India.

Will open waste burning become India's largest air pollution source?

India struggles with frequent exceedances of the ambient air quality standard for particulate matter and benzene. In the past two decades, India has made considerable progress in tackling indoor air pollution, by phasing out kerosene lamps, and pushing biofuel using households towards Liquefied Petroleum Gas (LPG) usage. In this study, we use updated emission inventories and trends in residential fuel consumption, to explore changes in the contribution of different sectors towards India's largest air pollution problem. We find that residential fuel usage is still the largest air pollution source, and that the <10% households using cow dung as cooking fuel contribute ∼50% of the residential PM_2.5 emissions. However, if current trends persist, residential biofuel usage in India is likely to be phased out by 2035. India's renewable energy policies are likely to reduce emissions in the heat and electricity sector, and manufacturing industries, in the mid-term. PM_2.5 emissions from open waste burning, on the other hand, hardly changed in the decade from 2010 to 2020. We conclude that without strong policies to promote recycling and upcycling of non-biodegradable waste, and the conversion of biodegradable waste to biogas, open waste burning is likely to become India's largest source of air pollution by 2035. While our study is limited to India, our findings are of relevance for other countries in the global South suffering from similar waste management challenges.

Drivers of air pollution variability during second wave of COVID-19 in Delhi, India

To curb the 2nd wave of COVID-19 disease in April-May 2021, a night curfew followed by full lockdown was imposed over the National Capital Territory, Delhi. We have analyzed the observed variation in pollutants and meteorology, and role of local and transboundary emission sources during night-curfew and lockdown, as compared to pre-lockdown period and identical periods of 2020 lockdown as well as of 2018 and 2019. In 2021, concentration of pollutants (except O₃, SO₂, and toluene) declined by 4-16% during night-curfew as compared to the pre-lockdown period but these changes are not statistically significant. During lockdown in 2021, various pollutants decreased by 1-28% as compared to the night-curfew (except O₃ and PM₂.₅), but increased by 31-129% compared to the identical period of 2020 lockdown except O₃. Advection of pollutants from the region of moderate lockdown restrictions and an abrupt increase in crop-residue burning activity (120-587%) over Haryana and Punjab increased the air pollution levels over NCT during the lockdown period of 2021 as compared to 2020 in addition to a significant contribution of long-range transport. The increase in PM₂.₅ during the lockdown period of 2021 compared to 2020 might led to 5-29 additional premature mortalities.

Identification of air pollution hotspots in urban areas - An innovative approach using monitored concentrations data

Critical assessment of spatio-temporal variations in pollution levels is a crucial step for identifying and prioritizing air pollution hotspots (APH) in urban areas. There is no universally accepted methodology for defining and delineating air pollution hotspot which can be source-specific, pollutant-specific and time-specific. The present research article is an attempt to develop a protocol for identifying APH for any pollutant within a city where-in three criteria-based innovative methodology has been derived. The three criteria are frequency of exceedance (% of days), scale of exceedance and consistency in exceedance (consecutive number of days) to the specified standards that need to be met continuously for at least three years. The suggested methodology has been applied on a three-year database (2018-2021) of 37 continuous ambient air quality stations to identify PM_2.5 specific APH. The analysis indicates 11 APH in April, 9 in May, 2 in June and almost the entire city during the October-February months. Given prioritization of implementation of control actions, the identified APH during summer has been further physically examined to map source activity types and their suitability for ambient air quality monitoring stations as per the guidelines. The APH can be the priority areas for the implementation of control actions by urban local bodies. The management of air pollution at these priority areas would be more effective instead of city-scale management practice, which is difficult to implement and monitor.

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015-2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM_2.5, PM₁₀, PMC (coarse fraction of PM), NO₂, SO₂, NOx, CO, O₃ and the total gaseous oxidant (OX = NO₂ + O₃) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015-2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO₂ and NOx concentrations and peoples' mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO₂ and between 30% and 40% in mean PM_2.5 concentrations over 2020 full lockdown compared to the same period in 2015-2019. However, PM_2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO₂/CO ratio). Some Chinese cities showed similar increases in PM_2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O₃ concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO₂ anomalies were negative for 2020 compared to 2015-2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO₂/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO₂ decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO₂ + O₃) showed that primary NO₂ emissions at urban locations were greater than the O₃ production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO₂ and O₃ concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM_2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.

Importance of meteorology and chemistry in determining air pollutant levels during COVID-19 lockdown in Indian cities

Indian cities can experience severe air pollution, and the reduction in activity during the first national COVID-19 lockdown (2020) offered a natural experiment to study the contribution of local sources. The current work aimed to quantify the changes due to the lockdown in NO_x, O₃ and PM_2.5 in two contrasting cities in India (Delhi and Hyderabad) using a boosted regression tree model to account for the influence of meteorology. The median NO_x and PM_2.5 concentrations were observed to decrease after lockdown in both cities, up to 57% and 75% for PM_2.5 and NO_x, respectively when compared to previous years. After normalization due to meteorology the calculated reduction after lockdown for PM_2.5 was small (<8%) in both cities, and was likely less attributable to changes in local emissions, but rather due changes in background levels (i.e. regional source(s)). The reduction of NO_x due to lockdown varied by site (on average 5-30%), likely reflecting differences in relative proximity of local sources to the monitoring site, demonstrating the key influence of meteorology on ambient levels post-lockdown. Ozone was observed to increase after lockdown at both sites in Delhi, likely due to changes in relative amounts of precursor concentrations promoting ozone production, suggesting a volatile organic compound (VOC)-limited regime in Delhi. Thus, the calculated reduction in air pollutants due to lockdown in the current work cannot be extrapolated to be solely from a reduction in emissions and instead reflects the overall change in ambient levels, as meteorology and atmospheric chemical processes also contributed.

Prevalence of COPD among population above 30 years in India: A systematic review and meta-analysis

Background

By 2030, Sustainable Development Goal 3.4 aims to reduce the premature mortality caused by non-communicable diseases through prevention and treatment. Chronic obstructive pulmonary disease is the second leading cause of mortality and disability-adjusted life years in India. This review was conducted to estimate the prevalence of COPD using systematic review and meta-analysis technique.

Method

Search was conducted using six databases for studies on COPD among population above 30 years in India between years 2000 to 2020. Cross-sectional and cohort studies reporting prevalence of COPD and associated risk factors were included in the present review. Screening and data extraction was done by two authors independently. Studies were appraised for quality using the modified New Castle Ottawa scale and reporting quality was assessed using STROBE guidelines.

Result

Our search returned 8973 records, from which 23 records fulfilled the eligibility criteria. Overall, the prevalence of COPD among population aged 30 years and above in India was 7%. Risk factors like active and passive smoking, biomass fuel exposure, environmental tobacco smoke, occupational exposure to dust, indoor and outdoor pollution, and increasing age were reported to have a significant association with COPD among Indian population.

Conclusion

Our findings suggest the need for a multicentric national-level research study to understand COPD burden and its contributing risk factors. The findings also suggest the need for COPD sensitive health literacy program focused on early screening and primary prevention of risk factors for COPD, which may help early initiation of self-management practices, that are crucial for better quality of life.

Association between air pollution, body mass index, respiratory symptoms, and asthma among adolescent school children living in Delhi, India

Background:

Delhi is one of the most polluted cities in the world with annual average ambient PM₁₀ and PM_2.5 levels exceeding the World Health Organization standards by over 15 fold. We aimed to study the prevalence of respiratory and allergic symptoms and asthma among adolescent children living in Delhi (D) and compare it with children living in lesser polluted cities of Kottayam (K) and Mysore (M) located in Southern India.

Methods:

4361 boys and girls between the age group of 13–14 and 16–17 years from 12 randomly selected private schools from D, K, and M were invited to participate. Modified and expanded International Study for Asthma and Allergies in Children (ISAAC) questionnaires (Q) were filled by the students who also performed spirometry using the ultrasonic flow-sensor-based nDD Spirometer.

Results:

3157 students (50.4% boys) completed the Q and performed good quality spirometry. The prevalence of asthma and airflow obstruction among children living in Delhi was 21.7% using the ISAAC Q and 29.4% on spirometry, respectively. This was accompanied by significantly higher rates of self-reported cough, shortness of breath, chest tightness, sneezing, itchy and watery eyes, itchy skin, and eczema among Delhi children (vs. K-M, all P < 0.05). Delhi children were more overweight and obese (39.8% vs. 16.4%, P < 0.0001), and this was the only risk factor that was strongly associated with asthma (odds ratio [OR]: 1.79; confidence interval: 1.49–2.14), with a more pronounced effect in Delhi children (P = 0.04). Forced expiratory volume₁ and Forced vital capacity values were significantly higher in Delhi children (vs. K-M P < 0.0001). Preserved ratio impaired spirometry was more common in K-M children (P < 0.0001).

Conclusion:

Adolescent children living in the polluted city of Delhi had a high prevalence of asthma, respiratory symptoms, allergic rhinitis, and eczema that was strongly associated with a high body mass index (BMI). Our study suggests an association between air pollution, high BMI, and asthma/allergic diseases, which needs to be explored further.

Impact of COVID-19 lockdown on ambient air quality in megacities of India and implication for air pollution control strategies

The impact of restrictions during various phases of COVID-19 lockdown on daily mean PM2.5 concentration in five Indian megacities (New Delhi, Chennai, Kolkata, Mumbai, and Hyderabad) was studied. The impact was studied for pre-lockdown (1st Mar-24th Mar 2020), lockdown (25th Mar-31st May 2020), and unlocking (1st Jun-31st Aug 2020) phases. The lockdown period comprises 4 lockdown phases with distinct measures, whereas the unlocking period had 3 phases. PM2.5 concentration reduced significantly in all megacities and met the national standards during the lockdown period. The maximum reduction in PM2.5 level was observed in Kolkata (62%), followed by Mumbai (49%), Chennai (34%), and New Delhi (26%) during the lockdown period. Comparatively, Hyderabad exhibited a smaller reduction in PM2.5 concentration, i.e., 10%. The average PM2.5 levels during the lockdown in the peak hour (i.e., 07:00-11:00 h) in New Delhi, Chennai, Kolkata, Mumbai, and Hyderabad decreased by 21.3%, 48.5%, 63.4%, 56.4%, and 23.8%, respectively, compared to those before lockdown period. During the unlocking period, except for Chennai, all megacities showed a reduction in average PM2.5 levels compared to concentrations in the lockdown period, but these reductions were mainly linked with monsoon rains in India. The current study provided an opportunity to study air pollution in the absence of major anthropogenic activities and during limited activities in monsoon season having an ecological design. The study reports a new baseline of PM2.5, except for monsoon, and explores this knowledge to plan future air pollution reduction strategies. The study also discusses how this new learning of knowledge could strengthen air pollution control policies for better air quality and sustainability.Graphical abstract.

Short-term impacts of air pollutants in three megacities of India during COVID-19 lockdown

Lockdown was imposed by the Indian government in the month of March 2020 as an early precaution to the COVID-19 pandemic which obstructed the socio-economic growth globally. The main aim of this study was to analyse the impact of lockdown (imposed in March and continued in April 2020) on the existing air quality in three megacities of India (Delhi, Mumbai and Kolkata) by assessing the trends of PM10 and NO2 concentrations. A comparison of the percentage reduction in concentrations of lockdown period with respect to same period in year 2019 and pre-lockdown period (February 14-March 24) was made. It was observed from the study that an overall decrease of pollutant concentrations was in the ranges of 30-60% and 52-80% of PM10 and NO2, respectively, in the three cities during lockdown in comparison with previous year and pre-lockdown period. The overall decrease in concentrations of pollutants at urban sites was greater than the background sites. Highest decline in concentrations of PM10 were observed in Kolkata city, followed by Mumbai and Delhi, while decline in NO2 was highest in Mumbai. Results also highlighted that capital city Delhi had the worst air quality amongst three cities, with particulate matter (PM10) being the dominant pollutant. Although COVID-19 has significantly affected the human life considering the mortality and morbidity, lockdowns imposed to control the pandemic had significantly improved the air quality in the selected study locations, although for the short amount of period.

Impact of lockdown associated with COVID19 on air quality and emissions from transportation sector: case study in selected Indian metropolitan cities

This study examines the impact of air quality in selected Indian metropolitan cities during the COVID19 pandemic lockdown period. Concentrations of air quality parameters such as PM_2.5, NO₂, SO₂, and CO during the transition to lockdown and the actual lockdown period were compared with business as usual periods (a period prior to COVID19 lockdown and a corresponding period in 2019) to estimate the reduction in emission in four major IT hubs in India namely Bengaluru, Chennai, Hyderabad and Pune. A 40-45% reduction in PM_2.5 concentration was observed, in these cities, during the lockdown compared to the corresponding period in 2019 and a 20-45% reduction was observed compared to business as usual period in 2020. A vehicle kilometer traveled (VKT)-related questionnaire survey-based study in Hyderabad revealed that, with 48% of population utilizing work-from-home during the transition to lockdown period, vehicular PM_2.5 emission in Hyderabad reduced by 54% compared to usual traffic emissions prior to COVID19 lockdown. Furthermore, it was estimated that emission of up to 3243, 777, 113, and 54 tons/year of CO, NOx, PM_2.5, and SO₂, respectively, could be avoided in Hyderabad alone, if work-from-home is implemented on a 2 days/week basis. The experience from this study can be used to develop policies favoring reduced use of private vehicles or implementation of work-from-home to combat air pollution and reduce carbon emissions.

Impact of lockdown associated with COVID19 on air quality and emissions from transportation sector: case study in selected Indian metropolitan cities

This study examines the impact of air quality in selected Indian metropolitan cities during the COVID19 pandemic lockdown period. Concentrations of air quality parameters such as PM_2.5, NO₂, SO₂, and CO during the transition to lockdown and the actual lockdown period were compared with business as usual periods (a period prior to COVID19 lockdown and a corresponding period in 2019) to estimate the reduction in emission in four major IT hubs in India namely Bengaluru, Chennai, Hyderabad and Pune. A 40-45% reduction in PM_2.5 concentration was observed, in these cities, during the lockdown compared to the corresponding period in 2019 and a 20-45% reduction was observed compared to business as usual period in 2020. A vehicle kilometer traveled (VKT)-related questionnaire survey-based study in Hyderabad revealed that, with 48% of population utilizing work-from-home during the transition to lockdown period, vehicular PM_2.5 emission in Hyderabad reduced by 54% compared to usual traffic emissions prior to COVID19 lockdown. Furthermore, it was estimated that emission of up to 3243, 777, 113, and 54 tons/year of CO, NOx, PM_2.5, and SO₂, respectively, could be avoided in Hyderabad alone, if work-from-home is implemented on a 2 days/week basis. The experience from this study can be used to develop policies favoring reduced use of private vehicles or implementation of work-from-home to combat air pollution and reduce carbon emissions.

Diurnal and temporal changes in air pollution during COVID-19 strict lockdown over different regions of India

Lockdown measures to contain COVID-19 pandemic has resulted in a considerable change in air pollution worldwide. We estimate the temporal and diurnal changes of the six criteria air pollutants, including particulate matter (PM2.5 and PM10) and gaseous pollutants (NO2, O3, CO, and SO2) during lockdown (25th March - 3rd May 2020) across regions of India using the observations from 134 real-time monitoring sites of Central Pollution Control Board (CPCB). Significant reduction in PM2.5, PM10, NO2, and CO has been found in all the regions during the lockdown. SO2 showed mixed behavior, with a slight increase at some sites but a comparatively significant decrease at other locations. O3 also showed a mixed variation with a mild increase in IGP and a decrease in the South. The absolute decrease in PM2.5, PM10, and NO2 was observed during peak morning traffic hours (08-10 Hrs) and late evening (20-24 Hrs), but the percentage reduction is almost constant throughout the day. A significant decrease in day-time O3 has been found over Indo Gangetic plain (IGP) and central India, whereas night-time O3 has increased over IGP due to less O3 loss. The most significant reduction (∼40-60%) was found in PM2.5 and PM10. The highest decrease in PM was found for the north-west and IGP followed by South and central regions. A considerable reduction (∼30-70%) in NO2 was found except for a few sites in the central region. A similar pattern was observed for CO having a ∼20-40% reduction. The reduction observed for PM2.5, PM10, NO2, and enhancement in O3 was proportional to the population density. Delhi's air quality has improved with a significant reduction in primary pollutants, however, an increase in O3 was observed. The changes reported during the lockdown are combined effect of changes in the emissions, meteorology, and atmospheric chemistry that requires detailed investigations.