A Satellite-Based High-Resolution (1-km) Ambient PM2.5 Database for India over Two Decades (2000–2019): Applications for Air Quality Management

Nationwide estimation of daily ambient PM2.5 from 2008 to 2020 at 1 km2 in India using an ensemble approach

High-resolution assessment of historical levels is essential for assessing the health effects of ambient air pollution in the large Indian population. The diversity of geography, weather patterns, and progressive urbanization, combined with a sparse ground monitoring network makes it challenging to accurately capture the spatiotemporal patterns of ambient fine particulate matter (PM2.5) pollution in India. We developed a model for daily average ambient PM2.5 between 2008 and 2020 based on monitoring data, meteorology, land use, satellite observations, and emissions inventories. Daily average predictions at each 1 km × 1 km grid from each learner were ensembled using a Gaussian process regression with anisotropic smoothing over spatial coordinates, and regression calibration was used to account for exposure error. Cross-validating by leaving monitors out, the ensemble model had an R2 of 0.86 at the daily level in the validation data and outperformed each component learner (by 5-18%). Annual average levels in different zones ranged between 39.7 μg/m3 (interquartile range: 29.8-46.8) in 2008 and 30.4 μg/m3 (interquartile range: 22.7-37.2) in 2020, with a cross-validated (CV)-R2 of 0.94 at the annual level. Overall mean absolute daily errors (MAE) across the 13 years were between 14.4 and 25.4 μg/m3. We obtained high spatial accuracy with spatial R2 greater than 90% and spatial MAE ranging between 7.3-16.5 μg/m3 with relatively better performance in urban areas at low and moderate elevation. We have developed an important validated resource for studying PM2.5 at a very fine spatiotemporal resolution, which allows us to study the health effects of PM2.5 across India and to identify areas with exceedingly high levels.

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.

Excess Mortality Risk Due to Heat Stress in Different Climatic Zones of India

India is at a high risk of heat stress-induced health impacts and economic losses owing to its tropical climate, high population density, and inadequate adaptive planning. The health impacts of heat stress across climate zones in India have not been adequately explored. Here, we examine and report the vulnerability to heat stress in India using 42 years (1979-2020) of meteorological data from ERA-5 and developed climate-zone-specific percentile-based human comfort class thresholds. We found that the heat stress is usually 1-4 °C higher on heatwave (HW) days than on nonheatwave (NHW) days. However, the stress on NHW days remains considerable and cannot be neglected. We then showed the association of a newly formulated India heat index (IHI) with daily all-cause mortality in three cities - Delhi (semiarid), Varanasi (humid subtropical), and Chennai (tropical wet and dry), using a semiparametric quasi-Poisson regression model, adjusted for nonlinear confounding effects of time and PM2.5. The all-cause mortality risk was enhanced by 8.1% (95% confidence interval, CI: 6.0-10.3), 5.9% (4.6-7.2), and 8.0% (1.7-14.2) during "sweltering" days in Varanasi, Delhi, and Chennai, respectively, relative to "comfortable" days. Across four age groups, the impact was more severe in Varanasi (ranging from a 3.2 to 7.5% increase in mortality risk for a unit rise in IHI) than in Delhi (2.6-4.2% higher risk) and Chennai (0.9-5.7% higher risk). We observed a 3-6 days lag effect of heat stress on mortality in these cities. Our results reveal heterogeneity in heat stress impact across diverse climate zones in India and call for developing an early warning system keeping in mind these regional variations.

Addressing Biases in Ambient PM2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India

Ambient PM2.5 exposure statistics in countries with limited ground monitors are derived from satellite aerosol optical depth (AOD) products that have spatial gaps. Here, we quantified the biases in PM2.5 exposure and associated health burden in India due to the sampling gaps in AOD retrieved by a Moderate Resolution Imaging Spectroradiometer. We filled the sampling gaps and derived PM2.5 in recent years (2017-2022) over India, which showed fivefold cross-validation R2 of 0.92 and root mean square error (RMSE) of 11.8 μg m-3 on an annual scale against ground-based measurements. If the missing AOD values are not accounted for, the exposure would be overestimated by 19.1%, translating to an overestimation in the mortality burden by 93,986 (95% confidence interval: 78,638-110,597) during these years. With the gap-filled data, we found that the rising ambient PM2.5 trend in India has started showing a sign of stabilization in recent years. However, a reduction in population-weighted exposure balanced out the effect of the increasing population and maintained the mortality burden attributable to ambient PM2.5 for 2022 (991,058:798,220-1,183,896) comparable to the 2017 level (1,014,766:812,186-1,217,346). Therefore, a decline in exposure alone is not sufficient to significantly reduce the health burden attributable to ambient PM2.5 in India.

Cumulative effect of PM2.5 components is larger than the effect of PM2.5 mass on child health in India

While studies on ambient fine particulate matter (PM2.5) exposure effect on child health are available, the differential effects, if any, of exposure to PM2.5 species are unexplored in lower and middle-income countries. Using multiple logistic regression, we showed that for every 10 μg m-3 increase in PM2.5 exposure, anaemia, acute respiratory infection, and low birth weight prevalence increase by 10% (95% uncertainty interval, UI: 9-11), 11% (8-13), and 5% (4-6), respectively, among children in India. NO3-, elemental carbon, and NH4+ were more associated with the three health outcomes than other PM2.5 species. We found that the total PM2.5 mass as a surrogate marker for air pollution exposure could substantially underestimate the true composite impact of different components of PM2.5. Our findings provide key indigenous evidence to prioritize control strategies for reducing exposure to more toxic species for greater child health benefits in India.

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.

Pollution characteristics and ecological risks of trace elements in PM2.5 over three COALESCE network sites - Bhopal, Mesra, and Mysuru, India

Time-synchronized, 24 h integrated PM_2.5 trace element (TE) measurements made as a part of the COALESCE project (Venkataraman et al., 2020) at Bhopal, Mesra, and Mysore during all of 2019 were analyzed in this study. The concentrations of 15 key elements ranged between 0.05 ng m^-3 and 50 μg m^-3 across the study sites. Pronounced seasonal variation of elements from multiple source classes showed that the crustal origin elements (Al, Si, Fe, Ti, and Ca) peaked during the pre-monsoon season, while the anthropogenic activities driven element (P, S, K, V, Mn, Cu, Zn, and Pb) concentrations increased during the winter and post-monsoon seasons. Spearman correlation coupled with hierarchical clustering separated the matrix of elements into three common clusters at all sites, corresponding to crustal sources, combustion and biomass burning emissions, and industrial/non-exhaust vehicular emissions, respectively. Furthermore, episodes of metal pollution throughout the year were examined using characteristic radar charts of TEs to identify the association between TE sources and poor air quality. For example, maximum metal pollution in Bhopal occurred during the post-monsoon season, attributable to biomass burning, dust storms, industrial and non-exhaust vehicular emissions. Finally, an ecological risk assessment revealed that the risk index was higher than the threshold value of 600 for all heavy metals at all sites. Pb, Cu, and Zn were the top contributors to 'extremely high risk' amongst all heavy metals. Overall, the results show that although TE concentrations at all three locations were much lower than in other urban locations in India, the risk from heavy metals to the ecosystem (and likely to human health) cannot be ignored. The findings warrant a full source apportionment of fine PM to better identify TE-rich source contributions and future studies to examine the atmospheric processing and eco-system uptake of TEs.

Analysis and Variation of the Maiac Aerosol Optical Depth in Underexplored Urbanized Area of National Capital Region, India

Aerosol monitoring is the emerging application field of satellite remote sensing. As a satellite-based indicator of aerosol concentration, aerosol optical depth (AOD) can aid in assessing the crucial effects of aerosols on the global environment. Among various satellite-based aerosol product, Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6), Multiangle Implementation of Atmospheric Correction (MAIAC) aerosol product (1 km resolution) has still untapped potential in Indian regions. Considering the importance of regional validation of such high-resolution aerosol product, the present study attempts to fill this gap by validating MAIAC aerosol estimates (AODMAIAC) in highly polluted districts (Faridabad, Ghaziabad, Gautam Budh Nagar, Gurugram) of National Capital Region (NCR) with heavy aerosol loading using limited AErosol RObotic NETwork (AERONET) observations obtained from AERONET sites at Amity University (AU) and Gual Pahari (GP). Such evaluation of satellite-retrieved aerosol product with ground data confirms its practicality based on retrieval errors (Expected Error (EE) values (EE = 0.05 + 15 %*AOD) (EE: 78.85 % at AU, 73.58 % at GP), root mean square error (RMSE) values (RMSE: 0.15 at AU, 0.24 at GP), and correlation coefficient (R) values (R: 0.86 at AU, 0.73 at GP). The seasonal variation in AOD over the study area from 2010-2019 reveals increasing trend of AOD in the monsoon and post-monsoon season due to natural and anthropogenic factors. In addition to contributing to a holistic assessment of MAIAC aerosol estimates as a recent, high-resolution aerosol product, present results provide a basis for further research into NCR aerosols.

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.

Low-cost nature-inspired deep learning system for PM2.5 forecast over Delhi, India

Air quality has a tremendous impact on India's health and prosperity. Air quality models are crucial tools for surveying and projecting air pollution episodes, which can be used to issue health advisories to take action ahead of time. Short-term increases in air pollution trigger many adverse health events; a fast, efficient, cost-effective, and reliable air quality prediction model would aid in minimizing the effect on health and prosperity. Deterministic models, on the other hand, are less robust in predicting the pollutant series since it is non-stationary and non-linear. Atmospheric chemistry models are computationally expensive and often rely on outdated emissions information. We propose a deep learning model in this study that integrates neural networks, fuzzy inference systems, and wavelet transforms to predict the most prominent air pollutant affecting Delhi, India i.e., PM2.5 (particulate matter of aerodynamic diameter less than or equal to 2.5 µm). We have included the main aspects of air quality models in this research i.e., less computational time (7 min approximately using I5-1035G1, 1.19 GHz processor), less resource-intensive (dependent only on the pollutant lagged values), and high spatial resolution (1 km) for forecasting air quality three days ahead. The model predictions show a significant correlation coefficient lying in [0.96,0.98], [0.86,0.93], and [0.82,0.91] with Central Pollution Control Board (CPCB) monitored data at various sites in Delhi for one, two, and three days of forecast respectively.

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.

Association between Acute Exposure to PM2.5 Chemical Species and Mortality in Megacity Delhi, India

The association between daily all-cause mortality and short-term fine particulate matter (PM_2.5) exposure is well established in the literature. However, association between acute exposure to PM_2.5 chemical species and mortality is not well known, especially in developing countries like India. Here we examined associations between mortality and acute exposure to PM_2.5 mass concentration and their 15 chemical components using data from 2013 to 2016 in megacity Delhi using a semiparametric quasi-Poisson regression model, adjusting for mean temperature, relative humidity, and long-term time trend as the major potential confounders. Mortality estimates were further checked for effect modification by sex, age group, and season. The subspecies of NO₃^-, NH₄NO₃, Cr, NH₄⁺, EC, and OC showed a higher mortality impact than the total PM_2.5 mass. Males were at higher risk from NO₃^-, SO₄^2-, and their NH₄⁺ compounds along with carcinogen Cr, whereas female group was at higher risk from EC and OC. Among all age groups, the elderly above 65 years were the most vulnerable group prone to mortality effects from maximum species. The major mortality risk from all hazardous species arose from their winter exposures. Our study provides the first evidence of association between acute exposure to PM_2.5 chemical species and mortality anywhere in India and recommends similar studies in other regions so that sectoral mitigation emitting the most toxic species can be prioritized to maximize the health benefits.

Impact of Air Pollution on Child Health in India and the Way Forward

Recent research in epidemiological modelling reveals that air pollution affects child health in various ways resulting in low birthweight, stillbirth, preterm birth, developmental delay, growth failure, poor respiratory and cardiovascular health, and a higher risk of anemia. India has embarked on the national clean air program, but a much stronger coordinated multi-sectoral approach is required to minimize the child health burden caused by air pollution. Air pollution should be treated as a public health crisis that can only be managed with policy backed by science, gradual transition to clean energy use, emission reduction supported by clean air technologies, long-term commitment from the Government, and cooperation of the citizens.

Robust relationship between ambient air pollution and infant mortality in India

BACKGROUND

Ambient exposure to fine particulate matter (PM_2.5) is one of the top global health concerns. We estimate the associations between in-utero and perinatal exposure to PM_2.5 and infant, neonatal and postneonatal mortality in India. We evaluate the sensitivity of this association to two widely-used exposure assessments.

METHOD

We linked nationally representative anthropometric data from India's 2015-2016 Demographic and Health Survey (n = 259,627 children under five across 640 districts of India) with satellite-based PM_2.5 concentrations during the month of birth of each child. We then estimated the associations between PM_2.5 from each dataset and child mortality, after controlling for child, mother and household factors including trends in time and seasonality. We examined if factors: urban/rural, sex, wealth quintile and state modified the associations derived from the two datasets using Wald tests.

RESULTS

We found evidence that PM_2.5 impacts infant mortality primarily through neonatal mortality. The estimated association between neonatal mortality and PM_2.5 in trimester 3 was OR: 1.016 (95% CI: 1.003, 1.030) for every 10 μg/m³ increase in exposure. This association was robust to the exposure assessment used. Child sex was a significant effect modifier, with PM_2.5 impacting mortality in infant girls more than boys.

CONCLUSIONS

Our results revealed a robust association between ambient exposure to PM_2.5 in the latter period of pregnancy and early life with infant and neonatal mortality in India. Urgent air pollution management plans are needed to improve infant mortality in India.