Background: Numerous epidemiological studies indicated high levels of particulate matter less than2.5 μm diameter (PM_2.5) as a major cardiovascular risk factor. Most of the studies have been conducted in high-income countries (HICs), where average levels of PM_2.5 are far less compared to low- and middle- income countries (LMICs), and their socio-economic profile, disease burden, and PM speciation/composition are very different. We systematically reviewed the association of long-term exposure to PM_2.5 and cardio-metabolic diseases (CMDs) in LMICs. Methods: Multiple databases were searched for English articles with date limits until March 2018. We included studies investigating the association of long-term exposure to PM_2.5 (defined as an annual average/average measure for 3 more days of PM_2.5 exposure) and CMDs, such as hospital admissions, prevalence, and deaths due to CMDs, conducted in LMICs as defined by World Bank. We excluded studies which employed exposure proxy measures, studies among specific occupational groups, and specific episodes of air pollution. Results: A total of 5567 unique articles were identified, of which only 17 articles were included for final review, and these studies were from Brazil, Bulgaria, China, India, and Mexico. Outcome assessed were hypertension, type 2 diabetes mellitus and insulin resistance, and cardiovascular disease (CVD)-related emergency room visits/admissions, death, and mortality. Largely a positive association between exposure to PM_2.5 and CMDs was found, and CVD mortality with effect estimates ranging from 0.24% to 6.11% increased per 10 μg/m³ in PM_2.5. CVD-related hospitalizations and emergency room visits increased by 0.3% to 19.6%. Risk factors like hypertension had an odds ratio of 1.14, and type 2 diabetes mellitus had an odds ratio ranging from 1.14–1.32. Diversity of exposure assessment and health outcomes limited the ability to perform a meta-analysis. Conclusion: Limited evidence on the association of long-term exposure to PM_2.5 and CMDs in the LMICs context warrants cohort studies to establish the association.

Introduction: Exposure to fine particulate matter has been associated with several cardiovascular and cardiometabolic diseases. However, such evidence mostly originates from low-pollution settings or cross-sectional studies, thus necessitating evidence from regions with high air pollution levels, such as India, where the burden of non-communicable diseases is high. Research design and methods: We studied the associations between ambient PM_2.5 levels and fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c) and incident type 2 diabetes mellitus (T2DM) among 12 064 participants in an adult cohort from urban Chennai and Delhi, India. A meta-analytic approach was used to combine estimates, obtained from mixed-effects models and proportional hazards models, from the two cities. Results: We observed that 10 μg/m³ differences in monthly average exposure to PM_2.5 was associated with a 0.40 mg/dL increase in FPG (95% CI 0.22 to 0.58) and 0.021 unit increase in HbA1c (95% CI 0.009 to 0.032). Further, 10 μg/m³ differences in annual average PM_2.5 was associated with 1.22 (95% CI 1.09 to 1.36) times increased risk of incident T2DM, with non-linear exposure response. Conclusions: We observed evidence of temporal association between PM_2.5 exposure, and higher FPG and incident T2DM in two urban environments in India, thus highlighting the potential for population-based mitigation policies to reduce the growing burden of diabetes.

India experiences high levels of air pollution as measured by fine particulate matter <2.5 μm (PM_2.5) across the country. With limited resources, it is imperative to identify the most impacted areas. We aimed to identify air pollution hotspots in India and analyze temporal trends. We conducted a geospatial analysis using Getis-Ord Gi statistics on gridded-annual levels of PM_2.5 disaggregated for every state/UT and three largest cities [Delhi, Kolkata & Mumbai] of India from 2008 to 2019. The annual average PM_2.5 was derived from a validated and robust nationwide spatiotemporal model(1kmx1km). Hotspots were identified annually using Gi∗ score and p-value and temporal trends across 2 periods [T1:2008-2013 & T2:2014-2019] for each spatial unit. We classified temporal trends based on the number of occurrences of hotspots in T1 and in T2 as consistent (similar in T1 & T2), declining (decreasing in T2) and emerging (increasing in T2) hotspots. We identified consistent hotspots in 9.9% followed by emerging hotspots in 2.6% of the country where 16% and 4.9% people live. In addition, we identified declining hotspots in 2.6% area with 3.4% of the population. Rajasthan had largest share of area identified as consistent hotspots while Uttar Pradesh had densely populated consistent hotspots. Maharashtra had both higher number of areas identified as emerging and declining hotspots. Among the largest cities, Kolkata had highest proportion of consistent hotspots. We identified 170 additional cities with either consistent or emerging hotspots beyond the non-attainment cities as defined by the National Clean Air Programme. India continues to have large areas of consistent and emerging hotspots of air pollution where close to a fifth of India's population live. Identifying hotspots can inform strategic approach for targeted action in air quality management, appropriate resource allocation and a baseline for assessing intervention effectiveness and future programs and policies, including health.

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 (PM_2.5) pollution in India. We developed a model for daily average ambient PM_2.5 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 PM_2.5 at a very fine spatiotemporal resolution, which allows us to study the health effects of PM_2.5 across India and to identify areas with exceedingly high levels.

Introduction: Air pollution presents a major public health threat to India, affecting more than three quarters of the country’s population. In the current project, GEOHealth Health Effects of Selected Environmental Exposomes Across the Life CourSe–India, we aim to study the effect of environmental exposomes—fine particulate matter (PM _2.5 ), nitrogen dioxide (NO ₂ ), ozone (O ₃ ) and extremes of temperature—on multiple health outcomes using a modified life course approach. The associated training grant aims to build capacity in India to address the unique environmental health problems. Methods and analysis: The project aims to (A) Develop exposure assessments in seven cities, namely Delhi, Chennai, Sonipat, Vizag, Pune, Hyderabad and Bikaner, for: (1) A fine-scale spatiotemporal model for multiple pollutants (PM _2.5 , NO ₂ , O _3, temperature); (2) Combined ground monitoring and modelling for major chemical species of ambient PM _2.5 at seven cities; and (3) Personal exposure assessment in a subsample from the six cities, except Pune, and (B) Conduct health association studies covering a range of chronic non-communicable diseases and their risk factors leveraging a unique approach using interdigitating cohorts. We have assembled existing pregnancy, child, adolescent, adult and older adult cohorts across India to explore health effects of exposomes using causal analyses. We propose to use Bayesian kernel machine regression to assess the effects of mixtures of all pollutants including species of PM _2.5 on health while accounting for potential non-linearities and interactions between exposures. This builds on earlier work that constructed a fine spatiotemporal model for PM _2.5 exposure to study health outcomes in two Indian cities. Ethics and dissemination: Ethical clearance for conduct of the study was obtained from the Institutional Ethics Committee (IEC) of the Centre for Chronic Disease Control, and all the participating institutes and organisations. National-level permission was provided by the Indian Council of Medical Research. The research findings will be disseminated through peer-reviewed publications, policy briefs, print and social media, and communicating with the participating communities and stakeholders. Training of Indian scientists will build the capacity to undertake research on selected adverse environmental exposures on population health in India.

Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences. Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences.