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.

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.

The Asian Indian Beta Cell function (ABCs) in Infants Study examined the associations of maternal weight on infant pancreatic beta cell function across 7 months postpartum. Pregnant women aged 18–35 years were recruited in Hyderabad, India. Women were classified by first trimester weight as underweight (UW), BMI < 18.5 kg/m²; normal weight (NW), BMI 18.5–22.9 kg/m²; or overweight (OW), BMI 23.0 through 7 months, infants had an oral glucose tolerance test (OGTT, 1.75 g glucose/kg bodyweight) following a 3 h fast. Infant blood samples were assayed for C-peptide and glucose. Infant beta cell function (HOMA2-B; disposition index, DI) and insulin resistance (HOMA2-IR) were compared across maternal weight groups. Mothers (UW n = 63; NW n = 43; OW n = 29) had similar age at delivery and second trimester 50 g glucose challenge test results. Cord HOMA2-B values were 51% greater for I_UW (83.5, SD 55.2) and 44% greater for I_OW (79.9, SD 60.8) vs. I_NW (55.4, SD 51.5), forming a U-shaped relationship between maternal weight and HOMA2-B. No qualitative differences in HOMA2-IR were found at birth. However, at 7 months postpartum, HOMA2-IR changed most within I_UW (−64% median reduction) and changed the least in I_OW (−7% median reduction). At seven months postpartum, DI was higher in I_UW vs. the other groups (geometric mean I_UW 1.9 SD 2.5; I_NW 1.3 SD 2.6 or vs. I_OW mean 1.2 SD 3.7), reflecting a +49% difference in DI. Evidence from this study illustrates adaptations in the pancreatic functional response of infants associated with the maternal nutritional environment.