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Introduction: 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., Methods: 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., Results: 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., Conclusion: 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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Evidence for long-term mortality risks of PM 2.5 comes mostly from large administrative studies with incomplete individual information and limited exposure definitions. Here we assess PM 2.5 -mortality associations in the UK Biobank cohort using detailed information on confounders and exposure., Methods: We reconstructed detailed exposure histories for 498,090 subjects by linking residential data with high-resolution PM 2.5 concentrations from spatiotemporal machine-learning models. We split the time-to-event data and assigned yearly exposures over a lag window of 8 years. We fitted Cox proportional hazard models with time-varying exposure controlling for contextual- and individual-level factors, as well as trends. In secondary analyses, we inspected the lag structure using distributed lag models and compared results with alternative exposure sources and definitions., Results: In fully adjusted models, an increase of 10 μg/m³ in PM 2.5 was associated with hazard ratios of 1.27 (95% confidence interval: 1.06, 1.53) for all-cause, 1.24 (1.03, 1.50) for nonaccidental, 2.07 (1.04, 4.10) for respiratory, and 1.66 (0.86, 3.19) for lung cancer mortality. We found no evidence of association with cardiovascular deaths (hazard ratio = 0.88, 95% confidence interval: 0.59, 1.31). We identified strong confounding by both contextual- and individual-level lifestyle factors. The distributed lag analysis suggested differences in relevant exposure windows across mortality causes. Using more informative exposure summaries and sources resulted in higher risk estimates., Conclusions: We found associations of long-term PM 2.5 exposure with all-cause, nonaccidental, respiratory, and lung cancer mortality, but not with cardiovascular mortality. This study benefits from finely reconstructed time-varying exposures and extensive control for confounding, further supporting a plausible causal link between long-term PM 2.5 and mortality., Competing Interests: Disclosure: The authors report no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)