The authors estimated the impact of PM_(2.5) exposure on non-trauma all-cause mortality in the megacity Delhi using a quasi-Poisson regression model and satellite-derived high-resolution (1 km) exposure data. The satellite PM_(2.5) data set was calibrated and validated against coincident measurements from ground-based reference-grade monitors. This allowed us to minimize exposure misclassification, which otherwise would have happened due to reliance on limited ground-based monitoring sites with a large spatial gap. The annual average (median) PM_(2.5) exposure (with interquartile range) during this period was 108.5 (63.5-133.6) μg/m~3.The authors found a 0.52%(95% confidence interval, CI: 0.42-0.62%) increase in non-trauma all-cause mortality for every 10 μg/m~3 increase in 6 days cumulative PM_(2.5) exposure with a relatively higher impact on men (0.57%, 95% CI: 0.46-0.69%) than on women (0.52%, 0.38-0.65%). The impact of PM_(2.5) was almost twofold higher in winter (0.55%, 0.45-0.66%) per 10 μg/m~3 increase in PM_(2.5) than in summer (0.29%, 0.14-0.45%). The district-level aggregated 6 days cumulative PM_(2.5) exposure had the largest impact on mortality (1.10%, 0.84-1.35%). The impact of acute exposure to PM_(2.5) on all-cause mortality was larger than that of PM_(10) exposure (from previous studies). This study adds to the global evidence pool, fills the critical gap of evidence of acute PM_(2.5) exposure impact on mortality in India, and most importantly demonstrates the significance of disaggregatedexposureassessmentusingsatellite data to reduce exposure misclassification in health impact studies, particularly in data-poor regions.
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