Estimating the impact of exposure to multi-pollutant on mortality in an Indian city over middle Indo-Gangetic Plain

Background: At a time, individuals are exposed to multi air pollutants which may have independent or synergistic effects on human health. Given the paucity in multi-pollutant based health effect studies, assessment of human health on exposure to multi air pollutant may be more plausible and important than taking under consideration the effects of individual air pollutants.Objectives: We examined the association between multiple air pollutants (black carbon; BC, PM2.5, PM10, NO2, SO2 and O3) and non-accidental mortality in an urban pollution hotspot located in middle Indo-Gangetic Plain (IGP). Method: Daily mortality data for the years 2009-2016 are taken from office of Municipal Corporation of Varanasi. Meteorological data like temperature and relative humidity were collected from the India Meteorological Department, New Delhi. Semiparametric quasi-Poisson regression model were used to study the relationship between multi air pollutants and daily mortality. Result: Among multiple air pollutants, individual impact of BC was highest and significant on mortality. Every 10-unit increase lead to an increase in mortality by 4.95% (95% CI: 2.16-7.74) for BC, followed by 2.38% (95% CI: 0.88-3.87%) for NO2, 1.06% (95% CI: 0.45-1.66%) for PM2.5 and 0.26% (95% CI: 0.13-0.38%) for PM10. The multi-pollutant model showed increase in mortality risks for BC aerosols (7.3%) and PM2.5 (residual PM2.5) compared to their individual impact. Effect modification based on sex, age, place of death and season didn’t show statistically significant differences except PM10. We also found that there exist a lag effect of up to 0-6 days from exposure to mortality. Furthermore, air pollutants displayed mostly but not strictly linear exposure-response association, that shows increase in mortality risk associated with BC exposure >15 µg m-3 and PM2.5>60 µg m-3. Conclusion: The study concludes with certain confidence that multi pollutant model shows improvement in effect estimates associated with air pollutants in combination than their individual counterpart.