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Title: Assessing public health burden associated with exposure to ambient black carbon in the United States.

Authors: Li, Ying; Henze, Daven K; Jack, Darby; Henderson, Barron H; Kinney, Patrick L

Published In Sci Total Environ, (2016 Jan 01)

Abstract: Black carbon (BC) is a significant component of fine particulate matter (PM2.5) air pollution, which has been linked to a series of adverse health effects, in particular premature mortality. Recent scientific research indicates that BC also plays an important role in climate change. Therefore, controlling black carbon emissions provides an opportunity for a double dividend. This study quantifies the national burden of mortality and morbidity attributable to exposure to ambient BC in the United States (US). We use GEOS-Chem, a global 3-D model of atmospheric composition to estimate the 2010 annual average BC levels at 0.5×0.667° resolution, and then re-grid to 12-km grid resolution across the continental US. Using PM2.5 mortality risk coefficient drawn from the American Cancer Society cohort study, the numbers of deaths due to BC exposure were estimated for each 12-km grid, and then aggregated to the county, state and national level. Given evidence that BC particles may pose a greater risk on human health than other components of PM2.5, we also conducted sensitivity analysis using BC-specific risk coefficients drawn from recent literature. We estimated approximately 14,000 deaths to result from the 2010 BC levels, and hundreds of thousands of illness cases, ranging from hospitalizations and emergency department visits to minor respiratory symptoms. Sensitivity analysis indicates that the total BC-related mortality could be even significantly larger than the above mortality estimate. Our findings indicate that controlling BC emissions would have substantial benefits for public health in the US.

PubMed ID: 26383853 Exiting the NIEHS site

MeSH Terms: Air Pollutants/analysis*; Air Pollution/statistics & numerical data*; Environmental Exposure/statistics & numerical data*; Humans; Models, Chemical; Public Health; Risk Assessment; Soot/analysis*; United States

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