Using atmospheric models to estimate global air pollution mortality
Public Deposited
Add to collection
You do not have access to any existing collections. You may create a new collection.
Citation
MLA
Anenberg, Susan C. Using Atmospheric Models to Estimate Global Air Pollution Mortality. Chapel Hill, NC: University of North Carolina at Chapel Hill, 2011. https://doi.org/10.17615/vqcz-zn96APA
Anenberg, S. (2011). Using atmospheric models to estimate global air pollution mortality. Chapel Hill, NC: University of North Carolina at Chapel Hill. https://doi.org/10.17615/vqcz-zn96Chicago
Anenberg, Susan C. 2011. Using Atmospheric Models to Estimate Global Air Pollution Mortality. Chapel Hill, NC: University of North Carolina at Chapel Hill. https://doi.org/10.17615/vqcz-zn96- Last Modified
- March 22, 2019
- Creator
-
Anenberg, Susan C.
- Affiliation: Gillings School of Global Public Health, Department of Environmental Sciences and Engineering
- Abstract
- Ground-level ozone and fine particulate matter (PM2.5) are associated with premature mortality and can influence air quality on global scales. This work examines the global health impacts of ozone and PM2.5 using concentrations simulated by global chemical transport models (CTMs), which allow full spatial coverage and analysis of hypothetical changes in emissions. Here, previous methods using global models are improved by using cause-specific and country-specific baseline mortality rates, and by using area-weighted average rates where gridcells overlap multiple countries. Using these methods, we estimate 0.7 [plus or minus] 0.3 and 3.7 [plus or minus] 1.0 million global premature deaths annually due to anthropogenic ozone and PM2.5, found as the difference between simulations with and without anthropogenic emissions. PM2.5 mortality estimates are ~50% higher than previous measurement-based estimates based on common assumptions, mainly because rural populations are included, suggesting higher estimates, although the coarsely resolved global atmospheric model may underestimate urban PM2.5 exposures. Estimating the mortality impacts of intercontinental transport of ozone shows that for North America, East Asia, South Asia, and Europe, foreign ozone precursor emission reductions contribute ~30%, 30%, 20%, and >50% of the deaths avoided by reducing emissions in all regions together. For North America and Europe, reducing precursor emissions avoids more deaths outside the source region than within, due mainly to larger foreign populations. Finally, using the MOZART-4 global CTM, we estimate that halving global anthropogenic black carbon (BC) emissions reduces population-weighted average PM2.5 by 542 ng/m3 (1.8%) and avoids 157,000 (95% confidence interval, 120,000-194,000) annual premature deaths globally, with the vast majority occurring within the source region. Over 80% of these deaths occur in Asia, with 50% greater mortality impacts per unit BC emitted for South Asian versus East Asian emissions. Globally, the contribution of residential, industrial, and transportation BC emissions to BC-related mortality is 1.3, 1.2, and 0.6 times each sector's contribution to anthropogenic BC emissions, owing to the degree of co-location with population. Future research should improve upon the many sources of uncertainty, incorporate shifting demographics, and examine the health impacts of realistic emission control technologies, which would affect emissions of multiple species simultaneously.
- Date of publication
- May 2011
- DOI
- Resource type
- Rights statement
- In Copyright
- Note
- "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Environmental Sciences and Engineering."
- Advisor
- West, J. Jason
- Degree granting institution
- University of North Carolina at Chapel Hill
- Language
- Publisher
- Place of publication
- Chapel Hill, NC
- Access right
- Open access
- Date uploaded
- March 18, 2013
Relations
- Parents:
This work has no parents.
Items
Thumbnail | Title | Date Uploaded | Visibility | Actions |
---|---|---|---|---|
|
2019-04-10 | Public | Download |