Estimation of Speciated PM2.5 Values from the Chemical Speciation Network Using the Bayesian Maximum Entropy Method
Public Deposited- Last Modified
- March 22, 2019
- Creator
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Teet, Stephen B.
- Affiliation: Gillings School of Global Public Health, Department of Environmental Sciences and Engineering
- Abstract
- Fine particulate air pollution is of particular concern to health and mortality. Until recent years, only general PM2.5 data were available; but with the inception of the Chemical Speciation Network, interest has mounted regarding the effects of individual components of PM2.5. However, due to geographic and temporal scarcity of available data, researchers have been able to conclude little about the effects of these pollutants. The use of a Bayesian Maximum Entropy model could address gaps in data and lead to better analysis of specific effects of PM2.5 components. Data for three different pollutants were collected from the Chemical Speciation Network for seven sites in California over a period spanning five years. These data were entered into a Bayesian Maximum Entropy model to check the validity of estimations made by this model. The three models output a range of estimations, both good and bad, but overall show potential for future use.
- Date of publication
- August 2012
- DOI
- Resource type
- Rights statement
- In Copyright
- Advisor
- Leith, David
- Degree
- Master of Science
- Graduation year
- 2012
- Language
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