| Type of Document |
Dissertation |
| Author |
Nail, Amy Jeanette, |
| Author's Email Address |
ajnail@stat.ncsu.edu |
| URN |
etd-08092007-123658 |
| Title |
Quantifying local creation and regional transport using a hierarchical space-time model of ozone as a function of observed NOx, a latent space-time VOC process, emissions, and meteorology |
| Degree |
PhD |
| Graduate Program |
Statistics |
| Advisory Committee |
| Advisor Name |
Title |
| Jacqueline M. Hughes-Oliver |
Committee Co-Chair |
| John F. Monahan |
Committee Co-Chair |
|
| Keywords |
- spatial covariance
- ozone
- NOx
- VOC
- space-time
- latent
- hierarchical
|
| Date of Defense |
2007-08-13 |
| Availability |
unrestricted |
Abstract
We explore the ability of a space-time model to decompose the 8-hour ozone concentration on a given day at a given site into the parts attributable to local emissions and regional transport, and ultimately to assess the efficacy of past and future emission control programs. We model ozone as created plus transported ozone plus an error term that has a seasonally varying spatial covariance. The created component uses atmospheric chemistry results to express ozone created on a given day at a given site as a function of the observed NOx concentration, the latent VOC concentration, and temperature. The ozone transported to a given day at a given site is expressed as a weighted average of the ozone observed at all sites on the previous day, where the weights are a function of wind speed and direction that appropriately distribute weight across redundant information. The latent VOC process model has a mean trend that includes emissions from various source types, temperature, a workday indicator variable, and an error term that has a seasonally varying spatial covariance. We fit the model using likelihood methods, and we compare our predictions to observations from a withheld dataset and to those predictions of CMAQ, the deterministic model used by EPA to assess emission control programs. We find that the model predictions based on the mean trend and the random deviations from this mean outperform CMAQ predictions according to multiple criteria, but predictions based on the mean trend alone underperform CMAQ predictions.
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Approximate Download Time
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etd.pdf |
18.05 Mb |
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