Abstract Details
Activity Number:
|
351
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Section on Statistics and the Environment
|
Abstract - #310053 |
Title:
|
Bias in CMAQ Prediction for Ozone Concentration
|
Author(s):
|
Ryan Durden*+ and Sarah Cummings
|
Companies:
|
NC State University and North Carolina State University
|
Keywords:
|
CMAQ ;
EPA ;
Ozone ;
Statistics ;
Air Quality
|
Abstract:
|
Global scale pollution is among the most controversial topics in society today. The decisions of scientists and policy makers rely heavily on the results of pollution research. Deterministic atmospheric chemistry models help us understand the potential impacts of policy decisions on future air pollution levels. Our goal is to generate a model that allows simulation of future air quality under different conditions and makes improvement on ozone concentration predictions. We accessed and modified a large-scale dataset containing various variables such as the actual measurements of ozone concentrations and CMAQ (Community Multiscale Air Quality) predictions of weather conditions from 82 sites. By selecting the most important variables, we generated a linear model to make predictions for ozone concentration. This would allow the EPA and other CMAQ users to more accurately predict ozone levels throughout the country. In model development, we utilized the statistical procedure of stepwise selection. Exploratory data analysis focused on both physical conditions of weather and chemical predictors for the model.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.