Sunday, 22 January 2017
4E (Washington State Convention Center )
Air pollutants considered harmful to public health and the environment, such as nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM), are regulated by the U.S. Environmental Protection Agency under the Clean Air Act. Accurate prediction of pollutant concentrations is critical to formulating air quality standards in order to reduce the harmful effects. Regional air quality modeling systems such as the Community Multi-Scale Air Quality (CMAQ) model driven by the Weather Research and Forecasting (WRF) meteorological model are often used to estimate ambient pollutant concentrations at high temporal and spatial resolutions. The WRF-CMAQ modeling framework has been evaluated on its performance for NO2, O3, and PM2.5 concentrations over 17 Western U.S. states from June 1997-December 1999. The 12-km by 12-km model results were compared to observational data from the EPA’s Air Quality Standard (AQS) Chemical Speciation Network (CSN). Questions of interest include which timescales are most important for each pollutant and how well does the model predict temporal components embedded in the time series. In order to observe variations at each site occurring at short-term (< 46 hours), synoptic (2.5-21 days), and long-term (> 21 days) time scales, a moving average filter was applied. Agreements between model estimates and observations were analyzed by site characteristics (rural, urban, sub-urban) and dominant emission source types (agricultural, residential, and mobile) for each temporal scale. For NOx the long-term and synoptic scales were the most accurately represented by the model. The short-term scale variance tended to be the most dominant which the model had the most difficulty reproducing. Similar analysis will be carried out for O3 and PM2.5.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner