Citation:

BATTYE, W., W. WARREN-HICKS, S. FUDGE, AND T. E. PIERCE. MODULATING EMISSIONS FROM ELECTRIC GENERATING UNITS AS A FUNCTION OF METEOROLOGICAL VARIABLES. Presented at 4th Annual CMAS Models-3 Conference, Chapel Hill, NC, September 26 - 28, 2005.

Impact/Purpose:

The objectives of this task include: (1) to continuously evaluate and analyze the forecast results to provide diagnostic information on model performance and inadequacies to guide further evolution and refinements to the CMAQ model, and (2) extending the utility of the daily air quality forecast model data being produced by NOAA's National Weather Service (NWS) as part of a NOAA/EPA collaboration in air quality forecasting, to EPA mission-oriented activities. These objectives include developing and maintaining a long-term database of air quality modeling results (ozone and PM2.5), performing periodic analysis and assessments using the data, and making the air quality database available and accessible to States, Regions, RPO's and others to use as input data for regional/local scale air quality modeling for policy/regulatory purposes.

Description:

Electric Generating Units (EGUs) are an important source of emissions of nitrogen oxides (NOx), which react with volatile organic compounds (VOCs) in the presence of sunlight to form ozone. Emissions from EGUs are believed to vary depending on short-term demands for electricity; for instance, increased use of air conditioning on hot summer days is expected to cause increases in electricity demand, and consequent increases in EGU emissions. The purpose of this effort is to analyze Continuous Emissions Monitoring Systems (CEMS) data from 2003 for more than 5000 EGUs across the country and to relate EGU NOx emissions to temperature and other meteorological variables. Linear regression models were developed to calculate daily modulation factors as a function of meteorological parameters and day type for individual regions across the country. The explained variance, r², for the regression model ranged from 0.54 in Montana to above 0.9 for several states. The r² statistic is above 0.75 for most regions. As an example, for North Carolina, the regression model improved the predicted emissions over seasonal averages, with the mean error dropping from 21% to 9%. This effort has demonstrated that NOx emissions from EGUs can be correlated to meteorological parameters. In addition, regression results indicate that meteorological parameters can have a significant impact on NOx emissions. NOAA is currently testing the regression models for NOx emissions in its Eta-CMAQ ozone forecast model system.

Record Details: