Daily air quality forecasts are used extensively throughout the country as a way to protect public health and to encourage reduction of emission-producing activities. As more cities begin forecasting air quality, new methods are needed to improve the reliability and accuracy of the forecasts. Such improvements can be obtained through the development of objective forecasting techniques. To this end, the EPA sponsored a project to develop regression equations to forecast ozone concentrations for four U.S. cities: Columbus, Ohio; Minneapolis, Minnesota; and Nashville and Memphis, Tennessee.

To develop these equations, five years of historical meteorological and air quality data were compiled into the developmental data set. Predictor variables were selected using factor analysis to eliminate co-linear variables. Statistical software was used to generate several possible linear regression equations. The predictor variables in each equation were checked for accessibility, accuracy, and consistent physical relationships between meteorological variables and ozone. Verification of the equations on small independent subsets of data showed that the equations for Columbus, Memphis, and Nashville predicted Air Quality Index categories as accurately as human forecasters, but human forecasters better predicted ozone concentrations. In addition, the equations tended to underpredict when observed ozone concentrations were actually high. After testing the equations for these cities, final equations were selected. Verification of the equations for Minneapolis showed poor results, so different forecasting techniques are currently being evaluated. The regression equations that were developed for Columbus, Memphis, and Nashville were put into databases designed to let the user easily enter available meteorological variables, print forecast forms, and store input and forecast data.