Development of a regression model to forecast ground-level ozone concentration in Louisville, KY

Abstract

To support ozone forecasting and episodic air pollution control initiatives in the Louisville metropolitan area, a multiple-linear regression model to predict daily domain-peak ground-level ozone concentration [O 3] has been developed and validated. Using only surface meteorological data from 1993–1996 and making extensive use of parametric transformations to improve accuracy, the ten parameter model has a standard error of prediction of 12.1 ppb and an explained variance of 0.70. Retrospective ozone forecasts were made for each day of the four ozone seasons (May–September) using archival meteorological data as input to the model. For the period 1993–1996 examined, 50% of days were forecast to within ±7.6 ppb, and on 80% of days the accuracy was within ±14.8 ppb. The model correctly predicted 74, 80, and 40% of occurrences of the daily “good” ([O 3]⩽60 ppb), “moderate” (60<[O 3]⩽95), and “approaching unhealthful” (95<[O 3]⩽120) air quality categories, respectively. The model did not predict any of the nine exceedances of the National Ambient Air Quality Standard ([O 3] >120) which occurred over the four year period. Simple supplementary meteorological criteria were developed that correctly forecast 89% of NAAQS exceedances. Used in combination with forecaster experience, synoptic weather information, and supplementary meteorological criteria, the regression model can be a useful tool for improving the accuracy of local O 3 forecasts.