||Automated Classification Scheme Designed to Better Elucidate the Dependence of Ozone on Meteorology.
Eder, B. K. ;
Davis, J. M. ;
Bloomfield, P. ;
||Environmental Protection Agency, Research Triangle Park, NC. Atmospheric Research and Exposure Assessment Lab. ;North Carolina State Univ. at Raleigh.
Regression analysis ;
Meteorological data ;
||Some EPA libraries have a fiche copy filed under the call number shown.
The paper utilizes a two-stage (average linkage then convergent k-means) clustering approach as part of an objective meteorological classification scheme designed to elucidate ozone's dependence on meteorology. When applied to ten years (1981-1990) of meteorological data for Birmingham, Alabama, the classification scheme identified seven statistically distinct meteorological regimes, which exhibited significantly different mean daily 1-hour maximum concentrations characteristics. Results from this two-stage clustering approach were then used to develop seven 'refined' stepwise regression models that were designed to (1) identify the optimum set of independent meteorological parameters influencing the ozone (O3) concentrations within each meteorological cluster and (2) to weigh each independent parameter according to its unique influence within that cluster.