USE OF REGRESSION-MODELS FOR ANALYZING HIGHWAY STORM-WATER LOADS

Storm-water data collected from an expressway in the Austin, Tex. area were used to develop regression models for predicting loads for a num ber of constituents commonly found in highway runoff. The goal of the model development was to identify the processes that affect the qualit y of highway runoff. Linear regression was selected as the most approp riate technique for analyzing the data because of its ability to ident ify constituent specific causal variables. The regression equations in dicate that the majority of variations observed in highway storm-water loading can be explained by causal variables measured during the rain storm event, the antecedent dry period, and the previous rainstorm eve nt. Loads for each of the constituents were dependent upon a unique su bset of the identified variables, indicating that processes responsibl e for the generation, accumulation, and washoff of storm-water polluta nts are constituent specific. Loads of some constituents, such as tota l suspended solids, were dependent on the characteristics of the curre nt storm, antecedent dry period, and the preceding storm indicating th e importance of buildup and washoff processes. Other constituents, suc h as oil and grease, were dependent only on conditions during the curr ent storm, such as runoff volume and number of vehicles during the eve nt. The identification of constituent-specific explanatory variables s uggests the type of mitigation that would be appropriate for specific constituents in non-point-source pollution control.