Sensitivity of a Complex Urban Air Quality Model to Input Data

Abstract

In recent years, urban-scale photochemical simulation models have been developed that are of practical value for predicting air quality and analyzing the impacts of alternative emission control strategies. Although the performance of some urban-scale models appears to be acceptable, the demanding data requirements of such models have prompted concern about the costs of data acquisition, which might be high enough to preclude use of photochemical models for many urban areas. To explore this issue, sensitivity studies with the Systems Applications, Inc. (SAI) Airshed Model, a grid-based time-dependent photochemical dispersion model, have been carried out for the Los Angeles basin. Reductions in the amount and quality of meteorological, air quality and emission data, as well as modifications of the model gridded structure, have been analyzed. This paper presents and interprets the results of 22 sensitivity studies. A sensitivity-uncertainty index is defined to rank input data needs for an urban photochemical model. The index takes into account the sensitivity of model predictions to the amount of input data, the costs of data acquisition, and the uncertainties in the air quality model input variables. The results of these sensitivity studies are considered in light of the limitations of specific attributes of the Los Angeles basin and of the modeling conditions (e.g., choice of wind model, length of simulation time). The extent to which the results may be applied to other urban areas also is discussed.