Reliability of optimal control strategies for photochemical air pollution.

This study illustrates how consideration of modeling uncertainties can affect optimal control strategies for urban ozone. Control strategies are investigated for illustrative cases of air parcel trajectories ending at Azusa, CA, and Riverside, CA, on August 28, 1987. The control strategies are designed to achieve a specified air quality target with a given reliability, considering uncertainties in the California Institute of Technology's trajectory model and its inputs, including uncertainties in emissions and in the SAPRC-97 chemical mechanism. A decoupled stochastic optimization scheme is used to solve the chance-constrained programming problem. Least-cost control strategies derived using nominal model inputs and parameter values have low reliability for some target O3 concentrations when uncertainties are taken into account. For the case considered, reducing volatile organic compound (VOC) emissions from motor vehicles is identified as the least-cost approach to meeting O3 targets at Azusa. However, the optimal control strategies for Riverside depend on the target O3 concentrations and the level of reliability required. Consideration of model uncertainty is found to shift the focus from VOC controls to nitrogen oxide controls for the Riverside trajectory.