An optimization model for photochemical air pollution control

One difficulty in developing effective ozone control strategies is to incorporate the complex and nonlinear relationship between photochemical pollutants and their precursors into a mathematical programming model. In this paper, we develop a locally linear approximation to the nonlinear relationship between photochemical pollutants and their precursors. We then develop a mathematical programming model for optimal control of photochemical pollutants. The model minimizes the net present value of precursor emission control costs from various emission sources which are subject to meeting ambient air quality goals for different pollutants at different geographical locations over the planning time periods. At the end of this paper, we provide a case study examining photochemical smog occurring in Los Angeles. We discuss what data inputs are required for the air quality management model, the kind of outputs that can be obtained from this model and policy implications of the model results.