Application of genetic algorithms for the design of ozone control strategies

Designing air quality management strategies is complicated by the difficult y in simultaneously considering large amounts of relevant data, sophisticat ed air quality models, competing design objectives, and unquantifiable issu es. For many problems, mathematical optimization can be used to simplify th e design process by identifying cost-effective solutions. Optimization appl ications for controlling nonlinearly reactive pollutants such as tropospher ic ozone, however, have been lacking because of the difficulty in represent ing nonlinear chemistry in mathematical programming models. We discuss the use of genetic algorithms (GAs) as an alternative optimizati on approach for developing ozone control strategies. A GA formulation is de scribed and demonstrated for an urban-scale ozone control problem in which controls are considered for thousands of pollutant sources simultaneously. A simple air quality model is integrated into the GA to represent ozone tra nsport and chemistry. Variations of the GA formulation for multiobjective a nd chance-constrained optimization are also resented. The paper concludes w ith a discussion of the practicality of using more sophisticated, regulator y-scale air quality models with the GA. We anticipate that such an approach will be practical in the near term for supporting regulatory decision-maki ng.