title: A genetic algorithm based stochastic programming model for air quality management.
abstract: This paper presents a model that can aid planners in defining the total allowable pollutant discharge in the planning region, accounting for the dynamic and stochastic character of meteorological conditions. This is accomplished by integrating Monte Carlo simulation and using genetic algorithm to solve the model. The model is demonstrated by using a realistic air urban-scale SO2 control problem in the Yuxi City of China. To evaluate effectiveness of the model, results of the approach are shown to compare with those of the linear deterministic procedures. This paper also provides a valuable insight into how air quality targets should be made when the air pollutant will not threat the residents' health. Finally, a discussion of the areas for further research are briefly delineated.
authors.affiliation: Center for Environmental Sciences, Peking University, Beijing 100871, China.
authors.forename: Xiao-Ming
authors.initials: XM
authors.lastname: Ma
authors.forename: Fan
authors.initials: F
authors.lastname: Zhang
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000465:Algorithms; D005544:Forecasting; D006801:Humans; D008685:Meteorological Concepts; D008957:Models, Genetic; D010316:Particle Size; D011634:Public Health; D013458:Sulfur Dioxide / Q000032:analysis*; D014505:Urban Population
chemical_list: D000393:Air Pollutants; D013458:Sulfur Dioxide