Large-Scale Optimization Of Coordinated Control Strategies Of Fine Particulate Matter And Ozone Using A Genetic Algorithm

In recent years,during the rapid economic growth of China,the accompanying air pollution problems have caused great negative effects on human health and the ecological environment.Regional composite air pollution problems represented by fine particulate matter(PM_2.5)and ozone?O₃?are getting worse.To reduce composite pollution and protect public health,the State Council of China has implemented the 13^th Five-Year Plan for Ecological Environmental Protection(hereafter 13^th Five-Year).The Pearl River Delta?PRD?,one of the key economic regions and populous regions of China,has taken the lead in effectively attaining the national annual averaged PM_2.5 standard of 35?g m^-3 due to the 13^th Five-Year.However,the results of air quality improvement are not stable,and the ozone concentration is rising.A scientifically sound integrated assessment modeling?IAM?system capable of providing optimized cost-benefit analysis is essential in effective air quality management and control strategy development.Yet scenario optimization for large-scale applications is limited by the computational expense of optimization over many control factors.In this study,a multi-pollutant cost-benefit optimization system based on a genetic algorithm?GA?has been developed to provide cost-effective air quality control strategies for large-scale applications(e.g.,solution spaces of?10³⁵).A cost-effectiveness evaluation system for air quality attainment based on the marginal cost curve and surface response model was constructed.Therefore,the system can establish a comprehensive relationship among multi-pollutant air quality targets,pollution control strategies,control costs,and health benefits,providing a scientific assessment of the effectiveness of the control strategies.In this study,the performance is demonstrated by providing optimal cost-benefit control pathways to attain air quality goals for PM_2.5 and O₃ over the Pearl River Delta?PRD?region of China using the traditional grid search method and genetic algorithm in the ABa CAS-OE.The GA is found to be>99%more efficient than the commonly used grid searching method while providing the same combination of optimized multi-pollutant control strategies.The GA method can therefore address air quality management problems that are intractable using the grid searching method.The annual attainment goals for PM_2.5(<35?g m^-3)and O₃(<160?g m^-3 can be achieved simultaneously over the PRD region and surrounding areas by reducing NO_x?22%?,VOCs?12%?,and primary PM?30%?emissions.However,to attain stricter PM_2.5goals,SO₂ reductions?>9%?are needed as well.The estimated benefit-to-cost ratio of the optimal control strategy reached 17.7 in our application,demonstrating the value of multi-pollutant control for cost-effective air quality management in the PRD region.