Abstract: This paper proposes an optimal control system for pollution reduction constrained by urban air quality compliance. Based on the air quality model (WRF-CMAQ), a “localized” air quality simulation platform is built, and an air quality compliance assessment model and emission reduction cost optimization model are constructed. The genetic algorithm is used to solve the optimal control strategy of pollution reduction of a city under the constraint of air quality target in winter. The results show that under the condition of keeping the ozone concentration unchanged, when the PM2.5 target concentration values are set as 55?g/m3, 60?g/m3, and 65?g/m3, respectively, the corresponding optimal control scheme of pollution reduction can be obtained. The PM2.5 target concentration values were improved by 30.4%, 24.1%, and 17.8%, and the corresponding total emission reduction costs were 16.6×106, 6.36×106, and 1.46×106 yuan, respectively. The optimal control system for urban pollution reduction and its model solving method constructed in this paper can not only provide effective scientific and technological support for the formulation of the urban heavy pollution weather response plan in winter, but also provide theoretical guidance and decision-making method for the development of “one city, one policy” urban air quality compliance strategic planning.

Key words: typical months of winter, air quality target, genetic algorithm, pollution reduction, optimal control strategy