A forecasting tool for optimized emission control strategies to achieve short-term air quality attainment.

Abstract

Optimizing an emergency air pollution control strategy for haze events presents a significant challenge due to the extensive computational demands required to quantify the complex nonlinearity associated with controls on diverse air pollutants and regional sources. In this study, we developed a forecasting tool for emergency air pollution control strategies based on a predictive response surface model that quantifies PM_2.5 responses to emission changes from different pollutants and regions. This tool is equipped to assess the effectiveness of emergency control measures corresponding to various air pollution alerts and to formulate an optimized control strategy aimed at specific PM_2.5 targets. A case study in the Yangtze River Delta demonstrates that our tool can conduct assessments and generate optimized control strategies for the forthcoming seven to ten days within a 6-h window. Results indicate that the haze event on November 3rd, 2017, was predominantly attributable to regional transport, while the episode on November 7th-8th resulted more from local emissions. The optimized control strategy for November 3rd involves coordinated control from 17 cities along the northwest regional transport pathway, whereas 9 cities around Shanghai should implement emergency emission reductions for PM_2.5 attainment in Shanghai on November 7th-8th. Additionally, the intensity of air pollution alerts is higher in the optimized strategy for November 3rd. The forecasting tool developed in this study can quickly and accurately assess the effectiveness of pollution emergency reduction plans and formulate optimal control strategies in advance, which is of great significance for enhancing the emergency response capabilities of authorities to address short-term air pollution events effectively.