Air pollution caused by particulate matter with a diameter of less than 2.5 μm (PM2.5) poses a serious threat to human health and the environment. Predicting PM2.5 concentrations and controlling emissions are crucial for pollution prevention and control. This study proposes a comprehensive solution based on weight-sharing deep learning and multi-objective optimization. The proposed approach first utilizes a model that combines the Convolutional Neural Network and Long Short-Term Memory Neural Network to analyze data from 13 air quality monitoring stations in Xi'an City. By simultaneously inputting data from different monitoring stations, the model can extract highly correlated spatiotemporal features, enabling accurate predictions of PM2.5 concentrations for specific monitoring stations using LSTM. In addition, a multi-objective optimization model is established with the primary goal of achieving maximum total emission reduction. This model takes into account four key factors: the total emission reduction, the task of emission reduction, the government subsidy, and the total cost of emission reduction. To obtain the emission reduction of PM2.5 concentration at 13 monitoring stations, 5 classical intelligence algorithms are employed to solve the model. Experimental results demonstrate the effectiveness of the proposed prediction model, with an average Root Mean Square Error (RMSE) of 12.820 and a fitting coefficient (R2) of 0.907, outperforming all comparison models. The proposed model exhibits strong generalization ability, making it applicable to different time and space conditions. Furthermore, it can be adapted for calculating emission reduction of other air pollutants. Lastly, the multi-objective optimization model achieves significant success in terms of total emission reduction. This study provides a new reference in the field of artificial intelligence and its application to air pollution control. The findings hold great significance for promoting public health and environmental protection.
