A deep learning approach predicts O3 increase and PM2.5 declines under high emission scenario across the Northern China urban agglomeration

Due to the complex interaction between varied pollutant emission sources and atmospheric circulation patterns, achieving reliable air quality prediction poses a formidable challenge. Consequently, the changes in PM 2.5 and O 3 pollution under future climate change scenarios remain largely unknown, particularly in regions that are frequently affected by severe air pollution, such as the Northern China urban agglomeration (NCUA). Here we developed an Integrated Graph Neural Network (IGNN) model that, trained by historical meteorological and emission data, is able to predict future PM 2.5 and O 3 concentrations over the NCUA. The IGNN model contains nodes and edges, with historical station observations being the graphical nodes, while spatiotemporal features of air quality variables, meteorological properties and emission information are defined as attributes of the nodes and edges. The results demonstrate that the IGNN model effectively captures the variability of historical PM 2.5 and O 3 , outperforming other state-of-the-art methods in accurately predicting air quality variability. In high carbon emission scenario with varying air pollutant strategies, all IGNN model simulations show a significant decrease of average concentration of PM 2.5 (the rate of decline ranges from −0.14 to −0.37 μg m −3 year −1 , p < 0.05), Conversely, there is a significant increase in average concentration of O 3 (+0.07 to +0.22 μg m −3 year −1 , p < 0.05). Our findings highlight the risk posed by elevated O₃ pollution levels under high carbon emission scenarios in the NCUA. This underscores the critical necessity for a coordinated approach that integrates air quality control with climate change mitigation efforts. • An Integrated Graph Neural Network model for predicting PM2.5 and O3 was developed. • IGNN displayed better skills in predicting PM2.5 and O3 than other approaches • Increase of O 3 and decrease of PM 2.5 under in high emission scenarios were projected. • Heightened levels of O3 pollution could lead to a high risk of respiratory mortality,