Strategies for the coordinated control of particulate matter and carbon dioxide under multiple combined pollution...

Air pollutants represented by fine particulate matter (PM_2.5) and the greenhouse effect caused by carbon dioxide (CO₂), are both urgent threats to public health. Tackling the synergistic reduction of PM_2.5 and CO₂ is critical to achieving improvements in clean air worldwide. A persistent issue is the identification of their common sources and integrated impacts under different environmental conditions. In this study, we investigated the characteristics of the pollution types captured by combined analysis through a comprehensive observational dataset for 2017-2020, and applied machine learning algorithms to quantify the effects of drivers on air pollutants and CO₂ formation. More importantly, detailed conclusions were drawn for the joint control of PM_2.5-CO₂ in multiple pollution types by using ensemble traceability technique. We demonstrated that reducing coal combustion emissions was an effective measure to maximize the benefits of PM_2.5-CO₂ in weather with low CO₂ levels and no PM_2.5 pollution. Correspondingly, on days with severe PM_2.5 episodes, prioritizing control of vehicle emissions can simultaneously mitigate PM_2.5 and CO₂. Similar conclusions were found at high CO₂ levels, accompanied by a more extensive role of vehicle emissions. Furthermore, a comparison of the differences in source impacts between PM_2.5-CO₂ and individual species suggests that focusing only on the sources that contribute significantly to one species may result in an underestimation or overestimation of PM_2.5-CO₂ source impacts. One such implication, as evidenced by our findings, is that synergistic controlling common sources of pollutants should be efficient. Thereby, common source management targeting PM_2.5-CO₂ under multiple pollution types is a more workable solution to alleviate environmental pollution.