Revealing the Covariation of Atmospheric O2 and Pollutants in an Industrial Metropolis by Explainable Machine Learning

In urban areas, atmospheric O2 actively participates in the process of anthropogenic emissions and energy consumption. However, the covariation between atmospheric O2 and the emitted pollutants has yet to be thoroughly explored. This study examines the covariations between atmospheric O2 and pollutants in Lanzhou, a semi-arid industrial metropolis. A machine learning (ML)-based O2 simulator coupled with a SHapley Additive exPlanation (SHAP) algorithm is established to explore and interpret their covariations under diverse conditions. Our findings indicate an increase of 16.3 ppm in the O2 concentration associated with the atmospheric transport of natural dust particles during dusty weather events. This suggests that natural dust transport can mitigate the depletion of atmospheric O2 caused by primary emissions and secondary formation of anthropogenic particulate matter. Furthermore, we identify a nonlinear relationship between the concentrations of O2 and pollutant concentrations, which likely arises from their distinct diffusive abilities. The study highlights the unique pollution characteristics in a semi-arid urban downtown and demonstrates the ability of ML-based methodology to reproduce and interpret the environmental and anthropogenic impacts on the local carbon–oxygen balance.