Satellite observations reveal heterogeneous atmospheric composition responses to rapid emission changes

Abstract. We developed a unified machine learning framework to retrieve daily, 1 km resolution, gap-free concentrations of six major atmospheric pollutants across China, providing a consistent basis for quantifying atmospheric composition responses to rapid emission perturbations. Our results reveal pronounced spatiotemporal variability across pollutant species, with recovery times ranging from two to eight weeks following abrupt emission reductions. Most air pollutants, such as particulate matter (PM) and NO₂, exhibited rapid declines and subsequent rebounds, consistent with changes in anthropogenic emissions, whereas O₃ showed the opposite response, reflecting nonlinear photochemical processes under reduced NO_x conditions. In contrast, SO₂ and CO displayed more sustained decreases, indicating longer-term structural changes in combustion-related sources. By integrating explainable artificial intelligence with atmospheric predictors, we disentangle meteorological and emission-driven contributions to the variability of secondary pollutants across spatial scales. In Wuhan, reduced anthropogenic emissions contributed to a 22 % decrease in PM_2.5 during the emission-reduction period, whereas enhanced atmospheric oxidation associated with meteorological variability led to a 40 % increase in O₃. During the subsequent recovery phase, meteorological factors dominated interannual variability, driving a 16 % rebound in PM_2.5 but a 5 % decline in O₃. These findings elucidate the chemical and physical mechanisms governing atmospheric composition under rapid perturbations in emissions and underscore the nonlinear coupling among primary emissions, secondary formation, and meteorological processes.