Widespread missing super-emitters of nitrogen oxides across China inferred from year-round satellite observations

Nitrogen oxides (NO_x ≡ NO + NO₂) play a central role in air pollution and are targeted for emission mitigation by environmental protection agencies globally. Unique challenges for mitigation are presented by super-emitters, typically with the potential to dominate localized NO_x budgets. Nevertheless, identifying super-emitters still challenges emission mitigation, while the spatial resolution of emission monitoring rises continuously. Here we develop an efficient, super-resolution (1 × 1 km²) inverse model based on year-round TROPOMI satellite observations over China. Consequently, we resolve hundreds of super-emitters in virtually every corner of China, even in remote and mountainous areas. They are attributed to individual plants or parks, mostly associated with industrial sectors, like energy, petrochemical, and iron and steel industries. State-of-the-art bottom-up emission estimates (i.e., MEICv1.3 and HTAPv2), as well as classic top-down inverse methods (e.g., a CTM coupled with the Ensemble Kalman Filter), do not adequately identify these super-emitters. Remarkably, more than one hundred super-emitters are unambiguously missed, while the establishments or discontinuations of the super-emitters potentially lead to under- or over-estimates, respectively. Moreover, evidence shows that these super-emitters generally dominate the NO_x budget in a localized area (e.g., equivalent to a spatial scale of a medium-sized county). Although our dataset is incomplete nationwide due to the undetectable super-emitters on top of high pollution, our results imply that super-emitters contribute significantly to national NO_x budgets and thus suggest the necessity to address the NO_x budget by revisiting super-emitters on a large scale. Integrating the results we obtain here with a multi-tiered observation system can lead to identification and mitigation of anomalous NO_x emissions.