A Joint Satellite NO2-CO Constraint Framework Reveals Emission Biases Driven by Extreme Events and Missing Sources in Emission Inventories

Bottom-up emission inventories often miss day-to-week variability, especially during extreme events, and they can also omit or underestimate sources that are sporadic, poorly monitored, or rapidly changing. Here we present a joint NO₂-CO remote-sensing constraint framework designed to diagnose these problems in a consistent way across regions and scales. The framework leverages the complementary information content of NO₂ (short-lived, strongly tied to local sources) and CO (longer-lived, sensitive to both combustion and atmospheric transport) to separate local emission signals from meteorology-driven redistribution, and to flag conditions where inventories are likely biased.

First, we use joint NO₂-CO signals to constrain plume injection and vertical placement, showing that simple plume-rise formulations can systematically underestimate injection heights (by ~33% on average) and that NO₂ and CO terms are essential predictors for capturing free-tropospheric lofting. Second, we apply top-down constraints on daily-to-weekly emissions to reproduce observed extremes in the Monsoon Asia free troposphere, where matching the magnitude and spatial reach of events requires substantially larger effective emissions. Third, we extend the concept to broader spatial domains, using satellite-derived NO₂ and CO to estimate emission variability with uncertainty bounds and to identify missing or underestimated sources; the inferred extra CO can further translate into non-negligible CO₂ mass equivalents through oxidation, highlighting a coupled air-quality–carbon implication.

A China-focused application illustrates how vertical information improves attribution: incorporating MOPITT vertical profiles strengthens surface–column consistency across 1577 sites and reveals episodes in which CO from major urban sources (e.g., Xi’an) is lofted to ~500 hPa and transported >2000 km downwind. Overall, the proposed NO₂-CO constraint framework provides a practical route to evaluate and refine emission inventories under extreme conditions, while explicitly accounting for vertical transport and source intermittency, while also helping models to better close the missing carbon budget.