Spatiotemporal optimization of NO _<i>x</i> and VOC emissions using a hybrid inversion framework and implications for ozone sensitivity-regime diagnosis

Abstract. Ozone (O3) over South Korea has risen in recent years, underscoring the need to accurately quantify emissions of nitrogen oxides (NOx) and volatile organic compounds (VOC). We develop a hybrid inverse modeling framework that couples the Finite Difference Mass Balance (FDMB) method with four-dimensional variational data assimilation (4D-Var) using the Community Multiscale Air Quality (CMAQ) model to jointly constrain spatiotemporal NOx and VOC emissions over South Korea. The inversion is constrained by Tropospheric Monitoring Instrument (TROPOMI) NO2 and HCHO columns and by surface NO2 and O3 concentrations from the Air Quality Monitoring Station (AQMS) network. The analysis covers 1–14 May 2022, during a month that exhibited the highest mean O3 over the past decade. Optimized NOx emissions exhibit strong diurnal adjustments relative to the prior (nighttime reductions up to 51 % and daytime increases up to 14 %). The joint NOx–VOC inversion produced the best consistency with assimilated AQMS O3 observations (Index of Agreement &gt; 0.8). Optimized emissions shift O3 sensitivity from VOC-sensitive to NOx-sensitive across much of the domain, improving spatial consistency with TROPOMI-derived formaldehyde-to-NO2 ratio diagnostics. Adjoint-based hourly ΔO3 responses reveal distinct temporal characteristics: O3 titration by NOx is immediate, whereas photochemical O3 production by VOCs requires a 1–2 h reaction time. Furthermore, reactive biogenic VOCs contribute to a slight nighttime O3 sink under NOx-sensitive conditions. These findings motivate hour-specific, regime-specific controls rather than uniform daily reductions. Overall, the hybrid framework improves O3 simulations and sensitivity-regime diagnosis, enabling spatiotemporally resolved precursor emission reduction guidance for effective O3 mitigation.