Spatiotemporal optimization of NO_x and VOC emissions using a hybrid inversion framework and its implication for ozone sensitivity diagnosis

Abstract. Ozone (O₃) over South Korea has risen in recent years, underscoring the need to accurately quantify emissions of nitrogen oxides (NO_x) 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 (4D-Var) assimilation using the Community Multiscale Air Quality (CMAQ) model to jointly constrain spatiotemporal NO_x and VOC emissions. The inversion is constrained by Tropospheric Monitoring Instrument (TROPOMI) NO₂ and HCHO columns and by surface NO₂ and O₃ from the air quality monitoring station network. The analysis covers 1–14 May 2022, a period of climatologically high O₃. Optimized NO_x emissions exhibit strong diurnal adjustments relative to the prior (nighttime reductions up to 51 % and daytime increases up to 14 %). The joint inversion of NO_x and VOC delivers the largest improvement in O₃ simulations, achieving the best agreement with observations (IOA > 0.8). Constrained emissions shift O₃ sensitivity from VOC-sensitive to NO_x-sensitive across much of the domain, improving spatial consistency with TROPOMI-derived formaldehyde-to-NO₂ ratio (FNR) diagnostics. Adjoint-based hourly ΔO₃ responses reveal regime- and hour-dependent behavior: VOC controls are most effective under VOC-sensitive conditions, whereas NO_x controls are more direct under NO_x-sensitive conditions. Importantly, because O₃ titration is immediate while photochemical production requires finite reaction time, emissions released approximately 1–2 hours earlier have the greatest influence on current O₃, motivating hour-specific, regime-specific controls. Overall, the hybrid framework improves O₃ simulations and sensitivity-regime diagnosis, enabling spatiotemporally resolved precursor emission reduction guidance for effective O₃ mitigation.