Unveiling the HONO Offsetting Effect: Rethinking NO_<i>x</i> Emission Controls during Urban Ozone Pollution Episodes

Conventional ozone (O₃) control typically targets nitrogen oxides (NO_<i>x</i>) and volatile organic compounds (VOCs), yet the role of nitrous acid (HONO) is often overlooked. Here, machine learning (ML)-derived HONO-NO_<i>x</i> reduction relationships in the real atmosphere are integrated into the process-based photochemical model (OBM-MCM) to diagnose the sensitivity of the O₃-NO_<i>x</i>-VOCs during high-pollution episodes in Shenzhen, China. OBM simulations constrained by observed HONO show a 95% increase in daytime net O₃ production rates [<i>P</i>_net(O₃)] compared to the conventional unconstrained case through enhanced OH radical formation that accelerated VOC oxidation and HO₂/RO₂ + NO pathways. Relative incremental reactivity (RIR) of HONO exhibits a strong anticorrelation with NO_<i>x</i> (<i>R</i>² = 0.86), indicating that a greater NO_<i>x</i>-driven increase in the level of O₃ corresponds to a greater HONO-driven decrease in the level of O₃. ML predicts that a 10% reduction in NO_<i>x</i> synchronically results in reducing atmospheric HONO and TVOCs by ∼7.6 and ∼3%, respectively, leading to a shift in <i>P</i>_net(O₃) from a maximum of 28% increase to a 14% decrease through the reshaping empirical kinetic modeling approach (EKMA), thereby demonstrating that HONO can offset the O₃ increase induced by NO_<i>x</i> reduction. These findings challenge traditional EKMA frameworks that NO_<i>x</i> control brings adverse effects under VOC-limited regimes, highlighting the feasibility of NO_<i>x</i> control strategies when HONO responses are considered.