Unveiling the HONO Offsetting Effect: Rethinking NOx Emission Controls during Urban Ozone Pollution Episodes.

Conventional ozone (O3) control typically targets nitrogen oxides (NOx) and volatile organic compounds (VOCs), yet the role of nitrous acid (HONO) is often overlooked. Here, machine learning (ML)-derived HONO-NOx reduction relationships in the real atmosphere are integrated into the process-based photochemical model (OBM-MCM) to diagnose the sensitivity of the O3-NOx-VOCs during high-pollution episodes in Shenzhen, China. OBM simulations constrained by observed HONO show a 95% increase in daytime net O3 production rates [Pnet(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 NOx (R2 = 0.86), indicating that a greater NOx-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 NOx synchronically results in reducing atmospheric HONO and TVOCs by ∼7.6 and ∼3%, respectively, leading to a shift in Pnet(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 NOx reduction. These findings challenge traditional EKMA frameworks that NOx control brings adverse effects under VOC-limited regimes, highlighting the feasibility of NOx control strategies when HONO responses are considered.