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

Conventional ozone (O₃) control typically targets nitrogen oxides (NO_x) and volatile organic compounds (VOCs), yet the role of nitrous acid (HONO) is often overlooked. Here, machine learning (ML)-derived HONO-NO_x reduction relationships in the real atmosphere are integrated into the process-based photochemical model (OBM-MCM) to diagnose O₃-NO_x-VOCs sensitivity during high pollution episodes in Shenzhen, China. OBM simulations constrained by observed HONO show a 95% increase in daytime net O₃ production rates (P_net(O₃)) compared to the conventional unconstrained case, through enhanced OH radical formation that accelerated VOCs oxidation and HO₂/RO₂+NO pathways. Relative incremental reactivity (RIR) of HONO exhibits a strong anticorrelation with NO_x(R²=0.86), indicating that greater NO_x-driven O₃ increase corresponds to greater HONO-driven O₃ decrease. The ML predicts that a 10% reduction in NO_x synchronically results in reducing atmospheric HONO and TVOCs by ~7.6% and ~3%, respectively, leading to a shift in P_net(O₃) from a maximum 28% increase to a 14% decrease through reshaping Empirical Kinetic Modeling Approach (EKMA), thereby demonstrating that HONO can offset the O₃ increase induced by NO_x reduction. These findings challenge traditional EKMA frameworks that NO_x control brings adverse effects under VOCs-limited regimes, highlighting feasibility of NO_x control strategies when HONO responses are considered.