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

Abstract

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 the sensitivity of the O₃–NO_x–VOCs 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 VOC oxidation and HO₂/RO₂ + NO pathways. Relative incremental reactivity (RIR) of HONO exhibits a strong anticorrelation with NO_x (R² = 0.86), indicating that a greater NO_x-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_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 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_x reduction. These findings challenge traditional EKMA frameworks that NO_x control brings adverse effects under VOC-limited regimes, highlighting the feasibility of NO_x control strategies when HONO responses are considered.