The role of HONO in O₃ formation and insight into its formation mechanism during the KORUS-AQ Campaign

Abstract. Photolysis of nitrous acid (HONO) has long been recognized as an early morning source of OH radicals in urban air, but the detailed mechanism of its formation is still unclear. During the Korea-US Air Quality (KORUS-AQ) campaign, HONO was measured using Quantum Cascade Tunable Diode Laser Absorption Spectroscopy (QC-TDLAS) at Olympic Park in Seoul from 17 May to 10 June, 2016. HONO concentrations ranged from 0.07 ppbv to 3.46 ppbv with an average of 0.93 ppbv. HONO remained high at night from 1 am to 5 am, during which the mean concentration was higher in high-O₃ episodes (1.82 ppbv) than non-episode (1.20 ppbv). In the morning, OH budget due to HONO photolysis were higher by 50 % (0.95 pptv) during high-O₃ episodes compared to non-episode. Diurnal variations of HO_x and O₃ simulated by the F0AM model demonstrated a difference of ~ 20 ppbv in daily maximum O₃ between the two periods. The HONO concentration increased with relative humidity (RH) until 80 %, of which the highest HONO was associated with the top 10 % NO_x, confirming that NO_x is a crucial precursor of HONO and its formation is facilitated by humidity. The conversion ratio of NO_x to HONO was estimated to be 0.86 × 10⁻² h⁻¹ at night and also increased with RH. As surrogate for the catalyst surface, the mass concentrations of black carbon (eBC) and the surface areas of particles smaller than 120 nm showed a tendency for RH similar to conversion ratio. Using an Artificial Neuron Network (ANN) model, HONO concentrations were successfully simulated with measured variables (r = 0.8 for the best suite), among which NO_x, surface area, and RH were found to be main factors affecting ambient HONO concentrations with weigh values of 26.2 %, 11.9 %, and 10.6 %, respectively. This study demonstrates the coupling of HONO with HO_x-VOCs-O₃ cycle in Seoul Metropolitan Areas (SMA) and provides practical evidence for heterogeneous formation of HONO by employing the ANN model to atmospheric chemistry.