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 HOx 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 % NOx, confirming that NOx is a crucial precursor of HONO and its formation is facilitated by humidity. The conversion ratio of NOx 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 NOx, 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 HOx-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.

Existing studies suggest various potential daytime sources of atmospheric nitrous acid (HONO), including photolysis surface reactions and photo-enhanced NO2 conversion on organic surfaces. However, the understanding of daytime HONO sources is still inadequate. In this study, we report the HONO formation on asphalt surfaces under various NO2, VOCs (toluene and hexane), and UV irradiance conditions using a continuous flow chamber. Although no HONO formation was found without light exposure, the light threshold for HONO formation on the asphalt surface was very low, with a total UV (TUV) of 0.7 W m−2. HONO formation on the asphalt surface was linearly dependent on NO2 up to 300 ppb in the presence of VOCs, but no HONO formation was observed with humified air and NO2. HONO production was saturated at high hydrocarbon concentrations and light intensities. The calculated first-order NO2 conversion rate to HONO on the asphalt surface was 1.2 × 10−4 s −1. The observed mean HONO emission flux was 1.3 × 109 molecules cm−2 s −1 with a similar range of those on other urban covered surfaces. The calculated vertical HONO profile using the measured HONO emission flux and 1-D steady state model revealed that the asphalt surface may account for 13% of daytime HONO in the elevated on-road pollutant concentrations in Seoul. However, we show that its HONO contribution could be much higher on real-life road surfaces directly exposed to much higher NO2 emissions from vehicle exhaust.

This study was performed to determine the optimal composition of Cheonggukjang added with garlic. The experiment utilized a central composite design (CCD). The evaluation was carried out by means of response surface methodology (RSM), which included 18 experimental points with three independent variables : the content of the garlic (1.3∼9.7%, X1), the steaming time of garlic (0∼15.1 min, X2), and the fermentation time of Cheonggukjang (48.2∼71.8 h, X3). The viscous substance (Y1), acidity (Y2), amino-type nitrogen (Y3), γ-GTP activity (Y4) and ABTS radical scavenging activity (Y5). were assessed in four replicates with five dependent variables. The maximum content of the viscous substance was 13.02% at 6.53% (X1), 6.81 min (X2) and 55.18 h (X3). The acidity was increased when the fermentation time was longer, and the minimum acidity point was 0.50% at 7.75% (X1), 3.42 min (X2) and 58.60 h (X3), respectively. The content of the amino-type nitrogen at the experimental range studied was was 80.58∼158.82 mg%, and the stationary point was at saddle point. Using ridge analysis, the maximum point was 156.97 mg% at 6.21% (X1), 14.85 min (X2) and 58.04 h(X3). The optimum conditions of γ-GTP activity was 5.73% (X1), 6.99 min (X2) and 57.96 h(X3), respectively, at the maximum point was 353.66 mU/mL. The maximum point of ABTS radical scavenging activity was 76.43% at 3.78% (X1), 14.28 min (X2) and 57.99 h(X3) at the saddle point, when the garlic steaming time was longer.

Abstract. Nitrous acid (HONO), one of the reactive nitrogen oxides (NOy), plays an important role in the formation of ozone (O3) and fine aerosols (PM2.5) in the urban atmosphere. In this study, a simulation model of Reactive Nitrogen species using Deep neural network model (RND) was constructed to calculate the HONO mixing ratios through a deep learning technique using measured variables. A Python-based Deep Neural Network (DNN) was trained, validated, and tested with HONO measurement data obtained in Seoul during the warm months from 2016 to 2019. A k-fold cross validation and test results confirmed the performance of RND v1.0 with an Index Of Agreement (IOA) of 0.79 ~ 0.89 and a Mean Absolute Error (MAE) of 0.21 ~ 0.31 ppbv. The RNDV1.0 adequately represents the main characteristics of HONO and thus, RND v1.0 is proposed as a supplementary model for calculating the HONO mixing ratio in a high- NOx environment.