# Assessment of Daytime HONO Emission Source from Asphal Surface to Urban Air
## 3 Results
### Vertical Profile of HONO above the Asphalt Surface  
Table 2 shows observed daytime HONO concentrations in selected urban areas, including two field studies in Seoul performed in this study. It confirms again that substantial amounts of daytime HONO could not be accounted only for well-identified homogeneous HONO formation with NO and OH in urban areas. Observed HONO means in Seoul were 0.6 and 0.45 ppbv at Olympic Park site during the 2016 KORUS-AQ campaign and at an urban site of Jungrang district in 2019, respectively. These values in Seoul are in the middle range of typical urban conditions. We attempted to determine the fraction of HONO that can be explained by asphalt-surface HONO production. If we exclude other HONO sources and sinks, such as gaseous HONO formation (NO+OH) and other ground and aerosol sources and sinks, then the changing rate of daytime HONO formation can be expressed using vertical eddy transport, photolysis of HONO, and asphalt surface production, as shown in Equation (3). \[\frac{\partial[HONO]}{\partial t}=\ -\frac{\partial}{\partial z}(K_{z}\frac{ \partial[HONO]}{\partial z})-J_{homo}[HONO]+Flux_{asphalt\ surface} \tag{3}\]  
If we assume that the HONO concentration is in a steady state (\(\frac{\partial[HONO]}{\partial t}=0\)), then we can solve the 1-D differential Equation (3) with flux boundary conditions using the forward Euler method with discretizing diffusion operators for allowable time steps. Implementation of numerical computations for Equation (3) requires additional known parameters, such as eddy diffusivity (\(K_{z}\)), photolysis rate of HONO (\(J_{homo}\)), and asphalt surface flux of HONO (\(Flux_{asphalt\ surface}\)). Based on typical summer meteorological conditions during the study period, the atmosphere in the boundary layer is generally considered to be moderately unstable. We estimated the vertical diffusivities (\(K_{z}\)) along with heights using a simplified diffusivity profile derived from a nonhydrostatic, semi-implicit model for a mod  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Location** & **Sampling Period** & **Instrument** & **Min** & **Mean** & **Max** & **Reference** \\ \hline London, UK & July–Aug 2012 & LOPAP a  & 0.20 & 0.44 & 0.60 & [43] \\ New York, USA & July–Aug 2001 & HPLC b  & 0.40 & 0.46 & 1.40 & [44] \\ Houston, USA & Apr 21, 2009 & LP-DOAS c  & 0.05 & 0.1 & 0.15 & [21] \\ Bakersfield, USA & May–July, 2010 & AIM d  & 0.03 & 0.08 & 0.13 & [19] \\ Beijing, China & Aug 2007 & UV-Vis & 0.80 & - & 1.60 & [45] \\  & July 2008–April 2009 & MAX-DOAS e [FOOTNOTE:e]Footnote e: High-performance liquid chromatography. \({}^{\text{e}}\) Long-path differential optical absorption spectrometer. \({}^{\text{g}}\) Long-path differential optical absorption spectrometer. \({}^{\text{g}}\) Multi-axis differential optical absorption spectrometer. erately unstable 1-km boundary layer [48; 49; 50], as shown in Figure 5. The photolysis rate of HONO (\(J_{hono}\)) was set to a constant value of 0.0017 s\({}^{-1}\) for typical daytime light conditions. To estimate the average value of the asphalt-surface HONO flux in Seoul, we built a random forest regression model and a least square regression model with dependent variables (HONO asphalt surface flux) and controlled features (NO\({}_{2}\), VOCs, and UV intensities) of the independent variables. The model-calculated HONO flux values were well matched with experimental data with r-square of 0.932 and 0.931 for random forest regression and a least square model, respectively. Using these models, we estimated an asphalt surface HONO flux of 1.3 \(\times\) 10\({}^{9}\) molecules cm\({}^{-2}\) s\({}^{-1}\) for on-road pollutant conditions with 160 ppbv for NO\({}_{2}\) and 100 ppbv for total hydrocarbons (TVOCs) observed in tunnel studies in Seoul, which are three to five times higher than those found in the roadside [51; 52].