## 3 Results  
### Simple Functionalization Scheme (1-Bin Case)  
The first set of simulations used the simple functionalization scheme (1-bin), negligible additional production of bSOA during aging (no bSOA aging) and no fragmentation of the organic compounds. Implicitly, this scheme assumes that the simulated functionalization represents the net effect of these pathways. This scheme, until now, even though it is the simplest, has shown surprising success in all its previous applications [18; 19; 20].  
The predicted average diurnal profile of OA mass concentration for the 1-bin scheme is shown in Figure 2. The average predicted OA is equal to 2.3 \(\mu\)g m\({}^{-3}\) and the average measured OA mass concentration at the site was equal to 2.1 \(\mu\)g m\({}^{-3}\) (Table 2). The measured OA concentrations during the PEGASOS campaign were quite typical for the season, even if significant intra-annual variability is observed in the site, as discussed in detail by [PERSON] et al. [31]. Once again, this simple aging scheme is the most successful of all parameterizations used in predicting the OA concentration (Table 2). It had the lowest root mean square error, as well as the lowest absolute bias (0.23) and one of the lowest fractional errors (38%). Most of the error is present during the first hours of the day when the model overpredicts the nighttime OA levels (Figure 2). The prediction skill metrics of the model 4-h average OA concentration against the average hourly ground measurements for the selected days during the PEGASOS 2013 campaign are summarized in Table 2. In Figure 3, the total average OA concentration is presented together with the OA composition predicted by all seven different parameterization schemes. Similarly to their application in the polluted Po Valley [20], the seven aging schemes predict similar average concentrations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**2D-VBS** & **Predicted** & **Average** & **Fractional** & **Fractional** & **Absolute Error** & **Absolute Bias** & **Root Mean** \\
**Parameterization** & **(\(\mu\)g m\({}^{-3}\))** & **Error** & **Bias** & **(\(\mu\)g m\({}^{-3}\))** & **(\(\mu\)g m\({}^{-3}\))** & **Square Error** \\ \hline
1-bin & 2.32 & 0.38 & \(-\)0.05 & 0.92 & 0.23 & 1.16 \\
1-bin/bSOA/\(b\) = 0.15 & 2.91 & 0.34 & 0.17 & 1.08 & 0.82 & 1.69 \\
2-bin & 2.59 & 0.31 & 0.07 & 0.92 & 0.5 & 1.35 \\
2-bin/\(b\) = 0.1 & 2.46 & 0.34 & 0.02 & 0.91 & 0.38 & 1.25 \\
2-bin/bSOA/\(b\) = 0.4 & 2.98 & 0.31 & 0.22 & 1.06 & 0.90 & 1.67 \\ DET/\(b\) = 0.3 & 2.45 & 0.33 & 0.02 & 0.89 & 0.36 & 1.21 \\ DET/bSOA/\(b\) = 0.7 & 2.73 & 0.26 & 0.15 & 0.84 & 0.64 & 1.33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance metrics of the seven parameterizations for hourly ground-level OA concentration. The measured average OA mass concentration was 2.1 \(\mu\)g m\({}^{-3}\).  
Biogenic SOA dominates the predicted OA composition, as expected in a forested environment, contributing 41% of the total OA on average (Figure 4). Please note that the events simulated do not include stagnation periods during which the bSOA contribution should be much higher. The present results are not representative of the average conditions in Hyytiala, but only of the limited periods simulated, which are dominated by transport. SOA from the oxidation of intermediate-volatility compounds contributed 27% according to PMCAMx-Trij and anthropogenic SOA from VOCs represented another 16%. The remaining material was fresh primary OA (4%), SOA from evaporation of the POA and subsequent oxidation (6%) and OA from long-range transport (6%).  
Figure 3: Predicted OA composition for the schemes with good performance for Hyytiala. The red line indicates the average measured OA equal to 2.1 \(\upmu\)g m\({}^{-3}\).  
Figure 2: Average diurnal OA mass concentration at the ground level in Hyytiala for the 1-bin simulation. The red line shows the model predictions and the black symbols represent the ACSM measurements. The error bars correspond to one standard deviation.