# Stomatal and Non-Stomatal Turbulent Deposition Flux of Ozone to a Managed Peatland
## 3 Results and Discussion
### \(O_{3}\)-Flux Partitioning  
The turbulent deposition flux of ozone, \(F_{O_{3}}\) was partitioned into \(F_{sto}\) and \(F_{nsto}\) (Equation (6)) for each half hour. Figure 4 shows the median diurnal courses of these individual fluxes. \(F_{nsto}\) is generally larger than \(F_{sto}\). Both \(F_{sto}\) and \(F_{nsto}\) follow a diurnal pattern. \(F_{sto}\) is driven by the conductance of the stomata (and thus related to _GPP_) and by \(c_{O_{3}}\). It is zero during the nights, and the total \(F_{O_{3}}\) is established by \(F_{nsto}\). \(F_{nsto}\) is larger than \(F_{sto}\) for most of the daytime as well, except during the afternoon hours, when \(F_{nsto}\) and \(F_{sto}\) are about equal to each other. For situations when no evaporation is present, \(g_{sto,PM}\) (Equation (9)) can be used to calculate \(F_{sto}\) instead of \(g_{sto}\) (Equation (11)). Using this simplification leads to an overestimation of the amount of the nocturnal \(F_{sto}\) by about 1 nmol m\({}^{-2}\) s\({}^{-1}\), (nocturnal \(F_{sto}\approx-1\) nmol m\({}^{-2}\) s\({}^{-1}\)) and a root mean square error (RMSE) between the simplified and the correct approach of 1.3 nmol m\({}^{-2}\) s\({}^{-1}\) (details not shown in detail). This overestimate stems from nocturnal evaporation, of which a considerable portion was interpreted to be a stomatal flux when applying the simplification. For daytime conditions, the RMSE between the two methods was 1.7 nmol m\({}^{-2}\) s\({}^{-1}\) and \(F_{sto}\) as calculated with the simplified approach is mostly higher, which suggests that also during daytime, a certain amount of evaporation falsely contributed to the computed transpiration flux and thus to \(F_{sto}\). Therefore, taking the _GPP_ into account when calculating the stomatal conductance (Equation (11)) and thus \(F_{sto}\) leads to much better results. The partitioning of \(F_{O_{3}}\) into \(F_{sto}\) and \(F_{nsto}\) allows us to disentangle different processes driving \(F_{O_{3}}\). Figure 5 shows a plot of the stomatal flux \(F_{sto}\) versus _GPP_ with \(\chi_{O_{3}}\) used to color-code individual data points. As _GPP_ and \(\chi_{O_{3}}\) are the main drivers of \(F_{sto}\), a dependence of the slopes of regressions for  
Figure 4: Ozone deposition flux (\(F_{O_{3}}\)) partitioning between stomatal (\(F_{sto}\)) and non- stomatal (\(F_{nsto}\)) contributions. Medians and interquartile ranges are shown for each 1-h interval of the day. various \(\chi_{O_{3}}\) classes is to be expected in this plot. The larger \(\chi_{O_{3}}\) is, the larger (with larger negative numbers) \(F_{sto}\) is. However, there remains a scatter in Figure 5 due to the fact that it is not the ambient ozone concentration \(c_{O_{3}}\) that is used to compute \(F_{sto}\) but the surface ozone concentration \(c_{O_{3 warf}}\) (Equation (7)). This surface concentration is computed for each half hour and it introduces the variability in the computation of \(F_{sto}\) from measured data. The more important non-stomatal flux \(F_{nsto}\) is the residual of flux computations (Equation (6)). According to general understanding, it should be associated with the surface area available for deposition and its properties (_e.g._, LAI and surface wetness) and atmospheric turbulence (expressed as u*). Figure 6 presents the results of the driver analysis for \(v_{d,nsto}\). Overall (lowest panel in Figure 6), an R\({}^{2}\) of about 26% was achieved, of which u* and \(R_{g}\) together account for 50%. The low R\({}^{2}\) value shows that a large portion of the variability of \(F_{nsto}\) cannot be explained with the data and analytical tools employed. A graphical presentation of the relationships between \(v_{d,nsto}\) and environmental drivers (Figure 6, top 6 panels) shows that most relationships are not linear and contain much scatter, which explains the low value of R\({}^{2}\) for the multiple linear model. Figure 5: Scatter plots of stomatal ozone flux (\(F_{sto}\)) versus gross primary productivity (\(GPP\)) during daytime hours (\(R_{g}>10\) W m\({}^{-2}\)).