## 4 Results and Discussion
### Discussion  
As mentioned earlier, in addition of filling missing data, an air pollutant proxy is advantageous for several reasons. This subsection discusses two scenarios where the developed proxy is beneficial to be deployed in practice. The first scenario is related to the proxy deployment in an official air pollution monitoring station. It is known that the pollutant O\({}_{3}\) concentration differs between weekend and weekday, as well as day and night, due to the variation of traffic volumes in big cities [63]. An investigation of weekday and weekend differences in O\({}_{3}\)-NOx levels is an important indicator to understand whether O\({}_{3}\) has its origin in local photochemical production or in transport processes [54, 64]. Figure 11 presents the average of the measured and estimated O\({}_{3}\) concentration on weekday and weekend. It can be seen that lower NOx levels and higher O\({}_{3}\) values at weekends than on weekdays took place in Jeddah. This phenomenon is common to be observed mainly within areas with an influence from urban emissions. This occurs due to weekly changes in emissions from human activities. The relationship of emission-concentration relationship at urban, suburban and rural sites is discussed in [PERSON] et al. [65]. Considering the significance of having both measurements of O\({}_{3}\) and NOx concentration, the developed proxy performance on the O\({}_{3}\) is evaluated on the concentration characteristic in the area as shown in Figure 11. It can be seen clearly that our developed estimated O\({}_{3}\) concentration fits very well the average measurement data on weekday and weekend. This suggests that the developed O\({}_{3}\) proxy is reliable and accurate as the substitution of O\({}_{3}\) real measurement. Having the O\({}_{3}\) virtual measurement is then beneficial to study the pollutant sources by reducing the cost and operator involvement. The second scenario concerns the proxy deployment to complement low-cost sensor measurements in dense air pollution networks. Air quality networks in cities are usually sparse because air pollution is difficult to measure, instrumental and operational costs of official measurement stations are typically expensive [66]. On the other hand, air pollutant concentrations vary highly over both space and time [67]. Therefore, real-time and high-resolution measurements are then required that enable the mapping of air pollutants and air quality index in higher resolution. Over the last few years, the deployment of low cost sensors have been assessed for their viability in monitoring ambient air quality [68]. However, their accuracy and reliability are still challenging. For example, although testing low-cost O\({}_{3}\) sensors demonstrates good performance under controlled laboratory conditions [69], their performances decline significantly under field conditions [70, 71]. The performance degradation is  
Figure 10: Regression plot (**a**) and the prediction bias (**b**) between the test data of O\({}_{3}\) concentration measurement and the estimated O\({}_{3}\) concentration generated by BNN. mainly related to interference with other air pollutants as well as with meteorological conditions [72; 73; 74; 75]. Therefore, it is also advantageous to substitute low-cost O\({}_{3}\) sensors by O\({}_{3}\) proxies. Although low-cost WS devices are also available, such as described in [PERSON] et al. [76], but this variable may be neglected as the proxy input because the proxy performance is still reasonable well (see Table 1). The O\({}_{3}\) proxy can then be deployed by taking inputs from the measurements of low-cost NO/NO\({}_{2}\)/NO\({}_{x}\) sensors. The combination of low-cost sensors and virtual sensors is promising because virtual sensors are not affected directly by physical sensor failures and environmental conditions. However, the proxy accuracy might drift over time because there may be new measured data that has not been involved at all in the process of proxy development, which is known as _extrapolation_.