## 3 Methodology
### Methods of Predictive Modeling
#### 3.4.2 Evaluation of Metrics of Prediction Models
* NO\({}_{2}\) is the only pollutant that is found at a higher level in Al Riyadh than Al Jubail, and it is especially high during October. * SO\({}_{2}\) has the highest mean level in Al Jubail, and it is distributed throughout the year. * Most of the emissions of HCHO in the three cities occur from June to September. * Most of the emissions of CO in the three cities in the year 2022 occurred in March. * Most of the O\({}_{3}\) formation in the air in the three cities occurs from April to September. * In Al Riyadh, there is a positive linear relationship between NO\({}_{2}\) and CO, which is stronger in 2023. * In Al Jubail, there is a positive linear relationship between HCHO and NO\({}_{2}\) and between HCHO and SO\({}_{2}\). * In Najran, there is a negative linear relationship between O\({}_{3}\) and CO.  
We present the correlation of NO\({}_{2}\) with meteorological parameters because we noticed a similar trend in the correlation between each pollutant and meteorological parameters. Additionally, we noted that NO\({}_{2}\) differs from all other pollutants, as it has a higher value in Al Riyadh than in Al Jubail. This correlation is presented in Table 5. These are the main points that can be concluded from this table:  
* There is a moderate positive linear relationship between NO\({}_{2}\) and the temperatures in Al Jubail and Najran. * There is a moderate negative relationship between NO\({}_{2}\) and wind speed, but this relationship is weak in Najran. Figure 3: Distribution of the NO\({}_{2}\) concentration in the three cities, where all Arabic names are translations of city names. * There is a moderate negative relationship between NO2 and the humidity in Al Riyadh, despite the generally dry weather in the city. The above results agree with research conducted in other areas around the world [51; 52]. The major sources of NO2 and volatile organic compounds (VOCs), including HCHO, are the large number of vehicles and emissions from industrial and chemical manufacturing [51]. The high levels of NO2 in Al Riyadh can be attributed to the large number of vehicles, which is likely due to this city's high population. Using the EPA Multiscale Air Quality Model, [PERSON] et al. [53] analyzed the relationship between several atmospheric parameters and NO2 using observed concentrations from ground stations and satellites. They found that boundary layer height, wind speed, temperature, and relative humidity were the most important variables in determining the variation in NO2 near the land surface. In their results, the NO2 concentration was positively correlated with temperature and negatively correlated with wind speed. Therefore, weather parameters play a crucial role in air quality monitoring. Based on the results in Table 5, we can conclude that the impact of weather parameters on the NO2 concentration varies across cities due to differing urban characteristics. In Najran, temperature and humidity have the strongest influence, with humidity being inversely correlated with NO2 and temperature being positively correlated with NO2. Meanwhile, in Al Jubail, temperature and wind speed are the primary factors affecting the NO2 concentration, with temperature being positively correlated and wind speed being negatively correlated with NO2. In Al Riyadh, wind speed and humidity are the most significant weather parameters affecting the NO2 concentration, with both being inversely related to the NO2 concentration, meaning that increasing humidity and wind speed lead to decreased NO2 levels. These results are important for developing a model to predict NO2 concentrations that include the most important weather factors. These weather parameters have varying degrees of influence when training a prediction model for each city. Therefore, using a model trained on Al Riyadh data to predict NO2 levels in Al Jubail, for instance, would be inappropriate, as we will demonstrate in our predictive modeling study.