## 5 Conclusions  
This study conducted a comprehensive analysis of air pollution in three Saudi Arabian cities, Al Riyadh, Al Jubail, and Najran, with distinct environmental characteristics. By utilizing Sentinel-5 satellite data and the Google Earth Engine, we performed a descriptive analysis of air pollutant trends from 2022 to 2023 and developed a robust deep learning model for NO\({}_{2}\) prediction.  
Our descriptive analysis using two-way ANOVA, as shown in Table 7, revealed significant differences in air pollutant levels across the three cities over the two years, highlighting the impact of industrial activities, population density, and weather factors on air pollution. It is worth noting that from Table 4, we observed that Al Jubail had the highest concentrations of most of the pollutants studied due to its industrial nature, while Al Riyadh showed high concentrations of NO\({}_{2}\), which is likely attributed to traffic emissions due to its population density. However, we observed that in Najran city, the concentrations of most pollutants were lower due to its agricultural nature, which helps to reduce air pollution. This study also confirmed a strong relationship between city characteristics and the rate of increase or decrease in the air pollutants.  
We developed a deep learning model using LSTM and embedded the Time2 Vec algorithm to enhance its air pollution monitoring and forecasting capabilities. This model demonstrated superior accuracy in predicting NO\({}_{2}\) concentrations compared to traditional LSTM models, effectively capturing temporal patterns and achieving minimal prediction errors. The integration of Time2 Vec proved particularly valuable in improving prediction quality. The developed deep learning framework can be a useful tool for local authorities and environmental agencies in Saudi Arabia to enhance their air quality monitoring and forecasting capabilities. By providing accurate predictions, this framework can support the implementation of careful measures to reduce air pollution, such as traffic management, industrial emission controls, and public awareness campaigns.  
Figure 8: Box plots showing the expected deviation during each month of each year for the three cities. The line in the box represents the mean absolute error between the original and predicted values. The small circles represent extreme values. (**a**) Al Jubail; (**b**) Al Riyadh; and (**c**) Najran.  
This study provides insights into air pollution dynamics and forecasting but has some limitations. First, we used data from a single satellite air quality monitoring station. We recommend using ground monitoring stations with diverse emission sources to achieve more effective planning for air quality management strategies with satellite stations to achieve more accurate forecasting. Second, urban nitrogen dioxide levels are closely related to traffic composition and flow, which requires detailed characterization in future models and should be included among the factors affecting air pollution.  
Conceptualization, S.A. and H.A.; methodology, S.A. and H.A.; software, S.A.; validation, S.A.; formal analysis, S.A. and H.A.; investigation, S.A. and H.A.; resources, S.A.; data curation, S.A.; writing--original draft preparation, S.A.; writing--review and editing, S.A., H.A., S.B. and E.A.; visualization, S.A.; supervision, H.A. and S.B.; project administration, S.A., H.A. and S.B.; statistical analysis, E.A.; final manuscript revision, E.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data can be available on an appropriate request.  
The authors declare no conflicts of interest.