## 5 Discussion
### Comparison of Analysis Results in Line with Learning Variables and Observation Conditions  
This section discusses the analysis results based on the classification of observation conditions and model application (learning variables and outlier removal algorithm application). As for the observation conditions, in Experiment 1, we compared the result from the application of the dataset excluding the nighttime, and in Experiment 3, we compared the result from applying a distribution per PM class to simulate a real air pollution environment. In Experiment 1, the dataset's predictive power, excluding nighttime hours, was slightly higher. The assumption that the quality of CCTV images decreases at night due to the lack of light and that the PM predictive power also decreases as a result was reflected in the experiment. However, there is a point that should not be neglected. While daytime is when people are most active outside, diverse activities occur at night (e.g., exercise and outdoor performance). In this sense, PM analysis is also required during nighttime hours, not just during the day. It is assumed that various attempts should be made to improve the techniques and reliability of analyzing the nighttime. Experiment 3 showed a slightly higher predictive power of the dataset with the distribution per PM class. The predictive power of the dataset that simulates an actual air pollution environment was higher, but it is assumed to not be an absolute factor. This experiment is assumed to be meaningful in that it attempted to construct a dataset in line with the actual air pollution condition rather than focusing on improving the predictive power. Next, we discuss the results of the comparative analysis based on the model application, such as the learning variables and the application of the outlier removal algorithm. Experiment 2 showed the high predictive power of the dataset with the solar incidence angle variable. The assumption in the solar incidence angle directly affects the chroma and brightness of the image and causes shadow positions to be reflected. Image analysis based on RGB cameras has the limitation that the sun's position highly influences it. The same result was found in Experiment 1, which excluded the nighttime, as in Experiment 2. It is expected that the limitations of sun-related RGB camera-based PM image analysis will be overcome to some extent if future research continues to analyze not only the position of the sun but also the specific illuminance differences according to time of day and the CCTVs in each direction. In Experiment 4, we found that the predictive power of the dataset with the outlier removal algorithm was improved. The reliability of data was secured by removing information on unexpected events (e.g., vehicles, birds, and poor focus), PM error values (inversion of PM2.5 to PM10), and values with large sudden drops (e.g., smoking and street cleaning) applied to outlier removal algorithms. In any study, outlier removal is important to improve predictive power. In this study, the main algorithm was to remove local outliers based on MAD, but the inversion of PM2.5 and values with large sudden drops (e.g., smoking and street cleaning) were added, leading to higher PM predictive power. It is assumed to be important to consider not only the optimal variables but also the targeting of outliers that align with the study's purpose. In this study, the hybrid DL-ML model with the machine learning algorithm XGBoost based on ResNet152 had the highest predictive power, with an RMSE of 3.32 and a MAPE of 25.52% in the comparison experiment of the deep learning model structure. When comparing learning variables and observation conditions, the RMSE for the dataset with the added sun incidence angle was the lowest (2.94), and the MAPE was 19.74% for the dataset with the outlier removal algorithm, showing the highest relative predictive power.