## 6 Conclusions  
We compared and analyzed the effects of various experimental conditions, such as the deep learning model structure, machine learning application and configuration, exclusion of nighttime hours, the addition of solar incidence angle variable, the application of distributions per PM class, and the use of the outlier removal algorithm, to suggest the optimal process for PM image analysis. In the deep learning model structure and process comparison experiment, the hybrid DL-ML model using ResNet152 and XGBoost showed the highest predictive power, with an RMSE of 3.32 and MAPE of 25.52%. We concluded that the classification model was better than the ResNet regression model, and the hybrid DL-ML model with post-processed XGBoost is better than the single ResNet152 model in terms of the prediction of PM based on AI. In the comparison experiment, according to learning variables and observation conditions, all four experiments excluded the nighttime, added the solar incidence angle variable, applied the distribution per PM class, and removed the outlier removal algorithm, which showed high predictive power. In particular, Experiment 4, which satisfied all experimental conditions, was able to derive predictive values that are expected to be commercialized, with an RMSE of 3.61 and MAPE of 19.74%. This study's findings suggest the possibility and framework of CCTV image-based PM analysis to provide dense PM spatial distribution as a public service. This study has the following limitations: First, we controlled the analysis conditions of each experiment but could not control all eight experiments due to the nature of the experiments. However, phased experiments with controlled analysis conditions may have suggested a clearer PM prediction framework. Second, methodologically, the experiments were conducted only on the CNN series, especially ResNet models. The selection was made due to the frequent utilization of the ResNet technique in the PM prediction research and the focus on other experiments. In future studies, different deep learning models, such as LSTM, RNN, and CNN, should be investigated for their strength in predicting time series data. Third, the vertical values of the ROI were not considered. It is judged that there will be a variation in fine dust depending on the vertical value, but this experiment was carried out ground-based fine dust measurement and estimation. For more precise vertical distribution and three-dimensional estimation, it is judged that fine dust estimation analysis via elevation should be performed. Fourth, we did not analyze the different CCTV channels. In particular, the CCTV direction and time of day must be analyzed, as these may vary significantly in reproducing the sunlight value. Future studies should address the analysis of PM images, such as analyzing different deep learning models, CCTV directions, and additional high-quality datasets. Additionally, continuous research should be conducted on developing PM control systems for citizen services, such as spatial informatization of PM predictions and three-dimensional interpolating measurement blind spots. Such attempts will enable the provision of dense PM information that reflects citizens' rights to live based on the existing CCTV infrastructure, which is expected to contribute to establishing a social safety net to protect people's health from PM. Conceptualization, W.C., H.S. and K.C. ; methodology, W.C. and K.C. ; software, W.C.; validation, H.S. and K.C. ; formal analysis, W.C.; investigation, W.C. and H.S. ; data curation, W.C.; writing--original draft preparation, W.C.; writing--review and editing, W.C. and K.C. ; visualization, W.C.; supervision, K.C. All authors have read and agreed to the published version of the manuscript. The research for this paper was carried out under the KICT Research Program (project no. 20240437-001) funded by the Ministry of Science and ICT. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request.