###### Abstract  
Despite the high public interest in particulate matter (PM), a key determinant for indoor and outdoor activities, the current PM information provided by monitoring stations (e.g., data per administrative district) is insufficient. This study employed the closed-circuit television (CCTV) cameras densely installed within a city to explore the spatial expansion of PM information. It conducted a comparative analysis of PM estimation effects under diverse experimental conditions based on AI image recognition. It also fills a gap by providing an optimal analysis framework that comprehensively considers the combination of variables, including the sun's position, day and night settings, and the PM distribution per class. In the deep learning model structure and process comparison experiment, the hybrid DL-ML model using ResNet152 and XGBoost showed the highest predictive power. The classification model was better than the ResNet regression model, and the hybrid DL-ML model with the post-processed XGBoost was better than the single ResNet152 model regarding AI prediction of PM. All four experiments that excluded the nighttime, added the solar incidence angle variable, applied the distribution of PM per class, and removed the outlier removal algorithm showed high predictive power. In particular, the final experiment that satisfied all conditions, including the exclusion of nighttime, addition of solar incidence angle variable, and application of outlier removal algorithm, derived predictive values that are expected to be commercialized. f +
Footnote †: journal: Applied sciences  
operation and management. Even if PM monitoring stations are installed, it is insufficient to reflect all the citizens' rights to live. In this regard, there are studies predicting PM based on AI with images from existing closed-circuit television (CCTV) that attempt to expand PM information spatially [1; 2; 3; 4]. South Korea has CCTV control centers in the 226 basic local governments. Based on this, 1,767,894 public CCTVs thoroughly controlled urban areas in 2023 to monitor traffic, crime prevention, and disasters [5]. Considering that 530 national monitoring stations are currently measuring urban air quality levels [6], the AI analysis based on CCTV infrastructure can provide citizens with more detailed PM information. Rather than monitoring the local PM2.5 concentrations with a high accuracy, it is necessary to increase the spatial density of observations using urban CCTVs. The current research utilizes video images but mostly conducts methodological experiments on deep learning models to estimate PM. Research on deep learning models also focuses on comparing the predictive power of each model, and experimental research under various conditions, such as model structure and application of machine learning, is insufficient. There are a few studies that used meteorological variables as input data. However, no study has suggested an optimal analysis framework that comprehensively considers the combination of variables, including the sun's position, day and night settings, and the PM distribution per class. In this sense, this study attempts to find the optimal modeling combination for PM image analysis by comparing and analyzing the model structure, the types within the ResNet series, and the application and configuration of machine learning based on ResNet of the representative deep learning model series, the Convolutional Neural Network (CNN) series, to propose an optimal process for PM image analysis. Furthermore, this study aims to propose an optimal PM analysis framework based on AI by comparing the predictive power of PM under different experimental conditions, such as excluding the nighttime, adding the angle of sun incidence, distributing the PM per class, and applying an outlier removal algorithm. To this end, we review previous studies in Section 2 and propose new research materials and methods in Section 3.