## 2 Literature Review  
Research on PM estimation based on images can be divided into analytical, empirical, and experimental studies depending on the choice of experimental conditions and variables. The analytical and empirical studies are as follows: The recent studies based on images obtained by cameras mainly employ CNNs. CNNs were first introduced in 1989 in [PERSON]'s presentation on \"Backpropagation applied to handwritten zip\" [7]. It was specifically designed in 2003 in [PERSON]'s presentation on \"Hierarchical Neural Network for image interpretation\", which enabled us to overcome the problem of overfitting with a huge number of parameters and large computational load depending on the image resolution [8]. Since then, various CNN-based models (AlexNet, ZFNet, GoogleNet, VGGNet, and ResNet) have been studied to improve the extraction and classification of data features with geometric and textual associations between the data [9].  
[PERSON] et al. [10] applied a CNN model to analyze PM2.5 based on images stage by stage; images were analyzed according to the PM2.5 concentrations based on two types of main transfer learning strategies: random forests based on CNN fine tuning and CNN features. There have been other studies that have predicted various PM levels based on numerous CNNs [2; 3; 11; 12; 13; 14]. [PERSON] et al. [15] employed ResNet and SVR, both types of CNNs, to improve the index estimation of PM2.5 for input images. [PERSON] et al. [16] utilized the ResNet-101 model to estimate dust concentrations from vehicle traffic on unpaved roads. [PERSON] and [PERSON] [17] employed an Image Pyramid and an improved ResNet technique to detect real-time dust patterns. [PERSON] et al. [18] combined data augmentation, particle size constraints, and ResNet techniques to improve the accuracy of PM2.5 concentration estimation. [PERSON] [19] also proposed an efficient ResNet model for atmospheric visibility classification. It was found that ResNet techniques were mainly used to analyze PM images. In addition, there are studies using various deep learning algorithms, such as LSTM (Long Short-Term Memory), or combined machine learning models, DNNs (Deep Neural Networks), and RNNs (Recurrent Neural Networks) [20; 21; 22; 23; 24; 25; 26; 27].  
More studies have been conducted that apply various influencing variables and experimental conditions to improve the predictive power regarding PM. Most studies have indicated a close correlation between PM and meteorological factors [28; 29; 30; 31]. [PERSON] and [PERSON] [32] utilized meteorological and PM data and derived influencing variables on PM generation through multiple regression analysis. They suggested a PM prediction model using machine learning, such as linear regression and a Support Vector Machine. [PERSON] et al. [1; 2] experimented with learning with all or partial CCTV images and compared analysis results when different meteorological variables, such as humidity and wind velocity, were applied. They also performed experiments during the nighttime and presented the predictability of PM based on CCTV images. [PERSON] and [PERSON] [3] calculated the edge intensity of the region of interest (ROI) and analyzed its correlation with PM prediction. Thus, in the prediction of PM based on images, the model selection, variables, and experimental conditions need to be considered to provide public services. Based on these previous studies, this study proposes an optimal framework for the prediction of PM based on AI.