## 4 Empirical Analysis  
### Data Analysis Design  
In this study, we designed the data analysis frame by classifying various experimental conditions, including the deep learning model structure and variable settings, to estimate PM concentrations in the images by combining image and weather condition data. The dependent variable is PM2.5, and the independent variables are the field weather observation data, public weather data, and solar incidence angle. As for the field weather observation data, the factors influencing the PM concentrations (i.e., humidity, temperature, wind direction, and wind velocity) and illuminance, making it possible to estimate the weather status, were simultaneously input. We utilized the humidity, temperature, wind velocity, wind direction, and precipitation as input data for public weather data. The data collection period was from 21 April 2021 to 14 February 2022 (between 8:00 and 22:00), and the data composition varies depending on the purpose of the experiment. The deep learning model uses ResNet, which is a CNN series mainly utilized for PM image analysis, and the hyperparameter values were set using repeated comparisons for optimal modeling. In terms of epochs, as a result of conducting a comparative analysis after applying values such as 10, 100, 200, and 300 with batch sizes of 32, 128, and 256, the optimal hyperparameter setting value of this model was derived as follows: epochs of 100 and a batch size of 256. Additionally, the data used for analysis were set to 60% for learning data, 20% for verification data, and 20% for test data. The analysis results of the experiments are interpreted with the RMSE (Root Mean Square Error), which can be considered PM errors, and the Mean Absolute Percentage Error (MAPE), which is easy to understand relatively between performances, is utilized. We conducted four experiments on deep learning model structures. In all four experiments, only the deep learning analysis structures were set differently depending on the purpose of the experiments; all other conditions were controlled. First, we compared the PM predictive power of the ResNet regression model and the classification model. Second, we compared the predictive power of ResNet50, in which the number of ResNet layers was set as 49, and of ResNet152, with 151 layers set. Third, we compared the predictive  
Figure 4: Hybrid DL-ML model process. power of the hybrid DL-ML model with the post-processed machine learning based on ResNet152 by applying the machine learning techniques, including SVR and XGBoost, respectively. Fourth, we compared the predictive power of the single DL model and the hybrid DL-ML model. We conducted four additional experiments with different learning variables and observation conditions following the deep learning analysis. First, we compared the PM predictive power of the daytime dataset (08:00-18:00), excluding the nighttime between 18.00 and 22.00, with that of the entire dataset (08.00-22.00). This experiment aimed to identify how resolution degradation from a lack of nighttime light affects predictive power. Second, we compared the predictive power with or without applying the solar incidence angle variable. The variable angle of incidence is important because it results in differences in the illuminance and shadow position depending on the position of the sun. The sun azimuth (right and left positions) and the sun elevation (upper and lower elevations) were calculated and entered as inputs to vary the position of the sun. The sun azimuth is the angle between the projection of the sun's rays and a line to the south and can be calculated by measuring the angle clockwise from north (0 degrees) (see Equation (1)). The position of the sun is the angle between the sun and the Earth's surface, which is used to determine the height of the sun. As shown in Equation (2), it can be calculated from the hour angle, the declination of the sun, and the local latitude at which the height of the sun can be observed.