## 1 Introduction  
The negative effects of fine mode particles on human health have been significantly examined, and are more serious at the present time. For example, in addition to the threat to the well-known respiratory and cardiovascular problems, people are currently concerned about the damage to mental health due to the high concentration of aerosols and even precursor gases [1]. This effect has usually been inspected based on the mass density of particulate matter (PM), especially PM having an aerodynamic diameter <2.5 \(\upmu\)m (PM\({}_{2.5}\)). To conduct an accurate evaluation, the monitoring of local PM\({}_{2.5}\) is therefore an essential task. To date, the platform for the in situ measurement of PM\({}_{2.5}\) has been improved and advanced in many places, resulting in the accumulation of long-term records. Nonetheless, the spatial coverage of in-situ PM\({}_{2.5}\) observations is still limited, and does not provide abundant data for the evaluation of health effects, particularly in rural regions.  
As an alternative approach, satellite remote sensing has been used for the monitoring of aerosol pollution. The aerosol optical depth (AOD) is a product from satellite remote sensing that indicates the atmospheric turbidity associated with the density of airborne particles. However, PM\({}_{2.5}\) is not directly produced from satellite measurements. Thus, PM\({}_{2.5}\) estimation using satellite AOD has been widely conducted based on various statistical methods, which are well summarized in [2; 3]. Multiple-linear regression (MLR) modeling is a representative method that has been widely used to date, indicating the contribution of various factors (e.g., meteorology) to the estimation of PM\({}_{2.5}\). Recently, machine learning and deep learning algorithms have been applied to PM\({}_{2.5}\) estimation, and better results have been achieved compared to the previous approaches.  
Nonetheless, the accuracy of PM\({}_{2.5}\) estimation can vary according to the utilized dataset and methodology. For example, PM estimation can differ between the usage of polar-orbit and geostationary satellite AOD [4], or in accordance with the temporal period [5]. The combination of factors also relates to the quality of the PM estimate [6]. In addition, PM estimation using the same dataset and methodology can result in different levels of quality due only to the difference in spatial location [7].  
Although many studies have presented various methods of PM\({}_{2.5}\) estimation using AOD values, the sensitivity to the type of data used has not been significantly investigated. In particular, the quality of PM\({}_{2.5}\) estimation according to the kind of AOD, one of the main parameters considered to be significant in the process of PM\({}_{2.5}\) estimation, has rarely been evaluated. Although some previous works directly compared the spatiotemporal pattern consistency between AOD and PM\({}_{2.5}\), and diagnosed the difference in terms of the kind of selected AOD values [8; 9], evaluation of the quality of estimated PM\({}_{2.5}\) after advanced statistical data processing was not undertaken.  
Thus, this study attempted to examine the impact on the accuracy of PM\({}_{2.5}\) estimation according to the application of different AOD products, because previous studies showing the effect of different AOD products on the quality of PM\({}_{2.5}\) estimates are rare. Based on several MLR models, we estimated PM\({}_{2.5}\) values using multiple AOD values from satellite and ground-based measurements in Seoul. Then, these estimated values were compared with in situ PM\({}_{2.5}\) observations for validation. Based on the generated statistical parameters, e.g., correlation coefficient (R) and root mean square error (RMSE), we evaluated the PM\({}_{2.5}\) estimate in terms of sensitivity to the kind of AOD products, seasonal difference, and wavelengths. To identify the seasonal pattern, we used full yearly measurements of AODs from May 2016 to April 2017.