## 3 Methodology  
Although AOD from space-borne satellite measurement provides a quantitative pattern of aerosol loadings in the Earth's atmosphere, AOD cannot be converted directly into a PM\({}_{2.5}\) value because AOD is a columnar amount and indicator of atmospheric turbidity; this is different from the physical meaning of PM\({}_{2.5}\), which is a surface mass density. Thus, a specific process is required to convert the columnar AOD to the surface PM\({}_{2.5}\). MLR modeling has been widely utilized for this purpose. Although the application of machine learning or deep learning techniques have recently started to produce meaningful results for the conversion between AOD and PM\({}_{2.5}\)[4; 5; 6; 7], the MLR method continues to be frequently used for PM estimation using AOD values due to its simplicity and convenience in assessing the influence of considering factors [27; 28]. In addition, the performance of the MLR method for PM estimation is comparable with that of other advanced techniques [29]. Therefore, to evaluate how different AOD values influence the PM estimation, we used the MLR method in this study. A number of factors are considered in the MLR model for PM\({}_{2.5}\) estimation from AOD values. PBLH plays an important role in determining the effective height of homogeneous mixing of local particles near the surface, and assisting in the detection of the contribution of lower layer air pollution to the total column of aerosol amount. Wind speed indicates the effect of the degree of dispersion on the accumulation or ventilation of airborne aerosols [4; 30]. Relative humidity relates to the growth in the particle size and mass [24], and air temperature can be associated with the source of long-range transport [31] or the extent of precursor emissions from biogenic sources [32]. Visibility is another well-known indicator of aerosol pollutions [33]. Ozone and NO\({}_{2}\) can show the local photochemical activity [34] and atmospheric chemical conditions related to the aerosol pollutions [35; 36]. Based on these data, we constructed MLR models having different combinations of variables. The description and variables used for each MLR model are shown in Table 3. MLR Model 1 (M1) is the simplest model, and estimates PM\({}_{2.5}\) only based on the direct linear regression with AOD, which is still significantly investigated for the diagnosis of PM exposure [37]. MLR Model 2 (M2) estimates PM\({}_{2.5}\) using PBLH in addition to AOD, considering the effective height of aerosol accumulation near the surface. MLR Model 3 (M3) includes not only meteorological factors (temperature, wind speed, relative humidity, and visibility) but also chemical conditions in the atmosphere (ozone and NO\({}_{2}\)). For M2 and M3, we utilized two different PBLH data, which were obtained from ecolometer observation (related to M2-1 and M3-1) and from ECMWF reanalysis (related to M2-2 and M3-2). MLR models were established using 50% of all available data selected randomly, and evaluated with the remaining 50% of the data. Figure 1 shows the validation example of all MLR models (M1, M2-1, M2-2, M3-1, and M3-2) using AODs retrieved from the MODIS-DT algorithm. Considering the reverse pattern of AOD and PM\({}_{2.5}\) in East Asia [38], the relationship between observed and estimated PM\({}_{2.5}\) from the M1 model (i.e., only AOD is considered) is acceptable, having a linear shape and moderate correlation coefficient (R). However, the slope was 0.15, indicating that the estimated PM\({}_{2.5}\) cannot follow the variation range of observed PM\({}_{2.5}\). When we included the PBLH parameter in the MLRmodel (M2), the correlation improved and the slope increased slightly. However, the quality enhancement of M2 model does not look to be significant. After including the mentioned meteorological and chemical factors (M3), the performance of the MLR model was finally significantly enhanced: R > 0.8 between observed and estimated PM\({}_{2.5}\) and the slope was ~0.7.