###### Abstract
However, its usability in SEA is still limited due to large missing data series as a result of heavy and extended cloudiness [18; 30; 31; 32]. On the other hand, geostationary satellites provide high temporal resolution (\(\sim\)15 min) data, thus limiting the problem of cloudiness. In the past few years, geostationary satellites such as Geostationary Operational Environmental Satellite (GOES) [33], Geostationary Ocean Color Imager (GOCI) [34], Fengyun-4 [35] and Himawari-8 [36; 37; 38] are available and provide continuous data over the SEA region. Significant progress has been made in developing and establishing various techniques for estimating PM concentrations from space at local, regional and global scales. The progress in usage of linear and multi-linear statistics, regression-based, machine learning and hybrid models for estimations of PM\({}_{2.5}\) and PM\({}_{10}\) concentrations during the last decades is reviewed in recent works [1; 39; 40]. In the early 2000s, most researchers predicted PM using only AOD, by means of simple linear regression techniques [41; 42]. Later on, more advanced techniques were developed to incorporate AOD and other important parameters that may influence PM distribution spatially and temporarily, starting from multiple linear regressions [18; 43; 44; 45; 46], chemical transport models (CTM) [47; 48; 49; 50], mixed effect models (MEM) [51; 52; 53; 54], artificial neural networks (ANN) [18; 55; 56; 57; 58], geographic weighted regression (GWR) [59; 60; 61; 62] and generalized additive models (GAM) [63; 64; 65; 66]. These techniques were used to capture the non-linear relationships that exist between the variables. Consequently, complex techniques have been developed by combining two or more statistical techniques; for instance, merging MEM and GWR [67] or incorporating MEM into GAM [68]. Nowadays, Machine Learning (ML) techniques such as deep neural network (DNN), support vector regression (SVR) and random forest (RF) enable to capture the complex relationships between parameters, exhibiting greater performance in estimating PM\({}_{2.5}\)[69; 70] and are increasingly used in air quality studies [71; 72; 73; 74]. Furthermore, some studies have incorporated meteorological factors with land use variables to predict the spatial and temporal variation of aeolian erosion and PM [75; 76; 77; 78; 79]. However, only few studies have incorporated air pollutant concentrations for PM\({}_{2.5}\) estimations [65; 80; 61]. [PERSON] et al. [65] explored the use of generalized additive model to estimate PM\({}_{2.5}\) concentrations in the Xi'an City, China (3581 square kilometres) using a combination of air pollutants (SO\({}_{2}\), CO, NO\({}_{2}\), and O\({}_{3}\)), AOD and meteorological variables. The model was found to explain \(\sim\)70% of the variance in PM\({}_{2.5}\) concentrations, with CO concentration and AOD represented most of the variation. The influence of air pollutants on the seasonal variability of PM\({}_{2.5}\) in an urban-industrial environment in Malaysia was investigated by [81] for one year. This study concluded that only gases (CO, NO\({}_{2}\), NO and SO\({}_{2}\)) significantly affected the PM\({}_{2.5}\) mass, but not the meteorological factors (rainfall, wind speed and wind direction). Based on the prediction model of [80], temperature, Normalized Difference Vegetation Index (NDVI), humidity and residential area were found to be important parameters affecting the spatial variation of PM\({}_{2.5}\) in Jakarta, Indonesia. The same study [80], also showed that several parameters (PM\({}_{10}\), NO\({}_{2}\), SO\({}_{2}\), UV, rainfall, land use and NDVI) influenced the distribution of PM\({}_{2.5}\) in Taipei, Taiwan. More studies are therefore needed to find out the role of gases and meteorology in affecting the spatial and seasonal patterns of PM\({}_{2.5}\), even using meteorological normalization techniques in order to exclude the effect of changing meteorology on PM concentrations trends [82].