## 2 Study Background
### Relevance of Satellite Data and the Industrial Census Survey in Thailand  
Several studies have examined the relationship between socio-economic factors and satellite data indicators in Thailand. [PERSON] et al. [13] suggested the novel integration of satellite data and spatial analytical methods. [PERSON] and [PERSON] [14] explored the use of nighttime light (NTL) data from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) as an economic indicator. [PERSON] and [PERSON] [15] aimed to predict economic growth and urban land-use change in one of the fastest-growing areas in Thailand--the Ban Chang District--from 2021 to 2030.  
This study is inspired by the work of [16; 17; 18]. Building on their approach, this research advances the concept by utilizing air pollution data from the MERRA-2 satellite, which provides provincial-level data for Thailand. Spatial analysis and spatial econometrics are applied to investigate the spatial spillover effects of PM\({}_{2.5}\) pollution. The use of MERRA-2 satellite data helps address gaps caused by incomplete air pollution records in Thailand. Additionally, this study identifies an inflection point that reflects the turning point of the relationship between PM\({}_{2.5}\) pollution and economic growth. These findings could support the development of environmental policies in Thailand, particularly in addressing PM\({}_{2.5}\) pollution issues. Furthermore, this research contributes to the academic understanding of environmental issues by utilizing publicly available satellite data, which offers a low-cost alternative for pollution studies. The use of this dataset has significant potential for air pollution research in both developed and developing countries, providing a novel approach to studying air pollution problems in the future.  
Studies on the accuracy of nighttime light (NTL) data from the Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS) found that using DMSP nighttime light data for spatial inequality analyses in the United States and China led to biased estimations and inconsistent trends [19; 20]. Moreover, the accuracy of the DMSP was found to be lower than that of the VIIRS. A literature review also indicated that air pollution data obtained from MERRA-2 tends to underestimate pollution levels by about 30% because of the coarse resolution of its spatial satellite images. Many studies recommend combining satellite data with ground-based data to enhance the accuracy and reliability of air pollution measurements [21; 22; 23]. It is known that although MERRA-2 data cannot provide 100% accuracy in measuring air pollution levels, it can effectively reflect air pollution concentration patterns in different areas with an accuracy comparable to ground-based data [24; 25].  
Therefore, it is recommended that MERRA-2 data be used to fill gaps in incomplete datasets in various areas, improving data coverage. This approach is particularly useful for tracking air pollution in regions where ground-based data are unavailable.  
Figure 1: PM\({}_{2.5}\) concentration in each province of Thailand during 2012-2022.