The study aims to monitor PM2.5 concentrations in the north-eastern and eastern regions of Thailand for the past 5 years (from 2017 to 2021) from satellite observations for monitoring air quality. To ensure the PM2.5 concentrations from satellite data of NASA’s MEERA2 products, it is necessary to evaluate and analyze the relationship between PM2.5 data from satellite observations and higher precision data from ground-based stations of the Pollution Control Department (PCD) in terms of various statistical values, such as squared correlation coefficient (R2), mean (Bias) and Root Mean Square Error (RMSE). Then, the estimation of the monthly average for PM2.5 concentrations was performed using satellite data of MERRA2 products by bias correction method with twenty PCD’s ground stations. The result shows that before correcting the relationship value is relatively low, the performance of estimating PM2.5 concentrations from satellite data needs to be improved from the monthly average using the bias correction method. After bias-corrected, a correlation coefficient (R2) value of 0.73 was improved from 0.56. A mean (Bias) of 0.03 µg/m3 and an RMSE of 6.39 µg/m3 were decreased. It is clear that bias correction can help to improve estimating PM2.5 concentrations from MEERA2 products.

Presently, the risk of natural hazards is continuously increasing which may cause noticeable casualties and destruction under the influence of climate change. Information regarding the hazard characteristics, capacity, susceptibility, and vulnerability in pre-disaster phases is critical in realizing efficient disaster management. There are numerous GIS-based hazard assessment studies have been done by researchers every year; however, most of their GIS-based outcomes have rarely been effectively used for supporting the decision-making processes in real situations. The objective of the study is to propose an appropriate way of using these GIS-based hazard assessment outcomes to develop a Web-based platform to effectively support the decision-making processes. The GIS-based hazard assessment outcomes from previous work are used in this study to develop a Web-based spatial decision support system as an example of practicing the proposed approach. Finally, there are 3 modules including general module, thematic single factor maps module, and results module have been designed and developed in the system. This approach provides a new way of making better use of all kinds of GIS-based hazard assessment outcomes, which can be used as an effective decision-making support tool. In addition, it can be used as a guideline for future research.