A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Environmental Engineering and Management

Thesis (M. Sc.) - Asian Institute of Technology, 2020

Air quality monitoring station is a traditional assessment tool of the ambient pollutant concentration, including PM2.5. Although the monitoring stations provide the continuous online data on a real time basis, the limitations of this tool are the complication in sampling techniques and cost of investment, operating and maintenance. For these reasons, number of monitoring stations are very limited, and the air quality monitoring stations could not provide spatial coverage of the pollutant concentration of the interested area. Satellite measurement has been used to apply the Aerosol Optical Depths (AODs) to estimate PM2.5 concentration with larger spatial coverage. However, the AOD products from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite which are commonly used to predict PM2.5 concentration are only available two times per day. This research used the advantages of the AOD products from Himawari-8 satellite to fill the temporal data gaps for PM2.5 concentration estimation at the near real-time basis during February to April in Chiang Mai. In this study, the statistical model was selected to develop the relationship between hourly AOD and PM2.5 concentration. Two models were investigated: (1) Model I - AOD and PM2.5 without meteorological parameters; and (2) Model II - AOD and PM2.5 with meteorological parameters. The results revealed that the coefficient of determination (R2) of the Model I and II were 0.39 and 0.75, respectively. Then, the Model II (higher R2) which included AOD, wind speed and visibility as the independent variables, was used to estimate PM2.5 concentration in Chiang Mai during February to April in 2017 {u2013} 2018. The model evaluation showed that the estimated hourly PM2.5 concentrations were consistent with observed hourly PM2.5 with the R2 of 0.69 for all data (from 09.00 to 16.00 LT). The mean bias (MB) and root mean square error (RMSE) were 4.1 and 15.4 æg/m3, respectively. Additionally, the results of the R2 between the estimated and the observed hourly PM2.5 concentration in the different hours varied from 0.61 to 0.77 with the highest agreement at 12.00 LT. Then, the Model II was also used to develop a map of the spatial distribution of hourly PM2.5 from 09.00 to 16.00 LT. Finally, the relationship between daily AOD and PM2.5 was used to estimate the annual average PM2.5 concentration in 2017with the R2 of 0.85. Then, the health impacts associated with PM2.5 concentration were estimated for the long-term exposures to PM2.5. The results showed that reducing the current PM2.5 concentration to the level of WHO guideline could reduce the highest number of deaths in Ischemic Heart Disease by 45.2 %, followed by Lung Cancer (26.2%) and All-cause (12.6%).