04261nas a2200421 a 4500 20260818112519.0 260616s20269999th u ms t 000 eng d .b12507659 AIT Thesis no.EV-26-01 Jang, Beomgeun Estimation of fine particulate matter concentration using geostationary satellite over the Lower Mekong Region Pathum Thani, Thailand : Asian Institute of Technology, 2026 96 leaves : ill.+ 1 online resource Thesis ; no. EV-26-01 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, 2026 This study aims to predict ground-level PM₂.₅ concentrations in the Lower Mekong River region by exploiting the high temporal resolution of the Geostationary Environment Monitoring Spectrometer (GEMS). Satellite remote exploration provides an important alternative to the supplement lack of ground monitoring networks in Southeast Asia. Accurate monitoring of ground-level particulate matter PM₂.₅ in Southeast Asia is often challenging by the retrieval biases and seasonal and diurnal noises in geostationary satellite retrievals. This study addresses these challenges by developing two-stage machine learning framework designed to enhance the utility of GEMS data over the Lower Mekong Region.In the first stage, an XGBoost-based calibration model was implemented to refine GEMS Aerosol Optical Depth (AOD) across three primary spectral bands (350 nm, 440 nm, and 550 nm). By integrating ground-based observations from AERONET and PANDORA networks, the model successfully mitigated systematic dual-biases, correcting overestimation at low AOD levels and underestimation during high AOD loads. The calibration significantly improved retrieval accuracy, raising R 2 values from an initial range of 0.49{u2013}0.59 to 0.92{u2013}0.95. Furthermore, the framework effectively neutralized diurnal variations and early-morning noise, ensuring a physically consistent aerosol signal.The second stage XGBoost model leveraged these calibrated AOD products as primary predictors for estimating ground-level PM₂.₅ concentration. The model demonstrated sophisticated predictive logic, particularly utilizing the UV-spectrum AOD and auxiliary indices sensitive to biomass burning aerosols, which are prevalent in the study area. Validation via 10-fold cross-validation revealed an overall R 2 of 0.87, a dramatic improvement over the operational GEMS Level 4 PM₂.₅ product{u2019}s performance (R 2 = 0.43) in the same region. These results highlight the critical necessity of localized, multi-stage modeling to overcome the limitations of global operational products. This research provides a expandable architecture for incorporating multiple ground observation and satellite datasets from various operating entities and data quality, to enable high spatio-temporal resolution assessment of air quality management in Southeast Asia. Air quality Mekong River Region Air Pollution Mekong River Region Environmental monitoring Mekong River Region Geostationary satellites Ekbordin Winijkul, Chairperson Thammarat Koottatep, Examination Committee Ghimire, Anish, Examination Committee AIT Scholarship, Scholarship Donor Asian Institute of Technology. Thesis ; no. EV-26-01 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B24435 .b12507659 mnarc a 260622 0 260617 m h a 0 mnarc mnarc 67 40 Barcode : - CREATED : 2026-06-16 RECORD # : i13604909 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050120421085 CREATED : 2026-06-16 RECORD # : i13605665 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 40231 40231 0 0 0 0 MAIN MAIN mnarc 2026-08-18 0 AIT Thesis no.EV-26-01 2026-08-18 11:25:19 1 2026-08-18 67 0 0 0 0 MAIN MAIN mnarc 2026-08-18 0 AIT Thesis no.EV-26-01 30050120421085 2026-08-18 11:25:19 1 2026-08-18 40