Estimation of high-resolution fine particulate matter concentration in Bangkok Metropolitan Region using satellite-derived aerosol optical depth, land-use and meteorological parameters | |
| Author | Thanapat Jansakoo |
| Call Number | AIT Thesis no.EV-20-12 |
| Subject(s) | Air--Pollution--Thailand--Bangkok Remote sensing--Environmental aspects Air quality management--Thailand--Bangkok |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Environmental Engineering and Management |
| Publisher | Asian Institute of Technology |
| Abstract | Air quality situations in the developing countries have been deteriorated over the past decades due to the fast development of economic and urbanization. Bangkok Metropolitan Region (BMR) faces the fine particulate matter (PM2.5) problems every year, but the ambient air quality monitoring stations used to monitor the concentration of PM2.5 do not cover all areas in BMR. Although the Aerosol Optical Depth (AOD) products from the satellites have been used to solve this problem, the spatial resolutions were very low (such as 5, 10 km) to apply in the urban area where the sources of pollutants are complex. In this study, the Simplified high-resolution MODIS Aerosol Retrieval Algorithm (SARA) was used to develop the high spatial resolution (0.5x0.5 km2) of AOD based on the MODIS satellite. Then, the SARA-AOD, MODIS standard product (MOD/MYD04) and MAIAC AOD were validated with the ground-based AERONETs’ AOD data. The results of these analyses showed that the relationship between SARA-AOD and AERONET’s AOD (R2 = 0.73) was higher than the relationship of the MODIS standard product (MOD04_3K, R2 = 0.62) and the MAIAC product (R2 = 0.454). However, the relationship between the SARAAOD and the ground-based PM2.5 concentration at the monitoring stations were low (R2 = 0.123). To improve the relationship between the SARA-AOD and the ground-based PM2.5 concentration, meteorological and land-use parameters were used as the additional inputs to the multi-linear regression (MLR), artificial neuron network (ANN) and land use regression (LUR) to develop the empirical models for estimating the concentration of PM2.5 in BMR. When evaluating, the performance of the models with an independent dataset, the MLR provided the highest accuracy (R2 = 0.420), followed by the LUR (R2 = 0.291), and the ANN (R2 = 0.125). Finally, the spatial distribution of the PM2.5 concentration on a spatial resolution of 0.5x0.5 km2 was developed for BMR based on the MLR technique. |
| Year | 2020 |
| Type | Thesis |
| School | School of Environment, Resources, and Development (SERD) |
| Department | Department of Energy and Climate Change (Former title: Department of Energy, Environment, and Climate Change (DEECC)) |
| Academic Program/FoS | Environmental Engineering and Management (EV) |
| Chairperson(s) | Ekbordin Winijkul; |
| Examination Committee(s) | Visvanathan, Chettiyappan;Virdis, Salvatore G.P.; |
| Scholarship Donor(s) | Her Majesty the Queen's Scholarships (Thailand); |
| Degree | Thesis (M. Sc.) - Asian Institute of Technology, 2020 |