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| 099 | 9 | _aAIT Thesis no.EV-22-10 | |
| 100 | 0 | _aPyae Phyo Kyaw | |
| 245 | 1 | 0 | _aEstimation of high spatial and temporal resolution of pm2.5 concentrations in Chiang Mai using satellite-derived aerosol optical depths and meteorological parameters |
| 260 |
_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2022 |
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| 300 |
_a51 leaves : _bill. |
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| 490 | 1 |
_aThesis ; _vno. EV-22-10 |
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| 500 | _aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Environmental Engineering and Management, School of Environment, Resources and Development | ||
| 502 | _aThesis (M. Sc.) - Asian Institute of Technology, 2022 | ||
| 520 | _aParticulate Matter (PM) is one of the most common pollutants emitted from anthropogenic sources because they can be formed from the numerous types of sources. Chiang Mai faces high level of PM2.5 concentration during dry season of every year. To monitor PM2.5 condition, there are only two air quality monitoring stations in Chiang Mai. Insufficient monitoring stations makes the limitation in air quality management. Nowadays, satellite remote sensing become a tool to monitor air pollutants on the earth surface where there are not enough monitoring stations. Aerosol Optical Depth (AOD) is one of the parameters to show the condition of air quality in the atmosphere. There are many different satellites that provide AOD products with different temporal and spatial resolution. In this study, AOD from MAIAC, having 1x1 km2 spatial resolution and AOD from Himawari-8 satellite having 5x5 km2 spatial resolution, were used to develop hourly 1x1 km2 resolution AOD to estimate PM2.5 concentration in Chiang Mai for the year 2020. These AOD were validated with the ground-based AOD from Chiang Mai AERONET station, and the result showed a correlation coefficient (R 2 ) of 0.63. This hourly high-resolution AOD data was used to estimate PM2.5 concentration by using Multiple Linear Regression with 1- hr average ground-based PM2.5 concentration data from the monitoring stations. The results showed that the R2 was 0.56 for the relationship between AOD and PM2.5 only. The meteorological parameters, i.e., temperature, relative humidity, wind speed, and wind direction were added in the model, and the R 2 was improved to 0.62. Random Forest Regression was also applied to compare the results of correlation, and the R square results were 0.95 in the training set and 0.53 in the testing set. This regression showed the most important variable was aerosol optical depth in estimation of PM2.5. The estimated PM2.5 concentrations were correlated with the observed PM2.5 with an R2 of 0.71. The mean bias was 1.115 and the root mean squared error was 23.28 æg/m3 . The correlation results of different hours from 09:00 to 16:00 hr. varied from 0.55 at 14:00 hr. to 0.81 at 10:00 hr. Finally, the hourly spatial distribution maps of PM2.5 concentration over Chiang Mai were developed with the relationship between hourly 1x1km2 spatial resolution AOD and PM2.5 with meteorological parameters. | ||
| 650 | 0 |
_aParticulate matter _xEnvironmental aspects _zThailand _zChiang Mai |
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| 650 | 0 |
_aAir quality _zThailand _zChiang Mai |
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| 650 | 0 |
_aAir quality _xRemote sensing |
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| 700 | 0 |
_aEkbordin Winijkul, _eChairperson |
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| 700 | 1 |
_aXue, Wenchao, _eExamination committee |
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| 700 | 1 |
_aVirdis, Salvatore G.P., _eExamination committee |
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| 710 | 2 |
_aEnvironmental Conservation Department (ECD), Myanmar, _eScholarship Donor |
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| 710 | 2 |
_aAsian Institute of Technology Scholarships, _eScholarship Donor |
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| 810 | 2 |
_aAsian Institute of Technology. _tThesis ; _vno. EV-22-10 |
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| 856 | 4 | 0 |
_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B18631 |
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