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Study the change in air pollution after the COVID-19 outbreak in Thailand |
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| รหัสดีโอไอ | |
| Creator | 1. Natcha Nitinattrakul 2. Pichnaree Lalitaporn |
| Title | Study the change in air pollution after the COVID-19 outbreak in Thailand |
| Publisher | Faculty of Engineering, Khon Kaen University |
| Publication Year | 2566 |
| Journal Title | Engineering and Applied Science Research |
| Journal Vol. | 50 |
| Journal No. | 2 |
| Page no. | 137-148 |
| Keyword | Air pollution, Satellite, Thailand, Traffic-volume, Number of people travel, COVID-19 |
| URL Website | https://ph01.tci-thaijo.org/index.php/easr/index |
| Website title | Engineering and Applied Science Research |
| ISSN | 2539-6161 |
| Abstract | In Thailand, the outbreak of COVID-19 occurred, resulting in a lifestyle change, which could affect the sources of air pollution. This study aims to examine the trend of air pollution changes during COVID-19 in Thailand by air quality measurement data from the ground and satellites, as well as the effect of the traffic volume. From the results, during the normal period, CO, NO2, SO2, PM10 and PM2.5 from the ground station tended to be consistently high (+6.58–56.60%), while satellite data showed that only CO, NO2 and SO2 were likely to be higher (+1.20–29.29%). When entering the first lockdown period, the ground data began to tend to decrease, including NO2 (-10.22%). For the satellite values, there was a similar downward trend, except for the AOD and O3 (+0.94 and +0.79%). For the ‘new normal’ period, all parameters of ground data tended to be consistently lower, as well as that of satellites. Furthermore, as a result of the traffic volume that affected the change in air pollutants, during the first lockdown, CO, NO2, PM10 and PM2.5, tended to decrease in line with the decrease in traffic. However, during the COVID-19 crisis, it was still found that air pollution remained high, because of the summer (March–May) and from the activities on weekdays. Also, in the correlation of ground and satellite measurements, it was found that the only high correlations in the data were the NO2 data (r = 0.74), and the correlations were not high for PM2.5 (r = 0.54), PM10 (r = 0.45), SO2 (r = 0.30), or CO (r = 0.12), and the lowest was O3 (r = -0.40). The values from both stations were different, because there may be other factors involved. The relevant information on dependent variables and variables affecting the measurements should also be included to make the forecast more accurate. |
