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Ground-Level Ozone Pollution in Upper Northern, Thailand : An ArcGIS-Based Approach |
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| รหัสดีโอไอ | |
| Creator | Supawan Srirattana |
| Title | Ground-Level Ozone Pollution in Upper Northern, Thailand : An ArcGIS-Based Approach |
| Publisher | Faculty of Engineeing Naresuan University |
| Publication Year | 2565 |
| Journal Title | Naresuan University Engineering Journal |
| Journal Vol. | 17 |
| Journal No. | 1 |
| Page no. | 56-68 |
| Keyword | Ground-Level Ozone, ArcGIS, Spatial Interpolation, Inverse Distance Weighted, Ground-Level Ozone, ArcGIS, Spatial Interpolation, Inverse Distance Weighted |
| URL Website | https://ph01.tci-thaijo.org/index.php/nuej |
| Website title | https://ph01.tci-thaijo.org/index.php/nuej |
| ISSN | 1905-615x |
| Abstract | Ground-level ozone in the air we breathe even relatively low levels can cause health effects. Thus, it is important to analyze the spatial-temporal ozone concentrations. The IDW interpolation technique by ArcMap 10.5ยฎ software was used to simulate and access ground-level ozone data in areas where without ambient air quality monitoring stations in 8 provinces (Chiang Rai, Chiang Mai, Phrae, Nan, Phayao, Lampang, Lamphun, and Mae Hong Son) during the year 2017 โ" 2019. The ground-level zone input data were obtained from the pollution control department, Thailand. The Mean Error (ME) and Root Mean Squared Error (RMSE) were used to find the most suitable power for IDW interpolation. The IDW interpolation with power 3 was represented the best condition. IDW interpolation of monthly maximum 1-hour reveal that orange and red were found as the major of AQI colors in all 8 provinces. Orange was found in every province, while red was only distributed in Chiang Rai and some areas in Chiang Mai during ozone crisis (Febry to June). For monthly maximum 8-hour, AQI ratings were ranging from green to purple, and most areas were faced with ozone pollution in the red to the purple. Nevertheless, July to January was rarely reached a high level of ozone. Additionally, the IDW interpolation map of Chiang Mai in May 2017 was chosen for discussion as an example to converted data from 2D - spatiotemporal interpolation map to the number of sub-districts in which ozone AQI levels were got to the unhealthy zone. |
