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Comparison of AERMOD Performanceusing Observed and Prognostic Meteorological Data |
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| รหัสดีโอไอ | |
| Creator | 1. Wissawa Malakan 2. Jutarat Keawboonchu 3. Sarawut Thepanondh |
| Title | Comparison of AERMOD Performanceusing Observed and Prognostic Meteorological Data |
| Publisher | The Thai Society of Higher Education Institutes on Environment |
| Publication Year | 2561 |
| Journal Title | EnvironmentAsia |
| Journal Vol. | 11 |
| Journal No. | 2 |
| Page no. | 38-52 |
| Keyword | AERMOD, Maptaphut industrial area, Prognostic meteorological simulations, SO2 |
| URL Website | http://www.tshe.org/ea/index.html |
| Website title | EnvironmentAsia |
| ISSN | 2586-8861 |
| Abstract | This study is aimed to compare the performance of AERMOD dispersion model by using actual and prognostic meteorological data in predicting ground level sulfur dioxide (SO2) concentrations and spatial dispersion in the largest petrochemical industrial complex in Thailand. Three SO2 monitoring stations having the highest percentage of data completeness were selected among the air quality monitoring network in the study area to serve the evaluation purpose. Emission data in this study comprised of 472 combustion stacks and 11 roads. Those emissions were assumed as constant value for each source over the simulated period. The observed air quality and meteorological data in May, 2013 were then also selected due to the occurring of hourly extreme concentration (episode) of SO2 as well as having highest completeness of measureddata. Hourly meteorological data during this period obtained from direct measurement and prognostic meteorological data were used as input independent variables in the model simulation. Evaluation of model performance was accomplished by statistical comparison between observed and modeled SO2 concentrations. Results from statistical analysis indicated that there were no different between predicted SO2 concentrations from using of prognostic and actual meteorological simulations. However, predicted SO2 concentrations by AERMOD from both meteorological data provide over-estimate results when compare with those monitoring results. |
