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    A study of AERMOD tiering approach for nitrogen dioxide prediction in Maptaphut Industrial Area
    (Mahidol University. Mahidol University Library and Knowledge Center, 2015) Supitchaya Tunlathorntham; Sarawut Thepanondh; Suphaphat Kwonpongsagoon
    The Maptaphut industrial area, Rayong Province is the largest industrial complex in Thailand. There has been concern about many air pollutants over this area. This study presents the methodologies and results of an application of the AERMOD model to predict the air quality impacts of NO2 emitted by industrial emission sources in Maptaphut Industrial Estate. These emissions are typically composed of a mixture of nitric oxide (NO) and nitrogen dioxide (NO2), an oxide of nitrogen (NOx). NO is subsequently oxidized to NO2, which is an air pollutant found in the environment. Taking into consideration the chemistry of NOx and characteristics of the conversion of NO to NO2, three different tiers, recommended by the USEPA are tested and evaluated for their ability in predicting NO2 ambient concentration in the study area. The performance evaluation of the AERMOD dispersion model in predicting 1-hour average concentrations in the vicinity of the Maptaphut industrial complex was conducted for the years 2012 and 2013 (1 January, 2012 to 31 December, 2013). Measured data from 10 ambient air monitoring stations were used to compare with those modeled results. The results from the model indicated that Tier 1 (100% conversion of NO2 to NO2) provided less bias with those measured data as compared with other tiers. It also performed very well in predicting the extreme end of NO2 concentrations. Therefore, Tier 1 may be considered as appropriate for prediction of the annual average as well as in determining the maximum ground level concentration of NO2 in the Maptaphut industrial area.
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    PublicationOpen Access
    Effect of the Waste Heat Recovery System to Buoyancy and Momentum Flux of Combustion Stack in the Cement Industry
    (2019) Jutarat Keawboonchu; Wissawa Malakan; Wisit Thongkum; Sarawut Thepanondh; Mahidol University. Faculty of Public health. Department of Sanitary Engineering
    to the rise of plume for both scenarios. There were no differences in the predicted PM-10 ground level concentrations at receptorsaround the cement plant for the model simulation under two scenarios. Therefore, it was concluded
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    PublicationOpen Access
    Diversity and Habitat Use of Terrestrial Mammals in the Area Proposed for Water Resource Development in Khao Soi Dao Wildlife Sanctuary, Thailand
    (2021) Intanon Kolasartsanee; อินทนนท์ กลศาสตร์เสนี; Mahidol University. Faculty of Science. Department of Biology
    The Khlong Ta Liu dam construction plan was re-proposed to be constructed in the forest area of Khao Soi Dao Wildlife Sanctuary in the Ta-riu tributary without a biodiversity impact assessment. Five camera traps were mounted at the hotspot points for terrestrial mammals next to the main trail for 376 trap nights from the forest edge to the forest interior. Fifteen species of terrestrial mammals were found. Two species found were categorized as having endangered status, while seven have vulnerable status. Grazers and browsers, i.e., Sambar, Gaur, Northern Red Muntjac, and Asian Elephant were mostlydetected at the forest edge, while omnivores and frugivores, i.e., Wild Boar,Greater Hog Badger, and Northern Pig-tailed Macaque were largely detected inthe forest interior. Dhole should be a carnivore species specific to the forestedge while Clouded Leopard should be in the forest interior. The NormalizedDifference Vegetation Index (NDVI) may relate to species of terrestrialmammals detected at each habitat. Among the five habitats, three would bedestroyed upon construction of the dam, including the habitat with the highestdiversity of terrestrial mammals. The habitat that would likely be least impactedhad the lowest species richness of terrestrial mammals.
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    ItemOpen Access
    Annual Report 2011 College of Management Mahidol University
    (2012) Mahidol University. College of Management