Pimpunchat, Busayamas

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Pimpunchat, Busayamas
Alternative Name
Pimpunchat, B.
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Email
busayamas.pi@kmitl.ac.th

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    Item type:Publication,
    Modelling greenhouse gas generation for landfill
    (2018-01-01)
    Sirimangkhala, Khwansiri
    ;
    Pimpunchat, Busayamas
    ;
    Amornsamankul, Somkid
    ;
    Triampo, Wannapong
    Landfill has been widely used in many countries for the final disposal of solid waste material due to its economic advantages. Landfills emit mostly methane and carbon dioxide. In this work, the dynamic characteristics of greenhouse gases generated from the closed landfill system were studied. The chemical reaction processes involved are considered and a mathematical model is formulated and analyzed. The behaviors of the key greenhouse gases, namely, carbon dioxide and methane, are carefully studied. It was found that our proposed model is qualitatively in good agreement with the real world phenomena. The characteristics of greenhouse gases of interest namely carbon dioxide and methane were found to depend on the reaction rate in a complex manner. Since the system is closed, each gas species becomes a constraint to one another. We can utilize these findings to control the landfill system and to take benefit from the useful products. Moreover, it suggests that methane and carbon dioxide could be used for electricity generation which helps reduce greenhouse gases in atmosphere.
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    Item type:Publication,
    Modeling haze problems in the North of Thailand using logistic regression
    (2014-01-01)
    Pimpunchat, Busayamas
    ;
    Sirimangkhala, Khwansiri
    ;
    Junyapoon, Suwannee
    At present, air pollution is a major problem in the upper northern region of Thailand. Air pollutants have an effect on human health, the economy and the traveling industry. The severity of this problem clearly appears every year during the dry season, from February to April. In particular it becomes very serious in March, especially in Chiang Mai province where smoke haze is a major issue. This study looked into related data from 2005-2010 covering eight principal parameters: PM10 (particulate matter with a diameter smaller than 10 micrometer), CO (carbon monoxide), NO<inf>2</inf> (nitrogen dioxide), SO<inf>2</inf> (sulphur dioxide), RH (relative humidity), NO (nitrogen oxide), pressure, and rainfall. Overall haze problem occurrence was calculated from a logistic regression model. Its dependence on the eight parameters stated above was determined for design conditions using the correlation coefficients with PM10. The proposed overall haze problem modeling can be used as a quantitative assessment criterion for supporting decision making to protect human health. This study proposed to predict haze problem occurrence in 2011. The agreement of the results from the mathematical model with actual measured PM10 concentration data from the Pollution Control Department was quite satisfactory.