Modeling Haze Problems in the North of Thailand using Logistic Regression


  • Busayamas Pimpunchat Industrial Mathematics Research Unit & Department of Mathematics, Faculty of Science, King Mongkut??s Institute of Technology Ladkrabang
  • Khwansiri Sirimangkhala Industrial Mathematics Research Unit & Department of Mathematics, Faculty of Science, King Mongkut??s Institute of Technology Ladkrabang
  • Suwannee Junyapoon Department of Chemistry, Faculty of Science, King Mongkut??s Institute of Technology Ladkrabang



forecasting, haze problem, multivariate logistic regression, mathematical model, PM10


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), NO2 (nitrogen dioxide), SO2 (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.


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