Modeling Haze Problems in the North of Thailand using Logistic Regression

Abstract

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: PM 10 (particulate matter with a diameter smaller than 10 micrometer), CO (carbon monoxide), NO 2 (nitrogen dioxide), SO 2 (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 PM 10. 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 PM 10 concentration data from the Pollution Control Department was quite satisfactory.

Analysis of Tropospheric Nitrogen Dioxide Using Satellite and Ground Based Data over Northern Thailand

“…Lalitaporn et al [26] found high concentrations of NO2 in Chiang Mai during biomass burning season of March-April which agreed well with PM10 and CO concentrations. Similarly, Pimpunchat et al [33] presented high concentrations of PM10 during March-April in Chiang Mai owing to haze situation. They also reported high correlation of PM10 concentrations versus NO2, CO, and O3 concentrations.…”

Factors Leading To Success Of Tourism Business In Bangkok Thailand

“…The data collected were subsequently analyzed using the path modeling technique, utilizing SPSS as a tool for data analysis. (Pimpunchat, Sirimangkhala, & Junyapoon, 2014;Taro, 1973).…”

Investigation of PM10 prediction utilizing data mining techniques: Analyze by topic

“…Many researchers used their national AQI to identify class for their classification model (P. Perez & Reyes, 2006;Díaz-Robles et al, 2008;Stadlober et al, 2008;P. Perez, 2012;Sabri & Tarek, 2012;Pimpunchat et al, 2014;Fu et al, 2015;Saithanu & Mekparyup, 2015b, 2015cFaganeli Pucer et al, 2018;Srijiranon & Eiamkanitchat, 2018 Apart from the aforementioned outputs, a small number of research reported average values between monitoring stations, the likelihood of pollution to enter hazardous levels, and the differences between daily and annual average air quality.…”

“…This method was generally implemented to find relationships between input features and PM 10 . Many researchers calculate the r value, then select only input features that passed specific thresholds (García Nieto et al, 2018b;Liu et al, 2015;Mekparyup & Saithanu, 2013Pimpunchat et al, 2014;Saithanu & Mekparyup, 2015a, 2015b, 2015cTaşpınar, 2015).…”