Publication: Particulate matter (PM<inf>10</inf>) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand
Issued Date
2021-12-01
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ISSN
14712458
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2-s2.0-85119859085
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Mahidol University
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SCOPUS
Bibliographic Citation
BMC Public Health. Vol.21, No.1 (2021)
Suggested Citation
Wissanupong Kliengchuay, Rachodbun Srimanus, Wechapraan Srimanus, Sarima Niampradit, Nopadol Preecha, Rachaneekorn Mingkhwan, Suwalee Worakhunpiset, Yanin Limpanont, Kamontat Moonsri, Kraichat Tantrakarnapa Particulate matter (PM<inf>10</inf>) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand. BMC Public Health. Vol.21, No.1 (2021). doi:10.1186/s12889-021-12217-2 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/77474
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Title
Particulate matter (PM<inf>10</inf>) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand
Abstract
Background: The northern regions of Thailand have been facing haze episodes and transboundary air pollution every year in which particulate matter, particularly PM10, accumulates in the air, detrimentally affecting human health. Chiang Rai province is one of the country’s most popular tourist destinations as well as an important economic hub. This study aims to develop and compare the best-fitted model for PM10 prediction for different seasons using meteorological factors. Method: The air pollution and weather data acquired from the Pollution Control Department (PCD) spanned from the years 2011 until 2018 at two stations on an hourly basis. Four different stepwise Multiple Linear Regression (MLR) models for predicting the PM10 concentration were then developed, namely annual, summer, rainy, and winter seasons. Results: The maximum daily PM10 concentration was observed in the summer season for both stations. The minimum daily concentration was detected in the rainy season. The seasonal variation of PM10 was significantly different for both stations. CO was moderately related to PM10 in the summer season. The PM10 summer model was the best MLR model to predict PM10 during haze episodes. In both stations, it revealed an R2 of 0.73 and 0.61 in stations 65 and 71, respectively. Relative humidity and atmospheric pressure display negative relationships, although temperature is positively correlated with PM10 concentrations in summer and rainy seasons. Whereas pressure plays a positive relationship with PM10 in the winter season. Conclusions: In conclusion, the MLR models are effective at estimating PM10 concentrations at the local level for each seasonal. The annual MLR model at both stations indicates a good prediction with an R2 of 0.61 and 0.52 for stations 65 and 73, respectively.