Particulate matter (PM10) 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 touris...

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Autores principales:	Wissanupong Kliengchuay, Rachodbun Srimanus, Wechapraan Srimanus, Sarima Niampradit, Nopadol Preecha, Rachaneekorn Mingkhwan, Suwalee Worakhunpiset, Yanin Limpanont, Kamontat Moonsri, Kraichat Tantrakarnapa
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/66cd34f47b8f4e0e9e107dfc306d773c
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spelling	oai:doaj.org-article:66cd34f47b8f4e0e9e107dfc306d773c2021-11-28T12:13:00ZParticulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand10.1186/s12889-021-12217-21471-2458https://doaj.org/article/66cd34f47b8f4e0e9e107dfc306d773c2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12889-021-12217-2https://doaj.org/toc/1471-2458Abstract 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.Wissanupong KliengchuayRachodbun SrimanusWechapraan SrimanusSarima NiampraditNopadol PreechaRachaneekorn MingkhwanSuwalee WorakhunpisetYanin LimpanontKamontat MoonsriKraichat TantrakarnapaBMCarticlePublic aspects of medicineRA1-1270ENBMC Public Health, Vol 21, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Public aspects of medicine RA1-1270
spellingShingle	Public aspects of medicine RA1-1270 Wissanupong Kliengchuay Rachodbun Srimanus Wechapraan Srimanus Sarima Niampradit Nopadol Preecha Rachaneekorn Mingkhwan Suwalee Worakhunpiset Yanin Limpanont Kamontat Moonsri Kraichat Tantrakarnapa Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand
description	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.
format	article
author	Wissanupong Kliengchuay Rachodbun Srimanus Wechapraan Srimanus Sarima Niampradit Nopadol Preecha Rachaneekorn Mingkhwan Suwalee Worakhunpiset Yanin Limpanont Kamontat Moonsri Kraichat Tantrakarnapa
author_facet	Wissanupong Kliengchuay Rachodbun Srimanus Wechapraan Srimanus Sarima Niampradit Nopadol Preecha Rachaneekorn Mingkhwan Suwalee Worakhunpiset Yanin Limpanont Kamontat Moonsri Kraichat Tantrakarnapa
author_sort	Wissanupong Kliengchuay
title	Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand
title_short	Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand
title_full	Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand
title_fullStr	Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand
title_full_unstemmed	Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand
title_sort	particulate matter (pm10) prediction based on multiple linear regression: a case study in chiang rai province, thailand
publisher	BMC
publishDate	2021
url	https://doaj.org/article/66cd34f47b8f4e0e9e107dfc306d773c
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Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand

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