气候变化领域集成服务门户(CCPortal): Land use regression modeling for fine particulate matters in Bangkok; Thailand; using time-variant predictors: Effects of seasonal factors; open biomass burning; and traffic-related factors

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DOI	10.1016/j.atmosenv.2020.118128
	Land use regression modeling for fine particulate matters in Bangkok; Thailand; using time-variant predictors: Effects of seasonal factors; open biomass burning; and traffic-related factors
	Chalermpong S.; Thaithatkul P.; Anuchitchanchai O.; Sanghatawatana P.
发表日期	2021
ISSN	1352-2310
卷号	246
英文摘要	In recent years, as the level of fine particulate matter (PM2.5) concentration has become more closely monitored in Thailand and its harmful effects on health have been widely recognized by the public, the Thai government has debated various measures to improve air quality. In this paper, the Land Use Regression (LUR) technique was used to model the relationship between the daily PM2.5 concentration and various predictor variables using data from the entire year of 2019. The results confirmed strong seasonal effects on PM2.5 and substantial effects of time-variant predictors, including open biomass burning and meteorological conditions. However, time-invariant variables, including traffic, transportation, and land use characteristics were generally weaker predictors in the LUR models. The results of the model based on data for the entire year showed better statistical fit and robustness than the seasonal models. The relatively low adjusted R2 of the models developed in this study compared with previous LUR studies suggests that more detailed data, especially the traffic volume on roads nearby monitoring sites, might be necessary to improve the model's performance. Finally, the large buffer size of the open biomass burning predictor implied that the measures to reduce PM2.5 by limiting open biomass burning would require international cooperation as some fires within the buffer area occurred in neighboring countries outside the borders of Thailand. © 2020 Elsevier Ltd
英文关键词	Air quality; Biomass; International cooperation; Bangkok , Thailand; Fine particulate matter; Fine particulate matter (PM2.5); Land use regression; Land-use regression models; Meteorological condition; PM2.5 concentration; Predictor variables; Land use; biomass burning; concentration (composition); land use change; particulate matter; prediction; seasonality; traffic emission; air quality; Article; biomass; environmental monitoring; fire; government; humidity; international cooperation; land use; meteorology; particulate matter 2.5; predictor variable; priority journal; rainy season; regression analysis; seasonal variation; Thailand; time; traffic; traffic pollution; wind speed; winter; Bangkok; Central Region [Thailand]; Krung Thep Mahanakhon; Thailand
语种	英语
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/169075
作者单位	Transportation Institute, Chulalongkorn University, Bangkok, 10330, Thailand; Department of Civil Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand
推荐引用方式 GB/T 7714	Chalermpong S.,Thaithatkul P.,Anuchitchanchai O.,et al. Land use regression modeling for fine particulate matters in Bangkok; Thailand; using time-variant predictors: Effects of seasonal factors; open biomass burning; and traffic-related factors[J],2021,246.
APA	Chalermpong S.,Thaithatkul P.,Anuchitchanchai O.,&Sanghatawatana P..(2021).Land use regression modeling for fine particulate matters in Bangkok; Thailand; using time-variant predictors: Effects of seasonal factors; open biomass burning; and traffic-related factors.Atmospheric Environment,246.
MLA	Chalermpong S.,et al."Land use regression modeling for fine particulate matters in Bangkok; Thailand; using time-variant predictors: Effects of seasonal factors; open biomass burning; and traffic-related factors".Atmospheric Environment 246(2021).