气候变化领域集成服务门户(CCPortal): Concurrent spatiotemporal daily land use regression modeling and missing data imputation of fine particulate matter using distributed space-time expectation maximization

CCPortal

DOI	10.1016/j.atmosenv.2019.117202
	Concurrent spatiotemporal daily land use regression modeling and missing data imputation of fine particulate matter using distributed space-time expectation maximization
	Taghavi-Shahri S.M.; Fassò A.; Mahaki B.; Amini H.
发表日期	2020
ISSN	13522310
英文摘要	In this study, a spatiotemporal land use regression (LUR) model using distributed space-time expectation maximization (D-STEM) software was developed. We trained the model using daily mean ambient particulate matter ≤2.5 μm (PM2.5) data measured hourly in 2015 at 30 regulatory monitoring network stations within the megacity of Tehran, Iran. Since a substantial amount of measured data were missing (48% of the total number of daily PM2.5 observations), we used the D-STEM to impute missing data and compared the missing imputation performance between different fitted models and the mean substitution method. We used h-block cross-validation (h-block CV) method in order to account for spatial autocorrelation in the model building and validation. In the imputation of missing data, the D-STEM LUR model had a mean absolute percentage error (MAPE) of 25.3%, outperforming the mean substitution method, which resulted in MAPE of 28.3%. The spatiotemporal R-squared was 0.73 and the average CV R-squared of 2-block and 5-block cross-validations was 0.60. These values were 0.68 and 0.47 when the spatial aspect of the LUR model was assessed, and 0.995 and 0.992 when the temporal aspect of the LUR model was assessed. This study demonstrated the competence of D-STEM software in spatiotemporal modeling, missing data imputation, and mapping of daily ambient PM2.5 at a very high spatial resolution (20 m × 20 m). These estimations are available for future research, especially for epidemiological studies on short- and/or long-term health effects of ambient PM2.5. Generally, we found D-STEM as a promising tool for spatiotemporal LUR modeling of ambient air pollution, especially for those models that rely on regulatory network monitoring stations with a considerable amount of missing data. © 2020 Elsevier Ltd
英文关键词	Air pollution; D-STEM; Exposure assessment; LUR; Missing data
学科领域	Air pollution; Land use; Maximum principle; Ambient particulate Matter; Expectation - maximizations; Exposure assessment; Land-use regression models; Mean absolute percentage error; Missing data; Missing data imputations; Very high spatial resolutions; Particles (particulate matter); air pollution; ambient air; article; autocorrelation; cross validation; expectation; Iran; land use; particulate matter; software; validation process
语种	英语
scopus关键词	Air pollution; Land use; Maximum principle; Ambient particulate Matter; Expectation - maximizations; Exposure assessment; Land-use regression models; Mean absolute percentage error; Missing data; Missing data imputations; Very high spatial resolutions; Particles (particulate matter); air pollution; ambient air; article; autocorrelation; cross validation; expectation; Iran; land use; particulate matter; software; validation process
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/120856
作者单位	Department of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical SciencesIsfahan, Iran; Student Research Committee, School of Health, Isfahan University of Medical SciencesIsfahan, Iran; Department of Management, Information and Production Engineering, University of BergamoBergamo, Italy; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
推荐引用方式 GB/T 7714	Taghavi-Shahri S.M.,Fassò A.,Mahaki B.,et al. Concurrent spatiotemporal daily land use regression modeling and missing data imputation of fine particulate matter using distributed space-time expectation maximization[J],2020.
APA	Taghavi-Shahri S.M.,Fassò A.,Mahaki B.,&Amini H..(2020).Concurrent spatiotemporal daily land use regression modeling and missing data imputation of fine particulate matter using distributed space-time expectation maximization.Atmospheric Environment.
MLA	Taghavi-Shahri S.M.,et al."Concurrent spatiotemporal daily land use regression modeling and missing data imputation of fine particulate matter using distributed space-time expectation maximization".Atmospheric Environment (2020).