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DOI	10.1016/j.atmosenv.2020.117757
	Prediction of PM2.5 concentrations at the locations of monitoring sites measuring PM10 and NOx, using generalized additive models and machine learning methods: A case study in London
	Analitis A.; Barratt B.; Green D.; Beddows A.; Samoli E.; Schwartz J.; Katsouyanni K.
发表日期	2020
ISSN	1352-2310
卷号	240
英文摘要	The adverse health effects of air pollutants, especially those of PM2.5, are well documented. However, a lack of adequate monitoring and weaknesses in modelling approaches do not allow a good assessment of health effects in many areas of the World. Advances in computational methods and the availability of new data sets, e.g. satellite remote observations, have enlarged the possibilities of modelling for application in large scale health effects studies. However, PM2.5 monitoring is very recent in most of the World and more limited compared to other pollutants, and understanding how to use PM10 monitors to estimate PM2.5 exposure is therefore important. Since interest in these methods is relatively recent, there is a need for testing their performance against ambient measurements, but long term PM2.5 datasets are less readily available than PM10 in many regions. In the present study we report the methodology and results of using regression modelling and a machine learning method (Random Forest-RF), as well as a combination of the two, to enhance a PM2.5 measurement data base in London using PM10 and NOx measurements as well as other predictors and compare the relative performance of each method. We found that the combination of predictions by the regression model and the RF performs best and we obtain a cross-validation R2 of 99.29% and 98.22% for the 5-year periods 2004–2008 and 2009–2013, respectively, and a Mean Square Error near 1. Our enhanced data base for PM2.5 is available for use by other researchers. © 2020
关键词	Ensemble methods Environmental exposure London case study PM2.5 prediction Random forest
语种	英语
scopus关键词	Decision trees; Health; Mean square error; Nitrogen oxides; Pollution; Predictive analytics; Random forests; Regression analysis; Turing machines; Adverse health effects; Ambient measurement; Generalized additive model; Machine learning methods; PM2.5 concentration; Regression modelling; Relative performance; Remote observation; Machine learning; concentration (composition); machine learning; measurement method; modeling; monitoring; nitric oxide; particulate matter; prediction; spatiotemporal analysis; article; cross validation; England; environmental exposure; pollutant; prediction; random forest; England; London [England]; United Kingdom
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/248976
作者单位	National and Kapodistrian University of Athens, Faculty of Medicine, Department of Hygiene, Epidemiology and Medical Statistics, Athens, Greece; MRC Centre for Environment and Health, King's College London, United Kingdom; HPRU in Environmental Exposures and Health, Imperial College London, United Kingdom; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, United States
推荐引用方式 GB/T 7714	Analitis A.,Barratt B.,Green D.,et al. Prediction of PM2.5 concentrations at the locations of monitoring sites measuring PM10 and NOx, using generalized additive models and machine learning methods: A case study in London[J],2020,240.
APA	Analitis A..,Barratt B..,Green D..,Beddows A..,Samoli E..,...&Katsouyanni K..(2020).Prediction of PM2.5 concentrations at the locations of monitoring sites measuring PM10 and NOx, using generalized additive models and machine learning methods: A case study in London.ATMOSPHERIC ENVIRONMENT,240.
MLA	Analitis A.,et al."Prediction of PM2.5 concentrations at the locations of monitoring sites measuring PM10 and NOx, using generalized additive models and machine learning methods: A case study in London".ATMOSPHERIC ENVIRONMENT 240(2020).