Effect of sample number and location on accuracy of land use regression model in NO2 prediction

doi:10.1016/j.atmosenv.2020.118057

	Effect of sample number and location on accuracy of land use regression model in NO2 prediction
	Dong, Jin 1; Ma, Rui 1; Cai, Panli 1; Liu, Peng 2; Yue, Handong 1; Zhang, Xiaoping 1; Xu, Qun 3,4; Li, Runkui 1,5,6; Song, Xianfeng 1,5,7,8
刊名	ATMOSPHERIC ENVIRONMENT
	2021-02-01
卷号	246 页码:8
关键词	Land use regression Sample number Sample location Purposive sampling Accuracy
ISSN号	1352-2310
DOI	10.1016/j.atmosenv.2020.118057
通讯作者	Li, Runkui(lirk@ucas.ac.cn)
英文摘要	Land use regression model (LUR) is one of the most commonly used methods to project the spatial concentration of ambient pollutants. The number and location of samples are two key factors affecting the accuracy of LUR, yet limited detail is known to us. In order to explore such effect, we collected NO2 monitoring data in high spatial density with a total of 263 sites in Shijiazhuang city of China, and designed four sampling strategies: random sampling, regular sampling, attribute hierarchical sampling, and purposive sampling. Under each strategy, LUR model was repeatedly built with increasing number of modeling site (NMS). Results showed that NMS and their locations affected model performance largely especially when NMS was less than 30. With the increase of NMS, the accuracy of LUR models gradually stabilized. The minimum NMS required for LUR would be 30, and the ideal number would be 60 for the study area. Purposive sampling was the most efficient strategies. R-2 during modeling and cross validation was greatly inflated comparing to hold-out validation, which was more obvious with less NMS.
资助项目	National Natural Science Foundation of China[41771435] ; National Natural Science Foundation of China[41771133] ; National Key Research and Development Program of China[2017YFB0503605] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040403] ; Key Deployment Project of Center for Ocean Mega-Research of Science, Chinese academy of sciences[COMS 2019Q15] ; CAMS Innovation Fund for Medical Sciences[2017-I2M-1-009] ; Fundamental Research Funds for the Central Universities
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000613546400005
资助机构	National Natural Science Foundation of China ; National Key Research and Development Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Key Deployment Project of Center for Ocean Mega-Research of Science, Chinese academy of sciences ; CAMS Innovation Fund for Medical Sciences ; Fundamental Research Funds for the Central Universities
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/136069]
专题	中国科学院地理科学与资源研究所
通讯作者	Li, Runkui
作者单位	1.Univ Chinese Acad Sci, Coll Resources & Environm, 19A, Beijing 100049, Peoples R China 2.Henan Polytech Univ, Inst Resources & Environm, Jiaozuo 454003, Henan, Peoples R China 3.Chinese Acad Med Sci, Sch Basic Med, Dept Epidemiol & Biostat, Inst Basic Med Sci,Peking Union Med Coll, Beijing 100005, Peoples R China 4.Chinese Acad Med Sci, Peking Union Med Coll, Ctr Environm & Hlth Sci, Beijing 100005, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 6.Chinese Acad Sci, Ctr Ocean Mega Res Sci, Beijing, Peoples R China 7.Univ Chinese Acad Sci, Sino Danish Coll, Beijing 100049, Peoples R China 8.Univ Chinese Acad Sci, Sino Danish Educ & Res Ctr, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Dong, Jin,Ma, Rui,Cai, Panli,et al. Effect of sample number and location on accuracy of land use regression model in NO2 prediction[J]. ATMOSPHERIC ENVIRONMENT,2021,246:8.
APA	Dong, Jin.,Ma, Rui.,Cai, Panli.,Liu, Peng.,Yue, Handong.,...&Song, Xianfeng.(2021).Effect of sample number and location on accuracy of land use regression model in NO2 prediction.ATMOSPHERIC ENVIRONMENT,246,8.
MLA	Dong, Jin,et al."Effect of sample number and location on accuracy of land use regression model in NO2 prediction".ATMOSPHERIC ENVIRONMENT 246(2021):8.