气候变化领域集成服务门户(CCPortal): Effect of sample number and location on accuracy of land use regression model in NO2 prediction

CCPortal

DOI	10.1016/j.atmosenv.2020.118057
	Effect of sample number and location on accuracy of land use regression model in NO2 prediction
	Dong J.; Ma R.; Cai P.; Liu P.; Yue H.; Zhang X.; Xu Q.; Li R.; Song X.
发表日期	2021
ISSN	1352-2310
卷号	246
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. R2 during modeling and cross validation was greatly inflated comparing to hold-out validation, which was more obvious with less NMS. © 2020 Elsevier Ltd
关键词	Accuracy Land use regression Purposive sampling Sample location Sample number
语种	英语
scopus关键词	Location; Nitrogen oxides; Regression analysis; Efficient strategy; Hierarchical sampling; High spatial density; Land-use regression models; Model performance; Regular samplings; Sampling strategies; Spatial concentration; Land use; nitrogen dioxide; accuracy assessment; ambient air; atmospheric pollution; concentration (composition); hierarchical system; land use change; nitrogen dioxide; prediction; sampling; spatial analysis; air pollutant; air pollution control; air quality; air sampling; China; cross validation; data analysis software; fuzzy c means clustering; land use; population density; prediction; priority journal; purposive sample; remote sensing; China; Hebei; Shijiazhuang
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/248720
作者单位	College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China; Institute of Resources & Environment, Henan Polytechnic University, Jiaozuo, 454003, China; Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100005, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; Center for Ocean Mega-Research of Science, Chinese Academy of Sciences, China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100049, China; Sino-Danish Educational and Research Centre, University of Chinese Academy of Sciences, Beijing, 100190, China
推荐引用方式 GB/T 7714	Dong J.,Ma R.,Cai P.,et al. Effect of sample number and location on accuracy of land use regression model in NO2 prediction[J],2021,246.
APA	Dong J..,Ma R..,Cai P..,Liu P..,Yue H..,...&Song X..(2021).Effect of sample number and location on accuracy of land use regression model in NO2 prediction.ATMOSPHERIC ENVIRONMENT,246.
MLA	Dong J.,et al."Effect of sample number and location on accuracy of land use regression model in NO2 prediction".ATMOSPHERIC ENVIRONMENT 246(2021).