气候变化领域集成服务门户(CCPortal): Adaptive spatial sampling design for environmental field prediction using low-cost sensing technologies

CCPortal

DOI	10.1016/j.atmosenv.2019.117091
	Adaptive spatial sampling design for environmental field prediction using low-cost sensing technologies
	Yoo E.-H.; Zammit-Mangion A.; Chipeta M.G.
发表日期	2020
ISSN	1352-2310
卷号	221
英文摘要	The last decade has seen an explosion in data sources available for monitoring and prediction of environmental phenomena. While several inferential methods have been developed to make predictions on the underlying process by combining these data, an optimal sampling design for additional data collection in the presence of multiple heterogeneous sources has not yet been developed. Here, we provide an adaptive spatial design strategy based on a utility function that combines both prediction uncertainty and risk-factor criteria. Prediction uncertainty is obtained through a spatial data fusion approach based on fixed rank kriging that can tackle data with differing spatial supports and signal-to-noise ratios. We focus on the application of low-cost portable sensors, which tend to be relatively noisy, for air pollution monitoring, where data from regulatory stations as well as numeric modeling systems are also available. Although we find that spatial adaptive sampling designs can help to improve predictions and reduce prediction uncertainty, low-cost portable sensors are only likely to be beneficial if they are sufficient in number and quality. Our conclusions are based on a multi-factorial simulation experiment, and on a realistic simulation of pollutants in the Erie and Niagara counties in Western New York. © 2019 Elsevier Ltd
关键词	Adaptive spatial sampling design Change-of-support problem Fixed rank kriging Low-cost portable air sensors Measurement uncertainty
语种	英语
scopus关键词	Costs; Data fusion; Ecodesign; Environmental technology; Forecasting; Interpolation; Pollution; Reactor cores; Signal to noise ratio; Uncertainty analysis; Adaptive spatial samplings; Change-of-support problems; Kriging; Low costs; Measurement uncertainty; Monitoring; adaptive management; atmospheric pollution; design method; environmental conditions; environmental monitoring; environmental technology; experimental study; field method; kriging; prediction; sampling; sensor; spatial analysis; uncertainty analysis; air pollutant; air pollution; air sampling; Article; cost control; decision making; information processing; kriging; pollution monitoring; prediction; priority journal; risk factor; signal noise ratio; simulation; uncertainty; Canada; Erie; Michigan; New York [United States]; Niagara; Ontario [Canada]; United States
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/249495
作者单位	Department of Geography, University of Buffalo, SUNY, United States; School of Mathematics and Applied Statistics, University of Wollongong, Australia; Researcher in Geospatial Epidemiology, Big data institute, University of Oxford, United Kingdom
推荐引用方式 GB/T 7714	Yoo E.-H.,Zammit-Mangion A.,Chipeta M.G.. Adaptive spatial sampling design for environmental field prediction using low-cost sensing technologies[J],2020,221.
APA	Yoo E.-H.,Zammit-Mangion A.,&Chipeta M.G..(2020).Adaptive spatial sampling design for environmental field prediction using low-cost sensing technologies.ATMOSPHERIC ENVIRONMENT,221.
MLA	Yoo E.-H.,et al."Adaptive spatial sampling design for environmental field prediction using low-cost sensing technologies".ATMOSPHERIC ENVIRONMENT 221(2020).