气候变化领域集成服务门户(CCPortal): Low-cost sensor networks and land-use regression: Interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California

CCPortal

DOI	10.1016/j.atmosenv.2020.117287
	Low-cost sensor networks and land-use regression: Interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California
	Weissert L.; Alberti K.; Miles E.; Miskell G.; Feenstra B.; Henshaw G.S.; Papapostolou V.; Patel H.; Polidori A.; Salmond J.A.; Williams D.E.
发表日期	2020
ISSN	13522310
卷号	223
英文摘要	The development of low-cost sensors and novel calibration algorithms offer new opportunities to supplement existing regulatory networks to measure air pollutants at a high spatial resolution and at hourly and sub-hourly timescales. We use a random forest model on data from a network of low-cost sensors to describe the effect of land use features on local-scale air quality, extend this model to describe the hourly-scale variation of air quality at high spatial resolution, and show that deviations from the model can be used to identify particular conditions and locations where air quality differs from the expected land-use effect. The conditions and locations under which deviations were detected conform to expectations based on general experience. © 2020 Elsevier Ltd
英文关键词	Air quality sensor network; Land-use regression; Nitrogen dioxide; Ozone
学科领域	Air quality; Costs; Decision trees; Image resolution; Nitrogen oxides; Ozone; Sensor networks; Calibration algorithm; High spatial resolution; Land use regression; Nitrogen dioxides; Particular condition; Random forest modeling; Southern California; Temporal and spatial; Land use; nitrogen dioxide; ozone; air quality; atmospheric pollution; calibration; concentration (composition); interpolation; land use; land use change; network analysis; nitric oxide; ozone; regression analysis; sensor; spatial resolution; air monitoring; air pollutant; air pollution; air quality; Article; California; circadian rhythm; environment; instrument validation; land use; meteorology; prediction; priority journal; random forest; spatiotemporal analysis; toxic concentration; wind; California; United States
语种	英语
scopus关键词	Air quality; Costs; Decision trees; Image resolution; Nitrogen oxides; Ozone; Sensor networks; Calibration algorithm; High spatial resolution; Land use regression; Nitrogen dioxides; Particular condition; Random forest modeling; Southern California; Temporal and spatial; Land use; nitrogen dioxide; ozone; air quality; atmospheric pollution; calibration; concentration (composition); interpolation; land use; land use change; network analysis; nitric oxide; ozone; regression analysis; sensor; spatial resolution; air monitoring; air pollutant; air pollution; air quality; Article; California; circadian rhythm; environment; instrument validation; land use; meteorology; prediction; priority journal; random forest; spatiotemporal analysis; toxic concentration; wind; California; 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/120766
作者单位	School of Chemical Sciences and MacDiarmid Institute for Advanced Materials and Nanotechnology, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand; Aeroqual Ltd, 460 Rosebank Road, Avondale, Auckland, 1026, New Zealand; School of Environment, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand; South Coast Air Quality Management District, 21865 Copley Drive, Diamond Bar, CA 91765, United States
推荐引用方式 GB/T 7714	Weissert L.,Alberti K.,Miles E.,et al. Low-cost sensor networks and land-use regression: Interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California[J],2020,223.
APA	Weissert L..,Alberti K..,Miles E..,Miskell G..,Feenstra B..,...&Williams D.E..(2020).Low-cost sensor networks and land-use regression: Interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California.Atmospheric Environment,223.
MLA	Weissert L.,et al."Low-cost sensor networks and land-use regression: Interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California".Atmospheric Environment 223(2020).