气候变化领域集成服务门户(CCPortal): Quantifying wintertime O3 and NOx formation with relevance vector machines

CCPortal

DOI	10.1016/j.atmosenv.2021.118538
	Quantifying wintertime O3 and NOx formation with relevance vector machines
	Olson D.A.; Riedel T.P.; Offenberg J.H.; Lewandowski M.; Long R.; Kleindienst T.E.
发表日期	2021
ISSN	1352-2310
卷号	259
英文摘要	This paper uses a machine learning model called a relevance vector machine (RVM) to quantify ozone (O3) and nitrogen oxides (NOx) formation under wintertime conditions. Field study measurements were based on previous work described by Olson et al. (2019), where continuous measurements were reported from a wintertime field study in Utah. RVMs were formulated using either O3 or nitrogen dioxide (NO2) as the output variable. Values of the correlation coefficient (r2) between predicted and measured concentrations were 0.944 for O3 and 0.931 for NO2. RVMs are constructed from the observed measurements and result in sparse model formulations, meaning that only a subset of the data is used to approximate the entire dataset. For this study, the RVM with O3 as the output variable used only 20% of the measurement data while the RVM with NO2 used 16%. RVMs were then used as a predictive model to assess the importance of individual precursors. Using O3 as the output variable, increases in three species resulted in increased O3 concentrations: hydrogen peroxide (H2O2), dinitrogen pentoxide (N2O5), and molecular chlorine (Cl2). For the two termination products measured during the study, nitric acid (HNO3) and formic acid (CH2O2), no change in O3 concentration was observed. Using NO2 as the output variable, only increases in N2O5 resulted in increased NO2 concentrations. © 2021
关键词	Dinitrogen pentoxide Hydrogen peroxide Machine learning Nitrous acid Nitryl chloride Support vector machine
语种	英语
scopus关键词	Hydrogen peroxide; Nitric acid; Nitrogen oxides; Oxidation; Support vector machines; Dinitrogen pentoxide; Field studies; Machine-learning; N$-2$/O; Nitrous acid; Nitryl chloride; NO $-2$; Output variables; Relevance Vector Machine; Support vectors machine; Chlorine compounds; chlorine; dinitrogen pentoxide; formic acid; hydrogen peroxide; nitric acid; nitrogen dioxide; nitrogen oxide; nitrous acid; ozone; correlation; field method; formic acid; hydrogen peroxide; machine learning; nitrate; nitric acid; nitrogen dioxide; nitrogen oxides; ozone; support vector machine; winter; Article; calibration; correlation coefficient; field study; measurement; model; quantitative analysis; relevance vector machine; time series analysis; winter; United States; Utah
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/248361
作者单位	Office of Research and Development, United States Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle ParkNC 27711, United States
推荐引用方式 GB/T 7714	Olson D.A.,Riedel T.P.,Offenberg J.H.,et al. Quantifying wintertime O3 and NOx formation with relevance vector machines[J],2021,259.
APA	Olson D.A.,Riedel T.P.,Offenberg J.H.,Lewandowski M.,Long R.,&Kleindienst T.E..(2021).Quantifying wintertime O3 and NOx formation with relevance vector machines.ATMOSPHERIC ENVIRONMENT,259.
MLA	Olson D.A.,et al."Quantifying wintertime O3 and NOx formation with relevance vector machines".ATMOSPHERIC ENVIRONMENT 259(2021).