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DOI	10.1016/j.atmosenv.2020.117500
	A strategy for modelling heavy-tailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean?
	Dhanoa M.S.; Louro A.; Cardenas L.M.; Shepherd A.; Sanderson R.; López S.; France J.
发表日期	2020
ISSN	13522310
卷号	237
英文摘要	In this study, we draw up a strategy for analysis of greenhouse gas (GHG) field data. The distribution of GHG flux data generally exhibits excessive skewness and kurtosis. This results in a heavy tailed distribution that is much longer than the tail of a log-normal distribution or outlier induced skewness. The generalised extreme value (GEV) distribution is well-suited to model such data. We evaluated GEV as a model for the analysis and a means of extraction of a robust average of carbon dioxide (CO2) and nitrous oxide (N2O) flux data measured in an agricultural field. The option of transforming CO2 flux data to the Box-Cox scale in order to make the distribution normal was also investigated. The results showed that average CO2 estimates from GEV are less affected by data in the long tail compared to the sample mean. The data for N2O flux were much more complex than CO2 flux data due to the presence of negative fluxes. The estimate of the average value from GEV was much more consistent with maximum data frequency position. The analysis of GEV, which considers the effects of hot-spot-like observations, suggests that sample means and log-means may overestimate GHG fluxes from agricultural fields. In this study, the arithmetic CO2 sample mean of 65.6 (mean log-scale 65.9) kg CO2–C ha−1 d−1 was reduced to GEV mean of 60.1 kg CO2–C ha−1 d−1. The arithmetic N2O sample mean of 1.038 (mean log-scale 1.038) kg N2O–N ha−1 d−1 was substantially reduced to GEV mean of 0.0157 kg N2O–N ha−1 d−1. Our analysis suggests that GHG data should be analysed assuming a GEV distribution of the data, including a Box-Cox transformation when negative data are observed, rather than only calculating basic log and log-normal summaries. Results of GHG studies may end up in national inventories. Thus, it is necessary and important to follow all procedures that contribute to minimise any bias in the data. © 2020 Elsevier Ltd
英文关键词	Carbon dioxide; Finney correction; Generalised extreme value; Heavy-tailed data; Nitrous oxide; Skewness correction
语种	英语
scopus关键词	Agricultural robots; Agriculture; Carbon dioxide; Greenhouse gases; Higher order statistics; Nitrogen oxides; Normal distribution; Agricultural fields; Box Cox transformation; Generalised extreme value distributions; GEV distributions; Greenhouse gas (GHG); Greenhouse gases (GHG); Heavy-tailed distribution; Log-normal distribution; Metadata; ammonium nitrate; carbon dioxide; nitrous oxide; air sampling; atmospheric modeling; atmospheric pollution; carbon dioxide; carbon flux; greenhouse gas; nitrous oxide; skewness; spatial distribution; strategic approach; transformation; agricultural slurry; agriculture; arithmetic; Article; controlled study; data analysis; greenhouse gas; mathematical model; priority journal
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/145038
作者单位	Centre for Nutrition Modelling, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada; Rothamsted Research, North Wyke, Okehampton, Devon EX20 2SB, United Kingdom; Institute of Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, 23 St Machar Drive, Aberdeen, AB24 3UU, United Kingdom; Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Gogerddan, Aberystwyth, Ceredigion, SY23 3EB, United Kingdom; Departamento de Producción Animal, Universidad de León, León, E-24007, Spain; Instituto de Ganadería de Montaña, CSIC-Universidad de León, Finca Marzanas S/n, Grulleros, Leon, 24346, Spain
推荐引用方式 GB/T 7714	Dhanoa M.S.,Louro A.,Cardenas L.M.,et al. A strategy for modelling heavy-tailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean?[J],2020,237.
APA	Dhanoa M.S..,Louro A..,Cardenas L.M..,Shepherd A..,Sanderson R..,...&France J..(2020).A strategy for modelling heavy-tailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean?.Atmospheric Environment,237.
MLA	Dhanoa M.S.,et al."A strategy for modelling heavy-tailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean?".Atmospheric Environment 237(2020).