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Abstract
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 lognormal distribution or outlier induced skewness. The generalised extreme value (GEV) distribution is wellsuited 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 (CO _{2}) and nitrous oxide (N _{2}O) flux data measured in an agricultural field. The option of transforming CO _{2} flux data to the BoxCox scale in order to make the distribution normal was also investigated. The results showed that average CO _{2} estimates from GEV are less affected by data in the long tail compared to the sample mean. The data for N _{2}O flux were much more complex than CO _{2} 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 hotspotlike observations, suggests that sample means and logmeans may overestimate GHG fluxes from agricultural fields. In this study, the arithmetic CO _{2} sample mean of 65.6 (mean logscale 65.9) kg CO _{2}–C ha ^{−1} d ^{−1} was reduced to GEV mean of 60.1 kg CO _{2}–C ha ^{−1} d ^{−1}. The arithmetic N _{2}O sample mean of 1.038 (mean logscale 1.038) kg N _{2}O–N ha ^{−1} d ^{−1} was substantially reduced to GEV mean of 0.0157 kg N _{2}O–N ha ^{−1} d ^{−1}. Our analysis suggests that GHG data should be analysed assuming a GEV distribution of the data, including a BoxCox transformation when negative data are observed, rather than only calculating basic log and lognormal 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.
Original language  English 

Article number  117500 
Number of pages  8 
Journal  Atmospheric Environment 
Volume  237 
Early online date  17 Apr 2020 
DOIs  
Publication status  Published  15 Sep 2020 
Keywords
 nitrous oxide
 carbon dioxide
 Generalised extreme value
 Finney correction
 Heavytailed data
 skewness correction
 Carbon dioxide
 Nitrous oxide
 Skewness correction
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Dive into the research topics of 'A strategy for modelling heavytailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean?'. Together they form a unique fingerprint.Activities
 1 Publication Peer review

Science of the Total Environment (Journal)
Mewa Singh Dhanoa (Peer Review), Laura M. Cardenas (Peer Review) & Anita Shepherd (Peer Review)
Sep 2019 → Oct 2019Activity: Publication peerreview and editorial work › Publication Peer review