Document details for 'Analyzing Wildland Fire Smoke Emissions Data Using Compositional Data Techniques'

Authors Weise, D.R., Palarea Albaladejo, J., Johnson, T.J. and Jung, H.
Publication details Journal of Geophysical Research - Atmospheres 125(6), e2019JD032128. Wiley.
Publisher details Wiley
Keywords simplex, compositional data analysis, balance, log-ratio, MCE
Abstract By conservation of mass, the mass of wildland fuel that is pyrolyzed and combusted must equal the mass of smoke emissions, residual char and ash. For a given set of conditions, these amounts are fixed. This places a constraint on smoke emissions data which violates statistical assumptions for many of the methods currently used to analyze these data such as linear regression, analysis of variance, and t-tests. These data are inherently multivariate and non-negative parts of a whole. This paper introduces the field of compositional data analysis to the emissions community and provides examples of appropriate statistical treatment of emissions data. It is shown that modified combustion efficiency should not be used as a predictor variable for other smoke emissions because it is not an independent variable. An alternative method based on compositional linear trends to estimate trace gas composition using CO and CO2 is presented.
Last updated 2020-03-16

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