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 that violates key assumptions for many of the statistical methods ordinarily used to analyze these data such as linear regression, analysis of variance, and t tests. These data are inherently multivariate, relative, and nonnegative parts of a

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... and are then characterized as so-called compositional data. This paper introduces the field of compositional data analysis to the biomass burning emissions community and provides examples of statistical treatment of emissions data. Measures and tests of proportionality, unlike ordinary correlation, allow one to coherently investigate associations between parts of the smoke composition. An alternative method based on compositional linear trends was applied to estimate trace gas composition over a range of combustion efficiency that reduced prediction error by 4% while avoiding use of modified combustion efficiency as if it were an independent variable. Use of log-ratio balances to create meaningful contrasts between compositional parts definitively stressed differences in smoke emissions from fuel types originating in the southeastern and southwestern United States. Application of compositional statistical methods as an appropriate approach to account for the relative nature of data about the composition of smoke emissions and the atmosphere is recommended. Because of the chemical complexity of wood, Byram approximated the proportion of C, H, and O atoms in wood by C 6 H 9 O 4 . Complete combustion with no dissociation is an idealized situation which explains the maximum product mass possible. Incomplete combustion and thermal dissociation will yield additional products and less CO 2 . The foliage of woody plants has a different chemical composition from the wood component which can affect both combustion and combustion products (Hough, 1969; Jolly et al., 2016; Key Points: • Emissions data involve multiple and interrelated elements that can be more effectively analyzed using multivariate statistical techniques • By mass conservation, the range of emissions data is inherently constrained, making techniques for compositional data appropriate • Estimating trace gas emissions using a compositional linear trend based on modified combustion efficiency recognizes these features Supporting Information: • Supporting Information S1 • Data Set S1 • Data Set S2 • Data Set S3 • Data Set S4