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 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 non-negative parts of a whole
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 which
reduced prediction error by 4 percent 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 U.S. 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.