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Stochastic Diffusion Model for Estimating Trace Gas Emissions with Static Chambers
Author(s) -
Pedersen Asger R.,
Petersen Søren O.,
Vinther Finn P.
Publication year - 2001
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2001.65149x
Subject(s) - estimator , linear regression , statistics , trace (psycholinguistics) , trace gas , diffusion , mathematics , standard deviation , regression analysis , confidence interval , data set , regression , standard error , environmental science , econometrics , meteorology , thermodynamics , physics , philosophy , linguistics
Trace gas emission measurements are frequently based on static chamber methods, where the trace gas accumulation within an enclosed headspace is followed over time. This study addressed the statistical part of trace gas measurements by comparing the typical approach, linear regression analysis, with a new method proposed by A.R. Pedersen, which is based on a stochastic extension of the diffusion model described by G.L. Hutchinson and A.R. Mosier. The new method provides an estimate of the emission rate, the standard error, P values, confidence intervals, estimates of model parameters, and a set of methods for validation of the assumed model. It was applied to data of N 2 O emissions from a peat meadow with the groundwater level at 20‐ and 40‐cm depths, respectively. Furthermore, the three models mentioned above were compared in a simulation study using parameter values representative for the observed data. The simulations demonstrated that the assumptions underlying linear regression were violated, that the standard t test for significance did not have the expected properties, and that R 2 was a poor diagnostic for detecting deviations from these assumptions. The Hutchinson and Mosier estimator was not as biased as the linear regression estimator, but the method often failed because a necessary condition was not satisfied by the data, a large standard error was indicated, and the method did not provide a test of significance for the estimated emission rate. The new method provided a good description of the data and useful diagnostics for testing it, and due to its ability to use more observations (longer time series), it had a negligible failure rate and bias.