Stochastic diffusion model for estimating trace gas emissions with static chambers

Trace pas emission measurements are frequently based on static chamber meth ods, where the trace gas accumulation within an enclosed headspace is follo wed over time. This study addressed the statistical part of trace gas measu rements by comparing the typical approach, linear regression analysis, with a new method proposed by A.R, Pedersen, which is based oil a stochastic ex tension of the di fusion model described by G,L, Hutchinson and A.R. Mosier . The new method provides an estimate of the emission rate, the standard er ror, P values, confidence intervals, estimates of model parameters, and a s et of methods fur validation of the assumed model. It was applied to data o f N2O emissions from a peat meadow with the groundwater level at 20- and 40 -cm depths, respectively; Furthermore, the three models mentioned above wer e compared in a simulation study using parameter values representative fur the observed data. The simulations demonstrated that the assumptions underl ying linear regression were violated, that the standard t test for signific ance did not have the expected properties, and that R-2 was a poor diagnost ic for detecting deviations from these assumptions. The Hutchinson and Mosi er estimator was not as biased as the linear regression estimator, but the method often failed because ii 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 pro vided 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 h ad a negligible failure rate and bias.