Stochastic Diffusion Model for Estimating Trace Gas Emissions with Static Chambers
Stochastic Diffusion Model for Estimating Trace Gas Emissions with Static Chambers
复制标题
用于估算静态室痕量气体排放的随机扩散模型
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
F. Vinther
中科院分区：
文献类型：
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作者：
A. Pedersen;S. O. Petersen;F. Vinther
痕量气体排放测量通常基于静态室方法，其中随时间跟踪封闭顶部空间内的痕量气体积累。本研究通过比较典型的方法，线性回归分析，与A.R. Pedersen的扩散模型是基于G.L.哈钦森和A.R.莫西尔新的方法提供了一个估计的排放率，标准误差，P值，置信区间，模型参数的估计，和一套方法来验证假设的模型。应用该模型对地下水位分别为20 cm和40 cm的泥炭草甸的N2 O排放数据进行了分析。此外，在模拟研究中使用代表观察数据的参数值对上述三种模型进行了比较。模拟结果表明，线性回归的基本假设被违反了，显著性的标准t检验没有预期的特性，R2是检测这些假设偏差的一个很差的诊断方法。哈钦森和莫西尔估计量不像线性回归估计量那样有偏，但该方法经常失败，因为数据不满足必要条件，显示出较大的标准误差，并且该方法没有提供估计排放率的显著性检验。新方法提供了对数据的良好描述和用于测试数据的有用诊断，并且由于其能够使用更多的观察结果（更长的时间序列），它的失败率和偏差可以忽略不计。
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.