Jansen Boris, Nierop Klaas G J, Vrugt Jasper A, Verstraten Jacobus M
Centre for Geo-ecological Research (ICG), Institute for Biodiversity and Ecosystem Dynamics (IBED), Physical Geography, Universiteit van Amsterdam, Nieuwe Achtergracht 166, NL-1018 WV Amsterdam, The Netherlands.
Water Res. 2004 Mar;38(5):1270-80. doi: 10.1016/j.watres.2003.11.017.
One of the best approaches to date to obtain overall binding constants (Ko) for Al and dissolved organic matter (DOM) from acidic soil solutions is to collect 'free' Al data with diffusive gradients in thin films (DGT) and to infer the Ko values by fitting a continuous distribution model based on Scatchard plots. Although there is clear established literature demonstrating the usefulness of the Scatchard approach, relatively little attention has been given to a realistic assessment of the uncertainties associated with the final fitted Ko values. In this study we present an uncertainty analysis of the fitted Ko values using a synthetic dataset with different levels of random noise and a real data set using DGT data from an acidic soil solution. The parameters in the continuous distribution model and their corresponding upper and lower 95% uncertainty bounds were determined using the Shuffled Complex Evolution Metropolis (SCEM) algorithm. Although reasonable fits of the distribution model to the experimental data were obtained in all cases, an appreciable uncertainty in the resulting Ko values was found due to three main reasons. Firstly, obtaining 'free' Al data even with the DGT method is relatively difficult, leading to uncertainty in the data. Secondly, before Scatchard plots can be constructed, the maximum binding capacity (MBC) must be estimated. Any uncertainty in this MBC propagates into uncertainty associated with the final plots. Thirdly, as the final fitted Ko values are largely based on extrapolation, a small uncertainty in the fit of the binding data results in an appreciable uncertainty in the obtained Ko. Therefore, while trends in Ko for Al and DOM could easily be discerned and compared, the uncertainty in the Ko values hinders the application in quantitative speciation calculation. More comprehensive speciation models that avoid the use of Ko seem to fit better for this purpose.
迄今为止，从酸性土壤溶液中获取铝（Al）与溶解有机物（DOM）的总体结合常数（Ko）的最佳方法之一，是利用薄膜扩散梯度（DGT）收集“游离”铝数据，并通过基于斯卡查德图拟合连续分布模型来推断Ko值。尽管有明确的文献证明了斯卡查德方法的实用性，但相对较少关注对最终拟合的Ko值相关不确定性的实际评估。在本研究中，我们使用具有不同随机噪声水平的合成数据集以及来自酸性土壤溶液的DGT实际数据集，对拟合的Ko值进行了不确定性分析。使用洗牌复合进化 metropolis（SCEM）算法确定了连续分布模型中的参数及其相应的95%上下不确定性界限。尽管在所有情况下分布模型对实验数据都有合理的拟合，但由于三个主要原因，发现所得Ko值存在明显的不确定性。首先，即使使用DGT方法获取“游离”铝数据也相对困难，导致数据存在不确定性。其次，在构建斯卡查德图之前，必须估计最大结合容量（MBC）。该MBC的任何不确定性都会传播到与最终图相关的不确定性中。第三，由于最终拟合的Ko值在很大程度上基于外推，结合数据拟合中的小不确定性会导致所得Ko值存在明显的不确定性。因此，虽然可以轻松辨别和比较Al与DOM的Ko趋势，但Ko值的不确定性阻碍了其在定量形态计算中的应用。为此，避免使用Ko的更全面的形态模型似乎更合适。
