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(Un)certainty of overall binding constants of Al with dissolved organic matter determined by the Scatchard approach

机译:用Scatchard方法测定Al与溶解有机物的总结合常数的(不确定性)

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One of the best approaches to date to obtain overall binding constants (K_o) 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 K_o 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 K_o values. In this study we present an uncertainty analysis of the fitted K_o 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 K_o 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 K_o values are largely based on extrapolation, a small uncertainty in the fit of the binding data results in an appreciable uncertainty in the obtained K_o. Therefore, while trends in K_o for Al and DOM could easily be discerned and compared, the uncertainty in the K_o values hinders the application in quantitative speciation calculation. More comprehensive speciation models that avoid the use of K_o seem to fit better for this purpose.
机译:迄今为止,从酸性土壤溶液中获得铝和溶解有机物(DOM)的总结合常数(K_o)的最佳方法之一是收集薄膜中具有扩散梯度的“自由”铝数据(DGT)并推断出K_o通过基于Scatchard图拟合连续分布模型来确定值。尽管已有明确的文献证明了Scatchard方法的有用性,但相对较少地关注与最终拟合K_o值相关的不确定性的实际评估。在这项研究中,我们使用具有不同水平随机噪声的合成数据集和使用来自酸性土壤溶液的DGT数据的真实数据集,对拟合K_o值进行了不确定性分析。连续分布模型中的参数及其相应的不确定性上下95%范围是使用改组复杂演化都会(SCEM)算法确定的。尽管在所有情况下都获得了分布模型与实验数据的合理拟合,但是由于三个主要原因,在所得的K_o值中发现了明显的不确定性。首先,即使使用DGT方法也难以获得“免费”的Al数据,从而导致数据的不确定性。其次,在构造斯卡查德图之前,必须估计最大结合能力(MBC)。此MBC中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K_o值主要基于外推法,因此绑定数据拟合的较小不确定性会导致获得的K_o出现相当大的不确定性。因此,尽管可以容易地辨别和比较Al和DOM的K_o趋势,但K_o值的不确定性阻碍了其在定量形态形成计算中的应用。避免使用K_o的更全面的物种模型似乎更适合此目的。

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