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Predicting organic contaminant concentrations in sediment porewater using solid-phase microextraction

机译:固相微萃取预测沉积物孔隙水中的有机污染物浓度

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摘要

Because of its cost and time saving features, solid-phase microextraction (SPME) is a leading candidate as a biomimic technique in assessing the bioavailable fraction of hydrophobic organic contaminants (HOCs) in sediment porewater. However, no predictive modeling framework in which to systematically address the effect of key parameters on SPME performance for this application exists. In this study, we derived two governing equations to predict (1) the minimum sediment volume (V_s~(min)) required to achieve non-depletive conditions, and (2) dissolved phase HOC porewater concentrations (C_(pw)) as functions of HOC- and sediment specific characteristics in a conceptual three compartment system. The resulting model predicted that V_s~(min) was independent of HOC concentrations both in sediment and porewater, but did vary with hydrophobicity (characterized by log K_(ow)), the fraction of sediment porewater (f_(pw)), and the volume (V_f) of the SPME sorbent phase. Moreover, the effects of these parameters were minimized (i.e., V_s~(min) reached plateaus) as log K_(ow) approached 4-5. Model predictions of C_(pw), a surrogate for SPME-based detection limits in porewater, decreased with increasing sediment volume (V_s) at low V_s values, but rapidly leveled off as V_s increased. A third result suggested that the sediment HOC concentration required for SPME is completely independent of K_(ow). These results suggest that relatively small sediment volumes participate in exchange equilibria among sediment, porewater and the SPME fiber, and that large sediment HOC reservoirs are not needed to improve the detection sensitivity of SPME-based porewater samplers. The ultimate utility of this modeling framework will be to assist future experimental designs and help predict in situ bioavailability of sediment-associated HOCs.
机译:由于其成本节省和时间节省的特点,固相微萃取(SPME)作为生物仿生技术在评估沉积物孔隙水中疏水性有机污染物(HOC)的生物利用度方面是领先的候选者。但是,不存在用于系统解决此应用程序的关键参数对SPME性能影响的预测建模框架。在这项研究中,我们导出了两个控制方程,以预测(1)实现非枯竭条件所需的最小沉积物体积(V_s〜(min)),以及(2)溶解相HOC孔隙水浓度(C_(pw))作为函数概念性三室系统中HOC和沉积物的特定特征所得模型预测V_s〜(min)与沉积物和孔隙水中的HOC浓度无关,但随疏水性(以log K_(ow)为特征),沉积物孔隙水的比例(f_(pw))和SPME吸附剂相的体积(V_f)。此外,随着log K_(ow)接近4-5,这些参数的影响被最小化(即,V_s_(min)达到平稳)。 C_(pw)的模型预测是孔隙水中基于SPME的检测极限的替代物,在低V_s值下随着沉积物体积(V_s)的增加而降低,但随着V_s的增加而迅速趋于平稳。第三个结果表明,SPME所需的沉积物HOC浓度与K_(ow)完全无关。这些结果表明,相对较小的沉积物体积参与了沉积物,孔隙水和SPME纤维之间的交换平衡,并且不需要大型的沉积物HOC储层来提高基于SPME的孔隙水采样器的检测灵敏度。该建模框架的最终用途将是协助将来的实验设计,并帮助预测与沉积物相关的HOC的原位生物利用度。

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