Yang Ze-Yu, Zeng Eddy Y, Maruya Keith A, Mai Bi-Xian, Ran Yong
State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, P.O. Box 1131, Guangzhou, Guangdong 510640, China.
Chemosphere. 2007 Jan;66(8):1408-14. doi: 10.1016/j.chemosphere.2006.09.050. Epub 2006 Nov 7.
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 logK(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 logK(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.
由于具有成本低和节省时间的特点,固相微萃取(SPME)作为一种仿生技术,在评估沉积物孔隙水中疏水性有机污染物(HOCs)的生物可利用部分方面是主要候选方法。然而,目前不存在一个可系统解决关键参数对该应用中SPME性能影响的预测建模框架。在本研究中,我们推导了两个控制方程,以预测(1)实现非消耗性条件所需的最小沉积物体积(V(s)min),以及(2)在一个概念性的三室系统中,溶解相HOC孔隙水浓度(C(pw))作为HOC和沉积物特定特征的函数。所得模型预测,V(s)min与沉积物和孔隙水中的HOC浓度无关,但会随疏水性(以logK(ow)表征)、沉积物孔隙水分数(f(pw))以及SPME吸附剂相的体积(V(f))而变化。此外,当logK(ow)接近4 - 5时,这些参数的影响最小化(即V(s)min达到平稳状态)。孔隙水中基于SPME的检测限的替代指标C(pw)的模型预测,在低V(s)值时随沉积物体积(V(s))增加而降低,但随着V(s)增加迅速趋于平稳。第三个结果表明,SPME所需的沉积物HOC浓度完全独立于K(ow)。这些结果表明,相对较小体积的沉积物参与沉积物、孔隙水和SPME纤维之间的交换平衡,并且不需要大量的沉积物HOC储存库来提高基于SPME的孔隙水采样器的检测灵敏度。这个建模框架的最终用途将是协助未来的实验设计,并帮助预测沉积物相关HOCs的原位生物可利用性。