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17 种邻苯二甲酸酯在三种土壤上的吸附行为:pH 和溶解有机质的影响、吸附系数测定和 QSPR 研究。

Sorption behavior of 17 phthalic acid esters on three soils: effects of pH and dissolved organic matter, sorption coefficient measurement and QSPR study.

机构信息

State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Xianlin Campus, Nanjing University, Nanjing 210046, PR China.

出版信息

Chemosphere. 2013 Sep;93(1):82-9. doi: 10.1016/j.chemosphere.2013.04.081. Epub 2013 Jun 4.

Abstract

This work studies the sorption behaviors of phthalic acid esters (PAEs) on three soils by batch equilibration experiments and quantitative structure property relationship (QSPR) methodology. Firstly, the effects of soil type, dissolved organic matter and pH on the sorption of four PAEs (DMP, DEP, DAP, DBP) are investigated. The results indicate that the soil organic carbon content has a crucial influence on sorption progress. In addition, a negative correlation between pH values and the sorption capacities was found for these four PAEs. However, the effect of DOM on PAEs sorption may be more complicated. The sorption of four PAEs was promoted by low concentrations of DOM, while, in the case of high concentrations, the influence of DOM on the sorption was complicated. Then the organic carbon content normalized sorption coefficient (logKoc) values of 17 PAEs on three soils were measured, and the mean values ranged from 1.50 to 7.57. The logKoc values showed good correlation with the corresponding logKow values. Finally, two QSPR models were developed with 13 theoretical parameters to get reliable logKoc predictions. The leave-one-out cross validation (CV-LOO) indicated that the internal predictive power of the two models was satisfactory.

摘要

本研究通过批量平衡实验和定量结构-性质关系(QSPR)方法研究了邻苯二甲酸酯(PAEs)在三种土壤上的吸附行为。首先,考察了土壤类型、溶解有机质和 pH 值对四种 PAEs(DMP、DEP、DAP、DBP)吸附的影响。结果表明,土壤有机碳含量对吸附过程有重要影响。此外,对于这四种 PAEs,发现 pH 值与吸附容量之间存在负相关关系。然而,DOM 对 PAEs 吸附的影响可能更为复杂。在低浓度 DOM 的情况下,四种 PAEs 的吸附得到促进,而在高浓度的情况下,DOM 对吸附的影响较为复杂。然后,测量了 17 种 PAEs 在三种土壤上的有机碳归一化吸附系数(logKoc)值,平均值范围为 1.50 至 7.57。logKoc 值与相应的 logKow 值具有良好的相关性。最后,建立了两个包含 13 个理论参数的 QSPR 模型,以获得可靠的 logKoc 预测。留一法交叉验证(CV-LOO)表明,两个模型的内部预测能力令人满意。

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