表层沉积物上多溴联苯醚的吸附行为及其毒性特征研究
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摘要
本文利用正交试验设计、遗传神经网络、单因素实验研究了十溴联苯醚的分散液液微萃取前处理方法；采用选择性萃取、连续萃取、因子分析、多元线性回归研究了多溴联苯醚污染沉积物不同组分中多溴联苯醚的分布规律；利用连续性萃取、吸附动力学实验、吸附热力学实验、线性回归方法研究了十溴联苯醚在沉积物上的吸附行为及吸附机理；利用发光菌实验、量子化学从头计算法、逐步多元线性回归研究了十溴联苯醚的发光菌毒性、十溴联苯醚和重金属铜、锌的联合毒性以及多溴联苯醚的剂量效应关系模型。研究得出：
离子强度是水样中十溴联苯醚分散液液微萃取方法中萃取回收率及富集倍数的主要影响因素。同时,萃取剂体积也影响分散液液微萃取的富集倍数。分散液液微萃取的最优化条件：10μL四氯乙烯、0.71mL丙酮、pH5、NaCl2.00%、萃取时间10min。丙酮体积可同时影响超声辅助萃取-分散液液微萃取-上浮溶剂固化方法的萃取回收率和萃取峰面积。超声辅助萃取-分散液液微萃取-上浮溶剂固化的最优化条件为：1.00g样品、丙酮体积3mL、超声20min、分散剂0.59mL、萃取剂为20.5μL十一醇、离子强度20%、萃取时间10.00min。
松花江吉林市段上游采样点中多溴联苯醚含量普遍高于下游采样点,十溴联苯醚(deca-BDEs)是多溴联苯醚同系物中的主要组分。商品deca-BDEs是松花江吉林市江段沉积物多溴联苯醚的主要来源,其次为商品penta-BDEs和octa-BDEs。表层沉积物各组分对十溴联苯醚的吸附作用大于对其他多溴联苯醚的吸附。表层沉积物中锰氧化物和其他组分的交互作用会抑制对多溴联苯醚的吸附,铁氧化物和有机质的交互作用为协同作用,锰氧化物可能分别通过氧化作用改变铁氧化物表面电荷电性、通过氧化作用抑制有机质的官能团来抑制铁氧化物、有机质对多溴联苯醚的吸附。
十溴联苯醚在松花江沉积物上的吸附量在48h时达到最大值,Elovich方程能较好地拟合十溴联苯醚的动力学吸附曲线。对于十溴联苯醚的热力学曲线而言Freundlich方程的拟合效果最好。沉积物中有机质含量和十溴联苯醚的最大吸附量间具有显著的相关性。表层沉积物中铁氧化物、锰氧化物和有机质对十溴联苯醚均有吸附能力。浓度低时十溴联苯醚在沉积物上的吸附机理以吸附作用为主,浓度高至十溴联苯醚的最大吸附量时,其吸附机理以分配作用为主。
暴露于十溴联苯醚溶液中后,发光菌的发光强度先降低而后趋于平稳。十溴联苯醚发光菌毒性的剂量效应曲线可用Weibull模型和Logit模型拟合。助溶剂二甲亚砜和十溴联苯醚对发光菌毒性的交互作用为协同作用,十溴联苯醚和重金属铜、锌的交互作用为拮抗作用。多溴联苯醚的芳香烃受体的相对结合能力主要与多溴联苯醚的极化率、超极化率相关。建立的剂量效应关系模型可较好预测多溴联苯醚的毒性(R=0.957),预测残差的标准偏差为0.158,预测模型稳定,不存在多重共线性。
Orthogonal test, genetic algorithm neural network and single factor experiment were used to optimize detection method of decabrominated diphenyl ether (BDE-209) based on dispersive liquid-liquid microextraction (DLLME) method in this paper. Distribution of polybrominated diphenyl ethers (PBDEs) on components of surficial sediments from Songhua River of Jilin City was investigated using selective extraction, sequential extraction, factor analysis and multiple linear regressions. Sequential extraction, sorption dynamics, sorption thermodynamics experiments and linear regression were used to investigate sorption behavior and sorption mechanism of BDE-209onto surficial sediments. Toxity of PBDEs was studied by luminous bacteria, Hartree-Fock metod and multiple linear stepwise regressions. The results of research of this paper were shown as follow:
Ionic strength had significant effect on the enrichment factor (EF) and extraction recovery (ER) of BDE-209. In addition, volume of extraction solvents affected the EF of BDE-209as well as ionic strength. The optimum conditions of DLLME was to use10μL of tetrachloroethylene,0.71mL of acetone, pH=5,2.00%of NaCl and5min of extraction time. The ER and peak area of dispersive liquid-liquid microextraction based on the solidification of floating organic drop coupled with ultrasonic-assisted extraction (UAE-DLLME-SFO) was affected by volume of acetone. The optimum UAE-DLLME-SFO was to use1.00g of sediment sample,3mL of acetone,10min of UAE,0.59mL of disperser solvents,20.5μL of undecanol,20%of NaCl,10min of DLLME.
PBDEs concentration of sediment samples from downstream sample locations of Songhua River (Jilin City) was higher than samples collected from upstream. BDE-209was principal component of PBDEs in all the sediment samples mentioned above. Commercial deca-BDEs was the primary source of PBDEs in Songhua River (Jilin City), followed by penta-BDEs and octa-BDEs. Sorption ability of BDE-209on components of sediment is higher than the ability of other PBDEs. Interaction of Mn oxides with other sediments components on sorption of PBDEs was inhibition. While the interaction between Fe oxides and organic matters contributed to sorption of PBDEs. The inhibition between Mn oxides and other components of sediments may due to the oxidation of Mn oxides.
The BDE-209sorption onto sediments reached maximum after48h of sorption dynamics experement. Elovich model achieved relatively good simulating effect on the dynamics of BDE-209sorption. The predicting effect of Freundlich model on sorption thermodynamic of BDE-209was the best among the3models. Contents of organic matters in sediments were related with the maximum sorption capacitys of sediments to BDE-209. Fe oxides, Mn oxides and organic matters in sediments all showed sorption capacity to BDE-209in water. When the concentration was low, the sorption of BDE-209by sediments was mainly adsorption action. However, the sorption of BDE-209was mainly patitioned into organic matters by the action of patitioning when the BDE-209concentration up to the maximum sorption amounts of sediments to BDE-209can be achieved.
Luminous intensity of luminous bacteria decreased and then went steadily after been exposed to BDE-209solution. Weibull model and Logit model are used to fit dose-effect curve of BDE-209to luminous bacteria. Interaction of dimethyl sulfoxide (cosolvent) with BDE-209promoted the toxicity of BDE-209to luminous bacteria. Interaction of BDE-209with Cu or Zn on luminous bacteria was inhibition. The relative binding affinities (RBAs) to aryl hydro carbon receptor (AhR) of PBDEs were mainly explained by polarizabilities and hyperpolarizabilities of PBDEs calculated by Hartree-Fock analysis. The optimized regression model was selected through Adj R2and F. The optimized quantitative structure-activity relationships (QSAR) model included αyz,△a, βyzz, βxxy and αxx. Multi-collinearity among the variables was not found in this model.
离子强度是水样中十溴联苯醚分散液液微萃取方法中萃取回收率及富集倍数的主要影响因素。同时,萃取剂体积也影响分散液液微萃取的富集倍数。分散液液微萃取的最优化条件：10μL四氯乙烯、0.71mL丙酮、pH5、NaCl2.00%、萃取时间10min。丙酮体积可同时影响超声辅助萃取-分散液液微萃取-上浮溶剂固化方法的萃取回收率和萃取峰面积。超声辅助萃取-分散液液微萃取-上浮溶剂固化的最优化条件为：1.00g样品、丙酮体积3mL、超声20min、分散剂0.59mL、萃取剂为20.5μL十一醇、离子强度20%、萃取时间10.00min。
松花江吉林市段上游采样点中多溴联苯醚含量普遍高于下游采样点,十溴联苯醚(deca-BDEs)是多溴联苯醚同系物中的主要组分。商品deca-BDEs是松花江吉林市江段沉积物多溴联苯醚的主要来源,其次为商品penta-BDEs和octa-BDEs。表层沉积物各组分对十溴联苯醚的吸附作用大于对其他多溴联苯醚的吸附。表层沉积物中锰氧化物和其他组分的交互作用会抑制对多溴联苯醚的吸附,铁氧化物和有机质的交互作用为协同作用,锰氧化物可能分别通过氧化作用改变铁氧化物表面电荷电性、通过氧化作用抑制有机质的官能团来抑制铁氧化物、有机质对多溴联苯醚的吸附。
十溴联苯醚在松花江沉积物上的吸附量在48h时达到最大值,Elovich方程能较好地拟合十溴联苯醚的动力学吸附曲线。对于十溴联苯醚的热力学曲线而言Freundlich方程的拟合效果最好。沉积物中有机质含量和十溴联苯醚的最大吸附量间具有显著的相关性。表层沉积物中铁氧化物、锰氧化物和有机质对十溴联苯醚均有吸附能力。浓度低时十溴联苯醚在沉积物上的吸附机理以吸附作用为主,浓度高至十溴联苯醚的最大吸附量时,其吸附机理以分配作用为主。
暴露于十溴联苯醚溶液中后,发光菌的发光强度先降低而后趋于平稳。十溴联苯醚发光菌毒性的剂量效应曲线可用Weibull模型和Logit模型拟合。助溶剂二甲亚砜和十溴联苯醚对发光菌毒性的交互作用为协同作用,十溴联苯醚和重金属铜、锌的交互作用为拮抗作用。多溴联苯醚的芳香烃受体的相对结合能力主要与多溴联苯醚的极化率、超极化率相关。建立的剂量效应关系模型可较好预测多溴联苯醚的毒性(R=0.957),预测残差的标准偏差为0.158,预测模型稳定,不存在多重共线性。
Orthogonal test, genetic algorithm neural network and single factor experiment were used to optimize detection method of decabrominated diphenyl ether (BDE-209) based on dispersive liquid-liquid microextraction (DLLME) method in this paper. Distribution of polybrominated diphenyl ethers (PBDEs) on components of surficial sediments from Songhua River of Jilin City was investigated using selective extraction, sequential extraction, factor analysis and multiple linear regressions. Sequential extraction, sorption dynamics, sorption thermodynamics experiments and linear regression were used to investigate sorption behavior and sorption mechanism of BDE-209onto surficial sediments. Toxity of PBDEs was studied by luminous bacteria, Hartree-Fock metod and multiple linear stepwise regressions. The results of research of this paper were shown as follow:
Ionic strength had significant effect on the enrichment factor (EF) and extraction recovery (ER) of BDE-209. In addition, volume of extraction solvents affected the EF of BDE-209as well as ionic strength. The optimum conditions of DLLME was to use10μL of tetrachloroethylene,0.71mL of acetone, pH=5,2.00%of NaCl and5min of extraction time. The ER and peak area of dispersive liquid-liquid microextraction based on the solidification of floating organic drop coupled with ultrasonic-assisted extraction (UAE-DLLME-SFO) was affected by volume of acetone. The optimum UAE-DLLME-SFO was to use1.00g of sediment sample,3mL of acetone,10min of UAE,0.59mL of disperser solvents,20.5μL of undecanol,20%of NaCl,10min of DLLME.
PBDEs concentration of sediment samples from downstream sample locations of Songhua River (Jilin City) was higher than samples collected from upstream. BDE-209was principal component of PBDEs in all the sediment samples mentioned above. Commercial deca-BDEs was the primary source of PBDEs in Songhua River (Jilin City), followed by penta-BDEs and octa-BDEs. Sorption ability of BDE-209on components of sediment is higher than the ability of other PBDEs. Interaction of Mn oxides with other sediments components on sorption of PBDEs was inhibition. While the interaction between Fe oxides and organic matters contributed to sorption of PBDEs. The inhibition between Mn oxides and other components of sediments may due to the oxidation of Mn oxides.
The BDE-209sorption onto sediments reached maximum after48h of sorption dynamics experement. Elovich model achieved relatively good simulating effect on the dynamics of BDE-209sorption. The predicting effect of Freundlich model on sorption thermodynamic of BDE-209was the best among the3models. Contents of organic matters in sediments were related with the maximum sorption capacitys of sediments to BDE-209. Fe oxides, Mn oxides and organic matters in sediments all showed sorption capacity to BDE-209in water. When the concentration was low, the sorption of BDE-209by sediments was mainly adsorption action. However, the sorption of BDE-209was mainly patitioned into organic matters by the action of patitioning when the BDE-209concentration up to the maximum sorption amounts of sediments to BDE-209can be achieved.
Luminous intensity of luminous bacteria decreased and then went steadily after been exposed to BDE-209solution. Weibull model and Logit model are used to fit dose-effect curve of BDE-209to luminous bacteria. Interaction of dimethyl sulfoxide (cosolvent) with BDE-209promoted the toxicity of BDE-209to luminous bacteria. Interaction of BDE-209with Cu or Zn on luminous bacteria was inhibition. The relative binding affinities (RBAs) to aryl hydro carbon receptor (AhR) of PBDEs were mainly explained by polarizabilities and hyperpolarizabilities of PBDEs calculated by Hartree-Fock analysis. The optimized regression model was selected through Adj R2and F. The optimized quantitative structure-activity relationships (QSAR) model included αyz,△a, βyzz, βxxy and αxx. Multi-collinearity among the variables was not found in this model.
引文
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