几种农药生物富集和消解行为的动力学模型研究
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摘要
本文首先建立了斜生栅藻和培养液中四种氯乙酰胺类除草剂(甲草胺、乙草胺、丙草胺和丁草胺)的含量测定方法。使用纤维素滤膜真空抽滤的方式基本可以分离栅藻细胞和培养液。然后对提取溶剂种类、用量和提取藻细胞样品时是否加水进行了优化,方法采用20mL乙酸乙酯直接提取,然后蒸干乙腈定容,最后使用气相色谱-质谱分析,内标法定量。测定藻细胞和培养液中目标物的方法回收率在86.4%-102.9%,相对标准偏差均低于15.1%,栅藻细胞样品中四种化合物检出限均不高于0.005μg,培养液样品中检出限均不高于0.0002mg/L。本方法具有良好的准确度、精密度和灵敏度。本方法可以用于测定栅藻细胞和培养液中甲草胺、乙草胺、丙草胺和丁草胺的含量并用于研究藻类对这四种氯乙酰胺类除草剂的生物富集。
进而对斜生栅藻中甲草胺和乙草胺的生物富集进行了测定。从36h至96h,培养液中甲草胺和乙草胺浓度均基本保持恒定。栅藻细胞中甲草胺和乙草胺的质量随时间持续增长。生长稀释效应对栅藻细胞中甲草胺和乙草胺的浓度产生了很大的影响,甲草胺和乙草胺的浓度在前期基本稳定或下降,后期上升。栅藻细胞对甲草胺和乙草胺的生物富集因子分别在572-915和376-1068之间。结果表明,斜生栅藻对氯乙酰胺类除草剂有较强的生物富集作用,该类药剂的大量使用可能会对水生生态带来风险。
为了将栅藻细胞的生长考虑在内,对生物配体模型重新进行了推导,得到了3组分别以指数生长、线性生长和对数生长推导得到的模型公式,每组中包含生长曲线方程和一个藻细胞内农药质量对时间的方程。对生长曲线的拟合中,对数生长的方程的拟合效果最优,决定系数R2均达到0.9920。而且藻细胞内农药质量对时间方程的拟合效果同样是基于对数生长的最优,决定系数R2均达到0.9568。结果表明,基于对数生长的生物配体模型可以吻合斜生栅藻对甲草胺和乙草胺的富集动力学。这是生物配体模型首次被应用在有机污染物的富集动力学上。结果说明有机污染物进入生物体的机理可能与生物配体模型相符。
本文还对斜生栅藻对甲草胺和乙草胺生物富集的复合效应进行了测定。部分数据呈现了一定的富集复合效应。例如,在另一化合物存在下,藻细胞富集的甲草胺或乙草胺的质量受到一定的抑制。但总的来说,缺乏一致性的规律,因此需要采用模型对数据进一步评价。复合效应的现象揭示了生物体对有机污染物富集的复杂性,有助于更准确地评估有机污染物的环境风险。
使用推导得到生物配体模型公式对复合富集数据进行了拟合。栅藻细胞生长曲线选择对数生长模型拟合,决定系数R2均不低于0.9911,与实验中实际藻细胞生长状况最吻合。使用基于对数生长的生物配体模型的藻细胞内农药质量对时间方程拟合实验结果很好,决定系数R2均高于0.95。复合条件下,甲草胺和乙草胺的富集均受到了抑制,甲草胺和乙草胺的摄入速率分别降低了约46%和约68%。复合作用对乙草胺富集的影响程度要高于甲草胺。模型公式经过进一步推导得到了两个化合物竞争情况下的模型方程,能够解释甲草胺和乙草胺共同存在下斜生栅藻对甲草胺和乙草胺富集的相互拮抗作用的可能机理。经过模型推理,乙草胺的富集米氏常数较高,甲草胺的富集米氏常数较低,所以栅藻细胞对乙草胺的摄入速率受甲草胺影响较大,而藻细胞对甲草胺的摄入速率受乙草胺影响较小。这是生物配体模型首次被应用于解释有机污染物生物富集的复合效应。生物配体模型克服了传统毒物代谢动力学模型无法解释可能存在的富集复合效应的缺陷,为定量评价有机污染物生物富集的复合效应提供了工具。
此外,本文还对氟硅唑在柑橘和土壤中的消解动态及其消解动力学模型进行了研究。分析方法样品用乙腈提取,PSA净化,气相色谱-质谱分析。方法在柑橘和土壤中的平均回收率在93.1%-107.7%,相对标准偏差均不超过5.1%,检出限分别达到0.003mg/kg和0.001mg/kg。方法具有良好的准确度、精密度和灵敏度。消解实验在湖南、广西和浙江三地进行,结果发现一级动力学模型拟合氟硅唑在柑橘和土壤中的消解动力学比二级动力学模型更合适。根据一级动力学拟合得到的氟硅唑在柑橘中的半衰期为6.3-8.4天,在土壤中的半衰期为5.5-13.4天；氟硅唑在浙江消解最快,其次是广西,在湖南地区消解最慢。
In this work, quantitation methods for analyzing four chloroacetamide herbicides (alachlor, acetochlor, pretilachlor, butachlor) in algae (Scendesmus obliquus) and culture medium were developed. Vacuum filtration with mixed cellulose filter was applied for the purpose of separating algal cells with culture medium. Then extraction solvent, solvent volume and water content for extracting analyte from algal cells samples were optimized. The sample was extracted with20mL ethyl acetate, and then concentrated by rotary evaporator followed by addition certain amount of acetonitrile. All analytes were detected by GC-MS with internal standard calibration method. Average fortified recoveries ranged from86.4%-102.9%with relative standard deviations below15.1%in algal cells and culture medium. The LODs of four compounds in algal cells samples were not below0.005μg, while those in cluture medium were not below0.0002mg/L. The method is of good accuracy, precision, sensitivity and linear relationship. It can be used for analyzing alachlor, acetochlor, pretilachlor, butachlor and also for investigating their bioconcentration in algae.
And then, the bioconcentration of alachlor, acetochlor in Scenedesmus obliquus were investigated. The results showed that the concentration of alachlor and acetochlor in culture medium remained constant from36h to96h. However the mass of the two herbicides in S. obliquus continuously increased with the time. The concentrations of alachlor and acetochlor, which were greatly influenced by growth dilution effect, were stable or decreased at initial growth stage but increased at the end of growth stage. High bioconcentration factors were obtained, ranging572-915for alachlor and376-1068for acetochlor respectively. The results suggested that S. obliquus has a great bioconcentration capability for chloroacetamide herbicides, which may cause the risk for aquatic ecosystem if the herbicides were used largely.
Taking the growth of algae into account, three groups of biotic ligand models were derived based on exponential growth, linear growth, and logarithmic growth of S. obliquus, respectively. Each model was consisted of a growth curve equation and an equation about pesticide mass versus time. Logarithmic growth model acquired the best fitting results (coefficient of determination above0.9920) compared with other growth curve models. Furthermore, fitting the relationship between pesticide mass in algal cells versus time, model based on logarithmic growth acquired the best fitting results (coefficient of determination were more than0.9568). The results suggested that biotic ligand model based on logarithmic growth fits the bioconcentration kinetic of alachlor and acetochlor in S. obliquus. It was the first time for biotic ligand model applied in bioconcentration study of organic pollutants. The results suggested that mechanism of organic pollutants uptake by biont may be the same as biotic ligand model.
The combined effect of alachlor and acetochlor on S. obliquus bioconcentration was also studied. Some of the data suggested combined effects of alachlor and acetochlor on the bioconcentration. For example, in the presence of alachlor or acetochlor, the bioconcentration mass of the other compound in algal cells was inhibited. However there was no consistent pattern for all results, therefore the kinetic models needed further evaluation. The combined effects reveal the complexity of organic pollutants bioconcentration, which will contribute to preciser environmental risk evaluation of organic pollutants.
The combined effect results were further fitted by biotic ligand model mentioned previously. Logarithmic growth model acquired the best fitting results of growth curve of algae (coefficient of determination above0.9911) compared with others, which was consistent with the actual growth situation of algae. Considering the relationship between pesticide mass versus time, model based on logarithmic growth also acquired the best fitting results (coefficient of determination were more than0.95). The bioconcentration of alachlor and acetochlor was inhibited due to the combined effect. The uptake rate of alachlor and acetochlor decreased approximately by46%and68%, respectively. The results indicated that the combined effect on the bioconcentration of acetochlor was more than that on alachlor. Model Equation for competing study was derived, which could explain the possible mechanism of mutual antagonistic effect on bioconcentration of alachlor and acetochlor in S. obliquus in the presence of both alachlor and acetochlor. It was ratiocinated that acetochlor has a higher Michaelis constant than alachlor, which result in that the uptake rate of acetochlor by algal cells was significantly affected by alchlor, and the uptake rate of alachlor by algal cells was less significantly affected by acetochlor. This was the first study of combined effect on bioconcentration of two organic pollutants, and also the first time that biotic ligand model was applied in explaining combined effect of bioconcentration of two organic pollutants. Biotic ligand model overcame the defect of traditional toxicokinetics model which was incapable for explaining potential combined effect of bioconcentration, and provided a quantitative evaluation tool for combined effect of organic pollutants bioconcentration.
Besides, the dissipation dynamics of flusilazole in mandarin and soil was investigated using different kinetic models. The samples were extracted by acetonitrile, cleaned up with PSA, and then analyzed by gas chromatography-mass spectrometry. The average recoveries were93.1%-107.7%in mandarin and soil with relative standard deviations not above5.1%. The LOD (limit of detection) was0.003μg/kg and0.001μg/kg for mandarin and soil, respectively. The method was of good accuracy, precision and sensitivity. Field trials were conducted in Hunan, Guangxi and Zhejiang province. The results showed that the dissipation of flusilazole in mandarin and soil followed first-order kinetics model more than that of second-order kinetics model. Based on first-order kinetics model, the half-lives of flusilazole were6.3-8.4days in mandarin and5.5-13.4days in soil; and flusilazole dissipated the fastest in Zhejiang, intermediate in Guangxi, and the slowest in Hunan.
进而对斜生栅藻中甲草胺和乙草胺的生物富集进行了测定。从36h至96h,培养液中甲草胺和乙草胺浓度均基本保持恒定。栅藻细胞中甲草胺和乙草胺的质量随时间持续增长。生长稀释效应对栅藻细胞中甲草胺和乙草胺的浓度产生了很大的影响,甲草胺和乙草胺的浓度在前期基本稳定或下降,后期上升。栅藻细胞对甲草胺和乙草胺的生物富集因子分别在572-915和376-1068之间。结果表明,斜生栅藻对氯乙酰胺类除草剂有较强的生物富集作用,该类药剂的大量使用可能会对水生生态带来风险。
为了将栅藻细胞的生长考虑在内,对生物配体模型重新进行了推导,得到了3组分别以指数生长、线性生长和对数生长推导得到的模型公式,每组中包含生长曲线方程和一个藻细胞内农药质量对时间的方程。对生长曲线的拟合中,对数生长的方程的拟合效果最优,决定系数R2均达到0.9920。而且藻细胞内农药质量对时间方程的拟合效果同样是基于对数生长的最优,决定系数R2均达到0.9568。结果表明,基于对数生长的生物配体模型可以吻合斜生栅藻对甲草胺和乙草胺的富集动力学。这是生物配体模型首次被应用在有机污染物的富集动力学上。结果说明有机污染物进入生物体的机理可能与生物配体模型相符。
本文还对斜生栅藻对甲草胺和乙草胺生物富集的复合效应进行了测定。部分数据呈现了一定的富集复合效应。例如,在另一化合物存在下,藻细胞富集的甲草胺或乙草胺的质量受到一定的抑制。但总的来说,缺乏一致性的规律,因此需要采用模型对数据进一步评价。复合效应的现象揭示了生物体对有机污染物富集的复杂性,有助于更准确地评估有机污染物的环境风险。
使用推导得到生物配体模型公式对复合富集数据进行了拟合。栅藻细胞生长曲线选择对数生长模型拟合,决定系数R2均不低于0.9911,与实验中实际藻细胞生长状况最吻合。使用基于对数生长的生物配体模型的藻细胞内农药质量对时间方程拟合实验结果很好,决定系数R2均高于0.95。复合条件下,甲草胺和乙草胺的富集均受到了抑制,甲草胺和乙草胺的摄入速率分别降低了约46%和约68%。复合作用对乙草胺富集的影响程度要高于甲草胺。模型公式经过进一步推导得到了两个化合物竞争情况下的模型方程,能够解释甲草胺和乙草胺共同存在下斜生栅藻对甲草胺和乙草胺富集的相互拮抗作用的可能机理。经过模型推理,乙草胺的富集米氏常数较高,甲草胺的富集米氏常数较低,所以栅藻细胞对乙草胺的摄入速率受甲草胺影响较大,而藻细胞对甲草胺的摄入速率受乙草胺影响较小。这是生物配体模型首次被应用于解释有机污染物生物富集的复合效应。生物配体模型克服了传统毒物代谢动力学模型无法解释可能存在的富集复合效应的缺陷,为定量评价有机污染物生物富集的复合效应提供了工具。
此外,本文还对氟硅唑在柑橘和土壤中的消解动态及其消解动力学模型进行了研究。分析方法样品用乙腈提取,PSA净化,气相色谱-质谱分析。方法在柑橘和土壤中的平均回收率在93.1%-107.7%,相对标准偏差均不超过5.1%,检出限分别达到0.003mg/kg和0.001mg/kg。方法具有良好的准确度、精密度和灵敏度。消解实验在湖南、广西和浙江三地进行,结果发现一级动力学模型拟合氟硅唑在柑橘和土壤中的消解动力学比二级动力学模型更合适。根据一级动力学拟合得到的氟硅唑在柑橘中的半衰期为6.3-8.4天,在土壤中的半衰期为5.5-13.4天；氟硅唑在浙江消解最快,其次是广西,在湖南地区消解最慢。
In this work, quantitation methods for analyzing four chloroacetamide herbicides (alachlor, acetochlor, pretilachlor, butachlor) in algae (Scendesmus obliquus) and culture medium were developed. Vacuum filtration with mixed cellulose filter was applied for the purpose of separating algal cells with culture medium. Then extraction solvent, solvent volume and water content for extracting analyte from algal cells samples were optimized. The sample was extracted with20mL ethyl acetate, and then concentrated by rotary evaporator followed by addition certain amount of acetonitrile. All analytes were detected by GC-MS with internal standard calibration method. Average fortified recoveries ranged from86.4%-102.9%with relative standard deviations below15.1%in algal cells and culture medium. The LODs of four compounds in algal cells samples were not below0.005μg, while those in cluture medium were not below0.0002mg/L. The method is of good accuracy, precision, sensitivity and linear relationship. It can be used for analyzing alachlor, acetochlor, pretilachlor, butachlor and also for investigating their bioconcentration in algae.
And then, the bioconcentration of alachlor, acetochlor in Scenedesmus obliquus were investigated. The results showed that the concentration of alachlor and acetochlor in culture medium remained constant from36h to96h. However the mass of the two herbicides in S. obliquus continuously increased with the time. The concentrations of alachlor and acetochlor, which were greatly influenced by growth dilution effect, were stable or decreased at initial growth stage but increased at the end of growth stage. High bioconcentration factors were obtained, ranging572-915for alachlor and376-1068for acetochlor respectively. The results suggested that S. obliquus has a great bioconcentration capability for chloroacetamide herbicides, which may cause the risk for aquatic ecosystem if the herbicides were used largely.
Taking the growth of algae into account, three groups of biotic ligand models were derived based on exponential growth, linear growth, and logarithmic growth of S. obliquus, respectively. Each model was consisted of a growth curve equation and an equation about pesticide mass versus time. Logarithmic growth model acquired the best fitting results (coefficient of determination above0.9920) compared with other growth curve models. Furthermore, fitting the relationship between pesticide mass in algal cells versus time, model based on logarithmic growth acquired the best fitting results (coefficient of determination were more than0.9568). The results suggested that biotic ligand model based on logarithmic growth fits the bioconcentration kinetic of alachlor and acetochlor in S. obliquus. It was the first time for biotic ligand model applied in bioconcentration study of organic pollutants. The results suggested that mechanism of organic pollutants uptake by biont may be the same as biotic ligand model.
The combined effect of alachlor and acetochlor on S. obliquus bioconcentration was also studied. Some of the data suggested combined effects of alachlor and acetochlor on the bioconcentration. For example, in the presence of alachlor or acetochlor, the bioconcentration mass of the other compound in algal cells was inhibited. However there was no consistent pattern for all results, therefore the kinetic models needed further evaluation. The combined effects reveal the complexity of organic pollutants bioconcentration, which will contribute to preciser environmental risk evaluation of organic pollutants.
The combined effect results were further fitted by biotic ligand model mentioned previously. Logarithmic growth model acquired the best fitting results of growth curve of algae (coefficient of determination above0.9911) compared with others, which was consistent with the actual growth situation of algae. Considering the relationship between pesticide mass versus time, model based on logarithmic growth also acquired the best fitting results (coefficient of determination were more than0.95). The bioconcentration of alachlor and acetochlor was inhibited due to the combined effect. The uptake rate of alachlor and acetochlor decreased approximately by46%and68%, respectively. The results indicated that the combined effect on the bioconcentration of acetochlor was more than that on alachlor. Model Equation for competing study was derived, which could explain the possible mechanism of mutual antagonistic effect on bioconcentration of alachlor and acetochlor in S. obliquus in the presence of both alachlor and acetochlor. It was ratiocinated that acetochlor has a higher Michaelis constant than alachlor, which result in that the uptake rate of acetochlor by algal cells was significantly affected by alchlor, and the uptake rate of alachlor by algal cells was less significantly affected by acetochlor. This was the first study of combined effect on bioconcentration of two organic pollutants, and also the first time that biotic ligand model was applied in explaining combined effect of bioconcentration of two organic pollutants. Biotic ligand model overcame the defect of traditional toxicokinetics model which was incapable for explaining potential combined effect of bioconcentration, and provided a quantitative evaluation tool for combined effect of organic pollutants bioconcentration.
Besides, the dissipation dynamics of flusilazole in mandarin and soil was investigated using different kinetic models. The samples were extracted by acetonitrile, cleaned up with PSA, and then analyzed by gas chromatography-mass spectrometry. The average recoveries were93.1%-107.7%in mandarin and soil with relative standard deviations not above5.1%. The LOD (limit of detection) was0.003μg/kg and0.001μg/kg for mandarin and soil, respectively. The method was of good accuracy, precision and sensitivity. Field trials were conducted in Hunan, Guangxi and Zhejiang province. The results showed that the dissipation of flusilazole in mandarin and soil followed first-order kinetics model more than that of second-order kinetics model. Based on first-order kinetics model, the half-lives of flusilazole were6.3-8.4days in mandarin and5.5-13.4days in soil; and flusilazole dissipated the fastest in Zhejiang, intermediate in Guangxi, and the slowest in Hunan.
引文
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