重金属水生生物毒性和基准预测模型研究

发布时间：2018-04-04 15:50

本文选题：QSAR　切入点：重金属　出处：《中国环境科学研究院》2017年硕士论文

【摘要】：重金属污染已经成为我国的主要环境问题之一。系统全面的毒性测试需要消耗大量的人力、物力和生物资源,加之许多介质条件下(尤其是海水)的标准毒性测试方法很不完善,目前重金属的生物毒性数据十分有限,严重制约了水质基准制定和环境管理工作。定量结构活性相关(Quantitative structure-activity relationship,QSAR)方法作为现代毒理学最重要的非实验手段之一,能够利用有限的污染物毒性数据,快速预测、评价结构类型相似污染物的生物毒性,在水质基准推导和生态风险评估中有广阔的应用前景。本研究搜集、筛选了重金属对6门8科海洋生物的慢性毒性数据,通过线性回归建立金属毒性与其理化参数的QSAR模型,并预测了 21种未知金属的毒性。结果与物种敏感度分布(Species sensitivity distribution,SSD)法相耦合,推导了保护海洋水生生物的重金属慢性水质基准。金属的毒性不仅与其自身理化参数有关,还受到水环境条件的影响。搜集和筛选了不同盐度、温度条件下,重金属对海洋水生生物的毒性数据;构建基于金属理化参数和水环境条件的QSAR-SSD模型;在此基础上预测了不同温度、盐度条件下重金属的毒性和基准阈值,并通过"原位毒性数据"得到验证。除了简单金属离子体系之外,本研究还构建了预测纳米金属氧化物细胞毒性的nano-QSAR模型。共筛选了 16种纳米金属氧化物对大肠杆菌的毒性数据,分析了它们与金属离子或金属氧化物纳米颗粒的26种理化参数之间的相关关系,构建多元线性回归模型。研究成功预测了 51种纳米金属氧化物的毒性,并基于nano-QSAR模型揭示了纳米金属氧化物潜在的毒理学机制。综上所述,本研究基于有限的毒性数据,构建了金属类无机物的QSAR预测模型,并率先应用于水质基准研究领域,取得了较好的效果,为金属类污染物的基准/标准制定,风险评估和环境管控提供了理论依据。
[Abstract]:Heavy metal pollution has become one of the main environmental problems in China.Systematic and comprehensive toxicity testing requires a large amount of manpower, material resources and biological resources. In addition, the standard toxicity test methods under many medium conditions (especially seawater) are not perfect, so the biotoxicity data of heavy metals are very limited.Seriously restricted the water quality standard formulation and the environment management work.As one of the most important non-experimental methods of modern toxicology, quantitative structure-activity correlation structure-activity correlation (QSAR) method can rapidly predict and evaluate the biotoxicity of similar structural pollutants by using limited pollutant toxicity data.It has a broad application prospect in water quality reference derivation and ecological risk assessment.In this study, the chronic toxicity data of heavy metals to marine organisms of 6 phylum and 8 families were collected and screened. The QSAR model of metal toxicity and its physical and chemical parameters was established by linear regression, and the toxicity of 21 unknown metals was predicted.Results coupled with the species sensitivity distribution and species sensitivity distribution method, the chronic water quality criteria for protecting marine aquatic organisms were derived.The toxicity of metals is not only related to their own physical and chemical parameters, but also affected by the water environment.The toxicity data of heavy metals to marine aquatic organisms at different salinity and temperature were collected and screened. The QSAR-SSD model based on the physical and chemical parameters of metals and the water environment conditions was constructed.The toxicity and baseline threshold of heavy metals under salinity were verified by in situ toxicity data.In addition to the simple metal ion system, a nano-QSAR model was constructed to predict the cytotoxicity of nanometallic oxides.The toxicity data of 16 kinds of nanometallic oxides to Escherichia coli were screened. The correlation between them and 26 physical and chemical parameters of metal ions or metal oxide nanoparticles was analyzed and a multivariate linear regression model was established.The toxicity of 51 nanometallic oxides was successfully predicted, and the potential toxicological mechanism of nano-metal oxides was revealed based on nano-QSAR model.To sum up, based on the limited toxicity data, the QSAR prediction model of metallic inorganic compounds was constructed, and the model was first applied in the field of water quality benchmark research, which achieved good results and was used to establish the standard / standard for metal pollutants.Risk assessment and environmental control provide theoretical basis.
【学位授予单位】：中国环境科学研究院
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X171.5

【参考文献】