发现新型有机污染物的色谱—高分辨质谱系统方法研究

Identification of unknown persistent organic pollutants (POPs) existed in complex environmental compartments is an important and challenging task in the environmental scientific community. The applicant begun researches on the screening and identification of potential POPs in the environment since 2006, with thirty-four emerging compounds in eight groups recognized covering different molecular structures such as heterocyclic, polyfluoralkyl and halogenated bisphenol functional groups. The current research strategies on identification of these compounds are based on specific method development of particular candidate chemicals, which are mainly “accidental” discoveries. By mean of the work experience, the project proposal intends to further develop a robust systematic analytical methodology with the following processes: summarize the general structure features of POPs chemicals, and build independent distinguish arithmetic modules on distinct functional groups of molecular structures for high-resolution mass spectrum, synchronously obtain the physical-chemical property data during the chromatography separation procedures, combine the chemical structure information collected from diverse analytical procedures in the identification strategy and optimize weight scoring equations to increase molecular structure confirmation accuracy. The novelty of the proposal is that it does not target on particular analyte, but emphasize on compatibility of the identification strategy for various kinds of potential chemical candidates. By optimization of the sample pretreatment, instrument qualitative monitoring and data interpretation procedures for modularization and standardization purposes, the goal of the identification of unknown environmental pollutants could be inevitably achieved.

在复杂环境基质中发现未知持久性有机污染物（POPs）是一个挑战性课题。申请人所在课题组2006年以来提出筛选和发现新型POPs的研究方向。到目前为止，共发现氮杂环、全氟烷基、卤代双酚等不同官能团结构的新污染物共8大类34种。这些新型污染物发现的基本技术路线首先是通过选取特定类型的化合物，然后建立特异性分析方法，多为“偶然性”事例，尚未建立起可靠的系统方法体系。本申请项目拟在现有工作经验的基础上，通过归纳POPs物质结构共性，编写针对不同官能团特征的高分辨质谱信号识别算法模块；注重基于色谱分离原理的物理-化学性质信息的同步获取；并将多重结构信息进行整合和赋值加权分析，提高结构确认的可靠性。拟研究方法不以特定化合物为研究对象，强调识别策略的普遍适用性；拟对样品前处理、仪器定性检测和数据分析步骤进行体系化整合，突出识别策略的模块化和标准化，实现未知污染物发现从偶然走向必然。

在复杂环境基质中发现具有持久性有机污染物（POPs）特征的新型环境污染物是一个挑战性课题。新型环境污染物的发现过程多为“偶然性”事例，当前研究思路主要基于常规分析方法进一步深化探索，潜在目标物类型单一，尚未建立可靠的系统方法体系。本项目研究内容紧密围绕申请书拟定的技术路线，通过总结归纳POPs物质的结构共性，以质谱信号数据为主要对象，编写化合物模块化识别的数据挖掘算法，将样品前处理、仪器分析和数据处理流程进行优化开发和整合。研究内容主要包括：（1）在样品前处理方法的普适性研究方面，针对复杂环境样本，优化选择了QuEChERs等萃取净化流程，便于在消除环境基质干扰的前提下尽可能多地保留化合物信息。（2）在化合物信息处理的模块化开发方面，编写了官能团特征模块的识别算法，实现对多类新型污染物的识别和环境行为研究。（3）在化合物信息整合和评价方面，突出了多重化合物信息的同步获取和有机利用，提升目标物结构判别的准确性。..在污泥、大气细颗粒物等复杂环境介质样本中发现合成酚抗氧化剂、芳香族苯酮/硫杂蒽酮/苯胺光引发剂、多氟醚基磺酸、多氟烷基硅烷、羰基硫酸酯/硝酸酯/酮酸等新型污染物共8大类114种。目前已在Environ. Sci. Technol.、TrAC-Trend. Anal. Chem.等环境化学、分析化学重要期刊上发表SCI论文10篇，中文核心期刊论文2篇，撰写《发现新型有机污染物的理论与方法》（科学出版社） 专著一部。形成了新型环境污染物识别研究方法框架，能够为我国化学品管理和风险评估提供技术支持。..综上，研究成果较好地完成并超出了项目的预期目标。

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