Characterization of Organic Compounds as Complex Mixtures in the Environment: Method Development and Applications

数以万计的有机化学品已被生产并推向全球市场。大量的人造化学品可能与其转化产物一起释放到环境中，因此很难描述复杂的有机混合物的环境风险。这是因为混合物中的大多数成分都是未经鉴定的，或者环境化学家和风险评估人员不太了解。在环境中鲜为人知的化学品中，有些可能对生态系统和人类构成高风险。有必要查明高风险的污染物，并说明其来源、环境归宿和影响。我的研究计划的长期目标是识别环境中具有高潜在风险的鲜为人知的有机污染物，并描述其来源，环境命运和影响。为了实现这一目标，我将使用高分辨率质谱技术进行非目标化学分析和计算机模型模拟有机化学品的环境归宿和风险。将开发不同的方法，将从仪器获得的信息与化学特性和环境危害联系起来。作为一个短期目标，将开发一个基于卷积神经网络的机器学习模型，并将从非目标分析中获得的信息作为输入（GC和LC保留指数、准确质量、质量亏损、同位素丰度、碎片损失）。该模型的输出包括环境持久性、生物累积潜力和毒性。卷积神经网络将使用经验和计算机预测数据进行训练。另一个有待开发的方法是将在非目标分析中检测到的化学峰的所有可能结构投射到能够揭示所检测化学品的环境危害的化学空间上。使用这些方法，可以根据其环境危害对检测到的化学品进行排序和选择。此外，还将根据其随时间、空间和其他环境因素趋势的系统性变化，确定鲜为人知的环境关切化学品。为从非目标分析中提取重要化学品而制定的不同方法将适用于来源区域的大气和水生样品。通过所开发的方法发现的具有高潜在风险的化学品将成为进一步评估其来源、环境归宿和影响的候选物质。围绕确定化学品的潜在环境问题与新开发的方法的目标，拟议的研究计划将促进表征化学压力的复杂混合物对生态系统的影响，并提供预警之前，化学品达到阈值，将不可逆转地恶化生态系统和影响人类健康。该研究也将为化学品管理提供科学支持。

Hundreds-of-thousands of organic chemicals have been produced and introduced to global market. The large number of man-made chemicals potentially released to the environment together with their transformation products makes it difficult to characterize the environmental risks from the complex organic mixture. This is because most constituents of the mixture are unidentified or lesser-known to environmental chemist and risk assessors. Among the lesser-known chemicals in the environment, some can possibly pose high risk to the ecosystem and human population. There is a need to identify the high-risk ones and characterize their sources, environmental fate and impact. The long-term objective of my research program is to identify lesser-known organic contaminants with high potential risk in the environment and to characterize their sources, environmental fate and impact. To achieve the objective, I will use high-resolution mass spectrometry based techniques for non-targeted chemical analysis and computer model simulations on environmental fate and risk of organic chemicals. Different methods will be developed to link information gained from the instrument with chemical properties and environmental hazards. As one short-term objective, a convolutional neutral network based machine-learning model will be developed with information gained from non-targeted analysis as input (GC and LC retention indices, accurate mass, mass defect, abundances of isotopes, fragment losses). Output of the model includes environmental persistence, bioaccumulation potential and toxicity. The convolutional neutral network will be trained with empirical and in silico predicted data. Another method to be developed is to project all possible structures of chemical peaks detected in non-targeted analysis onto chemical space that can disclose environmental hazards of detected chemicals. Using the approaches, detected chemicals can be ranked and selected based on their environmental hazards. In addition, lesser-known chemicals of environmental concerns will also be identified based on their systematic variation with temporal, spatial and other trends in environmental factors. The different approaches developed for retrieval important chemicals from non-targeted analysis will be applied to atmospheric and aquatic samples in source regions. Chemicals with high potential risk revealed by the developed methods will become candidates for further assessment on their sources, environmental fate and impact. Revolving around the objective of identifying chemicals of potential environmental concerns with newly developed methods, the proposed research program will facilitate the characterization of the influence of chemical stressors as complex mixtures on the ecosystems and provide an early warning before chemicals reaching thresholds that would irreversibly deteriorate ecosystem and influence human health. This research will also provide scientific support for chemical management.