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

环境中有机化合物作为复杂混合物的表征：方法开发和应用

• 批准号: RGPIN-2021-04132
• 负责人: ZHANG, Xianming
• 金额: $ 2.11万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=748499
• 关键词: Characterization; Organic; Compounds; Complex; Mixtures

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.

成千上万的有机化学品已被生产并推向全球市场。大量的人造化学品及其转化产物可能释放到环境中，因此很难描述复杂有机混合物的环境风险。这是因为大多数混合物的成分是未知的，或者对环境化学家和风险评估人员知之甚少。在环境中不太为人所知的化学物质中，有些可能对生态系统和人类构成高风险。有必要查明高风险因素，并说明其来源、环境命运和影响。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，并表征其来源，环境命运和影响。为了实现这一目标，我将使用基于高分辨率质谱的技术进行非靶向化学分析，并对有机化学品的环境命运和风险进行计算机模型模拟。将开发不同的方法，将仪器获得的信息与化学性质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，将从非目标分析中获得的信息作为输入（GC和LC保留指数、精确质量、质量缺陷、同位素丰度、碎片损失）。该模型的输出包括环境持久性、生物蓄积潜力和毒性。卷积神经网络将使用经验数据和计算机预测数据进行训练。另一种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化学空间，从而可以揭示被检测化学品的环境危害。使用这些方法，可以根据检测到的化学品的环境危害对其进行排序和选择。此外，还将根据其随环境因素的时间、空间和其他趋势的系统变化来确定对环境有影响的鲜为人知的化学品。为从非目标分析中检索重要化学物质而开发的不同方法将应用于源区的大气和水生样品。开发的方法所揭示的高潜在风险化学品将成为进一步评估其来源、环境命运和影响的候选者。围绕用新开发的方法识别潜在环境问题的化学品这一目标，拟议的研究计划将有助于描述化学压力源作为复杂混合物对生态系统的影响，并在化学品达到不可逆转地恶化生态系统和影响人类健康的阈值之前提供早期预警。本研究也将为化学品管理提供科学依据。