Emerging contaminants and their transformation products are widely distributed in the environment. These pollutants carry unknown risks owing to their persistence, migration, and toxicity. The wide variety and complex structures of these substances render them difficult to identify using only target analysis. Suspect screening analysis can identify more substances than target analysis in a single run. However, this analysis method is based on limited data and cannot meet the growing demand for compound identification, especially for emerging contaminants and their transformation products with unknown information. The development of high-resolution mass spectrometry technology has promoted the applications of nontarget analysis in the environmental field, especially for identifying unknown transformation products. At present, the challenges of nontarget analysis include the difficulty of finding compounds of interest and their transformation products from complex data. Molecular networking calculates the similarity between mass spectra based on an improved cosine similarity algorithm. This method can cluster molecular families with similar structures, achieve visualization and a collection of massive mass spectral datasets, and promote the annotation of pollutants through networks and communities. Molecular networking can globally organize and systematically interpret complex tandem mass spectral datasets, providing a new direction for nontarget analysis. This technology was first used in proteomics and gradually introduced into metabolomics for the discovery of new natural products. Recently, it has been introduced into the environmental field for the study of various man-made chemicals, particularly for the discovery of emerging contaminants and their transformation products. In this paper, we introduce a molecular networking analysis method based on high-resolution tandem mass spectrometry and describe its applications in the nontargeted screening of emerging contaminants, focusing on the technical principles, workflow, application status, and future development prospects. This paper discusses the applications of molecular networking technology in the detection of emerging contaminants and their transformation products such as drugs, perfluorinated compounds, and disinfection byproducts. Molecular networking technology is widely applicable to the screening of emerging contaminants in various environmental media, revealing the full range of pollutants in the environment and promoting studies on the environmental behavior and toxicological properties of these compounds.
环境中新污染物及其转化产物分布广泛且风险未知。其种类繁多且结构复杂,使得传统的靶向分析和可疑物筛查分析难以满足分析需求。高分辨质谱技术的发展促进了基于液相色谱-高分辨率串联质谱的非靶向分析在环境领域的应用,尤其在识别未知转化产物方面有所贡献。然而,非靶向分析面临着如何从复杂数据中找到关注化合物及其转化产物的挑战。分子网络反映质谱之间的相似性,将结构彼此相似的分子家族成簇,实现海量质谱数据集的可视化和社区化,并通过网络和社区对污染物进行传播注释,可以全局展示和系统理解复杂串联质谱数据集,为非靶向分析提供了新方向。本文介绍了基于高分辨率串联质谱的分子网络分析方法及其在新污染物的非靶向筛查中的应用,包括技术原理、工作流程、应用现状和未来展望。本文重点展示了分子网络技术在药物、全氟化合物和消毒副产物等新污染物及其转化产物中的应用状况,强调分子网络技术普遍适用于各种环境介质中新污染物的筛查、揭示环境中污染物的全貌、推进化合物的环境行为和毒理学特性的研究。
Keywords: emerging contaminants; high-resolution tandem mass spectrometry; molecular networking; transformation products.