Meher Anil Kumar, Zarouri Akli
Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, MN 55108, USA.
Molecules. 2025 Jan 17;30(2):364. doi: 10.3390/molecules30020364.
Emerging contaminants (ECs), encompassing pharmaceuticals, personal care products, pesticides, and industrial chemicals, represent a growing threat to ecosystems and human health due to their persistence, bioaccumulation potential, and often-unknown toxicological profiles. Addressing these challenges necessitates advanced analytical tools capable of detecting and quantifying trace levels of ECs in complex environmental matrices. This review highlights the pivotal role of mass spectrometry (MS) in monitoring ECs, emphasizing its high sensitivity, specificity, and versatility across various techniques such as Gas Chromatography-Mass Spectrometry (GC-MS), Liquid Chromatography-Mass Spectrometry (LC-MS), and High-Resolution Mass Spectrometry (HR-MS). The application of MS has facilitated the real-time detection of volatile organic compounds, the comprehensive non-targeted screening of unknown contaminants, and accurate quantification in diverse matrices including water, soil, and air. Despite its effectiveness, challenges such as matrix interferences, a lack of standardized methodologies, and limited spectral libraries persist. However, recent advancements, including hybrid MS systems and the integration of artificial intelligence (AI), are paving the way for more efficient environmental monitoring and predictive modeling of contaminant behavior. Continued innovation in MS technologies and collaborative efforts are essential to overcome existing challenges and ensure sustainable solutions for mitigating the risks associated with emerging contaminants.
新兴污染物(ECs)包括药品、个人护理产品、农药和工业化学品,由于其持久性、生物累积潜力以及通常未知的毒理学特征,对生态系统和人类健康构成了日益严重的威胁。应对这些挑战需要先进的分析工具,能够检测和量化复杂环境基质中痕量水平的新兴污染物。本综述强调了质谱(MS)在监测新兴污染物中的关键作用,强调了其在气相色谱 - 质谱(GC-MS)、液相色谱 - 质谱(LC-MS)和高分辨率质谱(HR-MS)等各种技术中的高灵敏度、特异性和通用性。质谱的应用促进了挥发性有机化合物的实时检测、未知污染物的全面非靶向筛查以及在包括水、土壤和空气在内的各种基质中的准确定量。尽管其有效,但基质干扰、缺乏标准化方法以及光谱库有限等挑战仍然存在。然而,包括混合质谱系统和人工智能(AI)集成在内的最新进展,正在为更高效的环境监测和污染物行为预测建模铺平道路。质谱技术的持续创新和合作努力对于克服现有挑战以及确保减轻与新兴污染物相关风险的可持续解决方案至关重要。