The number of chemicals with potential to reach the environment is still largely unknown, which poses great challenges for both environmental scientists and analytical chemists. Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is currently the instrumentation of choice for identification of wide-scope polar chemicals of concern (CECs) in water. This review critically evaluates all steps involved in screening for polar CECs in water, including sampling and extraction, analysis by LC-HRMS, data (pre-)treatment, evaluation and reporting. Passive samplers and direct injection, in combination with LC-HRMS, provide new opportunities compared with conventional grab water sampling, as do instrumental advances such as ion-mobility spectrometry coupled to HRMS (IM-HRMS). In this paper, we argue that target, suspect and non-target screening should not be viewed as three separate principles, but rather as conceptual approaches to general data treatment strategies that can be linked together. Due to the large amount of data generated, smart prioritisation strategies are needed, in particular for non-target screening, to reduce complexity and focus on data of high interest. We critically evaluate existing strategies and consider that each prioritisation step will result in data loss (as any other step in a screening study), requiring compromises depending on the research question to be tackled. Many different data treatment strategies have been developed in recent years, but structure elucidation remains a challenging and time-consuming task. We discuss current and potential future trends, e.g. effect-based methods that can be used as future prioritisation tools, technological advances like IM-HRMS and improved software solutions that can enable new data treatment strategies.

仍然有可能到达环境的化学物质数量仍然未知，这对环境科学家和分析化学家都构成了巨大的挑战。液相色谱与高分辨率质谱法（LC-HRMS）耦合是目前鉴定水中广泛关注的极性化学品（CEC）的首选仪器。这项审查严格评估了筛选水中极性CEC的所有步骤，包括采样和提取，LC-HRMS分析，数据（预处理），评估和报告。与传统的抓取水采样相比，无源采样器和直接进样与LC-HRMS相结合提供了新的机遇，诸如离子迁移谱仪与HRMS（IM-HRMS）耦合等仪器技术的进步也为我们提供了新的机遇。在本文中，我们认为该目标 可疑筛查和非目标筛查不应被视为三个独立的原则，而应视为可联系在一起的一般数据处理策略的概念方法。由于生成了大量数据，因此需要智能优先级排序策略，尤其是对于非目标筛选，以降低复杂性并关注高关注度数据。我们严格评估现有策略，并认为每个优先步骤都会导致数据丢失（与筛查研究中的任何其他步骤一样），这需要根据要解决的研究问题做出妥协。近年来已经开发了许多不同的数据处理策略，但是结构阐明仍然是一项艰巨而耗时的任务。我们讨论当前和潜在的未来趋势，例如