Effluent from wastewater treatment plants is considered an important source of micropollutants (MPs) in aquatic environments. However, monitoring MPs in effluents is often inefficient owing to the variety in their types. Thus, this study derived marker constituents to estimate the behavior of MPs in each cluster using the self-organizing map (SOM), a machine learning-based clustering analysis method. In SOM analysis, the physicochemical properties, functional groups, and the initial biotransformation rules of 29 out 42 MPs were used to ultimately estimate the degradation rate constants of 13 MPs. Consequently, when the physicochemical properties and functional groups were considered, SOM analysis showed outstanding performance to label MPs with an accuracy value of 0.75 for each aerobic and anoxic condition. Based on the clustering results, 11 MPs were determined to be marker constituents under each aerobic and anoxic condition. Moreover, an estimation method for the rate constants of unlabeled MPs was successfully developed using the identified markers with the random forest classifier. The proposed algorithm could estimate both sorption and biotransformation of MPs regardless of dominant removal mechanisms, whether the MPs were removed by sorption or biotransformation. An accuracy of 0.77 was calculated for estimating rate constants under both aerobic and anoxic conditions, which is remarkably higher than those reported previously. The proposed procedure could be extended further to efficiently monitor MPs in effluents.

废水处理厂的污水被认为是水生环境中微污染物（MP）的重要来源。然而，由于其类型多种多样，监测废水中的 MP 往往效率低下。因此，本研究使用自组织图（SOM）（一种基于机器学习的聚类分析方法）导出标记成分来估计每个聚类中 MP 的行为。在SOM分析中，利用42个MP中29个的理化性质、官能团和初始生物转化规则，最终估计了13个MP的降解速率常数。因此，当考虑物理化学性质和官能团时，SOM 分析显示出标记 MP 的出色性能，对于每种有氧和缺氧条件，准确度值为 0.75。根据聚类结果，在各有氧和缺氧条件下，确定 11 个 MP 为标记成分。此外，使用随机森林分类器识别的标记成功开发了未标记 MP 速率常数的估计方法。所提出的算法可以估计 MP 的吸附和生物转化，无论主要的去除机制如何，无论 MP 是通过吸附还是生物转化去除。在有氧和缺氧条件下估算速率常数的准确度为 0.77，明显高于之前报道的准确度。拟议的程序可以进一步扩展，以有效监测废水中的 MP。