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.

This study investigated the birnessite (δ-MnO₂) catalyzed oxidative removal of 1,4-naphthoquinone (1,4-NPQ) in the presence of phenolic mediators; specifically, the kinetics of 1,4-NPQ removal under various conditions was examined, and the reaction pathway of 1,4-NPQ was verified by liquid chromatography–tandem mass spectrometry (LC–MS/MS). The removal rate of 1,4-NPQ by birnessite-catalyzed oxidation (pH = 5) was faster in the presence of phenolic mediators with electron-donating substituents (pseudo-first-order initial stage rate constant (k₁) = 0.380–0.733 h⁻¹) than with electron-withdrawing substituents (k₁ = 0.071–0.244 h⁻¹), and the effect on the substituents showed a positive correlation with the Hammett constant (Σσ) (r² = 0.85, p < 0.001). The rate constants obtained using variable birnessite loadings (0.1–1.0 g L⁻¹), catechol concentrations (0.1–1.0 mM), and reaction sequences indicate that phenolic mediators are the major limiting factor for the cross-coupling reaction of 1,4-NPQ in the initial reaction stages, whereas the birnessite-catalyzed surface reaction acts as the major limiting factor in the later reaction stages. This was explained by the operation of two different reaction mechanisms and reaction products identified by LC-MS/MS.

This study evaluated the cytotoxicity of mixtures of citral (CTR) and either benzisothiazolinone (BIT, Mix-CTR-BIT) or triclosan (TCS, Mix-CTR-TCS) in human A549 lung epithelial cells. We investigated the effects of various mix ratios of these common air freshener ingredients on cell viability, cell proliferation, reactive oxygen species (ROS) generation, and DNA damage. Mix-CTR-BIT and Mix-CTR-TCS significantly decreased the viability of lung epithelial cells and inhibited cell growth in a dose-dependent manner. In addition, both mixtures increased ROS generation, compared to that observed in control cells. In particular, cell viability, growth, and morphology were affected upon increase in the proportion of BIT or TCS in the mixture. However, comet analysis showed that treatment of cells with Mix-CTR-BIT or Mix-CTR-TCS did not increase DNA damage. Taken together, these data suggested that increasing the content of biocides in air fresheners might induce cytotoxicity, and that screening these compounds using lung epithelial cells may contribute to hazard assessment.