Supercritical Fluid Chromatography Coupled to High-Resolution Mass Spectrometry Reveals Persistent Mobile Organic Compounds with Unknown Toxicity in Wastewater Effluents.

Broad screening approaches for monitoring wastewater are normally based on reversed-phase liquid chromatography (LC) coupled to high-resolution mass spectrometry (HRMS). This method is not sufficient for the very polar micropollutants, neglected in the past due to a lack of suitable analytical methods. In this study, we used supercritical fluid chromatography (SFC) to detect very polar and yet-undetected micropollutants in wastewater effluents. We tentatively identified 85 compounds, whereas 18 have only rarely been detected and 11 have not previously been detected in wastewater effluents such as 17α-hydroxypregnenolone, a likely transformation product (TP) of steroids, and 1H-indole-3-carboxamide, a likely TP from new synthetic cannabinoids. Suspect screening of 25 effluent wastewater samples from 8 wastewater treatment plants revealed several distinct potential pollution sources such as a pharmaceutical company and a golf court. The analysis of the same samples with LC-HRMS showed clearly how SFC increases the ionization efficiency for low-molecular-weight micropollutants (m/z < 300 Da) by a factor 2 to 87 times, which significantly improved the mass spectra for identifying very polar compounds. In order to assess which micropollutants might be of environmental concern, literature and toxicological databases were screened. There was a lack of available hazard and bio-activity data for regulatory-relevant in vitro and in vivo assays for >50% of the micropollutants. Especially, 70% of the data were lacking for the whole organism (in vivo) tests.

Constructed wetlands as nature-based solutions in managing per-and poly-fluoroalkyl substances (PFAS): Evidence, mechanisms, and modelling.

Per- and poly-fluoroalkyl substances (PFAS) have emerged as newly regulated micropollutants, characterised by extreme recalcitrance and environmental toxicity. Constructed wetlands (CWs), as a nature-based solution, have gained widespread application in sustainable water and wastewater treatment and offer multiple environmental and societal benefits. Despite CWs potential, knowledge gaps persist in their PFAS removal capacities, associated mechanisms, and modelling of PFAS fate. This study carried out a systematic literature review, supplemented by unpublished experimental data, demonstrating the promise of CWs for PFAS removal from the influents of varying sources and characteristics. Median removal performances of 64, 46, and 0 % were observed in five free water surface (FWS), four horizontal subsurface flow (HF), and 18 vertical flow (VF) wetlands, respectively. PFAS adsorption by the substrate or plant root/rhizosphere was deemed as a key removal mechanism. Nevertheless, the available dataset resulted unsuitable for a quantitative analysis. Data-driven models, including multiple regression models and machine learning-based Artificial Neural Networks (ANN), were employed to predict PFAS removal. These models showed better predictive performance compared to various mechanistic models, which include two adsorption isotherms. The results affirmed that artificial intelligence is an efficient tool for modelling the removal of emerging contaminants with limited knowledge of chemical properties. In summary, this study consolidated evidence supporting the use of CWs for mitigating new legacy PFAS contaminants. Further research, especially long-term monitoring of full-scale CWs treating real wastewater, is crucial to obtain additional data for model development and validation.