Photocatalysis has become an attractive process to treat wastewater since it allows a rapid and efficient degradation of micropollutants in water. A solution of ciprofloxacin (CIP) was photocatalytically treated by ultraviolet A light (UVA) and titanium dioxide (TiO₂) or zinc oxide (ZnO) nanoparticles. Toxicity of CIP and of the treated CIP solutions, as well as the toxicity of TiO₂ and ZnO irradiated nanoparticles, was evaluated towards Vibrio fischeri. The lowest concentration of CIP tested, 10 μg L⁻¹, leads to 50% of luminescence inhibition. Regarding irradiated nanoparticles, ZnO presented higher bacteria luminescence inhibition than TiO₂, 97 and 38%, respectively. Due to high toxicity of ZnO, it was only possible to evaluate the CIP solution treated by UVA/TiO₂. Initially, the toxicity decreased with the time of the process, but after 15 min the toxicity increased significantly (55%) and after 45 min of treatment, was 70%. High-performance liquid chromatography (HPLC) and Fourier transform infrared spectroscopy (FTIR) analysis proved that the initial decrease of toxicity was caused by CIP adsorption on catalyst surface, which latter increased due to the generation of by-products and toxicity contribution of soluble nanoparticles. Ten by-products were identified by liquid chromatography-mass spectrometry (LC-MS) and the mechanism of CIP photocatalytic degradation was proposed.

Per- and polyfluoroalkyl substances (PFAS) are recalcitrant pollutants which tend to persist in soils and aquatic environments and their remediation is among the most challenging with respect to organic pollutants. Anaerobic digestion (AD) supplemented with low amounts of carbon materials (CM), acting as electron drivers, has proved to be an efficient process for the removal of organic compounds from wastewater. This work explores the impact of PFAS on different trophic groups in anaerobic communities, and the effect of carbon nanotubes (CNT), activated carbon (AC), and oxidized AC (AC-HNO₃), as electron shuttles on the anaerobic bioremoval of these compounds, based on CH₄ production. The inhibition of the specific methanogenic activity (SMA) exerted by perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), at a concentration of 0.1 mg L⁻¹, was below 10% for acetoclastic and below 15%, for acetogenic communities. Hydrogenotrophic methanogens were not affected by the presence of PFAS. All CM reduced the negative impact of PFAS on the CH₄ production rate, but AC was the best. Moreover, the methanization percentage (MP) of sewage sludge (SS) increased 41% in the presence of PFOS (1.2 g L⁻¹) and AC. In addition, AC fostered an increase of 11% in the MP of SS+PFOS, relative to the condition without AC. AC promoted detoxification of PFOA- and PFOS-treated samples by 51% and 35%, respectively, as assessed by Vibrio fischeri assays, demonstrating the advantage of bringing AD and CM together for PFAS remediation.

Environmental pollution has become one of society’s main concerns, which is why it is increasingly necessary to look for new effective remediation strategies. The applicability of artificial intelligence (AI) tools in different areas of knowledge has attracted the attention of the academic community due to their ability to process large datasets efficiently and deliver rapid, reliable predictions. Among them, artificial neural networks (ANNs) have stood out as some of the main aids in remediation and environmental control research. In this review, we systematically assess the current state of research on ANN applications in environmental remediation, particularly focusing on the remediation of dyes in water and wastewater treatment, through a comprehensive bibliographical analysis using the Methodi Ordinatio 2.0 methodology. This advanced methodology adds value compared to traditional systematic reviews by offering a structured and data-driven ranking of relevant publications based on impact, recency, and citation scores. The results demonstrate a clear trend toward adopting ANN models in water treatment processes, with the multilayer perceptron neural network emerging as the most widely used model due to its high predictability and adaptability. Key validation criteria for ANN models are discussed, and recent experimental results related to their application in adsorptive systems for wastewater treatment are highlighted. This review not only underscores the potential of ANN tools for improving environmental control but also identifies key challenges and emerging trends in this rapidly evolving field.