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.

Endocrine disruptors (EDs), including natural estrogens, such as 17β-estradiol (E2) and synthetic chemicals (e.g., bisphenol A (BPA) and per- and polyfluoroalkyl substances (PFAS)), pose environmental and human health risks due to their ability to interfere with hormonal systems, even at trace concentrations and can lead to developmental, reproductive, and carcinogenic effects. These persistent compounds often escape removal in conventional wastewater treatment processes, leading to environmental contamination and human exposure. Given their widespread presence in wastewater and resistance to conventional treatments, the use of fungi offers a promising bioremediation strategy. This review explores the potential of fungal biodegradation, particularly using the white-rot fungus Trametes versicolor, in mitigating the estrogenic activity of EDs in wastewater. Laccase, an oxidative enzyme produced by white-rot fungus, shows high efficiency in degrading EDs, positioning fungal treatment as an eco-friendly alternative to conventional technologies. This systematic literature review was conducted using the Methodi Ordinatio, a multi-criteria decision-making methodology that allows for a structured selection of relevant studies and underscores the significant potential of fungal-based systems in addressing the global challenge of ED contamination in water environments.

This work sought to evaluate the ability of fungi Phanerochaete chryosporium to degrade congo red azo dye in batch reactor, evaluate the influence of glucose and wheat bran as co-substrates on the removal of the dye in the medium and investigate the influence of ammonium chloride, ammonium nitrate and ammonium sulfate as the inorganic nitrogen source for the process. Wheat bran was not effective satisfactorily for the removal of dye and organic matter had no desired effect for the removal of color and organic matter and showed the lowest values of k2, 0.008 M-1.d-1and 0.0004 M-1.d-1, respectively. Glucose presented the best response with the highest final percentage of dye removal (97%) and rate of dye removal (0.017 M-1.d-1), without adding an external source of nitrogen

This study evaluated the glucose effect on the removal of methyl parathion by Aspergillus niger AN400. The study was conducted in two stages: toxicity tests on plates and assays in flasks, under an agitation of 200 rpm. The methyl parathion concentrations in the toxicity test ranged from 0.075 to 60 mg/L. The second stage consisted on evaluating reactors: six control reactors with methyl parathion solution; six reactors with fungi and methyl parathion, and six reactors containing fungi, methyl parathion, and glucose. The reaction times studied ranged from 1 to 27 days. Methyl parathion concentrations of up to 60 mg/L were not toxic for Aspergillus niger AN400. The first-order kinetic model served as a good representation of the methyl parathion conversion rate. The first-order kinetic constant was 0.063 ± 0.005 h-1 for flasks without addition of glucose, while a value of 0.162 ± 0.014 h-1 was obtained when glucose was added.