The removal yield of organic substances present in water depends on the environmental conditions, on the chemical composition of the water and on the chemical substance dissolved in the water, which constitutes the substrate of the metabolic activities of the microalgae that use these substances in the biochemical reactions of cellular enzyme complexes. o-Chlorobenzylidene malononitrile (CS, to use its military designation) was synthesized in-house, for research purposes, by a condensing reaction between o-chlorobenzaldehide and malononitrilein the presence of diethylamine. The detection, identification and confirmation of o-chlorobenzylidenemalononitrile (coded CBM in this experimental study) was performed using gas chromatography–mass spectrometry (GC-MS) and the purity of CBM was 99%. The biodegradation capacity in the samples that contained the biological suspension, after 24 h and 96 h of incubation, was determined via GC-MS analysis, and no evidence of the presence of CBM or some metabolites of CBM was detected. In the parallel samples, a hydrolysis process of CBM at room temperature, without biological treatment, revealed two main metabolites, malononitrile and o-chlorobenzaldehyde, respectively. This study is focused on evaluating the biodegradation capacity of o-chlorobenzylidene malononitrile in the presence of a biological material, culture of Chlorella sp., in comparison with a classical hydrolysis process. The tests performed indicate that the suspension of Chlorella sp. consumed the entire amount of CBM and metabolites from the analyzed samples. The tests prove that the biological material can be used to decontaminate the affected areas.

Toxic substances used as chemical weapons present a number of particularities that affect the surrounding environment, having a wide range of action by disrupting the ecological balance: they may infect soil or air, or form aerosols through smoke or toxic fog. Such substances can have a long duration of action, from minutes to weeks, which is why they are used in military attacks. This study evaluated the toxicological character of o-chlorobenzyliden malonitrile (CBM) in order to study the toxicity limit of this substance using microbiological cultures of Saccharomyces sp., Chlorella sp., Lactobacillus sp. and Paramecium sp., which were used to determine their rate of growth in the presence of different concentrations of o-chlorobenzyliden malonitrile and their ability to respond to this toxic stimulus.

The integration of noble metal nanoparticles (NPs) effectively modifies the electronic properties of semiconductor photocatalysts, leading to improved charge separation and enhanced photocatalytic performance. TiO₂ nanorods decorated with Au NPs were successfully synthesized using a cost-effective, rapid microwave-assisted method in H₂O₂ and HF media for methylene blue (MB) degradation under visible light illumination. X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), N2 physisorption, and UV–vis spectroscopy were employed to characterize the structures, morphologies, compositions, and photoelectronic properties of the as-synthesized materials. The fusing of Au NPs effectively alters the electronic structure of TiO₂, enhancing the charge separation efficiency and improved electrical conductivity. The HF treatment promotes the exposure of the highly reactive (001) and (101) crystalline facets. The improved photocatalytic activity of Au/TiO₂, achieving 97% efficiency, is attributed to the surface plasmon resonance (SPR) effect of the Au NPs and the presence of oxygen vacancies. The photodegradation of MB using the TiO₂/Au photocatalysts follows pseudo-first-order kinetics, highlighting the enhanced catalytic efficiency of the synthesized nanostructures. The exceptional properties of the binary Au/TiO₂ photocatalysts, including the SPR effect, exposed crystallographic faces, and efficient charge carrier separation through a decrease in the recombination of electrons and holes, contribute to the photocatalytic degradation of MB.

Knowledge of the impact of chemicals on the environment is important for assessing the risks that chemicals can generate in ecosystems. With the help of pilot-scale micro-tests, it was possible to evaluate the biological sludge in terms of its chemical and biological composition, information that can be applied on an industrial scale in treatment plants. The important parameters analyzed in the evaluation of the biodegradability of wastewater were pH, chemical composition (NH₄⁺, NO₃⁻, NO₂⁻, and PO₄³⁻), dry substance (DS), inorganic substance (IS), and organic substance (OS), and the biological oxygen demand (BOD)/chemical oxygen consumption (COD) ratio. The examination revealed the presence of free active ciliates Aspidisca polystyla, Lyndonotus setigerum, Vorticella microstoma, fixed by Zooglee, Paramecium sp., Opercularia, Colpoda colpidium, Euplotes, Didinum nasutum, Stentor, and Acineta tuberosa, metazoa Rotifers, filamentous algae, Nostoc and Anabena, and bacteria Bacillus subtilis, Nocardia, and Microccocus luteus. The novelty of this study lies in the fact that we carried out a study to evaluate the population of microorganisms starting from the premise that the probability of biodegradation of substances is directly proportional to the number of microorganisms existing in the environment and their enzymatic equipment.

This study proposes HGBRCond, a machine learning model for conductivity prediction in controlled biodegradation processes. Eight regression algorithms were evaluated using experimental data (n = 424) from a micro-pilot treatment system. HGBRCond, based on Histogram-Gradient Boosting Regression (best performing ML model), achieved optimal performance (R² = 0.877 ± 0.011, RMSE = 10.235 ± 0.54 µS/cm) through 10-fold cross-validation. Unlike standard HGBR and previous conductivity models that lack comprehensive validation frameworks, HGBRCond integrates rigorous statistical validation (cross-validation, sensitivity analysis, confidence intervals) with multi-level interpretability (Morris screening, SHAP analysis, feature importance), achieving a 6.8% performance improvement over standard gradient boosting approaches while addressing mechanistic interpretability gaps present in prior work. However, limitations constrain direct potential industrial applicability: limited dataset (n = 424), narrow conductivity range (285–360 µS/cm), strong dissolved oxygen dependence, sensitivity across two critical parameters, constant flowrate, and validation restricted to controlled conditions. These constraints require model recalibration for potential industrial application. Future work will focus on model validation across extended operational ranges using industrial samples and full-scale testing to establish applicability beyond controlled experimental settings.