Cellulose acetate (CA) is a semisynthetic, biodegradable polymer. Due to its characteristics, CA has several applications, including water membranes, filament-forming matrices, biomedical nanocomposites, household tools, and photographic films. This review deals with topics related to the CA membranes, which are prepared using different techniques, such as the phase inversion technique. CA membranes are considered very important since they can be used as microfiltration membranes (MF), ultrafiltration membranes (UF), nanofiltration membranes (NF), reverse osmosis (RO) membranes, and forward osmosis (FO) membranes. Membrane fouling results from the accumulation of materials that the membrane rejects on the surface or in the membrane’s pores, lowering the membrane’s flux and rejection rates. There are various forms of CA membrane fouling, for instance, organic, inorganic, particulate fouling, and biofouling. In this review, strategies used for CA membrane antifouling are discussed and summarized into four main techniques: feed solution pretreatment, cleaning of the membrane surface, membrane surface modification, which can be applied using either nanoparticles, polymer reactions, surface grafting, or surface topography, and surface coating.

Accurate monitoring of chlorophyll-a (Chl-a) is critical for assessing eutrophication and water quality in inland aquatic ecosystems. While integrating Sentinel-2 MultiSpectral Instrument (MSI) imagery with machine learning (ML) is widely used for water quality assessment, challenges remain in optimizing Chl-a retrieval in optically complex small-scale reservoirs, particularly in semi-arid environments where traditional models often fail. This study introduces a machine learning (ML)-based approach to enhance Sentinel-2 MSI Chl-a retrieval, systematically evaluating six ML models: Artificial Neural Networks (ANN), CatBoost (CB), random forest (RF), ridge regression (RR), support vector regression (SVR), and extreme gradient boosting (XGB). By integrating Sentinel-2 MSI reflectance data with in-situ measurements from reservoirs exhibiting diverse trophic conditions, this study not only compares model performance but also applies feature importance analysis to refine spectral band selection for improved Chl-a retrieval. Among the models, RF demonstrated the highest predictive accuracy (R² = 0.93 calibration, R² = 0.91 validation), outperforming CB and XGB, whereas linear models such as RR and SVR were inadequate for capturing the nonlinear spectral relationships of Chl-a. The results highlight the advantages of ensemble-based ML models in handling complex water quality datasets, particularly in semi-arid and data-scarce regions. The proposed framework provides a robust, data-driven approach for integrating Sentinel-2 MSI imagery with AI techniques, supporting real-time water quality assessment and environmental management in diverse aquatic ecosystems. This research advances the application of ML for remote sensing-based water quality monitoring by systematically assessing model reliability, feature selection, and efficient application in semi-arid small-scale reservoirs. The proposed approach enhances the scalability of ML-driven remote sensing, offering a transferable framework for Chl-a estimation in data-scarce and environmentally vulnerable regions.

This study presents a pilot-scale investigation into the treatment of olive mill wastewater (OMW), a high-strength effluent characterized by high organic load and phenolic content. An integrated system combining physicochemical treatment, mainly coagulation/flocculation followed by Dissolved Air Flotation (DAF), and biological processes using activated sludge were evaluated. The coagulation/flocculation stage was optimized at the bench scale using Response Surface Methodology (RSM), focusing on key operational parameters: pH, lime dose, mixing rate, and mixing time. Optimal conditions, pH 10, lime dose of approximately 30,000 mg L⁻¹, mixing rate of 250 rpm, and mixing time of 5 minutes, yielded removal efficiencies of 21% for Chemical Oxygen Demand (COD), 90% for phenol, and 100% for turbidity. The biological treatment was further optimized by varying the volumetric percentage of return activated sludge (RAS) in the feed. A 50% RAS ratio resulted in 65% COD and 91% phenol removal over a 12-day retention time. A 20% RAS ratio also demonstrated high phenol removal efficiency, suggesting its suitability where shorter retention or lower sludge generation is preferred. Overall, the integrated coagulation–DAF and biological treatment pilot-scale system achieved combined removal efficiencies of 89.5% for COD and 87.7% for phenol. These results highlight the potential of the proposed setup as a sustainable first-stage treatment for recalcitrant industrial wastewater, enabling significant pollutant reduction prior to discharge or further polishing in municipal treatment systems.