Microplastics (MPs) and their associated chemical additives, such as bisphenol A (BPA), nonylphenol (NP), nonylphenol ethoxylate (NEPO), and tetrabromobisphenol (TBBPA), are widely recognized as significant environmental contaminants due to their widespread presence in ecosystems and their potential impact on human health. This review evaluates advanced treatment technologies, such as membrane filtration and oxidation processes, for mitigating these risks and highlights gaps in sustainability and efficiency. Actionable strategies for improving the removal of MPs and MP additives were presented in the assessment of innovative hybrid treatment tailored to different water matrices. The importance of anti-fouling technologies, comprehensive life cycle assessments (LCAs), and standardizing treatment methods to enhance the sustainability and applicability of these technologies were further highlighted. To further improve and optimize treatment processes and ensure sustainability, existing knowledge gaps must be addressed by framing a comprehensive understanding of the long-term ecological effects of MPs and their additives.

Studies on plastic pollution conducted in freshwaters mainly focused on monitoring plastic debris in the water column or in the sediments. Few studies have investigated the occurrence of plastic debris in benthic biofilms (periphyton). Yet, algal biofilms may potentially act as a sink for plastic debris, trapping it within their mucilaginous or filamentous matrix. Biofilms may also represent a source of plastic debris by sloughing when they become senescent. In addition, plastic debris accumulated within biofilms may enter the food web via primary consumers. Considering these observations, this study aims to quantify nanoplastics (NPs) accumulated in biofilms growing on aquatic vegetation from Lake Saint Pierre (LSP), a fluvial lake of the St. Lawrence River, and its archipelago. Biofilms were removed from submerged plants and the presence of NPs was assessed by spectrophotometry using the fluorescent molecular rotor probe 9(dicyanovinyl)-julolidine (DCVJ). The results of this study confirm the biofilms’ ability to act as a sink for NPs. Despite the fact that the determination of the absolute nanoparticle number and size distribution remains a challenge, we estimated a median concentration of 1.05 × 10⁹ NP/mg of biofilm dry weight (DW) when using 100 nm polystyrene beads for calibration. Concentrations were significantly different between water masses, with higher concentrations in samples collected in the two lateral water masses compared to the central water mass. Our study provides, for the first time, a quantitative assessment of NPs from epiphytic biofilms in a large river under the influence of anthropogenic sources.

Water contamination by heavy metals has emerged as a global environmental problem. Their toxicity, non-degradability, and persistent nature make them a serious threat to human health, flora, and fauna. Therefore, several techniques have been developed for the removal of these pollutants from wastewater. Recently, linear aromatic polymers have received increasing attention for wastewater treatment due to the presence of various heterocyclic moieties containing electron-donating atoms such as nitrogen, oxygen, or sulfur on their backbone, which can be easily coordinated with metals, resulting in excellent affinity for heavy metals. This review article is specifically devoted to providing an overview of the various linear-architecture heterocyclic polymers that have been synthesized to be used as adsorbent phases for the removal of heavy metals from wastewater over the past fifteen years. The importance of incorporating heterocyclic units as efficient chelating sites for ion binding is highlighted. The adsorption mechanisms of different aromatic polymers are presented; their adsorption isotherms can primarily be modeled with the Langmuir model and their kinetics follow a pseudo-second-order kinetic model. The ways to improve the adsorption capacities of the linear aromatic polymers by increasing their specific surface area are discussed in the perspective paragraph, along with strategies to improve their reusability by choosing the proper acidic washing step.

Heavy metals such as arsenic can be effectively removed through adsorption. Through material property evaluation and adsorption parameter optimization, machine learning (ML) modeling provides an alternative to lengthy laboratory experimentation. In this work, adsorption data from an earlier study employing a waste-material composite were used. To create prediction models, four non-neural network algorithms - support vector machines (SVM), Gaussian process regression (GPR), linear regression, and ensemble approaches - were used and contrasted with neural network algorithms. Nine predictors were utilized, ranging from adsorbent composition alterations to experimental circumstances. Using principal component analysis (PCA) and feature selection, together with the F-test and minimum redundancy maximum relevance (MRMR) algorithms for feature reduction, optimization was accomplished. With an R-squared of 0.939, mean absolute error (MAE) of 5.778, and root mean squared error (RMSE) of 7.119 for training and an R-squared of 0.942, MAE of 5.450, and RMSE of 6.870 for testing, the optimized GPR method offered the best predictive performance. The best R-squared values found for other algorithms were: SVM (0.922), linear regression (0.925), and ensemble (0.927). The most important variables influencing adsorption efficiency were initial arsenic concentration, time, and the iron salt content. Local interpretable model-agnostic explanations (LIME), partial dependence plot (PDP), and Shapley additive explanations (SHAP) plots were used to explain these results. This work shows that, based on model-derived parameters, non-neural network algorithms may efficiently simulate and optimize arsenic adsorption tests, providing a trustworthy substitute for neural network techniques and markedly increasing adsorption efficiency.

With increasing population and waste generation, organic waste disposal has presented an unprecedented challenge. Valorization of the waste for wastewater treatment emerges as a feasible way to recycle or upcycle the waste. Agricultural and non-agricultural waste has been successfully converted into biosorbents to remove various pollutants. Through reviewing 126 papers, this review aims to provide a comprehensive overview of the effectiveness and mechanisms of biosorbents from organic waste in adsorbing different organic and inorganic waste. Most recent studies have focused on using biosorbents to remove dyes, pharmaceuticals, and heavy metals. The biosorbents were synthesized primarily through drying and pulverization, or pyrolysis. Some biosorbents have been chemically treated with acids, alkalis, or salts to increase their surface functional groups. Furthermore, different biomass materials have also been combined to synthesize biocomposite sorbents. The extraction of lignocellulose and chitin from biomass as biosorbents is also common. Biosorption occurs via chemisorption and physisorption, with the former more prevalent among organic pollutants and the latter among inorganic pollutants. The Langmuir isotherm model, which indicates monolayer sorption, and the pseudo-second-order kinetic model, which implies chemisorption as rate-limiting, best describe most of the biosorption data. Biosorption is governed mainly by pH, temperature, initial pollutant concentrations, dosage and size of biosorbents, and contact time. This review highlights the need to standardize optimization procedures and develop cost-effective and scalable biosorption systems. It highlights the potential of biosorbents, especially biochar, as potential substitutes for activated carbon in the column adsorption process of tertiary wastewater treatment.

Microplastic (MP; plastic particles < 5 mm) pollution is pervasive in the marine environment, including remote polar environments. This study provides the first pan-Antarctic survey of MP pollution in Southern Ocean sea ice by analyzing sea ice cores from several diverse Antarctic regions. Abundance, chemical composition, and particle size data were obtained from 19 archived ice core samples. The cores were melted, filtered, and chemically analyzed using Fourier-transform infrared spectroscopy and 4,090 MP particles were identified. Nineteen polymer types were found across all samples, with an average concentration of 44.8 (± 50.9) particles·L^-1. Abundance and composition varied with ice type and geographical location. Pack ice exhibited significantly higher particle concentrations than landfast ice, suggesting open ocean sources of pollution. Winter sea ice cores had significantly more MPs than spring and summer-drilled cores, suggesting ice formation processes play a role in particle incorporation. Smaller particles dominated across samples. Polyethylene (PE) and polypropylene (PP) were the most common polymers, mirroring those most identified across marine habitats. Higher average MP concentrations in developing sea ice during autumn and winter, contrasting lower levels observed in spring and summer, suggest turbulent conditions and faster growth rates are likely responsible for the increased incorporation of particles. Southern Ocean MP contamination likely stems from both local and distant sources. However, the circulation of deep waters and long-range transport likely contribute to the accumulation of MPs in regional gyres, coastlines, and their eventual incorporation into sea ice. Additionally, seasonal sea ice variations likely influence regional polymer compositions, reflecting the MP composition of the underlying waters.