Occurrence, bioaccumulation, fate, and risk assessment of emerging pollutants in aquatic environments: A review

Melamine sponge loaded anionic covalent organic framework by sodium alginate cross-linking for selective dye removal with high adsorption capacity and reusability

Ionic dyes are widely used and emitted in large quantities by modern industries. It is of great importance to develop efficient and practical adsorbent materials for the removal of such pollutants. Ionic covalent organic frameworks (COFs) with charged pore environments and stable backbone structures are excellent candidates for dye adsorbents. To improve the drawbacks of COF powder, which is not easy to be recycled and prone to secondary pollution, we report an effective strategy to prepare the composite material by immobilizing dispersed anionic COF on melamine foam sponge (MF@COF). Sodium alginate cross-linking method is developed as a powerful combination of COF and MF, with no powder falling off during adsorption. The composite material can quickly adsorb dyes, and the removal rate of cationic dyes can reach >99 % in 10 min; at the same time, it can selectively separate anionic dyes. The adsorption capacity of MF@COF for methylene blue (MB), crystal violet (CV), and malachite green (MG), was 947 mg g-1, 466 mg g-1 and 1689 mg g-1 in terms of the weight of COF, respectively. Compared with using the COF powder alone, the adsorption capacity of the composite material has been improved to a certain extent, with MB's adsorption capacity increasing by 6.16 %. Furthermore, MF@COF composite showed its practicality in practical water adsorption tests and could be recycled >5 times, which makes it a simple and practical adsorbent for water pollution control.

Accumulation of heavy metals in soil, medicinal plants and agricultural crops irrigated with drain water. Case study of Bahr El-Baqar Drain, Egypt

Heavy metal pollution from industrial and sewage waste in Egypt's Bahr Al-Baqar drain threatens ecosystems and human health. This study analyzed metal accumulation (Mn, Cr, Ni, Co, Fe, Cu, Zn) in water, soil, and vegetation, assessing associated risks. Mn in drain water ranged 0.12-0.63 mg L-1, peaking at the endpoint. Hazard quotients (HQ) for Mn and Fe were < 1, indicating low immediate risk. Soil metal concentrations exceeded background levels: Co (1.18×), Cr (2.63×), Ni (1.15×), and Mn (1.03×), with contamination factors (CF >1) confirming moderate pollution. The soil hazard index (HI) for ingestion was 0.944, below risk thresholds. Medicinal plants exhibited high bioaccumulation: Arctostaphylos uva-ursi roots accumulated Cu (BF: 30.44) and Zn (20.59), while Rumex acetosa roots showed extreme Ni uptake (BF: 248.43). Transfer factors revealed Sonchus oleraceus translocated Cu efficiently, Triticum aestivum (wheat) transferred Fe, Ni, and Zn, and Urtica dioica (nettle) mobilized Mn. Consumption of crops/plants irrigated with drain water posed critical risks, with hazard indices (HI) reaching 7.5 (children) and 8.8 (adults), far exceeding safety limits. These results confirm the drain's water is unsafe for irrigation without treatment. Plants like Rumex acetosa and Triticum aestivum act as hazardous bioindicators due to excessive metal uptake, requiring strict monitoring. Immediate actions-including wastewater treatment, pollution control, and soil remediation-are vital to reduce health and environmental threats. The study highlights the need to regulate agricultural and medicinal use of plants from contaminated areas, as their metal accumulation poses direct exposure risks.

Predictive modeling and interpretability analysis of bioconcentration factors for organic chemicals in fish using machine learning

Chemicals are misused and released into the environment, causing adverse effects on people and ecosystems. Assessing the potential environmental risks of these chemicals before their use is crucial. The bioconcentration factor (BCF) is a key parameter used to describe the extent of chemical bioaccumulation. However, previous experiments to determine BCF values are often time-consuming and costly. In this study, a machine learning (ML) model was developed to predict BCF values using molecular descriptors and 9 algorithms. The random forest (RF) model demonstrated strong predictive performance, achieving and values of 0.949 and 0.935. Moreover, it required only 10 easily obtainable features. The Tanimoto similarity coefficient based on molecular structure was used to characterize the applicability domain (AD). We employed SHAP method, which identified primary factors, including hydrophobicity, molecular volume and shape, polarizability and lipophilicity, that have significantly affected BCF values. Furthermore, partial dependence plots (PDP) and 2D interaction were utilized to delve deeper into the relationship between feature values and model predictions. Results showed that MollogP>4.5, SM1_Dzv>0, SM1_Dzp>0, and ZM1C1>35 were linked to higher lgBCF values (3.2 L/kg), indicating stronger bioconcentration potential. Conversely, under other conditions that suggested weaker bioconcentration capacities, the focus should move to environmental migration. The study provided valuable insights into the factors that influence the bioaccumulation of chemicals, while the RF models can be an effective tool for assessing the bioconcentration potential of chemicals.

Impacts of sex and gestation on bioaccumulation and transfer of per-and polyfluoroalkyl substances in loaches

Per- and polyfluoroalkyl substances (PFAS) are global pollutants, and understanding their bioaccumulation and transfer behavior in wild fish is crucial for assessing their potential impact on aquatic ecosystems. This study investigated the effects of sex and gestation on PFAS bioaccumulation and transfer in loaches, a topic with limited existing research. We measured the concentrations of 14 PFAS in 41 loaches (17 males and 24 females) from a freshwater lake in northwestern China. All loaches had detectable PFAS, with bioaccumulation factors tending to correlate positively with protein-water partition coefficients (Kpw). Notably, female loaches generally exhibited lower PFAS concentrations than males, and the chemical compositions differed, potentially linked to maternal transfer efficiency, quantified by comparing PFAS concentration ratios in eggs versus muscle tissue (EMR). The EMR (1.84-20.27) appeared to vary with Kpw and maternal reproductive investment. The PFAS mixture's PFOS equivalent varied from 3.23 to 18.69 ng/g ww, and the hazard quotient of 0.008 suggests a low likelihood of reproductive damage from current PFAS concentrations in loach eggs. This study provides valuable insights into the behavior and risk of PFAS in the aquatic environment, but further research with a larger sample size is recommended to confirm and extend these findings.

Analysis of multi-class unregulated organic compounds in soil and biosolids using LC-MS/MS

Numerous unregulated organic compounds (UOCs) including pharmaceuticals, opioids, and personal care products (PCPs) end up in wastewater. UOC presence in biosolids (a wastewater treatment byproduct), which are applied to soil for different reasons raises environmental and health risk concerns. In this study, two multi-class extraction methods were developed and validated to target 111 UOCs from 8 different major families simultaneously in biosolids and biosolids-impacted soil. One method (M-SPE) is a modified version of EPA 1694, that uses triple solid-liquid extraction and solid phase extraction (SPE). The second method (EMR) is a super-fast method consisting in a single solvent extraction and EMR (enhanced matrix removal)-Lipid dispersive SPE. M-SPE performed better overall with 72 and 54 UOCs extracted with 50-130% recovery for soil and biosolids, respectively, compared to EMR for which only 49 and 43 UOCs achieved within the same range, respectively. EMR performed particularly well for the extraction of low concentration opioids from biosolids. The use of ENVI-Carb as an additional cleanup step and its potential to sorb analytes was also evaluated. Although >75% sorption of 27 UOCs occurred, ENVI-Carb (graphitized carbon) was needed to sufficiently clean extracts prior to injection to avoid precipitation and protect analytical systems. Application of these methods to environmental samples resulted in detection of some flame retardants, opioids, pharmaceuticals, PCPs and phthalates totaling 30 and 26 UOCs in biosolids and soil historically applied with biosolids, respectively. This methodology will be an asset to determining UOC concentrations in biosolids and biosolids-impacted soils.

A comprehensive review on TiO2-based heterogeneous photocatalytic technologies for emerging pollutants removal from water and wastewater: From engineering aspects to modeling approaches

The increasing presence of emerging pollutants (EPs) in water poses significant environmental and health risks, necessitating effective treatment solutions. Originating from industrial, agricultural, and domestic sources, these contaminants threaten ecological and public health, underscoring the urgent need for innovative and efficient treatment methods. TiO2-based semiconductor photocatalysts have emerged as a promising approach for the degradation of EPs, leveraging their unique band structures and heterojunction schemes. However, few studies have examined the synergistic effects of operating conditions on these contaminants, representing a key knowledge gap in the field. This review addresses this gap by exploring recent trends in TiO2-driven heterogeneous photocatalysis for water and wastewater treatment, with an emphasis on photoreactor setups and configurations. Challenges in scaling up these photoreactors are also discussed. Furthermore, Machine Learning (ML) models play a crucial role in developing predictive frameworks for complex processes, highlighting intricate temporal dynamics essential for understanding EPs behavior. This capability integrates seamlessly with Computational Fluid Dynamics (CFD) modeling, which is also addressed in this review. Together, these approaches illustrate how CFD can simulate the degradation of EPs by effectively coupling chemical kinetics, radiative transfer, and hydrodynamics in both suspended and immobilized photocatalysts. By elucidating the synergy between ML and CFD models, this study offers new insights into overcoming traditional limitations in photocatalytic process design and optimizing operating conditions. Finally, this review presents recommendations for future directions and insights on optimizing and modeling photocatalytic processes.

Food web bioaccumulation model for ecological risk assessment of emerging organic pollutants in marine ecosystems: Principles, advances and challenges

The bioaccumulation and trophic transfer of pollutants in marine ecosystem members determine their ultimate ecological risks. Food web bioaccumulation models are widely used in scientific and regulatory programs to assess the bioaccumulation and ecological risks of pollutants at the ecosystem scale. The food web models are mainly established through concentration- and fugacity-based modeling approaches and include some chemical, food web-related, physiological and environmental factors. The models applied in the "forward approach" predict bioaccumulation and conduct internal exposure level-based ecological risk assessment (IEL-ERA), whereas those in the "reverse approach" are used to back-calculate the IEL-based predicted no-effect concentrations (PNECs) or environmental criteria. However, some challenges still exist in the application of food web model integrated risk assessment, including the lack of standardized/generalized frameworks, the lack of chemical- and species-specific toxicokinetic data and internal exposure (or tissue residue)-based toxicity data, and the lack of uncertainty-control methods in model estimation and parameterization. There are urgent requirements to improve models, integrate methods and update study designs in the assessment and prediction of "system-scale risks" of marine emerging organic pollutants.

Profiling the bacterial microbiome diversity and assessing the potential to detect antimicrobial resistance bacteria in wastewater in Kimberley, South Africa

Wastewater treatment plants (WWTPs) are hotspots for pathogens, and can facilitate horizontal gene transfer, potentially releasing harmful genetic material and antimicrobial resistance genes into the environment. Little information exists on the composition and behavior of microbes in WWTPs, especially in developing countries. This study used environmental DNA (eDNA) techniques to examine the microbiome load of wastewater from WWTPs. The DNA was isolated from wastewater samples collected from the treatment trains of three WWTPs in Kimberley, South Africa, and the microbial diversity and composition was compared through 16 S rRNA gene sequencing. The microbes detected were of the Kingdom Bacteria, and of these, 48.27% were successfully identified to genus level. The majority of reads from the combined bacterial data fall within the class Gammaproteobacteria, which is known to adversely impact ecological and human health. Arcobacteraceae constituted 19% of the bacterial reads, which is expected as this family is widespread in aquatic environments. Interestingly, the most abundant bacterial group was Bacteroides, which contain a variety of antibiotic-resistant members. Overall, various antibiotic-resistant taxa were detected in the wastewater, indicating a concerning level of antibiotic resistance within the bacterial community. Therefore, eDNA analysis can be a valuable tool in monitoring and assessing the bacterial microbiome in wastewater, thus providing important information for the optimization and improvement of wastewater treatment systems and mitigate public health risks.

An overview of the recent advances and future prospects of three-dimensional particle electrode systems for treating wastewater

Three-dimensional (3D) electrochemical technology is considered a very effective industrial wastewater treatment method for its high treatment efficiency, high current efficiency, low energy consumption, and, especially, ability to completely mineralize nonbiodegradable organic contaminants. Particle electrodes, which are the fundamental components of 3D electrochemical technology, have multiple functions in the electrochemical reaction process. Various types of particle electrodes have been created and applied for wastewater treatment. Herein, we present a thorough analysis of the research and development of particle electrodes used for electrocatalyzing pollutants. Initially, reactor designs, factors affecting the removal efficiency of pollutants and degradation mechanisms are introduced. In particular, a detailed investigation is conducted into the selection of particle electrode materials and the roles they play in the 3D electrochemical treatment of wastewater. Subsequently, the degradation efficiency and energy consumption associated with 3D electrochemical technology for different pollutants are investigated. Finally, the directions and outlook for further studies on particle electrodes are discussed. We believe that this review will offer a useful perspective on the development and application of particle electrodes for wastewater purification.

Hormesis in the Assessment of Toxicity Assessment by Luminescent Bacterial Methods

The threat posed by water pollutants to aquatic ecosystems and human health cannot be overlooked, and the assessment of the toxicity of these contaminants is paramount to understanding their risks and formulating effective control measures. Luminescent bacteria-based assays, as a vital tool in evaluating contaminant toxicity, encounter a challenge in ensuring accuracy due to the phenomenon of "Hormesis" exhibited by pollutants towards biological entities, which may skew toxicity assessments. This study elucidated the specific effects of pollutants on luminescent bacteria at different concentrations, used modeling to characterize the effects and predict their toxicity trends, and explored the applicable concentration ranges for different pollutants. Research revealed that six typical pollutants, namely PAHs, endocrine disruptors, antibiotics, pesticides, heavy metals, and phytosensory substances, could promote the luminescence intensity of luminescent bacteria at low concentrations, and the promotional effect increased and then decreased. However, when the concentration of the substances reached a certain threshold, the effect changed from promotional to inhibitory, and the rate of inhibition was directly proportional to the concentration. The EC50 values of six types of substances to luminescent bacteria is as follows: endocrine disruptors > pesticides > antibiotics > heavy metals > polycyclic aromatic hydrocarbons > chemosensory agents. The effect curves were further fitted using the model to analyze the maximum point of the promotion of luminescence intensity by different substances, the threshold concentration, and the tolerance of luminescent bacteria to different substances. The maximum promotion of bacterial luminescence intensity was 29% for Bisphenol A at 0.005 mg/L and the minimum threshold concentration of chromium was 0.004 mg/L, and the maximum bacterial tolerance to erythromycin is 6.74. In addition, most of the current environmental concentrations had a positive effect on luminescent bacteria and may still be in the range of concentrations that promote luminescence as the substances continue to accumulate. These findings will enhance the accuracy and comprehensiveness of toxicity assessments, thereby facilitating more informed and effective decision-making in the realms of environmental protection and pollution management.

Antagonistic effects of a COX1/2 inhibitor drug in human HepG2 cells exposed to an environmental carcinogen

Understanding interactions between legacy and emerging environmental contaminants has important implications for risk assessment, especially when mutagens and carcinogens are involved, whose critical effects are chronic and therefore difficult to predict. The current work aimed to investigate potential interactions between benzo[a]pyrene (B[a]P), a carcinogenic polycyclic aromatic hydrocarbon and legacy pollutant, and diclofenac (DFC), a non-steroidal anti-inflammatory drug and pollutant of emerging concern, and how DFC affects B[a]P toxicity. Exposure to binary mixtures of these chemicals resulted in substantially reduced cytotoxicity in human HepG2 cells compared to single-chemical exposures. Significant antagonistic effects were observed in response to high concentrations of B[a]P in combination with DFC at IC50 and ⅕ IC50. While additive effects were found for levels of intracellular reactive oxygen species, antagonistic mixture effects were observed for genotoxicity. B[a]P induced DNA strand breaks, γH2AX activation, and micronuclei formation at ½ IC50 concentrations or lower, whereas DFC induced only low levels of DNA strand breaks. Their mixture caused significantly lower levels of genotoxicity by all three endpoints compared to those expected based on concentration additivity. In addition, antagonistic mixture effects on CYP1 enzyme activity suggested that the observed reduced genotoxicity of B[a]P was due to its reduced metabolic activation as a result of enzymatic inhibition by DFC. Overall, the findings further support the growing concern that co-exposure to environmental toxicants and their non-additive interactions may be a confounding factor that should not be neglected in environmental and human health risk assessment.