Chemosphere最新文献

As nanotechnology advances, metal oxide nanoparticles (MeONPs) increasingly come into contact with humans. The inhaled MeONPs cannot be effectively cleared by cilia or lung mucus. In the last decade, potential immune toxicity arising from exposure to MeONPs has been extensively debated, as lung macrophage is the main pathway for cleaning inhaled exogenous particles. However, their toxicity on lung macrophages has rarely been quantitatively predicted in silico due to the complexity of responses in macrophages and the intricate properties of MeONPs. Here, machine learning (ML) methods were used to establish models for quantitatively predicting the toxicity of MeONPs in macrophages. A multidimensional dataset including 240 data points covering the lethality, biochemical behaviors, and physicochemical properties of 30 MeONPs was obtained. ML models based on different algorithms with high prediction accuracy were constructed by addressing the issue of class imbalance during the training process. The models were verified by 10-fold cross-validation and external validation. The best-performed model has an R² of 0.85 and 0.90 in the 10-fold cross-validation and external test set, respectively; and Q² of 0.88 and 0.90 in the 10-fold cross-validation and test set, respectively. Five parameters that impact toxicity were identified and the toxicity mechanisms were elucidated by ML analysis. The prediction results can be used to fill the data gap in the risk assessment of nanomaterials. The framework offers valuable insights for designing and utilizing safe nanoparticles, as well as aiding in decision-making processes aimed at protecting the environment and public health.

Microplastics (MPs) are one of the most widespread environmental pollutants, but their risk assessment to freshwater ecosystems has not been clearly investigated. Risk assessment has been constrained by the absence of MP concentration in some environment, the diverse types and shapes of MPs, and limitations of polystyrene (PS)-biased toxicity studies. This study examined exposure to MPs in rivers and lakes worldwide, including China (the Three Gorges Dam & Yangtze River (TGD & YR) and the lakes of Wuhan city (WL)), Vietnam (seven lakes of Da Nang city (7UL)), Europe (the Rhine River (RR)), Finland (Kallavesi Lake (KL)), Argentina (nine lakes in the Patagonia region (9LP)), Brazil (Guaiba Lake (GL)), and South Korea (Nakdong River (NR), Han River (HR), and Anyang Stream (AS)), and assessed the risks to aquatic ecosystems based on the toxicity information and morphology of MPs. We also examine the limitations of the traditional risk quotient (RQ)-based risk assessment method for PS-biased toxicity studies. Potential ecological risks were assessed using pollution load index (PLI) and potential ecological risk index (PERI) considering the hazard scores of MP types. RQ was approximately 10^-6 to 10^-4, indicating negligible risk to aquatic organisms. In contrast, the calculated PLI (>30: extreme danger) and PERI (>1200: extreme danger) values suggest that MPs represent serious ecological threats at all the study locations. Furthermore, principal component analysis (PCA) indicated that MP fibers and fragments have a significant impact on the risks for freshwater systems. These MP morphologies derive from surrounding fishing and agricultural activities, and household and clothing industries. The areas surrounding these rivers and lakes are expected to become more densely populated, potentially leading to increased MP emissions and higher risks, suggesting a need to expand wastewater treatment facilities, reduce consumption of single-use plastics, and raise societal awareness of waste plastics.

2,4-dichlorophenoxy acetic acid (2,4-D) and 1,1-dimethyl-4,4-bipiridinium chloride (paraquat) are among the most widely used herbicides and are known to be toxic. Fabrication of green adsorbents which are capable of removing both herbicides remains a challenge. Here, we fabricate a novel adsorbent from tropical waste wood and use a facile, chitosan-mediated N-heteroatom functionalization technique to augment surface nitrogen and improve specific surface area. The addition of 20 wt% chitosan to the waste wood feedstock prior to activation, increased specific surface area by 300 m²/g (∼25%) and nitrogen content by 7-fold. This functionalized material removed 69% of 2,4-D and 82% of paraquat at initial concentrations of 4 ppm and 40 ppm from model solutions at pH 7. It also removed 39% 2,4-D and 93% paraquat from binary mixtures demonstrating its versatility. 2,4-D adsorption increased with chitosan addition suggesting synergistic effects between protonated amine functions and the anionic herbicide form. Paraquat adsorption was negatively correlated with chitosan addition, implying antagonistic interaction between protonated amine functions and quaternary nitrogen atoms on herbicide molecules. Adsorption of both herbicides was spontaneous, entropically favored and exothermic with ΔG °: 19.2 kJ/mol and -28.8 kJ/mol; ΔS °: 7.42 and 28.6 J/Kmol and ΔH °: 17.0 kJ/mol and -20.1 kJ/mol for 2,4-D and paraquat respectively. Chitosan addition therefore provides a facile and green alternative for N-heteroatom functionalization, and these nitrogen-doped materials are promising candidates for the removal of multiple herbicides from aqueous systems.

The selective oxidation of NH₄⁺-N into dinitrogen (N₂) is still a challenge. Currently, traditional advanced oxidation processes often involve in the chlorine free radicals to increase the selectivity of NH₄⁺-N oxidation products towards N₂ but is usually accompanied by the production of many toxic disinfection by-product. Herein, we reported a novel catalytic ozonation system (UV/O₃/MgO/Na₂SO₃) for selective NH₄⁺-N oxidation based on the reducing capability and photochemical properties of Na₂SO₃. In the UV/O₃/MgO/Na₂SO₃/NH₄⁺-N system, Na₂SO₃ could not only reduce the intermediate of NO₂^- or NO₃^- to N₂ by inducing the generation of hydrated electrons under UV irradiation, but also reduce the gaseous intermediate of NO_x to N₂, thus achieving a high N₂ selectivity (>85 %). Based on the analyses of each component roles, the determination of reactive oxygen species and the evolution of NH₄⁺-N oxidation intermediates, the possible mechanisms of NH₄⁺-N selective oxidation by UV/O₃/MgO/Na₂SO₃ system were revealed. This system exhibits a great potential for the NH₄⁺-N removal from water/wastewater. This work provides a new strategy for NH₄⁺-N oxidation into N₂ by advanced oxidation processes independent of the action of chlorine free radicals.

Microplastics (MPs) in aquatic environments constitute an ideal surface for biofilm formation, facilitating or hindering the transport of contaminants. This study aims to provide knowledge on the sorption behavior of high-density polyethylene (μ-HDPE) after algal degradation toward UV filters. Up to now, the oxidation of μ-HDPE using the microalga Acutodesmus obliquus has not been studied. The results obtained by infrared spectroscopy (IR), scanning electron microscopy (SEM), and porosimetry analysis revealed a biofilm formation on the surface of μ-HDPE and the presence of carbonyl and double bond functional groups. Also, this is the first time that the simultaneous sorption of benzophenone (BPh), 4-methylbenzylidene camphor (4MBC), benzophenone 3 (BPh3), and benzophenone 2 (BPh2) onto biofilm-covered HDPE (biofilm-HDPE) in water have been studied. Filters' sorption on biofilm-HDPE particles follows pseudo-second-order kinetics, and film diffusion was the stage that limited the sorption rate. The Langmuir isothermal model describes the adsorption process for 4MBC, BPh, and BPh2 well, and the linear model is fit for the sorption of BPh3. Hydrophobic interactions, van der Waals forces, electrostatic, and π-π bon are the main mechanisms responsible for the sorption. Biological analysis indicated that HDPE at concentrations of 500 mg L^-1 inhibits A. obliquus growth and reduces the levels of proteins, sugars, and chlorophylls. In contrast, the activity of antioxidant enzymes and the contents of small molecular weight antioxidants significantly increased in algal cells treated with microplastic. These findings confirm the toxicity of μ-HDPE and demonstrate the induction of defense mechanisms in A. obliquus as a response to environmental pollutants.

Even at trace concentrations, micropollutants, including pesticides and pharmaceuticals, pose considerable ecological risks, and the increasing presence of synthetic chemical substances in aquatic systems has emerged as a growing concern. Moreover, limited machine-learning (ML) approaches exist for analyzing environmental data, and the increasing complexity of ML models has made it challenging to understand predictor-outcome relationships. In particular, understanding complex interactions among multiple variables remains challenging. This study applies and integrates explainable ML techniques and network analysis to identify the sources of micropollutants in a large watershed and determine the factors affecting micropollutant levels. We assessed the performance of four ML algorithms-support vector machine, random forest, extreme gradient boosting (XGB), and autoencoder-XGB-in predicting micropollutant levels based on the spatial characteristics of the watershed. We applied the synthetic minority oversampling technique to address the data imbalance. The XGB model demonstrated superior predictive performance, particularly for high concentration levels, achieving an accuracy of 87%-99%. Shapley additive explanations (SHAP) analysis identified temperature and rainfall as significant factors. Moreover, agricultural activities contributed to pesticide pollution, whereas urban activities contributed to pharmaceutical contamination. The network analysis corroborated the SHAP findings and revealed event-specific contamination characteristics. This included distinct discharge pathways during a dry summer event and shared pathways during a wet winter event. This approach enhances an understanding of contamination sources and pathways and subsequently aids in developing control measures and making informed policy decisions to preserve water quality in mixed land-use areas.

For decades, studies have shown how exposure to non-essential trace metals such as lead (Pb) and cadmium (Cd) largely impact global wildlife. Ecoimmunotoxicology has emerged in the past two decades and focuses on the effects of pollutants on the immune system of free-ranging organisms. Adverse outcome pathways (AOPs) represent a conceptual approach to explore the mechanistic linkage between a molecular initiating event and adverse outcomes, potentially at all biological levels of organisation. The present paper proposes putative AOPs related to the effects of Cd, Pb, and the mixture Cd-Pb, on the immune system of mammals to address future questions in ecoimmunotoxicology. Molecular Initiating Events for both metals relate to entrance in cells through Ca²⁺ channels or bond to cell surfaces. Exposure to Cd, Pb and Cd-Pb share several similar Key Events (KEs), primarily an increase of oxidative stress (OS) in immune cells through production of reactive oxygen species. For both metals and the mixture, OS affects mitochondrial membranes, and induces apoptosis, ultimately decreasing immune cell number. Both metals affect innate immune system through nuclear factor kappa B (NF-κB) and mitogen-activated protein kinase (MAPK) inflammatory signalling pathways, leading to an upregulation of inflammatory markers and mediators. Adaptive immune system is also affected by the exposure to both metals though a decrease of CD4+/CD8+ ratio, a decrease of MHCII, an inactivation of T_H1 and T_H2 response, and an inhibition of the humoral response mediated by various Ig. Mixture effects of Cd-Pb are less documented resulting in a more speculative AOP, but potential synergic and antagonistic effects were identified. According to our AOPs, further research in ecoimmunotoxicology of metals in free-ranging mammals should focus on KEs related to NF-κB/MAPK inflammatory signalling pathways, changes in CD4+/CD8+ ratio and MHCII complexes, and on AOs related to auto-immune disorders and on the effective increase of infection rate, particularly in case of exposure to metal mixtures.

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This study focused on analyzing the spatial and vertical distributions of 28 per- and polyfluoroalkyl substances (PFASs), which comprised five precursors and three alternatives, in the water columns of the regional seas surrounding South Korea, such as the Yellow Sea (YS, Y1-Y10), East China Sea (ECS, EC1-EC6), South Sea (SS, S1-S5), and East Sea (ES, E1-E7). The concentrations of these PFASs detected in 204 seawater samples varied from below the limit of detection (-1 in the YS, 0.26-17 ng L^-1 in the ECS, 0.08-3.4 ng L^-1 in the SS, and -1 in the ES, with perfluorooctanoic acid being identified as the most abundant compound. Principal component analysis grouped water masses and regions based on PFASs concentrations and compositions, enabling the identification of PFASs sources and their fate. PFASs are mainly derived from land and are transported via ocean currents, where their compositions tend to remain conservative. PFASs entering the YS are likely conveyed to the ES through ECS and SS, following the northward movement of the Taiwan Warm Current and Kuroshio Current. The ECS serves as a mixing zone for PFASs from various sources. This study provides valuable baseline data for understanding PFASs transport and the characteristics of water masses in the regional seas around South Korea.

The photocatalytic degradation of rhodamine B (RhB), a cationic dye, and bromocresol green (BCG), an anionic dye, was investigated using oxygen vacancy-enriched ZnO as the catalyst. These dyes were selected due to their differing charges and molecular structures, allowing for a deeper exploration of how these characteristics impact the degradation process. The catalyst was prepared by reducing ZnO with 10% H₂/Ar gas at 500 °C, and the introduction of oxygen vacancies was confirmed using various characterization techniques. A detailed kinetic model was developed to track dye degradation, accounting for adsorption and photocatalytic degradation simultaneously, both in solution and on the catalyst surface. The model incorporated the effect of pH on adsorption by considering the dissociation behavior of the dyes and their respective pK_a values. The study revealed that degradation primarily occurs on the catalyst surface at acidic pH, while at basic pH, degradation is more pronounced in the solution. DFT calculations supported these findings, showing that the electrostatic potential of the dyes shifts depending on pH, influencing their interaction with hydroxyl radicals or the catalyst surface. Quantum yield calculations indicate peak values of 6.32 10^-5 molecules per photon for RhB at pH 11, and 4.20 10^-5 for BCG at pH 3.