Xiaoli Zhao

The roles of clothing and personal hygiene in regulating dermal exposure to organophosphate esters (OPEs) have received increasing attention. Herein, we performed an experiment on 15 participants subjected to three 10-day OPE exposure rounds to quantify their exposure reduction efficiencies (EREs) under real-world conditions, controlling for laundering, showering, and their combined laundering–showering frequencies. All tested fabrics (cotton, polyester, acrylic fiber, viscose, and modal) effectively reduced OPE residues on the skin, and EREs were negatively correlated with fabric density (r = -0.923), with cotton exhibiting the highest ERE of 69%. As laundering frequency decreased, ∑28OPE concentrations in clothes increased by 4–8-fold, whereas OPE residues on the skin remained relatively stable, indicating that laundering exerts slight modulatory effects on ERE and that clothes retain their protective efficacy for at least 10 days. Without showering, peak ∑28OPEs concentrations were observed after 2–7 days, indicating that daily showering further limits progressive OPE accumulation. Weighted quantile sum regression results showed that clothing contributed most to EREs (56–87%), followed by showering (12–39%) and laundering (0.8–4%). Urinary biomonitoring confirmed that reductions in dermal exposure lowered internal exposure. Thus, wearing cotton clothes and daily showering are feasible measures to mitigate OPE exposure.

Persulfate (PDS)-based advanced oxidation processes have attracted considerable attention because of their excellent pollutant removal performance. However, precisely regulating the PDS activation pathway to shift conventional radical oxidation toward a highly selective nonradical oxidation process remains a major challenge in environmental catalysis. Herein, we report a curvature-engineered Zn single-atom catalyst (C-ZnN4) for regulating PDS activation through a nonradical electron-transfer pathway (ETP). A comparative study between curved Zn single-atom sites (C-ZnN4) and planar Zn single-atom sites (P-ZnN4) revealed that atomic-scale curvature effectively modulated the local electronic structure of the Zn active centers, redistributed the electron density, and enhanced interfacial electron transfer. The C-ZnN4/PDS system exhibited excellent acetaminophen (APAP) degradation performance, with a pseudo-first-order rate constant 19.22 times higher than that of the planar ZnN4 system. Electron paramagnetic resonance (EPR), quenching experiments, electrochemical measurements, in situ Raman spectroscopy, and theoretical calculations collectively demonstrated that the ETP dominated APAP degradation in the C-ZnN4/PDS system, contributing 99.3% to the overall process. Density functional theory calculations further confirmed that the curved structure enhanced PDS adsorption at the Zn single-atom sites and promoted interfacial electron transfer between APAP and PDS through the Zn active centers. In addition, the C-ZnN4/PDS system exhibited excellent cycling stability and robust continuous-flow treatment performance, maintaining an APAP removal efficiency of 99.47% after 120 h of continuous operation. This study elucidates the critical role of atomic-scale curvature in regulating the catalytic mechanism of single-atom sites and provides a theoretical basis and design strategy for developing efficient and highly selective nonradical advanced oxidation systems for water pollution control.

Anaerobic digestion (AD) of lipid-rich food waste (FW) is frequently destabilized by long-chain fatty acids (LCFAs) toxicity and persistent foaming. Here, nitrogen and oxygen nanobubbles (N2-NBs and O2-NBs) were compared under identical generation and dosing conditions in 80-day biomethane potential assays. The comparison integrated fatty acids (FAs) methanation assays, targeted volatile- and medium-chain fatty-acid profiling, electrochemical characterization, and aminophenyl fluorescein measurements of reactive oxygen species (ROS)-associated oxidant signals. N2-NBs produced the highest methane yield (718.6 NmL/g-VS, 30% higher than the deionized-water control) during AD of lipid-rich FW and decreased the maximum transient medium-chain fatty acids (MCFAs) concentration by 44.4%. N2-NBs also yielded the highest capacitance, electron-accepting capacity, electron-donating capacity, and total electron-transfer capacity of the sludge in the AD, consistent with greater extracellular redox capacity and improved FAs conversion. O2-NBs exhibited the highest reactive oxidant signal and the strongest apparent foam mitigation, suggesting a possible contribution of oxidative interfacial processes to foam destabilization. N2-NBs-focused omics further revealed coordinated enrichment of hydrolytic/fermentative bacteria, methanogens, and lipid- and redox-related functions, alongside metabolite patterns indicative of reduced persistence of inhibitory FAs intermediates. Overall, N2-NBs enhanced methane recovery and redox capacity, whereas O2-NBs improved foam control, supporting gas-core-specific optimization of lipid-rich AD.

High-valent metal-oxo species offer selective oxidation in complex water matrices, yet controlling their formation on heterogeneous catalysts remains challenging. Here, a CeO2@V2C heterointerface was constructed to regulate the spin state of vanadium sites and direct peroxymonosulfate (PMS) activation toward high-valent vanadium-oxo species (HVVOS). Interfacial electronic reconstruction transferred electrons from V2C to CeO2, altered the V 3d orbital occupation, and induced a low-spin-to-high-spin transition. Under optimized conditions, the CeO2@V2C/PMS system removed 98.39% of acetaminophen within 10 min, with an apparent rate constant of 0.33043 min-1. Multiple lines of evidence, including scavenging tests, electron paramagnetic resonance, a methyl phenyl sulfoxide probe, in situ Raman spectroscopy, and H218O isotope labeling, identified HVVOS as the dominant oxidants, accounting for 88.39% of the overall degradation activity. Coupling CeO2 with V2C increased the conversion of methyl phenyl sulfoxide to its sulfone by 4.9-fold. X-ray absorption near-edge structure spectroscopy further confirmed the enhanced spin state of vanadium. Density functional theory calculations revealed stronger PMS adsorption (-2.815 eV), enhanced interfacial charge transfer, an upward shift of the V d-band center, and lower energy barriers for HVVOS formation. The system remained effective over pH 3.14–9.07, resisted common inorganic ions and natural organic matter, achieved near-complete removal in tap and river water, degraded structurally diverse pollutants by 92.72–100%, and retained 97.21% acetaminophen removal after five cycles. These findings establish interfacial spin-state engineering as an effective strategy for selective PMS activation and robust water decontamination.

Plasticizers are widely used polymer additives whose release into aquatic environments poses ecological risks, while their structural diversity challenges conventional toxicological assessment. To address this data gap, an integrated modeling framework was established for hazard assessment and prioritization of plasticizers. Species-specific QSAR models were developed for six species across three trophic levels, enabling accurate toxicity prediction for 600 plasticizers. Eighty-five plasticizers were identified as high-priority due to potent toxicity (LC50 < 10 μM) across trophic levels, 54.1% of which are alternative plasticizers. Approximately half of these high-priority plasticizers are widely used in polyvinyl chloride plastics, and 21.2% occur in bioplastics, indicating their potential contribution to the chemical risk of conventional plastics and “green” polymers. Across multiple taxa, predicted toxicity differences among plasticizer subclasses were linked to structural features captured by 2D descriptors. By integrating QSAR predictions with interspecies correlation estimation, the hazardous concentrations for 5% of species (HC5: 0.04–274 μg/L) were derived for high-priority plasticizers using species sensitivity distributions, with crustaceans identified as the most sensitive taxa. Linking hazard prediction and prioritization with polymer occurrence enables the systematic identification of high-priority plasticizers and highlights polymers that contain plasticizers of potential concern.

Understanding and predicting the ecotoxicity of nanomaterials (NMs) in untested aquatic species remains a major challenge due to the absence of quantitative and mechanistically meaningful species descriptors. Here, we propose a functional genomics-based species descriptor that captures molecular functions, antioxidant defenses, and detoxification capacities and evaluate its utility in improving deep-learning (DL) models for NM toxicity prediction. Using toxicity data for 14 metal-containing NMs across 31 aquatic organisms, we integrated the proposed descriptor into four supervised machine-learning algorithms, the models achieved a highly balanced and satisfactory predictive performance across different structures, with cross-validation Q2 values stabilizing around 0.61–0.65 for both random forest (RF) and DL architectures. Feature-importance analysis further showed that functional genomic traits contributed comparably to NM physicochemical properties and exposure conditions, indicating that the descriptor provides mechanistic relevance rather than functioning as a purely statistical feature set. This approach enables the quantitative characterization of species-specific sensitivity to NMs and substantially enhances cross-species generalizability of toxicity prediction. Consequently, the proposed genomics-based descriptor provides a scalable approach for hazard assessment of NMs in untested, rare, and data-deficient aquatic species of conservation concern.

Nanoplastics inevitably interact with human sweat upon dermal contact, where their aggregation behavior governs the subsequent migration and penetration risks. However, the driving factors and underlying mechanisms have remained poorly understood. Herein, polyethylene nanoplastics (PENPs), the main type of nanoplastics detected in skin care products and airborne particles, were studied in batch experiments with data analyzed by machine learning. The nanoplastic concentration level (49.8%), photoaging (17.1%), and organic constituents (8.7%, mainly l-histidine) were identified as dominant factors governing aggregation. Increasing PENP concentration promoted aggregation, thereby reducing penetration possibility through sweat glands and hair follicle pores. In contrast, photoaging suppressed aggregation and elevated the penetration possibility of photoaged PENPs (APENPs). l-Histidine adsorbed on PENPs via electrostatic attraction and hydrophobic interactions enhanced their dispersion and penetration possibility, whereas l-histidine promoted the aggregation of APENPs through cooperative LH-mediated electrostatic bridging among APENPs, thus reducing their penetration possibility. Moreover, model predictions indicated that higher penetration possibility in females, overweight individuals, trunk skin regions, and populations in China and Europe was attributed to higher dispersion due to lower particle concentration, stronger photoaging, and higher lactic acid in sweat. These findings contribute to understanding realistic exposure of PENPs and their molecular mechanisms of aggregation in sweat, thus providing useful insights into human health risks from dermal exposures.