ACS Environmental Au最新文献

The insect growth regulator novaluron is a benzoylurea compound that disrupts the polymerization of chitin filaments. It is commonly used to control agricultural pests, particularly during their immature stages, and is generally considered nontoxic to adult insects. However, there is a lack of studies addressing the potential side effects of this insecticide on nontarget organisms, such as pollinating bees. In honey bees, the midgut is the primary organ responsible for digestion and nutrient absorption, where ingested food is surrounded by the peritrophic matrix, a structure composed of chitin microfibrils, glycosaminoglycans, and glycoproteins synthesized by digestive cells along the midgut. This study investigated whether chronic oral exposure to novaluron affects adult workers of the honey bee Apis mellifera. Specifically, we assessed the effects of the insecticide on the composition and permeability of the peritrophic matrix, the histopathology of the midgut, and worker mortality. Bees exposed chronically to a sublethal concentration of novaluron for 10 days showed reduced chitin levels in the peritrophic matrix, which appeared disorganized and diffuse, along with increased permeability of this barrier. Furthermore, exposed bees exhibited histopathological alterations in the midgut epithelium and elevated mortality rates. These findings indicate that, in the context of chronic oral exposure, commercial formulation of the insecticide novaluron, although classified as an insect growth regulator, is toxic to adult A. mellifera workers at the tissue level.

While ultraviolet-driven photochemistry influences organic aerosols in typical indoor environments, the impact of high-energy medical X-raysdespite their greater energy and direct health relevanceremains unexplored in radiotherapy rooms. Here, we employed Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to characterize the organic molecular compositions of fine particles (PM_2.5) in three radiotherapy rooms and adjacent waiting areas. Results revealed a 1.3-2.3-fold increase in molecular formulas under X-ray exposure, driven by fragmentation-oligomerization cycles. Diurnal oxygen-to-carbon (O/C) ratios varied from 0.31 to 0.35 in the daytime and from 0.53 to 0.61 at night in three radiotherapy rooms, which indicate a light-modulated oxidation. Critically, PAH precursors, dominated by CHO/CHON species with double bond equivalence (DBE) ≥ 10, were enriched by 1.31-2.83-fold in radiotherapy environments. These compounds correlated strongly with oxidative stress biomarkers, implying potential health risks. Mechanistically, fragmentation and oligomerization prevail, likely enhancing gas-phase oxidation and particle-phase dimerization. Our findings necessitate air purification targeting reactive organics in medical radiation facilities to mitigate exposure risks.

The Bonn Challenge and the UN Decade on Ecosystem Restoration promote global forest restoration, while the implementation mechanisms and their ecological effects remain insufficiently understood. This study focuses on China's Natural Forest Protection Program (NFPP) as a case study to address this gap. The study uses a causal machine learning approach, i.e., the forest doubly robust learner, to investigate the individual treatment effect by dividing the NFPP implementation into three phases (1-5, 6-14, and 15-20 years) to capture short-term and long-term policy impacts. Provincial-level panel data (1998-2020), incorporating indicators of the natural environment, socioeconomic factors, and ecological governance are used. The results show that the NFPP significantly reduced soil erosion after 15 years of implementation. The policy's effectiveness differed regionally, contingent on nonlinear thresholds that delineate specific ″efficiency traps″ and ″safe operating spaces″. Crucially, driving mechanisms underwent a structural transition, shifting from early anthropogenic disturbance dominance to mature natural background regulation. Mitigation outcomes were constrained by stressors such as extreme rainfall and population density. Notably, excessive afforestation in specific regions failed to yield benefits, exemplifying the adverse trade-offs of violating ecological thresholds. These findings underscore the critical need for long-term commitment and precision governance to ensure sustainable ecological resilience.

Safe and optimized utilization of waste-to-energy (WtE) fly ash (FA) requires a detailed understanding of the physicochemical properties of its metal constituents. This study provides a comprehensive analysis of the chemical form of Zn in fine (<1 μm) and coarse (>1 μm) FA particles, hypothesized to originate from different formation mechanisms. Size-selective aerosol sampling was performed during standard operation in the flue gas channel at a WtE facility. Additionally, FA samples from the air pollution control filters at the facility and boiler deposits were analyzed. Speciation was determined primarily using synchrotron-based X-ray absorption spectroscopy, complemented by XRD, SEM-EDS, and total elemental analysis. Significant differences in terms of elemental composition, crystalline phases, and Zn chemical forms were observed between fine- and coarse FA particles. Fine particles were dominated by Cl, K, and Na with Zn almost exclusively present as potassium zinc chlorides. Coarse particles were heterogeneous, with Zn occurring in stable forms such as aluminate, ferrite, and silicates (e.g., gehlenite). The major elemental constituents were Ca, Si, and Al. Although coarse particles constitute the major mass of the FA, about 50% of the Zn was found in the fine fraction. These findings support strategies for efficient secondary use and recycling of FA, such as targeted Zn extraction from fine particles and potential utilization of the Ca-rich coarse particles in construction, reducing the reliance on virgin materials.

Soil organic carbon (SOC) is crucial in climate change mitigation, yet its spatiotemporal patterns and regional heterogeneity in China remain insufficiently resolved. By integrating field observations with multisource satellite data, we mapped surface SOC dynamics at 1 km for 1986-2023 using XGBoost (R ² = 0.71, RMSE = 1.73 kg C m^-2, MAE = 1.21 kg C m^-2). Model interpretability was advanced with Shapley additive explanation (SHAP) and piecewise structural equation modeling (piecewiseSEM) to identify key drivers and causal pathways. China's SOC distribution exhibits pronounced heterogeneity and can be partitioned into four regions. Regions I-II together hold 56.90% of national SOC stock while covering 43.24% of the area. Nationally, SOC increased at 0.015 Pg C yr^-1 during 1986-2023, but trends diverged regionally: Region I acted as a net carbon source, whereas Region II functioned predominantly as a sink. Soil properties govern the spatial pattern of SOC (strongest direct effects), while climate exerts the greatest overall influence via both direct and indirect pathways. Total nitrogen (TN), temperature (Temp), and soil organic matter (SOM) were the most influential drivers nationally. These findings underscore two policy priorities: (i) protect existing SOC-rich areas to achieve higher mitigation efficiency, and (ii) adopt region-differentiated conservation and management strategies to enhance land resilience and carbon sequestration under China's dual-carbon goals.

Biosolids, the solid byproducts of wastewater treatment, are widely applied to soils to enhance nutrient levels and organic matter. However, their use raises environmental and human health concerns due to the presence of anthropogenic organic contaminants. As such, there is a need to develop treatment strategies that can help remove these compounds before biosolids are land applied. This study investigates the potential of two white-rot fungal species to remove nine psychoactive pharmaceuticals from biosolids. Each species degraded eight compounds, achieving removal efficiencies between 48 and 99% after 60 days. Pleurotus ostreatus nearly completely (>90%) degraded desvenlafaxine, trazodone, and citalopram, while Trametes versicolor achieved over 75% degradation of desvenlafaxine, trazodone, and lamotrigine. Liquid culture (without biosolids) and biosolid experiments tentatively identified 41 fungal transformation products (27 for P. ostreatus and 36 for T. versicolor), of which many were formed from cleavage, hydroxylation, or demethylation reactions. These findings demonstrate that white-rot fungi can effectively grow on biosolids and degrade sorbed psychoactive pharmaceuticals. Overall, the results highlight mycoaugmentation as a promising and sustainable approach for mitigating pharmaceutical contamination in biosolids prior to land application.

Gas ebullition plays a critical role in facilitating contaminant transport from the sediment to the water column. Predicting gas ebullition accurately is necessary for managing sediments and assessing sediment/water flux, and many researchers have published models to predict gas ebullition in lakes, ponds, and rivers. The need for site-specific data and a strong emphasis on water column parameters in published models (rather than sediment data where gas ebullition occurs), coupled with the narrow range of systems studied, limits the application of current published methods to predict ebullition. This study develops machine learning (ML) generalized models to predict gas ebullition accurately across a diverse range of waterways under various ecohydrological conditions. Input data are common sediment parameters, depth and temperature, and gas ebullition flux rates acquired through various methods. We trained and evaluated Multivariate Linear Regression (MLR), Random Forest (RF), eXtreme Gradient Boosting (XGB), and Neural Network (NN) models to predict gas flux using data from over 40 sites across all seasons. ML approaches significantly enhance prediction accuracy compared to results predicted by ten published regression models, most of which had low/no predictive capability for the waterway data set. Among the ML models, the results show that RF and XGB performed significantly better (r ² = 0.79 and 0.80, respectively), and temperature, COD/TOC ratio, and water depth are the most influential parameters, consistent with known mechanisms of methane production and sediment fracture. The results further show that currently published regression models for ebullition based on lacustrine systems have little predictive capability for waterways. The ability to predict gas ebullition accurately using commonly measured parameters suggests immense potential to enhance ebullition modeling, site assessment, and remediation design.

The complexity of chemical mixtures in the environment challenges their in-depth risk assessment due to the diverse compounds in use and the lack of experimental toxicity data. In silico models can be used to fill data gaps for compounds with unknown toxic potency. QSAR models typically distinguish only between active and inactive compounds, providing no information about the levels of activity. In this study, a quantitative structure-activity relationship (QSAR) model that classifies compounds into multiple activity levels was developed to address data gaps in the levels of aryl hydrocarbon receptor-mediated (AhR) activity of compounds commonly detected in environmental samples. Its practical applicability has been demonstrated on highly complex mixtures of aquatic pollutants from the Joined Danube Survey to prioritize the most relevant compounds for experimental assessment. The model's performance showed high sensitivity and specificity, with weighted overall accuracy ranging from 77 to 87%. The combination of experimental and QSAR predicted data was used to calculate site-specific AhR activity, which was compared to the overall AhR activity detected by in vitro bioassays. Experimental testing confirmed the ability of the QSAR model to identify compounds with high AhR activity, including benzonaphthothiophene, perylene, acridone, and triphenylene, and prioritize the most relevant suspected effect drivers. Our model can predict toxic potency and thus prioritize the potential bioactive compounds based on specific activity levels. Our study shows that when QSAR models are used for compound prioritization, several factors must be considered: cytotoxicity, solubility, the high rate of false positives for low-toxicity compounds, and the model's applicability domain.

Photothermal membrane distillation (PMD) offers a sustainable pathway for freshwater production, yet its progress depends on developing high-performance membranes made from environmentally benign materials. To address the growing concern over the environmental persistence of fluorinated polymers, this study utilizes non-fluorinated, postindustrial polyphenylsulfone (w-PPSU) waste as a sustainable polymer source for fabricating photothermal membranes. Electrospun w-PPSU nanofibers were surface-modified with magnetite/black titania (Fe₃O₄/b-TiO₂) nanocomposites synthesized at varying b-TiO₂ concentrations and subsequently sealed with a thin hydrophobic polydimethylsiloxane (PDMS) coating. The best-performing membrane (M-F/bT-50) demonstrated rapid solar-driven heating, elevating its surface temperature to 91.1 °C within 120 s under 1 kWm^-2 irradiation. In desalination tests at a minimal temperature difference (ΔT = 15 °C), this membrane achieved a water flux of 3.27 Lm^-2h^-1, a salt rejection of 99.74%, and a photothermal conversion efficiency of 69.87%. Furthermore, the membrane maintained performance over multiple acidic cleaning cycles, demonstrating high flux recovery and regenerability. This work not only introduces an effective material system for efficient desalination but also establishes a viable pathway for valorizing industrial polymer waste into advanced, environmentally responsible technologies, contributing directly to the principles of a circular economy.