Environmental Science: Processes & Impacts最新文献

Industrial emissions contribute to air pollution in industrialized cities in North America, where higher concentrations of chemicals are linked to adverse effects on human health. In the present study, polyurethane foam passive air samplers (PUF-PAS) were deployed over a one-year period in two industrialized cities (5 sites per city – rural, residential, and urban) to determine the spatial and temporal distribution of 30 semi-volatile organic compounds (SVOCs) and 12 metals. Targeted SVOCs included polychlorinated biphenyls (PCBs), flame retardants (PBDEs and TDCPP), and organochlorine pesticides (OCPs; cis-chlordane, endosulfan I and 4,4′-DDE), and most pollutants were elevated at industrial locations. The annual average air concentrations of PCBs, PBDEs and OCPs ranged between 17–631 pg m⁻³, while those of the polycyclic aromatic hydrocarbons (PAHs) ranged from 2–42 ng m⁻³. Concentrations of benzo[a]pyrene were greatest during the Winter and Spring, and exceeded the ambient air quality criterion (0.01 ng m⁻³) at all sites (0.03–0.50 ng m⁻³). Iron was the most abundant metal detected, and its spatial distribution likely reflected the presence of steel production. Chemical activated luciferase gene expression (CALUX®) assays for estrogenicity were below detection limits, while anti-androgenicity activity was measured at all sites with the greatest activity observed at locations most impacted by industrial emissions.

Correction for ‘Tiny pollutants, big consequences: investigating the influence of nano- and microplastics on soil properties and plant health with mitigation strategies’ by W. Hamd et al., Environ. Sci.: Processes Impacts, 2025, 27, 860–877, https://doi.org/10.1039/D4EM00688G.

Accurate quantification of inorganic fluoride (F⁻) release is an essential metric for evaluating the efficacy of poly- and perfluoroalkyl substance (PFAS) degradation processes. However, current analytical methods for F⁻ measurement, including ion chromatography (IC), the use of ion selective electrodes (ISEs), and combustion ion chromatography (CIC), possess inherent limitations and susceptibility to interferences within complex environmental matrices. This perspective critically examines these under-recognized analytical pitfalls, such as the co-elution of inorganic fluoride with short-chain organic acids in IC, the inherent sensitivity limitations and stability issues of ISEs at trace concentrations, and the variable recovery of ultra-short-chain or volatile fluorinated species in combustion-based sum parameter analyses. We explain how these analytical discrepancies can lead to mischaracterization of defluorination efficiency and potentially result in overestimation of technological performance, misinformed risk assessments, and regulatory ambiguity. We propose a series of methodological recommendations, emphasizing the routine application of hyphenated techniques such as IC-mass spectrometry (IC-MS) for enhanced F⁻ validation, and the strategic utilization of fluorine-19 nuclear magnetic resonance (¹⁹F NMR) spectroscopy for comprehensive tracking of diverse organofluorine species. Emphasizing analytical cross-validation and holistic fluorine mass balance approaches is paramount for enabling reliable advancements in PFAS remediation.

Mealworm larvae (Tenebrio molitor) exhibit the potential for biodegrading synthetic plastics, providing a sustainable strategy to reduce plastic waste. Real-time data on plastic consumption rates and degradation mechanisms, particularly those linked to oxygen consumption, remain limited. This study aimed to quantify the ability of mealworms to consume commercial plastics—polystyrene (PS) and polyethylene (PE)—over a 28 day period under controlled conditions (75% ± 5% humidity, 25 ± 0.5 °C) using a respirometer. Twenty-eight-day survival rates exceeded 80% in plastic-fed groups, versus 44.2% in the unfed control. Daily plastic consumption per 100 larvae was 15.9 ± 0.5 mg (PS) and 17.9 ± 0.9 mg (PE). Reductions in the molecular weights (Mw, Mn, and Mz) of residual PS and PE in larval frass, compared to feedstock, confirmed plastic depolymerization and biodegradation. GC-MS identified surface chemical changes with oxygen-rich functional groups and short alkanes, while ¹H-NMR and FTIR analyses revealed chemical modifications consistent with the partial oxidation of the polymer. The gut microbiome of T. molitor adapted significantly to PS and PE exposure, reshaping microbial diversity and ecological niches. Notably, Spiroplasma sp., Lactococcus sp., and Enterococcus sp. were associated with all four plastics, whereas Staphylococcus sp. and Providencia sp. played key roles in PS metabolism. Our finding demonstrates for the first time that oxygen consumption can serve as a quantitative indicator of plastic biodegradation, highlighting the mealworm gut microbiome as a promising tool for plastic biodegradation while expanding scientific insights into its microbial functions.

Biomagnification, the process by which chemical concentrations increase in organisms at higher trophic levels, can pose significant risks to wildlife and ecosystems. Despite its importance, our understanding of species-specific differences in biomagnification potential remains limited. The analysis of the critical biotransformation half-life, the maximum half-life to avoid biomagnification of a chemical, can help address this gap. Here, I present a comprehensive analysis of critical biotransformation half-lives across diverse air-breathing wildlife species, providing novel insights into the factors influencing biomagnification. By constructing species-specific contour plots in chemical partition space, I reveal substantial variations in biomagnification potential among different organisms, with differences in critical biotransformation half-lives reaching more than two orders of magnitude. These substantial interspecies differences underscore the need for species-specific biotransformation data and biomagnification modelling. This analysis also demonstrates that model normalisation methods significantly impact these species-specific differences, suggesting that the choice of normalisation can alter biomagnification assessments. I further delineate the chemical partition space regions where elimination is dominated by urination versus respiration, highlighting important interspecies variations. Finally, I introduce a weight-of-evidence approach for assessing potential food-chain biomagnification, illustrated through a case study on methoxychlor, which is a generalizable approach that differs from current approaches by its stronger focus on biotransformation. A critical discussion of allometric scaling and sources of uncertainty identifies further research needs. This work enhances our ability to predict and assess biomagnification risks across diverse ecosystems and species, offering valuable tools for environmental risk assessment and conservation efforts.

Microplastics (MPs) are persistent pollutants that pose serious ecological and health hazards across terrestrial and aquatic ecosystems. Compared with physical and chemical degradation methods, the biological degradation of MPs is more pronounced and eco-friendlier. Although bacterial and fungal contributions to MP biodegradation have been extensively studied, the role of protists remains comparatively underexplored. Earlier laboratory studies have demonstrated that various protistan taxa can ingest latex microspheres through phagocytosis and influence their fate in an ecosystem. However, beyond ingestion and transfer, the potential of protists to transform and partially degrade MPs via enzymatic or oxidative processes has only recently attracted attention. Therefore, beyond existing summaries on protist–latex bead interactions, this review proposes a novel conceptual framework that not only positions protists as vectors that transfer MPs within food webs, but also as active agents in degradation processes and facilitators of microbial colonization. By introducing emerging evidence, we highlight protists as overlooked yet promising components of MP fate and outline future research directions to establish them as part of integrated microbial tools for environmental microplastic remediation.

Chromium (Cr) and vanadium(V) are redox-active, geogenic contaminants observed to co-occur in groundwater in the North Carolina (NC) Piedmont region. On a landscape-scale, factors controlling Cr and V solubilization to groundwater in the Piedmont are understood to be largely associated with the regional geology. However, the specific mechanisms mediating (bio)geochemical interactions among heterogeneous geologic materials and redox active chemical inputs in the subsurface are poorly understood. The specific goal of this research was to elucidate the chemical controls on the solubilization of Cr and V from saprolite – chemically weathered rock between soil and bedrock – to groundwater. We conducted 40-day batch incubation experiments using chemically variable saprolites from the NC Piedmont to evaluate dynamics of Cr and V solubilization as influenced by interactions between common chemical inputs. Organic carbon (citric acid) additions stimulated dissolution of Cr and V to the aqueous phase, with abiotic controls generating greater concentrations of Cr and V than biotic incubations. Addition of the oxidant manganese (Mn)-oxide suppressed solubilization of Cr and V from the saprolites. Across all experiments, dissolved Cr and V concentrations were positively correlated (R² = 0.81–0.99) with dissolved iron (Fe) concentrations. Overall, these results highlight how organic carbon inputs can modulate the cycling and solubilization of Cr and V in heterogeneous media, and our results may be impactful in making better predictive and vulnerability assessments plans, particularly in delineating abiotic vs. biotic roles driving Cr and V dissolution to groundwater.

Acid deposition, nitrogen (N) fertilizer and wastewater discharge are multiple stressors producing great impacts on natural water chemistry. However, few studies have quantitatively estimated the effects of these stressors on Taihu water chemistry. Here, the MAGIC model was used to simulate and predict long-term changes in lake water chemistry after adaptive modifications. Long term historical water chemistry data and our field data on soil properties were used to calibrate the model. The result indicated that Cl⁻, Na⁺, and SO₄²⁻ were the most sensitive to wastewater discharges, resulting in around an 80% increase in Cl⁻ and Na⁺; and after “wastewater calibration” the modelled results were in good agreement with calculated ones. Modelled Ca²⁺ and Mg²⁺ losses were consistent with measured results before 2000, and the acid deposition-induced effect was not different from the combined effect of acid deposition and N fertilization application; while after that base cation losses caused by dual acidification were significantly higher than those caused by acid deposition alone, which corresponded well with the N fertilizer consumption and SO₂ emissions. The modelled results indicated that after 2000, the annual loss of Ca²⁺ and Mg²⁺ caused by double acidification (acid deposition and N fertilization) was 27% and 11% higher than that caused by acid deposition, respectively. The MAGIC prediction based on different scenarios showed that the reduction of wastewater discharge would effectively inhibit the increase of Cl⁻, Na⁺, and SO₄²⁻, while the effect of acidification would last longer than expected even under SO₂ reduction. This work is expected to provide a scientific basis for integrated watershed management and recovery planning.

This study introduces a novel task transfer learning framework for predicting transient emissions (NOx, PN, and THC) in non-road diesel engines. Our key innovation lies in eliminating model re-optimization through a fixed-architecture approach where pretrained hyperparameters are preserved and only task-specific layers are fine-tuned. Validated on NRTC data across all emission transfer scenarios, the method achieves near-identical accuracy to pretrained models (R² difference ≤0.0044), peak R² values of 98.87% (NOx), 99.54% (PN), and 99.52% (THC) and computational cost reduction by 72% versus conventional methods. The framework surpasses operational vehicle sensor accuracy and matches laboratory-grade equipment precision. Analysis confirms the efficacy of transfer learning for emission prediction and establishes an efficient pre-trained model organization paradigm.

To explore the use of molecular docking as a high throughput in silico screening tool for identifying chemicals of environmental health concern, we conducted a case study to assess endocrine disruption effects due to targeting of nuclear receptors (NRs) by chemicals with backbone structures like bisphenols, but with varied functional groups. The molecular docking analysis elucidates how functional groups of the chemicals, such as NH₂, Cl, and OCH₃, influence their interaction with the human estrogen receptor alpha (hERα), a key player in endocrine regulation. Through comparative docking analysis, we examined how bisphenol analogs interact with three distinct conformations of hERα: the apo structure and two structures with bound agonist and antagonist ligands. Water molecules within the protein and surrounding the ligand binding domain (LBD) were found to have little impact on the affinity of compounds binding to the receptor across various conformations. This can be attributed to the hydrophobic nature of the ligand-binding pocket, which consists mainly of hydrophobic amino acid residues and binding sites. In the assessment of bisphenol analogs compared to well established endocrine disrupting chemicals (EDCs), it was observed that these analogs exhibit characteristics commonly associated with endocrine disruptors. While compounds like BPA and BPF exhibited partial agonist activity, stimulating hERα activity to varying degrees, other compounds displayed non-agonist behavior, suggesting a different mode of interaction with the receptor. Further analysis revealed the significance of specific functional groups, such as hydroxyl or amine groups, on the aromatic ring of these compounds in modulating their binding affinity to hERα. Within the ligand binding site of hERα, amino acid residues Glu353, Arg394, and His524 have the capacity to form hydrogen bonds with hydroxyl or amine groups. Protonation or deprotonation of these groups can further alter their binding affinity, thereby influencing their interaction with estrogen receptors and subsequent estrogenic effects. Via this case study, we demonstrate the potential and provide best practices of using molecular docking as a new approach methodology (NAM) for chemical assessments and regulations.