环境科学与技术最新文献

The marine environment is grappling with microplastic (MP) pollution, necessitating an understanding of its distribution patterns, influencing factors, and potential ecological risks. However, the vast area of the ocean and budgetary constraints make conducting comprehensive surveys to assess MP pollution impractical. Interpretable machine learning (ML) offers an effective solution. Herein, we used four ML algorithms based on MP data calibrated to the size range of 20–5000 μm and considered various factors to construct a robust predictive ML model of marine MP distribution. Interpretation of the ML model indicated that biogeochemical and anthropogenic factors substantially influence global marine MP pollution, while atmospheric and physical factors exert lesser effects. However, the extent of the influence of each factor may vary within specific marine regions and their underlying mechanisms may differ across regions. The predicted results indicated that the global marine MP concentrations ranged from 0.176 to 27.055 particles/m³ and that MPs in the 20–5000-μm size range did not pose a potential ecological risk. The interpretable ML framework developed in this study covered MP data preprocessing, MP distribution prediction, and interpretation of the influencing factors of MPs, providing an essential reference for marine MP pollution management and decision making.

Polycyclic aromatic hydrocarbons (PAHs) are toxic and persistent pollutants that are widely distributed in the environment. PAHs are toxic to microorganisms and pose ecological risks. Bacteria encode enzymes for PAH degradation through specific genes, thereby mitigating PAH pollution. However, due to PAHs’ complexity, information on the global degradation potential, diversity, and associated risks of PAH-degrading microbes in soils is lacking. In this study, we analyzed 121 PAH-degrading genes and selected 33 as marker genes to predict the degradation potential within the soil microbiome. By constructing a Hidden Markov Model, we identified 4990 species carrying PAH-degrading genes in 40,039 soil metagenomic assembly genomes, with Burkholderiaceae and Stellaceae emerging as high-potential degraders. We demonstrated that the candidate PAH degraders predominantly emerged in artificial soil and farmland, with significantly fewer present in extreme environments, driven by factors such as average annual rainfall, organic carbon, and human modification of terrestrial systems. Furthermore, we comprehensively quantified the potential risks of each potential host in future practical applications using three indicators (antibiotic resistance genes, virulence factors, and pathogenic bacteria). We found that the degrader Stellaceae has significant application prospects. Our research will help determine the biosynthetic potential of PAH-degrading enzymes globally and further identify potential PAH-degrading bacteria at lower risk.

Agricultural intensification has driven global biodiversity loss through land management change. However, there is no consensus on assessing the biodiversity impacts of changes in land management practices and intensity levels using life cycle assessment (LCA). This study reviews 7 expert scoring-based (ESB) and 19 biodiversity indicator-based (BIB) LCA methods used to assess biodiversity impacts, aiming to evaluate their quality and identify research needs for incorporating land management change in LCA. Overall, BIB methods outperformed ESB methods across general criteria, especially in robustness (95% higher). BIB methods assess biodiversity impacts based on land management intensity levels, whereas ESB methods emphasize specific land management practices. Neither approach fully captures biodiversity impacts across supply chains. For future studies, it is advisable to (1) model the direct (on-farm) impacts of land management change at the midpoint level; (2) establish cause-effect relationships between key land management practices and biodiversity indicators, while distinguishing between direct (on-site) and indirect (off-site) biodiversity impacts resulting from land management change; (3) characterize land-use intensity levels with specific land management practices and include the positive impacts from agroecological practices. This Review examines LCA methods for biodiversity concerning land management practices and discusses improvements to better account for the biodiversity impacts of agricultural land management.

Copper (Cu) has long been a concern for human health. While previous studies have explored the toxic effects of Cu, no study is available on the relationship between the Cu redox state transformation and biotoxicity in higher organisms. In this study, we explored the gut and liver toxicity caused by the overflow of Cu(I) at low doses of Cu exposure. Here, we first elucidated the digestive and metabolic systems as the main toxic target sites by a systematic epidemiological analysis. Then, ICP-MS analysis verified that the gut and liver were the top two Cu-high-accumulated organs in zebrafish exposed to 10 and 100 μg/L waterborne Cu for 72 h. In-situ Cu(I) and Cu(II) imaging techniques demonstrated that exogenous Cu(II) was converted to Cu(I) in the zebrafish gut. Furthermore, transcriptomic sequencing revealed that the high overflow of Cu(I) induced gut toxicity by cell cycle arrest in the G phase. However, the substantial accumulation of Cu(I) disrupted the metabolism of energy source nutrients and energy supply, leading to hepatic toxicity. This study provides new insights into the toxic mechanism based on Cu redox state and emphasizes the health risks associated with Cu exposure in the digestive and metabolic systems.