Understanding the mechanism of microplastic-associated antibiotic resistance genes in aquatic ecosystems: Insights from metagenomic analyses and machine learning.

The pervasive presence of microplastics (MPs) in aquatic systems facilitates the transmission of antibiotic resistance genes (ARGs), thereby posing risks to ecosystems and human well-being. However, owing to variations in environmental backgrounds and the limited scope of research subjects, studies on ARGs in MPs lack unified conclusions, particularly regarding whether different types of MPs selectively promote ARG enrichment. Analysing large-scale datasets can better encompass broad spatiotemporal scales and diverse samples, facilitating a more extensive exploration of the complex ecological relationships between MPs and ARGs. The present study integrated existing metagenomic datasets to conduct a comprehensive risk assessment and comparative analysis of resistance groups across various MPs. In addition, we endeavoured to elucidate potential associations between ARGs and bacterial taxa, as well as MP structural features, using machine learning (ML) methods. The findings of our research highlight the pivotal role of MP type in shaping plastispheres, accounting for 9.56 % of the biotic variation (Adonis index) and explaining 18.59 % of the ARG variance. Compared to conventional MPs, biodegradable MPs, such as polyhydroxyalkanoates (PHA) and polylactic acid (PLA), exhibit lower species uniformity and diversity but pose a higher risk of ARG occurrence. These ML approaches effectively forecasted ARG abundance by using the bacterial taxa and molecular structure descriptors (MDs) of MPs (average R2tra = 0.882, R2test = 0.759). Feature analysis showed that MDs associated with lipophilicity, solubility, toxicity, and surface potential significantly influenced the relative abundance of ARGs in the plastispheres. The interpretable multiple linear regression (MLR) model, particularly notable, elucidated a linear relationship between bacterial genera and ARGs, offering promise for identifying potential ARG hosts. This study offers novel insights into ARG dynamics and ecological risks within aquatic plastispheres, highlighting the importance of comprehensive MP monitoring initiatives.

Development of pp-LFER and QSPR models for predicting the diffusion coefficients of hydrophobic organic compounds in LDPE.

Diffusion coefficient (D) is important to evaluate the performance of passive samplers and to monitor the concentration of chemicals effectively. Herein, we developed a polyparameter linear free energy relationship (pp-LFER) model and a quantitative structure-property relationship (QSPR) model for the prediction of diffusion coefficients of hydrophobic organic contaminants (HOCs) in low density polyethylene (LDPE). A dataset of 120 various chemicals was used to develop both models. The pp-LFER model was developed with two descriptors (V and E) and the statistical parameters of the model showed satisfactory results. As a further exploration of the diffusion behavior of the compounds, a QSPR model with five descriptors (ETA_Alpha, ASP-6, IC1, TDB6r and ATSC2v) was constructed with adjusted determination coefficient (R2) of 0.949 and cross-validation coefficient (QLoo2) of 0.941. The regression results indicated that both models had satisfactory goodness-of-fit and robustness. This study proves that pp-LFER and QSPR approaches are available for the prediction of log D values for the hydrophobic organic compounds within the applicability domain.

Residues of the fungicide epoxiconazole in rice and paddy in the Chinese field ecosystem.

BACKGROUND: Epoxiconazole is extensively used as fungicide in cereals, grapes and other crops worldwide. Rice is one of the world's most important food crops. Many people who depend on rice for their food live in Asia. A method employing liquid chromatography-tandem mass spectrometry was developed for determination of epoxiconazole in brown rice, straw, rice hull, paddy water and soils. Epoxiconazole residues in rice hull, brown rice, straw and soil were also determined. RESULTS: The limit of quantitation was set at 0.01 mg kg(-1) for the matrices studied. Epoxiconazole degradation in straw, paddy water and soil was studied. The epoxiconazole residues in brown rice, straw, hull and paddy soil were determined. Concurrent recoveries were between 89.2 and 104.1%, with relative standard deviations ranging from 4.6 to 14.4% at three fortification levels between 0.01 and 5.0 mg kg(-1). The half-lives in straw, paddy water and soils were found to be 4.7-5.9, 2.9-6.0 and 2.9-6.4 days respectively. The maximum residues in brown rice, straw, hull and paddy soil samples were 0.18, 2.47, 2.54 and 0.09 mg kg(-1) respectively. CONCLUSION: Compared with the maximum residue levels (MRLs) for epoxiconazole in rice that have been set by the European Union (0.1 mg kg(-1)) and by China (0.5 mg kg(-1)), the epoxiconazole residue on rice at an application rate of 112.5 g AI ha(-1) with two applications at an interval of 7 days, and with a 28 day preharvest interval (PHI), is below the MRL, and thus the use of epoxiconazole is considered to be safe. Epoxiconazole should be applied correctly, according to good agricultural practice, using only the recommended amounts, frequencies and appropriate PHI.