Chrome Extension
WeChat Mini Program
Use on ChatGLM

Occurrence, Dissipation Kinetics and Environmental Risk Assessment of Antibiotics and Their Metabolites in Agricultural Soils

Journal of Hazardous Materials(2024)

Univ Seville

Cited 0|Views9
Abstract
Antibiotics are among the emerging contaminants of greatest concern to the scientific community. However, the occurrence and behaviour of their metabolites in soils have been scarcely studied. To address this research gap, this study investigates the occurrence, sorption, dissipation kinetics, and environmental risk of highly important antibiotics (sulfamethazine, sulfadiazine, sulfamethoxazole, trimethoprim) and their main metabolites in Mediterranean agricultural soils. Batch experiments were conducted under natural conditions for 120 days. Five different dissipation kinetics models were applied to elucidate antibiotics degradation. The sorption isotherms were evaluated by three different models. Most of the antibiotics and metabolites tested showed a good fit with the Linear Isotherm model (R2>0.96) and biphasic dissipation kinetic models (R2>0.90). The dissipation and the endpoints values (DT50 and DT90) depended on the soil type properties. A Lixisol soil demonstrated reduced degradation of the investigated compounds. Trimethoprim showed the highest persistence, followed by sulfamethazine, sulfamethoxazole, and sulfadiazine. Parent compounds exhibited lower degradation rates than their metabolites. Remaining antibiotic concentrations were found to be below the predicted no-effect concentration in soil, suggesting that they may not pose a risk to terrestrial biota. This study provides valuable insights into the behaviour of these antibiotics and their metabolites in soil.
More
Translated text
Key words
Antibiotics,Metabolites,Agricultural soils,Occurrence,Degradation
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest