对生物动力学参数的分析揭示了水生物种积的模式
Louise M Stevenson1, Paul G Matson1, Rachel M Pilla1
1Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, TN, United States of America.
The Science of the total environment
|December 21, 2024
概括
(Hg) 是一种在鱼类中发现的神经毒剂. 这项研究分析了水生物种中Hg和甲基 (MeHg) 积累的生物动力学参数,揭示了影响生物积累的关键因素. 了解这些因素对于管理Hg风险至关重要.
科学领域:
- 环境科学 环境科学
- 毒理学 毒理学 毒理学
- 生态毒理学 生态毒理学
背景情况:
- (Hg) 是一种强烈的神经毒剂,甲基 (MeHg) 通过食用鱼类构成风险.
- MeHg在水生食物链中生物扩大,导致即使在低环境水平的鱼类中,鱼类的度也很高.
- 了解水生物种中的Hg生物积累因子对于评估生态和人类健康风险至关重要.
研究的目的:
- 在鱼类和水生无脊椎动物中编制和分析Hg和MeHg的生物动力学参数 (吸收率,同化效率,排放率).
- 使用机器学习识别实验/生理变量与这些生动力学参数之间的关系.
- 为各种水生生物体提供Hg生物积累因子的综合数据集和分析.
主要方法:
- 编制了38种鱼类和34种无脊椎动物的生物动力学参数值 (吸收率[ku],同化效率[AE],排泄率[ke).
- 从现有文献中收集了总共502个参数值.
- 应用机器学习来识别生物动力学参数的重要预测因素,包括Hg形式,暴露时间,水类型和生物体重量.
主要成果:
- 的形式显著影响了大多数生物动力学参数,除了无脊椎动物吸收率 (ku).
- 无脊椎动物吸收率 (ku) 仅仅由水性暴露时间来预测.
- 流率 (ke) 受多种变量影响,包括鱼类和无脊椎动物中的水类型,生物体重量和Hg形式.
结论:
- 的形式和流出率是水生物种中的生物积累的关键因素.
- 鉴定出Hg生物积累的新模式,与以往强调环境参数的研究不同.
- 这种广泛的数据集和分析提供了对水生生态系统中Hg生物积累管理的更好的理解.
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