一个模型-实验循环,以优化中生态系统生态毒理学风险评估的数据要求

V. Grimm
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引用次数: 0

摘要

在生态毒理学中,化学品的毒性通常是在实验室条件下对个人进行量化的,而在现实中,个人在群体和社区中与其他个人相互作用,并暴露在空间和时间变化的条件下。因此,微观和中观实验被用于增加毒理学风险评估的生态现实性。然而,此类实验是劳动密集型的,成本高昂,并且由于后勤原因,无法实现所有可能的因素或利益(Henry等人,2017)。此外,由于这类实验通常包括动物,因此必须尽量减少实验的数量,以尽可能减少动物试验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A model-experiment loop to optimise data requirements for ecotoxicological risk assessment with mesocosms
Recommendation In Ecotoxicology, the toxicity of chemicals is usually quantified for individuals under laboratory conditions, while in reality individuals interact with other individuals in populations and communities, and are exposed to conditions that vary in space and time. Microand mesocosm experiments are therefore used to increase the ecological realism of toxicological risk assessments. Such experiments are, however, labour-intensive, costly, and cannot, due to logistical reasons, implement all possible factors or interests (Henry et al. 2017). Moreover, as such experiments often include animals, the number of experiments performed has to be minimized to reduce animal testing as much as possible.
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