Selection of scenarios for landscape-level risk assessment of chemicals: case studies for mammals

IF 5.9 3区 环境科学与生态学 Q1 Environmental Science Environmental Sciences Europe Pub Date : 2022-04-09 DOI:10.1186/s12302-022-00612-4
Magnus Wang, Su-Yeong Park, Christian Dietrich, Joachim Kleinmann
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引用次数: 2

Abstract

Background

For landscape-level risk assessments of pesticides, the choice of the scenario is a key question, since it determines the outcome of a risk assessment. Typically, the aim is to select a realistic worst-case scenario. In the present study, landscapes from an area with a high proportion of cereal fields in France were analysed and simulations with population models for wood mouse, common vole, brown hare and European rabbit were conducted to understand if the worst-case character regarding pesticide exposure and population survival can be determined based on landscape features alone. Furthermore, it was analysed which landscape features relate with population survival and the magnitude of effects due to pesticide application. Answers to these question may help to decide whether landscape scenarios can be selected based on expert decision and whether the same scenarios may be used for different species or not.

Results

There were species-specific landscape features relating to long-term population survival. A landscape that is worst-case for one species, was not necessarily worst-case for another. Furthermore, landscapes that were worst-case regarding population survival were often not worst-case regarding the magnitude of effects resulting from pesticide application. We also found that small landscapes were sometimes, but not always worst-case compared to larger landscapes. When small landscapes were worst-case, this was typical because of the artificial borders of the digitised landscape.

Conclusions

Landscape analyses can help to obtain an approximate impression of the worst-case character of a landscape scenario. However, since it was difficult to consistently and reliably do this for single landscapes, it may be advisable to use a set of different landscapes for each risk assessment, which covers the natural variability. Depending on whether population survival shall be ensured or the magnitude of effects due to pesticides, different landscape structure and composition needs to be considered to establish a worst-case landscape scenario.

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化学品景观级风险评估情景的选择:哺乳动物案例研究
背景:对于农药的景观级风险评估,情景的选择是一个关键问题,因为它决定了风险评估的结果。通常,目标是选择一个现实的最坏情况。在本研究中,研究人员分析了法国一个谷物田高比例地区的景观,并使用木鼠、普通田鼠、褐兔和欧洲兔的种群模型进行了模拟,以了解是否可以仅根据景观特征确定农药暴露和种群生存的最坏情况。此外,还分析了与种群生存有关的景观特征和农药施用的影响程度。这些问题的答案可能有助于决定景观场景是否可以根据专家的决定来选择,以及相同的场景是否可以用于不同的物种。结果存在与种群长期生存有关的物种特有景观特征。对一个物种来说是最坏情况的景观,对另一个物种来说不一定是最坏情况。此外,在种群生存方面最糟糕的景观往往不是在农药施用造成的影响程度方面最糟糕的。我们还发现,与大型景观相比,小型景观有时是最糟糕的,但并非总是如此。当小型景观最糟糕的时候,这是典型的,因为数字化景观的人工边界。结论景观分析有助于获得景观情景最坏情况特征的近似印象。然而,由于很难对单个景观进行一致和可靠的评估,因此为每个风险评估使用一组不同的景观可能是明智的,这些景观涵盖了自然的可变性。根据是否需要确保种群生存或农药影响的程度,需要考虑不同的景观结构和组成,以建立最坏的景观情景。
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来源期刊
Environmental Sciences Europe
Environmental Sciences Europe Environmental Science-Pollution
CiteScore
9.20
自引率
1.70%
发文量
110
审稿时长
13 weeks
期刊介绍: ESEU is an international journal, focusing primarily on Europe, with a broad scope covering all aspects of environmental sciences, including the main topic regulation. ESEU will discuss the entanglement between environmental sciences and regulation because, in recent years, there have been misunderstandings and even disagreement between stakeholders in these two areas. ESEU will help to improve the comprehension of issues between environmental sciences and regulation. ESEU will be an outlet from the German-speaking (DACH) countries to Europe and an inlet from Europe to the DACH countries regarding environmental sciences and regulation. Moreover, ESEU will facilitate the exchange of ideas and interaction between Europe and the DACH countries regarding environmental regulatory issues. Although Europe is at the center of ESEU, the journal will not exclude the rest of the world, because regulatory issues pertaining to environmental sciences can be fully seen only from a global perspective.
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