Department of Environmental Sciences, University of Milano Bicocca, Milano, Italy.
Ecotoxicology. 2012 May;21(4):1050-62. doi: 10.1007/s10646-012-0858-7. Epub 2012 Jan 22.
A novel approach, based on Species sensitivity distribution (SSD), is proposed for the development of an index for classifying ecotoxicological pesticide risk in surface waters. In this approach, the concept of TER (Toxicity Exposure Ratio), commonly used in traditional risk indices, is substituted by the concept of PAF (Potentially Affected Fraction), which takes into account several species within the biological community of interest, rather than just a small number of indicator species assumed as being representative of the ecosystem. The procedure represents a probabilistic tool to quantitatively assess the ecotoxicological risk on biodiversity considering the distribution of toxicological sensitivity. It can be applied to assess chemical risk on generic aquatic and terrestrial communities as well as on site-specific natural communities. Examples of its application are shown for some pesticides in freshwater ecosystems. In order to overcome the problem of insufficient reliable ecotoxicological data, a methodology and related algorithms are proposed for predicting SSD curves for chemicals that do not have sufficient available data. The methodology is applicable within congeneric classes of chemicals and has been tested and statistically validated on a group of organophosphorus insecticides. Values and limitations of the approach are discussed.
提出了一种基于物种敏感性分布(SSD)的新方法,用于开发地表水生态毒理学农药风险分类指数。在该方法中,传统风险指数中常用的 TER(毒性暴露比)概念被 PAF(潜在受影响分数)所取代,PAF 考虑了生物群落中多个物种,而不仅仅是少数被认为代表生态系统的指示物种。该程序代表了一种概率工具,用于定量评估考虑毒理学敏感性分布的生物多样性的生态毒理学风险。它可用于评估通用水生和陆生群落以及特定地点的自然群落的化学风险。本文以一些淡水生态系统中的农药为例,说明了其应用示例。为了克服可靠的生态毒理学数据不足的问题,提出了一种用于预测缺乏足够可用数据的化学品 SSD 曲线的方法和相关算法。该方法适用于同种类的化学品,并已在一组有机磷杀虫剂上进行了测试和统计验证。讨论了该方法的优缺点。