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生物积累模型中生态、化学和生理参数的不确定性:对体内浓度和基于组织的风险系数的影响。

Uncertainties in ecological, chemical and physiological parameters of a bioaccumulation model: implications for internal concentrations and tissue based risk quotients.

机构信息

NIOO-CEME, Netherlands institute of ecology, centre for estuarine and marine ecology, Korringaweg 7, 4400 Yerseke, The Netherlands.

出版信息

Ecotoxicol Environ Saf. 2010 Mar;73(3):240-6. doi: 10.1016/j.ecoenv.2009.11.011. Epub 2010 Jan 4.

DOI:10.1016/j.ecoenv.2009.11.011
PMID:20045560
Abstract

Bioaccumulation models predict internal contaminant concentrations (c(i)) using ecological, chemical and physiological parameters. Here we analyse the effect of uncertainties on these parameters on bioaccumulation model predictions. Simultaneously considering the uncertainties on all these parameters in a bioaccumulation model resulted in uncertainty ranges of c(i) that increased with the octanol water partition coefficient K(ow) and reached maxima of up to 1.25 log units for mesozooplankton and up to 1.45 log units fish at logK(ow)=8. A global sensitivity analysis (SA) was performed to rank the contribution of different parameters to the observed uncertainty. The SA demonstrated that this interspecies difference resulted predominantly from uncertain production rates of fish. The K(ow), the water concentration and organic carbon-octanol proportionality constant were important drivers of uncertainty on c(i) for both species. A tissue based risk quotient (RQ(tissue)) combining uncertainty on c(i) with realistic tissue based effect thresholds indicated that fish were up to 10 times more probable to have RQ(tissue)>1 than mesozooplankton, depending on the considered threshold value. Conventional exposure based risk quotients were up to 5 times less probable to exceed one than were corresponding RQ(tissue), and this for both species.

摘要

生物蓄积模型使用生态、化学和生理参数来预测体内污染物浓度 (c(i))。在这里,我们分析了这些参数的不确定性对生物蓄积模型预测的影响。在生物蓄积模型中同时考虑所有这些参数的不确定性,导致 c(i) 的不确定性范围随着辛醇-水分配系数 K(ow) 的增加而增加,对于中型浮游动物达到 1.25 个对数单位,对于鱼类达到 1.45 个对数单位,logK(ow)=8。进行了全局敏感性分析 (SA) 以对不同参数对观察到的不确定性的贡献进行排序。SA 表明,这种种间差异主要源自鱼类的不确定生产速率。K(ow)、水浓度和有机碳-辛醇比例常数是两种物种 c(i) 不确定性的重要驱动因素。将不确定性与现实的基于组织的效应阈值相结合的基于组织的风险商 (RQ(tissue)) 表明,取决于所考虑的阈值值,鱼类的 RQ(tissue)>1 的可能性是中型浮游动物的 10 倍。基于常规暴露的风险商比相应的 RQ(tissue)更不可能超过 1,对于这两种物种都是如此。

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