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Global Uncertainty And Sensitivity Analysis Of A Food-web Bioaccumulation Model

机译:食物网生物蓄积模型的全球不确定性和敏感性分析

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A global uncertainty and sensitivity analysis (UA/S A) of a state-of-the-art, food-web bioaccumulation model was carried out. We used an efficient screening analysis technique to identify the subset of the most relevant input factors among the whole set of 227 model parameters. A quantitative UA/SA was then applied to this subset to rank the relevance of the parameters and to partition the variance of the model output among them by means of a nonlinear regression of the outcomes of 1,000 Monte Carlo simulations. The concentrations of four representative persistent organic pollutants (POPs) in two representative species of the coastal marine food web of the Lagoon of Venice (Italy) were taken as model outputs. The screening analysis showed that the ranking was remarkably different in relation to the species and chemical being considered. The subsequent Monte Carlo-based quantitative analysis pointed out that the relationships among some of the parameters and the model outputs were nonlinear. The nonlinear regression showed that the fraction of output variance accounted for by each parameter was strongly dependent on the range of the octanol-water partition coefficient (K_(ow)) values being considered. For the less hydrophobic chemicals, the main sources of model uncertainty were the parameters related to the respiratory bioaccumulation, whereas for the more hydrophobic ones, A_(ow) and the other parameters related to the dietary uptake explained the largest fractions of the variance of the chemical concentrations in the organisms. The analysis highlighted that efforts are still needed for reducing uncertainty of model parameters to get reliable results from the application of food web bioaccumulation models.
机译:对最先进的食物网生物蓄积模型进行了全球不确定性和敏感性分析(UA / SA)。我们使用了一种有效的筛选分析技术来识别整个227个模型参数集中最相关的输入因子的子集。然后将定量的UA / SA应用于此子集,以对参数的相关性进行排名,并通过对1000个蒙特卡洛模拟结果的非线性回归来在其中对模型输出的方差进行划分。模型输出是威尼斯泻湖(意大利)沿海海洋食物网中两个代表性物种中四种代表性持久性有机污染物(POPs)的浓度。筛选分析表明,在考虑的物种和化学物质方面,排名明显不同。随后基于蒙特卡洛的定量分析指出,某些参数与模型输出之间的关系是非线性的。非线性回归表明,每个参数所占的输出方差分数在很大程度上取决于所考虑的辛醇-水分配系数(K_(ow))值的范围。对于疏水性较低的化学物质,模型不确定性的主要来源是与呼吸生物蓄积有关的参数,而对于疏水性较高的化学物质,A_(ow)和与膳食摄入有关的其他参数则解释了最大的方差变化。生物中的化学浓度。分析强调,仍然需要努力减少模型参数的不确定性,以便通过应用食物网生物蓄积模型获得可靠的结果。

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