Department of Geography and Environmental Science, School of Archaeology, Geography and Environmental Science, University of Reading, Reading, RG6 6DW, UK.
Environ Sci Pollut Res Int. 2017 Feb;24(5):4252-4260. doi: 10.1007/s11356-015-5176-1. Epub 2015 Sep 4.
New models for estimating bioaccumulation of persistent organic pollutants in the agricultural food chain were developed using recent improvements to plant uptake and cattle transfer models. One model named AgriSim was based on K regressions of bioaccumulation in plants and cattle, while the other was a steady-state mechanistic model, AgriCom. The two developed models and European Union System for the Evaluation of Substances (EUSES), as a benchmark, were applied to four reported food chain (soil/air-grass-cow-milk) scenarios to evaluate the performance of each model simulation against the observed data. The four scenarios considered were as follows: (1) polluted soil and air, (2) polluted soil, (3) highly polluted soil surface and polluted subsurface and (4) polluted soil and air at different mountain elevations. AgriCom reproduced observed milk bioaccumulation well for all four scenarios, as did AgriSim for scenarios 1 and 2, but EUSES only did this for scenario 1. The main causes of the deviation for EUSES and AgriSim were the lack of the soil-air-plant pathway and the ambient air-plant pathway, respectively. Based on the results, it is recommended that soil-air-plant and ambient air-plant pathway should be calculated separately and the K regression of transfer factor to milk used in EUSES be avoided. AgriCom satisfied the recommendations that led to the low residual errors between the simulated and the observed bioaccumulation in agricultural food chain for the four scenarios considered. It is therefore recommended that this model should be incorporated into regulatory exposure assessment tools. The model uncertainty of the three models should be noted since the simulated concentration in milk from 5th to 95th percentile of the uncertainty analysis often varied over two orders of magnitude. Using a measured value of soil organic carbon content was effective to reduce this uncertainty by one order of magnitude.
开发了新的模型来估算农业食物链中持久性有机污染物的生物积累,这些模型利用了植物吸收和牛转移模型的最新改进。一个名为 AgriSim 的模型是基于植物和牛体内生物积累的 K 回归,而另一个是稳态机械模型 AgriCom。这两个开发的模型和欧洲物质评估系统(EUSES)作为基准,被应用于四个报告的食物链(土壤/空气-草-牛-奶)情景,以评估每个模型模拟与观察数据的吻合程度。考虑的四个情景如下:(1)污染的土壤和空气,(2)污染的土壤,(3)高度污染的土壤表面和污染的地下部分,以及(4)不同海拔高度的污染土壤和空气。AgriCom 很好地再现了所有四个情景的牛奶生物积累,AgriSim 对情景 1 和 2 也是如此,但 EUSES 只对情景 1 这样做。EUSES 和 AgriSim 出现偏差的主要原因分别是缺乏土壤-空气-植物途径和环境空气-植物途径。基于这些结果,建议分别计算土壤-空气-植物和环境空气-植物途径,并避免在 EUSES 中使用转移因子对牛奶的 K 回归。AgriCom 满足了建议,这些建议导致了四个考虑情景中农业食物链中模拟和观察到的生物积累之间的低残余误差。因此,建议将该模型纳入监管暴露评估工具中。由于不确定性分析中第 5 至 95 百分位数的牛奶模拟浓度通常相差两个数量级,因此应注意三个模型的模型不确定性。使用土壤有机碳含量的实测值可以有效地将不确定性降低一个数量级。