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利用食物网生物积累模型揭示底特律河运动鱼类中空间综合多氯联苯的暴露情况。

Use of a Food Web Bioaccumulation Model to Uncover Spatially Integrated Polychlorinated Biphenyl Exposures in Detroit River Sport Fish.

机构信息

Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario, Canada.

Department of Biology, Memorial University of Newfoundland, St. John's, Newfoundland, Canada.

出版信息

Environ Toxicol Chem. 2019 Dec;38(12):2771-2784. doi: 10.1002/etc.4569. Epub 2019 Oct 29.

Abstract

We applied and tested a bioenergetic-based, steady-state food web bioaccumulation model to predict polychlorinated biphenyl (PCB) exposures in sport fish of the Detroit River (USA-Canada), which is a Great Lakes area of concern. The PCB concentrations in the sediment and water of the river were found to exhibit high spatial variation. The previously contained areas of high contamination may have spread to adjacent food webs as a result of fish movements. This process may cause biased predictions in single-compartment bioaccumulation models. We performed multiple simulations and contrasted model predictions against a database of 1152 fish sample records comprising 19 sport fish species. The simulations evaluated 4 spatial scales (river-wide, 2-nation, 4-zone, and 6-zone models) to reveal how the spatial heterogeneity of contamination and species-specific movements contribute to variation in fish PCB exposures. The model testing demonstrated that the 2-nation model provided the most accurate global prediction of fish contamination. However, these improvements were not equally observed across all species. The model was subsequently calibrated for poorly performing species, by allowing cross-zone exposures, demonstrating the importance of accounting for specific ecological factors, such as fish movement, to improve PCB bioaccumulation prediction, especially in highly heterogeneous water systems. Environ Toxicol Chem 2019;38:2771-2784. © 2019 SETAC.

摘要

我们应用并测试了一个基于生物能量的稳态食物网生物累积模型,以预测底特律河(美国-加拿大)运动鱼类中的多氯联苯(PCB)暴露情况,该河是五大湖关注区域之一。研究发现,该河的沉积物和水中的 PCB 浓度具有很高的空间变异性。由于鱼类的移动,先前包含高污染区域的范围可能已经扩散到相邻的食物网中。这个过程可能会导致单室生物累积模型的预测出现偏差。我们进行了多次模拟,并将模型预测与包含 19 种运动鱼类物种的 1152 个鱼类样本记录数据库进行对比。模拟评估了 4 个空间尺度(全流域、两国、4 区和 6 区模型),以揭示污染的空间异质性和特定物种的运动如何导致鱼类 PCB 暴露的变化。模型测试表明,两国模型提供了对鱼类污染最准确的全球预测。然而,并非所有物种都观察到了这些改进。通过允许跨区暴露,对表现不佳的物种进行了模型校准,这表明需要考虑鱼类运动等特定生态因素,以改善 PCB 生物累积预测,特别是在高度异质的水系统中。环境毒理化学 2019;38:2771-2784。©2019SETAC。

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