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利用动态线性建模检测伊利湖鱼类群落中 α-和γ-氯丹的时间趋势。

Detection of temporal trends of α- and γ-chlordane in Lake Erie fish communities using dynamic linear modeling.

机构信息

Ecological Modeling Laboratory, Department of Physical and Environmental Sciences, University of Toronto, Toronto, Ontario, Canada.

出版信息

Ecotoxicol Environ Saf. 2011 Jul;74(5):1107-21. doi: 10.1016/j.ecoenv.2011.04.019. Epub 2011 May 4.

DOI:10.1016/j.ecoenv.2011.04.019
PMID:21536330
Abstract

Dynamic linear modeling (DLM) analysis was performed to identify the long-term temporal trends of two toxic components of the technical chlordane pesticide, α- and γ-chlordane, in skinless-boneless muscle tissues of a number of sport fish species in Lake Erie. Our analysis considers the fish length as a covariate of the chlordane concentrations. The α-chlordane models for the coho salmon, channel catfish, rainbow trout, and common carp showed continuously decreasing trends during the entire 30+ year survey period (1976-2007). The γ-chlordane models demonstrated similar trends for the coho salmon, channel catfish, and common carp. These fish species had higher levels of α- and γ-chlordane in their muscle tissues. The α- and γ-chlordane levels in freshwater drum, smallmouth bass, walleye, white bass, whitefish, and yellow perch decreased until the mid-1980s and hovered at levels around the detection limits for the remaining period. The pesticide biotransformation process, the reduction of contaminant emissions to the environment, the feeding habits of the different fish species, and the food-web alterations induced by the introduction of aquatic invasive species are some of the hypotheses proposed to explain the observed temporal trends in different fish species in Lake Erie.

摘要

采用动态线性模型(DLM)分析,以鉴定湖中的若干种运动鱼类去皮去骨肌肉组织中技术氯丹农药的两种有毒成分(α-和γ-氯丹)的长期时间趋势。我们的分析将鱼的长度视为氯丹浓度的协变量。对于银大麻哈鱼、斑点叉尾鮰、虹鳟和鲤鱼,α-氯丹模型在整个 30 多年的调查期间(1976-2007 年)显示出持续下降的趋势。γ-氯丹模型对于银大麻哈鱼、斑点叉尾鮰和鲤鱼也表现出类似的趋势。这些鱼类肌肉组织中的α-和γ-氯丹含量较高。在其余时期,鼓鱼、小口黑鲈、大眼梭鲈、白鲈、白鱼和黄鲈的α-和γ-氯丹水平下降,直到 20 世纪 80 年代中期,并在检测限附近徘徊。提出了一些假设来解释在安大略湖不同鱼类中观察到的时间趋势,这些假设包括农药生物转化过程、向环境中减少污染物排放、不同鱼类的摄食习性以及引入水生入侵物种引起的食物网变化。

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