Suppr超能文献

使用多元数据分析模拟波罗的海鲱鱼(Clupea harengus)生物学与污染物浓度之间的关系。

Modeling relationships between Baltic Sea herring (Clupea harengus) biology and contaminant concentrations using multivariate data analysis.

机构信息

Environmental Toxicology, Department of Organismal Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18A, SE-752 36, Sweden.

出版信息

Environ Sci Technol. 2010 Dec 1;44(23):9018-23. doi: 10.1021/es102448b. Epub 2010 Nov 4.

Abstract

Baltic Sea herring (Clupea harengus) is a pelagic, zoo-planktivorous fish and young (2-5 years old) individuals of this species are sampled annually in the Swedish marine monitoring program. This study determined concentrations of organochlorines (OCs) and brominated flame retardants (BFRs) in dorsal muscle from herring (n = 60) of varying age (2-13 years), weight (25-200 g), and body length (16-29 cm) caught at three locations in the Swedish part of the Baltic Proper. In order to ensure that the fish biology was as varied as possible, though still similar from all sampling sites, the fish to be chemically analyzed were selected from a large number of fish with determined biology using Multivariate Design. In statistical evaluation of the data, univariate and multivariate data analysis techniques, e.g. principal components analysis (PCA), partial least-squares regression (PLS), and orthogonal PLS (OPLS), were used. The results showed that the fish are exposed to a cocktail of contaminants and levels are presented. Significant OPLS models were found for all biological variables versus concentrations of OCs and BFRs, showing that fish biology covaries with fish contaminant concentrations. Correlation coefficients were as high as 0.98 for e.g. βHCH concentration (wet weight) versus the lipid content. Lastly, the OC concentrations in herring muscle were modeled against the BFR concentrations to determine whether concentrations of either could be used to predict the other. It was found that OPLS models allowed BFR concentrations to be predicted from OC concentrations with high, but varying, accuracy (R(2)Ys between 0.93 to 0.75). Thus, fish biology and contaminant concentrations are interwoven, and fish biological parameters can be used to calculate (predict) contaminant concentrations. It is also possible to predict the BFR concentrations in an individual fish from its concentrations of OCs with very high accuracy.

摘要

波罗的海鲱(Clupea harengus)是一种洄游性、浮游动物食性的鱼类,每年在瑞典海洋监测计划中都会对其幼鱼(2-5 岁)进行采样。本研究测定了在波罗的海瑞典部分的三个地点捕获的不同年龄(2-13 岁)、体重(25-200 克)和体长(16-29 厘米)的鲱鱼背部肌肉中的有机氯(OCs)和溴化阻燃剂(BFRs)浓度。为了确保鱼类生物学尽可能多样化,尽管仍与所有采样地点相似,使用多元设计从大量具有确定生物学特性的鱼类中选择要进行化学分析的鱼类。在数据的统计评估中,使用了单变量和多变量数据分析技术,例如主成分分析(PCA)、偏最小二乘回归(PLS)和正交偏最小二乘(OPLS)。结果表明,这些鱼暴露于多种污染物中,并呈现出不同的浓度。对于所有生物变量与 OCs 和 BFR 浓度的关系,均发现了显著的 OPLS 模型,表明鱼类生物学与鱼类污染物浓度存在相关性。例如,β-HCH 浓度(湿重)与脂质含量之间的相关系数高达 0.98。最后,将鲱鱼肌肉中的 OC 浓度与 BFR 浓度进行建模,以确定是否可以使用任何一种浓度来预测另一种浓度。结果发现,OPLS 模型允许使用 OC 浓度来预测 BFR 浓度,且准确性较高,但存在差异(R2Y 值在 0.93 到 0.75 之间)。因此,鱼类生物学和污染物浓度是交织在一起的,可以使用鱼类生物学参数来计算(预测)污染物浓度。也可以从个体鱼的 OC 浓度非常准确地预测其 BFR 浓度。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验