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首页> 外文期刊>Environmental Science & Technology >A Statistical Resampling Method To Calculate Biomagnification Factors Exemplified with Organochlorine Data from Herring (Clupea harengus) Muscle and Guillemot (Uria aalge) Egg from the Baltic Sea
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A Statistical Resampling Method To Calculate Biomagnification Factors Exemplified with Organochlorine Data from Herring (Clupea harengus) Muscle and Guillemot (Uria aalge) Egg from the Baltic Sea

机译：一种统计重采样方法，用于计算波罗的海鲱鱼（Clupea harengus）肌肉和海雀科的鸟（Uria aalge）卵中有机氯数据所举例说明的生物放大因子

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A novel method for calculating biomagnification factors is presented and demonstrated using contaminant concentration data from the Swedish national monitoring program regarding organochlorine contaminants (OCs) in herring (Clupea harengus) muscle and guillemot (Uria aalge) egg, sampled from 1996 to 1999 from the Baltic Sea. With this randomly sampled ratios (RSR) method, biomagnification factors (BMF_(RSR)) were generated and denoted with standard deviation (SD) as a measure of the variation. The BMF_(RSR) were calculated by randomly selecting one guillemot egg out of a total of 29 and one herring out of a total of 74, and the ratio was determined between the concentration of a given OC in that egg and the concentration of the same OC in that herring. With the resampling technique, this was performed 50 000 times for any given OC, and from this new distribution of ratios, BMF_(RSR) for each OC were calculated and given as geometric mean(GM) with GM standard deviation (GMSD) range, arithmetic mean (AM) with AMSD range, and minimum (BMF_(MIN)) as well as maximum (BMF_(MAX)) biomagnification factors. The 14 analyzed OCs were p,p'DDT and its metabolites p,p'DDE and p,p'DDD, polychlorinated biphenyls (PCB congeners: CB28, CB52, CB101, CB118, CB138, CB153, and CB180), hexachlorocyclohexane isomers (α-, β-, and γHCH), and hexachlorobenzene (HCB). Multivariate data analysis (MVDA) methods, including principal components analysis (PCA), partial least squares regression (PLS), and PLS discriminant analyses (PLS-DA), were first used to extract information from the complex biological and chemical data generated from each individual animal. MVDA were used to model similarities/dissimilarities regarding species (PCA, PLS-DA), sample years (PLS), and sample location (PLS-DA) to give a deeper understanding of the data that the BMF modeling was based upon. Contaminants that biomagnify, that had BMF_(RSR) significantly higher than one, were p.p'DDE, CB118, HCB, CB138, CB180, CB153, βHCH, and CB28. The contaminants that did not biomagnify were p,p'DDT, p,p'DDD,αHCH,CB101, and CB52. Eventual biomagnification for γHCH could not be determined. The BMF_(RSR) for OCs present in herring muscle and guillemot egg showed a broad span with large variations for each contaminant. To be able to make reliable calculations of BMFs for different contaminants, we emphasize the importance of using data based upon large numbers of, as well as well-defined, individuals.
机译：介绍了一种新的计算生物放大因子的方法，并使用了瑞典国家监测计划中有关鲱鱼（Clupea harengus）肌肉和海雀科的蛋（Uria aalge）卵中有机氯污染物（OCs）的污染物浓度数据，该数据于1996年至1999年从波罗的海采样海。使用这种随机采样比率（RSR）方法，生成了生物放大系数（BMF_（RSR）），并用标准差（SD）表示该差异作为度量变化。 BMF_（RSR）的计算方法是：从总共29个鸡蛋中随机选择一个海雀科鸡蛋，从总共74个鲱鱼中随机选择一个鲱鱼，并确定鸡蛋中给定OC的浓度与鸡蛋中特定OC的浓度之比。 OC在那鲱鱼。使用重采样技术，对任何给定的OC都执行了5万次，并且从这种新的比率分布中，计算出每个OC的BMF_（RSR），并给出了具有GM标准偏差（GMSD）范围的几何平均值（GM），具有AMSD范围的算术平均值（AM），以及最小（BMF_（MIN））和最大（BMF_（MAX））生物放大系数。分析的14种OC为p，p'DDT及其代谢物p，p'DDE和p，p'DDD，多氯联苯（PCB同类：CB28，CB52，CB101，CB118，CB138，CB153和CB180），六氯环己烷异构体（ α-，β-和γHCH）和六氯苯（HCB）。多元数据分析（MVDA）方法，包括主成分分析（PCA），偏最小二乘回归（PLS）和PLS判别分析（PLS-DA），首先用于从各自产生的复杂生物和化学数据中提取信息个别动物。 MVDA用于对物种（PCA，PLS-DA），样本年份（PLS）和样本位置（PLS-DA）的相似性/异同性进行建模，以更深入地了解BMF建模所基于的数据。 p.p'DDE，CB118，HCB，CB138，CB180，CB153，βHCH和CB28是生物放大的BMF_（RSR）明显高于1的污染物。未生物放大的污染物为p，p'DDT，p，p'DDD，αHCH，CB101和CB52。无法确定γHCH的最终生物放大率。鲱鱼的肌肉和海雀科的卵中存在的OC的BMF_（RSR）显示出很大的跨度，每种污染物的变化很大。为了能够对不同污染物的BMF进行可靠的计算，我们强调使用基于大量以及定义明确的个体的数据的重要性。

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