Lundstedt-Enkel Katrin, Tysklind Mats, Trygg Johan, Schüller Peter, Asplund Lillemor, Eriksson Ulla, Häggberg Lisbeth, Odsjö Tjelvar, Hjelmberg Mats, Olsson Mats, Orberg Jan
Environmental Toxicology, Department of Physiology and Developmental Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18A, SE-752 36, Sweden.
Environ Sci Technol. 2005 Nov 1;39(21):8395-402. doi: 10.1021/es048415y.
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 BMFRsR 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 (alpha-, beta-, and gammaHCH), 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, ,betaHCH, and CB28. The contaminants that did not biomagnifywere p,p'DDT, p,p'DDD, alphaHCH, CB101, and CB52. Eventual biomagnification for gammaHCH 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.
本文提出了一种计算生物放大因子的新方法，并利用1996年至1999年从波罗的海采集的鲱鱼（Clupea harengus）肌肉和海鸠（Uria aalge）蛋中有机氯污染物（OCs）的污染物浓度数据进行了验证。采用这种随机抽样比率（RSR）方法生成了生物放大因子（BMF(RSR)），并以标准差（SD）作为变异度量。BMFRsR的计算方法是从总共29个海鸠蛋中随机选择一个，从总共74条鲱鱼中随机选择一条，然后确定该蛋中给定OC的浓度与该鲱鱼中相同OC的浓度之比。通过重采样技术，对任何给定的OC进行50000次这样的操作，从这个新的比率分布中，计算出每个OC的BMF(RSR)，并给出几何平均值（GM）及其GM标准差（GMSD）范围、算术平均值（AM）及其AM标准差（AMSD）范围，以及最小（BMF(MIN)）和最大（BMF(MAX)）生物放大因子。分析的14种OCs为p,p'-滴滴涕及其代谢物p,p'-滴滴伊和p,p'-滴滴滴、多氯联苯（PCB同系物：CB28、CB52、CB101、CB118、CB138、CB153和CB180）、六氯环己烷异构体（α-、β-和γ-六氯环己烷）以及六氯苯（HCB）。多变量数据分析（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），首先用于从每个个体动物产生的复杂生物和化学数据中提取信息。MVDA用于对物种（PCA、PLS-DA）、采样年份（PLS）和采样地点（PLS-DA）的相似性/差异进行建模，以便更深入地理解BMF建模所基于的数据。生物放大的污染物，即BMF(RSR)显著高于1的污染物，有p,p'-滴滴伊、CB118、HCB、CB138、CB180、CB153、β-六氯环己烷和CB28。未发生生物放大的污染物有p,p'-滴滴涕、p,p'-滴滴滴、α-六氯环己烷、CB101和CB52。无法确定γ-六氯环己烷是否最终会发生生物放大。鲱鱼肌肉和海鸠蛋中存在的OCs的BMF(RSR)显示出很大的跨度，每种污染物的变化都很大。为了能够对不同污染物的BMF进行可靠计算，我们强调使用基于大量且定义明确的个体的数据的重要性。
