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From Sediment to Tissue and Tissue to Sediment: An Evaluation of Statistical Bioaccumulation Models

机译:从沉积物到组织以及组织到沉积物:统计生物蓄积模型的评估

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摘要

Biota-sediment accumulation factors (BSAFs) and biota-sediment accumulation regressions (BSARs) are statistical models that may be used to estimate tissue chemical concentrations from sediment chemical concentrations or vice versa. Biota-sediment accumulation factors and BSARs are used to fill tissue concentration data gaps, set sediment preliminary remediation goals (PRGs), and make projections about the effectiveness of potential sediment cleanup projects in reducing tissue chemical concentrations. We explored field-based, benthic invertebrate biota-sediment chemical concentration relationships using data from the US Environmental Protection Agency (USEPA) Mid-Continent Ecology Division (MED) BSAF database. Approximately two thirds of the 262 relationships investigated were very poor (r~2 < 0.3 or p-value ≥ 0.05); for some of the biota-sediment relationships that did have a significant nonzero slope (p-value<0.05), lipid-normalized tissue concentrations tended to decrease as the colocated organic carbon (OC)-normalized sediment concentration increased. Biota-sediment relationships were further evaluated for 3 of the 262 datasets. Biota-sediment accumulation factors, linear regressions, model Ⅱ regressions, illustrative sediment PRGs, and confidence intervals (Cis) were calculated for each of the three examples. These examples illustrate some basic but important statistical practices that should be followed before selecting a BSAR or BSAF or relying on these simple models of biota-sediment relationships to support consequential management decisions. These practices include the following: one should not assume that the relationship between chemical concentrations in tissue and sediment is necessarily linear, one should not assume the model intercept to be zero, and one should not place too much stock on models that are heavily influenced by one or a few high chemical concentration data points. People will continue to use statistical models of field-based biota-sediment chemical concentration relationships to support sediment investigations and remedial action decisions. However, it should not be assumed that the models will be reliable. In developing and applying BSAFs and BSARs, it is essential that best practices are followed and model limitations and uncertainties are understood, acknowledged, and quantified as much as possible.
机译:生物沉积物累积因子(BSAF)和生物沉积物累积回归(BSAR)是统计模型,可用于根据沉积物化学浓度估算组织化学浓度,反之亦然。利用生物沉积物累积因子和BSAR来填补组织浓度数据的空白,设定沉积物初步修复目标(PRG),并对可能的沉积物清除项目在降低组织化学浓度方面的效果做出预测。我们使用来自美国环境保护局(USEPA)中部大陆生态部(MED)BSAF数据库的数据,探索了基于野外底栖无脊椎动物生物沉积物化学浓度的关系。在研究的262个关系中,约有三分之二非常差(r〜2 <0.3或p值≥0.05);对于某些确实具有显着非零斜率(p值<0.05)的生物-沉积物关系,脂质归一化的组织浓度倾向于随着共置有机碳(OC)归一化沉积物浓度的增加而降低。对262个数据集中的3个进一步评估了生物群-沉积物的关系。对于这三个实例,分别计算了生物沉积物累积因子,线性回归,Ⅱ型回归,示例性沉积物PRG和置信区间(Cis)。这些示例说明了在选择BSAR或BSAF或依靠这些简单的生物-沉积物关系模型来支持相应管理决策之前应遵循的一些基本但重要的统计实践。这些做法包括:一不应该假设组织和沉积物中化学浓度之间的关系必然是线性的,一不应该假设模型截距为零,一不应该在受以下因素严重影响的模型上放置过多的库存一个或几个高化学浓度数据点。人们将继续使用基于现场的生物沉积物化学浓度关系的统计模型来支持沉积物调查和补救措施决策。但是,不应假定模型是可靠的。在开发和应用BSAF和BSAR时,必须遵循最佳实践,并尽可能理解,认可和量化模型的局限性和不确定性。

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