Centre for Ecology & Hydrology, Lancaster Environment Centre, Lancaster, LA1 4AP, UK; School of Environment & Life Sciences, Peel Building, University of Salford, Manchester M5 4WT, UK.
Centre for Research in Bioscience, Dept. of Applied Sciences, University of the West of England, Frenchay, Bristol, UK.
J Environ Radioact. 2019 Nov;208-209:106020. doi: 10.1016/j.jenvrad.2019.106020. Epub 2019 Jul 20.
Radionuclide activity concentrations in food crops and wildlife are most often predicted using empirical concentration ratios (CRs). The CR approach is simple to apply and some data exist with which to parameterise models. However, the parameter is highly variable leading to considerable uncertainty in predictions. Furthermore, for both crops and wildlife we have no, or few, data for many radionuclides and realistically, we are never going to have specific data for every radionuclide - wildlife/crop combination. In this paper, we present an alternative approach using residual maximum likelihood (REML) fitting of a linear mixed effects model; the model output is an estimate of the rank-order of relative values. This methodology gives a less uncertain approach than the CR approach, as it takes into account the effect of site; it also gives a scientifically based extrapolation approach. We demonstrate the approach using the examples of Cs for plants and Pb for terrestrial wildlife. This is the first published application of the REML approach to terrestrial wildlife (previous applications being limited to the consideration of plants). The model presented gives reasonable predictions for a blind test dataset.
放射性核素在农作物和野生动物中的活度浓度通常使用经验浓度比 (CR) 进行预测。CR 方法简单易用,并且有一些数据可用于参数化模型。然而,该参数变化很大,导致预测存在很大的不确定性。此外,对于农作物和野生动物,我们没有或很少有许多放射性核素的数据,实际上,我们永远不会为每个放射性核素 - 野生动物/农作物组合提供具体数据。在本文中,我们提出了一种替代方法,使用剩余最大似然 (REML) 拟合线性混合效应模型;模型输出是相对值的秩估计。与 CR 方法相比,这种方法不确定性更小,因为它考虑了地点的影响;它还提供了一种基于科学的外推方法。我们使用 Cs 对植物和 Pb 对陆地野生动物的例子来说明该方法。这是 REML 方法首次应用于陆地野生动物(以前的应用仅限于考虑植物)。所提出的模型对盲测试数据集进行了合理的预测。