When data on the degradation of a chemical substance have been collected in a number of environmental media (e.g., in different soils), two strategies can be followed for data evaluation. Currently, each individual dataset is evaluated separately, and representative degradation parameters are obtained by calculating averages of the kinetic parameters. However, such averages often take on unrealistic values if certain degradation parameters are ill-defined in some of the datasets. Moreover, the most appropriate degradation model is selected for each individual dataset, which is time consuming and then requires workarounds for averaging parameters from different models. Therefore, a simultaneous evaluation of all available data is desirable. If the environmental media are viewed as random samples from a population, an advanced strategy based on assumptions about the statistical distribution of the kinetic parameters across the population can be used. Here, we show the advantages of such simultaneous evaluations based on nonlinear mixed-effects models that incorporate such assumptions in the evaluation process. The advantages of this approach are demonstrated using synthetically generated data with known statistical properties and using publicly available experimental degradation data on two pesticidal active substances.

中文翻译：

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使用非线性混合效应模型将退化数据的动力学评估提升到一个新的水平

当在多种环境介质中（例如，在不同的土壤中）收集了有关化学物质降解的数据时，可以采用两种策略进行数据评估。目前，每个单独的数据集都是单独评估的，并且通过计算动力学参数的平均值来获得具有代表性的降解参数。但是，如果某些数据集中的某些退化参数定义不明确，则此类平均值通常会采用不切实际的值。此外，为每个单独的数据集选择最合适的退化模型，这很耗时，然后需要解决方法来平均来自不同模型的参数。因此，需要同时评估所有可用数据。如果将环境介质视为来自总体的随机样本，可以使用基于关于整个群体的动力学参数的统计分布的假设的高级策略。在这里，我们展示了基于非线性混合效应模型的这种同时评估的优势，这些模型将此类假设纳入评估过程。使用具有已知统计特性的合成数据和公开可用的两种杀虫活性物质的实验降解数据证明了这种方法的优点。