Although the exposure assessment of wastewater-derived micropollutants via chemical, bioanalytical, and modeling methods in environmental compartments is becoming more frequent, the whole-body burden (i.e., internal concentrations) in nontarget organisms is rarely assessed. An understanding of the internal concentration fluctuation is especially important when exploring the mechanistic linkage between exposure and effects. In this study, we coupled a simple river model with a first-order toxicokinetic (TK) model to predict the concentrations of wastewater-derived micropollutants in freshwater invertebrates (Gammarus spp.). We applied Monte Carlo simulations and conducted laboratory experiments to account for the uncertain input data and the lack of uptake/depuration rate constants required for the TK model. The internal concentrations in field gammarids were predicted well, and the estimates varied only by a factor of 0.1-1.9. Fast equilibrium may also be assumed such that bioconcentration factors (BCFs) are used together with the daily river dilution patterns to predict internal concentrations. While this assumption is suitable for compounds observed in our experiment to reach the steady state within 48 h in gammarids, the model overpredicted the concentrations of substances that reach this condition after longer periods. Nevertheless, this approach provides conservative estimates and simplifies the coupling of models as BCFs are slightly more accessible than the rate constants. However, if one is interested in a more detailed exposure information (e.g., peak concentration and the whole-body burden recovery after a spill), then the nonsteady-state formulation should be employed.

中文翻译：

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将河水浓度模拟与毒物动力学模型耦合，可有效预测田间Gammarids中废水衍生的微污染物的内部浓度。

尽管通过化学，生物分析和建模方法在环境隔室中对源自废水的微量污染物的暴露评估越来越频繁，但很少评估非目标生物的全身负担（即内部浓度）。当探索暴露与效应之间的机械联系时，了解内部浓度波动尤其重要。在这项研究中，我们将简单的河流模型与一阶毒性动力学（TK）模型结合在一起，以预测淡水无脊椎动物（Gammarus spp。）中废水衍生的微污染物的浓度。我们应用蒙特卡洛模拟并进行了实验室实验，以解决TK模型所需的不确定输入数据和缺乏摄取/提纯速率常数的问题。田间γ-内酰胺的内部浓度被很好地预测，估计值仅相差0.1-1.9。也可以假设达到快速平衡，以便将生物浓缩因子（BCF）与每日河流稀释模式一起用于预测内部浓度。尽管此假设适合在我们的实验中观察到的化合物在48小时内在γ-射线中达到稳态，但该模型过长地预测了达到此条件的物质的浓度。尽管如此，这种方法提供了保守的估计，并简化了模型的耦合，因为BCF比速率常数更易于访问。但是，如果您对更详细的接触信息感兴趣（例如，峰值浓度和溢出后的全身负担恢复），