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A User-Friendly Kinetic Model Incorporating Regression Models for Estimating Pesticide Accumulation in Diverse Earthworm Species Across Varied Soils
Environmental Science & Technology ( IF 10.8 ) Pub Date : 2024-07-31 , DOI: 10.1021/acs.est.4c06642 Jun Li 1 , Mark E Hodson 1 , Colin D Brown 1 , Melanie J Bottoms 2 , Roman Ashauer 1, 3 , Tania Alvarez 2
Environmental Science & Technology ( IF 10.8 ) Pub Date : 2024-07-31 , DOI: 10.1021/acs.est.4c06642 Jun Li 1 , Mark E Hodson 1 , Colin D Brown 1 , Melanie J Bottoms 2 , Roman Ashauer 1, 3 , Tania Alvarez 2
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Existing models for estimating pesticide bioconcentration in earthworms exhibit limited applicability across different chemicals, soils and species which restricts their potential as an alternative, intermediate tier for risk assessment. We used experimental data from uptake and elimination studies using three earthworm species (Lumbricus terrestris, Aporrectodea caliginosa, Eisenia fetida), five pesticides (log Kow 1.69–6.63) and five soils (organic matter content = 0.972–39.9 wt %) to produce a first-order kinetic accumulation model. Model applicability was evaluated against a data set of 402 internal earthworm concentrations reported from the literature including chemical and soil properties outside the data range used to produce the model. Our models accurately predict body load using either porewater or bulk soil concentrations, with at least 93.5 and 84.3% of body load predictions within a factor of 10 and 5 of corresponding observed values, respectively. This suggests that there is no need to distinguish between porewater and soil exposure routes or to consider different uptake and elimination pathways when predicting earthworm bioconcentration. Our new model not only outperformed existing models in characterizing earthworm exposure to pesticides in soil, but it could also be integrated with models that account for earthworm movement and fluctuating soil pesticide concentrations due to degradation and transport.
中文翻译：
一种结合回归模型的用户友好的动力学模型，用于估计不同土壤中不同种类蚯蚓的农药积累
估计蚯蚓体内农药生物浓度的现有模型在不同化学品、土壤和物种中的适用性有限，这限制了它们作为风险评估的替代中间层的潜力。我们使用三种蚯蚓（ Lumbricus terrestris 、 Aporectodea caliginosa 、 Eisenia fetida ）、五种农药（log K ow 1.69–6.63）和五种土壤（有机质含量 = 0.972–39.9 wt %）的吸收和消除研究的实验数据来生产一级动力学累积模型。根据文献报道的 402 条体内蚯蚓浓度的数据集评估了模型的适用性，其中包括用于生成模型的数据范围之外的化学和土壤特性。我们的模型使用孔隙水或散装土壤浓度准确预测身体负荷，至少 93.5% 和 84.3% 的身体负荷预测分别在相应观测值的 10 倍和 5 倍之内。这表明在预测蚯蚓生物富集时无需区分孔隙水和土壤暴露途径或考虑不同的吸收和消除途径。我们的新模型不仅在描述蚯蚓接触土壤中农药的特征方面优于现有模型，而且还可以与解释蚯蚓运动和由于降解和运输而引起的土壤农药浓度波动的模型相结合。
更新日期：2024-07-31
中文翻译：
一种结合回归模型的用户友好的动力学模型，用于估计不同土壤中不同种类蚯蚓的农药积累
估计蚯蚓体内农药生物浓度的现有模型在不同化学品、土壤和物种中的适用性有限，这限制了它们作为风险评估的替代中间层的潜力。我们使用三种蚯蚓（ Lumbricus terrestris 、 Aporectodea caliginosa 、 Eisenia fetida ）、五种农药（log K ow 1.69–6.63）和五种土壤（有机质含量 = 0.972–39.9 wt %）的吸收和消除研究的实验数据来生产一级动力学累积模型。根据文献报道的 402 条体内蚯蚓浓度的数据集评估了模型的适用性，其中包括用于生成模型的数据范围之外的化学和土壤特性。我们的模型使用孔隙水或散装土壤浓度准确预测身体负荷，至少 93.5% 和 84.3% 的身体负荷预测分别在相应观测值的 10 倍和 5 倍之内。这表明在预测蚯蚓生物富集时无需区分孔隙水和土壤暴露途径或考虑不同的吸收和消除途径。我们的新模型不仅在描述蚯蚓接触土壤中农药的特征方面优于现有模型，而且还可以与解释蚯蚓运动和由于降解和运输而引起的土壤农药浓度波动的模型相结合。
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