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一种用户友好的动力学模型,结合回归模型,用于估算不同土壤中多种蚯蚓物种对农药的积累。

A User-Friendly Kinetic Model Incorporating Regression Models for Estimating Pesticide Accumulation in Diverse Earthworm Species Across Varied Soils.

机构信息

Department of Environment and Geography, University of York, York YO10 5NG, U.K.

Jealotts Hill International Research Centre, Syngenta Ltd, Warfield, Bracknell RG42 6EY, U.K.

出版信息

Environ Sci Technol. 2024 Aug 13;58(32):14555-14564. doi: 10.1021/acs.est.4c06642. Epub 2024 Jul 31.

Abstract

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 (, , ), five pesticides (log 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.

摘要

现有的估算蚯蚓体内农药生物浓缩的模型在不同的化学物质、土壤和物种中的适用性有限,这限制了它们作为替代的、中间层次风险评估方法的潜力。我们使用了三种蚯蚓物种( 、 、 )、五种农药(log 1.69-6.63)和五种土壤(有机质含量=0.972-39.9wt%)的吸收和消除研究的实验数据,生成了一个一级动力学积累模型。我们评估了模型的适用性,将其与从文献中报告的 402 个内部蚯蚓浓度数据集进行了比较,这些数据集包括了模型生成所用数据范围以外的化学物质和土壤特性。我们的模型可以准确地预测蚯蚓体内的负荷,无论是使用孔隙水还是土壤的浓度,至少有 93.5%和 84.3%的体内负荷预测值与相应的观测值相差 10 倍和 5 倍以内。这表明,在预测蚯蚓的生物浓缩时,没有必要区分孔隙水和土壤暴露途径,也不需要考虑不同的吸收和消除途径。我们的新模型不仅在描述土壤中蚯蚓暴露于农药方面优于现有的模型,而且还可以与考虑蚯蚓运动和由于降解和运输而导致的土壤农药浓度波动的模型相结合。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/592e/11325639/d91f2de72156/es4c06642_0001.jpg

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