Parameterization Models for Pesticide Exposure via Crop Consumption
Parameterization Models for Pesticide Exposure via Crop Consumption
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DOI：
10.1021/es301509u
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发表时间：
2012-12-04
影响因子：
11.4
通讯作者：
Jolliet, Olivier
中科院分区：
文献类型：
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作者：
Fantke, Peter;Wieland, Peter;Jolliet, Olivier
提出了一种通过消费六种重要粮食作物来估计人类接触农药的方法，该方法可用于扩展多媒体模型在健康风险和生命周期影响评估中的应用。我们首先使用矩阵代数评估了模型产量(每公斤施药的农药残留量)作为模型输入变量(物质、作物和环境属性)的函数的变化，包括它们可能的相关性。我们确定了影响农药残留变化的五个关键参数，即从施药到收获的时间、在作物和作物表面的降解半衰期、总在土壤中的停留时间和物质的相对分子质量。分配系数对果树和西红柿(Kow)、土豆(Koc)和生菜(Kow，Kow)也起着重要作用。围绕这些参数，我们通过将复杂的命运和暴露评估框架参数化来开发特定于作物的模型。因此，参数模型反映了框架的物理和化学机制，并使用作物、作物表面和土壤隔间的线性组合来预测收获时的农药残留。参数模型的结果与1540种物质-作物组合的复杂框架的结果很好地吻合，因子4(土豆)和因子66(生菜)之间的总偏差。预测的残基也与以前用于评估复杂框架的实验数据吻合得很好。最终可以将收获中的农药质量与食品加工中的减少系数结合起来，以估计人类从农作物消费中获得的暴露。所有参数模型都可以很容易地应用到现有的评估框架中。
An approach for estimating human exposure to pesticides via consumption of six important food crops is presented that can be used to extend multimedia models applied in health risk and life cycle impact assessment. We first assessed the variation of model output (pesticide residues per kg applied) as a function of model input variables (substance, crop, and environmental properties) including their possible correlations using matrix algebra. We identified five key parameters responsible for between 80% and 93% of the variation in pesticide residues, namely time between substance application and crop harvest, degradation half-lives in crops and on crop surfaces, overall residence times in soil, and substance molecular weight. Partition coefficients also play an important role for fruit trees and tomato (Kow), potato (Koc), and lettuce (Kaw, Kow). Focusing on these parameters, we develop crop-specific models by parametrizing a complex fate and exposure assessment framework. The parametric models thereby reflect the framework's physical and chemical mechanisms and predict pesticide residues in harvest using linear combinations of crop, crop surface, and soil compartments. Parametric model results correspond well with results from the complex framework for 1540 substance-crop combinations with total deviations between a factor 4 (potato) and a factor 66 (lettuce). Predicted residues also correspond well with experimental data previously used to evaluate the complex framework. Pesticide mass in harvest can finally be combined with reduction factors accounting for food processing to estimate human exposure from crop consumption. All parametric models can be easily implemented into existing assessment frameworks.