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A lognormal model for evaluating maximum residue levels of pesticides in crops
Environmental Pollution ( IF 8.9 ) Pub Date : 2021-03-01 , DOI: 10.1016/j.envpol.2021.116832
Yuan Guo , Zijian Li

To evaluate pesticide regulatory standards in agricultural crops, we introduced a regulatory modeling framework that can flexibly evaluate a population’s aggregate exposure risk via maximum residue levels (MRLs) under good agricultural practice (GAP). Based on the structure of the aggregate exposure model and the nature of variable distributions, we optimized the framework to achieve a simplified mathematical expression based on lognormal variables including the lognormal sum approximation and lognormal product theorem. The proposed model was validated using Monte Carlo simulation, which demonstrates a good match for both head and tail ends of the distribution (e.g., the maximum error = 2.01% at the 99th percentile). In comparison with the point estimate approach (i.e., theoretical maximum daily intake, TMDI), the proposed model produced higher simulated daily intake (SDI) values based on empirical and precautionary assumptions. For example, the values at the 75th percentile of the SDI distributions simulated from the European Union (EU) MRLs of 13 common pesticides in 12 common crops were equal to the estimated TMDI values, and the SDI values at the 99th percentile were over 1.6-times the corresponding TMDI values. Furthermore, the model was refined by incorporating the lognormal distributions of biometric variables (i.e., food intake rate, processing factor, and body weight) and varying the unit-to-unit variability factor (VF) of the pesticide residues in crops. This ensures that our proposed model is flexible across a broad spectrum of pesticide residues. Overall, our results show that the SDI is significantly reduced, which may better reflect reality. In addition, using a point estimate or lognormal PF distribution is effective as risk assessments typically focus on the upper end of the distribution.



中文翻译:

对数正态模型,用于评估农作物中农药的最大残留量

为了评估农作物中的农药法规标准,我们引入了法规建模框架,该框架可以根据良好农业规范(GAP)通过最大残留量(MRL)灵活评估人群的总暴露风险。基于集合暴露模型的结构和变量分布的性质,我们优化了框架,以基于对数正态变量(包括对数正态和逼近和对数正态积定理)实现简化的数学表达式。使用蒙特卡洛模拟对提出的模型进行了验证,该模型证明了该分布的头尾均具有良好的匹配性(例如,在第99个百分位数处,最大误差= 2.01%)。与积分估算方法(即理论上的每日最大摄入量TMDI)相比,基于经验和预防性假设,建议的模型产生了更高的模拟日摄入量(SDI)值。例如,根据欧盟(EU)对12种常见农作物中13种常见农药的最大残留限量(MRL)模拟的SDI分布的第75个百分点,其值等于估算的TMDI值,而第99个百分点的SDI值则超过1.6-乘以相应的TMDI值。此外,该模型通过结合生物特征变量(即摄食率,加工因子和体重)的对数正态分布并改变了农作物中农药残留的单位间变异性因子(VF)而得到了完善。这确保了我们提出的模型在多种农药残留中具有灵活性。总体而言,我们的结果表明SDI显着降低,可能会更好地反映现实。另外,使用点估计或对数正态PF分布是有效的,因为风险评估通常集中在分布的高端。

更新日期:2021-03-15
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