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A Bayesian generalized log-normal model to dynamically evaluate the distribution of pesticide residues in soil associated with population health risks
Environment International ( IF 11.8 ) Pub Date : 2018-10-09 , DOI: 10.1016/j.envint.2018.09.054
Zijian Li

Exploring better models for evaluating the distribution of pesticide residues in soil and sediment is necessary to assess and avoid population health risk. Frequentist philosophy and probability are widely used in many studies to apply a log-normal distribution associated with the maximum likelihood estimation, which assumes fixed parameters and relies on a large sample size for long-run frequency. However, frequentist probability might not be suitable for analyzing pesticide residue distribution, whose parameters are affected by many complex factors and should be treated as unfixed. This study aimed to implement a Bayesian generalized log-normal (GLN) model to better understand the distribution of pesticide residues in soil and quantify population risks. The Bayesian GLN model, including location, scale, and shape parameters, was applied for the first time to dynamically evaluate pesticide residue distribution in soil and sediments. In addition, a comprehensive human health risk assessment of exposure to lindane via soil was conducted using the lifetime cancer risk for carcinogenic effect, margin of exposure for non-carcinogenic effect, and disability-adjusted life year for health damage. The Bayesian posterior analysis results indicated that the distribution of the concentration of some pesticide was better fitted to a log-Laplace (e.g., the mode value of shape parameter for lindane is 1.079) or showed mixtures of distributions within the family of log-normal distributions (e.g., the mode value of shape parameter for p,p′-DDE is 2.395), which can better explain the long-tail phenomenon of pesticide residue distribution and dynamically evaluate distribution models. For lindane, the 95% uncertainty bounds on the 95th percentile computed from 95% highest probability density regions (credible intervals) of three parameters by using the Bayesian p-box method were [2.063, 1558.609] ng/g, which is several orders of magnitude larger than the computed frequentist 95% confidence interval of [4.690, 8.095] ng/g and indicates that the population could have cancer risk concerns. These uncertainty analysis results from the Bayesian GLN approach indicated a larger variation of Lindane soil residues, which might reflect the complex and unpredictable mechanism of pesticide residue distribution including both unfixed models and distribution parameters. In summary, Bayesian GLN model is more flexible for the dynamic evaluation of pesticide soil residue distribution, retains posteriors for future data analysis, and could better quantify the uncertainties in population health risks. Therefore, this study can provide a novel and dynamical perspective of pesticide residue distribution and help better quantify health risks.



中文翻译:

贝叶斯广义对数正态模型,用于动态评估与人口健康风险相关的土壤中农药残留的分布

为了评估和避免人口健康风险,有必要探索更好的模型来评估农药残留物在土壤和沉积物中的分布。频率主义哲学和概率在许多研究中被广泛使用,以应用与最大似然估计相关的对数正态分布,该分布采用固定参数并依赖大样本量来获得长期运行频率。但是,频繁出现的概率可能不适合分析农药残留分布,农药残留分布的参数受许多复杂因素的影响,因此应视为不确定的。这项研究旨在实施贝叶斯广义对数正态(GLN)模型,以更好地了解土壤中农药残留的分布并量化种群风险。贝叶斯GLN模型,包括位置,比例和形状参数,首次应用于动态评估土壤和沉积物中农药残留的分布。此外,通过使用终生致癌风险的癌症风险,非致癌作用的接触裕度以及因健康造成伤害的残障调整生命年,对通过土壤接触林丹进行了全面的人类健康风险评估。贝叶斯后验分析结果表明,某些农药的浓度分布更适合于对数拉普拉斯(例如,林丹的形状参数的众数值为1.079),或显示了对数正态分布族内分布的混合(例如,p,p'-DDE的形状参数的众数值为2.395),可以更好地解释农药残留的长尾现象并动态评估分布模型。对于林丹,使用贝叶斯p-box法从三个参数的95%最高概率密度区域(可信区间)计算得出的第95个百分位数的95%不确定性界限为[2.063,1558.609] ng / g,约为幅度大于计算的[46090,8.095] ng / g的常识患者95%置信区间,表明该人群可能有癌症风险担忧。这些来自贝叶斯GLN方法的不确定性分析结果表明,林丹土壤残留量变化较大,这可能反映了农药残留分布的复杂且不可预测的机制,包括不确定的模型和分布参数。总而言之,贝叶斯GLN模型对于农药残留量分布的动态评估更加灵活,保留了后代以用于将来的数据分析,并可以更好地量化人口健康风险的不确定性。因此,这项研究可以为农药残留的分布提供一种新颖而动态的观点,并有助于更好地量化健康风险。

更新日期:2018-10-11
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