Parsons Corporation, Chicago, IL 60606, USA; Department of Civil Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.
Environ Int. 2018 Dec;121(Pt 1):620-634. doi: 10.1016/j.envint.2018.09.054. Epub 2018 Oct 9.
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.
探索更好的模型来评估土壤和沉积物中农药残留的分布对于评估和避免人群健康风险是必要的。在许多研究中，经常使用频率主义哲学和概率来应用与最大似然估计相关的对数正态分布，该分布假设参数固定，并依赖于大样本量进行长期频率分析。然而，频率主义概率可能不适合分析农药残留分布，因为其参数受到许多复杂因素的影响，应视为不固定。本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人群风险。贝叶斯 GLN 模型，包括位置、规模和形状参数，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，还使用致癌效应终生癌症风险、非致癌效应暴露边际和健康损害残疾调整生命年来对通过土壤接触林丹进行综合人类健康风险评估。贝叶斯后验分析结果表明，某些农药浓度的分布更适合对数拉普拉斯分布（例如，林丹形状参数的模式值为 1.079）或呈现对数正态分布族内的分布混合（例如，p,p'-DDE 形状参数的模式值为 2.395），这可以更好地解释农药残留分布的长尾现象，并动态评估分布模型。对于林丹，使用贝叶斯 p 盒方法通过 95%最高概率密度区域（可信区间）计算的三个参数的第 95 个百分位数的 95%不确定性边界为 [2.063, 1558.609]ng/g，这比计算的频率主义 95%置信区间 [4.690, 8.095]ng/g 大几个数量级，表明人群可能存在癌症风险。贝叶斯 GLN 方法的这些不确定性分析结果表明林丹土壤残留的变化更大，这可能反映了包括不固定模型和分布参数在内的农药残留分布的复杂和不可预测机制。总之，贝叶斯 GLN 模型对于土壤中农药残留分布的动态评估更加灵活，保留了未来数据分析的后验概率，并且可以更好地量化人群健康风险的不确定性。因此，本研究可以为农药残留分布提供新的动态视角，并有助于更好地量化健康风险。
