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农药环境归趋数据的分布及方差/协方差结构

Distribution and variance/covariance structure of pesticide environmental fate data.

作者信息

Spurlock Frank

机构信息

California Department of Pesticide Regulation, Environmental Monitoring Branch, 1001 I Street, P.O. Box 4015, Sacramento, California 95812, USA.

出版信息

Environ Toxicol Chem. 2008 Aug;27(8):1683-90. doi: 10.1897/07-600.1.

Abstract

Hydrophobicity, persistence, and volatility data for individual pesticides are widely used in risk assessment and transport modeling, so it is important to understand their distribution, variation, and covariation. Correlations (normalized covariance) among properties across a range of multiple pesticides are also important for understanding fundamental relationships among the properties. For the present study, multiple determinations of 11 physicochemical properties of 262 individual pesticides were compiled, primarily from registrant submissions. A Z-score normality analysis indicates that, barring specific data to the contrary, log normality is a reasonable assumption for three properties commonly treated as random variables in modeling: Organic carbon-normalized soil sorption coefficient, aerobic soil metabolism half-life, and field dissipation half-life. Various percentiles for coefficients of variation of the variables are provided, allowing probabilistic modelers to choose realistic population parameters for sampling distributions. A second data set consisting of median values of individual properties for each pesticide was used to investigate the covariance structure of eight of the most important fate properties across 172 pesticides using correlation analysis and exploratory common factor analysis. That analysis demonstrated the use of common factor analysis for reducing the dimensionality of multicollinear environmental fate data, yielding three new orthogonal variables containing most of the information in the original data, and provided insight into the fundamental data structure.

摘要

单个农药的疏水性、持久性和挥发性数据在风险评估和迁移模型中被广泛使用,因此了解它们的分布、变化和协变情况很重要。多种农药的性质之间的相关性(归一化协方差)对于理解这些性质之间的基本关系也很重要。在本研究中,主要从注册者提交的数据中汇编了262种单个农药的11种物理化学性质的多次测定结果。Z分数正态性分析表明,除非有相反的特定数据,对于建模中通常视为随机变量的三种性质:有机碳归一化土壤吸附系数、好氧土壤代谢半衰期和田间消散半衰期,对数正态性是一个合理的假设。提供了变量变异系数的各种百分位数,使概率建模者能够为抽样分布选择现实的总体参数。第二个数据集由每种农药的单个性质的中值组成,用于通过相关分析和探索性共同因子分析研究172种农药中八种最重要的归宿性质的协变结构。该分析证明了使用共同因子分析来降低多共线性环境归宿数据的维度,产生三个新的正交变量,包含原始数据中的大部分信息,并提供了对基本数据结构的洞察。

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