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Evaluation of the Use of Bias Factors with Water Monitoring Data

机译：利用水监测数据评估偏见因素的使用

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Aquatic exposure assessments using surface water quality monitoring data are often challenged by missing extreme concentrations if sampling frequency is less than daily. A bias factor method has been previously proposed to address this concern for peak concentrations, where a bias factor is a multiplicative quantity to upwardly adjust estimates so that the true value is exceeded 95% of the time. In other words, bias factors are statistically protective adjustments. We evaluate this method using a research data set of 69 near-daily sampled site-years from the Atrazine Ecological Monitoring Program, dividing the data set into 23 reference and 46 validation site-years. Bias factors calculated from the reference data set are used to evaluate the method using the validation set for 1) point estimation, 2) interval estimation, and 3) decision-making. Sampling designs are every 7, 14, 28, and 90d; and target quantities of assessment interest are the 90th and 95th percentiles and maximum m-day rolling averages (m=1, 7, 21, 60, 90). We find that bias factors are poor point estimators in comparison with alternative methods. For interval estimation, average coverage is less than nominal, with coverage at individual site-years sometimes very low. Positive correlation of bias factors and target quantities, where present, adversely affects method performance. For decision rules or screening, the method typically shows very low false-negative rates but at the cost of extremely high false-positive rates. Environ Toxicol Chem 2018;37:1864-1876. (c) 2018 SETAC
机译：如果采样频率少于每日一次，则使用地表水水质监测数据进行的水生暴露评估往往会受到缺少极端浓度的挑战。先前已经提出了一种偏倚因子方法来解决对峰浓度的担忧，其中偏斜因子是向上调整估计值的乘数，因此真实值超过95％的时间。换句话说，偏差因素是统计上的保护性调整。我们使用来自阿特拉津生态监测计划的69个近日采样站点年的研究数据集对该方法进行评估，将数据集分为23个参考站点年和46个验证站点年。根据参考数据集计算的偏差因子用于评估方法的有效性，该验证集用于1）点估计，2）间隔估计和3）决策。采样设计是每7、14、28和90d。评估兴趣的目标数量是第90个和第95个百分位数以及最大m天滚动平均值（m = 1、7、21、60、90）。我们发现，与替代方法相比，偏差因子是较差的点估计器。对于间隔估计，平均覆盖率小于名义覆盖率，有时单个站点年的覆盖率很低。偏差因子和目标数量的正相关（如果存在）会对方法性能产生不利影响。对于决策规则或筛选，该方法通常显示出极低的假阴性率，但以极高的假阳性率为代价。 Environ Toxicol Chem 2018; 37：1864-1876。 （c）2018年SETAC

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