U.S. Geological Survey, 2280 Woodale Drive, Mounds View, MN 55112, USA.
U.S. Geological Survey, 8551 Research Way, Middleton, WI 53562, USA.
Sci Total Environ. 2022 Jul 15;830:154618. doi: 10.1016/j.scitotenv.2022.154618. Epub 2022 Mar 18.
Widespread occurrence of emerging contaminants in Great Lakes tributaries led to the development and publication of a vulnerability index (VI) to assess the potential exposure of aquatic communities to chemicals of emerging concern (CEC) in the Great Lakes basin. The robust nature of the VI was tested to evaluate the underlying statistical model and expand the spatial domain of the index. Data collected at 131 new sampling sites (Test 1) and published data from independent studies (Test 2) were used to test the model predictions. Test 1 water and sediment samples were analyzed for the same classes of CEC chemicals and compared to the predictions for the original VI. Concentrations and numbers of unique CECs detected in water and sediment samples were similar between the original data and the two test datasets, although CECs tended to have higher detection frequencies in the original dataset compared to the Test 1 and Test 2 datasets. For example, 69 CECs were detected in ≥30% of water samples in the original dataset compared with 17 CECs in the Test 1 data and 59 in the Test 2 data. Predicted vulnerability for test sites agreed with actual vulnerability 64% of the time for water and 71% of the time for sediment. Agreement percentage results were greater when individual sites were grouped by river, with 82% agreement between predictions and actual vulnerability for water and 78% agreement for sediment. For the entire dataset, the VI ranks correlated with an independent estimate of potential biological impact. Agreement percentage was the greatest for low or high vulnerability index values but highly variable for sites that are classified as having medium vulnerability. Despite the underlying variability, there is a significant correlation (R = 0.26; p < 0.01) between the VI ranking of tributaries and the independent ranking of potential negative biological impact.
在大湖支流中广泛存在的新兴污染物导致了脆弱性指数 (VI) 的开发和发表，以评估大湖流域水生群落接触新兴关注化学品 (CEC) 的潜在暴露情况。该 VI 的稳健性通过测试进行评估，以评估基础统计模型并扩展指数的空间域。使用在 131 个新采样点收集的数据（测试 1）和独立研究的已发表数据（测试 2）来测试模型预测。测试 1 的水和沉积物样品被分析了相同类别的 CEC 化学品，并与原始 VI 的预测进行了比较。原始数据和两个测试数据集之间的水和沉积物样品中检测到的 CEC 浓度和数量相似，尽管与原始数据集相比，CEC 在测试 1 和测试 2 数据集中的检测频率更高。例如，在原始数据集中有 69 种 CEC 在≥30%的水样中被检测到，而在测试 1 数据中只有 17 种，在测试 2 数据中只有 59 种。测试点的预测脆弱性有 64%的时间与水的实际脆弱性一致，有 71%的时间与沉积物的实际脆弱性一致。当按河流对个别站点进行分组时，结果的一致性百分比更高，水的预测与实际脆弱性之间有 82%的一致性，沉积物的预测与实际脆弱性之间有 78%的一致性。对于整个数据集，VI 排名与潜在生物影响的独立估计相关。对于低或高脆弱性指数值，一致性百分比最高，但对于被归类为具有中等脆弱性的站点，一致性百分比变化很大。尽管存在潜在的可变性，但支流的 VI 排名与潜在负面生物影响的独立排名之间存在显著相关性（R = 0.26；p < 0.01）。
