Walsh Eric S, Kreakie Betty J, Cantwell Mark G, Nacci Diane
U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Atlantic Ecology Division, Narragansett, Rhode Island, United States of America.
PLoS One. 2017 Jul 24;12(7):e0179473. doi: 10.1371/journal.pone.0179473. eCollection 2017.
Modeling the magnitude and distribution of sediment-bound pollutants in estuaries is often limited by incomplete knowledge of the site and inadequate sample density. To address these modeling limitations, a decision-support tool framework was conceived that predicts sediment contamination from the sub-estuary to broader estuary extent. For this study, a Random Forest (RF) model was implemented to predict the distribution of a model contaminant, triclosan (5-chloro-2-(2,4-dichlorophenoxy)phenol) (TCS), in Narragansett Bay, Rhode Island, USA. TCS is an unregulated contaminant used in many personal care products. The RF explanatory variables were associated with TCS transport and fate (proxies) and direct and indirect environmental entry. The continuous RF TCS concentration predictions were discretized into three levels of contamination (low, medium, and high) for three different quantile thresholds. The RF model explained 63% of the variance with a minimum number of variables. Total organic carbon (TOC) (transport and fate proxy) was a strong predictor of TCS contamination causing a mean squared error increase of 59% when compared to permutations of randomized values of TOC. Additionally, combined sewer overflow discharge (environmental entry) and sand (transport and fate proxy) were strong predictors. The discretization models identified a TCS area of greatest concern in the northern reach of Narragansett Bay (Providence River sub-estuary), which was validated with independent test samples. This decision-support tool performed well at the sub-estuary extent and provided the means to identify areas of concern and prioritize bay-wide sampling.
对河口沉积物结合污染物的数量和分布进行建模，往往受到对研究地点了解不全面以及样本密度不足的限制。为解决这些建模限制，构思了一个决策支持工具框架，该框架可预测从河口子区域到更广阔河口范围的沉积物污染情况。在本研究中，实施了随机森林（RF）模型，以预测美国罗德岛州纳拉甘西特湾中一种模型污染物三氯生（5-氯-2-(2,4-二氯苯氧基)苯酚）（TCS）的分布。三氯生是一种在许多个人护理产品中使用的未受监管的污染物。随机森林的解释变量与三氯生的迁移和归宿（替代指标）以及直接和间接的环境输入有关。针对三个不同的分位数阈值，将连续的随机森林三氯生浓度预测结果离散化为三个污染水平（低、中、高）。随机森林模型用最少的变量解释了63%的方差。总有机碳（TOC）（迁移和归宿替代指标）是三氯生污染的一个强预测因子，与TOC随机值的排列相比，其导致均方误差增加了59%。此外，合流污水溢流排放（环境输入）和沙子（迁移和归宿替代指标）也是强预测因子。离散化模型确定了纳拉甘西特湾北部区域（普罗维登斯河子河口）最受关注的三氯生区域，这一结果通过独立测试样本得到了验证。这个决策支持工具在河口子区域层面表现良好，并提供了识别关注区域和对全湾采样进行优先级排序的方法。
