Hollister Jeffrey W, August Peter V, Paul John F, Walker Henry A
USEPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Atlantic Ecology Division, 27 Tarzwell Drive, Narragansett, RI 02882, USA.
J Environ Qual. 2008 Jan 4;37(1):234-44. doi: 10.2134/jeq2007.0105. Print 2008 Jan-Feb.
Empirically derived relationships associating sediment metal concentrations with degraded ecological conditions provide important information to assess estuarine condition. Resources limit the number, magnitude, and frequency of monitoring activities to acquire these data. Models that use available information and simple statistical relationships to predict sediment metal concentrations could provide an important tool for environmental assessment. We developed 45 predictive models for the total concentrations of copper, lead, mercury, and cadmium in estuarine sediments along the Southern New England and Mid-Atlantic regions of the United States. Using information theoretic model-averaging approaches, we found total developed land and percent silt/clay of estuarine sediment were the most important variables for predicting the presence of all four metals. Estuary area, river flow, tidal range, and total agricultural land varied in their importance. The model-averaged predictions explained 78.4, 70.5, 56.4, and 50.3% of the variation for copper, lead, mercury, and cadmium, respectively. Overall prediction accuracies of selected sediment benchmark values (i.e., effects ranges) were 83.9, 84.8, 78.6, and 92.0% for copper, lead, mercury, and cadmium, respectively. Our results further support the generally accepted conclusion that sediment metal concentrations are best described by the physical characteristics of the estuarine sediment and the total amount of urban land in the contributing watershed. We demonstrated that broad-scale predictive models built from existing monitoring data with information theoretic model-averaging approaches provide valuable predictions of estuarine sediment metal concentrations and show promise for future environmental modeling efforts in other regions.
通过实证得出的沉积物金属浓度与退化生态条件之间的关系，为评估河口状况提供了重要信息。资源限制了获取这些数据的监测活动的数量、规模和频率。利用现有信息和简单统计关系来预测沉积物金属浓度的模型，可为环境评估提供重要工具。我们针对美国新英格兰南部和中大西洋地区河口沉积物中的铜、铅、汞和镉的总浓度，开发了45个预测模型。使用信息论模型平均方法，我们发现已开发土地总面积和河口沉积物的粉砂/粘土百分比是预测所有四种金属存在的最重要变量。河口面积、河流流量、潮差和农业用地总面积的重要性各不相同。模型平均预测分别解释了铜、铅、汞和镉变化的78.4%、70.5%、56.4%和50.3%。选定沉积物基准值（即效应范围）的总体预测准确率，铜、铅、汞和镉分别为83.9%、84.8%、78.6%和92.0%。我们的结果进一步支持了普遍接受的结论，即沉积物金属浓度最好由河口沉积物的物理特征和贡献流域内城市土地的总量来描述。我们证明，利用信息论模型平均方法从现有监测数据构建的大规模预测模型，能够对河口沉积物金属浓度做出有价值的预测，并为未来其他地区的环境建模工作展现出前景。
