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利用全球模型估算中国“随下水道流失”化学物质的地表水体浓度。

Estimating surface water concentrations of "down-the-drain" chemicals in China using a global model.

机构信息

Department of Environmental Science and Technology, School of Applied Sciences, Cranfield University, Cranfield, Bedfordshire, MK43 0AL, UK.

出版信息

Environ Pollut. 2012 Jun;165:233-40. doi: 10.1016/j.envpol.2011.10.035. Epub 2011 Dec 5.

Abstract

Predictions of surface water exposure to "down-the-drain" chemicals are presented which employ grid-based spatially-referenced data on average monthly runoff, population density, country-specific per capita domestic water and substance use rates and sewage treatment provision. Water and chemical load are routed through the landscape using flow directions derived from digital elevation data, accounting for in-stream chemical losses using simple first order kinetics. Although the spatial and temporal resolution of the model are relatively coarse, the model still has advantages over spatially inexplicit "unit-world" approaches, which apply arbitrary dilution factors, in terms of predicting the location of exposure hotspots and the statistical distribution of concentrations. The latter can be employed in probabilistic risk assessments. Here the model was applied to predict surface water exposure to "down-the-drain" chemicals in China for different levels of sewage treatment provision. Predicted spatial patterns of concentration were consistent with observed water quality classes for China.

摘要

本文提出了一种预测地表水污染的方法,该方法利用基于网格的空间参考数据,包括平均每月径流量、人口密度、国家特定的人均生活用水量和物质使用速率以及污水处理设施。利用数字高程数据得出的水流方向,将水和化学物质在景观中进行传输,同时考虑了河流中化学物质的简单一级动力学损耗。尽管模型的时空分辨率相对较低,但与空间不明确的“单元世界”方法相比,该模型在预测暴露热点的位置和浓度的统计分布方面仍具有优势,后者可用于概率风险评估。本文应用该模型预测了中国不同污水处理水平下“随污水流失”化学物质对地表水体的暴露情况。预测的浓度空间分布模式与中国实际水质类别一致。

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