Heise Susanne, Förstner Ulrich
Hamburg University of Technology, BIS, Eissendorfer Str. 40, 21073 Hamburg, Germany.
J Environ Monit. 2007 Sep;9(9):943-52. doi: 10.1039/b704071g. Epub 2007 Jul 30.
In order to ensure chemical quality of river basins, measures as required in the Water Framework Directive will also have to address contaminated sediments with subsequent monitoring of their successful application. Financial resources need to be allocated as part of a river basin plan to those contaminated sites that pose the biggest risk to the river basin. In order to differentiate between areas with elevated contaminant levels ("areas of concern") and those sites from which contaminated sediments can become resuspended and transported with the river, affecting the water phase and downstream sites in the catchment ("areas of risk"), the dynamics of sediment and suspended matter need to come into focus. Hydrological data have to be combined with concentration of suspended matter and its contaminant concentration to allow assessment of particle bound contaminant load. Each of these kinds of data, however, are subject to uncertainties--e.g. due to natural variability, heterogeneity of the matrix, challenges during sampling and chemical analyses of the suspended matter. Considering these uncertainties throughout the traceability chains of data collection, use of different lines of evidence and tools like fuzzy logic will increase the confidence of potentially costly management decisions.
为确保流域的化学质量，《水框架指令》要求的措施还必须解决受污染沉积物问题，并随后监测其实施成效。作为流域计划的一部分，需要将财政资源分配给那些对流域构成最大风险的受污染场地。为了区分污染物水平升高的区域（“关注区域”）和那些受污染沉积物可能重新悬浮并随河流输送、影响集水区水相和下游场地的场地（“风险区域”），沉积物和悬浮物的动态变化需要成为关注焦点。水文数据必须与悬浮物浓度及其污染物浓度相结合，以便评估颗粒结合污染物负荷。然而，这些数据中的每一种都存在不确定性——例如，由于自然变异性、基质的非均质性、采样过程中的挑战以及悬浮物的化学分析。在数据收集的整个可追溯链中考虑这些不确定性，使用不同的证据线和诸如模糊逻辑等工具，将增加潜在成本高昂的管理决策的可信度。
