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预测集水区的弥散微生物污染风险:SCIMAP 的表现及未来发展建议。

Predicting diffuse microbial pollution risk across catchments: The performance of SCIMAP and recommendations for future development.

机构信息

Biological & Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Stirling FK9 4LA, UK.

Department of Geography, Durham University, Durham DH1 3LE, UK.

出版信息

Sci Total Environ. 2017 Dec 31;609:456-465. doi: 10.1016/j.scitotenv.2017.07.186. Epub 2017 Jul 26.

Abstract

Microbial pollution of surface waters in agricultural catchments can be a consequence of poor farm management practices, such as excessive stocking of livestock on vulnerable land or inappropriate handling of manures and slurries. Catchment interventions such as fencing of watercourses, streamside buffer strips and constructed wetlands have the potential to reduce faecal pollution of watercourses. However these interventions are expensive and occupy valuable productive land. There is, therefore, a requirement for tools to assist in the spatial targeting of such interventions to areas where they will have the biggest impact on water quality improvements whist occupying the minimal amount of productive land. SCIMAP is a risk-based model that has been developed for this purpose but with a focus on diffuse sediment and nutrient pollution. In this study we investigated the performance of SCIMAP in predicting microbial pollution of watercourses and assessed modelled outputs of E. coli, a common faecal indicator organism (FIO), against observed water quality information. SCIMAP was applied to two river catchments in the UK. SCIMAP uses land cover risk weightings, which are routed through the landscape based on hydrological connectivity to generate catchment scale maps of relative in-stream pollution risk. Assessment of the model's performance and derivation of optimum land cover risk weightings was achieved using a Monte-Carlo sampling approach. Performance of the SCIMAP framework for informing on FIO risk was variable with better performance in the Yealm catchment (r=0.88; p<0.01) than the Wyre (r=-0.36; p>0.05). Across both catchments much uncertainty was associated with the application of optimum risk weightings attributed to different land use classes. Overall, SCIMAP showed potential as a useful tool in the spatial targeting of FIO diffuse pollution management strategies; however, improvements are required to transition the existing SCIMAP framework to a robust FIO risk-mapping tool.

摘要

农业集水区地表水的微生物污染可能是由于农场管理不善造成的,例如过度放牧或不当处理粪便和泥浆。集水区干预措施,如河道围栏、溪边缓冲区和人工湿地,有可能减少河道粪便污染。然而,这些干预措施成本高昂,占用了宝贵的生产用地。因此,需要有工具来帮助将这些干预措施有针对性地应用于对水质改善影响最大、占用生产用地最少的地区。SCIMAP 是为此目的而开发的一种基于风险的模型,但重点是扩散沉积物和营养物污染。在这项研究中,我们调查了 SCIMAP 预测地表水微生物污染的性能,并评估了模型输出的大肠杆菌(一种常见的粪便指示生物)与观测水质信息的吻合程度。SCIMAP 应用于英国的两个河流集水区。SCIMAP 使用土地覆盖风险权重,这些权重根据水文学连通性在景观中路由,生成相对河道污染风险的集水区尺度地图。通过蒙特卡罗抽样方法评估模型的性能并得出最优土地覆盖风险权重。SCIMAP 框架在指示 FIO 风险方面的性能各不相同,在 Yealm 集水区的性能更好(r=0.88;p<0.01),而在 Wyre 集水区的性能较差(r=-0.36;p>0.05)。在这两个集水区,最优风险权重的应用都存在很大的不确定性,这归因于不同的土地利用类别。总体而言,SCIMAP 作为一种有用的工具,具有在空间上针对 FIO 扩散污染管理策略的潜力;然而,需要改进现有的 SCIMAP 框架,以将其转化为一种可靠的 FIO 风险测绘工具。

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