Advancing techniques to fingerprint sediment sources in disturbed watersheds

先进技术对受干扰流域的沉积物来源进行指纹识别

• 批准号: RGPIN-2018-06360
• 负责人: Owens, Philip
• 金额: $ 2.62万
• 依托单位: University of Northern British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=713264
• 关键词: Advancing; techniques; fingerprint; sediment; sources

Soil erosion has been identified as one of the most serious threats leading to deteriorating water quality and lowering of crop yields throughout the world. Soil erosion and associated sediment-related impacts - such as sedimentation of reservoirs and salmonid spawning gravels, and the transport of pollutants attached to the sediment - cost billions of dollars each year to the Canadian economy. With an increasing population, and changes in climate and related wildfires and floods, these costs are expected to increase. Determining the origin of the erosion will allow for the mitigation of soil and channel bank erosion, leading to cost savings. Sediment fingerprinting is an emerging technique that directly links problematic sediment to its source by using key diagnostic properties or tracers like sediment colour, geochemistry and fallout radionuclides. The long-term objective of this research program is to improve and expand the sediment source fingerprinting technique by advancing the use of some recently identified tracers and by testing the use of other tracers in a novel way. Work will focus on two watersheds Quesnel (British Columbia, BC) and South Tobacco Creek (Manitoba) which are representative of contrasting conditions (e.g. mountainous with forests/grassland vs flat with cropland), and where a considerable amount of complementary data exists. One project will improve the use of compound-specific stable isotopes (CSSIs) as tracers to identify sediment coming from specific vegetation types, such as certain grasses or crops. A second project will evaluate the application of persistent organic pollutants (POPs, like PCBs and legacy pesticides) and polycyclic aromatic hydrocarbons (PAHs) as tracers to determine the sediment coming from areas with specific land use activities and from wildfires, such as those that occurred in BC in summer 2017. A third project will improve the use of models often referred to as mixing or unmixing models that are able to quantitatively apportion sediment to sources. This work will specifically use a Bayesian-based model (MixSIAR) with the goal of improving how the model utilises information on the variability associated with tracers in sediments and source materials like soils, and how the model incorporates tracer properties such as CSSIs, POPs and PAHs. This work will strengthen the use of the sediment source fingerprinting approach by testing and evaluating several novel and developing groups of tracers and by improving the ability of unmixing models to quantitatively identify the sources of problematic sediment. These improved techniques will advance the use of sediment fingerprinting and will directly benefit Canada's economy by providing an accurate means to protect our soil and water resources.

土壤侵蚀已被确定为导致全球水质恶化和作物产量下降的最严重威胁之一。土壤侵蚀和与沉积物有关的影响----例如水库和鲑鱼产卵砾石的沉积，以及附着在沉积物上的污染物的迁移----每年给加拿大经济造成数十亿美元的损失。随着人口的增加，气候的变化以及相关的野火和洪水，这些成本预计会增加。确定侵蚀的起源将允许减轻土壤和渠道银行侵蚀，从而节省成本。沉积物指纹识别是一种新兴技术，它利用沉积物颜色、地球化学和沉降放射性核素等关键诊断特性或示踪剂，将有问题的沉积物与其来源直接联系起来。这项研究计划的长期目标是通过推进使用一些最近确定的示踪剂，并通过测试使用其他示踪剂以一种新的方式来改善和扩大沉积物源指纹技术。工作重点将放在两个分水岭Quesnel（不列颠哥伦比亚省）和南烟草溪（马尼托巴），这两个分水岭代表了对比鲜明的条件（例如，多山的森林/草地与平坦的农田），并且存在大量的补充数据。其中一个项目将改进使用特定化合物稳定同位素作为示踪剂，以确定来自特定植被类型的沉积物，如某些草或作物。第二个项目将评估持久性有机污染物（如多氯联苯和遗留农药）和多环芳烃（PAH）作为示踪剂的应用，以确定来自特定土地使用活动地区和野火的沉积物，例如2017年夏天在BC发生的野火。第三个项目将改进通常被称为混合或非混合模型的模型的使用，这些模型能够定量地将沉积物分配给各个来源。这项工作将具体使用基于贝叶斯的模型（MixSIAR），目标是改进模型如何利用与沉积物和土壤等源材料中示踪剂相关的可变性信息，以及模型如何结合示踪剂特性，如CSSI，POP和PAH。 这项工作将通过测试和评价几组新的和正在开发的示踪剂，并通过提高分离模型定量确定问题沉积物来源的能力，加强使用沉积物来源指纹法。这些改进的技术将促进沉积物指纹的使用，并将通过提供保护我们的土壤和水资源的准确手段，直接造福于加拿大的经济。