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.

土壤侵蚀已被认为是导致全世界水质恶化和农作物产量下降的最严重威胁之一。土壤侵蚀和相关沉积物相关影响，例如水库沉积和鲑鱼产卵砾石，以及沉积物上附着的污染物的迁移，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。确定侵蚀的根源将有助于减轻土壤和河岸侵蚀，从而节省成本。沉积物指纹识别是一种新兴技术，通过使用沉积物颜色、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。该研究计划的长期目标是通过推进一些最近发现的示踪剂的使用并以新颖的方式测试其他示踪剂的使用来改进和扩展沉积物来源指纹识别技术。工作将集中在两个流域克内尔（不列颠哥伦比亚省，不列颠哥伦比亚省）和南烟草溪（马尼托巴省），这两个流域代表了对比条件（例如，山区有森林/草原与平坦的农田），并且存在大量补充数据。一个项目将改进化合物特异性稳定同位素（CSSI）作为示踪剂的使用，以识别来自特定植被类型（例如某些草或作物）的沉积物。第二个项目将评估持久性有机污染物（POP，如 PCB 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利用活动地区和野火（例如 2017 年夏季发生在 BC 省）的沉积物。第三个项目将改进通常被称为混合或分解模型的模型的使用，这些模型能够定量分配沉积物 到来源。这项工作将专门使用基于贝叶斯的模型 (MixSIAR)，目的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变异性信息，以及模型如何结合 CSSI、POP 和 PAH 等示踪剂特性。 这项工作将通过测试和评估几种新型和正在开发的示踪剂组，并提高分解模型定量识别有问题沉积物来源的能力，加强沉积物来源指纹识别方法的使用。这些改进的技术将促进沉积物指纹识别的使用，并通过提供准确的方法来保护我们的土壤和水资源，从而直接造福加拿大的经济。