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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753252
• 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最严重的威胁之一，导致水质恶化并降低了全世界的作物产量。土壤侵蚀和相关的与沉积物相关的影响 - 例如储层和鲑鱼产卵剂的沉积，以及附着在沉积物上的污染物的运输 - 每年耗资数十亿美元向加拿大经济体。随着人口增加，气候和相关野火和洪水的变化，这些成本有望增加。确定侵蚀的起源将允许减轻土壤和渠道库侵蚀，从而节省成本。沉积物指纹识别是一种新兴技术，它通过使用关键诊断性能或示踪剂（例如沉积物颜色，地球化学和辐射放射性核素）将有问题的沉积物直接连接到其来源。该研究计划的长期目标是通过推进一些最近确定的示踪剂并以新颖的方式测试其他示踪剂的使用来改善和扩展沉积物源指纹技术。工作将重点放在两个流域Quesnel（不列颠哥伦比亚省，卑诗省）和南烟草溪（曼尼托巴省）上，它们代表了对比条件（例如，与森林/草地与农田平坦的山区/草地）以及大量互补数据存在。一个项目将改善化合物特异性稳定同位素（CSSIS）作为示踪剂的使用，以鉴定来自特定植被类型的沉积物，例如某些草或农作物。第二个项目将评估持久有机污染物（如POP，PCB和遗产农药）和多环芳族烃（PAH）作为示踪剂，以确定来自具有特定土地使用活动的地区的沉积物，以及来自野生动物的特定区域，例如在夏季进行的野性或次要型号的野生动物。来源。这项工作将专门使用基于贝叶斯的模型（MIXSIAR），目的是改善该模型如何利用与沉积物和原始材料（如土壤）中的示踪剂相关的可变性信息，以及该模型如何结合CSSIS，POPS和PAH等示踪特性。这项工作将通过测试和评估几个新颖和开发的示踪剂组，并提高不混合模型定量识别有问题的沉积物来源的能力，从而增强沉积物源指纹方法的使用。这些改进的技术将提高沉积物指纹的使用，并通过提供准确的手段来保护我们的土壤和水资源，直接受益于加拿大的经济。