Microplastics fingerprinting at the watershed scale: from sources to receivers

分水岭范围内的微塑料指纹识别：从来源到接收者

• 批准号: 558435-2020
• 负责人: VanCappellen, Philippe
• 金额: $ 14.9万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 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=706817
• 关键词: Microplastics; fingerprinting; watershed; scale; sources

Plastics pollution is a global and growing environmental hazard with potentially far-reaching consequences for food webs, biodiversity, ecosystem services and human well-being. Of particular concern are microplastics because their small sizes enhance their long-range transport, toxicity to biota, and capacity to sorb and leach contaminants, including organic pollutants and toxic metals. We propose to implement a holistic data acquisition and modeling strategy that will link the sources, transport, fate and exposure risks of microplastics at the scale of entire watersheds. In addition to established spectroscopic and imaging techniques, we will explore novel biosensors and microwave sensing for the rapid screening and typing of microplastics in environmental samples. We will improve the processing of large analytical datasets on microplastics assemblages and develop standardized workflows for the sampling, sample preparation, screening, analysis, data processing and data management of microplastics. These workflows will be applied to characterize the entry pathways to, and transport along, the river network, identify (bio)degradation proxies, quantify accumulation in reservoirs and on riverbanks, and estimate the export of microplastics to receiving lakes. Past microplastics pollution will be reconstructed from dated sediment cores while the analysis of microplastics loading and transport rates under variable hydrometeorological conditions will help forecast the impacts of more extreme precipitation events in the future. Our growing database will be integrated into a mass balance modeling platform that will enable extensive scenario testing, including the response of microplastics emissions and loadings to hypothetical source reduction measures. The scenario results, together with cost-benefit analyses and knowledge exchanges with stakeholders, will contribute to identifying policy instruments and governance approaches that can effectively protect freshwater ecosystems and drinking water sources from microplastics pollution.

塑料污染是一种全球性的、日益严重的环境危害，可能对食物网、生物多样性、生态系统服务和人类福祉产生深远影响。特别值得关注的是微塑料，因为它们的小尺寸增强了它们的远距离迁移、对生物群的毒性以及吸收和浸出包括有机污染物和有毒金属在内的污染物的能力。我们建议实施一项全面的数据采集和建模策略，将整个流域范围内微塑料的来源、运输、命运和暴露风险联系起来。除了已建立的光谱和成像技术，我们将探索新的生物传感器和微波传感快速筛选和环境样品中的微塑料分型。完善微塑料组合大型分析数据处理，制定微塑料取样、制样、筛选、分析、数据处理、数据管理等标准化工作流程。这些工作流程将用于描述进入河网的途径和沿河运输的特征，确定（生物）降解代理，量化水库和河岸的积累，并估计微塑料向接收湖泊的出口。过去的微塑料污染将从年代久远的沉积物岩心中重建，而微塑料在不同水文气象条件下的装载和运输速率的分析将有助于预测未来更极端降水事件的影响。我们不断增长的数据库将集成到一个质量平衡建模平台中，该平台将实现广泛的情景测试，包括微塑料排放和负荷对假设的源头减少措施的响应。情景结果，以及成本效益分析和与利益攸关方的知识交流，将有助于确定有效保护淡水生态系统和饮用水源免受微塑料污染的政策工具和治理方法。