NSF Convergence Accelerator Track K: COMPASS: Comprehensive Prediction, Assessment, and Equitable Solutions for Storm-Induced Contamination of Freshwater Systems

NSF 融合加速器轨道 K：COMPASS：风暴引起的淡水系统污染的综合预测、评估和公平解决方案

• 批准号: 2344357
• 负责人: Jasim Imran
• 金额: $ 65万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2344357&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; COMPASS

Abstract for “NSF Convergence Accelerator Track K: COMPASS: Comprehensive Prediction, Assess-ment, and Equitable Solutions for Storm-Induced Contamination of Freshwater Systems”The project addresses the challenges of freshwater quality and quantity by integrating next-generation sensors, advanced flood modeling, and co-generated policy knowledge to enhance community resili-ency. Extreme weather events often result in the release of toxic chemicals, raw and partially treated sewage, and agricultural wastes into the environment. These disasters disproportionately affect un-derserved communities with outdated infrastructure and limited governmental resources. Focused on the Pearl River Watershed in Mississippi and the Santee River Basin in South Carolina, the research employs a modular sensor system, including unpiloted aerial vehicles and low-cost Nuclear Magnetic Resonance (NMR) spectrometers, to assess contaminant dispersion in watersheds. Through a data-driven and physics-based modeling approach, the project aims to provide reliable spatial and tem-poral projections for water quality and quantity, supporting decision-makers in monitoring freshwater systems and planning for water emergencies. The interdisciplinary team, combining expertise in social sciences, public policy, environmental justice, hydrologic modeling, distributed sensing, artificial intelli-gence, and quantum materials, seeks to empower communities to generate and implement equitable and sustainable solutions. The project's societal impacts extend to supporting policy decisions, incor-porating equity in adaptation solutions, and mitigating environmental impacts from flooding in vulner-able communities.The project focuses on developing low-cost, field-deployable NMR sensor systems, integrating data collection methods with hydrologic modeling, and adopting a system of socio-environmental systems approach. This integrated approach is required to overcome challenges in real-time data collection and the transition of academic research into actionable solutions. Low-cost and easy-to-deploy in situ NMR provides optimal sensing technology for developing a contaminant detection, quantification, and tracking system without constraining sensor development to focus on a specific contaminant. Key components of the project’s phase I scope of work include: (a) Refining an open-source compact NMR sensing system developed by the research team for in-situ monitoring of contaminants in aquatic envi-ronments. (b) Developing a coupled flood and contaminant modeling and monitoring framework to predict and respond to flooding and flood-borne contaminants with enhanced accuracy and timing. (c) Modeling the complexities and feedback loops inherent in integrated socio-environmental systems using a system of systems approach. The Phase I project establishes the groundwork for a potential Phase II initiative, aiming for an enhanced understanding of community vulnerability to storm-induced contaminants, advancements in scalable heterogeneous data acquisition for real-time flood and con-taminant tracking, and equipping communities with tools to design adaptive, active, and sustainable next-generation infrastructure.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

“NSF 融合加速器轨道 K：COMPASS：风暴引起的淡水系统污染的综合预测、评估和公平解决方案”摘要该项目通过集成下一代传感器、先进的洪水建模来解决淡水质量和数量的挑战，以及共同生成的政策知识，以增强社区的抵御能力。极端天气事件通常会导致有毒化学品、未经处理和部分处理的污水以及农业废物释放到环境中。该研究重点关注密西西比州的珠江流域和南卡罗来纳州的桑蒂河流域，采用了模块化传感器系统，包括无人驾驶飞行器和低成本核磁。共振（NMR）光谱仪，通过数据驱动和基于物理的建模方法来评估流域中的污染物扩散，该项目旨在提供可靠的空间和时间。水质和水量预测，支持决策者监测淡水系统和水紧急情况规划。跨学科团队结合了社会科学、公共政策、环境正义、水文建模、分布式传感、人工智能和量子方面的专业知识。该项目的社会影响延伸到支持政策决策、将公平性纳入适应解决方案以及减轻脆弱社区洪水造成的环境影响。低成本、可现场部署的核磁共振传感器系统，将数据收集方法与水文建模相结合，并采用社会环境系统方法，这种综合方法需要克服实时数据收集和学术研究转型的挑战。低成本且易于部署的原位 NMR 提供了用于开发污染物检测、定量和跟踪系统的最佳传感技术，而无需限制传感器开发以专注于项目的特定污染物的关键组件。第一阶段的工作范围包括： (a) 完善研究小组开发的开源紧凑型核磁共振传感系统，用于对水生环境中的污染物进行现场监测。 (b) 开发洪水和污染物耦合建模和监测框架。 (c) 使用系统方法的系统对综合社会环境系统固有的复杂性和反馈循环进行建模。潜在的第二阶段计划的基础，旨在增强对社区对风暴引起的污染物的脆弱性的了解，在实时洪水和污染物跟踪的可扩展异构数据采集方面取得进展，并为社区配备设计自适应、主动的工具和可持续的下一代基础设施。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。