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

摘要为“NSF收敛加速器轨道K：COMPASS：全面预测，评估，和淡水系统的风暴引起的污染公平的解决方案“该项目解决淡水质量和数量的挑战，通过集成下一代传感器，先进的洪水建模，并共同生成的政策知识，以提高社区的可持续性。极端天气事件往往导致有毒化学品、未经处理和部分处理的污水以及农业废物排放到环境中。这些灾害对基础设施陈旧、政府资源有限、得不到充分服务的社区的影响尤为严重。该研究以密西西比的珠江流域和南卡罗来纳州的桑蒂河流域为重点，采用模块化传感器系统，包括无人驾驶飞行器和低成本核磁共振（NMR）光谱仪，以评估流域中的污染物扩散。通过数据驱动和基于物理的建模方法，该项目旨在提供可靠的水质和水量空间和时间预测，支持决策者监测淡水系统和规划水紧急情况。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，人工智能和量子材料的专业知识，旨在使社区能够产生和实施公平和可持续的解决方案。该项目的社会影响扩展到支持政策决策，将公平纳入适应解决方案，并减轻可持续社区的洪水对环境的影响。该项目的重点是开发低成本，可现场部署的NMR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。需要这种综合方法来克服实时数据收集方面的挑战，并将学术研究转化为可行的解决方案。低成本和易于部署的原位NMR为开发污染物检测、量化和跟踪系统提供了最佳的传感技术，而不会将传感器开发局限于特定的污染物。该项目第一阶段工作范围的主要组成部分包括：（a）改进研究小组为现场监测水生环境中的污染物而开发的开放源码紧凑型核磁共振传感系统。(b)开发一个耦合的洪水和污染物建模和监测框架，以预测和应对洪水和洪水携带的污染物，提高准确性和时间。（c）第（1）款 使用系统方法的系统建模的复杂性和反馈回路的综合社会环境系统中固有的。第一阶段项目为潜在的第二阶段计划奠定了基础，旨在加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，并为社区提供设计自适应，主动，下一个可持续发展-该奖项反映了NSF的法定使命，并通过利用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

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 These disasters disproportionately affect un-derserved communities with outdated infrastructure and limited governmental resources.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.