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 These disasters disproportionately affect un-derserved communities with outdated infrastructure and limited governmental resources.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收敛加速器轨道K：Compass：全面的预测，评估和公平解决方案，用于暴风雨引起的淡水系统污染”，该项目通过整合下一代传感器，先进的洪水建模，高级洪水建模以及共同生成的政策知识来提高社区弹性弹性，以解决淡水质量和数量的挑战。极端天气事件通常会导致有毒化学物质，原始和部分处理的污水以及对环境的农业废物的释放。这些灾难不成比例地影响了没有服务的基础设施和有限的政府资源的社区。研究员工专注于密西西比州的珍珠河流域和南卡罗来纳州的Santee河盆地，一个模块化传感器系统，包括未插入的航空车和低成本的核磁共振（NMR）光谱仪，以评估流水中的污染物分散体。通过基于数据驱动和物理的建模方法，该项目旨在为水质和数量提供可靠的空间和临时项目，从而支持决策者监测淡水系统并为水面紧急情况计划。跨学科团队结合了社会科学，公共政策，环境正义，水文建模，分布式感应，人工智能和量子材料的专业知识，旨在使社区能够生成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，将公平性纳入适应性解决方案，并减轻易易受到的社区洪水所产生的环境影响。该项目着重于开发低成本的，可实地的可剥离NMR传感器系统，将数据收集方法与水文建模集成，并采用社会环境系统的系统。需要这种综合方法来克服实时数据收集的挑战，并将学术研究转变为可行的解决方案。低成本且易于掌握的原位NMR为开发污染物检测，量化和跟踪系统提供了最佳的灵敏度技术，而无需限制传感器开发以专注于特定的污染物。该项目第一阶段工作范围的关键组成部分包括：（a）研究团队开发的开源紧凑型NMR感应系统，用于在水生环境中对污染物的原位监测。 （b）开发一个耦合的洪水和污染物建模和监测框架，以增强的准确性和时机来预测和应对洪水和洪水传播污染物。 （c）使用系统方法系统建模在集成的社会环境系统中继承的复杂性和反馈回路。第一阶段项目为潜在的II期计划建立了基础，目的是增强对社区对风暴引起的污染物的脆弱性的了解，可伸缩的异质数据获取的进步，用于实时洪水和实时洪水跟踪，并为社区装备社区，并提供具有设计适应性的，活跃的，可持续的统计范围的工具，以表现出来。通过使用基金会的智力优点和更广泛影响的评论标准进行评估。