NSF Convergence Accelerator Track L: Innovative chemical microsensor development for in situ, real-time monitoring of priority water pollutants to protect water quality

Clean water is essential for the health and wellbeing of human life and of nature. Human activities, however, have far-reaching impacts on water quality, often leading to a decline in human and ecosystem health, reduction in food production, and escalation of poverty throughout the United States and globally. The increasingly high levels of water pollutants, most of which are not adequately monitored, are creating an “invisible water crisis” that disproportionately impacts low-income communities, tribal nations, and communities of color. This project aims to develop a compact, customizable chemical sensing system integrated with new microsensors and data management and analytic technologies to accurately quantify, and make visible, high-priority pollutants that threaten human and ecosystem health of freshwater and managed water systems. This collaboration of expertise and emerging technologies in engineering, environmental chemistry, artificial intelligence, and water resource management enhances the innovation of these real-time, cost-effective water-monitoring technologies. Their data outputs will ultimately improve capabilities for making timely, informed decisions and spurring progressive actions that ensure societal and planetary health. Engagement with diverse stakeholders (water advocacy non-profits, resource managers, tribal and state government groups, and environmental engineers) will ensure that these microsensors and data are accessible and user-friendly to benefit high-risk and underserved populations. Using microelectromechanical systems-based technologies through advanced microfabrication techniques, this project will develop miniaturized, portable chemical sensors with exceptional sensitivity (sub parts-per-billion level) and specificity of multiple target pollutants (specifically, nutrients and metals) in freshwater and managed water environments. Remote deployment of sensor modules and sensor arrays will be enabled by leveraging embedded systems design and ultra-low-power systems advances to extend the lifetime of deployments and enable wireless communications for real-time data transfer. Big Data technologies will be used to ensure that the data obtained from these sensor arrays are readily accessible, accurate, well-organized, and interpretable by end-users with wide-ranging expertise and needs, and spatial artificial intelligence methods will facilitate water quality prediction and forecasting. This innovative chemical sensing and data analytics platform will provide sensitive, selective, real-time, reliable, and cost-effective water quality monitoring for broad applications.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.

清水对于人类生活和自然的健康和福祉至关重要。然而，人类活动对水质的影响深远，通常会导致人类和生态系统健康的下降，粮食生产的减少以及整个美国和全球的贫困升级。越来越高的水污染物（其中大多数没有得到充分监控）正在造成“无形的水危机”，从而不成比例地影响低收入社区，部落国家和有色人种。该项目旨在开发一种与新的微传感器以及数据管理和分析技术集成的紧凑，可自定义的化学传感系统，以准确量化，并制造可见的高优先污染物，从而威胁淡水和管理水系统的人类和生态系统健康。这种在工程，环境化学，人工智能和水资源管理方面的专业知识和新兴技术的合作增强了这些实时，具有成本效益的水监控技术的创新。他们的数据输出最终将提高做出及时，明智的决策并激发确保社会健康的渐进式行动的能力。与潜水员利益相关者（水倡导非营利组织，资源经理，部落和州政府团体以及环境工程师）的参与将确保这些微传感器和数据可访问且用户友好，以使高风险和服务欠佳的人群受益。该项目通过高级微型制造技术利用基于微力学系统的技术，将在淡水和管理的水环境中开发具有特殊敏感性（次数分少量级别）的微型便携式化学传感器（次数分​​二十亿级）和多个目标污染物（特定于营养和金属）的特异性。传感器模块和传感器阵列的远程部署将使用大数据技术来确保从这些传感器阵列中获得的数据很容易被具有广泛的专业知识和需求的最终用户易于访问，准确，组织良好，并且空间人工智能方法将促进水质预测和预测。这种创新的化学敏感性和数据分析平台将为广泛的应用提供敏感，选择性，实时，可靠和具有成本效益的水质监测。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响标准来评估通过评估来获得的支持。