Collaborative Research: Ideas Lab: Light in the Dark: Fiber Optic Sensing of Climate-Critical Carbon Cycle Components at Water/Ice-Air Interfaces

湖泊在调节对地球气候至关重要的温室气体方面发挥着重要作用，但湖泊正面临着越来越多的人为压力和干扰，而气候变化又加剧了这种压力和干扰。监测湖泊，特别是那些在冬季被冰覆盖的湖泊，对于了解湖泊的变化至关重要。然而，由于常年在湖中安装和维护仪器的成本很高，因此很难进行此类测量。这个创意实验室：推进水下科学的工程技术 (ETAUS) 项目将通过开发一种可以全年监测多个水质参数的传感器来推动水质监测领域的发展，并填补这一知识空白。传感器开发的目标是同时测量参数，这些参数是测量湖泊对气候变化（二氧化碳、甲烷）、湖泊生态系统健康（温度、pH、盐度、溶解氧）以及人类影响（盐度、温度）影响的重要组成部分。易于部署、经济高效的传感器将有助于更好地了解所有湖泊系统的碳足迹，从而更好地为湖泊管理决策提供信息。该项目支持的教育计划还将通过与波士顿科学博物馆 (MOS) 的合作，促进儿童和成人对水质问题、科学和解决方案的学习和发现。 MOS 专注于与波士顿社区的女性和女孩在工程领域合作，并在领域内领先地强调通用设计。该项目的总体目标是通过开发一种能够在冰下长期部署的新型微型光纤多参数传感器（FOMS），增加我们对湖泊内温室气体循环的定量了解。对级联高 Q 环谐振器中波材料/结构相互作用的基本了解将有助于开发微型光子传感器，用于同时监测具有高分析物特异性和快速响应的多个参数。 FOMS 将被开发为同时测量七个参数，包括二氧化碳和甲烷，并部署在固定系泊和移动水下机器人平台上，以收集高时间和空间分辨率的数据。 FOMS 的开发和校准将以一种新颖的基于机器学习的传感器校准模型为指导，该模型将有助于将 FOMS 转变为智能传感系统，从而实现高保真“指纹”传感，从而解决硬件变化、监控环境中的噪声、传感器响应中的非线性和不确定性以及多个传感器输入之间的串扰问题。将使用 FOMS 跨固定和移动平台全年收集数据，这将产生四维数据。数据同化方法将与生成建模框架的目标进行比较，该框架可以为未来部署中的测量优化提供信息。总的来说，FOMS 的开发、测试和使用将产生一个测量工具和框架，用于定量了解冰覆盖湖泊中的温室气体产生、消耗和运输。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。

Lakes play an important role in regulating the greenhouse gases that are important to Earth’s climate, but lakes are under an increasing amount of human-induced stress and disturbance, exacerbated by a changing climate. Monitoring of lakes, especially those that are ice covered in winter months, is critical to understand how lakes are changing. However, it is difficult to make such measurements because of the high cost to install and maintain instruments in the lake year-round. This Ideas Lab: Engineering Technologies to Advance Underwater Sciences (ETAUS) project will advance the field of water quality monitoring by developing a sensor that can monitor multiple water quality parameters throughout the year and fill this knowledge gap. The goal for the sensor development is to simultaneously measure parameters that are significant components of measuring a lake’s influence on climate change (carbon dioxide, methane), the health of the lake ecosystem (temperature, pH, salinity, dissolved oxygen), and the impacts of human influence (salinity, temperature). An easy-to-deploy, cost-effective sensor will provide an improved understanding of the carbon footprint of all lake systems that will better inform lake management decisions. The education programs supported by this project will also promote learning and discovery of water quality issues, science, and solutions for children and adults through the partnership with the Museum of Science (MOS) in Boston. The MOS has a focus on working with women and girls from the Boston community in engineering and a field-leading emphasis on universal design. The overall aim of this project is to increase our quantitative understanding of greenhouse gas cycling within lakes through the development of a novel, miniature, fiber-optic multiparameter sensor (FOMS) capable of long-term, under-ice deployment. A fundamental understanding of the wave-material/structure interaction in cascaded high-Q ring resonators will be achieved to develop miniature photonic sensors for simultaneous monitoring of multiple parameters with high analyte specificity and fast response. The FOMS will be developed to measure seven parameters simultaneously, including CO2 and CH4, and deployed on a stationary mooring and mobile underwater robotic platforms for high temporal and spatial resolution data collection. The development and calibration of the FOMS will be guided by a novel machine learning-based sensor calibration model that will help transform the FOMS into an intelligent sensing system, leading to high-fidelity “fingerprint” sensing that can address hardware variations, noise in the monitoring environment, nonlinearities and uncertainties in the sensor response, and cross-talk between the multiple sensor inputs. Data will be collected year-round using the FOMS across stationary and mobile platforms, which will produce four-dimensional data. Data assimilation methods will be compared with the goal of producing a modeling framework that can inform measurement optimization in future deployments. Collectively, the development, testing, and use of the FOMS will produce a measurement tool and framework for a quantitative understanding of GHG production, consumption, and transport in ice-covered lakes.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.