NSF Award
2322281
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. <br/><br/>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.<br/><br/>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.
