C2H2 EAGER Collaborative Research: Explore a model-data framework to advance predictions of red tide and its impact on respiratory illnesses under changing temperature

About This Grant

Red tides, a type of harmful algal bloom, are becoming increasingly common in waters along the U.S. Gulf Coast due to warming ocean waters. Red tides are caused by the rapid growth of a type of marine microorganism that thrives in warm coastal ocean waters and contains dangerous neurotoxins. As a result, these tides pose a threat to public health. Sea spray, from breaking waves or storms during red tides, kicks aerosols into the air, and these are carried by the wind over beaches and coastal communities. During red tides, there are reports of increased incidences of human respiratory conditions, like asthma and pneumonia, among others. This research focuses on improving red tide forecasting and assessing and mitigating public health impacts. It does this through an innovative model-data integration framework designed to improve the prediction of red tides and their link to human health. Research involves an interdisciplinary team of experts in modeling, marine ecology, data science, and medicine. Southwest Florida, a hotspot for red tides with a large coastal population, serves as a testbed for the research. It was chosen because it has abundant, relevant, and accessible data for both red tide occurrences and population respiratory health. This research fills a present gap in our understanding of the link between red tides and respiratory illness burdens. Broader impacts include results that can lead to a red tide early-warning system. It also helps support adaptive strategies that enhance the resilience of coastal communities and helps guide public health preparedness. The research trains students and postdoctoral researchers to work on the boundary between geoscience and health. The research serves the national interest through advancing the national health and welfare by promoting the progress of science to address a growing health concern in coastal communities. This project explores a new model-data framework that advances predictions of red tides and their tie to human health conditions under changing coastal water conditions in the U.S. Gulf Coast. Earth system modeling that incorporates oceanographic, atmospheric aerosol, and other geoscience data will be combined with the occurrence of health issues to discern dynamic relationships between red tides and human health. The research involves analysis of spatial and temporal data of both issues and is designed to fill the gaps between Earth System modeling results and health records. This involves integration of simulations from the High-Resolution Model Intercomparison Project (HighResMIP) with machine learning to develop a framework for combined red tide prediction and health risk assessment. It harmonizes red tide occurrence records, environmental observations (e.g., ocean temperature, Loop Current dynamics, wind speed and direction, river discharge, nutrients), and public health data from hospitals and clinics. Models trained on these data will identify links between red tide dynamics and respiratory health burdens. Research results include generation of a linked database of red tide and respiratory illness, scenarios that identify land–ocean–atmosphere drivers of red tides, and creation of risk metrics for public health mitigation including red tide severity and a respiratory impact indices. The project provides an open-science framework that advances the NSF mission of promoting the progress of science and advancing the national health and welfare. 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.