Theoretical ecology can be used to explain microbial community organization and evolution, such as in the stratified layers of stromatolites, the oldest representation of microbial communities. In my current work, I am exploring how microbial models can be linked to fluid dynamics to predict the mortality and transport of fecal indicator bacteria in the Hudson River.
Mathematical models and remote sensing both offer cost effective methods for analyzing water quality. As a Future Investigator in Earth & Space Science & Technology (FINESST) grant recipient from NASA, I am studying optical properties throughout the Hudson River Estuary to support my future use of reflectance data captured by Landsat and Sentinel-2A/B satellites to predict fecal bacteria persistence patterns. In the Hudson River, fecal indicator bacteria concentrations are highly correlated to particle concentrations and a notable fraction of these bacteria are attached to particles. Research from my first chapter of my dissertation shows how being attached to particles increases the survival of fecal bacteria significantly. Because persistence is primarily dominated by light exposure, I am combining long-term data on water quality and optical parameters (like turbidity, secchi depth, light extinction), satellite data, and mathematical models to better predict water quality in various urban, coastal regions.
Inequality in human health and environmental exposure among racial and/or ethnic minorities and low socioeconomic status individuals is also a passion of mine in independent research. I have been studying and communicating the disproportionate effects of SARS-CoV-2/Covid-19 on these groups since the beginning of the Covid-19 pandemic.
focus: Environmental Microbiology & Oceanography
focus: Mathematical Biology