Liuhua Shi, ScD is Assistant Professor of Environmental Health, Rollins School of Public Health, Emory University. Dr. Shi's research focuses on employing massive datasets, including satellite-retrieved high resolution exposures and health data, to investigate how climate change and air pollution influence seniors' health. More specifically, her research focuses on: 1. application of remote sensing in environmental exposure modeling (e.g., predicting high-resolution PM2.5, ozone, NO2, and temperature); 2. estimating the health consequences of exposure to air pollution and climate change; 3. estimating the link between climate change and air quality, and the mediated health impacts; 4. estimating the joint and independent health effects of air pollutant mixtures; statistical modeling, e.g., causal modeling and big data approach.
Integrated Health Sciences Core of M-LEEaD (Michigan Center on Lifestage Environmental Exposures and Disease)Long-term Air Pollution and Incident Dementia in U.S.
Environmental Research Webinar, presented by Liuhua Shi, ScD
February 8, 2022
12:00 pm - 12:50 pm
Online in Zoom
Sponsored by: Integrated Health Sciences Core of M-LEEaD (Michigan Center on Lifestage Environmental Exposures and Disease)
Contact Information: Meredith McGehee (mcgehee@umich.edu | 647-0819)
Liuhua Shi, ScD is Assistant Professor of Environmental Health, Rollins School of Public Health, Emory University. Dr. Shi's research focuses on employing massive datasets, including satellite-retrieved high resolution exposures and health data, to investigate how climate change and air pollution influence seniors' health. More specifically, her research focuses on: 1. application of remote sensing in environmental exposure modeling (e.g., predicting high-resolution PM2.5, ozone, NO2, and temperature); 2. estimating the health consequences of exposure to air pollution and climate change; 3. estimating the link between climate change and air quality, and the mediated health impacts; 4. estimating the joint and independent health effects of air pollutant mixtures; statistical modeling, e.g., causal modeling and big data approach.
