Kai Zhang

Kai Zhang

Empire Innovation Associate Professor
Environmental Health Sciences

Ph.D. Environmental Health Sciences, University of Michigan

M.A. Statistics, University of Michigan

M.S. Environmental Engineering, Tsinghua University

B.S. Environmental Engineering, Southeast University


Dr. Zhang’s research explores emerging topics in urban environmental pollution at the interface among climate change, extreme weather events, air pollution and health.  His perspective is to apply multidisciplinary approaches to address complex environmental health problems.  His expertise includes exposome, air quality, environmental and occupational epidemiology, risk assessment, GIS, environmental statistics and data science. His previous work includes developing the first exposure model to estimate long-term exposures to PM10-2.5 species in the U.S.; developing a comprehensive framework to characterize exposures and risks due to traffic congestion; developing novel methods to characterize climate-related exposures and quantifying the health effects attributable to extreme weather (e.g., heat, cold, flooding, and hurricane).

Currently, Dr. Zhang research interests focus on: 1) exploring the human health effects associated with more frequent extreme weather events that are predicted to occur with a warming climate; 2) characterizing the sources and human health impacts of air pollution with a focus on the role of air pollution on chronic diseases development; 3) investigating the role of built and social environment in the development of chronic diseases; 4) investigating urban compact development (smart growth), transportation, environmental pollution, and sustainability; and 5) applying GIS and data science approaches in epidemiological and intervention studies.


Research interests

•    Climate change, extreme weather, disasters, and health
•    Urban exposome
•    State-of-the-art exposure models
•    Risk assessment and health impact assessment
•    Transportation, air quality and health
•    GIS, spatial analysis and data science in public health