Our group applies data driven methods to understand air pollutants, greenhouse gases, and their interactions with the climate and society. Projects in our group typically involve some combination of atmospheric chemistry modeling, satellite and in-situ observations, Bayesian statistics, data assimilation, and machine learning.
Our research aims to answer challenging environmental questions such as:
How to effectively trace greenhouse gas emissions and carbon neutral progress?
How do greenhouse gases and climate interact with each other?
What are the sources, distributions, and evolutions of major air pollutants (O3, PM2.5, NOx, SO2, CO, etc.)?