Prof. QIN Yiming (覃意茗)

PhD in Environmental Chemistry, Harvard University

Visiting address
YEUNG-B5428
Phone: +852 34424173

Author IDs

Willing to take PhD students: yes

Biography

Prof. Qin obtained her Ph.D. in Environmental Chemistry from the School of Engineering and Applied Sciences at Harvard University, MPhil in Chemical and Biomolecular Engineering at The Hong Kong University of Science and Technology, and BSc in Environmental Science at Shandong University. Before joining the City University of Hong Kong, she worked as Postdoc Fellow in the Department of Chemistry at the University of California Irvine. Prof. Qin’s research focuses on the source and transformation of atmospheric aerosol particles and their impacts on atmospheric chemistry, air pollution, and climate. She uses interdisciplinary approaches to unravel the complexities of aerosol gas-particle interaction, including analytical method development, laboratory experiment, field campaign, and machine learning. She was selected as Carnegie Mellon University Civil and Environmental Engineering Rising Star (launched at MIT) in 2022, and Seventeenth Atmospheric Chemistry Colloquium for Emerging Senior Scientists (ACCESS XVII) in 2023.

Research Interests/Areas

Atmospheric aerosol particles pose a major challenge for both regional and global environments, as they contribute to air pollution and climate change. These particles have far-reaching consequences on air quality, human health, and the Earth's overall climate, making them a critical environmental issue. My lab seeks to elucidate the molecular-level transformation mechanisms of both the traditional and emerging atmospheric aerosol particles and their impacts on many critical chemical and environmental systems with novel and interdisciplinary approaches. We use a variety of advanced analytical techniques, such as Ambient Ionization Mass Spectrometry, Chemical Ionization Mass Spectrometry, Aerosol Mass Spectrometry, and Raman Spectroscopy. My lab is also interested in employing cutting-edge statistical applications such as Machine Learning to understand the source and transformation of atmospheric particles. Current topics of interest include:

  • Mass spectrometry development
  • Aerosol gas-particle interaction and interfacial chemistry
  • Machine learning application in air pollution control
  • Atmospheric micro-and nano-plastics