Imperial College London

Dr. Gang Chen

Faculty of MedicineSchool of Public Health

Research Associate



+44 (0)20 7594 8731gang.chen Website CV




UREN 1003Building E - Sir Michael UrenWhite City Campus





Dr. Gang Chen is an expert in source apportionment of high-time resolution measurements of aerosols (e.g., aerosol mass spectrometer (AMS), aerosol chemical speciation monitor (ACSM), Xact). His research focuses on developing state-of-the-art source apportionment techniques to retrieve high-quality source information and improve the understanding of health outcomes of specific sources of aerosols by coupling epidemiology data or oxidative potential measurements.

  • PhD. in Environmental Systems Science (2022), Eidgenössische Technische Hochschule Zürich (ETH Zürich), Zürich, Switzerland (Thesis)
  • MASc. in Chemical Engineering & Applied Chemistry (2018), University of Toronto (U of T), Toronto, ON, Canada (Thesis )
  • BASc. in Chemical Engineering (2016), Western University (UWO), London, ON, Canada

Research Profiles

  • Aerosols II, Teaching Assistant, ETH Zurich 2021 
  • Chemical Engineering and Applied Chemistry–Laboratory III, Teaching Assistant, U of T 2018
  • Chemical Engineering and Applied Chemistry–Laboratory IV, Teaching Assistant, U of T 2017

Selected Publications

Journal Articles

Chen G, Canonaco F, Slowik JG, et al., 2022, Real-time source apportionment of organic aerosols in three European cities., Environmental Science & Technology, Vol:56, ISSN:0013-936X, Pages:15290-15297

Via M, Chen G, Canonaco F, et al., 2022, Rolling vs. seasonal PMF: real-world multi-site and synthetic dataset comparison, Atmospheric Measurement Techniques, Vol:15, ISSN:1867-1381, Pages:5479-5495

Chen G, Canonaco F, Tobler A, et al., 2022, European aerosol phenomenology - 8: Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets, Environment International, Vol:166, ISSN:0160-4120

Chen G, Sosedova Y, Canonaco F, et al., 2021, Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window, Atmospheric Chemistry and Physics, Vol:21, ISSN:1680-7316, Pages:15081-15101

Heikkinen L, Aijala M, Daellenbach KR, et al., 2021, Eight years of sub-micrometre organic aerosol composition data from the boreal forest characterized using a machine-learning approach, Atmospheric Chemistry and Physics, Vol:21, ISSN:1680-7316, Pages:10081-10109

Canonaco F, Tobler A, Chen G, et al., 2021, A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data, Atmospheric Measurement Techniques, Vol:14, ISSN:1867-1381, Pages:923-943

Chen G, Wang Q, Fan Y, et al., 2020, Improved method for the optical analysis of particulate black carbon (BC) using smartphones, Atmospheric Environment, Vol:224, ISSN:1352-2310, Pages:1-9

More Publications