Curriculum Vitae

Update: 2022/12/15


Dr. Congbo Song holds the position of Senior Research Scientist (2022.12-) in Data Science and Analytics in Atmospheric Air Pollution, at The National Centre for Atmospheric Science (NCAS) based in the Department of Earth and Environmental Science, the University of Manchester. He is working with Prof. Hugh Coe and David Topping. He has broad research interests in source emissions, source apportionment and air pollution. In particular, he has research interests and extensive expertise on studying air pollutant impacted by emissions from on-road vehicles, coal combustion and biomass burning through advanced data analysis of emission measurements and field measurements. He coordinated a number of large field campaigns in the UK and China, including a complex research cruise (RSS Discovery DY151) from Iceland to the Arctic. He has been extensively involved in several UK clean air projects, including SEANA – Shipping Emissions in the Arctic and North Atlantic atmosphere, Air Quality Supersite Triplets (UK-AQST), COP-AQ: UK-China collaboration to optimise net-zero policy options for air quality and health, West Midlands Air Quality Improvement Programme (WM-AIR), Integrated Research Observation System for Clean Air (OSCA).

Dr. Song was a Research Fellow (2019.08-2022.08) in Atmospheric Science, School of Geography, Earth and Environment Sciences, University of Birmingham. He worked with Prof. Zongbo Shi and Roy Harrison on a project of SEANA – Shipping Emissions in the Arctic and North Atlantic Atmosphere (SEANA), with particular interest in anthropogenic and natural sources of air pollutants. Prior the research fellow position, he studied at the College of Civil and Environmental Engineering in University of Science & Technology in Beijing between 2008 and 2012 (Bachelor’s degree), and the College of Environmental Science and Engineering in Nankai University between 2012 and 2019 (Master’s and PhD degrees). During his PhD study, he was supervised by Prof. Hongjun Mao to study on-road fleet average emissions and their impacts on multi-scale atmosphere environment.

Dr. Song has authored and coauthored over thirty peer-reviewed papers since 2017 (list), with total citations over 2300 and H-index of 20 (Google Scholar). His publications are mainly in the fields of Chain of Air Pollution Accountability, including Emissions, Air Quality, Public health and Air Quality Interventions. A key aspect of his research is to understand the impacts of source-specific air pollution on human health and climate through advanced receptor modeling. He is also interested in understanding environmental policies, environmental drivers and atmospheric processes controlling the air we breath using data-driven models such as machine learning and casual inference techniques.

Research interests

Field Measurement. Research Cruise, tunnel tests, near-road measurements and ambient measurements using a wide range of online and offline instruments.

Source Emission and Source Apportionment. Detailed characterization of particulate and gas emissions from anthropogenic and natural sources. Interpreting aerosol and gaseous chemical data using multivariate statistical methods (e.g., receptor models) and machine learning techniques; Revealing environmental drivers for source-specific air pollution. His recent focus is to develop real-time source apportionment techniques using single-particle mass spectrometry.

Machine Learning and Causal Inference. Using machine learning (deweathering) and causal inference techniques to decouple emissions and meteorology to evaluate causality of air quality management.

Methodological knowledge

Instrumentation: Single Particle Aerosol Mass Spectrometer (SPAMS), Aerosol Chemical Speciation Monitor (ACSM), Particle size spectrometers (APS, SMPS, PSM and NAIS), On-line metal elements (Xact), AiRRmonia, Single Particle Soot Photometer – Extended Range (SP2-XR), Aethalometer, Multi Angle Absorption Photometer (MAAP), Cavity Attenuated Phase Shift extinction (CAPS) and Cloud condensation nuclei (CCN).

Advanced Data Analysis. Multivariate data fusion- Integrating traffic data, meteorological data and pollutant data. Statistical analysis and data mining. Clustering techniques, receptor models (CMB, PMF, ME-2), machine learning techniques, causal inference techniques. Particularly, he is skilled at understanding sources and processes of air pollution (e.g., size-resolved aerosol) using a coupling receptor model and explainable machine learning technique.




Journal review

Atmospheric Chemistry and Physics, Atmospheric Environment, Atmosphere, Aerosol and Air Quality Research, Science of the Total Environment, Environmental Science: Processes & Impacts, Journal of the Air & Waste Management Association.