Song, Dr Congbo

Senior Research Scientist (NCAS) in Data Science and Analytics in Atmospheric Air Pollution

University of Manchester

School of Earth, Atmospheric and Environmental Sciences

The University of Manchester

Simon Building

Oxford Road

Manchester

M13 9PL

 

https://orcid.org/0000-0001-7948-4834

@congbosong

https://www.linkedin.com/in/congbo-song/

https://research.manchester.ac.uk/en/persons/congbo-song  

Dr. Congbo Song holds the position of Senior Research Scientist 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 has broad research interests in source emissions, source apportionment, air pollution and machine learning. In particular, he has research interests and extensive expertise on studying air pollution impacted by emissions from on-road vehicles, coal combustion and biomass burning through advanced data analysis on 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 has authored and coauthored over thirty peer-reviewed papers since 2017, with total citations over 2300 and H-index of 20. His publications are mainly in the field 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 modelling. 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. His recent focus is to develope data-driven models to understand causes and changes in air pollution in the real-time towards net-zero emissions.