Dr. Kezheng Liao’s research covers the data analysis of ambient observations and numerical simulations for air pollutants. He likes to explore the potentials of statistical and machine learning models in distilling new information from ambient measurements, while he also enjoys delving into the physiochemical principles to estimate the environmental impacts of certain pollutants. His on-going project about atmospheric brown carbon is one example. One on hand, a new algorithm is developed to quantify its ambient concentration and light-absorbing capability from online measurements; on the other hand, a numerical model is built to simulate its photosensitization reactions in the air.

Research Themes under ENVR

• Air and Health

Research Areas

• Atmospheric chemistry and air pollution
• Analytical chemistry and chemometrics

Research Interests

• Source apportionment of air pollutants
• Organic aerosols and its chemical characteristics
• Atmospheric brown carbon and its optical properties
• Chemical transport models and kinetic models
• Applications of statistical models and machine learning techniques

Research Projects

• Atmospheric brown carbon: a novel quantifying algorithm with aethalometer data, updated mechanisms for numeric simulation, and assessment of its regional impacts

Publications

Representative Publications

• Liao, K., Cheng, Y. Y., Yea, S. S., Chen, L. W. A., Seinfeld, J. H., & Yu, J. Z.* (2024). New analytical paradigm to determine concentration of brown carbon and its sample-by-sample mass absorption efficiency. Environmental Science & Technology, 58 (39), 17386-17395. https://doi.org/10.1021/acs.est.4c06831
• Wang, S.#, Liao, K.#, Zhang, Z., Cheng, Y. Y., Wang, Q., Chen, H., & Yu, J. Z.* (2024). Bayesian inference-based estimation of hourly primary and secondary organic carbon in suburban Hong Kong: multi-temporal-scale variations and evolution characteristics during PM2.5 episodes. Atmospheric Chemistry and Physics, 24(10), 5803–5821. https://doi.org/10.5194/acp-24-5803-2024
• Liao, K., Zhang, J., Chen, Y., Lu, X., Fung, J. C. H., Ying, Q.*, & Yu, J. Z.* (2023). Regional source apportionment of trace metals in fine particulate matter using an observation-constrained hybrid model. npj Climate and Atmospheric Science, 6, 65. https://doi.org/10.1038/s41612-023-00393-4
• Liao, K., Wang, Q., Wang, S., & Yu, J. Z.* (2023). Bayesian inference approach to quantify primary and secondary organic carbon in fine particulate matter using major species measurements. Environmental Science & Technology, 57(13), 5169-5179. https://doi.org/10.1021/acs.est.2c09412
• Liao, K., Park, E. S., Zhang, J., Cheng, L., Ji, D., Ying, Q.*, & Yu, J. Z.* (2021). A multiple linear regression model with multiplicative log-normal error term for atmospheric concentration data. Science of The Total Environment, 767, 144282. https://doi.org/10.1016/j.scitotenv.2020.144282

Full Publication List