We use physical modeling, IoT sensing technologies, lifecycle assessment, and various deep learning tools to decipher sustainability challenges in cities.
Full publication list can be found at at my Google Scholar and ORCID pages .
Urban Environmental Sensing
Transport Energy Transition
This work involves conducting tests using state-of-the-art sensing instrumentation, including portable emissions measurements (PEMS).
We characterize near-road air pollution using local-scale emission and dispersion models validated against in-situ measurements.
Study design and experiment design for emission testing at the vehicle, street, and city levels.
Researching the uncertainty in emission estimates for vehicle electrification and its impact on metropolitan GHG inventories.
Investigating marginal greenhouse gas emissions of electricity systems and the implications of EV charging patterns.
Investigating the combined effects of automated and electric transportation on metropolitan greenhouse gas emissions.
Evaluating the potential of machine learning models for predicting traffic-related air pollution levels.
Modeling urban brake wear particle emissions using ride-hailing data as a case study.
Constructing comprehensive multi-air pollutant emission inventories for urban transportation systems.