Please note that this RPG Seminar will be held virtually via Zoom at the normal time [25 February 2021 (Thursday), 13:00 p.m.]
Please be ready 5 minutes prior to the scheduled time.
The lockdown measures during the coronavirus disease 2019 (COVID-19) pandemic have led to a significant impact on air pollution around the world. Recent studies based on ground observations indicated considerable reductions in nitrogen dioxide (NO2) and fine particles (PM2.5), but slight increases in ozone (O3) in many cities. However, the limited coverage of ground-level observations makes it hard to capture the spatial and temporal dynamics of air pollution in different parts of the world at a fine-scale. In this context, this research proposes to examine the long-term impacts of lockdowns on air pollution (NO2, PM2.5, and O3), capture the spatial variations within and across different cities in selected countries and explore the factors that may contribute to these variations. By combining satellite remote sensing data with ground observations, a deep residual neural network (ResNet) will be developed to estimate air pollution at high spatiotemporal resolution and applied to identify changes in air pollution concentrations. This research will provide insights into how different air pollutants may respond to human restrictions in different ways and make suggestions for better coordinated multi-pollutant air pollution control strategies.
Keywords: air pollution; COVID-19; remote sensing; deep learning
About the Speaker
Ms. WANG SIYING is a second-year Ph.D. student in the Department of Urban Planning and Design at the University of Hong Kong. She obtained her Bachelor’s degree in Geoinformation Science and Technology from China University of Geosciences and a Master’s degree in Geographical Information Science from Wuhan University. Her research interests include air pollution, trajectory analytics, and land-use change.
~~ ALL INTERESTED ARE WELCOME ~~
Enquiries: 3917 2721
CENTRE OF URBAN STUDIES AND URBAN PLANNING
THE UNIVERSITY OF HONG KONG