Urban Informatics Programme

poster APDSS

Air-Pollution Decision Support System



Air pollution has become a life-threatening hazard that exerts a serious impact on the sustainable development of our cities. It has been exacerbating the already deteriorating living environment in most cities, particularly those in developing countries. Closer to home is the grave plight of air pollution in Chinese cities, including Hong Kong, the solution of which problem has become an imminent and significant air-quality policy challenge for national, provincial and municipal governance. An efficient and effective solution to the problem relies on our understanding of the air pollution process and the availability of historical, near real-time and real-time information about the spatial and temporal variability of air pollution.



The purpose of this proposed research is to develop an integrated approach to air quality monitoring and analysis in the urban environment. The study intends to capitalize on the power of the satellite-borne remote sensing technology, ground-based stations, mobile sensors, social media and chemical-transport models to construct a solid scientific foundation and methodology to generate, on a unified software platform, accurate and comprehensive spatio-temporal air pollution profiles crucial to the mitigation of air pollution and the corresponding urban design for high-density living.


Data Integration and Fusion: The development of such an approach requires basic research on several fronts. With respect to remote sensing, we aim at the fusion of remotely sensed data coming from different satellite sensors so that air pollution data of the highest spatial and temporal resolution can be composed through novel information fusion methods.


Platform Development: At the software development front, we plan to utilize state-of-the-art software engineering methodology to construct a decision support system for the collection, integration and management of multi-scale and multi-source spatial and temporal air pollution data.


Data Analytics: With reference to data analytic, our proposed research will also formulate novel data fusion and analysis methodology along the line of spatio-temporal data mining to unravel the intricate distributions of air pollution in the complex urban environment, to study the correlation/ causality of health conditions/diseases etc., to obtain trending public opinions on and sentiment about air quality from multimedia data in social networks, and to predict human behavior and lessen human exposure in space and time under air pollution episodes.



Investigators: Prof. LEUNG Kwong-Sak, Prof. LEUNG Yee, Prof. Man-Hon WONG