This project seeks to develop a publicly accessible smart platform to forecast air pollution exposure at an intraurban scale. This will be achieved by using a land-use regression integrating information on land-use type, conventional air quality monitoring data, real-time traffic flow, meteorological data, high-resolution mobile air pollution mapping using integrated air quality monitoring equipment, and other big data sources.
The project will demonstrate the technical feasibility to support a franchise-based rental model for a high-volume, low-cost, holistic air quality monitoring network. This innovative method of supply of air quality monitoring equipment allows data gaps to be filled in monitoring networks with significantly less capital cost and moreover addresses the lack of spatial resolution associated with traditional monitoring stations. This approach will foster sustainable city development.
Air pollution is a global threat with an estimated 7 million people dying worldwide from exposure to both indoor and outdoor air pollution every year, making it the world’s largest environmental health risk. This problem is particularly evident in China, as its burgeoning urban environments face substantial pressures to both grow economically as well as to simultaneously improve resident’s health, quality of life, and productivity.
This project seeks to develop a publicly accessible smart platform to forecast air pollution exposure at an intraurban scale. This will be achieved by using "big data" on land-use type, conventional air quality monitoring data, real-time traffic flow, meteorological data, high-resolution mobile air pollution mapping using integrated air quality monitoring equipment, and other big data sources. The project will demonstrate the technical feasibility to support a franchise based rental model for a high-volume, low-cost, holistic air quality monitoring network. This approach will foster sustainable development
