HKUST Annual Report 2018-19

29 HKUST TRACKING PERSONAL EXPOSURE TO AIR POLLUTION A mobile app to help users reduce their exposure to air pollution was launched in June 2019 as the first stage of the five-year PRAISE-HK project, led by Prof. Alexis LAU (Environment and Sustainability). PRAISE-HK stands for “Personalized Real-time Air-quality Informatics System for Exposure – Hong Kong”. The novel app combines state-of-the-art air quality and traffic modeling, real-time sensor and mobile technologies and big data analytics to provide highly accurate real-time and forecast air quality, and personal exposure health risk information down to street level. With resolution between two to 20 meters, users can better understand where and when they are exposed to the largest amount of air pollutants in their daily activities, and plan healthier routes to avoid pollution hotspots. The project is funded by the HSBC 150th Anniversary Charity Program. PUBLIC POLICY IN GREATER BAY AREA Two public policy projects, supported by the Hong Kong government’s Strategic Public Policy Research Funding Scheme, are seeking to informdevelopment of the Greater Bay Area. Prof. WU Xun (Public Policy, Social Science, Environment and Sustainability) is serving as principal investigator of both studies. One inter-university project is looking at the role of Hong Kong’s public research universities in driving forward a global innovation and technology hub in the Greater Bay Area by identifying global and regional good practices relevant to Hong Kong. The other HKUST study is charting the interplay between science and public policy on air pollution control in the Greater Bay Area. This is the first exploration of its kind in Hong Kong and Greater China. MEASURING REAL-TIME SHIP EMISSIONS A research team led by Prof. NING Zhi (Environment and Sustainability), and co-supervised by Profs. Jimmy FUNG and Alexis LAU, has been working with the Hong Kong government’s Environmental Protection Department to develop a proof-of-concept protocol to measure real-time fuel sulphur content from ship emissions. The project employs an unmanned aerial vehicle (UAV)-based system with a highly compact sensor package. The system is capable of measuring carbon dioxide (CO2), nitric oxide (NO), nitrogen dioxide (NO2), sulphur dioxide (SO2), carbon monoxide (CO) and particulate matter (PM) concentrations simultaneously and deriving the fuel sulphur content from the relationship between sulphur and carbon pollutants.