Interdisciplinary Programs Office Division of Environment and Sustainability 223 Air Quality Measurement Techniques Supervisor: GU Dasa / ENVR Student: CHAN Ching Ying / BIBU Course: UROP1000, Summer The team has measured the level of ambient volatile organic compounds (VOCs) in the HKUST supersite. There are also a lot of collaboration work with the other teams of ENVR and the EPD, focusing on wind, humidity and so on. The team collects samples in the supersite 3 times a day for doing analysis. Air samples will be collected in a canister which is convenient for us to further speculate the compounds in the ambient air. Air pollution is a serious problem in Hong Kong. It brings physical threats to humans, especially respiratory diseases. Various kinds of air pollutants have been created and dispersed in the air. There are several groups of VOCs commonly found in the air samples. They are alkanes, cycloalkanes, alkenes, aromatics, monoterpenes, alkyne, halogenated, alkyl nitrates and sulphur compounds. In this report, we will focus on alkanes, alkenes and alkynes. Machine Learning for Environmental Applications Supervisor: LAU Alexis Kai Hon / ENVR Co-supervisor: FUNG Jimmy Chi Hung Student: LEI Qianqian / MATH-SFM Course: UROP1100, Fall In order to reduce the losses caused by climate impacts, people need to be equipped with basic knowledges of possible climate risk. A well designed database, fromwhich people can access all the risks and be prepared for the climate risk, will be helpful. In this progress report, it first introduce the whole idea of the project, why and what we are going to do. In addition, is our basic framework of the project. Through this semester, what I have done is literature review. I will further give a thorough introduction of the literature review, which will be foundation for our future process. Machine Learning for Environmental Applications Supervisor: LAU Alexis Kai Hon / ENVR Co-supervisor: FUNG Jimmy Chi Hung Student: WONG Chi Ho / DSCT Course: UROP1100, Spring IoT sensors are prominent nowadays for detecting air quality, which let the public reference it or helps the commercial buildings to operate and do maintenance efficiently. However, anomalies may happen in those sensors and cause confusion before human intervention. We may meet the dilemma that whether should professionals trust the extraordinary data and respond with some follow-up actions on the system. Therefore, it is crucial to accurately detect anomalies with the help of a deep learning model MAD-GAN, which can model the temporal and spatial contextual information for better detection. We achieved around 80-90% accuracy in determining anomalies on the HKUST IoT Sensor dataset.
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