Academy of Interdisciplinary Studies Division of Environment and Sustainability 206 Apps and Tools to Encourage Responsible, Sustainable Consumption Supervisor: SAUERWEIN, Meike / ENVR Student: YEUNG, Man Lai / CHEM Course: UROP1100, Fall UROP2100, Spring UROP3200, Summer In the backdrop of escalating consumerism in various Asian societies, the imperative to manage resources, combat waste, and advocate sustainable consumption (SC) on a global scale becomes increasingly pronounced. Upfront awareness and comprehension of SC pathways among individuals is pivotal, primarily through education and promotion. While educational institutions embrace these endeavors, engaging the wider public poses challenges due to accessibility. Volume of research foresee the potential of games as effective tools for enhancing learning outcomes about sustainability. Nevertheless, a gap exists in investigating whether casual gameplay, occurring outside formal educational settings, can similarly raise awareness of SC concepts. Hence, this study is trying to investigate the impact of a serious commercially available SC-focused game, played both within a classroom context and casually with peers, on promoting SC awareness and knowledge. In these terms the focus of the research was on literature review to frame our results and draw conclusions from our study. Personalized Data Analytics and Visualization with Bottle Tracking IoT Devices on Behavioral Change Supervisor: SAUERWEIN, Meike / ENVR Student: ALI, Shahman / SENG Course: UROP1000, Summer The study was designed as a behavioral intervention research which aimed to investigate the factors that influence the water consumption behaviour of the HKUST student population, and the relative frequency of using water fountains available around them. The intervention made use of three surveys, a mobile application (My Water App integrated with USThing, the HKUST student application) and an Internet of Things (IOT) device that measures water withdrawal to collect strategic data. Although the data analysis is still in progress, preliminary findings suggest interesting patterns that highlight the importance of methodological choices for data collection through surveys (self-reported) and automated behaviour tracking. This study further allows deeper understanding of methods to encourage sustainable consumption, especially in tackling the intention-behaviour gap. Air Quality Study using Big Data and Artificial Intelligence Approach Supervisor: WANG, Zhe / ENVR Student: NG, Josiah Jian Wei / COMP Course: UROP1100, Spring Air pollution is responsible for 4.2 million deaths per year according to the World Health Organization (WHO). Hence, we should dedicate resources to understand and monitor air quality in our cities and neighbourhoods. With the increase of air monitoring data from nationwide and globally, more data mining and analysis is needed to better understand the air quality trends and causes behind this. We want to use the big data analytics and artificial intelligence approach to further analyse the air quality data in Hong Kong. This paper would include different machine learning approaches to analyse and predict air quality data.
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