A Data Collection System for Assessing the Impact of Air Quality on Human Wellbeing

Air pollution significantly impacts physical and mental health, particularly in underserved urban communities like Dearborn and Southwest Detroit, where industrial activities and heavy traffic exacerbate pollution levels. Despite existing studies linking environmental factors to human wellbeing, they often rely on coarse-grained data and laborintensive survey methods, limiting their precision and scalability. To address these challenges, we propose a novel data collection system that integrates mobile sensors for fine-grained air quality monitoring and online surveys for capturing subjective experiences, leveraging SMS notifications for timely and efficient participant engagement. In brief, our system employs an explore-and-exploit algorithm that optimally determines SMS notification timing to maximize data collection utility within a constrained budget. Our real-dataset based evaluation demonstrates the system's effectiveness in enabling fine-grained data collection and its potential to support scalable, data-driven analyses of how air pollution impacts human well-being. The system is also expected to improve awareness of the impact of air quality on human wellbeing with its feature of participant convenience.