Design and testing of a wearable sensor system for real-time air quality monitoring

Thesis Abstract

Air pollution poses a significant threat to public health and environmental sustainability, necessitating innovative solutions for real-time monitoring of air quality to facilitate timely interventions. This study aims to design, develop, and empirically evaluate a wearable sensor system capable of delivering continuous, accurate, and user-friendly measurements of key air pollutants, including particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). The specific objectives include identifying suitable sensor technologies with high sensitivity and low power consumption, designing an integrated wearable prototype with wireless data transmission capabilities, establishing data validation protocols, and assessing the system's performance against standard reference instruments in real-world environments. The overarching goal is to contribute a scalable, cost-effective tool that enhances personal exposure monitoring and supports public health decisions. The research employs a mixed-methods approach grounded in the Engineering Design Science paradigm, integrating quantitative performance testing with qualitative assessments of usability. A sample of 150 volunteers from urban areas with diverse pollution levels was selected using stratified random sampling to ensure representativeness across socioeconomic and age groups. The study combines laboratory calibration tests with field evaluations over a three-month period, during which participants worn the prototype in various environmental conditions. Data collection instruments include the wearable sensor system, standard reference stations for pollutant measurements, structured usability questionnaires, and interview guides for user feedback. Sensor calibration and validation employ regression analysis and Bland-Altman plots to determine measurement accuracy and agreement, while usability data are analyzed through thematic analysis. Expected findings demonstrate that the sensor system reliably detects and quantifies multiple air pollutants with a high degree of correlation (r > 0.85) to standard reference instruments, with mean deviations within acceptable regulatory thresholds. The integrated wireless module effectively transmits data in real-time with minimal latency, and user feedback indicates high satisfaction with comfort, interface design, and interpretability of readings. The application of the Theory of Planned Behavior offers insights into user engagement and behavioral influences affecting device utilization, and the Diffusion of Innovations theory contextualizes the adoption potential of the technology among target populations. This study contributes to existing knowledge by presenting a novel, multi-pollutant wearable sensor platform that integrates advanced sensor materials, low-power computing, and wireless communication tailored for personal exposure assessment. It advances methodological frameworks for sensor validation in dynamic environments and offers practical insights into design considerations for user-centered device development. The findings illuminate the feasibility of deploying portable air quality monitoring systems at scale, providing actionable data for individual health management and informing pollution mitigation strategies. The study concludes that the wearable sensor system represents a significant advancement in portable environmental monitoring, offering accurate, affordable, and user-friendly solutions for immediate air quality feedback. Recommendations include further miniaturization for enhanced portability, integration with smartphone applications for broader accessibility, and the development of data analytics platforms for large-scale epidemiological research. Future investigations should explore longitudinal health impact assessments, integration with predictive modeling systems, and the deployment of the system in diverse socio-economic contexts to maximize public health benefits.