Advancing the Applications of Low-Cost Sensors for Indoor and Outdoor Air Quality Monitoring

Abstract

Low-cost sensors (LCS) enable high-spatio-temporal monitoring of fine particulate matter (PM2.5), but standardised protocols for calibration, deployment, and data correction remain lacking. This thesis addresses this question by (1) integrating an LCS into a smoke alarm, developing a dual-purpose device, (2) analysing indoor-outdoor PM2.5 dynamics in mechanically ventilated offices, and (3) developing a real-time relative humidity correction for LCS networks. Together, these studies advance knowledge of LCS integration and deployment platforms, improve site-specific analysis and interpretation methods for indoor-outdoor PM2.5 relationships, and develop scalable, practical methods for improving the accuracy and quality of LCS data.