IoT-Based Real-Time Mold Detection and Prevention in Residential and Commercial Buildings Using Environmental Sensor Networks

Mold growth in the built environment is a persistent and costly problem that damages building fabric, degrades indoor air quality, and threatens occupant health. Conventional mold management is reactive: visible colonies or a musty odour are typically discovered only after extensive, often hidden, damage has already occurred. In this work we present the design, deployment, and evaluation of a low-cost Internet of Things (IoT) system for continuous, real-time mold-risk monitoring in residential and commercial buildings. The system combines a distributed network of environmental sensor nodes measuring air temperature, relative humidity, carbon dioxide (CO2), and total volatile organic compounds (TVOC) with a cloud dashboard and an AI-based alerting layer that translates raw measurements into actionable mold-risk indices. The platform was developed and tested in real-world residential environments, capturing the diurnal and seasonal humidity cycles that drive condensation risk. We show that sustained relative humidity above 70%, narrowing surface-to-dew-point margins, and rising TVOC concentrations together provide an early, interpretable signal of elevated mold risk, often days before visible growth.