Indoor Air Quality (IAQ) is a key component of a healthy productive learning environment, particularly in primary schools where children are more physiologically sensitive to indoor pollutants. While continuous IAQ monitoring provides valuable information regarding ventilation performance and risk of exposure, high-volume deployments in public buildings are con- strained by pragmatic costs of installation, power consumption, and redundancy of data. This dissertation deals with optimizing IAQ monitoring networks by reducing the number of operational sensors and the sampling frequency to ensure sufficient spatial and temporal representativeness. The study is founded on a 6–8 month mea- sured data set from several classrooms in a Finnish primary school for CO, PM., PM, temperature, and relative humidity.