A reproducible R workflow for harmonizing indoor air quality sensor data and cleaning activity logs in school-based field campaigns

This article presents a reproducible workflow developed in R to process indoor air quality data by integrating wearable multi sensor measurements with tablet-based activity logs. The data come from two field campaigns in four primary schools and involve 20 female maintenance staff members. The method supports occupational exposure assessment
under real world conditions, where sensor signals can be incomplete or noisy and self-reported activities may contain timing errors. The workflow organizes raw files by campaign, school and instrument, standardized timestamps (including daylight saving changes), and harmonizes quantitative signals (temperature, relative humidity, CO2, PM2.5 and VOC sensors) with qualitative descriptors from activity logs (location, activity, cleaning practice, product, time). It then builds an integrated dataset in which each sensor reading is linked to its activity context, making it possible to distinguish cleaning periods from daytime and night time storage of instruments. The approach produces graphical outputs and summary statistics that support sensor diagnostics, comparison of exposure profiles between micro
environments, and communication of semi quantitative exposure patterns to researchers and stakeholders.