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

Area of Science:

• Environmental Health
• Occupational Hygiene
• Data Science

Background:

• Indoor air quality monitoring is crucial for occupational health.
• Real-world data collection often involves incomplete sensor data and timing errors in activity logs.
• Integrating diverse data sources is challenging but necessary for accurate exposure assessment.

Purpose of the Study:

• To present a reproducible R workflow for processing integrated indoor air quality and activity data.
• To enable accurate occupational exposure assessment under real-world conditions.
• To link quantitative sensor measurements with qualitative contextual information.

Main Methods:

• Developed a workflow in R to process data from wearable multi-sensor devices and tablet-based activity logs.
• Standardized timestamps and harmonized quantitative sensor data (CO2, PM2.5, VOCs, temperature, humidity) with qualitative activity descriptors.
• Created an integrated dataset linking sensor readings to specific activities and contexts.

Main Results:

• The workflow successfully organized and standardized data from field campaigns in primary schools.
• Generated analysis-ready outputs linking indoor air quality exposures to specific activities (e.g., cleaning).
• Enabled distinction between exposure during work activities and instrument storage periods.

Conclusions:

• The R workflow provides a robust method for processing complex indoor air quality data.
• Facilitates occupational exposure assessment by contextualizing sensor measurements with user activities.
• Supports sensor diagnostics, exposure profile comparisons, and communication of findings to stakeholders.