Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign

Area of Science:

• Environmental Science
• Data Science
• Human-Computer Interaction

Background:

• Urban air quality monitoring presents challenges in data integration and participant reporting.
• Existing methods often lack holistic insights into personal exposure to urban stressors.
• The ICARUS H2020 project aimed to address these gaps using a multi-sensor approach.

Purpose of the Study:

• To develop and evaluate a data fusion and harmonization process for multi-sensor air quality data.
• To provide comprehensive and understandable individualized reports to research participants.
• To identify and address challenges in sensor data integration and retrieval.

Main Methods:

• Collected multi-sensor data streams from over 600 participants across seven European cities.
• Developed automated data fusion and harmonization protocols to address issues like non-uniform timestamps and data gaps.
• Streamlined coding for efficient data processing and report generation.

Main Results:

• Successfully harmonized diverse sensor data streams, improving data quality and quantity for participants.
• Automated the generation of detailed, individualized air quality and exposure reports.
• Identified necessary compromises in data visualization to achieve processing speed.

Conclusions:

• A robust data fusion and harmonization process significantly enhances the value of multi-sensor data for citizen science and research.
• Automation accelerates report production, but manual checks remain crucial for data integrity.
• This approach offers a scalable solution for personalized environmental monitoring and citizen engagement.