SensEURCity: A multi-city air quality dataset collected for 2020/2021 using open low-cost sensor systems

Area of Science:

• Environmental Science
• Sensor Technology
• Data Science

Background:

• Low-cost air quality sensors offer potential for high-density deployment but often lack reliable data quality.
• Assessing the performance of these sensors requires comprehensive datasets with co-located reference measurements.

Purpose of the Study:

• To present a unique, quality-controlled dataset for evaluating low-cost air quality sensor systems.
• To provide raw sensor data alongside co-located reference data for diverse meteorological and ambient conditions.

Main Methods:

• Deployment of 85 AirSensEUR sensor systems across three European cities (Antwerp, Oslo, Zagreb) for one year.
• Collection of raw sensor data for pollutants (NO, NO2, O3, CO, PM2.5, PM10, PM1, CO2) and meteorological parameters.
• Inclusion of co-location campaigns at Air Quality Monitoring Stations (AQMS) and diverse urban locations, with detailed metadata.

Main Results:

• A comprehensive dataset comprising raw sensor data and reference measurements from a year-long deployment.
• Data covers various meteorological and ambient conditions experienced in Antwerp, Oslo, and Zagreb.
• Detailed metadata on sensor systems, reference instruments, locations, and deployment dates are provided.

Conclusions:

• The presented dataset is valuable for the validation and quality assessment of low-cost air quality sensor networks.
• This resource facilitates research into improving the reliability and accuracy of sensor-based air quality monitoring.
• Enables further development and calibration strategies for low-cost sensor systems in diverse environmental settings.