Developing a Cloud-Based Air Quality Monitoring Platform Using Low-Cost Sensors

Area of Science:

• Environmental Science
• Sensor Technology
• Data Science

Background:

• Traditional air quality monitoring uses expensive equipment, leading to sparse data and missing pollution hotspots.
• Existing methods lack the spatial and temporal resolution needed for comprehensive area-wide air quality assessment.

Purpose of the Study:

• To develop a cost-effective, cloud-based network of air quality measurement platforms.
• To achieve higher spatial and temporal resolution in air quality data collection.
• To measure key air quality parameters including particulate matter and various gases.

Main Methods:

• Designed and built a low-cost, cloud-based air quality measurement platform.
• Integrated sensors for particulate matter (PM10, PM2.5, PM1) and gases (NO, NO2, O3, CO), plus temperature and humidity.
• Deployed three prototypes for field testing at urban traffic and remote railway sites.
• Investigated the use of a Proportional-Integral-Derivative (PID) controller to mitigate meteorological effects on sensor data.

Main Results:

• The developed platform successfully measured multiple air quality parameters with high frequency.
• Field tests at busy traffic and remote railway locations demonstrated the system's robustness.
• Material costs for one Air Quality Sensor Node were approximately 1500 €.
• The system transmitted sensor data to a cloud server for processing and storage.

Conclusions:

• The developed low-cost platform offers a viable solution for high-resolution air quality monitoring.
• The system's robustness and cost-effectiveness make it suitable for widespread deployment.
• Further improvements in data quality can be achieved by addressing meteorological influences, potentially using PID control.