Indoor Air Quality and Aerosol Technology

Department

Department of Energy “G. Ferraris”, Politecnico di Torino

Main research activities/topics/projects

The proposed research focuses on applying low-cost particulate matter (PM) sensors within air handling units (AHUs) to optimize indoor air quality control. Building on previous studies that demonstrated the potential of low-cost sensors (LCSs) for detecting PM2.5 and PM10 in HVAC systems, this research aims to enhance indoor air quality management by integrating these sensors into AHUs for real-time monitoring and adaptive control.

The research will involve selecting, calibrating, and deploying various LCSs within AHUs to monitor PM concentration continuously. Emphasis will be placed on the sensors' accuracy, particularly for PM2.5, under various environmental conditions, including airflow velocities, relative humidity levels, and aerosol compositions. The study will explore the sensors' potential for predictive maintenance by monitoring filter performance and detecting deviations in PM levels that indicate the need for maintenance or filter replacement.

Key experimental activities will include:

• Sensor Calibration and Validation: Calibration of LCSs against a reference-grade optical spectrometer to ensure accuracy and reliability for PM2.5 and PM10 measurements.
• In-Duct Testing: Deployment of LCSs within AHUs to assess their performance in real-world conditions, focusing on challenges such as varying air velocities and humidity levels.
• Data Analysis and Optimization: Statistical analysis of sensor data to identify patterns and correlations that inform adaptive control strategies for maintaining optimal indoor air quality.
• Predictive Maintenance Evaluation: Evaluation of the sensors' ability to predict filter degradation and optimize maintenance schedules, thereby improving system efficiency and air quality.

This research will contribute to developing cost-effective, scalable solutions for maintaining indoor air quality, leveraging the affordability and growing reliability of low-cost PM sensors.

Working language

 English

Special entry requirements

Technical Knowledge:

• Air Quality and HVAC Systems: Fundamental understanding of indoor air quality parameters, HVAC systems, and the role of air handling units in controlling particulate matter.
• Sensor Technology: Familiarity with low-cost sensor technology, particularly for particulate matter measurement (PM2.5 and PM10).

Technical Skills:

• Sensor Calibration and Data Analysis: Experience in calibrating environmental sensors and analyzing large datasets using software such as Excel, MATLAB, or Python.
• HVAC Integration: Practical experience in working with HVAC systems, including installing and integrating sensors within air ducts.

Problem-Solving Abilities:

• Troubleshooting and Adaptability: Ability to troubleshoot issues related to sensor performance, particularly under varying airflow conditions, and adapt experimental setups as needed.

Duration in months (min-max)

PhD sandwich: 6-12

Post Doc: 6-12