Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Sensor Technology

Background:

• Ambient particulate matter (PM) exposure poses health risks.
• Traditional monitoring networks lack spatial-temporal resolution.
• Low-cost optical particle counters (OPCs) offer potential but are sensitive to high relative humidity (RH).

Purpose of the Study:

• To develop a correction algorithm for OPCs affected by RH.
• To improve the accuracy of low-cost PM sensors under ambient conditions.
• To retain particle composition information during RH correction.

Main Methods:

• Developed a correction algorithm based on particle size distribution (PSD) data.
• Utilized kappa-Köhler theory to model RH influence on sensor measurements.
• Applied physically reasonable kappa values for the correction.

Main Results:

• The PSD-based correction significantly improved sensor performance.
• Overestimation of PM measurements reduced from ~5x to 1.05x.
• The method successfully accounted for RH effects on OPCs.

Conclusions:

• A PSD-based correction is essential for accurate RH compensation in OPCs.
• This approach enables low-cost sensors to provide reliable ambient PM data.
• Retains fundamental particle composition information, unlike PM mass-based corrections.