Improving the UNC Passive Aerosol Sampler Model Based on Comparison with Commonly Used Aerosol Sampling Methods

Area of Science:

• Environmental Science
• Occupational Health
• Aerosol Science

Background:

• Passive sampling offers a potential alternative to active sampling for dust monitoring in occupational environments.
• The University of North Carolina (UNC) passive aerosol sampler is a common device, but traditional analysis methods show underestimation for fine particles (PM2.5).

Purpose of the Study:

• To evaluate and potentially enhance the UNC passive aerosol sampler for stationary dust monitoring in a working environment.
• To address underestimations of fine particulate matter (PM2.5 and PM10) observed with existing UNC sampler analysis models.

Main Methods:

• Simultaneous sampling using UNC samplers, cyclones, PM10/PM2.5 impactors, and an aerodynamic particle sizer (APS) over 24 hours in an open-pit mine.
• Comparison of three UNC sampler quantification models: original (mesh factor), hybrid, and area factor.
• Assessment of the impact of increased microscopic imaging resolution on particle quantification.

Main Results:

• Employing the area factor model and higher resolution imaging eliminated underestimation for PM10 and PM2.5 particles.
• The area factor model demonstrated the lowest deviation compared to impactors and cyclones.
• Modified UNC sampler showed higher precision than respirable cyclones but lower than impactors, with improved agreement in particle size distribution compared to the APS.

Conclusions:

• The enhanced UNC sampler, utilizing high resolution and an area-based factor, accurately quantifies PM2.5, PM10, and respirable fractions without underestimation.
• This improved passive sampling method is suitable for monitoring in windless occupational environments.
• The UNC sampler with the area factor presents a viable and improved passive sampling strategy for workplace dust assessment.