Development of an approach to correcting MicroPEM baseline drift

Area of Science:

• Environmental Health Sciences
• Air Quality Monitoring
• Sensor Technology

Background:

• Fine particulate matter (PM2.5) exposure is linked to adverse health outcomes.
• The MicroPEM sensor is widely used for personal PM2.5 assessment, but suffers from baseline drift.
• Existing HEPA filter correction methods are insufficient, with over 40% of acquisitions showing drift.

Purpose of the Study:

• To develop and validate an algorithm for correcting MicroPEM sensor baseline drift.
• To improve the accuracy of real-time PM2.5 exposure data, particularly for children's health studies.

Main Methods:

• Developed a running baseline & gravimetric correction (RBGC) method.
• Utilized central site ambient PM2.5 data and gravimetric filter weights for correction.
• Validated the RBGC method using duplicate acquisitions and compared it to HEPA and gravimetric-only corrections.

Main Results:

• RBGC correction reduced baseline drift issues from 53% (HEPA) to 10%.
• Pearson correlation (R-squared) improved from 0.75 (raw) to 0.97 after RBGC.
• Regression slope improved from 0.60 (raw) to 1.00 after RBGC, indicating accurate data.

Conclusions:

• The RBGC algorithm effectively addresses MicroPEM sensor baseline drift.
• This method can be applied to other PM sensors with filters and is adaptable to various regions with ambient monitoring data.