Evaluation of Long-Term Performance of Six PM_2.5 Sensor Types

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Sensor Technology

Background:

• Low-cost PM2.5 sensors are increasingly used for air quality monitoring.
• Understanding sensor performance and limitations is crucial for reliable data.
• Existing data often lacks comprehensive, real-world deployment evaluations.

Purpose of the Study:

• To evaluate the performance of multiple PM2.5 air sensor models under real-world conditions.
• To identify common data quality issues and their causes.
• To provide a robust dataset for the air sensor community.

Main Methods:

• Operated six PM2.5 sensor models at seven U.S. air quality monitoring sites from July 2019 to January 2021.
• Colocated sensors with regulatory-grade monitors to assess performance.
• Analyzed data for common issues such as zero measurements, outliers, baseline shifts, and humidity influences.

Main Results:

• Identified several common PM sensor data issues, including repeat zero measurements, false high outliers, and baseline drift.
• Observed variable relationships between sensor readings and reference monitors, influenced by factors like relative humidity.
• Noted significant performance variability even among sensors with similar internal components.

Conclusions:

• Most evaluated PM2.5 sensors demonstrate potential to meet U.S. Environmental Protection Agency (EPA) performance targets.
• Addressing identified data issues can enhance sensor reliability and data accuracy.
• The publicly released dataset and findings will aid in sensor validation and method development.