Measuring Spatial and Temporal PM_2.5 Variations in Sacramento, California, Communities Using a Network of Low-Cost...

Area of Science:

• Environmental Science
• Air Quality Monitoring
• Sensor Technology

Background:

• Low-cost sensors are increasingly used for air pollution monitoring.
• Ensuring data quality is crucial for reliable insights into spatio-temporal air pollution variability.

Purpose of the Study:

• To determine the spatial variability of PM2.5 in Sacramento, California using low-cost sensors.
• To assess the data quality and correlation of low-cost PM sensors with regulatory monitors.

Main Methods:

• 19 AirBeam particulate matter (PM) sensors were deployed for spatial variability assessment.
• Sensors were collocated with regulatory monitors before and after the study for calibration and validation.
• Multi-linear regression analysis was used to identify factors influencing sensor deviations from reference measurements.

Main Results:

• Sensors demonstrated high precision (R² = 0.98-0.99) and minimal drift, with sensor-specific correction factors applied.
• Moderate correlation (R² = 0.60-0.68) was observed between sensors and regulatory monitors.
• Dew point and relative humidity were most correlated with deviations between sensor and reference PM2.5 measurements.
• PM2.5 distributions were largely homogeneous (average pairwise coefficient of divergence = 0.22).
• No significant difference in average PM concentrations was found between environmental justice and non-environmental justice communities (p=0.24).

Conclusions:

• Low-cost PM sensors, with proper quality assurance, can effectively monitor air pollution.
• The study found relatively homogeneous PM2.5 distribution in Sacramento.
• Air quality did not significantly differ between environmental justice and non-environmental justice communities in the study area.