Estimating PM_2.5 concentrations at public schools in North Carolina using multiple data sources and interpolation...

Area of Science:

• Environmental Science
• Public Health
• Education Policy

Background:

• Air pollution, especially fine particulate matter (PM2.5), presents significant health risks to children.
• Public schools are critical sites for evaluating children's exposure to air quality.
• Existing air quality monitoring networks have limitations in spatial coverage and real-time data.

Purpose of the Study:

• To estimate PM2.5 concentrations at North Carolina public schools.
• To compare the effectiveness of Kriging and Inverse Distance Weighting (IDW) interpolation methods.
• To assess the relationship between PM2.5 exposure and 8th-grade reading scores.

Main Methods:

• Utilized EPA and PurpleAir monitor data for PM2.5 estimation.
• Applied Kriging and IDW interpolation techniques, validated through cross-validation.
• Integrated data from sparse EPA monitors with dense PurpleAir monitors for enhanced spatial resolution.

Main Results:

• Kriging outperformed IDW in PM2.5 estimation accuracy, particularly near EPA sites.
• Combining EPA and PurpleAir data significantly improved predictive accuracy, especially in data-scarce areas.
• Suburban schools exhibited higher PM2.5 concentrations.
• A negative correlation was observed between PM2.5 exposure and 8th-grade reading scores.

Conclusions:

• Integrating PurpleAir monitors into school-based air quality monitoring is crucial for public health and education policy.
• Combined EPA and PurpleAir data with Kriging provide a robust method for assessing environmental determinants of student well-being.
• Findings suggest air pollution may negatively impact cognitive functions essential for academic achievement.