UNCW Data Science Seminar

The Data Science Seminar is interdisciplinary. All faculty and students are welcome. Email DataScienceUG@uncw.edu if you are interested in presenting.
Note: The time and location may vary.

Fall 2026 Schedule

Abstract

Unveiling the Invisible: PM2.5 at NC Schools & Its Academic Shadows by Dr. Carroll

Fine particulate matter (PM2.5) is a major environmental health concern, particularly for children, yet estimating exposure at school locations remains challenging due to limited monitoring coverage. This study estimates PM2.5 concentrations at North Carolina public schools by combining high-quality but sparse EPA monitoring data with dense, low-cost PurpleAir sensor data. Two interpolation approaches—Kriging and inverse distance weighting (IDW)—were compared using leave-one-out cross-validation. Results show that Kriging consistently outperformed IDW, and that incorporating PurpleAir data improved predictive accuracy, especially in areas lacking EPA monitors. School-level estimates revealed higher PM2.5 concentrations in suburban areas relative to urban and rural settings. Linking these estimates to educational outcomes, higher PM2.5 levels were associated with lower 8th-grade reading scores, though effects were modest. Overall, the study demonstrates that integrating low-cost sensors with traditional networks enhances air pollution exposure assessment and suggests potential implications of air quality for student academic performance.