Characterizing the Temporal Variation of Airborne Particulate Matter Using IoT-Based Air Quality Monitors in Urban Areas

This study aims to determine the optimal frequency for monitoring airborne pollutants in densely populated urban areas to effectively capture their temporal variations. While environmental organizations worldwide typically update air quality data hourly, there is no global consensus on the ideal monitoring frequency to adequately resolve pollutant (Particulate Matter) time series. By applying temporal variogram analysis to particulate matter (PM) data over time, we identified specific measurement intervals that accurately reflect fluctuations in pollution levels. Using January 2023 air quality data from the Joppa neighborhood of Dallas, TX, Temporal Variogram analysis was conducted on three distinct days with varying PM2.5 pollution levels. For the most polluted day, the optimal sampling interval for PM2.5 was determined to be 12.25 seconds. This analysis shows that highly polluted days are associated with shorter sampling intervals, highlighting the need for highly granular observations to accurately capture variations in PM levels. Using the variogram analysis results from the most polluted day, we trained Machine Learning models that can predict the sampling time using meteorological parameters. Feature importance analysis revealed that humidity, temperature and wind speed could significantly impact the measurement time for PM2.5. The study also extends to the other size fractions measured by the air quality monitor. Our findings highlight how local conditions influence the frequency required to reliably track changes in air quality.