Source apportionment of air pollution burden using geometric non-negative matrix factorization and high-throughput multi-pollutant air sensor data in Curtis Bay, Baltimore, USA
Air sensor networks provide hyperlocal, high-frequency data on multiple pollutants, but unlike speciated particulate matter (PM) measurements, they lack direct chemical signatures for source identification. However, these networks collect data at high temporal resolution and multiple spatial locations, creating new opportunities to interpret latent factors through their relationships with spatial proximity to known origins, temporal patterns, and meteorology. We analyze 451,946 one-minute air sensor records from Curtis Bay (Baltimore, USA; October 2022 - June 2023), covering size-resolved PM, black carbon (BC), carbon monoxide (CO), nitric oxide (NO), and nitrogen dioxide (NO2), using a geometric non-negative matrix factorization (NMF) approach that scales to large datasets and yields provably unique factor attribution percentages. Three stable latent factors emerge with converging evidence toward recognizable source categories: Factor 1 explains $>$ 70% of fine and coarse PM and $\sim$30% of BC; Factor 2 dominates CO and contributes $\sim$70% of BC, NO, and NO2; Factor 3 is specific to the larger PM fractions, PM10 to PM40. Regression analyses of the estimated factor intensities with wind direction and bulldozer activity at a nearby coal terminal and a case study on a known bulldozer incident link Factors 1 and 3 to the coal terminal. Extreme-intensity episodes from Factors 1 and 3 averaged $\sim$33 and $\sim$24 minutes per day at the site nearest the coal terminal, attenuating with distance. Factor 2 reflects diurnal traffic patterns. Together, these results show that dense air sensor networks paired with the geometric NMF method can move community air monitoring beyond pollution detection toward identifying likely source categories and informing actionable mitigation strategies.