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

Abstract

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. High temporal resolution and multiple spatial locations nonetheless create new opportunities to interpret latent sources through their relationships with spatial proximity to known origins, temporal patterns, and meteorology. We analyze 451946 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 source attribution percentages. Three stable latent sources emerge with converging evidence toward recognizable source categories: Source 1 explains $>$ 70% of fine and coarse PM and $\sim$30% of BC; Source 2 dominates CO and contributes $\sim$70% of BC, NO, and NO2; Source 3 is specific to the larger PM fractions, PM10 to PM40. Regression analyses and a case study on a known bulldozer incident link Sources 1 and 3 to a nearby coal terminal. Extreme-intensity episodes from Sources 1 and 3 averaged $\sim$33 and $\sim$24 minutes per day at the site nearest the terminal, attenuating with distance. Source 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.