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

TL;DR

This study applies a geometric non-negative matrix factorization method to high-resolution multi-pollutant air sensor data in Curtis Bay, Baltimore, identifying three stable pollution sources and linking them to specific local activities like coal terminal operations and traffic.

Contribution

It introduces a scalable geometric source apportionment framework that remains identifiable with large sensor datasets, improving source attribution accuracy in complex urban environments.

Findings

Identified three stable pollution sources with distinct profiles.

Linked sources to specific local activities such as coal terminal operations and traffic.

Demonstrated the method's stability and reliability through sensitivity analyses.

Abstract

Air sensor networks provide hyperlocal, high temporal resolution data on multiple pollutants that can support credible identification of common pollution sources. Source apportionment using least squares-based non-negative matrix factorization is non-unique and often does not scale. A recent geometric source apportionment framework focuses inference on the source attribution matrix, which is shown to remain identifiable even when the factorization is not. Recognizing that the method scales with and benefits from large data volumes, we use this geometric method to analyze 451,946 one-minute air sensor records from Curtis Bay, collected from October 21, 2022 to June 16, 2023, covering size-resolved particulate matter (PM), black carbon (BC), carbon monoxide (CO), nitric oxide (NO), and nitrogen dioxide (NO2). The analysis identifies three stable sources. Source 1 explains > 70% of fine and coarse PM and ~30% of BC. Source 2 dominates CO and contributes ~70% of BC, NO, and NO2. Source 3 is specific to the larger PM fractions, PM10 to PM40. Regression analyses show Source 1 and Source 3 rise during bulldozer activity at a nearby coal terminal and under winds from the terminal, indicating a direct coal terminal influence, while Source 2 exhibits diurnal patterns consistent with traffic. A case-study on the day with a known bulldozer incident at the coal terminal further confirms the association of terminal activities with Sources 1 and 3. Extreme episodes identified from Source 1 intensity affected ~33 minutes per day at the study site nearest the coal terminal, with impacts attenuating at locations farther from the terminal. The results are stable under sensitivity analyses. The analysis demonstrates that geometric source apportionment, paired with high temporal resolution data from multi-pollutant air sensor networks, delivers scalable and reliable evidence to inform mitigation strategies.