Improving Characterization of Cooking Organic Aerosols (COA) via Novel Source Apportionment Analysis of HR-ToF-AMS
ASHUTOSH KUMAR SHUKLA, Wenqing Jiang, Qi Zhang, University of California, Davis
Abstract Number: 515
Working Group: Source Apportionment
Abstract
Positive matrix factorization (PMF) is widely applied to high-resolution time-of-flight aerosol mass spectrometry (HR-ToF-AMS) data to identify PM pollution sources. However, traditional PMF on full organic data often struggles to resolve complex sources such as cooking, particularly in areas where multiple anthropogenic sources are extensively mixed. Cooking organic aerosols (COA) are increasingly recognized as a significant component of urban particulate pollution. Despite their importance for public health and regulation, cooking emissions remain difficult to resolve due to compositional and temporal similarity to other sources.
In this study, we present a novel and rigorously validated source apportionment (SA) approach to improve the resolution of COA using PMF and HR-ToF-AMS. Data were collected during a winter 2023–2024 field campaign in Fresno, located in California’s San Joaquin Valley (SJV)—one of the most polluted regions in the United States. Instrumentation included a HR-ToF-AMS and a chemical ionization mass spectrometer (CIMS) equipped with a Filter Inlet for Gases and AEROsols (FIGAERO). We applied PMF to full organic matrix (PMFFull), as well as separately to hydrocarbon ions (PMFCH) and remaining oxygenated ions (PMFOxi).
This approach successfully resolved a distinct COA factor along with multiple other OA sources from biomass burning and vehicular emissions. These sources exhibited substantial overlaps with COA, particularly during nighttime, reflecting increased emissions from cooking, residential wood burning, and traffic, combined with the reduced boundary layer height typical of winter conditions in the SJV. It was also supported by CIMS measurement which identified fatty acid species – key molecular markers of cooking emissions. On average, COA contributed approximately 10.6% to OA mass, emphasizing its significance as a major primary PM source in the region. This study demonstrates an advanced SA strategy that improves COA characterization through novel analysis of HR-ToF-AMS data, enabling clear resolution of complex emission sources in polluted urban environments.