Comparative Analysis of Cooking Emission Markers in Field Campaigns using the AMS and the Offline FIGAERO-CIMS Method

SUNHYE KIM, Abhishek Anand, Pavithra Ethi Rajan, Jenna DeVivo, Albert A. Presto, Carnegie Mellon University

     Abstract Number: 280
     Working Group: Urban Aerosols

Abstract
Cooking emissions serve as a significant contributor to Primary Organic Aerosol (POA) in urban environments, which is frequently quantified using the Aerosol Mass Spectrometer (AMS). Despite our recent AMS measurements revealing notable variations in Cooking Organic Aerosol (COA) composition, particularly a substantial presence of reduced N fragments at specific cooking sources, the application of the Chemical Ionization time-of-flight Mass Spectrometer (CIMS) can provide additional molecular insights into cooking emissions through the minimal fragmentation of molecular compounds.

During the summer of 2021, we investigated the OA concentrations and compositions across urban locations characterized by diverse levels of nearby anthropogenic emissions. Real-time measurements of NR-PM₁ were obtained via AMS using a mobile laboratory, while PM_2.5 samples were gathered on filters for subsequent analysis with iodide-based CIMS utilizing Filter Inlet for Gases and AEROsols (FIGAERO).

This study aims to identify cooking markers detected by the CIMS that exhibit robust correlations with both well-established COA markers identified from the AMS and the COA factor derived from the Positive Matrix Factorization (PMF) analysis. To achieve this, a list of cooking markers generated from prior research was utilized. Preliminary CIMS results from a high cooking activity source indicated that 53 % of the cooking markers displayed the highest signal at noon, followed by evening (31 %), morning (9.4 %), and afternoon (6.3 %), corresponding to the COA factor’s diurnal pattern observed in the AMS-PMF analysis. Notably, lactic acid (C₃H₆O₃) and palmitic acid (C₁₆H₃₂O₂) exhibited prominent peaks throughout the day.

This analysis will reveal spatiotemporal variations in correlations among COA markers by employing both CIMS and AMS, enabling a deeper understanding of potential factors influencing their relationships. Consequently, this integrated approach will not only enhance our understanding of the complexities of COA but also demonstrate how the CIMS can complement the molecular information detected by the AMS.