Particulate Oxalate‐To‐Sulfate Ratio as an Aqueous Processing Marker: Similarity Across Field Campaigns and Limitations

Abstract

Leveraging aerosol data from multiple airborne and surface‐based field campaigns encompassing diverse environmental conditions, we calculate statistics of the oxalate‐sulfate mass ratio (median: 0.0217; 95% confidence interval: 0.0154–0.0296; R = 0.76; N = 2,948). Ground‐based measurements of the oxalate‐sulfate ratio fall within our 95% confidence interval, suggesting the range is robust within the mixed layer for the submicrometer particle size range. We demonstrate that dust and biomass burning emissions can separately bias this ratio toward higher values by at least one order of magnitude. In the absence of these confounding factors, the 95% confidence interval of the ratio may be used to estimate the relative extent of aqueous processing by comparing inferred oxalate concentrations between air masses, with the assumption that sulfate primarily originates from aqueous processing.

Document Details

Document Type
Pub Defense Publication
Publication Date
Dec 07, 2021
Source ID
10.1029/2021gl096520

Entities

People

  • Alexander MacDonald
  • Andrea F Corral
  • Armin Sorooshian
  • Claire E. Robinson
  • Connor Stahl
  • Edward L. Winstead
  • Ewan Crosbie
  • Genevieve Rose Lorenzo
  • Grace Betito
  • Jack Dibb
  • James Bernard Simpas
  • Luke Ziemba
  • Maria Obiminda Cambaliza
  • Melliza Templonuevo Cruz
  • Michael Shook
  • Miguel Ricardo A. Hilario
  • Paola Angela Bañaga
  • Rachel A. Braun

Organizations

  • Arizona State University
  • Ateneo de Manila University
  • Langley Research Center
  • National Aeronautics and Space Administration
  • National Oceanic and Atmospheric Administration
  • National Science Foundation
  • Office of Naval Research
  • University of Arizona
  • University of New Hampshire

Tags

Fields of Study

  • Environmental science

Readers

  • Aerosol Science/Aerosol Physics
  • Regression Analysis.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference