Estimation of secondary organic aerosol formation parameters for the volatility basis set combining thermodenuder, isothermal dilution, and yield measurements
<p>Secondary organic aerosol (SOA) is a major fraction of the total organic aerosol (OA) in the atmosphere. SOA is formed by the partitioning onto pre-existent particles of low-vapor-pressure products of the oxidation of volatile, intermediate-volatility, and semivolatile organic compounds. Ox...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2023-06-01
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Series: | Atmospheric Measurement Techniques |
Online Access: | https://amt.copernicus.org/articles/16/3155/2023/amt-16-3155-2023.pdf |
Summary: | <p>Secondary organic aerosol (SOA) is a major fraction of the total organic
aerosol (OA) in the atmosphere. SOA is formed by the partitioning onto
pre-existent particles of low-vapor-pressure products of the oxidation of
volatile, intermediate-volatility, and semivolatile organic compounds.
Oxidation of the precursor molecules results in a myriad of organic products,
making the detailed analysis of smog chamber experiments difficult and the
incorporation of the corresponding results into chemical transport models
(CTMs) challenging. The volatility basis set (VBS) is a framework that has
been designed to help bridge the gap between laboratory measurements and
CTMs. The parametrization of SOA formation for the VBS has been
traditionally based on fitting yield measurements of smog chamber
experiments. To reduce the uncertainty in this approach, we developed an
algorithm to estimate the SOA product volatility distribution, effective
vaporization enthalpy, and effective accommodation coefficient combining SOA yield measurements with thermograms (from thermodenuders) and areograms
(from isothermal dilution chambers) from different experiments and
laboratories. The algorithm is evaluated with “pseudo-data” produced from
the simulation of the corresponding processes, assuming SOA with known
properties and introducing experimental error. One of the novel features of
our approach is that the proposed algorithm estimates the uncertainty in the predicted yields for different atmospheric conditions (temperature, SOA
concentration levels, etc.). The uncertainty in these predicted yields is
significantly smaller than that of the estimated volatility distributions
for all conditions tested.</p> |
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ISSN: | 1867-1381 1867-8548 |