A sulfuric acid nucleation potential model for the atmosphere
Observations over the last decade have demonstrated that the atmosphere contains potentially hundreds of compounds that can react with sulfuric acid to nucleate stable aerosol particles. Consequently, modeling atmospheric nucleation requires detailed knowledge of nucleation reaction kinetics and spa...
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Veröffentlicht in: | Atmospheric chemistry and physics 2022-06, Vol.22 (12), p.8287-8297 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Observations over the last decade have demonstrated that the
atmosphere contains potentially hundreds of compounds that can react with
sulfuric acid to nucleate stable aerosol particles. Consequently, modeling
atmospheric nucleation requires detailed knowledge of nucleation reaction
kinetics and spatially and temporally resolved measurements of numerous
precursor compounds. This study introduces the Nucleation Potential Model
(NPM), a novel nucleation model that dramatically simplifies the diverse
reactions between sulfuric acid and any combination of precursor gases. The NPM
predicts 1 nm nucleation rates from only two measurable gas concentrations,
regardless of whether all precursor gases are known. The NPM describes sulfuric
acid nucleating with a parameterized base compound at an effective base
concentration, [Beff]. [Beff] captures the ability of a compound
or mixture to form stable clusters with sulfuric acid and is estimated from
measured 1 nm particle concentrations. The NPM is applied to experimental and
field observations of sulfuric acid nucleation to demonstrate how
[Beff] varies for different stabilizing compounds, mixtures, and
sampling locations. Analysis of previous field observations shows distinct
differences in [Beff] between locations that follow the emission
sources and stabilizing compound concentrations for that region. Overall,
the NPM allows researchers to easily model nucleation across diverse
environments and estimate the concentration of non-sulfuric acid precursors
using a condensation particle counter. |
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ISSN: | 1680-7324 1680-7316 1680-7324 |
DOI: | 10.5194/acp-22-8287-2022 |