Exploring the processes controlling secondary inorganic aerosol: evaluating the global GEOS-Chem simulation using a suite of aircraft campaigns

<p>Secondary inorganic aerosols (sulfate, nitrate, and ammonium, SNA) are major contributors to fine particulate matter. Predicting concentrations of these species is complicated by the cascade of processes that control their abundance, including emissions, chemistry, thermodynamic partitionin...

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Main Authors: O. G. Norman, C. L. Heald, S. Bililign, P. Campuzano-Jost, H. Coe, M. N. Fiddler, J. R. Green, J. L. Jimenez, K. Kaiser, J. Liao, A. M. Middlebrook, B. A. Nault, J. B. Nowak, J. Schneider, A. Welti
Format: Article
Language:English
Published: Copernicus Publications 2025-01-01
Series:Atmospheric Chemistry and Physics
Online Access:https://acp.copernicus.org/articles/25/771/2025/acp-25-771-2025.pdf
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Summary:<p>Secondary inorganic aerosols (sulfate, nitrate, and ammonium, SNA) are major contributors to fine particulate matter. Predicting concentrations of these species is complicated by the cascade of processes that control their abundance, including emissions, chemistry, thermodynamic partitioning, and removal. In this study, we use 11 flight campaigns to evaluate the GEOS-Chem model performance for SNA. Across all the campaigns, the model performance is best for sulfate (<span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">=</span> 0.51; normalized mean bias (NMB) <span class="inline-formula">=</span> 0.11) and worst for nitrate (<span class="inline-formula"><i>R</i><sup>2</sup>=0.22</span>; NMB <span class="inline-formula">=</span> 1.76), indicating substantive model deficiencies in the nitrate simulation. Thermodynamic partitioning reproduces the total particulate nitrate well (<span class="inline-formula"><i>R</i><sup>2</sup>=0.79</span>; NMB <span class="inline-formula">=</span> 0.09), but actual partitioning (i.e., <span class="inline-formula"><i>ε</i></span>(NO<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M9" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi/><mn mathvariant="normal">3</mn><mo>-</mo></msubsup><mo>)</mo><mo>=</mo></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="22pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="3b35b7ceb952b67a7d90687e397a043a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-25-771-2025-ie00001.svg" width="22pt" height="16pt" src="acp-25-771-2025-ie00001.png"/></svg:svg></span></span> NO<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M10" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi/><mn mathvariant="normal">3</mn><mo>-</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="9pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="78ed0f7e81615226176402cdd6a1afd5"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-25-771-2025-ie00002.svg" width="9pt" height="16pt" src="acp-25-771-2025-ie00002.png"/></svg:svg></span></span> <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M11" display="inline" overflow="scroll" dspmath="mathml"><mo>/</mo></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="8pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="165b352473919034209a9d51d0eaf41d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-25-771-2025-ie00003.svg" width="8pt" height="14pt" src="acp-25-771-2025-ie00003.png"/></svg:svg></span></span> TNO<span class="inline-formula"><sub>3</sub></span>) is challenging to assess given the limited sets of full gas- and particle-phase observations needed for ISORROPIA II. In particular, ammonia observations are not often included in aircraft<span id="page772"/> campaigns, and more routine measurements would help constrain sources of SNA model bias. Model performance is sensitive to changes in emissions and dry and wet deposition, with modest improvements associated with the inclusion of different chemical loss and production pathways (i.e., acid uptake on dust, N<span class="inline-formula"><sub>2</sub></span>O<span class="inline-formula"><sub>5</sub></span> uptake, and NO<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M15" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi/><mn mathvariant="normal">3</mn><mo>-</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="9pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="9f81e901bf06635e082f559a787da68a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-25-771-2025-ie00004.svg" width="9pt" height="16pt" src="acp-25-771-2025-ie00004.png"/></svg:svg></span></span> photolysis). However, these sensitivity tests show only modest reduction in the nitrate bias, with no improvement to the model skill (i.e., <span class="inline-formula"><i>R</i><sup>2</sup></span>), implying that more work is needed to improve the description of loss and production of nitrate and SNA as a whole.</p>
ISSN:1680-7316
1680-7324