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<front>
<journal-meta>
<journal-id journal-id-type="publisher">EGUsphere</journal-id>
<journal-title-group>
<journal-title>EGUsphere</journal-title>
<abbrev-journal-title abbrev-type="publisher">EGUsphere</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">EGUsphere</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub"></issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/egusphere-2026-2663</article-id>
<title-group>
<article-title>Development of an offline Aerosol Mass Spectrometry method for organic aerosol characterization in a globally distributed Surface Particulate Matter Network</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Ren</surname>
<given-names>Yuxuan</given-names>
<ext-link>https://orcid.org/0000-0001-6608-9501</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Martin</surname>
<given-names>Randall V.</given-names>
<ext-link>https://orcid.org/0000-0003-2632-8402</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Chen</surname>
<given-names>Jhao-Hong</given-names>
<ext-link>https://orcid.org/0009-0001-3481-7444</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Oxford</surname>
<given-names>Christopher R.</given-names>
<ext-link>https://orcid.org/0000-0003-4799-9141</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Williams</surname>
<given-names>Brent J.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Weber</surname>
<given-names>Rodney J.</given-names>
<ext-link>https://orcid.org/0000-0003-0765-8035</ext-link>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Xu</surname>
<given-names>Lu</given-names>
<ext-link>https://orcid.org/0000-0002-0021-9876</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Department of Energy, Environmental &amp; Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States</addr-line>
</aff>
<aff id="aff3">
<label>3</label>
<addr-line>now at: Bureau of Air, Kansas Department of Health and Environment, Topeka, Kansas 66612, United States</addr-line>
</aff>
<aff id="aff4">
<label>4</label>
<addr-line>now at: Department of Soil, Water, and Climate, University of Minnesota, St. Paul, Minnesota 55108, United States</addr-line>
</aff>
<pub-date pub-type="epub">
<day>21</day>
<month>05</month>
<year>2026</year>
</pub-date>
<volume>2026</volume>
<fpage>1</fpage>
<lpage>43</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Yuxuan Ren et al.</copyright-statement>
<copyright-year>2026</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://egusphere.copernicus.org/preprints/2026/egusphere-2026-2663/">This article is available from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-2663/</self-uri>
<self-uri xlink:href="https://egusphere.copernicus.org/preprints/2026/egusphere-2026-2663/egusphere-2026-2663.pdf">The full text article is available as a PDF file from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-2663/egusphere-2026-2663.pdf</self-uri>
<abstract>
<p>Global measurements of organic aerosol (OA) concentrations and chemical composition remain limited and unevenly distributed. While monitoring networks, including the globally distributed Surface Particulate Matter Network (SPARTAN), provide an established framework for measurements, their current methodologies do not fully support comprehensive OA characterization. Aerosol Mass Spectrometry (AMS) is widely used for real-time OA composition measurements, but its cost, complexity, and logistical requirements limit long-term, multi-site online deployment, particularly at the global scale. Here we develop and evaluate an offline AMS methodology to characterize OA in particulate matter collected on Teflon filters routinely used by monitoring networks. Using a commercial ultrasonic nebulizer coupled with a syringe pump, this offline method is highly reproducible, requires small extract volumes (2 mL), offers low detection limits (1.7 &amp;micro;g OA and 0.43 &amp;micro;g sulfate per filter), and achieves higher nebulization efficiency than previous methods. We evaluate this offline AMS method by co-located online AMS observations. We find that oxygenated OA is effectively recovered (64 &amp;plusmn; 28 %) while the recovery is lower for hydrocarbon-like OA due to its limited water solubility. This approach offers new capability for SPARTAN and is readily adaptable to other monitoring networks. Its application across networks will broaden the spatiotemporal coverage of AMS-based OA measurements and improve methodological and instrumental consistency to support ongoing efforts to build a long-term, globally consistent OA dataset.</p>
</abstract>
<counts><page-count count="43"/></counts>
<funding-group>
<award-group id="gs1">
<funding-source>National Science Foundation</funding-source>
<award-id>Grant 2020673</award-id>
</award-group>
</funding-group>
</article-meta>
</front>
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