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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>
<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-2026-6352</article-id>
<title-group>
<article-title>Estimating Hourly Surface NO₂ Across the Contiguous United States Using TEMPO Observations and Machine Learning</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Aarons</surname>
<given-names>Steven</given-names>
<ext-link>https://orcid.org/0009-0001-0416-725X</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>Ni-Meister</surname>
<given-names>Wenge</given-names>
<ext-link>https://orcid.org/0000-0001-9723-2075</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>Ramamurthy</surname>
<given-names>Prathap</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Department of Geography and Environmental Science, Hunter College of CUNY, New York, NY 10065, United States of America</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Department of Mechanical Engineering, City College of New York, NY 10031, United States of America</addr-line>
</aff>
<pub-date pub-type="epub">
<day>23</day>
<month>10</month>
<year>2026</year>
</pub-date>
<volume>2026</volume>
<fpage>1</fpage>
<lpage>44</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Steven Aarons 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-2026-6352/">This article is available from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-6352/</self-uri>
<self-uri xlink:href="https://egusphere.copernicus.org/preprints/2026/egusphere-2026-6352/egusphere-2026-6352.pdf">The full text article is available as a PDF file from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-6352/egusphere-2026-6352.pdf</self-uri>
<abstract>
<p>Ground-level nitrogen dioxide (NO&lt;sub&gt;2&lt;/sub&gt;) poses significant health risks, yet hourly concentrations have not been estimated due to the limited temporal sampling of polar-orbiting satellites. NASA&amp;rsquo;s Tropospheric Emissions: Monitoring of Pollution (TEMPO) geostationary instrument provides the first hourly tropospheric NO&lt;sub&gt;2&lt;/sub&gt; observations over North America, creating new opportunities for estimating surface NO&lt;sub&gt;2&lt;/sub&gt; throughout the daytime. We develop XGBoost models using TEMPO tropospheric NO&lt;sub&gt;2&lt;/sub&gt;, ERA5 meteorology, road density, impervious surface percentage (ISP), and temporal features to predict hourly surface NO&lt;sub&gt;2&lt;/sub&gt; at 474 EPA stations across the contiguous United States from August 2023 through June 2025, and map concentrations at TEMPO&amp;rsquo;s L3 resolution. Separate TEMPO (daylight) and no-TEMPO (nighttime/imputation) models achieve test R&lt;sup&gt;2&lt;/sup&gt; = 0.78 (RMSE = 3.33 ppb) and R&lt;sup&gt;2&lt;/sup&gt; = 0.76 (RMSE = 4.36 ppb), respectively. TEMPO column NO&lt;sub&gt;2&lt;/sub&gt; is the strongest predictor by gain, while ISP dominates spatial prediction and boundary layer height is the most influential meteorological predictor. Importantly, TEMPO provides complementary spatial information beyond meteorological and temporal predictors, increasing R&lt;sup&gt;2&lt;/sup&gt; across all spatial cross-validation folds by an average of 17% and improving performance during data-scarce periods. Spatial cross-validation (R&lt;sup&gt;2&lt;/sup&gt; = 0.48) further highlights opportunities to improve predictions at unseen locations through higher-resolution spatial inputs, while extending satellite coverage into the evening could improve representation of the evening NO&lt;sub&gt;2&lt;/sub&gt; peak. To our knowledge, this is the first study to predict hourly surface NO&lt;sub&gt;2&lt;/sub&gt; across CONUS using geostationary satellite data, demonstrating the potential of TEMPO to support acute exposure assessments at previously infeasible temporal resolutions.</p>
</abstract>
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<funding-group>
<award-group id="gs1">
<funding-source>NASA Headquarters</funding-source>
<award-id>80NSSC25M0070</award-id>
<award-id>80NSSC21K0194</award-id>
</award-group>
</funding-group>
</article-meta>
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