The global daily High Spatial–Temporal Coverage Merged tropospheric NO<sub>2</sub> dataset (HSTCM-NO<sub>2</sub>) from 2007 to 2022 based on OMI and GOME-2
<p>Remote sensing based on satellites can provide long-term, consistent, and global coverage of <span class="inline-formula">NO<sub>2</sub></span> (an important atmospheric air pollutant) as well as other trace gases. However, satellites often miss data due to...
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Copernicus Publications
2024-11-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/16/5287/2024/essd-16-5287-2024.pdf |
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| author | K. Qin H. Gao X. Liu Q. He P. Tiwari J. B. Cohen |
| author_facet | K. Qin H. Gao X. Liu Q. He P. Tiwari J. B. Cohen |
| author_sort | K. Qin |
| collection | DOAJ |
| description | <p>Remote sensing based on satellites can provide long-term, consistent, and global coverage of <span class="inline-formula">NO<sub>2</sub></span> (an important atmospheric air pollutant) as well as other trace gases. However, satellites often miss data due to factors including but not limited to clouds, surface features, and aerosols. Moreover, as one of the longest continuous observational platforms of <span class="inline-formula">NO<sub>2</sub></span>, the Ozone Monitoring Instrument (OMI) has suffered from missing data over certain rows since 2007, significantly reducing its spatial coverage. This work uses the OMI-based tropospheric <span class="inline-formula">NO<sub>2</sub></span> (OMNO2) product as well as a <span class="inline-formula">NO<sub>2</sub></span> product from the Global Ozone Monitoring Experiment-2 (GOME-2) in combination with machine learning (eXtreme Gradient Boosting – XGBoost) and spatial interpolation (data-interpolating empirical orthogonal function – DINEOF) methods to produce the 16-year global daily High Spatial–Temporal Coverage Merged tropospheric <span class="inline-formula">NO<sub>2</sub></span> dataset (HSTCM-<span class="inline-formula">NO<sub>2</sub></span>; <span class="uri">https://doi.org/10.5281/zenodo.10968462</span>; Qin et al., 2024), which increases the average global spatial coverage of <span class="inline-formula">NO<sub>2</sub></span> from 39.5 % to 99.1 %. The HSTCM-<span class="inline-formula">NO<sub>2</sub></span> dataset is validated using upward-looking observations of <span class="inline-formula">NO<sub>2</sub></span> (multi-axis differential optical absorption spectroscopy – MAX-DOAS), other satellites (the Tropospheric Monitoring Instrument – TROPOMI), and reanalysis products. The comparisons show that HSTCM-<span class="inline-formula">NO<sub>2</sub></span> maintains a good correlation with the magnitudes of other observational datasets, except for under heavily polluted conditions (<span class="inline-formula">></span> 6 <span class="inline-formula">×</span> 10<span class="inline-formula"><sup>15</sup></span> <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M17" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><mi mathvariant="normal">molec</mi><mo>.</mo><mspace linebreak="nobreak" width="0.125em"/><msup><mi mathvariant="normal">cm</mi><mrow><mo>-</mo><mn mathvariant="normal">2</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="60pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="8c262684892ed39ced87d31c2f335c7a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-16-5287-2024-ie00001.svg" width="60pt" height="13pt" src="essd-16-5287-2024-ie00001.png"/></svg:svg></span></span>). This work also introduces a new validation technique to validate coherent spatial and temporal signals (empirical orthogonal function – EOF) and confirms that HSTCM-<span class="inline-formula">NO<sub>2</sub></span> is not only consistent with the original OMNO2 data but in some parts of the world also effectively fills in missing gaps and yields a superior result when analyzing long-range atmospheric transport of <span class="inline-formula">NO<sub>2</sub></span>. The few differences are also reported to be related to areas in which the original OMNO2 signal was very low, which has been shown elsewhere but not from this perspective, further confirming that applying a minimum cutoff to retrieved <span class="inline-formula">NO<sub>2</sub></span> data is essential. The reconstructed data product can effectively extend the utilization value of the original OMNO2 data, and the data quality of HSTCM-<span class="inline-formula">NO<sub>2</sub></span> can meet the needs of scientific research.</p> |
| format | Article |
| id | doaj-art-917677de874140cc8ec4cf78b01cf813 |
| institution | Kabale University |
| issn | 1866-3508 1866-3516 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Copernicus Publications |
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| series | Earth System Science Data |
| spelling | doaj-art-917677de874140cc8ec4cf78b01cf8132024-11-15T07:40:04ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162024-11-01165287531010.5194/essd-16-5287-2024The global daily High Spatial–Temporal Coverage Merged tropospheric NO<sub>2</sub> dataset (HSTCM-NO<sub>2</sub>) from 2007 to 2022 based on OMI and GOME-2K. Qin0H. Gao1X. Liu2Q. He3P. Tiwari4J. B. Cohen5School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China<p>Remote sensing based on satellites can provide long-term, consistent, and global coverage of <span class="inline-formula">NO<sub>2</sub></span> (an important atmospheric air pollutant) as well as other trace gases. However, satellites often miss data due to factors including but not limited to clouds, surface features, and aerosols. Moreover, as one of the longest continuous observational platforms of <span class="inline-formula">NO<sub>2</sub></span>, the Ozone Monitoring Instrument (OMI) has suffered from missing data over certain rows since 2007, significantly reducing its spatial coverage. This work uses the OMI-based tropospheric <span class="inline-formula">NO<sub>2</sub></span> (OMNO2) product as well as a <span class="inline-formula">NO<sub>2</sub></span> product from the Global Ozone Monitoring Experiment-2 (GOME-2) in combination with machine learning (eXtreme Gradient Boosting – XGBoost) and spatial interpolation (data-interpolating empirical orthogonal function – DINEOF) methods to produce the 16-year global daily High Spatial–Temporal Coverage Merged tropospheric <span class="inline-formula">NO<sub>2</sub></span> dataset (HSTCM-<span class="inline-formula">NO<sub>2</sub></span>; <span class="uri">https://doi.org/10.5281/zenodo.10968462</span>; Qin et al., 2024), which increases the average global spatial coverage of <span class="inline-formula">NO<sub>2</sub></span> from 39.5 % to 99.1 %. The HSTCM-<span class="inline-formula">NO<sub>2</sub></span> dataset is validated using upward-looking observations of <span class="inline-formula">NO<sub>2</sub></span> (multi-axis differential optical absorption spectroscopy – MAX-DOAS), other satellites (the Tropospheric Monitoring Instrument – TROPOMI), and reanalysis products. The comparisons show that HSTCM-<span class="inline-formula">NO<sub>2</sub></span> maintains a good correlation with the magnitudes of other observational datasets, except for under heavily polluted conditions (<span class="inline-formula">></span> 6 <span class="inline-formula">×</span> 10<span class="inline-formula"><sup>15</sup></span> <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M17" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><mi mathvariant="normal">molec</mi><mo>.</mo><mspace linebreak="nobreak" width="0.125em"/><msup><mi mathvariant="normal">cm</mi><mrow><mo>-</mo><mn mathvariant="normal">2</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="60pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="8c262684892ed39ced87d31c2f335c7a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-16-5287-2024-ie00001.svg" width="60pt" height="13pt" src="essd-16-5287-2024-ie00001.png"/></svg:svg></span></span>). This work also introduces a new validation technique to validate coherent spatial and temporal signals (empirical orthogonal function – EOF) and confirms that HSTCM-<span class="inline-formula">NO<sub>2</sub></span> is not only consistent with the original OMNO2 data but in some parts of the world also effectively fills in missing gaps and yields a superior result when analyzing long-range atmospheric transport of <span class="inline-formula">NO<sub>2</sub></span>. The few differences are also reported to be related to areas in which the original OMNO2 signal was very low, which has been shown elsewhere but not from this perspective, further confirming that applying a minimum cutoff to retrieved <span class="inline-formula">NO<sub>2</sub></span> data is essential. The reconstructed data product can effectively extend the utilization value of the original OMNO2 data, and the data quality of HSTCM-<span class="inline-formula">NO<sub>2</sub></span> can meet the needs of scientific research.</p>https://essd.copernicus.org/articles/16/5287/2024/essd-16-5287-2024.pdf |
| spellingShingle | K. Qin H. Gao X. Liu Q. He P. Tiwari J. B. Cohen The global daily High Spatial–Temporal Coverage Merged tropospheric NO<sub>2</sub> dataset (HSTCM-NO<sub>2</sub>) from 2007 to 2022 based on OMI and GOME-2 Earth System Science Data |
| title | The global daily High Spatial–Temporal Coverage Merged tropospheric NO<sub>2</sub> dataset (HSTCM-NO<sub>2</sub>) from 2007 to 2022 based on OMI and GOME-2 |
| title_full | The global daily High Spatial–Temporal Coverage Merged tropospheric NO<sub>2</sub> dataset (HSTCM-NO<sub>2</sub>) from 2007 to 2022 based on OMI and GOME-2 |
| title_fullStr | The global daily High Spatial–Temporal Coverage Merged tropospheric NO<sub>2</sub> dataset (HSTCM-NO<sub>2</sub>) from 2007 to 2022 based on OMI and GOME-2 |
| title_full_unstemmed | The global daily High Spatial–Temporal Coverage Merged tropospheric NO<sub>2</sub> dataset (HSTCM-NO<sub>2</sub>) from 2007 to 2022 based on OMI and GOME-2 |
| title_short | The global daily High Spatial–Temporal Coverage Merged tropospheric NO<sub>2</sub> dataset (HSTCM-NO<sub>2</sub>) from 2007 to 2022 based on OMI and GOME-2 |
| title_sort | global daily high spatial temporal coverage merged tropospheric no sub 2 sub dataset hstcm no sub 2 sub from 2007 to 2022 based on omi and gome 2 |
| url | https://essd.copernicus.org/articles/16/5287/2024/essd-16-5287-2024.pdf |
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