Identifying missing sources and reducing NO<sub><i>x</i></sub> emissions uncertainty over China using daily satellite data and a mass-conserving method
<p>This study applies a mass-conserving model-free analytical approach to daily observations on a grid-by-grid basis of NO<span class="inline-formula"><sub>2</sub></span> from the Tropospheric Monitoring Instrument (TROPOMI) to rapidly and flexibly quantify ch...
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Copernicus Publications
2025-02-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/25/2291/2025/acp-25-2291-2025.pdf |
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| author | L. Lu L. Lu J. B. Cohen J. B. Cohen K. Qin K. Qin X. Li X. Li Q. He Q. He |
| author_facet | L. Lu L. Lu J. B. Cohen J. B. Cohen K. Qin K. Qin X. Li X. Li Q. He Q. He |
| author_sort | L. Lu |
| collection | DOAJ |
| description | <p>This study applies a mass-conserving model-free analytical approach to daily observations on a grid-by-grid basis of NO<span class="inline-formula"><sub>2</sub></span> from the Tropospheric Monitoring Instrument (TROPOMI) to rapidly and flexibly quantify changing and emerging sources of NO<span class="inline-formula"><sub><i>x</i></sub></span> emissions at high spatial and daily temporal resolution. The inverted NO<span class="inline-formula"><sub><i>x</i></sub></span> emissions and optimized underlying ranges include quantification of the underlying atmospheric in situ processing, transport, and physics. The results are presented over three changing regions in China, including Shandong and Hubei, which are rapidly urbanizing and not frequently addressed in the global literature. The day-to-day and grid-by-grid emissions are found to be 1.96 <span class="inline-formula">±</span> 0.27 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−2</sup></span> s<span class="inline-formula"><sup>−1</sup></span> on pixels with available a priori values (1.94 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−2</sup></span> s<span class="inline-formula"><sup>−1</sup></span>), while 1.22 <span class="inline-formula">±</span> 0.63 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−2</sup></span> s<span class="inline-formula"><sup>−1</sup></span> extra emissions are found on pixels in which the a priori inventory is lower than 0.3 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−2</sup></span> s<span class="inline-formula"><sup>−1</sup></span>. Source attribution based on the thermodynamics of combustion temperature, atmospheric transport, and in situ atmospheric processing successfully identifies five different industrial source types. Emissions from these industrial sites adjacent to the Yangtze River are found to be 161. <span class="inline-formula">±</span> 68.9 Kt yr<span class="inline-formula"><sup>−1</sup></span> (163 % higher than the a priori), consistent with missing light and medium industries located along the river, contradicting previous studies attributing water as the source of NO<span class="inline-formula"><sub><i>x</i></sub></span> emissions. Finally, the results reveal pixels with an uncertainty larger than day-to-day variability, providing quantitative information for placement of future monitoring stations. It is hoped that these findings will drive a new approach to top-down emissions estimates, in which emissions are quantified and updated continuously based consistently on remotely sensed measurements and associated uncertainties that actively reflect land-use changes and quantify misidentified emissions, while quantifying new datasets to inform the bottom-up emissions community.</p> |
| format | Article |
| id | doaj-art-5518bc1d282c4d85a67a815858b3cf67 |
| institution | OA Journals |
| issn | 1680-7316 1680-7324 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Copernicus Publications |
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| series | Atmospheric Chemistry and Physics |
| spelling | doaj-art-5518bc1d282c4d85a67a815858b3cf672025-08-20T02:14:38ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242025-02-01252291230910.5194/acp-25-2291-2025Identifying missing sources and reducing NO<sub><i>x</i></sub> emissions uncertainty over China using daily satellite data and a mass-conserving methodL. Lu0L. Lu1J. B. Cohen2J. B. Cohen3K. Qin4K. Qin5X. Li6X. Li7Q. He8Q. He9School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaShanxi Key Laboratory of Environmental Remote Sensing Applications, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaShanxi Key Laboratory of Environmental Remote Sensing Applications, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaShanxi Key Laboratory of Environmental Remote Sensing Applications, China University of Mining and Technology, Xuzhou 221116, ChinaShanxi Key Laboratory of Environmental Remote Sensing Applications, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Geographic Sciences, Taiyuan Normal University, Jinzhong 030619, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaShanxi Key Laboratory of Environmental Remote Sensing Applications, China University of Mining and Technology, Xuzhou 221116, China<p>This study applies a mass-conserving model-free analytical approach to daily observations on a grid-by-grid basis of NO<span class="inline-formula"><sub>2</sub></span> from the Tropospheric Monitoring Instrument (TROPOMI) to rapidly and flexibly quantify changing and emerging sources of NO<span class="inline-formula"><sub><i>x</i></sub></span> emissions at high spatial and daily temporal resolution. The inverted NO<span class="inline-formula"><sub><i>x</i></sub></span> emissions and optimized underlying ranges include quantification of the underlying atmospheric in situ processing, transport, and physics. The results are presented over three changing regions in China, including Shandong and Hubei, which are rapidly urbanizing and not frequently addressed in the global literature. The day-to-day and grid-by-grid emissions are found to be 1.96 <span class="inline-formula">±</span> 0.27 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−2</sup></span> s<span class="inline-formula"><sup>−1</sup></span> on pixels with available a priori values (1.94 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−2</sup></span> s<span class="inline-formula"><sup>−1</sup></span>), while 1.22 <span class="inline-formula">±</span> 0.63 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−2</sup></span> s<span class="inline-formula"><sup>−1</sup></span> extra emissions are found on pixels in which the a priori inventory is lower than 0.3 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−2</sup></span> s<span class="inline-formula"><sup>−1</sup></span>. Source attribution based on the thermodynamics of combustion temperature, atmospheric transport, and in situ atmospheric processing successfully identifies five different industrial source types. Emissions from these industrial sites adjacent to the Yangtze River are found to be 161. <span class="inline-formula">±</span> 68.9 Kt yr<span class="inline-formula"><sup>−1</sup></span> (163 % higher than the a priori), consistent with missing light and medium industries located along the river, contradicting previous studies attributing water as the source of NO<span class="inline-formula"><sub><i>x</i></sub></span> emissions. Finally, the results reveal pixels with an uncertainty larger than day-to-day variability, providing quantitative information for placement of future monitoring stations. It is hoped that these findings will drive a new approach to top-down emissions estimates, in which emissions are quantified and updated continuously based consistently on remotely sensed measurements and associated uncertainties that actively reflect land-use changes and quantify misidentified emissions, while quantifying new datasets to inform the bottom-up emissions community.</p>https://acp.copernicus.org/articles/25/2291/2025/acp-25-2291-2025.pdf |
| spellingShingle | L. Lu L. Lu J. B. Cohen J. B. Cohen K. Qin K. Qin X. Li X. Li Q. He Q. He Identifying missing sources and reducing NO<sub><i>x</i></sub> emissions uncertainty over China using daily satellite data and a mass-conserving method Atmospheric Chemistry and Physics |
| title | Identifying missing sources and reducing NO<sub><i>x</i></sub> emissions uncertainty over China using daily satellite data and a mass-conserving method |
| title_full | Identifying missing sources and reducing NO<sub><i>x</i></sub> emissions uncertainty over China using daily satellite data and a mass-conserving method |
| title_fullStr | Identifying missing sources and reducing NO<sub><i>x</i></sub> emissions uncertainty over China using daily satellite data and a mass-conserving method |
| title_full_unstemmed | Identifying missing sources and reducing NO<sub><i>x</i></sub> emissions uncertainty over China using daily satellite data and a mass-conserving method |
| title_short | Identifying missing sources and reducing NO<sub><i>x</i></sub> emissions uncertainty over China using daily satellite data and a mass-conserving method |
| title_sort | identifying missing sources and reducing no sub i x i sub emissions uncertainty over china using daily satellite data and a mass conserving method |
| url | https://acp.copernicus.org/articles/25/2291/2025/acp-25-2291-2025.pdf |
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