Data · dataset · 2026
A rapid-updating method for anthropogenic NO<sub>x</sub> emissions based on convolutional neural networks and TROPOMI NO₂ observations
Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.6084/m9.figshare.34037813.v1
<p>Nitrogen oxides (NO<sub><i>x</i></sub> = NO + NO<sub>2</sub>) are air pollutants primarily emitted from anthropogenic sources, however, bottom-up anthropogenic NO<sub><i>x</i></sub> emission inventories often suffer from update delays.
Description
The TROPOspheric Monitoring Instrument (TROPOMI) provides near-real-time, high-resolution NO<sub>2</sub> column densities, offering new opportunities for timely NO<sub><i>x</i></sub> emission prediction.
Given the short lifetime of NO<sub><i>x</i></sub> in the lower troposphere, local emissions can influence observations over distances of ~100 km. In this study, we develop a convolutional neural network to predict anthropogenic NO<sub><i>x</i></sub> emissions from TROPOMI NO₂ observations. The model operates at a monthly temporal resolution and 0.1 ° × 0.1 ° spatial resolution over Europe and the continental United States, using TROPOMI NO<sub>2</sub> columns and ERA5 wind fields as inputs, with EDGARv8.1 emissions as training targets.
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Trained on 2019–2020 data, the model achieves an R<sup>2</sup> of 0.958 and an RMSE of 8.194 Mg/month/cell on the test set. It demonstrates strong temporal generalisation, with an average R<sup>2</sup> of 0.927 at the grid scale and 0.966 at the national scale during 2021–2022, although spatial generalisation remains limited. The model is applied to extend gridded NO<sub><i>x</i></sub> emissions to December 2025.
Overall, the proposed satellite-driven deep learning approach enables accurate, high-resolution, and near-real-time updates of anthropogenic NO<sub><i>x</i></sub> emissions.</p>
Links
Where it is published
- DOI doi.org/10.6084/m9.figshare.34037813.v1 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Chemistry · Chemistry · Chemistry · Computer Science & AI · Computer Science & AI · Computer Science & AI · Deep learning · Deep learning · Deep learning · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Ecology · Ecology · Ecology · Life Sciences · Life Sciences · Life Sciences
Provenance · 3 source records, 23 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/34037813 | 8 d ago | JSON v1 |
| Deakin Research Online | oai:figshare.com:article/34037813 | 8 d ago | JSON v1 |
| DMU Figshare | oai:figshare.com:article/34037813 | 8 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].anzsrc:field:461103 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['deep learning'] |
| concepts[field].anzsrc:field:461103 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['deep learning'] |
| concepts[field].anzsrc:field:461103 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['deep learning'] |
| concepts[field].anzsrc:group:3103 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Ecology'] |
| concepts[field].anzsrc:group:3103 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['Ecology'] |
| concepts[field].anzsrc:group:3103 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['Ecology'] |
| concepts[field].local:field:chemistry | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:chemistry | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
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| concepts[field].local:field:computer-science-ai | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
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| concepts[field].local:field:earth-environmental | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/rights |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |