Space-Based Constraints on North American Emissions of Nitrogen Oxides
Bibliographic record
Abstract
Global emission inventories of nitrogen oxides (NOx) remain uncertain to a factor of 2 with substantial implications for understanding of surface air quality, atmospheric oxidation, and climate. We will discuss recent progress in improving NOx emission inventories by combining traditional bottom-up inventories with top-down constraints from the GOME and SCIAMACHY satellite instruments. This integration results in an optimized surface NOx emission inventory with lower uncertainties. Additionally, comparisons with in-situ NO2 profile measurements during the ICARTT campaign (summer 2004) establish the validity and quantify the accuracy of the SCIAMACHY tropospheric NO2 column abundances. Conclusions •Growing confidence in top-down constraint on NOx emissions •Gross-underestimate in NOx emissions from megacities •Soil NOx emissions underestimated, especially from Northern Equatorial Africa (not shown here – ask me) •North American lightning NOx emissions underestimated (not shown here – ask me) Acknowledgements This research is supported by NASA, the Smithsonian Institution, the Canadian Foundation for Innovation (CFI), the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS), the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Nova Scotia Research and Innovation Trust (NSRIT). The Cooperation of ESA and the DLR in the GOME and SCIAMACHY programs is greatly appreciated. 1. Current Inventory Status: Global surface NOx emissions are uncertain by a factor of 2, with substantial implications for atmospheric pollution Here in Tg N yr-1 (source) Fossil Fuel 24 (GEIA) Biomass Burning 6 (Duncan et al., 2003) Soils 5 (Yienger and Levy, 1995) Uncertainty Range Fossil Fuel (20-33) Biomass Burning (3-13) Soils (4-21) 2005 Spring 2. Satellites Provide Top-Down Measurements 3. Global Distribution of Tropospheric NO2 from SCIAMACHY 4. Use Retrieved NO2 Columns to Map NOx Emissions 7. Global Top-Down Emission Inventory Reveals Major Discrepancy in NOx Emissions from Megacities Nadir measurements in the UV and visible provide measurements of tropospheric NO2, O3 (see A21A-13), HCHO (see A11A-06), and SO2 Used Here GOME (1995-2002) • Spatial resolution 320×40 km2 • Global coverage in 3 days SCIAMACHY (2002-present) • Spatial resolution 60×30 km2 • Global coverage in 6 days Not Yet OMI (2004) GOME-2 a,b,c (2006+) OMPS-n (2006+) NO NO2 Boundary Layer NO / NO2 vs. altitude
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".