Comparison and evaluation of anthropogenic emissions of SO<sub>2</sub>and NO<sub>x over China
Bibliographic record
Abstract
Abstract. Bottom-up emission inventories provide primary understanding of sources of air pollution and essential input of chemical transport models. Focusing on SO2 and NOx, we conducted a comprehensive evaluation of two widely-used anthropogenic emission inventories over China, ECLIPSE and MIX, to explore the potential sources of uncertainties and find the clues in improving emission inventories. We first compared the activity rates and emission factors used in two inventories, and investigated the reasons of differences and the impacts on emission estimates. We found that SO2 emission estimates are consistent between two inventories (with 1 % differences), while NOx emissions in ECLIPSE's estimates are 16 % lower than those of MIX. Discrepancies at sectorial and provincial level are much higher. We then examined the impacts of different inventories on model performance, by using the nested GEOS-Chem model. We finally derived top-down NOx emissions by using the NO2 columns from the Ozone Monitoring Instrument (OMI) and compared with the bottom-up estimates. To our knowledge, this is the first work where source-sector comparisons are made along with the remote sensing retrievals and chemical transport modeling. Through the comparison between bottom-up emission inventories and evaluation with top-down information, we summarized the potential directions for further improvement in inventory development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".