Monthly top‐down NO<sub><i>x</i></sub> emissions for China (2005–2012): A hybrid inversion method and trend analysis
Bibliographic record
Abstract
Abstract We develop an approach combining mass balance and four‐dimensional variational (4D‐Var) methods to facilitate inversion of decadal‐scale total nitrogen oxides (NO x = NO + NO 2 ) emissions. In 7 year pseudo‐observation tests, hybrid posterior emissions have smaller normalized mean square error (NMSE) than that of mass balance when compared to true emissions in most cases and perform slightly better in detecting NO x emission magnitudes and trends. Using this hybrid method, OMI NO 2 satellite observations and the GEOS‐Chem chemical transport model, we find more than 30% increases of emissions over most of East China at the 0.5° × 0.667° grid cell level, leading to a 16% growth of emissions over all of China from 2005 to 2012, whereas emissions in several urban centers have decreased by 10–26% in the same period. From 2010 to 2012, a decline is found in the North China Plain, Hubei Province, and Pearl River Delta area, coinciding with China's enforcement of its twelfth “Five Year Plan.” Changes in individual grid cell may be different from changes over the entire city or province, as exemplified by opposite trends in Beijing versus the Mentougou district of Beijing from 2005 to 2012. Also, NO 2 columns do not necessarily have the same trend as NO x emissions due to their nonlinear response to emissions and the influence of meteorology, the latter alone which can cause up to 30% interannual changes in NO 2 columns. Compared to recent bottom‐up inventories, hybrid posterior emissions have the same seasonality, smaller emissions, and emission growth rate at the national scale.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".