Growth in NO <sub>x</sub> emissions from power plants in China: bottom-up estimates and satellite observations
Bibliographic record
Abstract
Abstract. Using OMI (Ozone Monitoring Instrument) tropospheric NO2 columns and a nested-grid 3-D global chemical transport model (GEOS-Chem), we investigated the growth in NOx emissions from coal-fired power plants and their contributions to the growth in NO2 columns in 2005–2007 in China. We first developed a unit-based power plant NOx emission inventory for 2005–2007 to support this investigation. The total capacities of coal-fired power generation have increased by 48.8% in 2005–2007, with 92.2% of the total capacity additions coming from generator units with size ≥300 MW. The annual NOx emissions from coal-fired power plants were estimated to be 8.11 Tg NO2 for 2005 and 9.58 Tg NO2 for 2007, respectively. The modeled summer average tropospheric NO2 columns were highly correlated (R2 = 0.79–0.82) with OMI measurements over grids dominated by power plant emissions, with only 7–14% low bias, lending support to the high accuracy of the unit-based power plant NOx emission inventory. The ratios of OMI-derived annual and summer average tropospheric NO2 columns between 2007 and 2005 indicated that most of the grids with significant NO2 increases were related to power plant construction activities. OMI had the capability to trace the changes of NOx emissions from individual large power plants in cases where there is less interference from other NOx sources. Scenario runs from GEOS-Chem model suggested that the new power plants contributed 18.5% and 10% to the annual average NO2 columns in 2007 in Inner Mongolia and North China, respectively. The massive new power plant NOx emissions significantly changed the local NO2 profiles, especially in less polluted areas. A sensitivity study found that changes of NO2 shape factors due to including new power plant emissions increased the summer average OMI tropospheric NO2 columns by 3.8–17.2% for six selected locations, indicating that the updated emission information could help to improve the satellite retrievals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".