Seasonal variability of NO<sub>x</sub> emissions over east China constrained by satellite observations: Implications for combustion and microbial sources
Bibliographic record
Abstract
Observations of tropospheric column densities of NO2 obtained from the Global Ozone Monitoring Experiment (GOME) for a 3‐year period (1997, 1998, and 2000) are used to derive average seasonal variations in surface emissions of NOx from east China (100–123°E, 20–42°N). The retrieval allows for zonal variations in the contribution of the stratosphere to the NO2 column and removes a bias of ±10% on the seasonality of retrieved columns introduced by cloud screening. The top‐down inventory is constructed using an inversion approach with a global 3‐D chemical transport model (GEOS‐Chem) and combined subsequently with the a priori inventory to develop an a posteriori inventory. The contribution of background NO2 arising from nonsurface sources (lightning) and long‐range transport of emissions originating outside of east China is accounted for in the inversion. The a posteriori estimate of overall emissions for east China, 4.66 Tg N/yr (±30% uncertainty), is 33% higher than the a priori value and is shown to improve agreement with surface measurements of nitrate wet deposition and concentrations of NOy observed in China. On the basis of multiple constraints on the spatial and seasonal variations of combustion and microbial processes, the a posteriori inventory is partitioned among emissions from biomass burning, fuel combustion, and microbial activity (or soil emissions). Emission of NOx from biomass burning in east China is estimated as 0.08 TgN/yr ± 50% in the a posteriori inventory, increased by about a factor of 2 from the a priori estimate. The resulting a posteriori inventory for fuel combustion (3.72 TgN/yr ± 32%) is about 15% higher than the a priori and exhibits a distinct maximum in winter, in contrast to the weak seasonality indicated in the a priori inventory. The a posteriori value for the microbial source of NOx (0.85 TgN/yr ± 40%) is about a factor of 3 higher than the a priori value, amounting to 23% of combustion sources for east China and significantly higher than a priori value of 7%. The microbial source is unimportant in winter. It peaks in summer, accounting for as much as 43% of the combustion source for that season, and is significant also in spring and fall. This seasonality is attributed to the timing of fertilizer application and to the influence of seasonally variable environmental factors including temperature and precipitation.
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 | 0.000 | 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 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".