Comment on egusphere-2023-990
Bibliographic record
Abstract
Abstract. Elevated ground-level ozone concentrations have emerged as a major environmental issue in China. Nitrogen oxide (NOx) is a key precursor to ozone formation. Although control strategies aimed at reducing NOx emissions from conventional combustion sources are widely recognized, soil NOx emissions (mainly as NO) due to microbial processes have received little attention. The impact of soil NO emissions on ground-level ozone concentration is yet to be evaluated. This study estimated soil NO emissions in China using the Berkeley-Dalhousie soil NOx parameterization (BDSNP) algorithm. A typical modeling approach was used to quantify the contribution of soil NO emissions to surface ozone concentration. The Brute-force method (BFM) and the Ozone Source Apportionment Technology (OSAT) implemented in the Comprehensive Air Quality Model with extensions (CAMx) were used. The total soil NO emissions in China for 2018 were estimated to be 1157.9 Gg N, with an uncertainty range of 715.7~1902.6 Gg N. Spatially, soil NO emissions are mainly concentrated in Central China, North China, Northeast China, northern Yangtze River Delta (YRD) and eastern Sichuan Basin, with distinct diurnal and monthly variations that are mainly affected by temperature and the timing of fertilizer application. Both the BFM and OSAT results indicate a substantial contribution of soil NO emissions to the maximum daily 8-hour (MDA8) ozone concentrations by 8~12.5 µg/m3 on average for June 2018, with the OSAT results consistently higher than BFM. The results also showed that soil NO emissions led to a relative increase in ozone exceedance days by 10 %~43.5 % for selected regions. Reducing soil NO emissions resulted in a general decrease in monthly MDA8 ozone concentrations, and the magnitude of ozone reduction became more pronounced with increasing reductions. However, even with complete reductions in soil NO emissions, approximately 450.3 million people are still exposed to unhealthy ozone levels, necessitating additional control policies. This study highlights the importance of soil NO emissions for ground-level ozone concentrations and the potential of reducing NO emissions as a future control strategy for ozone mitigation in China.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.009 |
| Insufficient payload (model declined to judge) | 0.348 | 0.255 |
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".