Comment on acp-2022-502
Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> In this study, three methods, i.e., the random forest (RF) algorithm, boosted regression trees (BRTs) and the improved complete ensemble empirical-mode decomposition with adaptive noise (ICEEMDAN), were adopted for investigating emission-driven interannual variations in concentrations of air pollutants including <span class="inline-formula">PM<sub>2.5</sub></span>, <span class="inline-formula">PM<sub>10</sub></span>, <span class="inline-formula">O<sub>3</sub></span>, <span class="inline-formula">NO<sub>2</sub></span>, <span class="inline-formula">CO</span>, <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span>â<span class="inline-formula">+</span>â<span class="inline-formula">O<sub>3</sub></span> monitored in six cities in South China from May 2014 to April 2021. The first two methods were used to calculate the deweathered hourly concentrations, and the third one was used to calculate decomposed hourly residuals. To constrain the uncertainties in the calculated deweathered or decomposed hourly values, a self-developed method was applied to calculate the range of the deweathered percentage changes (DePCs) of air pollutant concentrations on an annual scale (each year covers May to the next April). These four methods were combined together to generate emission-driven trends and percentage changes (PCs) during the 7-year period. Consistent trends between the RF-deweathered and BRT-deweathered concentrations and the ICEEMDAN-decomposed residuals of an air pollutant in a city were obtained in approximately 70â% of a total of 42 cases (for seven pollutants in six cities), but consistent PCs calculated from the three methods, defined as the standard deviation being smaller than 10â% of the corresponding mean absolute value, were obtained in only approximately 30â% of all the cases. The remaining cases with inconsistent trends and/or PCs indicated large uncertainties produced by one or more of the three methods. The calculated PCs from the deweathered concentrations and decomposed residuals were thus combined with the corresponding range of DePCs calculated from the self-developed method to gain the robust range of DePCs where applicable. Based on the robust range of DePCs, we identified significant decreasing trends in <span class="inline-formula">PM<sub>2.5</sub></span> concentration from 2014 to 2020 in Guangzhou and Shenzhen, which were mainly caused by the reduced air pollutant emissions and to a much lesser extent by weather perturbations. A decreasing or probably decreasing emission-driven trend was identified in Haikou and Sanya with inconsistent PCs, and a stable or no trend was identified in Zhanjiang with positive PCs. For <span class="inline-formula">O<sub>3</sub></span>, a significant increasing trend from 2014 to 2020 was identified in Zhanjiang, Shenzhen, Guangzhou and Haikou. An increasing trend in <span class="inline-formula">NO<sub>2</sub></span>â<span class="inline-formula">+</span>â<span class="inline-formula">O<sub>3</sub></span> was also identified in Zhanjiang and Guangzhou and an increasing or probably increasing trend in Haikou, suggesting the contributions from enhanced formation of <span class="inline-formula">O<sub>3</sub></span>. The calculated PCs from using different methods implied that the emission changes in <span class="inline-formula">O<sub>3</sub></span> precursors and the associated atmospheric chemistry likely played a dominant role than did the perturbations from varying weather conditions. Results from this study also demonstrated the necessity of combining multiple decoupling methods in generating emission-driven trends in atmospheric pollutants.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.138 | 0.003 |
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; both teacher heads agree on what is shown here.
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".