Ground-based FTIR observation of hydrogen chloride (HCl) over Hefei, China, and comparisons with GEOS-Chem model data and other ground-based FTIR stations data
Bibliographic record
Abstract
In this study, the characterization of Hydrogen Chloride (HCl) seasonal variations and inter-annual linear trend are presented for the first time over the polluted region at Hefei (117°10’E, 31°54’N), China. The time series of HCl were retrieved by the mid-infrared (MIR) solar spectra recorded by the ground-based high-resolution Fourier transform infrared spectroscopy (FTIR) between July, 2015 and April, 2019. The magnitude of HCl reaches a peak in January (2.70 ± 0.16) × 1015 molecules*cm-2 and a minimum in September (2.27 ± 0.09) × 1015 molecules*cm-2. The four-year time series of HCl total column show a negative linear trend of (-1.83 ± 0.13) %. The FTIR data are compared with GEOS-Chem data in order to evaluate the performance of the GEOS-Chem model to simulate HCl. In general, total column FTIR data and GEOS-Chem model data are in a good agreement with a correlation coefficient of 0.82. GEOS-Chem model data present a good agreement with FTIR data in seasonal variation and inter-annul trend. The maximum differences occur in January and April with mean differences of 4%-6%. We also present HCl time series observed by 6 NDACC stations (Bremen, Toronto, Rikubetsu, Izana, Reunion.maido, Lauder) in low-middle-latitude sites of the northern and southern hemispheres and Hefei stations in order to investigate the seasonal and annual trends of HCl in low-middle-latitude sites. The HCl total column at the northern hemisphere stations reached the maximum in the late winter or early spring and the minimum in the early winter or late autumn. In general, the seasonal variations of HCl over Hefei is similar to that in other northern hemisphere mid-latitude FTIR stations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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".