Analysis of Tropospheric NO2 Observation Using Pandora and MAX-DOAS Instrument in Xianghe, North China
Bibliographic record
Abstract
This work presents a comprehensive investigation of tropospheric NO2 measurements using a portable ground-based Pandora spectrometer, incorporating an independently designed and implemented calibration and retrieval process (P-CAR v1.0). We designed and optimized a region-specific algorithm for retrieving tropospheric NO2 column densities in China. The measurement process began with establishing a spectral calibration system for processing the Pandora’s raw observations, followed by enhancing the differential optical absorption spectroscopy (DOAS) algorithm to retrieve both the slant column densities (SCDs) and tropospheric vertical column densities (VCDs) of NO2. To validate our retrieval products, comparative analyses were conducted against co-located MAX-DOAS measurements. The results demonstrate excellent agreement between Pandora-retrieved tropospheric NO2 and MAX-DOAS observations, with correlation coefficients exceeding 0.96 for both hourly and daily mean VCDs and fitting slopes greater than 0.90. Furthermore, the validation extended to multi-satellite observations from the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI), exhibiting pronounced consistency, as evidenced by the correlation coefficients all surpassing 0.90 for the hourly mean values. These findings confirm the high accuracy and reliability of NO2 retrievals from the portable Pandora instrument, significantly boosting its potential for atmospheric monitoring and application.
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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".