Comment on amt-2024-27
Bibliographic record
Abstract
Abstract. Ground-based remote sensing instruments have been widely used for atmospheric research but applications for air quality monitoring remain limited. Compared to an in situ instrument that provides air quality conditions at the ground level, most remote sensing instruments are sensitive to a broad range of altitudes, often providing only integrated column observations. These column data can be more difficult to interpret and to relate to surface values and hence to “nose-height-level” health factors. This research utilized ground-based remote sensing and in situ air quality observations in the Canadian Oil Sands Region to investigate some of their differences. Vertical column densities (VCDs) of SO2 and NO2 retrieved by Pandora spectrometers located at the Oski-Otin site at Fort McKay, (Alberta, Canada), from 2013–2019 were analyzed along with measurements of SO2 and NO2 surface concentrations and meteorological data. Aerosol optical depth (AOD) observations by CIMEL sunphotometer were compared with surface PM2.5 data. The Oski-Otin site is surrounded by several large bitumen mining operations within the Athabasca Oil Sands Region (AOSR) with significant NO2 emissions from the mining fleet. Two major bitumen upgraders that are 20 km south-east of the site have total SO2 and NO2 emissions of about 40 kt yr-1 and 20 kt yr-1 respectively. It was demonstrated that remote sensing data from Pandora and CIMEL combined with high vertical resolution wind profiles can provide information about pollution sources and plume characteristics. Elevated SO2 VCDs are clearly observed for times with south and south-eastern winds, particularly at 200–300 m altitude (above ground level). High NO2 VCD values were observed from other directions (e.g., north-west) with less prominent impacts from 200–300 m winds. In situ ground observations of SO2 and NO2 show a different sensitivity with wind profiles, indicating they are less sensitive to elevated plumes than remote sensing instruments. In addition to measured wind data and lidar observed boundary layer height (BLH), modelled wind profiles and BLH from ERA-5 have been used to further examine the correlation between column and surface observations. The results show that the ratio of measured column and surface concentration values could show positive or negative correlation with BLH, which depends on the height of emission sources (e.g., emissions from high stacks or near surface). This study explores differences between remote sensing and in situ instruments in terms of their vertical, horizontal, and temporal sampling differences. Understanding and resolving these differences are critical for future analyses linking satellite, ground-based remote sensing, and in situ observations in air quality monitoring and research.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.028 | 0.016 |
| Insufficient payload (model declined to judge) | 0.160 | 0.163 |
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".