AEROCAN, the Canadian sub-network of AERONET: Aerosol monitoring and air quality applications
Bibliographic record
Abstract
Previous studies have demonstrated the utility of AERONET (Aerosol Robotic Network) aerosol optical depth (AOD) data for monitoring the spatial variability of particulate matter (PM) in relatively polluted regions of the globe. AEROCAN, a Canadian sub-network of AERONET, was established 20 years ago and currently consists of twenty sites across the country. In this study, we examine whether the AEROCAN sunphotometer data provide evidence of anthropogenic contributions to ambient particulate matter concentrations in relatively clean Canadian locations. The similar weekly cycle of AOD and PM2.5 over Toronto provides insight into the impact of local pollution on observed AODs. High temporal correlations (up to r = 0.78) between daily mean AOD (or its fine-mode component) and PM2.5 are found at southern Ontario AEROCAN sites during May–August, implying that the variability in the aerosol load resides primarily in the boundary layer and that sunphotometers capture day-to-day PM2.5 variations at moderately polluted sites. The sensitivity of AEROCAN AOD data to anthropogenic surface-level aerosol enhancements is demonstrated using boundary-layer wind information for sites near sources of aerosol or its precursors. An advantage of AEROCAN relative to the Canadian in-situ National Air Pollution Surveillance (NAPS) network is the ability to detect free tropospheric aerosol enhancements, which can be large in the case of lofted forest fire smoke or desert dust. These aerosol plumes eventually descend to the surface, sometimes in populated areas, exacerbating air quality. In cases of large AOD (≥0.4), AEROCAN data are also useful in characterizing the aerosol type. The AEROCAN network includes three sites in the high Arctic, a region not sampled by the NAPS PM2.5 monitoring network. These polar sites show the importance of long-range transport and meteorology in the Arctic haze phenomenon. Also, AEROCAN sunphotometers are, by design and due to regular maintenance, the most valuable monitors available for long term aerosol trends. Using a variety of data analysis techniques and timescales, the usefulness of this ground-based remote-sensing sub-network for providing information relevant to air quality is demonstrated.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".