Evaluation and application of multi-decadal visibility data for trend analysis of atmospheric haze
Bibliographic record
Abstract
There are few multi-decadal observations of atmospheric aerosols worldwide. This study applies global hourly visibility (Vis) observations at more than 3000 stations to investigate historical trends in atmospheric haze over 1945–1996 for the US, and over 1973–2013 for Europe and eastern Asia. A comprehensive data screening and processing framework is developed and applied to minimize uncertainties and construct monthly statistics of inverse visibility (1/Vis). This data processing includes removal of relatively clean cases with high uncertainty, and change point detection to identify and separate methodological discontinuities such as the introduction of instrumentation. Although the relation between 1/Vis and atmospheric extinction coefficient ( b ext ) varies across different stations, spatially coherent trends of the screened 1/Vis data exhibit consistency with the temporal evolution of collocated aerosol measurements, including the b ext trend of −2.4 % yr −1 (95 % CI: −3.7, −1.1 % yr −1 ) vs. 1/Vis trend of −1.6 % yr −1 (95 % CI: −2.4, −0.8 % yr −1 ) over the US for 1989–1996, and the fine aerosol mass (PM 2.5 ) trend of −5.8 % yr −1 (95 % CI: −7.8, −4.2 % yr −1 ) vs. 1/Vis trend of −3.4 % yr −1 (95 % CI: −4.4, −2.4 % yr −1 ) over Europe for 2006–2013. Regional 1/Vis and Emissions Database for Global Atmospheric Research (EDGAR) sulfur dioxide (SO 2 ) emissions are significantly correlated over the eastern US for 1970–1995 ( r = 0.73), over Europe for 1973–2008 ( r ∼ 0.9) and over China for 1973–2008 ( r ∼ 0.9). Consistent "reversal points" from increasing to decreasing in SO 2 emission data are also captured by the regional 1/Vis time series (e.g., late 1970s for the eastern US, early 1980s for western Europe, late 1980s for eastern Europe, and mid 2000s for China). The consistency of 1/Vis trends with other in situ measurements and emission data demonstrates promise in applying these quality assured 1/Vis data for historical air quality studies.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".