Long-Term Atmospheric Visibility Trends and Characteristics of 31 Provincial Capital Cities in China during 1957–2016
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Millions of pulmonary diseases, respiratory diseases, and premature deaths are caused by poor ambient air quality in developing countries, especially in China. A proven indicator of ambient air quality, atmospheric visibility (AV), has displayed continuous decline in China’s urban areas. A better understanding of the characteristics and the factors affecting AV can help the public and policy makers manage their life and work. In this study, long-term AV trends (from 1957–2016, excluding 1965–1972) and spatial characteristics of 31 provincial capital cities (PCCs) of China (excluding Taipei, Hong Kong, and Macau) were investigated. Seasonal and annual mean values of AV, percentage of ‘good’ (≥20 km) and ‘bad’ AV (<10 km), cumulative percentiles and the correlation between AV, socioeconomic factors, air pollutants and meteorological factors were analyzed in this study. Results showed that annual mean AV of the 31 PCCs in China were 14.30 km, with a declining rate of −1.07 km/decade. The AV of the 31 PCCs declined dramatically between 1973–1986, then plateaued between 1987–2006, and rebounded slightly after 2007. Correlation analysis showed that impact factors (e.g., urban size, industrial activities, residents’ activities, urban greening, air quality, and meteorological factors) contributed to the variation of AV. We also reveal that residents’ activities are the primary direct socioeconomic factors on AV. This study hopes to help the public fully understand the characteristics of AV and make recommendations about improving the air environment in China’s urban areas.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it