Long-Term Trend of Ground-Level PM2.5 Concentrations Over 2012-2017 in China
Bibliographic record
Abstract
Ambient suspended fine particulate matter (PM2.5) is a greatest environmental risk factor for premature mortality. We adopted aerosol optical depth (AOD) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument to produce annual-mean PM2.5 concentrations from 2012 to 2017 with a spatial resolution of 3km. A geographically weighted regression model was conducted using vertical- and hydroscopic-corrected AOD and meteorological data. The PM2.5 estimates were validated by the ground measurements, with R2and RMSE (MPE) of 0.79 and 18.26 (12.03) μg/m3. The results show that national average of PM2.5 concentration represented a 31% decline over five years, from 69.37 μg/m3in 2013 to 43.85 μg/m3in 2017, after a slightly rise (6%) during 2012-2013. Significant reduction was revealed in the Beijing-Tianjin-Hebei region, decreasing by 37.31% from 2013 to 2017. Despite low decline in some southeastern provinces, the national-mean PM2.5 concentration has decreased by 31%, indicating the effectiveness of the control policies issued by Chinese government in 2013. Nevertheless, efforts to improve air quality are still required to further reduce the mass concentration in China.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".