Spatial distribution characteristics of air pollutants in major cities in China during the period of wide range haze pollution
Bibliographic record
Abstract
Based on the air pollutants monitoring data obtained from 667 meteorological stations of 118 major cities in China in the period from November to December in 2013,the spatial distribution maps of air pollutant concentrations every five days were obtained by selecting optimal spatial interpolation method. Kernel density and trend surface analysis were used to study the spatiotemporal distribution characteristics of pollutant concentrations during the period of wide range haze pollution,and the spatial heterogeneities of pollutant concentrations were explored by using global autocorrelation and local autocorrelation analysis methods. The results indicated that the variation trend of air pollutants was large at different time. The concentrations of air pollutants such as nitrogen dioxide( NO2),inhalable particles( PM10 and PM2. 5),and sulfur dioxide( SO2) showed an apparent tendency of the eastern region the western region and the northern region the southern region. PM10 and PM2. 5were the main contributors to the widespread haze weather. There were significant positive spatial autocorrelations in mean concentrations of all the air pollutants. The hot spots of mean concentrations of NO2 were concentrated in the central regions of Shandong Province,south regions of Heibei Province and the urban agglomerations of the Pearl River Delta. The hot spots of mean concentrations of PM10 were distributed in the south regions of Hebei Province,Huaihai and Guanzhong-Tianshui economic zones. The hot spots of mean concentrations of PM2. 5were concentrated mostly in Beijing-Tianjin-Hebei urban agglomerations,the Yangtze River Delta and the coast region of South China,and the hot spots of mean concentrations of SO2 mainly distributed in the central regions of Hebei Province and the northeastern region of Shandong Province.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".