Spatiotemporal Patterns and Characteristics of PM2.5 Pollution in the Yellow River Golden Triangle Demonstration Area
Bibliographic record
Abstract
Improving air quality in the Yellow River Golden Triangle Demonstration Area (YRGTDA) is an important practice for ecological protection and high-quality development in the Yellow River Basin. Preventing and controlling PM2.5 pollution in this region will require a scientific understanding of the spatiotemporal patterns and characteristics of PM2.5 pollution. PM2.5 data from different sources were combined in this study (the annual average of PM2.5 concentrations were obtained from the Atmospheric Composition Analysis Group of Dalhousie University, and the daily PM2.5 concentration data were obtained from the China National Environmental Monitoring Centre). Then, the temporal variation of PM2.5 concentrations at annual, seasonal, and monthly scales, the spatial variation of PM2.5 concentrations, and the variation of PM2.5 pollution classes were analyzed. Results showed that: (1) at the annual scale, the PM2.5 concentrations showed a decreasing trend from 2000 to 2021 in the study area. The variation of PM2.5 concentrations were divided into two different stages. (2) At the seasonal scale, high PM2.5 concentrations occurred mainly in winter, low PM2.5 concentrations occurred in summer. At the monthly scale, PM2.5 concentrations showed a U-shaped variation pattern from January to December each year. (3) The hotspot analysis of the PM2.5 concentrations in the study area showed a cyclical variation pattern. (4) The PM2.5 concentrations exhibited a spatial pattern of high values in the central and low values in the northern and southern parts of YRGTDA. (5) The number of days for different PM2.5 pollution classes from 2015 to 2021 followed the order of Good > Excellent > Light pollution > Moderate pollution > Heavy pollution > Severe pollution in YRGTDA. The results of this study have great theoretical and practical significance because they reveal the spatiotemporal patterns and pollution characteristics of PM2.5 and will lead to the development of scientifically based measures to reasonably prevent and control pollution in YRGTDA.
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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 | 0.000 | 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".