CHANGES OF SPATIAL DISPARITIES IN SATELLITE-DERIVED PM<sub>2.5</sub> EXPOSURES OVER 2010–2019 IN CHINA
Bibliographic record
Abstract
Abstract. Air pollution, especially fine particulate matter (PM2.5), has attracted extensive attention due to its adverse impacts on public health. Although PM2.5 pollution was significantly reduced in China over time, while little is known how the spatial disparity of PM2.5 exposure has evolved, especially from both absolute and relative perspectives. Here, we estimate the long-term PM2.5 exposures in China based on satellite observations and convolutional neural network, and characterize the spatial disparity of PM2.5 exposure using Theil index and rank-rank relationship. The result shows that both PM2.5 exposure and absolute spatial disparity were substantially reduced between 2010 and 2019. The nation-wide concentrations (Theil index) declined from 48.0µg/m3 (0.13) to 35.5µg/m3 (0.054). The inter-provincial disparities dominate the overall disparity in 2010, while the intra-provincial disparity contributed the most in 2019. However, while absolute disparities have diminished, relative disparities persist. PM2.5 exposures in the least 20th percentile polluted cities have increased over time, while exposures in other regions declined. On average, the more (less) polluted cities in 2010 were still the more (less) polluted cities in 2019 (except for the very most 2 percentile polluted cities), indicating that the population in more polluted cities still experiences more air pollution than others. Spatial pattern of relative disparity changes was also observed. Overall, understanding not only absolute spatial disparity but also relative disparity is required to help formulate targeted policies for an equitable environment, leaving nobody behind.
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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 | 0.001 |
| 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".