Global Inequality of PM <sub>2.5</sub> Exposure and Ecological Possession over 2001–2020
Bibliographic record
Abstract
Long-term exposure to ambient fine particulate matter (PM 2.5 ) air pollution presents a marked environmental risk factor affecting human health and has been demonstrated to increase human morbidity and mortality rates. In contrast to the health risks posed by air pollution, a healthy ecology serves as the foundation for human survival and well-being. However, there are still issues of ecological distribution and possession that are inequitable between humans and nature, as well as between different countries. This study scrutinizes the global, national, and grid-scale disparities in PM 2.5 exposure and ecological possession during the period 2001–2020. Our findings reveal that (a) PM 2.5 concentrations have been on the rise in several countries, including India, Saudi Arabia, Yemen, Russia, Turkey, Bangladesh, Ethiopia, Algeria, Iran, and Myanmar. Conversely, a decreasing trend in PM 2.5 levels is evident in China, the United States, Brazil, Canada, and most European countries. (b) A notable decrease in the risk of PM 2.5 exposure has been observed in densely populated regions in south-eastern China, specifically along the Heihe–Tengchong Line, attributable to a series of effective management measures. (c) Lower ecological quality possession was observed in parts of the Americas, Africa, Asia, and Europe, suggesting increased competition for ecological resources in these regions. We emphasize that humanity shares a common destiny within the global community, and strongly advocate for governments and relevant bodies to address these disparities in PM 2.5 exposure and ecological quality possession, with the aim of preserving the environment and attaining sustainable development goals.
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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) | 0.001 | 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".