Exploring Spatiotemporal Dynamics of PM2.5 Emission Based on Nighttime Light in China from 2012 to 2018
Bibliographic record
Abstract
China’s coal-based energy structure and rapid economic expansion have resulted in significant air pollution, notably PM2.5 pollution, which has harmed the environment, citizens’ health, and sustainable and cleaner development of China in recent years. Traditional ground monitoring stations have certain drawbacks, such as spatial distribution that is unequal. To better understand the spatial and temporal dynamic characteristics of PM2.5 emissions, this article studied the temporal and spatial changes of night light data along PM2.5 emission at the national, regional, and provincial scales. The Chinese Academy of Science’s Earth Luminous Data Set, Dalhousie University’s PM2.5 emission dataset, and the basic national geographical dataset from National Geographic were used for analysis. We found a significant positive correlation between nightlight data and PM2.5 emission data, which resulted in an accurate fitting of PM2.5 emissions using the proposed linear regression model, and the results showed that the spatiotemporal dynamics of PM2.5 emission and night light were different in various regions. In terms of spatial distribution, PM2.5 emission over the intermediate level (44% of China’s total area) was concentrated in the Sichuan Basin, North China Plain, and Northwest China, whereas PM2.5 emission below the middle level (55% of China’s total area) was concentrated in northeast China, Xizang, and West Sichuan. In terms of geographical and temporal dynamics, more than 65% of China’s total, area mainly located in the south of the Hu line, showed negative growth from 2012 to 2018, especially the North China Plain, the Sichuan Basin, and the Yangtze River’s Plains showed a lot of negative growth. The evolution of PM2.5 emission in China from 2012 to 2018 was visually exhibited by examining spatiotemporal dynamics and the interaction linkages between PM2.5 emission and nighttime light, which was useful for China’s air pollution control and sustainable development.
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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) | 0.005 | 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".