ESTIMATING PM2.5 IN THE BEIJING-TIANJIN-HEBEI REGION USING MODIS AOD PRODUCTS FROM 2014 TO 2015
Bibliographic record
Abstract
Fine particulate matter with a diameter less than 2.5 μm (PM2.5) has harmful impacts on regional climate, economic development and public health. The high PM2.5 concentrations in China’s urban areas are mainly caused by the combustion of coal and gasoline, industrial pollution and unknown/uncertain sources. The Beijing-Tianjin-Hebei (BTH) region with a land area of 218,000 km2, which contains 13 cities, is the biggest urbanized region in northern China. The huge population (110 million, 8% of the China’s population), local heavy industries and vehicle emissions have resulted in severe air pollution. Traditional models have used 10 km Moderate-resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) products and proved the statistical relationship between AOD and PM2.5. In 2014, the 3 km MODIS AOD product was released which made PM2.5 estimations with a higher resolution became possible. This study presents an estimation on PM2.5 distributions in the BTH region from September 2014 to August 2015 by combining the MODIS satellite data, ground measurements of PM2.5, meteorological parameters and social-economic factors based on the geographically weighted regression model. The results demonstrated that the 10 km AOD product provided results with a slightly higher accuracy although the 3 km AOD product could provide more information about the spatial variations of PM2.5 estimations. Additionally, compared with the global regression, the geographically weighed model was able to improve the estimation results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".