Changes in anthropogenic particulate matters and resulting global climate effects since the Industrial Revolution
Bibliographic record
Abstract
Abstract In order to quantify air pollution effects on climate change, we investigated the climate response associated with anthropogenic particulate matters (PMs) by dividing fine PM (PM2.5, particle size ≤2.5 μm) and coarse particulate matter (CPM, particle size >2.5 μm) in great detail in this work, with an aerosol‐climate coupled model. We find that the changes in PM2.5 and CPM are very different and thus result in different, even opposite effects on climate, especially on a regional scale. The column burden of PM2.5 increases globally from 1850 to the present, especially over Asia's southern and eastern parts, whereas the column concentration of CPM increases over high‐latitude regions and decreases over South Asia. The resulted global annual mean effective radiative forcing (ERF) values due to PM2.5 and CPM changes are −1.21 W·m−2 and −0.24 W·m−2, respectively. Increases in PM2.5 result in significant cooling effects on the climate, whereas changes in CPM produce small and even opposite effects. The global annual mean surface air temperature (SAT) decreases by 0.94 K due to PM2.5 increase. Coolings caused by increased PM2.5 are more apparent over Northern Hemisphere (NH) terrain and ocean at mid‐ and high latitudes. Increases in SATs caused by increased CPM are identified over high latitudes in the NH, whereas decreases are identified over mid‐latitude regions. Strong cooling due to increased PM2.5 causes a southward shift of the Intertropical Convergence Zone (ITCZ), whereas the Hadley circulation associated with CPM is enhanced slightly over both hemispheres, along with the weak movement of corresponding ITCZ. The global annual mean precipitation decreases by approximately 0.11 mm day−1 due to the increased PM2.5. Generally, PM2.5 concentration changes contribute more than 80% of the variation caused by all anthropogenic aerosols in ERF, SAT, cloud fraction, and precipitation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".