The Impact of Regional and Global Aerosol Changes on African Air Quality and Mortality: Feedbacks on Dust Emissions
Bibliographic record
Abstract
Future declines in anthropogenic aerosols will reduce fine particulate matter (PM2.5) concentrations; however, meteorological feedbacks alter dust emissions, offsetting or amplifying air quality gains. We use Regional Aerosol Model Intercomparison Project (RAMIP) simulations to assess African climate and air quality responses to regional and global anthropogenic precursor and aerosol emission perturbations, including meteorological feedbacks on dust emissions. By 2050, African and global anthropogenic emissions reductions yield the largest annual continent-average PM2.5 decrease (5% and 7%, respectively). Anthropogenic emissions reductions in the U.S. and Europe also lower PM2.5 by 2 %, due to teleconnections of Northern Hemisphere warming influencing the ITCZ intensity and location, and long-range transport of European aerosols. We find substantial inter-model variability in the magnitude and spatial distribution of dust emissions, dust PM2.5, and total PM2.5, reflecting differences in meteorological responses to the emissions reductions and dust emission parameterizations. Regression analysis using GISS-E2-1-G shows that meteorological responses explain 90 % of dust emissions variability across regions. Anthropogenic aerosol-driven climate feedbacks on dust accounts for up to 70 % of total PM2.5 changes in the Sahara and Namib deserts, while offsetting up to 20 % of anthropogenic PM2.5 reductions in Western and Eastern Africa. In 2050, under simulated global and Africa-wide anthropogenic aerosol reductions, 55,000 and 49,000 PM2.5-related deaths are avoided in Africa, with dust PM2.5 contributing 4.7 % and offsetting 1.3 %, respectively. These findings indicate that the climate impact of anthropogenic aerosol reductions offsets some of the direct air quality benefits.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".