The impact of historical land use change from 1850 to 2000 on secondary particulate matter and ozone
Bibliographic record
Abstract
Abstract. Anthropogenic land use change (LUC) since preindustrial (1850) has altered the vegetation distribution and density around the world. We use a global model (GEOS-Chem) to assess the attendant changes in surface air quality and the direct radiative forcing (DRF). We focus our analysis on secondary particulate matter and tropospheric ozone formation. The general trend of expansion of managed ecosystems (croplands and pasturelands) at the expense of natural ecosystems has led to an 11 % decline in global mean biogenic volatile organic compound emissions. Concomitant growth in agricultural activity has more than doubled ammonia emissions and increased emissions of nitrogen oxides from soils by more than 50 %. Conversion to croplands has also led to a widespread increase in ozone dry deposition velocity. Together these changes in biosphere–atmosphere exchange have led to a 14 % global mean increase in biogenic secondary organic aerosol (BSOA) surface concentrations, a doubling of surface aerosol nitrate concentrations, and local changes in surface ozone of up to 8.5 ppb. We assess a global mean LUC-DRF of +0.017, −0.071, and −0.01 W m−2 for BSOA, nitrate, and tropospheric ozone, respectively. We conclude that the DRF and the perturbations in surface air quality associated with LUC (and the associated changes in agricultural emissions) are substantial and should be considered alongside changes in anthropogenic emissions and climate feedbacks in chemistry–climate studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".