Bibliographic record
Abstract
Global change including changes in anthropogenic emissions, climate, land use and land cover have been imposing significant perturbations to atmospheric chemistry and air quality. In this work, we use a global 3-D chemical transport model (GEOS-Chem) to investigate the impacts of global change on two important air pollutants that have caused local, regional and global concerns: ozone and mercury. Ozone is a criteria air pollutant in the surface air. Tropospheric ozone is also a major greenhouse gas and has important implications for tropospheric chemistry. Mercury pollution poses risks to humans and wildlife, especially when it is converted to methylmercury in the aquatic system and bioaccumulate in the food chain. The stratospheric ozone level is predicted to recover towards its pre-1980 levels with the implementation of the Montreal Protocol and its amendments and adjustments. Our global model simulations show that the expected stratospheric ozone recovery would significantly decreases the photolysis rates for tropospheric ozone with the surface O3 photolysis rates being reduced by up to 22%. The photolysis rates for tropospheric NO2 show much weaker sensitivity to the changes in stratospheric ozone. In addition, the stratospheric ozone recovery causes strong seasonal variation and general increases in surface ozone, particularly over some ocean areas where surface ozone could increase by up to 5%. The lifetime of tropospheric ozone is augmented by stratospheric ozone recovery, which in turn enhances the intercontinental transport of ozone. We examine the impacts of changes in climate and land use and land cover on atmospheric mercury by coupling the GEOS-Chem model with a general circulation model (GISS GCM3) and a global dynamic vegetation model (LPJ). The land use and land cover change causes an increase in the annual mean Hg(0) dry deposition flux over the majority of the continental regions as a result of increasing leaf area index. Climate change drives the surface Hg(0) concentration to increase globally primarily due to suppressed tropospheric mercury oxidation and increased in-cloud mercury reduction and subsequent increase in Hg(0) dry deposition flux. Furthermore, the change in future precipitation greatly affects the mercury wet deposition flux with increases occurring over most continental regions and decreases over most of the mid-latitude and tropic oceans. Both changes in climate and land use and land cover would potentially drive more gross mercury deposition towards the terrestrial system and less to the ocean system. The growing concerns of elevated methyl mercury contamination have made it important to
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".