Causes of Large Increases in Atmospheric Ammonia in the Last Decade across North America
Bibliographic record
Abstract
Abstract Decadal trends of atmospheric ammonia (NH3) and their potential causes were explored through the analysis of monitored data collected at 15 sites in the United States and 7 sites in Canada. Large percentage increases in the annual average concentration of atmospheric NH3, for example, >100% at 6 sites and 40–100% at 10 sites, were observed over the most recent 8–13 year period. In contrast, a decrease or a narrow variation in NH3 emissions was reported at the state or provincial level in both countries during the same period. Decreased emissions of SO2 and NOx across North America in the past decade would have reduced the chemical loss of atmospheric NH3 to form particulate NH4+. Such a chemical mechanism was verified through regression analysis at about half of the monitored sites, where the increasing trends in atmospheric NH3 were partially explained by the reduced NH4+. Excluding the reduced contribution from this chemical loss to generate the adjusted annual NH3 concentration through two approaches, no decreasing trends can be obtained to align those in emissions at most sites, implying that other factors also contributed to the increase in the annual NH3 concentration. Correlation analysis results implied that enhanced drought conditions and increased ambient temperatures also likely contributed to the increasing trend in the annual NH3 concentration at some sites. The large percentage increases in the annual NH3 concentration cannot be fully explained by all the identified causes, leading to oppugning the reality of the decrease in NH3 emissions reported across North America in the recent decade.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".