Atmospheric ammonia and particulate inorganic nitrogen over the United States
Bibliographic record
Abstract
Abstract. We use in situ observations from the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network, the Midwest Ammonia Monitoring Project, 11 surface site campaigns as well as Infrared Atmospheric Sounding Interferometer (IASI) satellite measurements with the GEOS-Chem model to investigate inorganic aerosol loading and atmospheric ammonia concentrations over the United States. IASI observations suggest that current ammonia emissions are underestimated in California and in the springtime in the Midwest. In California this underestimate likely drives the underestimate in nitrate formation in the GEOS-Chem model. However in the remaining continental United States we find that the nitrate simulation is biased high (normalized mean bias > = 1.0) year-round, except in Spring (due to the underestimate in ammonia in this season). None of the uncertainties in precursor emissions, the uptake efficiency of N2O5 on aerosols, OH concentrations, the reaction rate for the formation of nitric acid, or the dry deposition velocity of nitric acid are able to explain this bias. We find that reducing nitric acid concentrations to 75% of their simulated values corrects the bias in nitrate (as well as ammonium) in the US. However the mechanism for this potential reduction is unclear and may be a combination of errors in chemistry, deposition and sub-grid near-surface gradients. This "updated" simulation reproduces PM and ammonia loading and captures the strong seasonal and spatial gradients in gas-particle partitioning across the United States. We estimate that nitrogen makes up 15−35% of inorganic fine PM mass over the US, and that this fraction is likely to increase in the coming decade, both with decreases in sulfur emissions and increases in ammonia emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".