Evaluation of ozone and its precursors using the Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) during the Michigan–Ontario Ozone Source Experiment (MOOSE)
Bibliographic record
Abstract
Surface ozone (O 3 ) in Southeast Michigan (SEMI) often exceeds US National Ambient Air Quality Standards, posing risks to human health and agroecosystems. SEMI, a relatively small region in the state of Michigan, contains most of the state's anthropogenic emission sources and more than half of the state's population and is also prone to long-range and transboundary pollutant transport. Here, we explore the distribution of O 3 and its precursors, such as nitrogen oxides (NO x ) and volatile organic compounds, over SEMI during the summer of 2021 using the chemistry–climate model MUSICAv0 (Multi-Scale Infrastructure for Chemistry and Aerosols Version 0). Using the regional refinement capabilities of MUSICAv0, we create a custom grid over the state of Michigan of 1/16° (∼ 7 km) to better understand the local-scale impacts of chemical and dynamic complexity in SEMI and compare it with a grid with 1/8° (∼ 14 km) resolution over the contiguous United States. Model simulations are evaluated using a comprehensive suite of observations from Phase I of the Michigan–Ontario Ozone Source Experiment (MOOSE) field campaign. MUSICAv0, with its higher horizontal grid resolution, showed excellent skill in capturing peak O 3 concentrations but showed larger variation in the simulation of O 3 precursors (e.g., NO x , HCHO, isoprene). In addition, we implement a diurnal cycle for anthropogenic nitric oxide (NO) emissions, which is generally not included in global models. As a result, modeled nighttime O 3 is improved because of lower NO x concentrations during the night. This work shows that when conceptualizing models in urban regions, it is important to consider a combination of high horizontal resolution and the diurnal cycle of emissions, as they can have important implications for the simulation of secondary air pollutants.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".