Bibliographic record
Abstract
Abstract. Surface ozone (O3) in Southeast Michigan (SEMI) often exceeds U.S. 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 O3 and its precursors, such as nitrogen oxides (NOx) 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 created 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 compared 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 higher horizontal grid resolution showed excellent skill in capturing peak O3 concentrations, but showed larger variation in the simulation of O3 precursors (e.g., NOx, HCHO, isoprene). In addition, we implemented a diurnal cycle for anthropogenic nitric oxide (NO) emissions, which is generally not included in global models. As a result, modeled nighttime O3 was improved because of lower NOx 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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.023 | 0.020 |
| Insufficient payload (model declined to judge) | 0.208 | 0.164 |
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".