Ozone Atmospheric Chemistry In Southeast Michigan
Notice bibliographique
Résumé
Surface ozone (O3) levels in Southeast Michigan (SEMI) have often exceeded U.S. National Ambient Air Quality Standards (NAAQS), posing threats to human and ecosystem health, and air quality. Although SEMI is generally small in comparison to the rest of the state, it is home to most businesses and industries, as well as more than half of the state’s population. With current advancements in high-resolution global chemistry-climate modeling, studying the impacts of O3 at exposure relevant scales has become more feasible. The central objective of this dissertation is to contribute to the understanding of O3 atmospheric chemistry in urban and semi-urban areas, using a high-resolution global modeling approach. To achieve this, an optimized high resolution modeling approach is used with observations to determine scenarios for mitigating O3 air pollution. To do this, two approaches are followed, using the summer of 2021 as a reference period: (i) to explore the distribution of O3 and its precursors (e.g., NOx and VOCs) in SEMI, I evaluate the global chemistry-climate model, MUSICAv0 (Multi-Scale Infrastructure for Chemistry and Aerosols, Version 0), with a custom regionally refined grid mesh over the state of Michigan and diurnally applied anthropogenic NO emissions, with field campaign measurements from the Michigan-Ontario Ozone Source Experiment (MOOSE); and (ii) use the optimized and evaluated model to understand the contribution of different anthropogenic emission sectors (e.g., power generation, transportation) and transport on O3 photochemical production and loss processes, and inform policy makers on different methods for mitigating O3 pollution in the region. In approach (i), the regional refinement capabilities of MUSICAv0 are used to create and test a custom grid over the state of Michigan of ~7 km (1/16˚). In addition, a diurnal cycle for anthropogenic emissions of NOx is applied within the simulation to better optimize and simulate O3 in the region, using sector- and country-specific temporal profiles. The model is then evaluated with stationary, mobile, and aircraft-based remote sensing during the MOOSE field campaign. This work shows that grid resolution is important for simulated O3, but becomes far more important for O3 precursors. Additionally, applying a diurnal cycle for anthropogenic NO emissions from CAMSv5.1 (Copernicus Atmosphere Modeling System Version 5.1) can play a large role in nighttime O3 formation. In the complementary approach (ii), I use the optimized MUSICAv0 model and configuration to explore the different impacts of anthropogenic sectors on O3 atmospheric chemistry in SEMI. This is done by removing global and Michigan-based anthropogenic emissions from different sectors and assessing their impact on O3 concentrations. This work showed that by removing global anthropogenic emissions from sectors, such as transportation, power, generation and industry, leads to large decreases in O3 due to reduced O3 precursors. Michigan-based anthropogenic emission reductions showed minimal changes to peak O3, and moderate changes to O3 in the early mornings. This is indicative that transport plays a key role in driving O3 processes in the region. This also highlights the need for local emission controls, in conjunction with more regional controls in nearby areas. The modeling framework and results described in this thesis are important for the design of effective O3 mitigation strategies in SEMI.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».