Comment on acp-2021-278
Notice bibliographique
Résumé
<strong class="journal-contentHeaderColor">Abstract.</strong> The Indian megacity of Delhi suffers from some of the poorest air quality in the world. While ambient NO<span class="inline-formula"><sub>2</sub></span> and particulate matter (PM) concentrations have received considerable attention in the city, high ground-level ozone (O<span class="inline-formula"><sub>3</sub></span>) concentrations are an often overlooked component of pollution. O<span class="inline-formula"><sub>3</sub></span> can lead to significant ecosystem damage and agricultural crop losses, and adversely affect human health. During October 2018, concentrations of speciated non-methane hydrocarbon volatile organic compounds (C<span class="inline-formula"><sub>2</sub></span>âC<span class="inline-formula"><sub>13</sub></span>), oxygenated volatile organic compounds (o-VOCs), NO, NO<span class="inline-formula"><sub>2</sub></span>, HONO, CO, SO<span class="inline-formula"><sub>2</sub></span>, O<span class="inline-formula"><sub>3</sub></span>, and photolysis rates, were continuously measured at an urban site in Old Delhi. These observations were used to constrain a detailed chemical box model utilising the Master Chemical Mechanism v3.3.1. VOCs and NO<span class="inline-formula"><sub><i>x</i></sub></span> (NOâ<span class="inline-formula">+</span>âNO<span class="inline-formula"><sub>2</sub></span>) were varied in the model to test their impact on local O<span class="inline-formula"><sub>3</sub></span> production rates, <span class="inline-formula"><i>P</i>(O<sub>3</sub>)</span>, which revealed a VOC-limited chemical regime. When only NO<span class="inline-formula"><sub><i>x</i></sub></span> concentrations were reduced, a significant increase in <span class="inline-formula"><i>P</i>(O<sub>3</sub>)</span> was observed; thus, VOC co-reduction approaches must also be considered in pollution abatement strategies. Of the VOCs examined in this work, mean morning <span class="inline-formula"><i>P</i>(O<sub>3</sub>)</span> rates were most sensitive to monoaromatic compounds, followed by<span id="page13610"/> monoterpenes and alkenes, where halving their concentrations in the model led to a 15.6â%, 13.1â%, and 12.9â% reduction in <span class="inline-formula"><i>P</i>(O<sub>3</sub>)</span>, respectively. <span class="inline-formula"><i>P</i>(O<sub>3</sub>)</span> was not sensitive to direct changes in aerosol surface area but was very sensitive to changes in photolysis rates, which may be influenced by future changes in PM concentrations. VOC and NO<span class="inline-formula"><sub><i>x</i></sub></span> concentrations were divided into emission source sectors, as described by the Emissions Database for Global Atmospheric Research (EDGAR) v5.0 Global Air Pollutant Emissions and EDGAR v4.3.2_VOC_spec inventories, allowing for the impact of individual emission sources on <span class="inline-formula"><i>P</i>(O<sub>3</sub>)</span> to be investigated. Reducing road transport emissions only, a common strategy in air pollution abatement strategies worldwide, was found to increase <span class="inline-formula"><i>P</i>(O<sub>3</sub>)</span>, even when the source was removed in its entirety. Effective reduction in <span class="inline-formula"><i>P</i>(O<sub>3</sub>)</span> was achieved by reducing road transport along with emissions from combustion for manufacturing and process emissions. Modelled <span class="inline-formula"><i>P</i>(O<sub>3</sub>)</span> reduced by <span class="inline-formula">â¼</span>â20âppbâh<span class="inline-formula"><sup>â1</sup></span> when these combined sources were halved. This study highlights the importance of reducing VOCs in parallel with NO<span class="inline-formula"><sub><i>x</i></sub></span> and PM in future pollution abatement strategies in Delhi.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,042 | 0,004 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».