Observations of speciated isoprene nitrates in Beijing: implicationsfor isoprene chemistry
Notice bibliographique
Résumé
Abstract. Isoprene is the most important biogenic volatile organic compound in the atmosphere. Its calculated impact on ozone (O3) is critically dependent on the model isoprene oxidation chemical scheme, in particular the way the isoprene-derived nitrates (IN) are treated. By combining gas chromatography with mass spectrometry, we have developed a system capable of separating, and unambiguously measuring, individual IN isomers. In this paper we report measurements from its first field deployment, which took place in Beijing as part of the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-Beijing) programme, along with box model simulations using the Master Chemical Mechanism (MCM) (v.3.3.1) to assess the key processes affecting the production and loss of the IN. Seven individual isoprene nitrates were identified and quantified during the summer campaign: two β-isoprene hydroxy nitrates (IHN); four δ isoprene carbonyl nitrates (ICN); and propanone nitrate. Whilst we had previously demonstrated that the system can measure the four δ-IHN, we found no evidence of them in Beijing. The two β-IHN mixing ratios are well correlated with an R2 value of 0.85. The mean for their ratio ((1-OH, 2-ONO2)-IHN : (4-OH, 3-ONO2)-IHN) is 3.4 and exhibits no clear diel cycle (the numbers in the names indicate the carbon (C) atom in the isoprene chain to which the radical is added). Examining this in a box model demonstrates its sensitivity to nitric oxide (NO), with lower NO mixing ratios favouring (1-OH, 2-ONO2)-IHN over (4-OH, 3-ONO2)-IHN. This is largely a reflection of the modelled ratios of their respective precursor peroxy radicals which, at NO mixing ratios of less than 1 part per billion (ppb), increase substantially with decreasing NO. Interestingly, this ratio in the peroxy radicals still exceeds the kinetic ratio (i.e. their initial ratio based on the yields of the adducts from OH addition to isoprene and the rates of reaction of the adducts with oxygen (O2)) even at NO mixing ratios as high as 100 ppb. The relationship of the observed β-IHN ratio with NO is much weaker than modelled, partly due to far fewer data points, but it agrees with the model simulation in so far as there tend to be larger ratios at sub 1 ppb amounts of NO. Of the δ-ICN, the two trans (E) isomers are observed to have the highest mixing ratios and the mean isomer ratio (E-(4-ONO2, 1-CO)-ICN to E-(1-ONO2, 4-CO)-ICN)) is 1.4, which is considerably lower than the expected ratio of 6 for addition of NO3 in the C1 and C4 carbon positions in the isoprene chain. The MCM produces far more δ-ICN than observed, particularly at night and it also simulates an increase in the daytime δ-ICN that greatly exceeds that seen in the observations. Interestingly, the modelled source of δ-ICN is predominantly during the daytime, due to the presence in Beijing of appreciable daytime amounts of NO3 along with isoprene. The modelled ratios of δ-ICN to propanone nitrate are very different to the observed. This study demonstrates the value of speciated IN measurements to test our understanding of the isoprene degradation chemistry. Our interpretation is limited by the uncertainties in our measurements and relatively small data set, but highlights areas of the isoprene chemistry that warrant further study, in particular the NO3 initiated isoprene degradation chemistry.
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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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,001 | 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 ».