Observations of speciated isoprene nitrates in Beijing: implicationsfor isoprene chemistry
Bibliographic record
Abstract
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".