In situ Ozone Production is highly sensitive to Volatile Organic Compounds in the Indian Megacity of Delhi
Bibliographic record
Abstract
Abstract. The Indian megacity of Delhi suffers from some of the poorest air quality in the world. While ambient NO2 and particulate matter (PM) concentrations have received considerable attention in the city, high ground level ozone (O3) concentrations are an often overlooked component of pollution. O3 can lead to significant ecosystem damage, agricultural crop losses, and adversely affect human health. During October 2018, concentrations of speciated non-methane hydrocarbons volatile organic compounds (C2 – C13), oxygenated volatile organic compounds (o-VOCs), NO, NO2, HONO, CO, SO2, O3, 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 NOx (NO + NO2) were varied in the model to test their impact on local O3 production rates, P(O3), which revealed a VOC-limited chemical regime. When only NOx concentrations were reduced, a significant increase in P(O3) was observed, thus VOC co-reduction approaches must also be considered in pollution abatement strategies. Of the VOCs examined in this work, mean morning P(O3) rates were most sensitive to monoaromatic compounds, followed by monoterpenes and alkenes, where halving their concentrations in the model led to a 15.6 %, 13.1 % and 12.9 % reduction in P(O3), respectively. P(O3) 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 NOx concentrations were divided into emission source sectors, as described by the EDGAR v5.0 Global Air Pollutant Emissions and EDGAR v4.3.2_VOC_spec inventories, allowing for the impact of individual emission sources on P(O3) to be investigated. Reducing road transport emissions only, a common strategy in air pollution abatement strategies worldwide, was found to increase P(O3), even when the source was removed in its entirety. Effective reduction in P(O3) was achieved by reducing road transport along with emissions from combustion for manufacturing and process emissions. Modelled P(O3) reduced by ~20 ppb h−1 when these combined sources were halved. This study highlights the importance of reducing VOCs in parallel with NOx and PM in future pollution abatement strategies in Delhi.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".