Tropospheric ozone (O3) pollution in Johannesburg, South Africa: Exceedances, diurnal cycles, seasonality, Ox chemistry and O3 production rates
Bibliographic record
Abstract
Ground-level ozone (O3) is an air pollutant of major health and environmental concern. The Johannesburg-Pretoria megacity in South Africa is the industrial and economical capital of the country with more than 10 million inhabitants experiencing poor air quality. In 2004, the City of Johannesburg (CoJ) began monitoring trace gases to assess ground-level O3 pollution. Here, we use CoJ’s publicly available air quality data, and present the first long-term data analysis of O3, nitric oxide (NO), nitrogen dioxide (NO2), NOx and carbon monoxide (CO) in the City from 2004 to 2011 at three air quality monitoring sites: Buccleuch, Delta Park and Newtown. We quantified CoJ’s South African National Ambient Air Quality Standards (NAAQS) exceedances for O3 and NO2, and demonstrate the City’s substantial O3 and NO2 air pollution problem. O3 mixing ratios peak in the early afternoon as expected due to photochemical production. To estimate O3 production rates, we summed O3 and NO2 diurnal profiles to obtain Ox mixing ratios at each site. This analysis provided insight into missing volatile organic compound (VOC) reactivity as well as primary NO2 emissions information necessary for developing tropospheric O3 pollution mitigation strategies. Furthermore, CoJ experiences high O3 mixing ratios on weekends due to lower NOx traffic emissions titrating the O3, thereby providing evidence of a VOC-limited regime for O3 production. Seasonal peak O3 occurs in the austral spring, a maximum that we link to increases in water (H2O) concentrations which in turn increases radical chemistry leading to O3. In addition, wintertime VOC and aerosol emissions from biomass burning over the winter add important precursors for O3 formation once radical chemistry is initiated during the first rain events in early spring. In all, this study will help inform air quality modelling and policy work on air pollutants in the City of Johannesburg, South Africa.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".