Slower ozone production in Houston, Texas following emission reductions: evidence from Texas Air Quality Studies in 2000 and 2006
Bibliographic record
Abstract
Abstract. Airborne measurements from two Texas Air Quality Study (TexAQS) field campaigns have been used to investigate changes of ozone production in Houston, Texas, from 2000 to 2006, a period of major emission reduction measures for petrochemical and other sources. Simultaneous declines in nitrogen oxides (NOx = NO + NO2) and highly reactive volatile organic compounds (HRVOCs) were observed between the two periods. We simulate HOx (OH and HO2) and organic radicals with a box model, the Dynamically Simple Model of Atmospheric Chemical Complexity, constrained by available airborne observations. Parameters such as total radical production, total OH reactivity of VOCs and ozone production rate (OPR) are computed to characterize the change of ozone production between 2000 and 2006 in the Houston area. The reduction in HRVOCs led to a decline in total radical production by 20–50%. Ozone production rates in the Houston area declined by 40–50% from 2000 to 2006, to which the reduction in NOx and HRVOCs made large contributions. Despite the significant decline in OPR, ozone production efficiency held steady, and VOC-sensitive conditions dominated during times of most rapid ozone formation, while the slow ozone formation continued to be NOx-limited. Our results highlight the importance of a balanced approach of ongoing HRVOC controls with NOx controls to further reduce O3 levels in the Houston area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".