Investigating NO<sub><i>x</i></sub> Concentrations on an Urban University Campus Using Passive Air Samplers and UV–Vis Spectroscopy
Bibliographic record
Abstract
Gas-phase nitrogen oxides are important in the formation of tropospheric ozone. NO x (NO and NO 2 ) as well as tropospheric ozone have been shown to have negative effects on human health. Therefore, accurately measuring NO x concentrations in the atmosphere is important. In this laboratory experience, students measured ambient NO x concentrations using a relatively simple and inexpensive passive sampling/UV–vis spectroscopy technique. The students used two different types of spectrophotometers to determine limits of detection and ambient NO x concentrations. Data demonstrated both spectrometers behaved similarly, proving laboratories utilizing different spectrophotometers could accurately perform the experiment. Although not statistically verified due to the limited number of passive samplers employed in the pilot experiment, measured NO x concentrations were similar to those calculated by a local air quality model (within approximately 50 parts per billion). At the end of the laboratory experience, students compared their measured NO 2 concentrations to the United States Environmental Protection Agency’s primary and secondary standard of 53 parts per billion (annual mean). The initial learning goals of the experiment included the following: the successful creation of calibration curves, the determination of spectrophotometer limit of detection, and the calculation of ambient NO x concentrations. The experiment is appropriate for students enrolled in analytical and environmental chemistry courses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".