Estimation of NO <sub> <i>x</i> </sub> and SO <sub>2</sub> Emissions from Sarnia, Ontario using Mobile-MAX-DOAS and a NO <sub> <i>x</i> </sub> -Analyzer
Bibliographic record
Abstract
Abstract. Sarnia, ON experiences pollutant emissions disproportionate to its relatively small size. The small size of the city limits traditional top-down emission estimate techniques (e.g., satellite) but a low-cost solution for emission monitoring is Mobile-MAX-DOAS. Measurements were made using this technique from 21/03/2017 to 23/03/2017 along various driving routes to retrieve vertical column densities (VCDs) of NO2 and SO2 and to estimate emissions of NOx and SO2 from the Sarnia region. A novel aspect of the current study was the installation of a NOx analyzer in the vehicle to allow real time measurement and characterization of near-surface NOx/NO2 ratios across the urban plumes, allowing improved accuracy of NOx emission estimates. Confidence in the use of near-surface measured NOx/NO2 ratios for estimation of NOx emissions was increased by relatively well-mixed boundary layer conditions. These conditions were indicated by similar temporal trends in NO2 VCDs and mixing ratios when measurements were sufficiently distant from the sources. Leighton ratios within transported plumes indicated peroxy radicals were likely disturbing the NO-NO2-O3 photostationary state through VOC oxidation. The average lower limit emission estimate of NOx from Sarnia was 1.60 ± 0.34 tonnes hr−1 using local 10 m elevation wind-speed measurements. Our estimates were larger than the downscaled annual 2017 NPRI reported industrial emissions of 0.9 tonnes NOx hr−1. Our lower limit estimate of SO2 emissions from Sarnia was 1.81 ± 0.83 tonnes SO2 hr−1, equal within uncertainty to the 2017 NPRI downscaled value of 1.85 tonnes SO2 hr−1. Satellite-derived NO2 VCDs over Sarnia from the Ozone Monitoring Instrument (OMI) were lower than Mobile-MAX-DOAS VCDs, likely due to the large pixel size relative to the city’s size. The results of this study support the utility of the Mobile-MAX-DOAS method for estimating NOx and SO2 emissions in relatively small, highly industrialized regions especially when supplemented with mobile NOx measurements.
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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 | 0.001 | 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".