Fugitive Particulate Matter Emissions to the Atmosphere from Tracked and Wheeled Vehicles in a Desert Region by Hybrid-Optical Remote Sensing
Bibliographic record
Abstract
A hybrid-optical remote sensing (hybrid-ORS) method was developed to quantify mass emission factors (EFs) for fugitive particulate matter with aerodynamic diameters ≤ 10 μm (PM10) and ≤ 2.5 μm (PM2.5). In-situ range-resolved extinction coefficient and concurrent point measurements of PM10 and PM2.5 mass concentrations are used to quantify two-dimensional (2-D) PM10 and PM2.5 mass concentration profiles. Integration of each 2-D mass concentration profile with wind data, event duration, and source type provides the corresponding fugitive PM10 and PM2.5 EFs. This method was used to quantify EFs for fugitive PM10 and PM2.5 emitted from tracked and wheeled vehicles travelling on unpaved roads in a desert region. The EFs for tracked vehicles ranged from 206 g/km to 1,738 g/km for PM10 and from 78 g/km to 684 g/km for PM2.5, depending on vehicle speed and vehicle type. The EFs for the wheeled vehicle ranged from 223 g/km to 4,339 g/km for PM10 and from 44 g/km to 1,627 g/km for PM2.5. Field implementation of the hybrid-ORS method demonstrates that the method can rapidly capture multiple profiles of the PM plumes and is well suited for improved quantification of fugitive PM EFs from vehicles traveling on unpaved roads.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".