Performance and Uncertainty in Measuring Atmospheric Plume Opacity Using Compact and Smartphone Digital Still Cameras
Bibliographic record
Abstract
Quantification of visible ambient plume opacity measurements using compact and smartphone digital still cameras (DSCs), and Digital Optical Method (DOM) are evaluated here. A new camera calibration method that employs exposure value compensation in place of exposure time or radiance of a surface is described and evaluated. This new and simpler method allows an automatic exposure controlled DSC to be calibrated using its own DSC settings. We also test the use of color in place of grayscale pixel values (PVs) to measure opacity. Finally, we determine the uncertainty of the opacity measurements. Two compact DSCs and two smartphone DSCs are tested to measure plume opacity values of smoke generated with an outdoor smoke generator, in comparison to the plume opacity values measured with an in-stack transmissometer. Results show that: 1) smartphone DSCs, like compact DSCs, can pass opacity measurement requirements set by USEPA; 2) the new simpler calibration method generates values within 5% in opacity on average compared to opacity values from the reference transmissometer; 3) non-uniform background color dominates the uncertainty of opacity measurements, and such uncertainty is wavelength dependent; and 4) the diffusive scattering parameter, used in DOM’s transmission model, is lower for black plumes than white plumes, and is wavelength dependent. These results improve our understanding of using DSCs and the parameters that introduce uncertainty to DOM to improve measurements of plume opacity that can improve protection of human health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".