Open-Path Tunable Diode Laser Absorption Spectroscopy for Acquisition of Fugitive Emission Flux Data
Bibliographic record
Abstract
Air pollutant emission from unconfined sources is an increasingly important environmental issue. The U.S. Environmental Protection Agency (EPA) has developed a ground-based optical remote-sensing method that enables direct measurement of fugitive emission flux from large area sources. Open-path Fourier transform infrared spectroscopy (OP-FTIR) has been the primary technique for acquisition of pollutant concentration data used in this emission measurement method. For a number of environmentally important compounds, such as ammonia and methane, open-path tunable diode laser absorption spectroscopy (OP-TDLAS) is shown to be a viable alternative to Fourier transform spectroscopy for pollutant concentration measurements. Near-IR diode laser spectroscopy systems offer significant operational and cost advantages over Fourier transform instruments enabling more efficient implementation of the measurement strategy. This article reviews the EPA's fugitive emission measurement method and describes its multipath tunable diode laser instrument. Validation testing of the system is discussed. OP-TDLAS versus OP-FTIR correlation testing results for ammonia (R2 = 0.980) and methane (R2 = 0.991) are reported. Two example applications of tunable diode laser-based fugitive emission measurements are presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".