Evaluation of Alternative Annular Denuder Systems for the Collection of Fine Aerosol Particulate Matter
Bibliographic record
Abstract
Due to the complex manner in which secondary inorganic aerosols (SIAs) form, a need exists to develop a methodology to measure PM2.5 emissions from agricultural operations to better understand the contribution of SIAs to the PM2.5 fraction. When sampling particulate matter (PM), annular denuder systems (ADS) are a United States Environmental Protection Agency (US EPA) approved system used to measure both gaseous and particulate components of aerosols. While collecting basic gases, such as ammonia, using nine denuders was feasibly demonstrated in poultry housing units but the ability of additional denuders to accurately collect the SIAs on the filters is yet to be demonstrated. An experiment was designed to assess particle deposition behaviors throughout three different ADS configurations. It was determined that the nine denuder configuration resulted in particles being impacted and retained, mainly in the U-bend junctions, prior to reaching the filters with only 87.2% of PM2.5 reaching the filter pack. The US EPA-prescribed ADS configuration had 99.4% of PM2.5 reaching the filters, indicating that there is an impact due to the U-Bend addition to the system. It was further demonstrated that having additional denuders in series with no U-Bend had no significant impact on PM2.5 deposition on the filters with 98.9% of PM2.5 being collected.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".