Indoor passive panel technologies: test methods to evaluate toluene and formaldehyde removal and re-emission, and by-product formation
Bibliographic record
Abstract
This test method was developed by the National Research Council of Canada (NRC) to determine the initial performance of indoor passive panel technologies (IPPT) in their removal of formaldehyde and toluene gas as well as assess re-emissions of captured gases and harmful by-product formation. This new protocol builds on existing sorption-based standards to evaluate performance of both sorptive- and PCO-based IPPT in removing indoor formaldehyde and toluene. Considering that some PCO-based IPPT may be influenced by sorptive effect, this new protocol differentiates removal performance attributed to light illumination and sorption. This protocol uses an improved chamber with proper control of air velocity and turbulence level to simulate indoor conditions instead of a photoreactor chamber. In addition, the chamber utilises indoor lighting (as opposed to UV) as a source of illumination to better represent indoor applications. By testing by-product formation, this new protocol is the first to address protection of building occupants to harmful pollutant exposures. The project was funded by the Government of Canada’s Clean Air Regulatory Agenda to develop three protocols for evaluating the effectiveness of “IAQ Solutions”. It was prepared by NRC researchers under the guidance of a Technical Advisory Committee (TAC) assembled for this task, whose members included participants representing Federal and Provincial Agencies, Industry Associations, Non-Governmental Organizations (NGOs), Municipal governments, and Standards Association from Canada. The protocol also considered consultations of stakeholders comprising builders, researchers, industry partners and health professionals. The contributions of the TAC members and stakeholders to this work are gratefully acknowledged. Compliance to the test method and to the data interpretation developed in this test method is voluntary.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".