Improved method for UV lamps irradiance characterization and life-cycle assessment for in-duct microbial inactivation
Bibliographic record
Abstract
Accurate characterization of ultraviolet (UV) irradiance is essential for the effective design and evaluation of in-duct air disinfection systems. This study aims to develop a novel angular correction factor to address sensor detection-angle limitations. Experimental irradiance measurements were conducted for 222 nm excimer lamp, 254 nm mercury lamp, 265 nm UVC LED and 365 nm UVA LEDs, and their environmental sensitivity was assessed under varying air temperature, velocity, and relative humidity. The comparative life-cycle assessment evaluated energy, costs, and environmental impacts for achieving a 3-log microbial reduction. Findings show that the correction factor reduced overestimation by up to 30% near lamp surfaces, with a maximum error (18%) observed farther from the 254 nm lamp. Model-based scalability from single LED modules to full arrays yielded an average relative error of ±13%, supporting flexible LED arrangements. The 254 nm lamp output increased by 18% as air temperature rose (25–35 °C) and decreased nearly to 80% as velocity increased (0.5–2 m/s). In contrast, the 222 nm lamp and both LED systems showed minimal sensitivity, indicating greater operational stability under dynamic conditions. While LEDs and 222 nm offer their own advantages, they require higher energy and cost to achieve equivalent disinfection. Therefore, under continuous in-duct application, the 254 nm lamp is the most sustainable and cost-effective option among those tested. This study provides a validated, building-scale framework that improves measurement accuracy and supports energy-aware implementation, offering actionable guidance for optimized, sustainable deployment of UVGI in building ventilation systems. • Sensor detection-angle limits can overestimate measured irradiance by up to 30%. • A novel correction factor with improved UV irradiance accuracy was proposed. • UV dose modeling reveals trade-offs between peak intensity and spatial coverage. • 254 nm lamp is the most economical and sustainable for continuous air disinfection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".