The effect of indoor daylight spectrum and intensity on viability of indoor pathogens on different surface materials
Bibliographic record
Abstract
ABSTRACT Built environments play a key role in the transmission of infectious diseases. Ventilation rates, air temperature and humidity affect airborne transmission while cleaning protocols, material properties and light exposure can influence viability of pathogens on surfaces. We investigated how indoor daylight intensity and spectrum through electrochromic (EC) windows can impact the growth rate and viability of indoor pathogens on different surface materials (polyvinyl chloride (PVC) fabric, polystyrene (PS), and glass) compared to traditional blinds. Our results showed that tinted EC windows let in higher energy, shorter wavelength daylight than those with clear window and blind. The growth rates of pathogenic bacteria and fungi were significantly lower in spaces with EC windows compared to blinds: nearly 100% growth rate reduction was observed when EC windows were in their clear state followed by 41-100% reduction in bacterial growth rate and 26-42% reduction in fungal growth rate when EC windows were in their darkest tint. Moreover, bacterial viabilities were significantly lower on PVC fabric when they were exposed to indoor light at EC-tinted window. These findings are deemed fundamental to the design of healthy modern buildings, especially those that encompass sick and vulnerable individuals. P ractical I mplications Light is an important factor that influences occupant health. Healthcare Associated Infections (HAI) bring substantial costs on the healthcare systems hence new disinfection methods are always needed to minimize fomites especially with the increasing antibiotic resistance. We found that indoor light modulated by the EC smart windows can significantly reduce the growth rate and viability of pathogenic bacteria and fungi, which is mainly due to the high energy blue light spectrum at wavelength of 400-500nm. Pathogenic fungi are found to be more affected by the indoor light intensity, while indoor bacteria on surfaces are more susceptible to the light spectrums. These results also demonstrate the promising potential of indoor daylight exposure as an alternative for fomite disinfection strategy and expand the benefits of EC window as part of healthy building design in the future.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".