Daylight and Electric Lighting in Primary and Secondary School Classrooms in the UK-An Observational Study.
Luke L A PriceAnnegret Hella Dahlmann-NoorMarina KhazovaPublished in: International journal of environmental research and public health (2024)
Only a few recent studies report direct assessment or monitoring of light levels in the indoor learning environment, and no consensus exists on minimum exposures for children's health. For instance, myopia is a common progressive condition, with genetic and environmental risk factors. Reduced daylight exposure, electric lighting changes, increased near-work for school children, greater academic focus, and use of display screens and white boards may have important detrimental influences. Published assessment methods had varied limitations, such as incomplete compliance from participants wearing light loggers for extended periods. Climate-Based Daylight Modelling is encouraged in UK school design, but design approaches are impractical for post-occupancy assessments of pre-existing classrooms or ad hoc modifications. In this study, we investigated the potential for direct assessment and monitoring of classroom daylight and lighting measurements. Combined with objective assessments of outdoor exposures and class time use, the classroom data could inform design and light exposure interventions to reduce the various health impacts of inadequate daylight exposure. The relevant environmental measure for myopia depends on the hypothesized mechanism, so the illuminance, spectral distribution, and temporal light modulation from the electric lighting was also assessed.
Keyphrases
- air pollution
- mental health
- physical activity
- risk factors
- human health
- healthcare
- public health
- particulate matter
- genome wide
- multiple sclerosis
- climate change
- health information
- young adults
- cross sectional
- systematic review
- dna methylation
- gene expression
- high throughput
- optical coherence tomography
- magnetic resonance imaging
- electronic health record
- computed tomography
- machine learning
- big data
- atomic force microscopy
- artificial intelligence
- copy number