Quantifying the impact of the transition to LED lighting on night sky brightness and colour using ground-based measurements and satellite imagery
Bibliographic record
Abstract
• The impact of the transition from HPS to LED on light pollution is unknown. • We quantified changes in night time brightness and color using remote sensing. • We compared three sensors: TESS-4C and LANcube (ground), SDGSAT-1 (satellite). • We applied machine learning to separate between cloudy and clear nights. • We found that the transition to LED resulted with brighter and whiter lights. Nighttime light pollution is gaining significant interest in the scientific community and the public due to its harmful effects on human health, ecosystems, and leisure activities. Recently, many countries worldwide have been retrofitting their streetlights with light emitting diodes (LEDs) to enable smart street lighting while reducing energy use for street lights. However, little is known about the rate of change in brightness and even less about the new composition of the nighttime light spectrum. In this study, we investigated the changes of brightness and colour of light pollution at night in a major road interchange, conducting measurements before and after the transition from high pressure sodium (HPS) yellow street lights to white LED lights. We used two ground-based photometers, one fixed (TESS-4C) and one mobile (LANcube), which provided long-term record of the artificial light at night at this location. We also quantified changes in nighttime lights as observed by the multispectral nighttime lights satellite SDGSAT-1. To ensure non-biased conditions, we used a random forest model to discriminate between clear and cloudy nights for our analysis of the TESS-4C measurements. These instruments offer a comprehensive analysis by measuring night-time brightness in various units and capturing distinct response spectra across different colour bands. We found an increase in nighttime brightness after the interchange was equipped with LED lamps compared to the previous HPS lamps. However, this increase was only identified by the ground sensors and not by the satellite. All sensors identified a shift in the emitted spectrum towards shorter wavelengths, with an increase in the red/green ratio being the most consistent across the sensors in quantifying the spectral change. We discuss the advantages of each instrument used and explore the expected changes in artificial nighttime light for areas that are retrofitting their streetlights with similar LED lamps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".