Several population-level studies explored the association between breast cancer (BC) incidence and artificial light-at-night (ALAN), and found higher BC rates in more lit areas. Most of these studies used ALAN satellite data, available from the United States Defence Meteorological Satellite Program (US-DMSP), while, in recent years, higher-resolution ALAN data sources, such as Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS-DNB), have become available. The present study aims to determine whether the use of different ALAN data sources may affect the BC–ALAN association. As the test case, we use data on BC incidence rates in women residing in the Greater Haifa Metropolitan Area (GHMA; Israel), matching them with US-DMSP and VIIRS-DNB data on ALAN intensities, and controlling for several potential confounders, including age, fertility, and socio-economic status (SES). Both ordinary least squares (OLS) and spatial dependency models were used in the analysis. ALAN emerged as a stronger predictor of BC rates in models based on better-resolution VIIRS-DNB estimates (t > 6.035; p < 0.01) than in models based on coarser US-DMSP data (t < 4.196; p < 0.01).

Several population-level studies revealed a positive association between breast cancer (BC) incidence and artificial light at night (ALAN) exposure. However, the effect of short-wavelength illumination, implicated by laboratory research and small-scale cohort studies as the main driving force behind BC–ALAN association, has not been supported by any population-level study carried out to date. We investigated a possible link between BC and ALAN of different subspectra using a multi-spectral year-2011 satellite image, taken from the International Space Station, and superimposing it with year-2013 BC incidence data available for the Great Haifa Metropolitan Area in Israel. The analysis was performed using both ordinary least square (OLS) and spatial dependency models, controlling for socioeconomic and locational attributes of the study area. The study revealed strong associations between BC and blue and green light subspectra (B = 0.336 ± 0.001 and B = 0.335 ± 0.002, respectively; p < 0.01), compared to a somewhat weaker effect for the red subspectrum (B = 0.056 ± 0.001; p < 0.01). However, spatial dependency models, controlling for spatial autocorrelation of regression residuals, confirmed only a positive association between BC incidence and short-wavelength (blue) ALAN subspectrum (z = 2.462, p < 0.05) while reporting insignificant associations between BC and either green (z = 1.425, p > 0.1) or red (z = −0.604, p > 0.1) subspectra. The obtained result is in line with the results of laboratory- and small-scale cohort studies linking short-wavelength nighttime illumination with circadian disruption and melatonin suppression. The detected effect of blue lights on BC incidence may help to develop informed illumination policies aimed at minimizing the adverse health effects of ALAN exposure on human health.

Artificial nighttime lights, emitted from residential, industrial, commercial and entertainment areas, and captured by satellites, have proven to be a reliable proxy for on-ground human activities

Artificial night-time light (NTL), emitted by various on-ground human activities, has become intensive in many regions worldwide. Its adverse effects on human and ecosystem health crucially depend on the light spectrum, making the remote discrimination between different lamp types a highly important task. However, such studies remain extremely limited, and none of them exploit freely available satellite imagery. In the present analysis, the possibility to remotely assess the relative contribution of different lamp types into outdoor lighting is tested. For this sake, we match two data sources: (i) the radiometrically calibrated RGB image provided by the ISS (coarse spectral resolution data), and (ii) a set of in situ measurements with detailed spectral signatures conducted by ourselves (fine spectral resolution data). First, we analyze the fine spectral resolution data: using spectral signatures of standard lamp types from the LICA UCM library as endmembers, we perform an unmixing analysis upon NTL in situ measurements; by this, we obtain the estimates for relative contributions of the standard lamp types in each examined in situ measurement. Afterward, we focus on the coarse spectral resolution data: by using various types of statistical models, we predict the estimated relative contributions of each lamp type via RGB characteristics of spatially corresponding pixels of the ISS image. The built models predict sufficiently well (with R² reaching ~0.87) the contributions of two standard lamp types: high-pressure sodium (HPS) and metal-halide (MH) lamps, the most widespread lamp types in the study area (Haifa, Israel). The restored map for HPS allocation demonstrates high concordance with the network of municipal roads, while that for MH shows notable coincidence with the industrial facilities and the airport.