Search Results (222)

Artificial Light at Night (ALAN) is a recent issue of concern for researchers primarily working on the anthropogenic impacts on animal and ecosystem health. Our concern is associated with the ALAN exposure to an aquatic ecosystem by disrupting the natural dark–light cycle, which is essential for maintaining the overall health of the ecosystem and its inhabitants. In this study, we have attempted to understand the adverse consequences of ALAN in inducing neuro-behavioural stress in a freshwater prawn species (aquatic arthropod) Macrobrachium lamarrei by considering grooming behaviour, a well-established indicator of neurological stress in animals. Our results show that continuous ALAN exposure (for seven days) can increase collective grooming activity in Macrobrachium lamarrei over time. In our experiment, we have used two intensities of ALAN (50 and 120 lux). Although the response (in terms collective grooming) to both intensities are apparently different, our fundamental hypothesis is confirmed, where it is evident that prolonged light exposure can induce an elevation in cumulative grooming performances in a freshwater prawn population. Full article

Light-emitting diodes (LEDs) in agricultural systems mainly contribute their capacity to create a precise and constant light spectral environment. However, the potential of LED in crop production was underestimated. LEDs serve not only as efficient artificial light sources for plant growth, but are also a good tool for enhancing biomass production with limited energy consumption. This article reviewed innovative applications of LED in facility agriculture, e.g., plant factory, and greenhouse. Compared to conventional application of LED, innovative lighting strategies such as intermittent lighting, night break, continuous lighting, alternate lighting, dynamic lighting, and end-of-day (EOD) far-red provided by LED light can elevate the production efficiency effectively. However, the scientific explanation of the above lighting strategies remains to be clearly revealed, providing theoretical support for the further optimization of conducting parameters. This review summarizes the physiological effects of different lighting strategies on crop cultivation and illustrates their future application in facility agriculture, aiming to provide novel methods for elevating the energy utilization efficiency and lowering the cost in facility agriculture using artificial light. Full article

Reliable early-stage yield forecasts are essential in precision viticulture, enabling timely interventions such as harvest planning, canopy management, and crop load regulation. Since grape yield is directly related to the number and size of bunches, the early detection of inflorescences and bunches, carried out even before flowering, provides a valuable foundation for estimating potential yield far in advance of veraison. Traditional yield prediction methods are labor-intensive, subjective, and often restricted to advanced phenological stages. This study presents a deep learning-based approach for detecting grapevine inflorescences and bunches during early development, assessing how phenological stage and illumination conditions influence detection performance using the YOLOv11 architecture under commercial field conditions. A total of 436 RGB images were collected across two phenological stages (pre-bloom and fruit-set), two lighting conditions (daylight and artificial night-time illumination), and six grapevine cultivars. All images were manually annotated following a consistent protocol, and models were trained using data augmentation to improve generalization. Five models were developed: four specific to each condition and one combining all scenarios. The results show that the fruit-set stage under daylight provided the best performance (F1 = 0.77, R² = 0.97), while for inflorescences, night-time imaging yielded the most accurate results (F1 = 0.71, R² = 0.76), confirming the benefits of artificial lighting in early stages. These findings define optimal scenarios for early-stage organ detection and support the integration of automated detection models into vineyard management systems. Future work will address scalability and robustness under diverse conditions. Full article

Light pollution is a rapidly growing environmental challenge, with the global brightness of the night sky increasing by an average of 9.6% per year. This study assessed the ecological impact of artificial light at night (ALAN) on protected areas in Poland, including all 23 national and 125 landscape parks, from 2012 to 2023. Based on VIIRS satellite radiance data and modelled sky surface brightness (Sa), we developed and applied the Ecological Light Pollution (ELP) scale, which classifies areas into four classes of ecological impact: strong (ELP-A), pronounced (ELP-B), noticeable (ELP-C), and weak or none (ELP-D). The analysis revealed that 38.5% of protected areas are affected by artificial skyglow at levels classified as ELP-B or ELP-C. Under cloudy conditions, which intensify light pollution effects, 22% of national parks and 41.8% of landscape parks fell into these classes. Notably, Wielkopolski National Park exhibited the most pronounced impact (ELP-B) even under clear skies, primarily due to its proximity to the Poznań metropolitan area. In contrast, Bieszczadzki and Białowieski National Parks recorded near-natural darkness (ELP-D). These light pollution effects can disrupt nocturnal species’ behaviour, reduce biodiversity, and degrade opportunities for dark-sky tourism. The findings emphasise the need for targeted mitigation, including stricter outdoor lighting regulations, formal dark-sky protection zones, and public education to preserve protected areas’ ecological integrity and tourism potential. Full article

The alterable light/dark cycle in a plant factory with artificial lighting eliminates the traditional concept of day and night in nature. Adjusting the light/dark cycle to closely align with the inherent circadian rhythm of plants can enhance biomass accumulation. In this study, we examined the effects of different light/dark cycles on the photosynthetic performance, growth, and energy use efficiency of two hydroponic lettuce cultivars (Lactuca sativa L. cv. ‘Frillice’ and ‘Crunchy’). The lettuces were subjected to four light/dark cycle treatments—16 h light/8 h dark (L₁₆D₈, as control), 12 h light/6 h dark (L₁₂D₆), 8 h light/4 h dark (L₈D₄), and 4 h light/2 h dark (L₄D₂), all under LED lamps with white combined red chips at the same light intensity of 250 μmol m⁻² s⁻¹. Photosynthetic performance and growth index were measured during the slow and rapid growth stages, corresponding to days 9 and 21 after transplanting, respectively. For Frillice, L₁₂D₆ achieved the highest shoot dry weight and light and electricity energy use efficiencies on days 9 and 21 after transplanting, primarily due to the largest leaf area, leaf number, and net photosynthetic rate. For Crunchy, L₁₂D₆ and L₈D₄ increased shoot fresh and dry weights due to larger leaf area and leaf number on day 9 after transplanting compared with L₁₆D₈. Subsequently, the lettuces in L₁₆D₈ exhibited a rapid increase in leaf area and leaf number, along with a high net photosynthetic rate during the rapid growth stage, resulting in fast shoot biomass accumulation. There were no significant differences in the shoot dry weight and energy use efficiency between L₁₆D₈ and L₁₂D₆ on day 21 after transplanting. Two lettuce cultivars in L₁₆D₈ both exhibited the highest water use efficiency on day 21 after transplanting. In conclusion, the light/dark cycle lighting can alter lettuce biomass accumulation by modifying plant morphology and leaf net photosynthetic rate. Additionally, the physiological response to the light/dark cycle was cultivar-dependent. Our findings provide valuable insights for optimizing hydroponic lettuce production to achieve high yield in LED plant factories. Full article

The long-term monitoring of nighttime lights is essential for understanding sources of light pollution. Nighttime lights observed in space are affected by atmospheric conditions as they transmit from the Earth surface through clouds and aerosols to the top of the atmosphere. In this study, based on the monthly cloud-free VIIRS/DNB products, we analyzed the long-term nighttime lights in Hong Kong (2012–2020). We found that the monthly variations in nighttime lights were large, especially in bright regions. The 12-month average of nighttime lights ranged from 13.0 to 18.9 nWcm⁻²sr⁻¹. Public transportation facilities, such as port facilities and the airport, were the brightest, twice as bright as other urban areas. Public residential areas were slightly brighter than private ones. These urban areas were at least four times brighter than undeveloped regions, showing a significant alteration in light at night due to artificial facilities. Further, we used an unsupervised clustering method to identify specific patterns. While nighttime lights were stable in most regions, increasing trends were found at construction sites of a new artificial island and the airport expansion. Abnormal patterns, such as wildfires, were also recognized. We found that the background nighttime lights were brighter in wet months (e.g., April) and dimmer in dry months (e.g., January). The amount of water in the atmosphere affects nighttime light scattering, with a linear correlation (R = 0.68) between humidity and the occurrence of bright nighttime lights each month. The diverse sources and variations in nighttime lights call for continuous monitoring and advanced analytical methods to better understand their environmental and societal impacts. Full article

Rising energy costs and a desire for sustainability have led municipalities to implement measures such as reducing artificial public lighting at night (ALAN). However, under pressure from residents, some municipalities are being forced to reverse their decision. Although important, the social relationship of individuals to public lighting and its impact on decisions to reduce or modify is still little considered. This study examines how residents of a municipality in southern France perceive a policy of reducing artificial night lighting from 1 a.m. to 5 a.m. A survey of 91 participants was used to assess their understanding of the positive (safety, nightlife…) and negative (effects on biodiversity, health…) impacts of ALAN, their attitudes towards the measure, and their opinions regarding its extension. The results indicate that participants who recognise the negative impacts of ALAN are more inclined to support its reduction (r = 0.56, p < 0.001; ß = 0.44, t = 3.12, p = 0.003), while those who appreciate its positive impacts are more reluctant to accept the measure (r = −0.57, p < 0.001; ß = −0.70, t = −5.30, p < 0.001). Hierarchical regression analysis revealed that specific attitudes towards these lighting-off measures were the main predictor of support for the policy (ß = 0.60, t = 6.70, p < 0.001) than general beliefs about the impacts of lighting. These results suggest that implementing such policies requires balancing ecological considerations with social expectations and building trust between local authorities and residents to improve public acceptance of lighting strategies. This study contributes to the still scarce research on the positioning of individuals in real projects to reduce or even switch off lighting and demonstrates the value of studying the relationship between individuals and policies to reduce public lighting. Full article

Driving at night is riskier in terms of crash involvement than it is during the day. Fortunately, it is clearly established that illumination on roadways can reduce the number and severity of nighttime crashes. However, state and municipal departments of transportation (DOTs) lack the available illumination data. Therefore, the objective of this research is threefold, as follows: (i) to develop machine learning models that use readily available roadway characteristic data to predict the severity of nighttime crashes; (ii) determine the effect that illumination has on crash severity; and (iii) develop a tool to assist DOT decision makers in collecting illumination data. To accomplish this objective, we have extracted data from the Texas Department of Transportation (TxDOT) Crash Record Information System (CRIS) database, which was then further split into a training and a test dataset. Then, seven machine learning techniques, namely binary logistic regression, k-nearest neighbors, naïve Bayes, random forest, artificial neural network, Extreme Gradient Boosting (XGBoost), and a Long Short-Term Memory (LSTM) model, were all applied to the unseen test data. The random forest model produced the most promising results by predicting severe crashes with 97.6% accuracy. In addition, we conducted a pilot study to test the collection of illumination data using a light meter. In the future, we aim to complete the development of a smartphone application, which can be used in conjunction with the random forest model presented in this paper, to collect crowdsourced illumination data and predict nighttime crash hotspots. This may assist DOT decision makers to prioritize funding for illumination at the hot spots. Full article

Artificial light at night (ALAN) is an expanding environmental issue, particularly in urban areas. This review aimed to present the state of the art regarding the impact of ALAN on specific and interrelated aspects related to physiological processes and life cycle events in tree species. The reviewed studies highlighted the multifaceted effects of artificial light on plants, offering insights and perspectives to guide future research in this evolving and stimulating field. ALAN disrupts circadian rhythms, alters photoperiodic responses, and affects photosynthesis and carbohydrate metabolism. Changes in phenology such as delayed senescence and altered budburst timing demonstrated species-specific responses, often compounded by other urban stressors like heat and drought. Despite an increased interest, knowledge gaps remain concerning the species-specific responses and the effects of light spectra as well as the long-term consequences on tree physiology. These gaps highlight the need for integrated research approaches and urban planning strategies to mitigate ALAN effects, ensuring the resilience of urban trees and preserving ecosystem services in the context of growing urbanization and climate change. Full article

Chrysanthemum × morifolium Ramat. is a globally renowned ornamental flower. It includes numerous varieties, most of which are typical short-day (SD) plants, and the flowering characteristics of different chrysanthemum varieties in response to the photoperiod vary greatly. In this study, seven representative chrysanthemum varieties were selected for a comparative analysis of flowering traits under long-day conditions (16 h/8 h day/night) and short-day conditions (12 h/12 h day/night). It was found that three varieties (‘A44’, ‘C60’, and ‘183’) belonged to obligatory short-day varieties and four varieties (‘A20’, ‘C1’, ‘C27’, and ‘C31’) belonged to facultative short-day varieties. The short-day conditions not only induced earlier flowering but also improved flowering quality in the facultative SD varieties. Different chrysanthemum varieties required different light conditions to complete the vegetative stage and reach the floral competent state. Seven chrysanthemum varieties, ‘A44’, ‘C60’, ‘183’, ‘A20’, ‘C1’, ‘C27’, and ‘C31’, reached a floral competent state in the L20, L20, L22, L22, L18, L20, and L24 periods, respectively, and were most sensitive to SD induction at this time. The expression patterns of key floral genes in the photoperiod pathway were analyzed and it was found that CmCRY1, CmCRY2, CmGI1, CmGI2, and CmCO were mainly expressed in leaves. Then, comparing the expression levels of these genes under LD and SD conditions, the expression of CmGI1, CmGI2, CmCO, and CmFTL were not significantly induced in the obligatory SD varieties, while the expression of them in the facultative SD varieties were induced by SD conditions. This may be the reason why the facultative varieties could respond to SD conditions more quickly to complete the floral transition. In addition, SD induction under different photoperiodic conditions and growth states resulted in differences in the phenotype of flowering. This result provides guidance for the artificial regulation of chrysanthemum flowering and improvement of ornamental quality, as well as clues for analyzing the flowering mechanism of chrysanthemums under different photoperiod conditions. Full article

This study investigates the impact of artificial light at night (ALAN) along the Sicilian coasts, using satellite data from 2016 to 2023, focusing on three distinct spatial domains: terrestrial areas within 1 km from the coastline, marine areas extending up to 1 km offshore, and marine areas up to 1 nautical mile from the coast. In coastal zones, ALAN is a significant anthropogenic pressure with potentially detrimental effects on ecosystems. By integrating satellite data with geographic datasets such as Corine Land Cover (CLC), Natura 2000 protected areas, and Posidonia oceanica meadows, this study aims to characterize and analyse the temporal and spatial variations in ALAN across these domains. The findings reveal substantial differences in light pollution between domains and over time, with coastal terrestrial areas exhibiting the highest levels of ALAN. In contrast, marine areas further offshore experience reduced light pollution, particularly within the 1-nautical-mile domain. This study also indicates that protected areas, especially those within the Natura 2000 network, show significantly lower ALAN levels than non-protected areas, highlighting the effectiveness of conservation efforts. Statistical analyses, including ANOVAs, demonstrate that factors such as geographic domain, year, province, and CLC classes significantly influence ALAN distribution. This study advocates for considering ALAN as a critical factor in environmental impact assessments, such as those under the Maritime Spatial Planning Directive (MSP) and Marine Strategy Framework Directive (MSFD), providing valuable insights to support policies aimed at mitigating the environmental impact of light pollution on coastal and marine ecosystems. Full article

The light pollution of the night sky is already a widespread phenomenon, the spatial extent and magnitude of which are increasingly represented in the form of thematic maps and cartographic visualization. Its leading cause needs to be correctly designed or adequately installed outdoor lighting. The problem of excessive artificial light emission at night, together with its adverse effects, has already reached such a level that it has become necessary to develop usable and comprehensible methods for the cartographic representation of the distribution of the phenomenon. In practice, there are several ways to measure the intensity of this pollution. However, there are no uniform legal standards for the use of outdoor lighting and no guidance and guidelines for the visualization of measurement data. Such visualization should provide a consistent, reliable, and, above all, readable picture of the phenomenon adapted to the needs of different audiences. Examples of the representation of the results of measurements of light pollution of the night sky can be found in the literature or a few atlases. Still, they often differ in color scales, value divisions, and measurement units used. This paper reviews the scales and units available in the literature to describe this phenomenon. The differences between the approaches of specialists from different branches and their influence on the final interpretation of the data are also presented. In addition, an authorial solution is proposed to standardize methods of cartographic visualization of the spatial distribution of light smog measurement results. The article attempts to draw attention to the importance of the graphical description of light smog, which will shortly be the subject of increasing research and work on the unification of cartographic communication. Full article

Artificial light at night (ALAN) and noise pollution are two important stressors associated with urbanisation that can have a profound impact on animal behaviour and physiology, potentially disrupting biological rhythms. Although the influence of ALAN and noise pollution on daily activity patterns of songbirds has been clearly demonstrated, studies often focus on males, and the few that examined females have not included the potential influence of males on female activity patterns. Using free-living pairs of great tits (Parus major) as a model, we examined for the first time the effects of ALAN and noise pollution and their interaction on the onset of (vocal) activity in both members of a pair. We focused on the egg-laying phase, when both sexes are most vocally active. The onset of male dawn song, female emergence time from the nest box and the onset of female calling in the nest box were measured and used as a proxy for the chronotype. The repeatabilities for all chronotype proxies were high, with higher repeatabilities for males. Consistent with previous studies, ALAN advanced the onset of male dawn song, while it did not elicit a strong response in female emergence time. Additionally, our results suggest an indirect effect of ALAN on the onset of female vocal activity via acoustic interaction with the male. Noise pollution advanced the emergence time in females, while an interaction between ALAN and noise pollution was found for the onset of female calling. In agreement with previous studies, several covariables were shown to have an influence on the activity onset. Taking several proxies for chronotype into account, this study has provided robust evidence of effects of ALAN on male and female cavity-nesting songbirds during the egg-laying period. Full article

Artificial light at night (ALAN), also known as light pollution, is a growing environmental problem worldwide. However, only a few studies have examined whether soil organisms that search for food at the surface at night can be affected by ALAN. We investigated the effects of ALAN on the above-ground foraging activity of anecic earthworms (Lumbricus terrestris), on the soil water infiltration and on the germination and growth of a cover crop (Phacelia tanacetifolia). In a full-factorial greenhouse experiment, we tested four factors: ALAN (about 5 lx during the night vs. total darkness), earthworms (two specimens vs. none), plant species (Phacelia alone vs. mixed with ragweed Ambrosia artemisiifolia) and sowing depth (surface-sown vs. sown in 5 cm depth). Data were analysed using multifactorial ANOVAs. Earthworms removed 51% less surface litter under ALAN than under dark conditions. ALAN had no effect on Phacelia germination but resulted in increased height growth and biomass production when the seeds were buried. Earthworms reduced Phacelia germination and biomass production. ALAN reduced water leaching through the experimental units, probably due to interactions between the subsurface casts and plant roots. We conclude that ALAN, as emitted from streetlights, can lead to complex ecological effects in ecosystems that merit further investigation. Full article

Night lighting is essential for urban life, and the occurrence of faults can significantly affect the presentation of lighting effects. Many reasons can cause lighting faults, including the damage of lamps and circuits, and the typical manifestation of the faults is that the lights do not light up. The current troubleshooting mainly relies on artificial visual inspection, making detecting faults difficult and time-consuming. Therefore, it is necessary to introduce technical means to detect lighting faults. However, current research on lighting fault detection mainly focuses on using non-visual methods such as sensor data analysis, which has the disadvantages of having a high cost and difficulty adapting to large-scale fault detection. Therefore, this study mainly focuses on solving the problem of the automatic detection of night lighting faults using machine vision methods, especially object detection methods. Based on the YOLOv5 model, two data fusion models have been developed based on the characteristics of lighting fault detection inverse problems: YOLOv5 Channel Concatenation and YOLOv5 Image Fusion. Based on the dataset obtained from the developed automatic image collection and annotation system, the training and evaluation of these three models, including the original YOLOv5, YOLOv5 Channel Concatenation, and YOLOv5 Image Fusion, have been completed. Research has found that applying complete lighting images is essential for the problem of lighting fault detection. The developed Image Fusion model can effectively fuse information and accurately detect the occurrence and area of faults, with a mAP value of 0.984. This study is expected to play an essential role in the intelligent development of urban night lighting. Full article