Search Results (105)

Mangrove forests serve as critical ecological barriers in coastal zones and play a vital role in global blue carbon sequestration strategies. In recent decades, China’s mangrove ecosystems have experienced complex interactions between degradation and restoration under intense coastal urbanization and systematic conservation efforts. However, the long-term spatiotemporal patterns and driving mechanisms of mangrove ecosystem health changes remain insufficiently quantified. This study developed a multi-temporal analytical framework using Landsat imagery (1986–2021) to derive kernel normalized difference vegetation index (kNDVI) time series—an advanced phenological indicator with enhanced sensitivity to vegetation dynamics. We systematically characterized mangrove growth patterns along China’s southeastern coast through integrated Theil–Sen slope estimation, Mann–Kendall trend analysis, and Hurst exponent forecasting. A Deep Forest regression model was subsequently applied to quantify the relative contributions of environmental drivers (mean annual sea surface temperature, precipitation, air temperature, tropical cyclone frequency, and relative sea-level rise rate) and anthropogenic pressures (nighttime light index). The results showed the following: (1) a nationally significant improvement in mangrove vitality (p < 0.05), with mean annual kNDVI increasing by 0.0072/yr during 1986–2021; (2) spatially divergent trajectories, with 58.68% of mangroves exhibiting significant improvement (p < 0.05), which was 2.89 times higher than the proportion of degraded areas (15.10%); (3) Hurst persistence analysis (H = 0.896) indicating that 74.97% of the mangrove regions were likely to maintain their growth trends, while 15.07% of the coastal zones faced potential degradation risks; and (4) Deep Forest regression id the relative rate of sea-level rise (importance = 0.91) and anthropogenic (nighttime light index, importance = 0.81) as dominant drivers, surpassing climatic factors. This study provides the first national-scale, 30 m resolution assessment of mangrove growth dynamics using kNDVI, offering a scientific basis for adaptive management and blue carbon strategies in subtropical coastal ecosystems. Full article

Obesity and metabolic disorders remain major global health concerns, traditionally attributed to excessive caloric intake and poor diet quality. Recent studies emphasize that the timing of meals plays a crucial role in determining metabolic health. This review explores chrononutrition, a growing field that examines how food intake patterns interact with endogenous circadian rhythms to influence energy balance, glucose and lipid metabolism, and cardiometabolic risk. The circadian system, which includes a central clock in the suprachiasmatic nucleus and peripheral clocks in metabolic tissues, regulates physiological functions on a 24 h cycle. While light entrains the central clock, feeding schedules act as key synchronizers for peripheral clocks. Disrupting this alignment—common in modern lifestyles involving shift work or late-night eating—can impair hormonal rhythms, reduce insulin sensitivity, and promote adiposity. Evidence from clinical and preclinical studies suggests that early time-restricted eating, where food intake is confined to the morning or early afternoon, offers significant benefits for weight control, glycemic regulation, lipid profiles, and mitochondrial efficiency, even in the absence of caloric restriction. These effects are particularly relevant for populations vulnerable to circadian disruption, such as adolescents, older adults, and night-shift workers. In conclusion, aligning food intake with circadian biology represents a promising, low-cost, and modifiable strategy to improve metabolic outcomes. Integrating chrononutrition into clinical and public health strategies may enhance dietary adherence and treatment efficacy. Future large-scale studies are needed to define optimal eating windows, assess long-term sustainability, and establish population-specific chrononutritional guidelines. Full article

The satellite remote sensing of Aerosol Optical Depth (AOD) products is crucial in environmental monitoring and atmospheric pollution research. However, data gaps in AOD products from satellites like Fengyun significantly hinder continuous, seamless environmental monitoring capabilities, posing challenges for the long-term analysis of atmospheric pollution trends, responses to sudden ecological events, and disaster management. This study aims to develop a high-precision method to fill spatial AOD missing values and generate daily full-coverage AOD products for the Beijing–Tianjin–Hebei region in 2021 by integrating multi-dimensional data, including meteorological models, multi-source remote sensing, surface conditions, and nighttime light parameters, and applying machine learning methods. A comparison of five machine learning models showed that the random forest model performed optimally in AOD inversion, achieving a root mean square error (RMSE) of 0.11 and a coefficient of determination (R²) of 0.93. Seasonal evaluation further indicated that the model’s simulation was best in winter. Variable importance analysis identified relative humidity (RH) as the most critical factor influencing model results. The reconstructed full-coverage AOD product exhibited a spatial distribution trend of significantly higher values in the southern plain areas compared to mountainous regions, consistent with the actual aerosol distribution patterns in the Beijing–Tianjin–Hebei area. Moreover, the product demonstrated overall smoothness and high accuracy. This research lays the foundation for establishing a long-term, 1 km resolution, daily spatially continuous AOD product for the Beijing–Tianjin–Hebei region and beyond, providing more robust data support for addressing regional and larger-scale environmental challenges. Full article

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of China’s three major urban agglomerations. Over the past thirty years, the region has undergone intensive economic development and urban expansion, resulting in significant changes in its ecological conditions. Due to the region’s humid and rainy climate, traditional remote sensing ecological indexes (RSEIs) struggle to ensure consistency in long-term ecological quality assessments. To address this, this study developed a unified RSEI (URSEI) model, incorporating optimized data selection, composite index construction, normalization using invariant regions, and multi-temporal principal component analysis. Using Landsat imagery from 1990 to 2020, this study examined the spatiotemporal evolution of ecological quality in the GBA. Building on this, spatial autocorrelation analysis was applied to explore the distribution characteristics of the URSEI, followed by geodetector analysis to investigate its driving factors, including temperature, precipitation, elevation, slope, land use, population density, GDP, and nighttime light. The results indicate that (1) the URSEI effectively mitigates the impact of cloudy and rainy conditions on data consistency, producing seamless ecological quality maps that accurately reflect the region’s ecological evolution; (2) ecological quality showed a “decline-then-improvement” trend during the study period, with the URSEI mean dropping from 0.65 in 1990 to 0.60 in 2000, then rising to 0.63 by 2020. Spatially, ecological quality was higher in the northwest and northeast, and poorer in the central urbanized areas; and (3) in terms of driving mechanisms, nighttime light, GDP, and temperature were the most influential, with the combined effect of “nighttime light + land use” being the primary driver of URSEI spatial heterogeneity. Human-activity-related factors showed the most notable variation in influence over time. Full article

Modern elite football is becoming increasingly physically demanding, often requiring training and matches to be played at night. This schedule may disrupt circadian rhythms and melatonin secretion, thereby impairing sleep and recovery. This study investigated the effects of training time on melatonin secretion, circadian phase markers, and sleep parameters in elite youth soccer players. Forty male players (aged 16–18 years) from an elite Spanish youth football club were studied. Two groups followed the same training program but trained either in the morning (MT) or in the evening (ET). Salivary melatonin was measured at six time points to determine the mean levels, dim light melatonin onset (DLMO), amplitude, and acrophase. Chronotype, sleep quality (PSQI), and daytime sleepiness (ESS) were assessed using validated questionnaires. Dietary intake and anthropometric variables were also recorded. The MT group had higher mean melatonin levels (p = 0.026) and earlier DLMO (p = 0.023) compared to the ET group. Sleep quality was significantly better in the MT group (p < 0.001), despite shorter sleep duration (p = 0.014). No major differences in diet or anthropometry were observed. The chronotype had a secondary effect on the circadian markers. Evening training is associated with alterations in melatonin rhythms and reduced sleep quality, possibly due to light-induced chronodisruption. These findings highlight the importance of training timing as a modifiable factor in the chronobiology and recovery of athletes. Incorporating circadian principles into training schedules may optimize resting time and thus performance and long-term health in athletes. 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

Nitrogen oxides (NOx) are known to be irritant gases, which present considerable risks to human health. TROPOMI NO₂ vertical column density (VCD) is commonly employed to estimate NOx emissions through the integration of complex models. However, satellite data often suffer from incompleteness, hindering the ability to achieve long-term and comprehensive estimates. In this study, we propose a reconstruction method to achieve comprehensive coverage of NO₂ VCD in China by leveraging the relationship between satellite data and meteorological variables. In addition, the CNN-BiLSTM-ATT model was developed to estimate China’s monthly NOx emissions from 2021 to 2023 in combination with other ancillary data, such as ERA5 meteorological data, topographic data, and nighttime light data, achieving a correlation coefficient (R) of 0.83 and a root mean squared error (RMSE) of 9.05 tons (T). The factors influencing NO₂ VCD were assessed using SHAP values, and the spatiotemporal characteristics and density distribution of NOx emissions were analyzed. Additionally, annual emission trends were evaluated. This study offers valuable insights for air quality management and policymaking, contributing to efforts focused on mitigating the adverse health and environmental impacts of NOx emissions. 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

Sustainable urban growth is an important issue in urbanization. Existing studies mainly focus on urban growth from the two-dimensional morphology perspective due to limited data. Therefore, this study aimed to construct a framework for estimating long-term time series of building volume by integrating nighttime light data, land use data, and existing building volume data. Indicators of urban horizontal expansion (UHE), urban vertical expansion (UVE), and comprehensive development intensity (CDI) were constructed to describe the spatiotemporal characteristics of the horizontal growth, vertical growth, and comprehensive intensity of the Beijing–Tianjin–Hebei (BTH) urban agglomeration from 2013 to 2023. The UHE and UVE increased from 0.44 and 0.30 to 0.50 and 0.53, respectively, indicating that BTH has simultaneously experienced horizontal growth and vertical growth and the rate of vertical growth was more significant. The UVE in urban areas and suburbs was higher and continuously increasing; in particular, the UVE in the suburbs changed from 0.35 to 0.60, showing the highest rate of increase. The most significant UHE growth was mainly concentrated in rural areas. The spatial pattern of the CDI was stable, showing a declining trend along the urban–suburb–rural gradient, and CDI growth from 2013 to 2023 was mainly concentrated in urban and surrounding areas. In terms of temporal variation, the CDI growth during 2013–2018 was significant, while it slowed after 2018 because economic development had leveled off. Economic scale, UHE, and UVE were the main positive factors. Due to the slowdown of CDI growth and population growth, economic activity intensity, population density, and improvement in the living environment showed a negative impact on CDI change. The results confirm the validity of estimating the multi-dimensional growth of regions using remote sensing data and provide a basis for differentiated spatial growth planning in urban, suburban, and rural areas. 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

Cities in cold regions face significant challenges, including high carbon emissions, intense energy use, and outdated energy structures, making them critical areas for achieving carbon neutrality and sustainable development. While studies have explored the impact of spatial structures on urban carbon emissions, the effects of multi-scale spatial structures remain insufficiently understood, limiting effective spatial planning strategies. This research examines Changchun, a city in a severe cold region, using data from 2012 to 2021, including road networks, land use, nighttime light, and energy statistics. Employing spatial syntax, landscape pattern indices, random forests, and segmented linear regression, this research establishes a carbon emission translation pathway to analyze the nonlinear effects of multi-scale spatial structures. Findings reveal a 26.70% annual decrease in carbon emissions, with winter emissions 1.84 times higher than summer ones. High-emission zones have shifted from industrial areas to transportation, commercial, and residential zones, reflecting growing seasonal variability and structural changes. Spatial complexity increased while connectivity declined. Multi-scale analysis identified a “decrease–increase–decrease” pattern, with macro-scale centrality declining and micro-scale hierarchy rising. These results provide both theoretical and practical guidance for urban planning in cold regions, supporting early carbon neutrality and long-term sustainable development goals. Full article

The ecological environment is fundamental to human survival and development, and China has seen a historical shift from localized to widespread improvements in its ecological conditions. Aksu, a typical ecologically sensitive region in Xinjiang, China, is significant for the study of vegetation dynamics and their driving factors, which is crucial for ecological conservation. This study evaluates the spatiotemporal changes in vegetation coverage in Aksu from 2000 to 2020 using long-term Normalized Difference Vegetation Index (NDVI) data and trend analysis. Additionally, this study explores key factors influencing vegetation changes through correlation analysis with temperature, precipitation, and nighttime light data. The results indicate the following: (1) vegetation coverage in Aksu exhibits significant spatial heterogeneity, with annual NDVI increasing at a rate of 0.83% per year (p < 0.05); (2) the influence of temperature and precipitation on NDVI was weakly correlated from 2000 to 2020; and (3) a strong positive correlation was found between nighttime light intensity and NDVI, suggesting that urban development plays a dominant role in vegetation change, while temperature and precipitation have comparatively minor impacts. The findings provide a scientific basis for ecological conservation and sustainable development in the region. Full article

The grassland ecosystem in the Inner Mongolia Autonomous Region (IMAR) serves as a vital ecological barrier in northern China, and the vegetation productivity in the grasslands exhibits considerable temporal and spatial variations. However, few studies have examined the long-term variations in the NPP in the IMAR and quantified the effects of natural factors and human activities on the NPP. The study modeled the net primary productivity (NPP) of the IMAR’s grasslands using the Carnegie–Ames–Stanford approach (CASA) model and employed linear regression, trend analysis, and spatial statistics to analyze the spatio-temporal patterns in vegetation productivity and explore the impact on the NPP of natural and socio-economic factors over the past two decades. The results reveal that the average NPP value from 2001 to 2021 was 293.80 gC∙m⁻² a⁻¹, characterized by spatial clustering of a relatively high NPP in the east, a low NPP in the west, and an annual increase of 3.26 gC∙m⁻² over the years. The NPP values varied significantly across different vegetation cover types, with meadows having the highest NPP, followed by typical steppe and desert grasslands. The spatial distribution pattern and temporal changes in the grassland productivity are the result of both natural factors and human activities, including topographical properties and socio-economic indicators such as gross domestic product, night-time light, and population. The results for the NPP in the IMAR were based solely on the CASA model and, therefore, to achieve improved data reliability, exact measurements in real field conditions will be conducted in the future. The findings from the spatial clustering and temporal trajectories of the NPP and the impacts from the factors can provide useful guidance to planning grassland vegetation protection policies for the IMAR. Full article

Coastal cities, as centers of economic and industrial activity, accommodate over 40% of the national population and generate more than 70% of the GDP. They are critical centers of carbon emissions, making the accurate and long-term analysis of spatiotemporal carbon emission patterns crucial for developing effective regional carbon reduction strategies. However, there is a scarcity of studies on continuous long-term carbon emissions in coastal cities. This study focuses on Qingdao and explores its carbon emission characteristics at the city, county, and grid scales. Data from multi-source are employed, integrating net primary production (NPP), energy consumption, and nighttime light data to construct a carbon emission estimation model. Additionally, the Tapio model is applied to examine the decoupling of GDP from carbon emissions. The results indicate that the R² of the carbon emission inversion model is 0.948. The central urban areas of Qingdao’s coastal region are identified as hotspots for carbon emissions, exhibiting significantly higher emissions compared to inland areas. There is a notable dependence of economic development on carbon emissions, and the disparities in economic development between coastal and inland areas have resulted in significant geographical differentiation in the decoupling state. Furthermore, optimizing and transitioning the energy structure has primarily contributed to carbon reduction, while exceptional circumstances, such as the COVID-19 pandemic, have led to passive fluctuations in emissions. This study provides a scientific reference for coastal cities to formulate targeted carbon reduction policies. Full article

A major source of carbon dioxide emissions (CO₂) arises from the household sector. Recent studies have reported increasing household CO₂ emissions (HCO₂) in many countries. Cities represent a key administrative level in China and can be managed to mitigate HCO₂ if spatial and temporal variations in HCO₂ are understood at fine scales. Here, we applied panel data analysis to map HCO₂ at a pixel scale of 1 km in China using remotely sensed time series nighttime light data, grid population density data, and provincial energy consumption statistics from 2000 to 2020. Spatial and temporal variations in HCO₂ were observed with four growth modes, including high growth (HG), low growth (LG), negative growth (NG), and high negative growth (HNG), for different periods, i.e., 2000–2010, 2010–2020, and 2000–2020. We proposed a local scale analysis of HCO₂ growth patterns within a global context to assess the contribution roles of 372 municipal cities to the changes in the national total HCO₂ (T-HCO₂). The results indicated that T-HCO₂ has tripled in the last two decades, but the roles of the contribution to the increase varied among cities. The local scale analysis revealed that more cities contributed to the rise in T-HCO₂ through HG and LG than those that suppressed it through NG and HNG. The majority of the cities displayed contributions to the rise in T-HCO₂ through two or more of the growth modes, confirming a significant variation in HCO₂ across locations, even within a city. This study provides a new approach to understanding the roles cities play in the long-term dynamics of T-HCO₂. We recommend increased efforts to encourage HCO₂ mitigation in cities that have contributed to the rise in T-HCO₂ to help neutralize carbon emissions at the national level. Full article