Search Results (10,069)

With global warming and rapid urbanization, short-duration summer rainstorms are becoming more intense and localized, posing growing challenges to urban flood resilience. However, their spatiotemporal characteristics, vertical structures, and environmental drivers remain poorly understood. Here, we combine multi-source remote sensing datasets and China’s new-generation satellite-borne dual-frequency precipitation radar observations to investigate summer rainstorms in Nanjing, China, during 2017–2024. Results reveal pronounced spatiotemporal heterogeneity, with higher rainfall intensities concentrated over urban and adjacent areas. During the study period, rainstorm intensity and duration increased by 7.44% and 38.63%, respectively, while the affected area decreased by 8.18%, indicating a transition toward more localized yet more intense rainfall events. Environmental analyses suggest that large-scale thermodynamic conditions and regional topographic forcing provide a favorable background for convection development, while local urban thermal effects may further modulate rainfall enhancement. Three-dimensional radar detection of an illustrative rainstorm event indicates an inverted-cone vertical structure, suggesting a mixed convective-stratiform precipitation structure involving both warm-rain and ice-phase processes. An Explainable Bayesian-Optimized XGBoost (EBOX) model further identifies near-surface air temperature and specific humidity as the primary environmental factors associated with rainstorm occurrence and development. Overall, this study highlights the value of integrating satellite remote sensing with explainable artificial intelligence to improve understanding of urban extreme rainfall and provide new insights into how climate change, topography, and urbanization jointly shape precipitation extremes in rapidly urbanizing monsoon regions. Full article

Surface congestion and taxiing delays at high-density airports increasingly constrain aviation sustainability, as ground-phase fuel consumption and emissions constitute a significant share of total airport emissions. Existing studies typically decouple air traffic flow management from ground resource scheduling, hindering coordinated optimization of punctuality, environmental benefits, and resource utilization. This paper proposes a multi-objective optimization method for runway-taxiway systems oriented toward air–ground collaborative decision-making, integrating Calculated Take-Off Time (CTOT) compliance constraints. A tri-objective mixed-integer programming model is formulated to minimize CTOT deviation, total taxiing time, and runway workload imbalance. A hybrid intelligent algorithm, SSA-SCA-NSGA-II, is designed with a bidirectional elite feedback mechanism to address this NP-hard problem. Validation uses real operational data of 58 departure flights during a peak period at Beijing Daxing International Airport. The results demonstrate that the proposed method achieves effective trade-offs on the Pareto front: CTOT compliance rate increased from 77.6% to 89.7–96.6%; total taxiing time decreased from 692 min to 551–635 min; and dual-runway utilization imbalance declined from 5.2% to 1.7–3.8%. These improvements translate into quantifiable sustainability gains: fuel consumption is reduced by 1425–3525 kg and CO₂ emissions by 4503–11,139 kg per peak hour, alongside a 19-percentage point improvement in punctuality that lowers passenger delay costs and reduces controller coordination workload. By simultaneously advancing environmental sustainability (carbon footprint reduction), economic sustainability (fuel and operational cost savings), and social sustainability (service punctuality and labor efficiency), the framework provides a measurable, monitorable, and policy-relevant decision-support tool for green airport surface operations aligned with sustainable development goals (SDGs). Full article

The present study investigates the presence, sources, and impacts of microplastics (MPs) in different soil types, including agricultural, urban, and forest areas, through a synthesis of results of published scientific papers. MPs originate from a variety of human activities, such as the widespread use of plastic mulch in agriculture and the application of organic fertilizers and treated sewage sludge, as well as from vehicle tire wear, industrial processes, and the gradual degradation of plastic products in the environment. In urban soils, the main sources of MPs are related to road traffic, industrial activity, and landfills, while in forest soils, concentrations are generally lower. However, MPs in forest areas are thought to be carried there by the air, by runoff, or from nearby areas with human activity. Available data show that larger MP particles tend to remain in the surface layers of the soil, while smaller particles can penetrate deeper soil layers, increasing their bioavailability and the likelihood of interaction with microorganisms and plant root systems. In terms of their chemical composition, polyethylene (PE) and polypropylene (PP) polymers dominate in agricultural soils, which is directly linked to agricultural practices, while polystyrene (PS) and polyvinyl chloride (PVC) are more frequently detected in urban soils. The morphological types of MPs include fragments, fibers, and films, while their color characteristics provide clues to possible sources of origin, such as plastic ground covers, tire wear, and packaging materials. Overall, the study’s results underscore the growing environmental significance of MP soil pollution and highlight the need for more effective management and recycling of plastic materials, as well as for further interdisciplinary research aimed at understanding the mechanisms of transport, accumulation, and long-term ecological effects of microplastics in terrestrial ecosystems. Full article

Forecasting fine particulate matter with a diameter of 2.5 μm (PM_2.5) is critically important due to its adverse effects on human health and environmental sustainability. Although ground-based monitoring stations provide accurate measurements, their limited spatial coverage restricts large-scale PM_2.5 assessment, especially in complex urban regions. Consequently, aerosol optical depth (AOD) derived from satellite imagery, combined with advanced deep learning (DL) techniques, has emerged as an effective alternative by offering wide spatial coverage and rich spatio-temporal information. This paper proposed an optimization-driven fuzzy transformer–deep belief network (ODFT-DBN) for accurate PM_2.5 air pollution prediction. The proposed framework integrates a fuzzy inference module to model uncertainty and nonlinear environmental relationships, a transformer encoder to capture long-range spatio-temporal dependencies, and a DBN to extract hierarchical features and improve prediction robustness. In addition, a novel multi-objective gray wolf optimizer (NMOGWO) is employed to jointly optimize the model hyper-parameters and fuzzy membership functions. The proposed approach is implemented for the city of Tehran, Iran, using meteorological variables, topographical features, ground-based PM_2.5 measurements, and satellite-derived AOD data. The ODFT-DBN model is compared with several benchmark methods, including bidirectional encoder representations from transformers (BERT), transformer, long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), DBN, and extreme gradient boosting (XGBoost). Experimental results demonstrate that the proposed framework achieves superior predictive performance, attaining an R² value of 0.94 and root mean square error (RMSE) of 0.8 μg/m³. Scatter plot analyses indicate a strong agreement between predicted and observed PM_2.5 values, while the proposed model exhibits low variance, stable convergence behavior, and acceptable computational time. Overall, the results confirm the effectiveness, robustness, and practical applicability of the proposed ODFT-DBN framework for spatio-temporal PM_2.5 forecasting. Full article

With the rapid development of commercial aerospace, emerging applications such as satellite constellations, space-based communications, and orbital computing platforms have significantly increased the demand for efficient and reliable spacecraft power systems. Abundant exploitable energy exists in the space environment, including Air Mass Zero (AM0) solar radiation, spacecraft surface temperature gradients, ambient electromagnetic radiation, and radioisotope thermal energy, making multi-source energy harvesting a promising approach for improving satellite energy autonomy and system redundancy. This paper reviews the following four key space energy harvesting technologies: photovoltaic power generation, radio frequency (RF) energy harvesting, thermoelectric energy harvesting, and radioisotope thermoelectric generators (RTGs). The impacts of harsh space environmental factors on device performance and reliability are analyzed, and the applicability of different technologies in low Earth orbit (LEO), geostationary orbit (GEO), and deep-space missions is discussed. Furthermore, a multi-source self-powered satellite energy architecture integrating energy harvesting, energy storage, and power management is proposed. Finally, the major challenges and future development trends of satellite energy harvesting systems are summarized. Full article

Stratospheric solar-powered unmanned aerial vehicles (UAVs), commonly operated as High-Altitude Pseudo-Satellites (HAPS), promise satellite-like persistence for Earth observation, communications and remote sensing, but their feasibility is governed by a tight coupling between solar energy availability and onboard energy demand. This study presents an energy-balance simulation framework that predicts the diurnal charge–discharge behaviour and endurance of solar-powered UAVs. The framework couples a physics-based environmental irradiance model—astronomical solar position, an air-mass and pressure-scaled broadband atmospheric transmission and an eccentricity-corrected extraterrestrial irradiance—with a wing-geometry photovoltaic collection model that reduces the airfoil camber, planform, dihedral and cell layout of a real wing to three scalar coefficients, replacing the flat-plate assumption common in solar-UAV sizing. The closed-form collection coefficient captures the full dependence of collected power on sun position and aircraft heading and admits an exact orbit-averaging result for circular loiter. The model is implemented as a reproducible, modular tool with single-day, annual and global analysis modes. It is validated against a ground-based photovoltaic charging campaign conducted on the as-built Aurora solar UAV demonstrator (5.6 m span, 8 kg) over three clear-sky days spanning a 90-day seasonal range: predicted and measured wing-collected power agree with a Pearson correlation of 0.998, a coefficient of determination of 0.993, an RMS error of 6.0% and a daily-energy agreement within 3.5%. A structured residual identifies an unmodelled photovoltaic temperature effect bounded at the 6% level. The framework provides HAPS designers and operators with a transparent, validated tool for feasibility screening, component selection and mission planning across latitude and season. Full article

This research evaluates Romania’s alignment with Sustainable Development Goal 9 by examining the relationship between digitalization, innovation capacity, sustainable infrastructure, and industrial environmental performance within the European Union. Using Eurostat data for 2015 and 2023, the research applies hierarchical cluster analysis with Ward’s method and squared Euclidean distance to classify EU Member States according to seven indicators: gross domestic expenditure on R&D, R&D personnel, patent applications to the European Patent Office, sustainable passenger transport, sustainable freight transport, air emission intensity from industry—PM10, and high-speed internet coverage. The analysis identifies five clusters for 2015 and three broader clusters for 2023. The two cross-sectional classifications reveal different patterns of similarity among EU Member States, while substantial structural heterogeneity remains. Leading countries combine strong R&D intensity, high patenting activity, advanced digital infrastructure, and low industrial emission intensity. Romania remains in the structurally constrained cluster in 2023, despite strong high-speed internet coverage and favourable freight-transport performance. The findings show that digital infrastructure alone is insufficient to ensure sustainable industrial growth without stronger innovation capacity, technological output, and cleaner industrial transformation. Full article

The post-COVID-19 tourism recovery exposes a structural divergence between economic resilience and environmental sustainability. Traditional tourism planning metrics consistently fail to diagnose how macroeconomic growth dynamics decouple from environmental pressures, leaving policymakers without empirical tools to identify structural vulnerabilities or prevent carbon-intensive recoupling during post-crisis transitions. This study integrates macroeconomic, environmental, and digital data across a global panel to map actionable pathways for sustainable tourism transitions. Employing a multi-stage methodology, the analysis first utilizes K-Means clustering (n = 80) to isolate the structural fixed effects of baseline destination archetypes driving a K-shaped recovery. Second, using a synchronized environmental panel (n = 41), a Decoupling Index evaluates eco-efficiency elasticity to test the alignment between tourism value recovery and aviation-induced CO₂ emissions. Third, regression analysis of an elite digital cohort (n = 18) measures dynamic exogenous catalysts, revealing that digital attractiveness, proxied by the global digital nomad market share, is a significantly stronger accelerator of recovery (β = 55.59, p = 0.019) than traditional physical air connectivity (β = −46.48, p = 0.036). Synthesizing these insights, a 2 × 2 Strategic Decision Matrix (n = 41) classifies destinations into Sustainable Leaders, Mass-Market Traps, Value Pivoters, and Vulnerable Laggards. The empirical results demonstrate that pre-pandemic structures do not deterministically dictate recovery (p > 0.05, Partial η² ≤ 0.077), yet rapid financial recovery often masks deep atmospheric vulnerabilities, with specific absolute decoupling leaders achieving exceptional value expansion alongside strict carbon contraction (e.g., Saudi Arabia, DE = −0.35; El Salvador, DE = −0.26). This framework provides a data-driven roadmap for policymakers to utilize “soft” digital infrastructure to transition from carbon-intensive, volume-dependent models toward value-optimized, low-emission ecosystems. Full article

Energy transition has established hydrogen and compressed natural gas (CNG) as key alternatives for reducing greenhouse gas emissions and promoting sustainable mobility in the transport sector. However, the safe deployment of this infrastructure is essential to ensure that decarbonisation strategies remain environmentally, socially and operationally sustainable. Filling stations handling flammable gases may release hydrogen or CNG into open environments where ventilation alone cannot always prevent unacceptable risk situations in populated and industrial areas. Although the current regulatory framework sets out essential design requirements, including safety distances, this article argues that risk management must go beyond minimum compliance, reconsidering the location of detectors, gas dispersion behaviour and the definition of hazard zones. The analysis takes into account the rapid dilution of these gases under open air ventilation conditions and supports a more risk-based approach to infrastructure planning. Since gas supply facilities operating at 25 MPa, the hazard zone from the vehicle receptacle is estimated at 3 m for hydrogen and 1.7 m for CNG. These findings contribute to a safer and more sustainable transport energy infrastructure. Full article

Fine particulate matter with an aerodynamic diameter of 2.5 μm or smaller (PM2.5) is a major environmental health risk that threatens individuals’ health, quality of life, and sustainable well-being. In the digital era, protective behaviors are increasingly shaped by people’s ability to access, evaluate, and use health information from online sources. This cross-sectional descriptive correlational study examined the levels of health literacy, digital health literacy, and self-care behaviors for PM2.5 protection and examined their associations with self-care behaviors among adults in Mueang District, Samut Songkhram Province, Thailand. A proportionate stratified sample of 375 adults from 11 subdistricts completed structured questionnaires. Data were analyzed using descriptive statistics and multiple linear regression. Most participants had moderate health literacy (55.2%), digital health literacy (52.0%), and self-care behaviors for PM2.5 protection (56.3%). The health literacy and digital health literacy dimensions jointly explained 28.1% of the variance in self-care behaviors. Using digital information for health decision-making showed the largest unique association with self-care behaviors (β = 0.31), followed by decision-making for PM2.5 protection (β = 0.26) and evaluation of information credibility (β = 0.24). Understanding PM2.5 information did not contribute independently after the other literacy dimensions were considered. PM2.5 risk communication should therefore move beyond information provision and strengthen credibility assessment, information appraisal, and action-oriented decision-making while addressing socioeconomic and digital-access barriers. Full article

Haze Pollution in China arises from the rapid enlargement of ultrafine particles into light-absorbing fine particulate matter through adsorption processes under atmospheric stagnation conditions. This study focuses on the sources of ultrafine particles (UFPs), the most critical component of haze pollutants during severe pollution periods in China. Utilizing methods including the spatial Durbin model and statistical data for the 28 cities (the “2 + 26” cities) within the Beijing–Tianjin–Hebei air pollution transmission channel—suffering the most severe haze pollution—it investigates the impact of pollution-intensive industries on haze pollution. This study reveals several key findings regarding China’s haze pollution. First, the principal source of ultrafine particles within China’s haze stems from the desulfurization, denitrification, and dust removal processes of pollution-intensive industries (the direct effect of these industries on haze is 0.028 * according to the SDM regression results). Crucially, the specific operational factors driving the abrupt increase in atmospheric UFPs during severe haze periods in China are identified as extensive management practices in desulfurization, the progressive tightening and annual escalation of denitrification emission standards, and the reliance on electrostatic precipitation which is ineffective against ultrafine particles. Second, haze pollution predominantly occurs in regions characterized by concentrations of pollution-intensive industries coupled with weak atmospheric environmental self-purification capacity (this carrying capacity for pollution-intensive industries exerts a significant negative impact on haze, demonstrated by a direct effect of −0.020 **; further analysis reveals that this is caused by regional differences in atmospheric self-purification capacity). Third, regional air transport acts as a contributing source, introducing UFPs from neighboring areas into local haze pollution, reflected by an indirect effect of pollution-intensive industries of 0.151 ** stemming from such spatial spillovers. Based on these conclusions, the study proposes a set of policy recommendations: relocate pollution-intensive industries using a gradient approach based on atmospheric self-purification capacity differences; systematically upgrade wet flue gas desulfurization technologies for industrial emissions; effectively promote technological innovation in denitrification processes; implement scientific controls on ammonia emissions; strengthen R&D in core technologies for UFP removal; innovate dust removal technologies to enhance overall system efficiency; reinforce regional coordinated governance; implement targeted training programs and select qualified management personnel; systematically enhance the environmental management capabilities of staff; and effectively mitigate the spillover effects of haze pollution. Full article

Rapid urbanization and climate change have intensified the interconnected challenges of surface heating, air pollution, and declining ecosystem functions, with important implications for regional sustainability. Taking Henan Province, China, as the study area, this study selected 2013, 2018, and 2023 as representative years and used land surface temperature (LST), fine particulate matter (PM2.5), ozone (O3), and net primary productivity (NPP) to characterize the thermal environment, air pollution, and carbon sequestration capacity. Pearson correlation analysis, multiple linear regression, and XGBoost-SHAP were integrated to examine bivariate associations, independent linear associations, factor importance, nonlinear responses, and potential threshold characteristics associated with natural, ecological, and anthropogenic factors. The results showed marked spatial differences in the four environmental variables. The multiple linear regression models explained 57.4–69.0% of the variation in LST, 23.8–72.0% in O3, 81.0–84.8% in PM2.5, and 57.4–62.5% in NPP. Natural factors generally showed relatively large and temporally stable standardized coefficients. Precipitation and potential evapotranspiration were positively associated with LST, whereas elevation and precipitation were negatively associated with PM2.5 and O3. NDVI showed an environmentally favorable pattern, being negatively associated with LST, PM2.5, and O3 but positively associated with NPP. Anthropogenic variables generally exhibited smaller and less temporally stable coefficients. The XGBoost models demonstrated good predictive performance, particularly for PM2.5, with R² values of 0.945, 0.920, and 0.905 in 2013, 2018, and 2023, respectively. SHAP analysis identified DEM, PRE, PET, and NDVI as the main contributors to model predictions and revealed nonlinear responses and potential threshold characteristics. These findings indicate that coordinated management of vegetation cover, hydrothermal conditions, and urban development can support heat mitigation, air pollution control, ecosystem productivity, and more sustainable, climate-resilient, and low-carbon development in rapidly urbanizing regions. Full article

China is expanding hydropower capacity as a key climate change mitigation strategy, yet greenhouse gas (GHG) emissions from reservoirs can substantially offset this benefit. The influence of specific environmental drivers on these emissions remains poorly understood, and previous studies have rarely quantified their relative importance under multifactorial conditions. To fill this gap, this study quantifies CO₂, CH₄, and N₂O emissions from 79 major hydroelectric reservoirs across China—representing over 60% of national hydropower generation—by integrating the G-res model and the IMAGE-DGNM model. We then employ a random forest (RF) model to evaluate the significance and marginal effects of 15 environmental drivers. Results show that reservoir-specific properties collectively explain 40.37% of the variance in total GHG emissions, and reservoir area emerges as the overwhelmingly dominant driver (MDI importance score = 1.41), far exceeding other key variables such as NH₄⁺ concentration, dissolved oxygen, altitude, water temperature, catchment area, total phosphorus, and air temperature (all with MDI importance > 0.5). Partial dependence analysis further reveals that emissions rise sharply with expanding reservoir area, NH₄⁺ concentrations above 0.15–0.2 mg/L, and catchment areas in the 360,000–680,000 km² range, while elevated dissolved oxygen (6–9 mg/L) and higher altitude suppress emissions. This study moves beyond simple emission inventories by providing a national-scale, data-driven attribution of reservoir GHG emissions to interacting environmental factors, thereby offering actionable insights for sustainable hydropower planning. Full article

Chromate-free conversion coatings are increasingly investigated as environmentally acceptable alternatives to conventional chromate conversion coatings for corrosion protection of aluminum alloys. In the present study, the electrochemical behaviour and long-term corrosion stability of several commercial conversion coating systems based on trivalent chromium (TCP), zirconium (ZrCC) and zirconium/titanium (Zr/TiCC) were systematically evaluated on AA3003 aluminum alloy and compared to chromate conversion coating (CCC) CR614. Three TCP coatings (ST650, MC1300 and B30002), two ZrCC (MC1700 and MC160/161), and one Zr/TiCC (B2040) were investigated. Coatings were prepared at pre-selected pH and concentration, but at varying conversion times. The protective performance of the coating was then tested across various exposure conditions using potentiodynamic polarization measurements: (i) after 24 h of exposure to air, (ii) after 24 h of immersion in 3.5 wt.% NaCl solution and (iii) simulated acid rain solution, and (iv) after exposure in a salt spray chamber for 500 h. The protective performance strongly depended on both the conversion conditions and the exposure environment. The optimal conversion times ranged between 40 s and 18 min, depending on the coating type. Differences between the investigated systems remained relatively limited when investigated after exposure to air and immersion in the simulated acid rain solution. However, in chloride-containing environments, substantially greater differentiation between the coatings was observed. Among the investigated systems, TCP coatings exhibited the most favourable overall corrosion performance, particularly after prolonged salt spray exposure, where ST650 and B30002 polarization resistance values were approximately 8800 and 5300 kΩ cm², respectively, together with corrosion current densities as low as 0.0004 and 0.001 μA cm⁻². ZrCC systems MC1700 and MC160/161 also provided significant corrosion protection, achieving polarization resistance values around 2700 and 2400 kΩ cm² after 500 h of salt spray exposure, whereas the Zr/TiCC coating B2040 exhibited poorer long-term performance. The results further demonstrated that prolonged salt spray exposure provides considerably more realistic evaluation of long-term coating protectiveness than short-term electrochemical measurements alone. Overall, optimized TCP and ZrCC systems provided corrosion protection under chloride-containing conditions comparable to or superior to the investigated conventional chromate conversion coating CR614 deposited on AA3003 alloy. Full article

As a critical insulation component in cable systems, the cable terminal is susceptible to defects caused by human and environmental factors during manufacturing, installation, and service. Such defects may lead to local electric-field distortion and insulation weaknesses at the cable terminal, posing a severe threat to the safe operation of the cable system. In this study, an electric-field simulation model of a 10 kV cable terminal was implemented to investigate the effects of various defects, such as insufficient stress-cone overlap, axial scratch, ring-cut defect, and moisture ingress on the cable terminal. The results show that insufficient stress-cone overlap produces a severe field distortion, and the distortion level is strongly correlated with the misalignment distance. For mechanical damage defects, axial scratches and ring-cut defects mainly distort the electric field inside the air gap, and defect position induces a stronger distortion level than that of defect depth. With increasing ring-cut depth, the maximum value of distorted electric field first decreases and then rises slightly. For moisture defects, the distorted field primarily occurs at the angle between the water-film tip and the stress cone, where the maximum value appears near the XLPE/SIR interface. These results provide a theoretical basis for defect diagnosis, structural optimization, and assembly process control of cable terminals. Full article