Search Results (27,356)

The emergence of Artificial Neural Networks (ANNs) and their deep learning form called Artificial Intelligence (AI) opened a new path to improve energy efficiency and the indoor environment. A small collaborating network team is now extending the passive house approach, in a book entitled Retrofitting, the Energy and Environment of Buildings (Gruyter Publishers), and presenting generalized AI modeling in the following paper. This concept uses a long-term neural network with a short-term memory (LSTM) and three stages (training, validation, and test) for optimalization to hourly data collected for one full year. The non-residential buildings are less affected by the space occupants. This paper examines the feasibility of a uniform, climate modified technology, as our objective is to create a universal and affordable approach to buildings assisting in slowing the rate of climate change. Hence, the idea of creating a generalized neural network for predicting electricity consumption linked with weather conditions was born. This network is to forecast the electricity consumption for buildings linked to the local weather conditions, but different categories of buildings are put together in one set. While this will lower the large set precision, still our question is if such a network would work. If so, in the future we will create multi-variant, local residential systems with the capability of predicting energy use. Full article

The frequent occurrence of extreme climate events poses a severe challenge to the reliability of building energy systems. Hydrogen energy, with its long-term storage capacity, has become a key technology carrier for enhancing building resilience. This study constructs a resilience–environment–economy co-optimization framework that couples dynamic simulation and emergy analysis. Through a five-in-one approach of physical modeling, climate scenario generation, resilience quantification, emergy accounting, and multi-objective optimization, the resilience performance of building hydrogen energy systems under the scenario of extreme heat waves combined with grid failure is evaluated. The results show that the thermal time constant deviation of the electrolyzer is 4.06%, the correlation coefficient between the generated heat wave scenario sequence and the historical measured data is 0.94, the prediction deviation of the once-in-a-century extreme temperature is 0.5%, the environmental load rate is 4.33, the Pareto front contains 127 non-dominated solutions, and the comprehensive performance of the co-optimal solution is improved by 42% to 88%. Engineering suggestions: For public buildings in hot summer and cold winter regions, the hydrogen energy system should adopt a configuration of 50–60 kW electrolyzers and 50–70 kg hydrogen storage tanks, with a key load guarantee rate of no less than 95%, and the ecological cost is 35% lower than that of diesel backup. This study provides a quantitative decision-making tool for the resilience planning of building hydrogen energy systems under extreme climate conditions and can be extended to other high climate risk areas. Full article

Climate change and urbanization have increased the precipitation variability and extreme hydrological events, highlighting the need for accurate river water level prediction. This study proposes a two-step sequential prediction framework based on a Long Short-Term Memory (LSTM) model and evaluates the impact of hydrological connectivity among observation stations on predictive performance. In Step 1, water levels at upstream and downstream stations are predicted. In Step 2, these predictions are incorporated as additional inputs for forecasting water levels at a target station. Input variables are selected using information gain (IG), and multicollinearity is assessed with the variance inflation factor (VIF). Results show that at Pojin Bridge, where short-term fluctuations are significant, incorporating predicted upstream and downstream water levels improves the coefficient of determination (R²) by approximately 3.9% to 9.24% as lead time increases. In contrast, at Andong Bridge, where hydrological responses are relatively stable, the additional inputs reduce model performance. These findings indicate that the effectiveness of incorporating hydrological connectivity depends on station-specific characteristics. The study provides practical guidance for designing data-driven river forecasting models under varying hydrological conditions. Full article

This study evaluates the performance of the Ain El Houtz wastewater treatment plant (WWTP) in Tlemcen, Algeria, by applying a fuzzy logic-based framework to multi-scale temporal data. A total of 2192 effluent samples collected between 2020 and 2022 were analyzed for Biochemical Oxygen Demand over five days (BOD₅), Chemical Oxygen Demand (COD), dissolved oxygen (O₂), pH, nitrate (NO₃⁻), phosphate (PO₄³⁻), and temperature. Expert-derived parameter weights were integrated into a Mamdani fuzzy inference system to compute a Fuzzy Water Quality Index (FWQI). Sensitivity analysis was conducted to assess the robustness of the model to variations in weights and membership functions. Results revealed satisfactory performance in 2020 and 2022 (FWQI > 85%), while 2021 showed critical degradation (FWQI ≈ 50%), unrelated to seasonal climate variability. Comparison with raw parameters and regulatory thresholds validated the FWQI’s ability to capture operational fluctuations. This work represents the first multi-scale fuzzy logic application to wastewater treatment monitoring in Algeria, highlighting both the potential and limitations of fuzzy indices in semi-arid contexts. The approach provides a transferable decision-support tool for improving effluent quality management and guiding corrective actions in WWTPs. Full article

Climate change and rising global temperature values lead to a cascade of effects on human health and well-being. Methodologies for assessing thermal conditions and identifying areas with increased thermal stress are important for enhancing the quality of life in urban environments. This study is aimed at developing a methodology that combines high-resolution simulation data with surface meteorological observations for application in urban thermal stress assessment. Eleven urban public sites within the metropolitan area of Athens, Greece (i.e., squares and parks) were simulated using the three-dimensional microclimate model ENVI-met. The model was validated using micrometeorological data from field campaigns conducted in summer, autumn and winter. The validation results confirmed that ENVI-met showed satisfactory performance for further research analysis. Subsequently, Physiologically Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI) were calculated using data from weather stations operated by the National Observatory of Athens and the Hellenic National Meteorological Service. PET and UTCI were then spatially interpolated using a mixed modeling and kriging method, with parameters optimized based on statistical validation metrics derived from the ENVI-met simulations. Finally, seasonal bioclimatic maps were produced to identify areas experiencing unfavorable thermal conditions. The spatial analysis revealed distinct seasonal patterns in the distribution of unfavorable thermal conditions across the Athens metropolitan area. Full article

The Polarization and Directionality of the Earth’s Reflectances (POLDER)-3/GRASP (Generalized Retrieval of Aerosol and Surface Properties) aerosol products have been widely used in studies on radiative balance and climate change. However, the stability and consistency of the products have yet to be comprehensively evaluated, despite their critical importance for long-term studies. POLDER-3/GRASP products mainly consist of three variants: High-Precision (HP), Components, and Models. This study aims to evaluate the accuracy, stability, and consistency of these aerosol products at global and regional scales, and to provide usage recommendations. Compared with AERONET observations, the Components product shows the best performance for both aerosol optical depth (AOD) and Ångström Exponent (AE) retrievals, with Root Mean Square Error (RMSE) of 0.114 for AOD and 0.319 for AE. The Models AOD and HP AE also demonstrate relatively high validation accuracy, with RMSE of 0.138 for Models AOD and 0.366 for HP AE. Regionally, Components AOD and AE outperform those from the HP and Models products in 8 out of 10 regions. Stability evaluation shows that the stability metrics of the three AOD products range from 0.034 to 0.036 per decade, and none of them meet the Global Climate Observing System (GCOS) stability requirement (i.e., 0.02 per decade), which indicates that caution should be exercised when using POLDER-3/GRASP products for long-term analysis. In terms of consistency, Components AOD and Models AOD exhibit high agreement, while HP AOD is systematically higher than them. The AE retrieved by the three products shows considerable discrepancies, highlighting uncertainties in AE and spectral-AOD retrievals and pointing toward directions for future algorithmic improvements. In summary, considering global and regional accuracy, stability, and consistency, the Components AOD and AE products are generally recommended for use. For different regions, users can choose the appropriate product based on detailed validation and intercomparison results. Full article

The climate emergency in Latin America and the Caribbean (LAC) has transformed sustainability from an aspirational goal into a strategic imperative, particularly in the context of decoupling economic growth from natural capital depletion. This research analyzes eco-innovation within the frameworks of the National Innovation System (NIS), open innovation, and absorptive capacity, with the objective of examining the moderating role of collaboration in overcoming financial, knowledge, and market-related obstacles to innovative behavior. Employing a quantitative methodology using firm-level microdata from the Latin American Harmonized Innovation Surveys (LAIS) between 2007 and 2017, this study focuses on eco-innovative outcomes specifically linked to reductions in energy and material consumption. By estimating models that assess the role of technical cooperation and public policy support, this study seeks to determine whether collaborative strategies operate as an effective buffer against uncertainty and the limitations of local innovation systems. Expanding the scope of previous analyses centered on a single country, this work provides a regional perspective that underscores institutional and sectoral disparities in emerging contexts. Ultimately, this research examines how integrating an environmental purpose into corporate strategy and strengthening absorptive capacity enable LAC firms to transform ecological pressures into sustainable competitive advantages, mitigating the barriers that traditionally hinder technological progress in the region. Full article

Forest soils serve as critical terrestrial carbon sinks. While broad hydrothermal controls on soil respiration (R_s) are established, uncertainties persist regarding high-frequency temporal dynamics and moisture-dependent variations in temperature sensitivity (Q₁₀). Specifically, conventional reliance on discrete, clear-day sampling obscures how precipitation disrupts diurnal patterns. To address this, we continuously monitored R_s and environmental factors in two Northeast Chinese mixed forests (Korean pine, Pinus koraiensis (KP), and Dahurian larch, Larix gmelinii (DL)) to quantify weather-driven daily dynamics and carbon fluxes. Precipitation primarily drove daily variability, but more importantly, it reshaped day–night asymmetry. Under clear-day conditions, R_s exhibited a consistent daytime-dominant pattern, with daytime fluxes being significantly higher than nighttime fluxes (p < 0.05). However, precipitation events fundamentally neutralized this asymmetry, resulting in no significant day–night differences across most phenological stages. Annual R_s effluxes (759 and 965 g C m⁻² yr⁻¹ for KP and DL, respectively) lacked significant inter-stand or temporal variations. Seasonal emissions peaked unimodally in July, with the non-growing season contributing merely 5%–8%. Notably, spring freeze–thaw R_s in the KP stand surged interannually by 143%. While R_s correlated positively with temperature (p < 0.001), Q₁₀ was co-regulated by forest stand and moisture. Under moderate moisture, the KP stand’s Q₁₀ (2.72) was significantly lower than the DL stand’s (3.81); however, this divergence neutralized under low moisture. Consequently, soil moisture acts as both a direct R_s driver and a fundamental regulator of its temperature sensitivity. These empirical findings provide critical data to calibrate forest carbon models, improving predictions of soil carbon feedbacks under future climate scenarios. Full article

The development of renewable energy is vital for addressing future climate change and environmental degradation. Nevertheless, the irregular and fluctuating essential features of wind power presents a considerable barrier to grid operational stability. Hence, precise prediction of wind energy output is crucial for improving power system management, boosting the reliability of the supply, and minimizing reserve expenditure. This study presents a predictive model designed for predicting short-term wind speeds using a stacking ensemble approach, which is based on an enhanced Multi-Feature Zebra Optimization Algorithm (IZOA-Stacking). In the data preprocessing phase, to minimize computational costs and prevent overfitting, a module tailored to the various features affecting wind power is developed for the IZOA-Stacking model. Grey relational analysis and Pearson correlation analysis are employed to determine and filter feature correlations. Critically, the preprocessing module demonstrates strong robustness: the One-Class Support Vector Machine (OneSVM) model is applied to identify and replace 100% of anomalous wind speed data, which leads to a substantial and measurable increase in feature correlation and overall model performance. For instance, when retaining wind speed features, the One-Class Support Vector Machine (OneSVM) model is employed to eliminate anomalous wind speed data. During model construction, a stacking ensemble learning strategy integrates multiple prediction models, including Long Short-Term Memory (LSTM) net-works, Extreme Gradient Boosting (XGBoost), ridge regression (RR), and Residual Networks (ResNets). This integration leverages the predictive strengths of each model. Additionally, the improved Zebra Optimization Algorithm (ZOA) optimizes the hyperparameters of each constituent model, further enhancing forecasting accuracy. The findings suggest that the proposed model demonstrates better performance than reference competitor models with regard to predictive accuracy. Full article

The Eastern Mediterranean, notably Cyprus, is a climate change hotspot facing severe heatwaves. Accurate numerical weather prediction of these extremes requires precise land–atmosphere modeling and initial and boundary conditions. This study assesses replacing the default USGS Land-Use and Land-Cover (LULC) dataset with the 10 m ESA WorldCover 2021 dataset in the Weather Research and Forecasting (WRF) model to simulate the 15–29 July 2023 Cyprus heatwave. The updated LULC increased urban representation six-fold. Statistical validations showed significant improvements in 2 m temperature, relative humidity, and 10 m wind speed predictions across 85% of observational sites. Dynamically, it restored urban thermal memory, effectively capturing the daytime Urban Cool Island effect and nocturnal heat release. Furthermore, radiosonde validations showed that the update corrected nocturnal Planetary Boundary Layer Height (PBLH) underestimations and dampened exaggerated daytime convective mixing. However, crucial limitations remain. High-frequency diagnostics indicated the model still suffers from damped thermal inertia, missing the abrupt temperature spikes and rapid nocturnal cooling typical of semi-arid microclimates. Additionally, the updated configuration failed to capture severe atmospheric stagnation during peak heatwave conditions, highlighting that deep-rooted kinetic errors persist within default boundary layer parameterizations despite static surface improvements. Full article

Endangered montane endemic species face dual threats from unresolved taxonomic controversies and climate change. The genus Abies, a keystone component of alpine and subalpine ecosystems in the Northern Hemisphere, encompasses numerous species with controversial taxonomy and inadequately understood climatic response patterns. In this study, we integrated morphological and phylogenetic evidence and ecological niche modeling approaches to fill existing knowledge gaps regarding Abies ernestii, an endemic species found in southwest China. Key results are summarized below: (1) Morphological comparisons strongly support A. ernestii as a distinct species, with significant morphological differentiation from its congeneric species; phylogenetic analyses based on plastid sequences further corroborate its close phylogenetic relationship with A. kawakamii and A. beshanzuensis, rather than A. chensiensis. (2) The natural distribution range of A. ernestii is narrower than previously documented in the literature, and a newly discovered population in northern Yunnan extends its documented southern distribution boundary southward. (3) Current suitable habitats of this species are concentrated in the eastern Hengduan Mountains, where temperature seasonality-related variables (BIO11, BIO3, BIO4) exert dominant control over its distribution. (4) Future climate projections indicate a dynamic habitat shift characterized by initial expansion followed by contraction, accompanied by severe habitat fragmentation and inadequate protected area coverage. Collectively, these lines of evidence demonstrate that A. ernestii represents an endemic Fir with underestimated vulnerability, warranting immediate conservation prioritization. Full article

Brominated flame retardants (BFRs) are widely used in automotive polymers and electronic components, yet vehicles remain an under-characterized and potentially high-exposure microenvironment, particularly in hot climates. This study provides the first comprehensive assessment of BFR occurrence, sources, and exposure risks in vehicle dust from Saudi Arabia, addressing a critical regional data gap. This study systematically investigates the occurrence, compositional patterns, sources, and human exposure risks of polybrominated diphenyl ethers (PBDEs) and selected alternative BFRs in dust from 80 vehicles (domestic cars and taxis; model years 2015–2022) operating in Jeddah, Saudi Arabia. Dust samples were collected using a standardized vacuuming protocol, extracted and cleaned using solvent extraction and silica SPE, and analyzed via GC–NCI–MS. Both legacy PBDE congeners and emerging alternatives (including DBDPE and TBB) were consistently detected, with BDE-209 dominating the overall BFR burden with mean concentrations of 6560 ng/g in domestic vehicles and 5454 ng/g in taxis, with maximum values reaching 220,860 ng/g. Lower-brominated PBDEs occurred at substantially lower concentrations, reflecting the ongoing global transition away from Penta- and Octa-BDE formulations. Taxis exhibited generally higher concentrations than domestic vehicles, likely due to prolonged occupancy, increased usage intensity, and enhanced dust resuspension dynamics. Multivariate analysis (PCA and correlation) revealed two distinct source categories: (i) legacy Penta-BDE-related congeners associated with polyurethane foam and textile materials and (ii) high-brominated PBDEs and DBDPE linked to hard plastics and electronic components. Human exposure assessment demonstrated that dust ingestion is the dominant exposure pathway, while dermal and inhalation routes contribute minimally. Non-carcinogenic hazard indices (HI) were well below unity for all compounds (HI < 1.67 × 10⁻⁶), and incremental lifetime cancer risks (ILCR) for BDE-209 remained within or near accepted risk thresholds (7.52 × 10⁻⁶–1.04 × 10⁻⁵), although occupational exposure among taxi drivers was consistently higher. Overall, the results demonstrate that modern vehicle cabins act as significant microenvironments for chronic BFR exposure, particularly under high-temperature conditions. Despite generally low estimated risks, the combined effects of chemical persistence, bioaccumulation potential, and mixture toxicity—amplified by extreme in-cabin temperatures—highlight vehicles as overlooked yet significant exposure environments. These findings provide the first comprehensive dataset for the Arabian Peninsula and emphasize the need for climate-sensitive exposure assessment, safer material design, and targeted mitigation strategies in vehicle interiors. Full article

Urban heating systems continue to rely heavily on fossil fuels, driving significant CO₂ emissions and underscoring the need for scalable renewable alternatives. This study evaluates a solar-assisted aquifer thermal energy storage (ATES) system for sustainable urban heating, operating within a relatively deep aquifer. A numerical model of the Mannville aquifer is developed to simulate charge–discharge cycles in a relatively deep open-loop ATES system, examining subsurface temperature evolution, storage efficiency, and long-term thermal stability under Canadian climatic conditions. Modeling results indicate that such aquifers act as an effective thermal buffer for solar energy storage operations, smoothing seasonal temperature fluctuations and stabilizing heat production. Surplus solar thermal energy injected during low-demand periods significantly reduces long-term temperature decline and preserves thermal availability for winter extraction. Balancing contributions from solar and aquifer storage maintains system efficiency during peak demand while improving overall thermal management. The integrated approach enhances renewable energy utilization, reduces reliance on conventional heating systems, and strengthens the resilience of urban energy networks. Our findings demonstrate that coupling solar thermal input with geothermal heat storage in relatively deep aquifers offers a practical pathway for advancing sustainable urban heating in cold-climate regions. The modeling framework provides a foundation for optimizing seasonal storage strategies and guiding the design of hybrid solar–geothermal systems for large-scale urban applications. Full article

Nepal has undergone significant land-use and land-cover (LULC) changes from 2000 to 2020, driven by urbanization, agricultural shifts, and broader socioeconomic dynamics. This study analyzes historical changes and projects LULC dynamics for 2030, 2040, and 2050 across four scenarios: Business-as-Usual (BAU), Rapid Urban Development (RUD), Forest Degradation and Terai Contraction (FDTC), and Agricultural Land Abandonment and Ecological Recovery (ALER). A CA–Markov modeling framework in TerrSet was used to simulate future land-use patterns, utilizing scenario-specific transition probability matrices and spatial constraints to reflect different socio-economic and policy assumptions. Under the BAU scenario, land-use change remains moderate, characterized by gradual urban expansion and limited forest decline. On the contrary, the RUD scenario predicts a drastic expansion of built-up areas by about 1.44 million ha, along with significant losses of cropland, bare soil, grassland, and forest, reflecting intensified development pressure. The FDTC scenario emphasizes agricultural expansion at the expense of forests, while urban growth remains limited. Conversely, the ALER scenario demonstrates strong ecological recovery driven by cropland abandonment and secondary vegetation regeneration, resulting in notable expansion of forest and other woody land. Overall, these four scenarios reveal sharply divergent land-use trajectories, ranging from rapid urban transformation to ecosystem restoration. These contrasting land-use pathways highlight the critical importance of integrated land-use policies that can proactively manage urban expansion, safeguard high-value agricultural and forest landscapes, and promote ecological restoration through incentives for agricultural land abandonment and secondary vegetation recovery, thereby ensuring long-term sustainability and climate resilience in Nepal. Full article

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) and the Yangtze River Delta (YRD) are key economic growth poles in China, playing a critical role in driving national economic development and facilitating international exchanges in commerce, culture, and ecology. However, rapid urbanization and industrialization have exerted considerable pressure on regional environments. In this study, we first assessed the dynamics of carbon storage (CS) and habitat quality (HQ) in the GBA and the YRD from 2000 to 2020 using the InVEST model and ArcGIS software, systematically analyzing their spatiotemporal changes and underlying driving mechanisms. Subsequently, we employed the PLUS model to predict land use changes by 2030 and evaluate their potential impacts on CS and HQ. The results indicate that: (1) Both regions have experienced increases in construction land and declines in cropland. (2) Between 2000 and 2020, CS in the GBA decreased by 33.65 × 10⁶ t and HQ declined by 0.0833, whereas in the YRD, CS decreased by 15.35 × 10⁶ t and HQ dropped by 0.0504. (3) By 2030, CS in the GBA is projected to decline further by 4.08%, with HQ decreasing to 0.4777, while in the YRD, CS is expected to fall by 2.71% and HQ decrease to 0.4115. (4) The spatial differentiation of CS and HQ in the GBA is primarily driven by anthropogenic processes, whereas in the YRD it is mainly constrained by natural factors such as topography. This study highlights the importance of understanding the spatiotemporal dynamics of CS and HQ, which can help enhance ecosystem service functions, mitigate the impacts of climate change, and provide a scientific basis for regional sustainable development. Full article