Search Results (926)

Seasonally dry tropical forests (SDTFs) are shaped by strong climatic and edaphic constraints, including pronounced rainfall seasonality, extended dry periods, and shallow karst soils with limited water retention. Understanding how tree species respond to these pressures is crucial for predicting ecosystem resilience under climate change. In the Yucatán Peninsula, we characterized sixteen tree species along a spatial and seasonal precipitation gradient, quantifying wood density, predawn and midday water potential, saturated and relative water content, and specific leaf area. Across sites, diameter classes, and seasons, we measured ≈4 individuals per species (n = 319), ensuring replication despite natural heterogeneity. Using a principal component analysis (PCA) based on individual-level data collected during the dry season, we identified five functional groups spanning a continuum from conservative hard-wood species, with high hydraulic safety and access to deep water sources, to acquisitive light-wood species that rely on stem water storage and drought avoidance. Intermediate-density species diverged into subgroups that employed contrasting strategies such as anisohydric tolerance, high leaf area efficiency, or strict stomatal regulation to maintain performance during the dry season. Functional traits were strongly associated with precipitation regimes, with wood density emerging as a key predictor of water storage capacity and specific leaf area responding plastically to spatial and seasonal variability. These findings refine functional group classifications in heterogeneous karst landscapes and highlight the value of trait-based approaches for predicting drought resilience and informing restoration strategies under climate change. Full article

Inadequate screening of features and insufficient extraction of multi-source time-series data potentially result in insensitivity to historical noise and poor extraction of features for PM_2.5 concentration prediction models. Precipitable water vapor (PWV) data obtained from the Global Navigation Satellite System (GNSS), along with air quality and meteorological data collected in Suzhou city from February 2021 to July 2023, were employed in this study. The Spearman correlation analysis and Random Forest (RF) feature importance assessment were used to select key input features, including PWV, PM₁₀, O₃, atmospheric pressure, temperature, and wind speed. Based on RF, Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP) algorithms, four PM_2.5 concentration prediction models were developed using sliding window and fusion algorithms. Experimental results show that the root mean square error (RMSE) of the 1 h PM_2.5 concentration prediction model using the RF-LSTM fusion algorithm is 4.36 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, while its mean absolute error (MAE) and mean absolute percentage error (MAPE) values are 2.63 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$ and 9.3%. Compared to the individual LSTM and MLP algorithms, the RMSE of the RF-LSTM PM_2.5 prediction model improves by 34.7% and 23.2%, respectively. Therefore, the RF-LSTM fusion algorithm significantly enhances the prediction accuracy of the 1 h PM_2.5 concentration model. As for the 2 h, 3 h, 6 h, 12 h, and 24 h PM_2.5 prediction models using the RF-LSTM fusion algorithm, their RMSEs are 5.6 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 6.9 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 9.9 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 12.6 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, and 15.3 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, and their corresponding MAPEs are 13.8%, 18.3%, 28.3%, 38.2%, and 48.2%, respectively. Their prediction accuracy decreases with longer forecasting time, but they can effectively capture the fluctuation trends of future PM_2.5 concentrations. The RF-LSTM PM_2.5 prediction models are efficient and reliable for early warning systems in Suzhou city. Full article

Camelina (Camelina sativa) is an oilseed crop of increasing importance, valued not only for its adaptability to diverse environmental conditions and potential for sustainable agriculture but also for its economic advantages, including low input requirements and suitability for biofuel production and niche markets. This study examines the relationship between camelina yield and climatic variables—specifically temperature and precipitation—based on a ten-year field experiment conducted in Poland. To capture the temporal dynamics of weather conditions, Functional Data Analysis (FDA) was applied to daily temperature and precipitation data. The analysis revealed that yield variability was strongly influenced by the length of the vegetative period and specific weather patterns in April and July. Higher yields were recorded in years characterized by moderate spring temperatures, elevated temperatures in July, and evenly distributed rainfall during the early generative growth stages. The Maximal Information Coefficient ($\mathrm{M}\mathrm{I}\mathrm{C}$) confirmed the relevance of these variables, with the duration of the vegetative phase showing the strongest correlation with yield. Cluster analysis further distinguished high- and low-yield years based on functional weather profiles. The FDA-based approach provided clear, interpretable insights into climate–yield interactions and demonstrated greater effectiveness than traditional regression models in capturing complex, time-dependent relationships. These findings enhance our understanding of camelina’s response to climatic variability and support the development of predictive tools for resilient, climate-smart crop management. Full article

Extreme precipitation in the Middle Reaches of the Yellow River Basin (MRYRB) has increased significantly and unevenly, heightening the urgency for rapid and accurate monitoring of such extremes. Satellite precipitation data have proved effective in capturing precipitation extremes but have not been validated in the MRYRB. Thus, station-interpolated data were used to validate the reliability of satellite data (GPM IMERG) in characterizing spatiotemporal changes in nine extreme precipitation indices across the entire MRYRB and its ten sub-basins from 2001 to 2022. The results show that all frequency, intensity, and cumulative amount indices exhibit significantly increasing trends. Spatially, extreme precipitation exhibits a clear southeast–northwest gradient. The higher values occur in the southeastern sub-basins. Characterized by high-intensity, short-duration precipitation, the central sub-basins exhibit the lower values of extreme precipitation indices, yet have experienced the most rapid upward trends in those indices. The comparative analysis demonstrates that GPM reliably reproduces indices such as the number of days and amounts with precipitation above a threshold (R10, R20, R95p), maximum precipitation over five days (RX5day), and total precipitation (PRCPTOT) (with regression slopes close to 1, coefficient of determination R² and Nash-Sutcliffe efficiency (NSE) greater than 0.7, and residual sum of squares ratio (RSR) less than 0.6, with negligible relative bias), particularly in the southern sub-basins. However, it tends to underestimate continuous wet days (CWD) and total precipitation when precipitation is over the 99th percentile (R99p). These findings advance current understanding of GPM applicability at watershed scales and offer actionable insight for water-sediment prediction under the world’s changing climate. Full article

This paper presents a novel methodology for predicting the tire–road friction coefficient in real-time under challenging climatic conditions based on a fuzzy logic inference system. The core innovation of the proposed approach lies in the integration and probabilistic weighting of a diverse set of input data, which includes signals from ambient temperature and precipitation intensity sensors, activation events of the anti-lock braking system (ABS) and electronic stability control (ESP), windshield wiper operation modes, and road marking recognition via a front-facing camera. This multi-sensor data fusion strategy significantly enhances prediction accuracy compared to traditional methods that rely on limited data sources (e.g., temperature and precipitation alone), especially in transient or non-uniform road conditions such as compacted snow or shortly after rainfall. The reliability of the fuzzy-logic-based predictor was experimentally validated through extensive road tests on dry asphalt, wet asphalt, and wet basalt (simulating packed snow). The results demonstrate a high degree of convergence between predicted and actual values, with a maximum modeling error of less than 10% across all tested scenarios. The developed methodology provides a robust and adaptive solution for enhancing the performance of Advanced Driver Assistance Systems (ADASs), particularly Automatic Emergency Braking (AEB), by enabling more accurate braking distance calculations. Full article

This study investigates the spatial characteristics of daily maximum precipitation for Prince Edward Island using a max-stable process model. The ssp126, ssp245, and ssp585 climate change scenarios, indicating low/optimistic, intermediate/in-between, and worst/pessimistic emissions scenarios, respectively, were extracted from 11 global climate model ensembles. For the time periods, the reference (historical) period was from 1971 to 2000, according to the World Meteorological Organization recommendations. Other time periods considered were 2011–2040, 2041–2070, and 2071–2100 as immediate, intermediate, and far future periods, respectively. The spatial trends analysis shows a west-to-east gradient throughout the entire study area. Return levels of 25 years were predicted for all the projections using the spatial generalized extreme value model fitted to the historical period, showing that topography should be included as a covariate in the spatial extreme model. Across the 134 grid points used in the study, the predicted return level for the historical period was 94 mm. Compared with the immediate time period, there is an increase of 47%, 53%, and 50% for the low, intermediate, and worst emission scenarios, respectively. For the intermediate period, there is an increase of 43%, 59%, and 56% for the low, intermediate, and worst emission scenarios, respectively. For the far future period, there is an increase of 49%, 48%, and 84% for the low, intermediate, and worst emission scenarios, respectively. There is a systematic increase in return levels based on the different periods. This shows a high chance of increased risks of extreme events of large magnitudes for this area in the immediate future through to the far future. This study will be useful for engineers, city planners, financial officials, and policymakers tasked with infrastructure development, long-term safety protocols, and sustainability and financial risk management. Full article

The Kingdom of Saudi Arabia launched a Regional Cloud Seeding Program in 2022 to enhance rainfall in central and southwestern regions. This study highlights a cloud seeding case on 1 November 2023, using convective development products derived from the Nowcasting Satellite Application Facility (NWCSAF), part of the SAF Network coordinated by the European Organization for the Exploitation of Meteorological Satellites. NWCSAF provided real-time satellite data for assessing cloud dynamics and precipitation. Analysis focused on Convection Initiation (CI) products issued 30–90 min before cloud seeding activities. Results showed the CI+30, +60, and +90 min outputs had high predictive accuracy, aligning with observed convection and demonstrating the value of satellite-based nowcasting in potential adaptation during cloud seeding operations. Full article

Acid mine drainage (AMD) poses significant environmental and health risks due to its high acidity and elevated metal and sulfate contents. Previous studies have primarily focused on short-term AMD monitoring, with limited attention paid to long-term, spatially resolved datasets and predictive modeling. In this 3.5-year study, six wells down-stream of a mine waste rock pile were monitored, and 132 sets of associated water quality (AWQ), geological (GEO), and climate history (CH) parameters were compiled to develop predictive models for Fe, Cu, and Zn concentrations. Random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) algorithms were applied using different combinations of input variables. The combined AWQ-GEO-CH dataset achieved the best overall performance, with XGBoost yielding the highest R² values for Fe (0.81) and Cu (0.77), and SVM performing best for Zn (0.94). CH variables, particularly precipitation and evaporation over 60-day periods, strongly influenced metal concentrations by driving hydrological and solute redistribution processes. AWQ parameters, especially F⁻ and S²⁻, were key predictors for Fe and Zn and ranked second for Cu, likely due to shared upstream sources and coupled geochemical processes such as FeF₃ dissolution. The most impactful GEO factor was the installation of a vertical barrier, which reduced metal concentrations by 73–80%. These findings highlight the value of integrating multi-source datasets with ML for long-term AMD prediction and management. Full article

The survival status of Lumnitzera littorea is near threatened globally and critically endangered in China. Clarifying its global distribution pattern and its changing trends under different future climate models is of great significance for the protection and restoration of its endangered status. To build a model for this purpose, this study selected 73 actual distribution points of Lumnitzera littorea worldwide, combined with 12 environmental factors, and simulated its potential suitable habitats in six periods: the Last Interglacial (130,000–115,000 years ago), the Last Glacial Maximum (27,000–19,000 years ago), the Mid-Holocene (6000 years ago), the present (1970–2000), and the future 2050s (2041–2060) and 2070s (2061–2080). The results show that the optimal model parameter combination is the regularization multiplier RM = 4.0 and the feature combination FC (Feature class) = L (Linear) + Q (Quadratic) + P (Product). The MaxEnt model has a low omission rate and a more concise model structure. The AUC values in each period are between 0.981 and 0.985, indicating relatively high prediction accuracy. Min temperature of the coldest month, mean diurnal range, clay content, precipitation of the warmest quarter, and elevation are the dominant environmental factors affecting its distribution. The environmental conditions for min temperature of the coldest month at ≥19.6 °C, mean diurnal range at <7.66 °C, clay content at 34.14%, precipitation of the warmest quarter at ≥570.04 mm, and elevation at >1.39 m are conducive to Lumnitzera littorea’s survival and distribution. The global potential distribution areas are located along coasts. Starting from the paleoclimate, the plant’s distribution has gradually expanded, and its adaptability has gradually improved. In China, the range of potential highly suitable habitats is relatively narrow. Hainan Island is the core potential habitat, but there are fragmented areas in regions such as Guangdong, Guangxi, and Taiwan. The modern centroid of Lumnitzera littorea is located at (109.81° E, 2.56° N), and it will shift to (108.44° E, 3.22° N) in the later stage of the high-emission scenario (2070s (SSP585)). Under global warming trends, it has a tendency to migrate to higher latitudes. The development of the aquaculture industry and human deforestation has damaged the habitats of Lumnitzera littorea, and its population size has been sharply and continuously decreasing. The breeding and renewal system has collapsed, seed abortion and seedling establishment failure are common, and genetic variation is too scarce. This may indicate why Lumnitzera littorea is near threatened globally and critically endangered in China. Therefore, the protection and restoration strategies we propose are as follows: strengthen the legislative guarantee and law enforcement supervision of the native distribution areas of Lumnitzera littorea, expanding its population size outside the native environment, and explore measures to improve its seed germination rate, systematically collecting and introducing foreign germplasm resources to increase its genetic diversity. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

Air pollution, particularly particulate matter (PM₁, PM_2.5, and PM₁₀), poses a significant environmental health risk globally. This study evaluates the predictive performance of three machine learning algorithms, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF), for forecasting particulate matter concentrations in four Brazilian cities (Porto Alegre, Recife, Goiânia, and Belém), which share similar demographic and urbanization characteristics but differ in geographic and climatic conditions. Using data from the Copernicus Atmosphere Monitoring Service, daily concentrations of PM₁, PM_2.5, and PM₁₀ were modeled based on meteorological variables, including air temperature, relative humidity, wind speed, atmospheric pressure, and accumulated precipitation. The models were tested under two climate change scenarios (+2 °C and +4 °C temperature increases). The results indicate that RF consistently outperformed the other models, achieving low RMSE values, around 0.3 µg/m³, across all cities, regardless of their geographic and climatic differences. KNN showed stable performance under moderate temperature increases (+2 °C) but exhibited higher errors under more extreme warming, while SVM demonstrated higher sensitivity to temperature changes, leading to greater variability in bivariate contexts. However, in multivariate contexts, SVM adjusted better, improving its predictive performance by accounting for the combined influence of multiple meteorological variables. These findings underscore the importance of selecting suitable machine learning models, with RF proving to be the most robust approach for particulate matter prediction across diverse environmental contexts. This study contributes valuable insights for the development of region-specific air quality management strategies in the face of climate change. Full article

This study examines the spatio-temporal evolution of precipitation in the State of Guanajuato, Mexico, from 1981 to 2016 by analyzing monthly series from 65 meteorological stations. A rigorous data quality protocol was implemented, selecting stations with more than 30 years of continuous data and less than 10% missing values. Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching was applied to handle missing data, preserving the statistical properties of the time series as validated by Kolmogorov–Smirnov tests ($p=1.000$ for all stations). Homogeneity was assessed using Pettitt, SNHT, Buishand, and von Neumann tests, classifying 60 stations (93.8%) as useful, 3 (4.7%) as doubtful, and 2 (3.1%) as suspicious for monthly analysis. Breakpoints were predominantly clustered around periods of instrumental changes (2000–2003 and 2011–2014), underscoring the necessity of homogenization prior to trend analysis. The Trend-Free Pre-Whitening Mann–Kendall (TFPW-MK) test was applied to account for significant first-order autocorrelation (${\rho }_1 > 0.3$) present in all series. The analysis revealed no statistically significant monotonic trends in monthly precipitation at any of the 65 stations ($\alpha =0.05$). While 75.4% of the stations showed slight non-significant increasing tendencies (Kendall’s $\tau$ range: 0.0016 to 0.0520) and 24.6% showed non-significant decreasing tendencies ($\tau$ range: −0.0377 to −0.0008), Sen’s slope estimates were negligible (range: −0.0029 to 0.0111 mm/year) and statistically indistinguishable from zero. No discernible spatial patterns or correlation between trend magnitude and altitude ($\rho =-0.022$, $p>0.05$) were found, indicating region-wide precipitation stability during the study period. The integration of advanced imputation, multi-test homogenization, and robust trend detection provides a comprehensive framework for hydroclimatic analysis in semi-arid regions. These findings suggest that Guanajuato’s severe water crisis cannot be attributed to declining precipitation but rather to anthropogenic factors, primarily unsustainable groundwater extraction for agriculture. Full article

Storm Daniel is arguably one of the most severe Mediterranean tropical-like cyclones (medicanes) ever recorded. Greece was one of the most affected areas, especially the central part of the country. The extreme precipitation that was observed along with the subsequent extensive flooding was considered a critical challenge to validate the regional version of the ICON (Icosahedral Non-Hydrostatic) numerical weather prediction (NWP) model. From a methodological standpoint, the short-range nature of the model was realized with 48 h runs over a sequence of cases that covered the storm period. The development of the medicane was highlighted via the tracking of the minimum mean sea level pressure (MSLP) in reference to the corresponding analysis of the European Center for Medium-Range Weather Forecasts (ECMWF). In a similar fashion, snapshots regarding the 500 hPa geopotential associated with the 850 hPa temperature were addressed at the 24th forecast hour of the model runs. Although the model’s performance over the four most affected synoptic stations of the Hellenic National Meteorological Service (HNMS) was mixed, the overall accumulated forecasted precipitation was in very good agreement with the corresponding total value of the observations over all the available synoptic stations. Full article

Background: Skin aging is mainly associated with oxidative stress and enzymatic degradation of collagen and elastin by protease activity. Peptides have antioxidant capacity and inhibitory effects on protease enzymes. Objective: The purpose of this study was to obtain peptides with in vitro anti-aging activity from the enzymatic hydrolysis of bovine casein with actinidin, a protease extracted from the green kiwi fruit (Actinidia deliciosa) Methodology: The enzyme actinidin was extracted from the pulp of the kiwi fruit, purified by ion exchange chromatography and characterized by polyacrylamide electrophoresis (SDS-PAGE). Subsequently, the extracted enzyme was used to hydrolyze commercial bovine casein at 37 °C for 30 min, precipitating the peptide fraction with trichloroacetic acid (TCA), and centrifuged. To determine the anti-aging potential of the peptides in vitro, antioxidant activity was evaluated using the ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) radical. Additionally, the inhibitory capacity of the peptides against collagenase and elastase enzymes was also studied. To complement the in vitro results, the enzymatic hydrolysis of casein with actinidin was simulated. The binding energy (ΔG) of each of the hydrolysates with the collagenase and elastase enzymes was calculated using molecular docking to predict the peptide sequences with the highest probability of interaction. Results: Actinidin was extracted and purified exhibiting a molecular weight close to 27 kDa. The enzyme hydrolyzed the substrate by 91.6%, and the resulting hydrolysates showed moderate in vitro anti-aging activity: antioxidant (17.5%), anticollagenase (18.55%), and antielastase (28.6%). In silico results revealed 66 peptide sequences of which 30.3% consisted of 4–8 amino acids, a suitable size to facilitate interaction with structural targets. The sequences with the highest affinity were FALPQYLK and VIPYVRYL for collagenase and elastase, respectively. Conclusions: Despite the modest inhibition values, the use of a fruit-derived enzyme and a food-grade substrate is in line with current trends in sustainable and natural cosmetics. These findings highlight the great potential for laying the groundwork for future research into actinidin-derived peptides as multifunctional and eco-conscious ingredients for the development of next-generation anti-aging formulations. Full article

Under the context of global climate change, the frequent occurrence of extreme low-temperature events poses a severe challenge to plant distribution and ecosystem stability. The arid and semi-arid regions of Northwestern China, as a sensitive response area to global change, have proven to possess significant development potential with their unique desert vegetation systems. This study focuses on the ecological adaptability mechanisms of Nitraria tangutorum Bobr., a key species of the desert ecosystem in Northwestern China, and systematically analyzes the evolution patterns of its geographical distribution under the coupled effects of climate change and human activities through a MaxEnt model. The research conclusions are as follows: (i) This study constructs a Human Footprint-MaxEnt (HF-MaxEnt) coupling model. After incorporating human footprint variables, the AUC value of the model increases to 0.914 (from 0.888), demonstrating higher accuracy and reliability. (ii) After incorporating human footprint variables, the predicted area of the model decreases from 2,248,000 km² to 1,976,000 km², with the High Suitability experiencing a particularly sharp decline of up to 79.4%, highlighting the significant negative impact of human disturbance on Nitraria tangutorum. (iii) Under the current climate baseline period, solar radiation, precipitation during the wettest season, and mean temperature of the coldest month are the core driving factors for suitable areas of Nitraria tangutorum. (iv) Under future climate scenarios, the potential distribution area of Nitraria tangutorum is significantly positively correlated with carbon emission levels. Under the SSP370 and SSP585 emission pathways, the area of potential distribution reaches 172.24% and 161.3% of that in the current climate baseline period. (v) Under future climate scenarios, the distribution center of potential suitable areas for Nitraria tangutorum shows a dual migration characteristic of “west–south” and “high altitude”, and the mean temperature of the hottest month will become the core constraint factor in the future. This study provides theoretical support and data backing for the delineation of habitat protection areas, population restoration, resource management, and future development prospects for Nitraria tangutorum. Full article