Search Results (6,613)

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

Receptor modeling of ambient pollutant concentrations plays a central role in urban air quality assessments. This study proposes an integrated framework combining Self-Organizing Maps (SOM), Positive Matrix Factorization (PMF), and Time-Series Analysis (TSA) for a comprehensive evaluation of urban air pollution patterns and source dynamics. The methodology was applied to multi-annual air quality and meteorological datasets (2009–2018) from two major Bulgarian cities, Plovdiv and Varna. The SOM was used for assessing the overall parameter patterns of the cities, leading to a clear clustering of the site samples on the map. Thus, PMF was run separately for the two sites, identifying a different number of sources (three and four, respectively). Traffic-related and sulfur-rich combustion sources were identified in both cities, while a crustal/resuspended dust factor was observed only in Varna. TSA revealed distinct temporal behaviors among source types. Traffic-related aerosol contributions decreased in both cities (−5.14% yr⁻¹ in Plovdiv; −9.30% yr⁻¹ in Varna), whereas sulfur-rich combustion factors showed increasing trends (+4.64% yr⁻¹ and +2.97% yr⁻¹, respectively). Traffic fresh exhaust factors exhibited pronounced seasonal variability and significant weekday–weekend differences in both cities. The integrated SOM–PMF–TSA framework enhanced source interpretability and temporal characterization, providing a robust approach for urban air quality assessment and supporting targeted air pollution management strategies. 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 integration of quantitative mathematical methods and artificial intelligence into agricultural monitoring systems represents a critical pathway toward data-driven decision-making in the contemporary precision agriculture economy. This study applies mathematical modeling and quantitative analysis to temporal NDVI (Normalized Difference Vegetation Index) raster datasets from six agricultural parcels in the Dobrogea region of Romania (2017 growing season), with the objective of supporting agronomic performance evaluation and operational decision-making. Higher-order statistical descriptors—variance, kurtosis, and skewness—were extracted from XML raster files and subjected to comprehensive visual analytics using kernel density estimation, three-dimensional surface modeling, and polynomial regression in Python. A feedforward Artificial Neural Network (ANN) with a 4-15-9-3-1 architecture was trained under four activation function and solver combinations (tanh/ReLU × Adam/SGD) to classify satellite sensing-date authenticity (is_sensing_date), a key data-quality indicator for operational crop monitoring workflows. Permutation-based feature importance analysis confirmed that variance is the dominant mathematical predictor (~35.8%), followed by kurtosis (~31.5%) and skewness (~26.6%), while the temporal month variable contributed least (~6.1%). The tanh–SGD configuration yielded the best training–test error balance for most individual datasets, while tanh–Adam performed optimally on the combined dataset. The inverse mathematical relationship between variance and kurtosis, and the direct co-variation between kurtosis and skewness, were consistent across all parcels, demonstrating the universality of these quantitative patterns in agricultural remote sensing data. These findings establish a replicable mathematical modeling framework applicable to predictive analytics, risk assessment of data quality, and performance evaluation in agricultural decision-making systems, with direct relevance to digital transformation strategies in the agri-economy sector. Full article

Accurate prediction of invasive species diffusion is essential for effective management and ecological conservation. Existing spatio-temporal Cox process models face limitations due to the separability assumption, which fails to capture spatio-temporal coupling dynamics inherent in biological diffusion processes. This study proposes a Spatio-Temporal Interaction Kernel Cox (STIK-Cox) model that constructs a non-separable conditional intensity function integrating baseline intensity, spatial and temporal proximity kernels, seasonal fluctuation, and a spatio-temporal interaction term. The model employs maximum likelihood estimation with Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Bounds (L-BFGS-B) optimisation and incorporates SHapley Additive exPlanations (SHAP) for interpretability analysis. Using the Vespa mandarinia (Hymenoptera, Vespidae) monitoring dataset from Washington State, the model achieves a comprehensive accuracy score of 0.957, a capture rate of 98.74% at a 0.5° threshold, and a mean prediction error of 0.0802°. K-function analysis confirms effective capture of spatial clustering patterns, while SHAP analysis reveals longitude as the primary predictive driver. The non-separable design outperforms conventional methods including inverse distance weighting and Poisson point processes. This framework demonstrates the potential of non-separable spatio-temporal point processes for invasive species early warning, providing a scientific basis for targeted monitoring and resource allocation in ecological management. 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

The Pinios River Basin, located in the water district of Thessaly in central Greece, is one of the most water-stressed agricultural regions in the country. This study investigates the spatio-temporal characteristics of drought in the basin using combined ground observations and remote sensing data over the common period October 1981–September 2002. Meteorological drought is assessed through the Standardized Precipitation Index (SPI) and the Standardized Precipitation–Evapotranspiration Index (SPEI), while hydrological drought is analyzed using the Standardized Runoff Index (SRI) in the Ali Efenti sub-basin of the Pinios River Basin. Ground-based station precipitation and temperature data were interpolated to a 5 km × 5 km grid using a multiple linear regression (MLR) approach and compared with CHIRPS satellite precipitation and ERA5 reanalysis temperature on the same grid. SPI and SPEI were calculated at multiple accumulation periods (1–12 months) from both ground-based and satellite-based datasets. Three major multi-year drought episodes (1988–1989, 1989–1990 and 2000–2001) were identified, with long duration, large spatial extent and of severe to extreme intensity. Satellite-based indices reproduced the timing and main spatial patterns of these events but tended to yield stronger drought magnitudes than ground-based indices. In the Ali Efenti sub-basin, SRI derived from simulated runoff using the calibrated University of Thessaly monthly water Balance model (UTHBAL) showed a clear propagation of meteorological deficits into streamflow drought with a short time lag. In the Ali Efenti sub-basin, the strongest linkage between meteorological and hydrological drought occurs at seasonal time scales (SPI-3/SPEI-3), with SRI-1 correlating best with SPI-3 (r = 0.67) and SPEI-3 (r = 0.63), indicating rapid drought propagation and supporting the use of 3-month indices for early warning of streamflow drought. Full article

Urban canals in rapidly growing cities often experience water quality deterioration from wastewater inputs and stormwater runoff, with impacts that vary across space and time. This study aimed to quantify five-year spatiotemporal patterns of key water quality indicators in the Mae Kha Canal, Chiang Mai, Thailand, and to identify persistent degradation hotspots to support management. Monthly longitudinal data (2020–2024) for dissolved oxygen (DO), biochemical oxygen demand (BOD), pH, and water temperature (WT) were collected at 18 monitoring stations and analyzed using locally estimated scatterplot smoothing (LOESS) for trend exploration, repeated-measures correlation for association between parameters, and Geographic Information Systems-based spatiotemporal mapping using inverse-distance-weighted interpolation. Results showed that DO remains very low across much of the canal, while BOD was persistently high; pH was relatively stable near neutral and WT exhibited clear seasonal variability. Spatial mapping indicated that upstream sections generally had better quality, whereas the urban middle reaches repeatedly exhibited hotspots of low DO and high BOD. BOD and DO levels positively correlate with pH level (p < 0.001). In conclusion, the Mae Kha Canal has sustained impairment over 2020–2024, highlighting the need for strengthened wastewater control, stormwater management, and targeted remediation guided by hotspot-based monitoring. Full article

The Dwarf Ombú, Phytolacca tetramera, is a rare and highly unusual plant endemic to the northeastern Pampean grasslands of Argentina and is currently considered of high conservation priority. In order to better understand its biology, ecology, and conservation requirements, we studied its anatomy, reproductive traits, life history, and distribution based on field observations and herbarium material. Our results show that P. tetramera possesses a combination of traits consistent with the concept of ecological anachronism. The species produces large fleshy fruits whose size and shape are comparable to those interpreted as adapted for dispersal by extinct megafauna. In addition, the plant exhibits morphological and ecological adaptations associated with intense grazing, trampling, and drought tolerance, including robust underground structures and a growth pattern comparable to underground trees from seasonally dry open habitats. These findings suggest that P. tetramera evolved under ecological conditions markedly different from those existing today, including megafaunal disclimax environments that disappeared after the late Pleistocene extinctions. This ecological mismatch may help to explain its present rarity, fragmented distribution, and low population numbers. Our results also indicate that current conservation strategies for P. tetramera should consider the role of disturbance regimes and extinct ecological interactions in shaping the biology of this species. Full article

Nearshore mariculture is a major contributor to the supply of “blue food”; however, its rapid expansion in bay systems has intensified sea-space competition and environmental pressures, underscoring the need for accurate and long-term monitoring. This study used multitemporal Sentinel-2 imagery processed using Google Earth Engine (GEE) to develop an automated identification framework for raft and cage aquaculture along the coast of Fujian, China, from 2017 to 2024. Three widely used classifiers—U-Net, DeepLabV3+, and random forest (RF)—were comparatively evaluated. Of these methods, U-Net had the most stable overall performance under optically complex nearshore conditions and was, therefore, used for province-scale mapping. Based on the U-Net-derived maps, the spatiotemporal evolution of mariculture was quantified. The results showed that mariculture in Fujian exhibited a persistent bay-oriented, dual-core clustering pattern, with major hotspots concentrated in Ningde and Zhangzhou. In the 2024 winter–summer comparison, raft aquaculture displayed a clear seasonal contrast, characterized by expansion in winter and contraction in summer, whereas cage aquaculture showed relatively smaller seasonal variation. Interannually, the mariculture system shifted from a mixed cage–raft configuration toward the dominance of raft aquaculture, accompanied by a spatial redistribution of mapped aquaculture density from inner nearshore waters toward bay mouths and more open waters. Overall, in this study, we demonstrate the potential of deep learning-enabled Sentinel-2 remote sensing for monitoring nearshore mariculture structures and provide mode-specific observational evidence for marine spatial planning, environmental risk management, and sustainable mariculture development in nearshore waters and semi-enclosed bay systems. Full article

Children living in agricultural regions are exposed to pesticides through multiple environmental and occupational exposure processes; however, the relative contribution of these processes remains insufficiently characterised in many rural contexts of the Global South. This study assessed pesticide exposure among children residing in an agricultural community in western Mexico characterised by close spatial proximity between residential areas and intensively cultivated fields. Urine samples were collected from children at two points in the agricultural cycle (March and December 2018). Pesticide concentrations were determined using liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS). Paired longitudinal analyses were conducted to evaluate intra-individual changes in detection frequencies and urinary concentrations across sampling periods. Multiple pesticides were detected, including compounds with near-universal presence across both sampling periods. Significant increases in urinary concentrations were observed between March and December for several pesticides, consistent with seasonal agricultural dynamics, while no systematic differences were identified between children from agricultural and non-agricultural households. These findings indicate that pesticide exposure in this setting operates as a community-level exposure regime that is both structurally produced and territorially embedded. Exposure patterns reflect the convergence of agricultural practices, environmental dispersion processes, and spatial configurations that extend beyond occupational boundaries. The results highlight the limitations of risk models focused exclusively on individual or occupational exposure and underscore the need for public health strategies that address pesticide exposure as a structurally produced and territorially embedded condition. Full article

This study evaluated spatial and seasonal patterns of physicochemical water quality in the Chiriquí Viejo River basin (western Panama), a tropical watershed characterized by strong seasonal variability. A total of 90 water samples were collected at ten stations during the rainy season (May to October 2024) and dry season (January to March 2025). Dissolved oxygen (DO), turbidity, potential of hydrogen (pH), apparent color, total dissolved solids (TDS), and electrical conductivity (EC) were analyzed following ISO/IEC 17025:2017 accredited methods, and precipitation patterns were characterized using spatial interpolation of meteorological data. Spatio-temporal variability was assessed using linear mixed-effects models, with season and basin position as fixed effects and sampling site as a random factor. Results showed a spatial and seasonal structuring of water quality, with the upper basin exhibiting high and stable DO concentrations and low turbidity and apparent color. In contrast, the middle and lower basin showed rainy-season increases in turbidity and apparent color, supported by a significant season × basin interaction, indicating that precipitation driven impacts are heterogeneous along the basin. EC and TDS displayed spatial gradients, while DO remained relatively stable across seasons and basin levels. These findings highlight turbidity and apparent color as sensitive indicators of precipitation-driven impacts. Full article

This study considers ocean–atmosphere influences on marine productivity over the shelf of Peru. Annual fish catch since 1961 and monthly satellite phytoplankton fluorescence (FLH) since 1997 in the area 7–14 S, 80–76 W provide a basis for statistical evaluation of environmental indicators from reanalysis fields. Monthly FLH is correlated with the year-on-year change in (anchovy) fish catch, wherein the autumn season (Mar–Aug) shows optimal association. The temporal record of FLH is regressed onto various fields, and the upper and lower 10 years are identified for composite analysis. Statistical results link the Southern Oscillation to wind patterns and oceanic response, wherein greater anchovy catch tends to follow La Niña. A case study is made of the change from El Niño in 2023 to La Niña in 2024. Composites indicate that cyclonic wind vorticity spreads phytoplankton across the Peruvian shelf under La Niña, resulting in a 33% increase in fluorescence from 0.26 to 0.39. Full article

Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are becoming convenient modes of transportation; however, their safety remains a critical concern as crashes continue to occur. To reveal crash trends and temporal variations, this study develops time-series forecasting models to predict future crash counts of such vehicles. The crash dataset released by the National Highway Traffic Safety Administration (NHTSA) has been used here. Two univariate forecasting models—the Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Facebook Prophet model—have been used here for different datasets. The models were trained on 30 months of data (July 2021 to December 2023) and validated on 6 months of data (January–June 2024). Validation metrics include Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Theil’s U1 statistic. Results showed that Facebook Prophet significantly outperformed SARIMA for both datasets, achieving an RMSE of 2.71 and an MAPE of 6.9% for ADAS, and an RMSE of 2.24 and an MAPE of 8.85% for ADS. For both systems, the model revealed empirically observed cyclical patterns and consistent rising trends. ADAS crashes exhibit a bimodal temporal pattern, with recurring peaks in January and May–June, alongside notable troughs in February–March and August–September. ADS displays a trimodal pattern, with recurring peaks in April–May, August and October, alongside notable troughs in December and the early winter months. These patterns represent empirically identified temporal regularities rather than causally attributed seasonality. From the future forecasts for July to December 2024, the model showed that ADAS crashes are expected to range between 40 and 80 per month, while ADS crashes are projected to remain between 20 and 40 per month. These findings underscore the need for proactive safety measures and enhanced regulatory oversight during identified high-risk periods to mitigate the growing trend in AV crashes. Full article

Accurate ship trajectory prediction is vital for intelligent maritime traffic management, yet conventional hybrid models often fail to balance local feature extraction, long-term dependency capture, and flexible feature weighting when processing AIS data. This paper proposes an MSC-BiLSTM-ATTENTION model that integrates trajectory clustering and an adaptive attention mechanism into a unified framework. Its fundamental advance over existing incremental hybrid architectures is twofold. First, a K-means clustering step groups trajectories with similar motion patterns before model training, effectively reducing the impact of data heterogeneity on prediction accuracy. Second, the deep learning backbone synergizes multi-scale convolution (MSC)—which captures local features at multiple temporal granularities via parallel kernels—with a bidirectional LSTM (BiLSTM) for forward–backward dependency learning, and an adaptive self-attention mechanism that dynamically optimizes feature weights to amplify critical navigation information. Extensive experiments on AIS data from the Gulf of Mexico and the U.S. Atlantic Coast, covering four seasons, benchmark the model against attention-enhanced architectures including Transformer, CNN-BiLSTM-ATTENTION, and DenseNet-BiGRU-ATTENTION across two distinct regions. The proposed model achieves significant improvements in predicting longitude, latitude, speed over ground, and course over ground, reducing MAE by over 76.9% and RMSE by over 65.3% compared with the strongest baseline. Ablation studies confirm that the synergy of all three modules is essential. The results demonstrate the model’s effectiveness and its practical value for intelligent maritime supervision, navigation risk warning, and waterborne traffic management. Full article