Search Results (1,031)

Polymetallic nodules constitute a strategically important deep sea mineral resource enriched in nickel, cobalt, copper, and other critical metals. Accurate prediction of their spatial distribution is crucial for resource assessment and sustainable exploitation. However, sparse sampling, high survey costs, and complex nonlinear interactions among environmental factors make this task difficult. To address these challenges, this study applies a quantile regression forest to analyze the data from 233 box-core stations across a 23,000 km² area in the eastern Pacific, integrating bathymetry and backscatter intensity. Cross-validation verifies model performance. The trained model predicts nodule abundance across an adjacent 3000 km² study area and validates it against 12 independent stations that were not involved in the training. This external validation gives a root mean square error of 4.11 kg/m². The quantile regression forest model also estimates uncertainty to quantify prediction reliability. The resulting uncertainty map distinguishes high-confidence zones from areas with elevated uncertainty. In 46% of the study area, normalized uncertainty falls below 0.50, indicating higher reliability. The remaining 54% shows higher uncertainty and requires additional sampling. This method combining independent spatial validation and uncertainty visualization provides a transparent tool for deep sea mineral resource assessment where data are sparse. Full article

The increasing co-occurrence of microplastics (MPs) and antibiotic resistance genes (ARGs) has led to growing ecological concerns. This study investigated the composition and spatial distribution of MPs, ARGs and microbial communities in the MP-containing membrane-retained particulate fraction of surface waters in the Haihe River estuary and Bohai Sea coastal waters, and examined their statistical correlations and co-occurrence patterns. The results showed MP abundances ranging from 150 to 1450 items/L, with pronounced hotspot accumulation in nearshore waters flanking the estuarine radial zone and polyvinyl chloride (PVC) as the dominant MP (51.95% of total MPs). The overall ARG profile remained compositionally stable, predominantly comprising peptide (8.97%), macrolide (8.92%), tetracycline (8.27%), and fluoroquinolone (8.01%) resistance genes, among which peptide ARGs exhibited a lower abundance in the estuarine radial zone and higher abundance in the nearshore waters. Correlation analysis revealed that Vulcanococcus (Cyanobacteria) positively correlated with both PVC (r = 0.73, p < 0.01) and peptide ARGs (r = 0.66, p < 0.05), suggesting a statistically significant co-occurrence pattern among PVC, Vulcanococcus, and peptide ARGs. Similarly, PA was positively correlated with Casp-actino8 and Pontimonas, which were also positively correlated with macrolide and penam ARGs. This study characterizes the co-pollution characteristics of MPs and ARGs in the Haihe estuary and Bohai coastal waters, and provides scientific evidence and dataset support for their ecological risk assessment. Full article

Trace element contamination represents a persistent environmental issue, particularly in industrialized areas where anthropogenic emissions overlap with natural geochemical backgrounds. This study investigates the atmospheric deposition of trace elements in the Milazzo district (Italy), which is characterized by intense industrial activity. Pinus pinea L. needles were used as biomonitors to assess the spatial distribution and sources of trace elements, combined with lead isotopic analysis for source apportionment. Forty needle samples were analyzed by ICP-OES and ICP-MS for Ca, K, Mg, Na, P, Al, As, Ba, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Sb, Ti, V, Zn, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu, while 25 samples were selected for Pb isotope ratio determination (²⁰⁶Pb/²⁰⁷Pb and ²⁰⁸Pb/²⁰⁶Pb). Multivariate statistical analyses identified source groups related to industrial and petrochemical emissions, vehicular traffic, crustal resuspension, and mixed combustion processes. Elevated concentrations of As, Cr, Mo, Ni, Pb, Sb, V, and Zn ranged from 16.6 μg g⁻¹ (Zn) to 0.09 μg g⁻¹ (Sb), with the following order of abundance: Zn > Cr > Ni > Pb > Mo > V > As > Sb; these elements were found near industrial facilities and urban areas. Enrichment Factor calculations indicated strong anthropogenic contributions to Cd, Cu, Mo, Sb, V, and Zn, with EF > 10, ranging from 10 (Cd) to 60 (Zn), whereas Al, Fe, and Ti exhibited EF values between 0.5 and 2, reflecting geogenic origins. Pb isotopic ratios (²⁰⁶Pb/²⁰⁷Pb = 1.153–1.192 and ²⁰⁸Pb/²⁰⁶Pb = 2.063–2.108) revealed mixing between industrial emissions and the local geological background, with limited influence from historical gasoline-derived Pb. This integrated geochemical and isotopic approach can effectively identify contamination sources in complex industrial environments. Full article

The Greater Cape Floristic Region (GCFR) in South Africa is a fire-prone biodiversity hotspot where high species richness, structural complexity, and small plant sizes (0.0001–4 m²) pose substantial challenges for remote sensing-based biodiversity assessment. Spectral similarity among species and the mismatch between plant size and sensor pixel dimensions limit the capacity of current and forthcoming spaceborne systems to resolve individual species and accurately detect plot-level diversity changes. We therefore developed a physics-based simulation framework that couples fynbos trait measurements with radiative transfer modeling in the DIRSIG (Digital Imaging and Remote Sensing Image Generation) environment towards quantifying information loss across spectral and spatial scales and to define theoretical limits for biodiversity monitoring. We constructed a three-dimensional virtual scene of post-fire fynbos communities in Grootbos Private Nature Reserve, integrating high-resolution imagery, terrestrial laser scanning (TLS), and structure-from-motion (SfM)-derived point clouds. Field measurements of mean diameter and percent cover were used to scale vegetation models and constrain species abundance. We distributed plant instances using a blue noise sampling algorithm, guided by density maps derived from unmanned aerial system (UAS) imagery. Species-specific optical properties were parameterized using field-measured reflectance data and the PROSPECT radiative transfer model, while terrain structure was derived from SfM-based digital terrain models. The integrated scene was used to simulate multispectral (DJI Mavic 3 MSI), hyperspectral (AVIRIS-NG), and light detection and ranging (LiDAR) observations. Agreement between simulated outputs were evaluated against corresponding field-acquired datasets using spectral signatures and vegetation indices. This framework enables systematic assessment of sensor specification effects on spectral biodiversity metrics and provides a pathway for evaluating theoretical limits of species discrimination across airborne and satellite platforms. Full article

Macroplastic pollution in rivers is an increasing environmental concern because rivers function simultaneously as active transport pathways and temporary storage compartments for land-based plastic waste. This study investigated the spatial distribution, composition, and size characteristics of riverbank and floating macroplastic debris in the Can Tho River, a tidal tributary of the Hau River in the Mekong Delta, Vietnam, to improve understanding of macroplastic transport, selective retention, and environmental partitioning between active transport and temporary storage compartments. Riverbank debris was surveyed at twelve sites spanning urban, peri-urban, and rural sections, while floating debris was quantified using a net-based sampling system. Riverbank accumulations exhibited pronounced local spatial heterogeneity, although litter density and mass density did not differ significantly among river sections. Plastics dominated both environmental compartments, accounting for 53–60% of accumulated debris and more than 95% of floating debris by abundance. Riverbank accumulations were dominated by plastic bags, food packaging, and beverage containers, whereas floating debris was dominated by expanded polystyrene foam products. Significant differences were also observed in material composition, plastic-product composition, and size distribution. Riverbank accumulations contained proportionally larger macroplastics (100–500 mm), whereas floating debris was dominated by smaller macroplastics (50–200 mm), supporting the role of size-dependent transport and selective retention in environmental partitioning. These findings show that floating debris and riverbank accumulations represent complementary components of the riverine plastic continuum, linking active transport and temporary storage through selective environmental partitioning. Integrating floating and riverbank monitoring provides a more comprehensive framework for understanding macroplastic transport and environmental fate while informing management strategies to reduce downstream plastic transport to the Hau River and ultimately estuarine and coastal ecosystems. Full article

To understand the epidemiological characteristics and patterns of mushroom poisoning in Zhejiang Province from 2012 to 2023, and to overcome the limitations of previous descriptive studies in precise early warning and spatial identification, this study explored the feasibility of identifying spatial distribution characteristics and high-risk spatiotemporal clusters. First, descriptive epidemiological analysis was conducted on 2276 cases from the Foodborne Disease Case Surveillance System and 408 outbreaks from the Foodborne Disease Outbreak Surveillance System reported over the 12-year period to clarify the basic characteristics and trends of poisoning. Subsequently, spatial autocorrelation analysis (Moran’s I) was employed to reveal spatial dependence and clustering patterns. Finally, spatiotemporal scan statistics (SatScan) were used to precisely identify high-risk spatiotemporal clusters, systematically analyzing the spatiotemporal distribution and clustering patterns of mushroom poisoning cases. The results showed a distinct summer–autumn seasonal peak (June–October), attributed to the subtropical monsoon climate with high temperatures and abundant rainfall, which is conducive to mushroom growth. Farmers were the most affected population (47.93%), and homes were the primary poisoning locations (71.7%), reflecting widespread foraging habits and insufficient risk awareness in rural areas. Chlorophyllum molybdites (36.27%) and Russula japonica (10.05%) were the dominant poisoning mushroom species, with gastrointestinal symptoms being the predominant clinical manifestation (84.07%). Spatial analysis revealed significant spatiotemporal clustering of mushroom poisoning in Zhejiang Province. The global Moran’s I index showed significant positive autocorrelation in some years (p < 0.05), with local hotspots mainly distributed in western Zhejiang counties. This pattern is driven by a dual model of environmental suitability and behavioral risk, resulting from the high forest coverage and humid climate of the western Zhejiang mountainous areas providing suitable habitats, combined with long-standing foraging habits among local residents. Retrospective spatiotemporal scanning identified high-risk clusters for each year from 2018 to 2023, with the Lishui area in 2023 being the most significant cluster (Relative Risk (RR) = 15.44, Log-Likelihood Ratio (LLR) = 114.49). The results confirm that mushroom poisoning in Zhejiang Province exhibits a stable and identifiable spatiotemporal clustering pattern, providing a quantitative basis for precise health education and targeted prevention and control in high-risk counties of western Zhejiang during June–October, thereby shifting the approach from passive reporting to targeted intervention. Full article

Microplastic contamination in coastal environments has become an increasing environmental concern due to its persistence, widespread distribution, and potential ecological impacts. However, comprehensive assessments that integrate microplastic abundance, polymer composition, and pollution indices for both coastal waters and sediments remain limited in the southern Black Sea. Therefore, this study presents an integrated assessment of microplastic contamination in surface waters and coastal sediments from eleven locations along the southern Black Sea coast by combining particle characterization, polymer identification, and pollution assessment. The mean microplastic abundance was determined as 138.6 ± 65.1 MP/L in surface waters and 195.8 ± 91.9 MP/kg dry weight in sediments. Fragments were the dominant particle type in both environmental compartments, where black and transparent particles constituted the most abundant colour categories, while smaller size fractions (<1000 µm) predominated in surface waters. ATR-FTIR analysis identified polyethylene and ethylene-vinyl acetate as the dominant polymers, followed by polyethylene terephthalate, acrylic polymers, polyvinyl stearate, and poly(α-methyl styrene). Spatial differences were evident among the sampling stations, with the highest microplastic abundance in surface water observed at Station S₁₁ and the highest sediment abundance at Station S₁, reflecting the influence of local anthropogenic activities and environmental conditions. Pollution index results revealed spatial variability among sampling locations, indicating differences in microplastic accumulation patterns and potential contamination levels associated with local anthropogenic pressures and coastal activities. Overall, the predominance of secondary microplastics and the spatial variation in pollution indices provide baseline information for future monitoring and support management strategies aimed at reducing plastic pollution along the southern Black Sea coast. Full article

The Shaximiao Formation in the Sichuan Basin hosts abundant tight oil resources; however, its reservoirs are typified by low porosity, low permeability, pronounced pore–throat structural heterogeneity, and highly complex microscopic crude oil seepage behavior. This study systematically investigates the microscopic flow mechanisms of crude oil in the Shaximiao Formation using a microfluidic experimental platform coupled with an integrated physical simulation system that enables real-time monitoring of dynamic imbibition throughout the fracturing–shut-in–flowback cycle. Experiments were conducted across three reservoir quality classes (Class I, II, and III), seven discrete shut-in durations (4, 8, 12, 24, 36, 42, and 54 h), and two representative fracturing fluid injection rates (12 and 20 m³/min). The results show that (1) residual-oil exhibits a distinct spatial distribution pattern: enrichment in large pores and large throats, with minimal retention in small pores and small throats; (2) moderate extension of shut-in duration significantly enhances movable oil saturation, whereas excessive shut-in time drives partial movable oil to transform into film flow or become trapped in dead-end pores, thereby exacerbating residual-oil retention; (3) the proportion of movable oil decreases gradiently with declining reservoir quality, following the order: Class I > Class II > Class III reservoirs; and (4) for the same reservoir type, a lower injection rate (12 m³/min) improves sweep efficiency in small pore–small throat regions and reduces residual oil retention, while a higher rate (20 m³/min) tends to induce an unbalanced seepage phenomenon, “preferential breakthrough in large pores and persistent retention in small pores”, which impairs the overall reservoir stimulation effect. Full article

Charybdis (Gonioneptunus) bimaculata (Miers, 1886), C. (Charybdis) japonica (A. Milne-Edwards, 1861), and C. (Charybdis) miles (De Haan, 1835) are dominant target species harvested by small-scale artisanal fisheries in the coastal waters of China. Characterizing their spatial distribution and interannual population variability is critical for sustainable crustacean stock conservation. Previous investigations of these three Charybdis congeners have been limited to short-duration, single-season, or geographically restricted descriptive surveys, with no systematic cross-species comparison of seasonal habitat use. This has created major knowledge gaps regarding their long-term spatiotemporal dynamics and ecological niche partitioning. We conducted standardized bottom-trawl surveys aboard research vessels across the southern Yellow Sea and East China Sea from 2018 to 2021 to quantify numerical abundance and biomass of the three congeneric crabs. Two species distribution modeling frameworks—Generalized Additive Models (GAMs) and Boosted Regression Trees (BRTs)—were implemented to generate fine-resolution seasonal habitat suitability maps for each species. We identified minimum thermal thresholds for species presence: bottom water temperature > 8 °C for C. bimaculata and >10 °C for C. miles. Core aggregation zones of C. bimaculata and C. japonica occurred in waters adjacent to the Yangtze River Estuary (32–34° N), whereas C. miles predominated in offshore habitats of the southern East China Sea (27–31° N). In this study, C. bimaculata favors shallow seas with warm bottom water and euryhaline conditions, C. japonica prefers low-salinity coastal waters with cold bottom water, and C. miles thrives in deep, high-salinity offshore waters with eurythermal tolerance. The findings of this study can contribute to the sustainable fisheries management and conservation of these species. Full article

Deadwood is widely recognized as an important structural component of forest ecosystems, yet its relationship with natural regeneration remains highly context-dependent, particularly in semi-arid forests. This study evaluated associations of plot-scale downed deadwood volume, litter depth, and stand basal area with Scots pine (Pinus sylvestris L.) seedling and sapling abundance in central Türkiye. Field measurements were conducted in 60 circular plots distributed across five naturally regenerated stands. Relationships were explored using Spearman rank correlations and evaluated using four unified zero-inflated negative binomial generalized linear mixed models (ZINB-GLMMs) that accounted for stand differences and paired seedling–sapling observations within plots. Downed deadwood was spatially heterogeneous and dominated by coarse woody debris, whereas fine woody debris contributed little to the total volume. Neither coarse woody debris, fine woody debris, total downed deadwood volume, nor basal area was significantly correlated with seedling or sapling abundance. In a supplementary unadjusted comparison, regeneration abundance did not differ between plots with and without deadwood (Wilcoxon tests, p > 0.05). Spearman correlations showed opposite associations between litter depth and seedlings (ρ = −0.312, p = 0.015) and saplings (ρ = 0.260, p = 0.045). However, AICc model comparison favored the baseline model containing the regeneration stage and stand identity (AICc = 517.26, Akaike weight = 0.672), and the unified model did not detect a significant regeneration stage × litter depth interaction (β = 0.138, p = 0.666). These findings indicate that contrasting bivariate litter-depth associations were not confirmed as a robust stage-dependent response after accounting for stand effects, zero inflation, and within-plot pairing. At the plot scale, total deadwood volume alone did not explain Scots pine regeneration abundance in the studied semi-arid forests. Full article

A previously overlooked isomer of okadaic acid (OA), which is structurally related to dinophysistoxin-2 (DTX2), was identified in bivalves and water columns from the Northwest Iberian Peninsula. This isomer elutes chromatographically after DTX2 and before DTX1, with mass spectrometric fragmentation patterns distinct from those of OA. It occurs at lower concentrations than OA but at levels comparable to DTX2, showing a consistent presence since monitoring by LC-MS/MS began in 2014. The abundance of this isomer correlated more strongly with OA than with DTX2, suggesting that it originates mainly from phytoplankton species that do not produce DTX2. Seasonal patterns revealed two maxima for the isomer, aligning with OA but differing from the single autumn–winter maximum of DTX2. Spatially, the isomer followed a northeast–southwest gradient along the Galician coast, similar to the OA and DTX2 distributions. The esterification rates of the isomer in mussels are lower than those of OA but similar to those of DTX2, likely resulting in slower depuration and longer persistence. Temporal trends indicate a recent increase in the concentration and seasonality of the isomer, rendering it more conspicuous. These findings highlight the growing significance of this isomer in shellfish toxin profiles throughout the studied period, underscoring the need to determine its structure and potential toxicity to better assess risks to human health. Full article

Restoration of natural sterlet (Acipenser ruthenus) stocks in the transboundary Irtysh River, shared by China, Kazakhstan, and Russia, is of critical importance for biodiversity conservation and represents a strategic priority for sustainable management of transboundary aquatic resources in all three countries. Over the past two decades, sterlet abundance and occurrence in the river have remained low, indicating an unfavorable population status. The Irtysh sterlet population is likely influenced by a combination of habitat degradation, fragmentation of spawning habitats, alterations in the hydrological regime, and increasing anthropogenic pressure. This study summarizes archival and recent data on sterlet catches, biological condition, and habitat characteristics within the Irtysh River basin. Hydrological and hydrochemical parameters, spatial distribution of catches, and the size–age structure of the population were analyzed. The results revealed a localized distribution pattern of sterlet, with the aggregation index reaching I ≥ 1 in some cases, indicating an uneven spatial distribution of individuals. Such aggregation patterns may partially reflect the presence of ecologically important habitats and provide a basis for future conservation planning and targeted monitoring, although this relationship could not be confirmed within the scope of the present study. A decline in the proportion of older age groups and deterioration of key biological indicators suggest weakening natural reproduction. The study initiated the establishment of a replacement broodstock and formed an initial broodstock of 34 sterlet individuals collected from the wild. This provides a foundation for future artificial propagation and the recovery of the endangered Irtysh River sterlet population. Full article

Climate change may redistribute forest pests, pathogens, natural enemies, and host trees, yet these components are still often projected independently. We used ensemble species distribution models (SDMs) to map potential climatic suitability for Monochamus alternatus, Dastarcus helophoroides, Scleroderma guani, Bursaphelenchus xylophilus, and a genus-level Pinus host layer, and then summarized niche overlap, range overlap, and multispecies co-suitability under SSP1-2.6, SSP3-7.0, and SSP5-8.5 for the 2050s and 2090s. A structural equation model (SEM) fitted to thresholded binary layers was retained only as an exploratory description of conditional spatial associations. Because its inputs were suitability classifications rather than abundance, infection, parasitism, nematode load, or transmission data, neither arrow direction nor coefficient sign is interpreted causally. The strongest joint modeled suitability for the vector, pathogen, host, and at least one natural enemy occurred in East Asia, whereas B. xylophilus alone also showed potential climatic suitability in parts of the Americas and Africa. Importantly, the current model underpredicted the established occurrence of B. xylophilus in Portugal and western Spain, demonstrating that mapped unsuitable cells cannot be interpreted as confirmed absence. The genus-level Pinus layer similarly represents broad host availability rather than species-specific susceptibility. Because the future maps are based on averages across three general circulation models and the archived outputs do not permit retrospective estimation of inter-model variability, these projections should be treated as screening-level, scenario-conditioned summaries rather than uncertainty-bounded forecasts. The results identify priorities for surveillance and field validation, but they do not quantify disease incidence, interaction strength, or biological-control efficacy. Full article

Intelligent transportation and aerial remote sensing scenes suffer from complex scene variations, abundant miniature targets and unpredictable out-of-distribution obstacles, which brings tough mathematical challenges to open-world detection tasks. Conventional detection algorithms lack rigorous frequency-domain separation and spatial constraint mathematical formulations, resulting in severe tiny-object feature attenuation, inefficient multimodal feature matching and catastrophic forgetting during incremental category iteration. To solve these mathematical bottlenecks, this paper constructs the TPCA-Net model built upon frequency decomposition and spatial dependency mathematical modelling. The entire framework consists of four fixed core modules: High-Frequency-Aware Multi-Scale Feature Enhancement (HSE), Reparameterized Adaptive Text–Visual Alignment (RTA), Double Wildcard Spatial Dependency Fusion (WSF), and Incremental Forgetting-Free Dual-Path Detection (DPD). From the mathematical perspective, the HSE module adopts discrete cosine transform-based filtering equations to split high-frequency object details from low-frequency background signals and establishes cross-attention spatial constraint formulas to make up for missing contextual information of small targets. The RTA module introduces low-rank decomposition mathematical optimization and reparameterized tensor fusion rules to realize domain-adaptive text embedding calibration and zero-cost cross-modal mapping at the inference stage. The WSF module constructs dual-wildcard self-supervised mathematical loss to finish unsupervised unknown-object identification and builds decoupled semantic–spatial fusion equations to improve the positioning precision of novel targets. The DPD module designs two sets of independent optimization objective functions and category-freezing incremental mathematical constraints to avoid conflicting parameter updates and eliminate forgetting defects in new-class expansion. Validated on COCO, DOTA and AI-TOD datasets, TPCA-Net achieves 56.0% AP on COCO, 79.30% mAP on DOTA, and 40.5% overall AP with 28.7% small-object AP on AI-TOD while delivering an inference throughput of 101.2 FPS on the Tesla T4 edge GPU. The proposed method outperforms existing mainstream open-world detection algorithms in tiny-object and rare-category recognition while maintaining efficient inference speed. Full article

The Shangri-La region in northwest Yunnan preserves extensive glacial moraines, which provide abundant loose material for landslides and debris flows. Mapping these moraines is therefore critical for susceptibility assessment. In this study, we used remote sensing interpretation to extract the spatial distribution of moraines. We then applied the Analytic Hierarchy Process (AHP) to eight factors—elevation, slope, curvature, vegetation cover, lithology, moraine distribution, rainfall, and human activities—and built a susceptibility model within a GIS framework. A Monte Carlo simulation was also conducted to test the sensitivity of the factor weights to potential uncertainties in expert judgments. The results show that moraines cover about 186.7 km² of the study area. Rainfall intensity and moraine distribution emerged as the two most influential factors. The simulation gave weights that differ from the standard AHP by less than 1.6%, suggesting the results are stable. The resulting susceptibility map divides the area into four zones: non-susceptible, low, moderate, and high susceptibility. The high-susceptibility zone covers 1656.48 km² (14.26% of the total area), mainly in high-altitude, steep terrain where moraines are present. The model performed reasonably well in validation, with an ROC curve AUC of 0.807. These findings could support local hazard prevention and land-use planning. Full article