Atmospheric and Oceanic Sciences

Marine Synechococcus is among the most widespread and productive autotrophs in the ocean, yet the quantitative role of mixotrophy in different lineages remains poorly constrained. Here, we compared organic nitrogen (urea and leucine) and carbon (glucose) utilization in nutrient-depleted versus nutrient-rich Synechococcus lineages by combining NanoSIMS-based single-cell measurements from field and laboratory incubations with omics analyses. Our findings revealed distinct mixotrophic strategies in different lineages. In nutrient-depleted lineages, elevated urea uptake supplied approximately 40–63% of the estimated total nitrogen demand. This pattern aligned with genomic evidence of enhanced urea transport, particularly the up-regulation of the high-affinity urea transporter DUR3 in low-nitrogen environments. In contrast, nutrient-rich lineages exhibited greater glucose uptake, although the amended organic substrates contributed only 2 to 4% to the estimated cellular carbon demand. These lineage-specific mixotrophic strategies underpinned niche partitioning in marine Synechococcus , refining our understanding of their trophic differentiation and its implications for marine biogeochemical cycling.

Airborne microplastics (AMPs) are increasingly recognized as an emerging air pollutant. However, observational data remain scarce in Southeast Asia. This study provides the first observations of AMPs in Phnom Penh, Cambodia, using µFTIR-ATR imaging. Number concentration, morphology, polymer composition, aerodynamic size distribution, Feret diameter, and surface aging characteristics were investigated together with meteorological parameters, gaseous pollutants, water-soluble ionic tracers, and HYSPLIT backward trajectories to examine possible source attribution. AMPs were dominated by polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET), with 52% classified as fragments and 82% having Feret diameter smaller than 30 µm. Across four independent 72-h sampling periods (n = 4), AMP concentrations ranged from 0.55 to 1.27 MP m−3 in TSP, with a mean ± standard deviation of 0.97 ± 0.30 MP m−3, and from 0.23 to 0.49 MP m−3 in the PM2.5 fraction, with a mean ± standard deviation of 0.33 ± 0.10 MP m−3. In total, 134 particles were identified in TSP, of which 46 were detected in the PM2.5 fraction. Carbonyl and hydroxyl indices indicated that PE and PP were relatively fresh and in low-to-moderate surface aging states. Pearson correlations suggested that the abundances of individual polymers were varied differently in relation to local environmental and precipitation-related variables; however, the limited number of sampling periods precludes source or process attribution. In addition, HYSPLIT backward trajectories showed that some air masses arriving in Phnom Penh had passed over marine regions under southwest monsoon flow. These findings provide the first baseline dataset for AMP pollution in Phnom Penh, Cambodia, and highlight the combined importance of local emissions and regional atmospheric transport in Southeast Asia.

Abstract. In complex hydrological systems, flow path dynamics, water storage and mixing, and biogeochemical processing vary in space and may change rapidly during events. Understanding source areas, connectivity and short-term dynamics in stream water quality therefore requires high-temporal-frequency, multi-source observations both within and across catchments. Revolutions in field-deployable analysers and sensors, together with advancement in automation techniques, now make such observations feasible via true “labs-in-the-field”. This paper details the technical realisation and proof-of-concept for the Water Analysis Trailer for Environmental Research (WATER). The WATER is a mobile, trailer-based platform for environmental sensing and automated, high-temporal-frequency sampling and analysis of water from multiple (currently up to 11) sources. It is currently equipped to measure stable water isotopes, nitrate, electrical conductivity, pH and temperature, though its modular design supports the integration of additional measurement devices in the future. A field test in the 1.03 km2 Schwingbach Environmental Observatory, Germany, demonstrated the ability of the WATER to successfully and autonomously collect and analyse samples from six water sources (2 × stream water, 3 × groundwater, 1 × precipitation) over a period of six months, with collected data offering potential for new understanding of catchment functioning. Insights were also gained into the practical considerations necessary when deploying the WATER for an extended period of time, such as ensuring an adequate self-sufficient power supply and scheduling routine maintenance visits. Simulation of the reduced sampling frequency that would result from extending the WATER to sample at its full capacity of 11 sources also indicated that, over multi-month periods, key distributional characteristics of the collected data would likely be maintained. Overall, the WATER provides a mobile and scalable solution for high-temporal-frequency, multi-source hydrological and hydrochemical monitoring that can be (re-)deployed in different locations or targeted to specific events.

With the rapid advancement of high-resolution remote sensing technology, Remote Sensing Semantic Change Detection (RS-SCD) has emerged as a core technology for monitoring dynamic Earth surfaces. RS-SCD enables simultaneous localization of changed regions and inference of their ‘From-To’ class transitions, overcoming the semantic ambiguity limitations of traditional binary change detection. In this systematic review, we first formalize the RS-SCD task and present a conceptual probabilistic perspective to elucidate the structural coupling between change detection and semantic segmentation. Moving beyond conventional architecture-centric surveys, we propose a task-oriented taxonomy based on five semantic-change coupling strategies: sequential decoupling (primarily CNN-based), parallel coupling (Transformer and Mamba architectures), relational coupling (graph neural networks), cross-modal fusion (optical, SAR, and vision-language modalities), and partial-evidence inference (weakly supervised, self-supervised, and foundation-model fine-tuning). For each strategy, we analyse representative models, highlighting trade-offs among accuracy, efficiency, and robustness. We further discuss practical bottlenecks, including pseudo-change suppression, annotation scarcity, and limited generalization, and outline future trends towards large-model-driven RS-SCD that is generalized, all-weather, and trustworthy. This review provides a systematic, conceptually grounded reference for both research and operational deployment of RS-SCD.

Abstract. Floating debris transport in marginal seas is controlled by a combination of geostrophic currents, wind-driven currents, wave-induced drift, and direct wind forcing, but the relative importance of these processes remains difficult to quantify without in-situ trajectory observations. This study evaluates near-surface forcing combinations for particle tracking in the East/Japan Sea using 33 GPS-tracked surface drifters deployed off the Korean coast in November 2021. The drifters revealed a clear cross-basin transport pathway toward the Japanese coastline and an early bifurcation near a hyperbolic-type Lagrangian structure. We used a particle tracking model to test combinations of geostrophic current, Ekman current, Stokes drift, and windage against the observed drifter trajectories using MAE and NCLS metrics. For undrogued DX drifters, the best performance was obtained by combining geostrophic current, Stokes drift, and windage. For drogued DO drifters, which sampled a deeper effective depth, the inclusion of all four forcing components provided the best agreement with observations. The optimized forcing combinations outperformed benchmark simulations driven by the GOFS analysis and GLORYS reanalysis in this specific event, primarily because they better reproduced the Lagrangian structure associated with the early trajectory bifurcation. Seasonal simulations showed that winter winds enhance eastward transport and beaching along Japan, whereas weaker summer winds allow mesoscale eddies to broaden particle dispersion. The present framework represents the fluid-dynamical component of near-surface particle transport and does not explicitly include plastic-specific fate processes such as fragmentation, biofouling-induced buoyancy changes, aggregation, settling, or resuspension.

Mangrove canopy height (MCH) is a fundamental structural variable for monitoring ecosystem health and quantifying carbon stocks. However, MCH retrieval from optical satellite imagery is often constrained by spectral saturation in dense stands and environmental noise in intertidal zones. This study investigates the utility of kernel-based spectral features (KVIs) as non-linear topological enhancements for MCH estimation by integrating GEDI spaceborne LiDAR with Sentinel-2 and Sentinel-1 data across three mangrove ecosystems along the South China coast. Utilizing four regression models under spatial cross-validation, we evaluated the performance of traditional indices, KVIs, and integrated feature sets against GEDI reference measurements. Results indicate that traditional optical indices exhibit limited linear sensitivity to MCH. Rather than serving as universal accuracy boosters, KVIs function as non-linear stabilizers by redistributing spectral values in Hilbert space, which effectively enhances feature representation in high biomass stands and mitigates background noise. Furthermore, model comparisons reveal that while traditional indices yield competitive baseline accuracy in specific architectures (e.g., 1D-CNN), kernel-based features provide nuanced advantages in spatial stability and ensemble dispersion reduction. Ultimately, this study demonstrates that kernel-based features enhance model robustness under rigorous cross-validation, providing a reliable structural foundation for large-scale ecological monitoring and regional carbon dynamics assessments in heterogeneous coastal environments.

Pixel-level annotation of remote sensing imagery is costly, motivating weakly supervised semantic segmentation (WSSS) using only image-level labels. However, class activation maps (CAMs) often highlight only discriminative sub-regions and fail to separate adjacent land-cover regions, particularly in remote sensing scenes characterized by densely co-occurring land-cover classes and substantial variations in object scale. To address these limitations, we propose a three-stage framework that integrates complementary priors from Contrastive Language–Image Pre-training (CLIP), Self-Distillation with No Labels version 2 (DINOv2), and the Segment Anything Model (SAM). First, a lightweight CLIP adapter aligns vision–language priors with remote sensing imagery, while sigmoid-based multi-label decoupled distillation replaces class-competitive distillation with independent class-wise supervision, producing more complete CAMs. Second, DINOv2-guided feature clustering decomposes large merged regions before SAM prompt generation, while Spatial–Semantic Constraints are used to construct confidence-guided point-and-box prompts and reject excessively expanded or semantically inconsistent masks, thereby generating reliable pseudo-labels. Finally, a compact segmentation network is initialized with the weights learned in Stage 1 and retrained using the refined pseudo-labels generated in Stage 2, eliminating the need for foundation models during inference. Experiments on the Potsdam, LoveDA, and DeepGlobe datasets show that the proposed method achieves mean intersection over union (mIoU) scores of 53.16%, 52.66%, and 62.98%, respectively, outperforming state-of-the-art WSSS baselines by 6.55, 1.16, and 1.27 percentage points, respectively. These results demonstrate the effectiveness and generalizability of the proposed framework across diverse remote sensing scenarios under image-level supervision.

In Deep Learning Remote Sensing, data quantity is rarely the limiting factor. A single high-resolution satellite image can yield thousands of training patches. What determines model performance, yet remains largely overlooked, is the quality of those patches. To date, the choice of sampling method has rarely been treated as a methodological decision. Conventional approaches, namely sliding-window and random sampling, introduce two compounding data-quality problems: severe class imbalance caused by the overproduction of background-only patches and negative learning arising from incomplete annotations, where unlabeled objects are implicitly treated as negative examples during training. To address these limitations at the data construction stage, we propose object-centric patch sampling, a model-independent strategy that anchors each training patch to the geometric centroid of an annotated object. This design ensures that every object-anchored patch contains at least one target instance and substantially reduces exposure to unlabeled regions that generate false-negative supervision signals; only a small, deliberately controlled proportion of background-only patches is retained to preserve contextual variety without reinstating background dominance. The method is evaluated on three heterogeneous remote sensing datasets spanning satellite (Sentinel-2, 10 m), aerial (NAIP, 1 m), and UAV (0.25 m) imagery, covering cotton field segmentation, rural building extraction, and water body delineation, respectively. Using a U-Net architecture under identical training conditions, the proposed approach achieves IoU scores of 0.929, 0.896, and 0.912 on the three datasets, respectively, outperforming sliding-window sampling by up to 19.6 percentage points in IoU and consistently delivering higher F1-scores across all experimental configurations. Evaluation under DeepLabV3+ gives a mean IoU of 0.9504 and a mean F1-score of 0.9772 in a multi-class segmentation task, indicating that the gains are not specific to a single architecture. Unlike model-level solutions such as focal loss or class reweighting, the proposed method improves training data quality at its source and integrates seamlessly into any deep learning pipeline without architectural modifications.

Abstract Dissolved oxygen (DO) is crucial for maintaining ecological integrity in tidal river tributaries. However, the relative roles of physical, chemical, and biological factors in urban tidal rivers remain unclear. In this study, controlled laboratory experiments were conducted to investigate the mechanisms by which these factors and their interactions influence DO dynamics in an urban tidal river. Temporal variations in DO and three nitrogen species were analyzed, and the implications for low-oxygen control were evaluated. The results indicate that physical factors mainly determine the background trajectory and overall range of DO variation. Higher flow velocities increase DO concentrations and accelerate recovery. Salinity showed no clear monotonic effect across the low-to-moderate range tested, whereas the highest salinity treatment produced lower DO levels. Among the chemical treatments, NH₃-N removal maintained substantially higher DO than COD removal or raw-water conditions. Sediment addition caused rapid DO depletion under both chemical and flow treatments, followed by only partial recovery. The influence of biota on DO was condition-dependent and became clearer mainly under high-temperature and still-water conditions. These findings suggest that prioritizing NH₃-N reduction, controlling sediment-related internal loading, and moderately enhancing mixing and reaeration are effective strategies for increasing and stabilizing DO levels.

Study region The study investigates the Corabia – Turnu Măgurele sector (rKm 601 – rKm626) of the Lower Danube River in Romania. This is a hydromorphologically dynamic area of regional importance for navigation. Spatial and temporal dynamics modify its hydrodynamic behaviour, sediment transport, and channel morphology, changing it into a critical navigation sector. Study focus Modern hydrographic and acoustic survey equipment was used to describe the study area's hydromorphological characteristics. The synergistic use of MBES, ADCP, and SSP delineated five morphological areas grouped into three broad classes: shallow-water zones associated with sandbar formation; moderate-depth sections characterized by relatively stable hydrodynamic conditions and requiring long-term monitoring; and deep-water zones within the main navigable channel, characterized by enhanced erosion and sediment transport. Principal component analysis, K-means clustering, and spatial principal component analysis were used to classify areas with different hydromorphological characteristics. The analysis offers insights into water flow patterns and areas for this regional critical area where the sedimentation rate is excessive. New hydrological insights for the region The integrated analysis identified water-flow patterns and zones of excessive sedimentation. The bathymetric surveys and ADCP measurements were complemented by sediment suspended profiling. The result reveals that the majority of sediment particles fall within the 100–1000 µm range (fine to medium sands). A dominant fraction (100–300 µm) underscores the prevalence of fine sand, indicative of moderate-energy depositional behavior. The low content of fine particles (<10 µm) reflects limited silt and clay retention. The statistical analysis (PCA, K-means, spatial PCA) grouped datasets into three types of areas with different hydro-morphological characteristics. Under extreme low-flow conditions, water depths within the navigable channel often locally drop below 2 m, imposing operational limitations on vessel draught and increasing the risk of navigation disruption. The proposed approach provides a valuable method to understand the importance of the synergistic application of bathymetric, hydrodynamic, and sedimentological analysis, contributes to increasing the level of knowledge on sediment dynamics in regional river systems, such as the Lower Danube, and provides support in decision-making regarding the prioritization of dredging interventions.

Climate change is driving hydroclimatic changes that are not fully captured by conventional trend analyses. This study presents a multidimensional assessment of hydroclimatic changes in Northern Cyprus using observational records from 27 precipitation stations and 12 temperature stations, with precipitation records spanning 35–46 years and temperature records spanning 21–30 years. Modified Mann–Kendall (MMK), Pettitt (PT), Innovative Trend Analysis (ITA), and Structural Trend and Variability Identification (STVI) methods were integrated to examine monotonic trends, abrupt shifts, distribution-dependent changes, and mean–variability interactions at annual and seasonal scales. Results revealed pronounced spatial heterogeneity and seasonal asymmetry in precipitation totals and their temporal evolution. Increasing tendencies were mainly concentrated in the Kyrenia mountainous region and parts of the western coast, whereas several eastern coastal stations showed drying tendencies, particularly in spring. Winter exhibited the most coherent wetting signal, while spring was more fragmented and drying-dominated. Monthly mean of daily maximum temperature (Tmax) and monthly mean of daily minimum temperature (Tmin) generally showed widespread warming, although Tmin responses were more localized and season-dependent. ITA indicated asymmetric precipitation behavior, with medium and high precipitation values generally increasing, while low values often decreased or showed mixed responses. STVI further revealed that precipitation changes involved substantial restructuring of both mean and variability components, whereas temperature changes were mainly mean-driven. The findings provide a more comprehensive understanding of evolving hydroclimatic conditions, which can support climate adaptation and water resources management in semi-arid Mediterranean regions.

Abstract Results are presented from a comprehensive analysis of the ability of CIN depth, relative to CIN and CAPE, to discriminate environments that support deep convection initiation (DCI) from those that do not. Evaluation is based on environments of DCI observed over the central US by the ThOR algorithm compared to nearby Null environments. A total of 62846 DCI/Null point pairs spanning a little over 7 years are used. CIN depth is calculated assuming three different parcel types – surface-based, mixed-layer, and most unstable – and two different definitions of depth – the distance between the LCL and LFC and the distance between the initial parcel height and the LFC. MLCD, the mixed-layer CIN depth, proved to be better than all CIN parameters in discriminating environments that support DCI from those that do not. For all parcel types, CD ORIG , the distance between the initial parcel height and the LFC, is better than CD LCL , the distance between the LCL and LFC, and both are better than CIN. CIN depth is also as good if not better than CAPE, the best discriminator based on prior work, at discriminating DCI from Null environments. When CIN differs very little between DCI and Null environments and CIN is small but generally non-zero, MLCD ORIG distinguishes DCI environments the best across the CIN depth varieties. For elevated DCI, CIN depth is generally less useful than CIN or CAPE at discriminating DCI from Null environments except when both DCI and Null environments have near zero CIN.

Atmospheric pollution associated with carbon monoxide (CO), nitrogen dioxide (NO 2 ), and sulfur dioxide (SO 2 ) represents an important environmental and public health concern in Andean regions influenced by urban growth, transportation, agricultural burning, mining-related activities, and complex topography. This study analyzed the spatiotemporal variability of CO, NO 2 , and SO 2 in the Apurimac region, Peru, during 2020–2023 using Sentinel-5P/TROPOMI satellite products processed in Google Earth Engine. The original column-density data, expressed in mol/m 2 , were converted into column-derived estimated concentrations using a simplified effective lower-atmospheric layer height of 2,000 m. These values were used only as relative indicators and were not interpreted as direct ground-level air quality measurements. The results showed heterogeneous spatial patterns across Apurimac. CO presented relatively higher values mainly in Abancay, Chincheros, and Andahuaylas, while NO 2 showed higher relative values in Cotabambas, Grau, Abancay, and Chincheros. SO 2 exhibited a more irregular and uncertain behavior, with localized positive values, negative retrievals, and high variability, indicating greater sensitivity to satellite retrieval noise. Therefore, SO 2 was interpreted as an exploratory indicator rather than robust evidence of province-level pollution hotspots. Temporal analysis showed seasonal fluctuations and isolated peaks, especially during dry-season months. Trend detection was performed using the Mann-Kendall test and Sen’s slope estimator. CO showed a statistically significant decreasing trend, NO 2 showed a weak but statistically significant increasing trend, and SO 2 did not show a statistically significant trend. Overall, Sentinel-5P/TROPOMI and Google Earth Engine proved useful for identifying relative pollutant patterns in a data-sparse Andean region. Future studies should integrate satellite observations with in situ measurements, meteorological data, boundary-layer information, emission inventories, and independent datasets to improve validation, source attribution, and regional air quality assessment.

Introduction The water–energy–food (WEF) nexus provides a critical framework for understanding the complex interactions among key resource systems under rapid urbanization and environmental change. However, limited attention has been paid to basin-scale internal heterogeneity and the differentiated mechanisms underlying system performance and coordination. Taking the Xiangjiang River Basin in China as a case study, this study examines the spatiotemporal evolution, spatial dependence, and driving mechanisms of the WEF system at the prefecture level from 2002 to 2022. Methods An integrated analytical framework combining the entropy weight–TOPSIS method, coupling coordination degree model, spatial autocorrelation analysis, and XGBoost–SHAP approach was employed. This framework was used to evaluate the development of individual WEF subsystems and overall system performance, characterize coupling coordination and spatial dependence, and identify the key factors driving system performance and coordination. Results The composite WEF system level improved steadily during the study period, primarily driven by the rapid development of the energy subsystem, while the water subsystem exhibited a fluctuating recovery and the food subsystem remained relatively stable. Despite this progress, the coupling coordination degree remained low, with most cities remaining in a state of moderate imbalance. Spatially, the WEF system exhibited weak global autocorrelation but distinct local clustering, indicating a fragmented yet locally structured spatial organization. The driving mechanisms also differed between system performance and coordination. System coordination was mainly influenced by agricultural production capacity and resource-use efficiency, whereas overall system performance was shaped by a broader combination of technological investment, fiscal support, and economic development. Discussion The findings demonstrate that improvements in overall WEF system performance do not necessarily translate into stronger subsystem coordination. Distinguishing between system growth and coordination is therefore essential for understanding basin-scale WEF dynamics. Region-specific and integrated management strategies that simultaneously enhance resource efficiency, balance subsystem development, and account for spatial heterogeneity are needed to promote sustainable resource management in river basins.

This study proposes an innovative framework that combines hydroclimatic and agro-environmental predictors, including nitrate concentration and NDVI, with climatic factors such as Rainfall, temperature, and evapotranspiration to forecast piezometric level variations in the Saïss Basin, Morocco. Eight Machine Learning (ML) models were benchmarked, and feature importance was assessed using Shapley Additive exPlanations (SHAP) to ensure model transparency and interpretability. In parallel, the PELT algorithm was applied to detect structural change points, while Sen’s slope estimator and the Mann–Kendall test were used to quantify long-term trends. The Extra Trees (ET) model achieved the best performance (R2 = 0.92), with Rainfall emerging as the most influential predictor, followed by nitrate concentration, confirming the added value of hydrochemical indicators for groundwater forecasting. Change-point analysis revealed significant declines during the 1980s and 1990s, followed by lower-amplitude fluctuations since the late 2000s. Projections toward 2050 suggest partial stabilization in the central part of the basin under favorable recharge conditions, whereas persistent declines are expected to continue in peripheral areas subjected to sustained groundwater abstraction pressure. These findings provide a robust and transferable decision-support tool for the sustainable management of groundwater resources in semi-arid agricultural area.

The Luzon Strait is a critical generation site for global internal tides. Their generation and propagation are significantly modulated by background currents, including the Kuroshio Current and mesoscale eddies. This study investigates nonlinear effects of these background currents on low-mode (modes 1–3) internal tides using a high-resolution numerical simulation. We apply the Taylor–Goldstein equation considering the Earth’s rotation and background currents to perform modal decomposition, and utilize a nonlinear internal tidal energy equation to quantify three crucial energy pathways: inter-modal energy conversion, nonlinear energy exchange with background currents, and nonlinear advection effects. Results demonstrate that while stationary mode-1 internal tides dominate in the generation region of the Luzon Strait, non-stationary energy increases significantly in the western and eastern propagation regions, driven largely by seasonal variability of the Kuroshio Current. Inter-modal energy conversion follows a cascade from lower to higher modes, with conversion efficiency increasing with mode number. Nonlinear exchanges between background currents and internal tides are one order of magnitude smaller than inter-modal conversions but exhibit a bidirectional transfer, where advection redistributes internal tidal energy within the eddy structures. This study provides a quantitative framework for understanding multiscale energy pathways of internal tides under complex ocean dynamics.

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.

This paper evaluates the sensitivity and calibration of the third-generation shallow-water wave model SWAN for Typhoon Doksuri (No. 202305) along the Fujian coast. Sensitivity analyses were conducted for model initialization, wind forcing, and key physical parameters. The results show that a 1-day spin-up period is sufficient to largely reduce the initial error caused by a cold start. A locally refined unstructured triangular grid was adopted, and ERA5 reanalysis winds were blended with the Holland empirical typhoon wind field to better represent extreme winds near the typhoon core. Further tests indicate that the combination of the Janssen wind input scheme, cds1 = 3.5, LTA triad wave interaction scheme, JONSWAP bottom friction scheme, and a wave-breaking parameter of 0.73 can effectively reproduce the typhoon wave process along the Fujian coast. The optimized simulations agree well with buoy observations and provide a reference for typhoon wave forecasting and coastal disaster risk assessment.

Ecosystem health is a measure that is used to describe the condition of the ecosystem. The stability of the ecosystem and sustainable growth are represented by ecosystem health which covers the overall condition of the environment. The loss of habitat and deterioration of ecosystem services are the outcomes of coastal urbanization and ecosystem health damage. Potential natural disasters and biodiversity loss are happening more frequently and intensely which makes an emerging issue of environmental resilience. That's why ecosystem health assessment is must. The 1,47,570 km2 of Bangladesh has 47,201 km2 of coastal zone consisting of 19 districts which is 32% of the country and about 43.8 million people live here. This study aims to assess the ecosystem health of 2013 and 2023 and to investigate the change in the coastal ecosystem health in evaluating with past and present conditions of the ecosystem. For assessment, a random forest machine learning model has been used in conjunction with remote sensing and GIS due to its high accuracy and efficiencies. The PSR (Pressure-State-Response) model has been used in this investigation. This study reveals that the ecosystem health of 2023 deteriorated mostly than in 2013. About 19.76% of the lower ecosystem health area of coastal zone has been increased over the decades. In addition, 6.92% and 12.84% of higher and medium ecosystem health areas have been degraded accordingly. The Satkhira district had higher ecosystem health and Chittagong had lower ecosystem health in 2013; whereas in 2023, it became Narail and Jhalokathi accordingly. The point is that the ecosystem is down falling from the time on. This concerning factor can assist the body in making decisions on how should interact with the environment with adequate controlling crucial measures to heal the environment's health.

Dengue remains a major public health problem in endemic regions, including Bangladesh. Forecasting algorithms relying on climatic variables may not capture epidemiological information such as dengue serotype patterns. This study proposes a horizon-dependent dengue forecasting framework and applies it to Bangladesh. This pipeline included temporal, meteorological, demographic, and epidemiological covariates in a district-panel pipeline and compares classical, machine-learning, and deep-learning algorithms using aggregate, horizon-wise, regime-wise, outbreak-detection, and uncertainty metrics. Coefficient-based interpretation, SHAP, permutation importance, and nonlinear causal-dependence analysis were used to examine predictor impacts across horizons. SARIMAX was used as the baseline model. TFT produced quantile forecasts but showed poor interval calibration during outbreak periods. Results show that no single model performed best across all forecasting horizons: SARIMAX ranked highest for one-step outbreak alerting (precision = 0.886, recall = 0.824, F1 score = 0.854, and ROC-AUC = 0.950), whereas Prophet performed best for pooled magnitude forecasting at horizons 2-6. MLR showed competitive performance in regime-wise evaluation. These findings show that dengue forecasting algorithms should be selected according to the intended decision objective and evaluated using task-relevant protocols.

Global environmental changes increasingly alter species distributions, yet their effects on plants serving both ecological and economic functions remain inadequately explored. We examined Xanthium strumarium, a species with medicinal and invasive properties, throughout China using integrated approaches: species distribution modeling (Biomod2), niche analysis (Ecospat), and rhizosphere microbiome profiling (Tax4Fun). Our findings demonstrate that human footprint index (66.6% variable importance), elevation, and topographic slope primarily determine current distribution patterns. Future climate scenarios predict habitat expansion of 8.9-28.6%, identifying high-risk invasion zones primarily concentrated in Yunnan, Guangdong, and Inner Mongolia provinces. Although niche overlap analysis indicates high conservatism (Schoener's D = 0.8986-0.9338), ecological adaptability shows a modest decline under elevated emission scenarios. Rhizosphere bacterial assemblages, characterized by Proteobacteria dominance and nitrogen-cycling taxa enrichment (Nitrospira, Verrucomicrobia), facilitate adaptation through enhanced metabolic pathways and environmental stress responses, promoting establishment in anthropogenically disturbed environments. Our results underscore the interactive effects of climate-mediated range shifts and microbiome-assisted resilience mechanisms underlying X. strumarium's invasive potential. This research offers essential guidance for managing dual-function species, emphasizing integrated strategies that consider both anthropogenic pressures and microbial associations in conservation planning under accelerating global change.

Abstract Machine learning (ML) has become a promising tool for image analysis, particularly for datasets with instrument-specific characteristics and variable image quality. The Balloon-borne Ice Cloud particle Imager (B-ICI) collects and images ice particles on a film strip during ascent through the troposphere. While the instrument provides high optical and pixel resolution, the background features of the film and oil coating introduce noise that complicates image analysis. To process these raw data obtained from B-ICI, a set of tools, BICI-NET, has been created. It consists of two convolutional neural network (CNN) models: BICI-NET-segmentation, which identifies ice particles in the images, and BICI-NET-classification, which categorises the particles by shape. A visual inspection step is included to ensure the quality of the segmentation results, allowing users to accept, correct, or reject individual mask images. These masks are then used to extract particle properties such as maximum dimension, cross-sectional area, and aspect ratio. The segmentation model was initially trained on data from 2013–2018, and its performance improves iteratively as new, inspected data are added to the training set. Using this model, the duration of the segmentation process is reduced by approximately 90%. Currently particles are classified into four different categories: columns; compacts; plates; and rosettes. These shape categories have been chosen based on current needs and because they had been used in previous studies with B-ICI data. We present the BICI-NET processing pipeline, its validation, and performance results. Comparisons with a manually analysed test dataset show good agreement, especially for particles larger than ≳ 100 μm. Minor biases affect smaller particles, and most misclassification occur in the plates category, which is under-represented in the training data.