Environmental and Earth Sciences

Abstract: Accurate prediction of significant wave height (SWH) is essential for fisheries management, coastal socio-economic activities, and marine ecological conservation. In recent years, deep learning-based bias correction has shown considerable potential for improving numerical wave forecasts. However, many existing approaches are still constrained by limited receptive fields and often struggle to capture long-range spatiotemporal dependencies in wave forecast errors. To deal with this issue, we adapt and improve a video prediction framework, namely the Vision Mamba Recurrent Neural Network (VMRNN), to model and correct the spatiotemporal patterns of SWH prediction biases. Comprehensive evaluations show that the multi-channel VMRNN achieves consistently high predictive accuracy across different forecast lead times and sea-state conditions. When validated against reanalysis data, the proposed model reduces the root mean square error (RMSE) of WAVEWATCH III forecasts by 28.2%, 26.1%, and 24.7% at lead times of 24, 48, and 72 hours, respectively. It also preserves the spatial structure of SWH fields quite well, with the spatial structural similarity index remaining as high as 0.945 even at the 72-hour lead time. Regional assessments over high-wave areas further indicate that VMRNN can effectively reduce both the mean error and the systematic overestimation commonly found in numerical wave models. Additional validation using in-situ buoy observations confirms that the model has a robust ability to correct systematic positive biases, especially for wave heights ranging from 0.5 m to 2 m. Taken together, these results suggest that VMRNN has strong spatiotemporal modeling capability and can serve as a promising post-processing framework for improving operational physics-based wave forecasting systems.

Abstract:

It is known that snowcover properties change rapidly due to effect of weather and radiation, detailed models mapping effect of weather and radiation processes to evolution of snowpack have been developed. These models are capable of accurately simulating entire evolution of snowpack at a specific point if a sufficiently detailed time-series of weather and radiation parameters affecting the point is known. In this study we consider the reverse problem of finding the weather and radiation parameters that lead to changes in snowpack parameters, we have used a simulation approach to study the feasibility of finding this reverse map. We mapped a time-series of snowcover states to their corresponding time-series of weather and radiation states using a machine learning model. The data of snowcover states was generated using a well known and rigorously validated snowcover simulation model (SNOWPACK). The results of our experiments show that snow surface time-series contains important information about the meteorological time-series affecting it. We were able to find the meteorological parameters from the simulated data under certain conditions, we expect these results to generalize with actual data. There maybe important applications of these results in optimization of weather data collection systems, weather interpolation algorithms and downscaling algorithms, combining the snowpack data with weather observations can lead to improvements in these algorithms. This study makes a preliminary feasibility study of the reverse problem, our results are positive and encourage further field work using actual data.

Abstract: The transition to a circular economy requires the safe management of sewage sludge through nutrient and energy recovery. However, the presence of pharmaceuticals and personal care products (PPCPs) poses a critical obstacle, as these compounds tend to accumulate in the sludge matrix through sorption processes, thereby shifting the environmental problem from the water stream to the sludge stream. This manuscript provides a comprehensive review of the scientific literature on technical alternatives for valorizing sewage sludge and removing emerging contaminants. The study evaluates the limitations of conventional biological methods, such as anaerobic digestion and composting, which exhibit variable efficacy and are often insufficient to degrade recalcitrant molecules, including some commonly used pharmaceuticals. On the contrary, thermal treatments (pyrolysis, gasification, and hydrothermal processes) are considered robust alternatives capable of achieving removals exceeding 90-99% thanks to the thermal degradation of contaminants. Furthermore, the article emphasizes the innovative potential of utilizing carbon-based byproducts (biochar and hydrochar) as adsorbents or catalysts to enhance the removal of PPCPs within the treatment infrastructure itself. The integration of advanced thermal technologies is essential to mitigate the risks of contaminant transfer to the food chain and ensure a safe and sustainable nutrient cycle.

Abstract: This paper presents the results of research aimed at identifying deep-seated natural sealed reservoirs for the isolation of chemically active gases, including CO₂. A comprehensive analysis of geological, geophysical, and hydrogeological data was conducted to identify potential structures within the aquifers of Almaty and the Almaty region (Southern Kazakhstan) capable of capturing and storing carbon dioxide under geographically favorable and economically viable conditions. The study utilizes seismic survey results and drilling data from the eastern part of the Ili Basin, demonstrating the efficacy of seismic exploration in identifying stratigraphic horizons and their structural-tectonic settings. Based on an integrated analysis of available geo-physic information, the lithological and stratigraphic characteristics of the sedimentary cover in the Ili Basin are substantiated. Key caprock sequences and reservoir units for potential storage sites are identified, and recommendations for further geological exploration are provided. Five reflecting horizons were identified within the geological section of the troughs. It was established that the Miocene-Paleogene and Jurassic horizons contain sandstone reservoirs with a thickness exceeding 10 m and enhanced filtration properties. Clay complexes are prevalent in the Upper Jurassic deposits, which can serve as a caprock for these reservoir rocks. Furthermore, the Upper Cretaceous clay sequence may act as a fluid seal for the Neogene-Paleogene sandy horizons. Such conditions meet the requirements for sealed reservoirs for the isolation of chemically active gases, including CO₂. According to hydrogeological studies, seven aquifer complexes are distinguished: Permian, Triassic, Jurassic, Cretaceous, Paleogene, Neogene and Quaternary. The novelty and practical significance of this research lie in obtaining new information on the geological structure of deep horizons in poorly studied areas of the Ili Basin and establishing favorable geological factors for identifying potential sites suitable for carbon dioxide sequestration.

Abstract: This study characterized standard biochars produced at 300, 400, and 500 °C alongside a locally made biochar (LBC, drum kiln method with newly devised method of Bababe) to assess fertilizer value and toxicity against IBI thresholds. Pyrolysis temperature strongly influenced properties: electrical conductivity and salt content increased with tempera-ture (BC300 and BC500 highest; LBC lowest). All standard biochars were highly alkaline (pH 10.26–10.57), while LBC was near-neutral (7.84). Maximum carbon content occurred at 300–400 °C (56.8–56.9 %). At 10 kg ha⁻¹, standard biochars supplied 308–331 kg ha⁻¹ K, with BC400 providing the highest Ca and Mg. LBC had the highest volatile micronu-trients (B, Cu, Fe, Mn), which decreased with rising temperature. It can be particularly well suited to fertilizer coating or blending systems, especially for salt-sensitive soils where application rates are kept low (< 10 t ha⁻¹), thereby limiting agronomic risks such as Mo contaminant loading. Nevertheless, molybdenum levels in all biochars were 5–8 times above IBI safe limits (5–75 mg kg⁻¹), posing toxicity risk at 10 t ha⁻¹ application. Cd was undetectable, reduced Pb by 90 % at 400–500 °C, and kept Ni and Pd within limits. SEM revealed BC400 had optimal honeycomb porosity and homogeneous mineral dis-tribution. BC400 is most suitable for agricultural fertilizer value, BC500 for carbon se-questration, BC300 for potassium supply, and LBC as a low-cost, low-salinity material. However, excessive molybdenum across all biochars relates feedstock composition as the paramount safety factor. The weakness and limitation of this studies lies in the resource constraints from use of one feedstock, absence of direct measurement of surface area and phosphorus, and absence of measurement of biochar stability.

Abstract: Ammonia-oxidizing bacteria (AOB) are vital for the nitrogen cycle and wastewater treatment, yet their recalcitrance to isolation and cultivation hampers industrial application. To isolate an efficient strain and optimize its culture conditions for high-ammonia wastewater treatment, we collected water samples from a polluted river in Zhongshan City. After enrichment, a strain was isolated via gradient dilution and silica gel plating, identified by scanning electron microscopy and 16S rDNA sequencing as Nitrosomonas europaea W4 (99.93% similarity to the type strain). Single-factor medium optimization examined CaCO₃ and Fe²⁺/Fe³⁺, while temperature and initial ammonia nitrogen effects were tested, and landfill leachate was used for verification. CaCO₃ shortened the lag phase without affecting maximum specific growth rate; replacing Fe³⁺ with Fe²⁺ further reduced lag and enhanced the ammonia oxidation rate. Optimal growth occurred at 25–30 °C and an initial ammonia nitrogen concentration of ~2000 mg/L. In landfill leachate, the strain increased the ammonia degradation rate 6.3-fold. Overall, N. europaea W4 exhibits high ammonia oxidation efficiency, and the optimized medium and conditions improve its proliferation and metabolic stability, providing a basis for cultivation and application in treating high-strength ammonia nitrogen wastewater.

Abstract: This research examines how climate change intensifies urban heat stress, particularly in public spaces where mechanical cooling is impractical. A climate-driven, systems-level numerical model is developed to evaluate the pre-installation feasibility of portable, solar-powered misting canopies. Hourly Typical Meteorological Year data (TMYx, 2009–2023) are analyzed for each city to estimate photovoltaic (PV) energy yield, electrical load, potential misting duration, water demand, and PV-to-load autonomy under summer daytime conditions. Misting operation is governed by an adaptive, rule-based control strategy based on air temperature, relative humidity, and solar radiation. To enable systematic comparison, K-means clustering is applied to classify the cities into six archetypal summer climate zones. Results indicate that evaporative cooling feasibility is driven more by ambient humidity than by air temperature. Hot-dry interior cities achieve the longest average misting duration (502.65 hours) and highest water consumption (30,486 L per module), but exhibit the lowest PV-to-load autonomy ratio (1.53) due to high energy demand for pumping. In contrast, humid Black Sea cities show minimal misting duration (13.11 hours) and water use (478 L) yet achieve the highest autonomy (40.91) because of limited system operation. It is important to note that the autonomy ratio reflects a seasonal energy balance rather than continuous off-grid capability. Overall, the adaptive control approach effectively aligns water and energy use with climatic suitability across all clusters. The proposed framework offers a scalable and quantitative screening tool to inform the design and deployment of PV-powered outdoor cooling systems across diverse urban environments.

Abstract: Remote sensing has become a key non-invasive tool in archaeological prospection, partic-ularly in regions where logistical constraints limit sustained fieldwork. This study pre-sents the results from Zar Tepe (1st–5th centuries AD), in the Surkhandarya province of southern Uzbekistan, within northwestern Bactria. The research aimed to document the site’s urban layout, accurately relocate Soviet-era excavation sectors within the pre-sent-day topography, and refine the interpretation of earlier interventions that were only partially documented and lacked precise georeferencing. A multiscale and multitemporal methodology was applied, integrating CORONA and WorldView-3 satellite imagery, UAV and terrestrial photogrammetry, GNSS Precise Point Positioning, magnetic prospection, and targeted archaeological verification. The workflow followed an iterative laboratory–field sequence, combining remote-sensing analysis, field checks, data refinement, and sys-tematic ground-truth validation. Fieldwork was conducted during two contrasting phe-nological periods, in June 2024 and December 2025, to assess seasonal variability in sur-face and subsurface visibility. The integrated approach enabled accurate spatial fitting of legacy excavation sectors and cross-validation of optical and salt-efflorescence-related anomalies with geophysical evidence. These results strengthen the interpretation of buried architectural features and provide a robust basis for reconstructing Zar Tepe’s urban or-ganization and occupational dynamics.

Abstract: The increasing presence of microplastics (MPs) in freshwater ecosystems poses significant threats to aquatic biota; yet, species-level information on the presence of MPs in Indian riverine ecosystems is scarce. This study assessed 220 fish samples from twelve species and various trophic levels for MP ingestion, organ-level accumulation, polymer type, and ecological risk at four locations along the River Yamuna in India. MPs were detected in all the studied species and organs, indicating their widespread distribution across various ecological habitats and trophic levels. A total of 1,678 MPs were quantified, which were significantly higher in fish from urban Delhi compared to upstream regions. The gastrointestinal tract had the highest MP concentrations (751), followed by gills (605) and muscle tissues (322), thus confirming ingestion as the primary route of MP uptake and their subsequent translocation into internal organs. Fibers dominated the MP community (>78%), with transparent (44%) and blue (19.5%) particles being the most abundant. ATR-FTIR analysis revealed the presence of ten different polymers, with polyethylene (≈24%) and polypropylene (≈21%) contributing to approximately 45% of MPs. Significant organ-level correlations (r/ρ = 0.635-0.958) and spatial variability (Kruskal-Wallis, H = 11.03, p = 0.011) indicated coordinated MP accumulation influenced by urban pollution. The Polymer Hazard Index analysis revealed a high PHI value (Category IV), mainly contributed by the widespread distribution of highly toxic polymers such as polycarbonate and polyimide.

Abstract: China’s pollutant discharge permit system mandates total-quantity emission control for industrial volatile organic compounds (VOCs), yet the actual utilization of permitted capacity remains poorly studied. This study developed an “emission idle rate” (IR = 1 − actual/permitted emissions) framework and applied it to 130 chemical enterprises across three cities in Jiangsu Province using 2020–2024 panel data. The mean idle rate reached 78.1%, with no significant inter-city differences (H = 0.96, p = 0.619), attributable to both production underutilization and systematic over-estimation of emission ceilings inherent in the design-capacity-based permit methodology. Ward hierarchical clustering revealed three emission behavioral patterns: Persistent Surplus (n = 74, IR = 0.95), Declining Surplus (n = 32, IR = 0.69), and Growing Surplus (n = 19, IR = 0.59), exhibiting distinct idle rate levels and temporal trajectories. Cluster differentiation was significantly associated only with production-side emission characteristics, while enterprise economic variables showed no significant effects. The estimated tradeable emission surplus reached 668.3 t/a, though its realization faces transaction cost barriers including the lack of standardized transfer mechanisms and formal VOC trading infrastructure. A quadrant-based strategy matrix integrating idle rate levels with temporal trends is proposed for differentiated permit management.

Abstract: This paper examines how the body of research and innovation on electric cooking for low and middle income countries has evolved to the extent that electric cooking is now influencing energy system performance. Methods: The paper synthesises recent evidence from pilots, market developments, and system-level analysis across Africa and Asia, focusing on demand patterns, utility economics, carbon finance mechanisms, and emerging digital and financing models. Results: Electric cooking is increasingly acting as a system-strengthening demand, rather than a system stressor. Two reinforcing mechanisms are identified: (i) an electricity revenue loop, in which increased consumption improves utility and mini-grid viability and supports further investment; and (ii) a carbon finance loop, enabled by metered methodologies and measurable emissions reductions, which can improve household affordability and accelerate adoption. The analysis also highlights the importance of diversified demand (household, commercial, and institutional), which improves load factors and aligns demand with generation. However, a persistent planning blind spot remains, with cooking demand largely excluded from energy models. Conclusions: Electric cooking is moving from proof of concept toward system integration, but scale is constrained by affordability, reliability, tariff design, fuel stacking, institutional fragmentation, and carbon market uncertainty. The findings suggest that electric cooking should be treated as a core component of energy system design, requiring coordinated policy, planning, and financing to realise its full potential.

Abstract: Soil health and irrigation water quality are fundamental to sustainable agricultural productivity, particularly in semi-arid environments. This study evaluated the influence of irrigation water quality on soil physical and chemical properties within the Kagitumba Irrigation Scheme in Eastern Rwanda. An observational analytical design integrated field sampling, laboratory analysis, and statistical evaluation. Soil samples (n = 20) were col-lected at depths of 0–30 cm and 30–60 cm, alongside irrigation water samples (n = 5) from intake and distribution points. Soil parameters analyzed included texture, bulk density, pH, electrical conductivity (EC), organic matter, and nutrient content, while water quality assessment focused on pH, EC, turbidity, dissolved oxygen (DO), and oxidation–reduction potential (ORP). Data were subjected to descriptive statistics, Pearson correlation, and ANOVA at a 95% confidence level. Findings revealed predominantly sandy loam soils with low bulk density, moderate water-holding capacity, and near-neutral pH. Soil salin-ity remained low, indicating limited risk of degradation. Irrigation water was generally suitable for agricultural use in terms of pH and salinity; however, elevated turbidity showed a strong negative correlation with infiltration rate (r = −0.73). Additionally, low soil nitrogen levels were significantly associated with water quality, suggesting nutrient leaching. These results underscore the critical role of irrigation water quality in shaping soil health and emphasize the need for improved water filtration and integrated nutrient management to enhance long-term sustainability.

Abstract: Swelling soil landslides pose severe challenges in geotechnical engineering due to non-linear deformation and strength degradation. Accurate characterisation of pore structure parameters remains the core difficulty. This study proposes a Physics-Informed Neural Network (PINNs) framework that utilises Mercury Intrusion Porosimetry (MIP) data to simultaneously invert three key physical parameters: pore fractal dimension (Ds), surface tension (γ), and contact angle (θ). By embedding the Washburn equation and fractal pore theory into the neural network loss function, the framework achieves high-precision inversion without requiring complete prior information. Validated on three expansive soil samples, the inverted Ds values were 2.47, 2.53, and 2.58, showing excellent agreement with classical models (R² > 0.99) and an average relative error below 2.3%. The inverted γ ranged from 0.476 to 0.480 N/m and θ from 142.3° to 144.2°, both satisfying physical plausibility requirements. Five-fold cross-validation confirmed the absence of overfitting (ΔR² < 0.001). Sensitivity analysis identified Ds as the dominant parameter controlling pore volume distribution; Ds exceeding 2.55 indicates elevated landslide susceptibility. This framework provides a rapid, automated approach for extracting pore structure parameters, offering parametric support for preliminary risk assessment of expansive soil slopes.

Abstract: Marginal ice zone (MIZ) is a transitional region between dense pack ice and open water. It is a highly dynamic zone under strong interactions between the atmosphere, ocean, sea ice and waves, playing a crucial role in the polar climate and ecosystem. Accurate determination of MIZ is therefore essential for advancing our understanding, modeling and prediction of the polar climate system. In this paper, we introduce and apply a suite of MIZ-related metrics to evaluate the performance of four satellite-derived high-resolution operational sea ice concentration (SIC) products in determination of the MIZs around Svalbard, using the Norwegian ice chart as reference. The metrics used for evaluation include sea ice extent (SIE), MIZ extent (MIZE), length of ice edge (LIE), integrated ice edge error (IIEE), integrated MIZ error (IME), ice edge distance error (IEDE), and MIZ width error (MWE). The evaluation is based on three years of daily SIC data (2023-2025) from four products, including the Bremen AMSR2 SIC data from the University of Bremen (Bremen SIC), the Resolution-enhanced AMSR2 SIC (RE SIC) and Multisensor SIC products (Multisensor SIC) from the Norwegian Meteorological Institute, and the Automated Sea Ice Product (ASIP) from the Copernicus Marine Environment and Monitoring Service (CMEMS) (ASIP SIC). To be consistent with the Norwegian ice chart, the MIZ is defined as MIZ70 and MIZ90, corresponding to SIC thresholds of up to 0.7 and 0.9, respectively. IEDE and MWE are calculated using two types of LIE, the reference LIE (LIEr) from the Norwegian ice chart and the average LIE (LIEa) by averaging the ice chart LIE and the concerned LIE from the four satellite products. The results demonstrate that all four satellite SIC products generally capture the evolution of the sea ice conditions around Svalbard well, but differ in their accuracy when determining the ice edge and MIZs. The Bremen SIC product tends to overlook areas with low SIC, leading to a significant underestimation of SIE and a large IIEE. However, it provides an overall close agreement with the ice chart for the MIZ90 metrics (MIZE, IME and MWE). The RE and ASIP SIC products exhibit strong performance in capturing the ice edge and total SIE, with the ASIP product particularly excelling in accurately representing the ice edge and MIZ70. The Multisensor provides the closest agreement with the ice chart for the IME90, MWE90 and MWEa70, and ranks as the second-best product for the IIEE, IME70 and MWEr70. These results suggest that SAR and Low-frequency AMSR2 channels are especially effective for capturing the lower bounds of the MIZ, while high-frequency channels are more suitable for defining the upper bounds. Despite these strengths, the complex summer surface conditions pose significant challenges for satellite sensors in determining the ice edge and the MIZ, resulting in higher IEDE and MWE values during this period. These results highlight both the capabilities and limitations of satellite-based data in determining the MIZ, particularly under challenging summer conditions. Accurate determination of MIZs may require significant advancements in satellite observation technologies, retrieval algorithms, and more robust methods for integrating multiple sources.

Abstract: The sealing behavior of fracture-filling minerals in the near-field of the deep geological repository (DGR) is critical for the safe disposal of high-level radioactive waste (HLW). In granite host rocks, natural fractures are often filled with polymineralic assemblages of calcite, quartz, and clay minerals; however, their coupled compaction–pressure solution mechanisms under thermal–hydraulic–mechanical–chemical (THMC) conditions remain poorly understood. In this study, 12 fracture sealing experiments were conducted on Beishan granite and its typical fracture fillings at 90 °C and 15 MPa effective stress, using different pore fluids and systematically varying grain size (75–250 μm), mineral proportions, and clay content. The results demonstrate that pressure solution–precipitation of calcite in saturated CaCO3 solution is the key mechanism for long-term fracture sealing, achieving a compaction strain of 24.6%—substantially higher than those obtained in deionized water (20.6%) and under dry conditions (14.8%). Fine-grained calcite compacts more effectively than its coarse-grained counterpart, reaching a porosity as low as 4.8%; rigid quartz accelerates calcite pressure solution via stress concentration at grain contacts; and a moderate amount of clay minerals (~20 wt%) further reduces porosity to 2.1% through lubrication and micropore filling. The study reveals a multi-stage process transitioning from mechanical compaction to pressure solution–precipitation, and a synergistic sealing mechanism dominated by calcite compaction–pressure solution, augmented by quartz stress transfer and clay lubrication. These findings revise the traditional monomineralic understanding and provide a scientific basis for safety assessment of HLW disposal and the design of natural sealing materials.

Abstract: Existing radio environment map (REM)-based emitter detection methods suffer from high false positives and missed detections in blurred or conjoined structures, or require large annotated datasets and heavy computation. This paper proposes an unsupervised method, persistent homology with agglomerative clustering (PH-AC), based on an improved persistent homology algorithm. A simulated spaceborne REM dataset is constructed via synthetic aperture passive interferometric imaging, covering isolated, adjacent-pair, and complex emitter distributions. Persistent homology tracks the birth, death, and merging of zero-dimensional connected components as the intensity threshold varies. To address missed detections for spatially proximate emitters, multidimensional topological features are constructed via feature contribution analysis. Agglomerative clustering with Ward linkage then adaptively separates emitters from noise without supervision. Experimental results show that PH-AC achieves a perfect F1 score of 1.000 in isolated scenarios; for adjacent emitters, it improves F1 by 15.7% over the best image processing method and stays within 4% of supervised deep learning methods, while requiring no annotations. In complex environments, it attains an F1 of 0.937, outperforming all compared methods. Its computational complexity is only 3.18e7 FLOPs, two orders lower than YOLO-based detectors. This work offers a lightweight, annotation-free topological paradigm for spaceborne REM emitter detection.

Abstract: Climate change is a major hazard to the agricultural systems of the world as it is changing the temperature regimes, precipitation patterns, and soil dynamics, which are weakening crop production and the stability of the ecosystems. The proposed research is a hybrid modeling framework that combines Multiple Linear Regression (MLR) with a deep learning architecture (PatchTST) based on the Transformer to quantify and predict the effect of climate variability on the productivity of agriculture. Multi-source data such as global weather data, crop data, and ISRIC-WISE soil data were harmonized with stringent preprocessing that included imputation, normalization, and spatial-temporal alignment. The regression analysis reveals a statistically significant negative impact of temperature on crop yield, while precipitation and soil fertility exhibit positive contributions. To capture complex non-linear dependencies and long-term temporal patterns, the PatchTST model was trained using time-series inputs enriched with satellite-derived vegetation indices. The proposed model significantly outperforms conventional deep learning approaches, achieving an R2 of 0.98, RMSE of 0.0172, and MAE of 0.0134. Attention-based interpretability highlights soil moisture and NDVI as dominant predictors, reinforcing the model’s physical and agronomic relevance. The findings instruct that integrating interpretable statistical models with advanced deep learning enhances predictive accuracy while addressing the transparency limitations of black-box approaches. This framework provides a robust decision-support tool for empathetic climate variability impacts on agricultural productivity.

Abstract: Arsenic represents a ubiquitous element in the environment, characterized by high mobility, complex chemical speciation and a strong sensitivity to redox conditions and biological activity. The present review provides an integrated synthesis of arsenic biogeochemical cycling across terrestrial, freshwater and marine ecosystems, emphasizing the central role of chemical speciation in controlling the arsenic levels, mobility and bioavailability. Natural processes regulating arsenic distribution are examined from mineralogical sources and soil–water interactions to biologically mediated transformations in aquatic and marine biotic compartments, highlighting the contrast between inorganic arsenic dominance in abiotic reservoirs and the prevalence of organoarsenicals in tissues of living organisms. The review further explores arsenic behaviour under natural environmental alterations and in extreme or unconventional ecosystems, where redox constraints, sulphide chemistry or intense fluid–sediment exchanges lead to deviations from the baseline speciation patterns. Against this framework, anthropogenic perturbations are discussed through several documented case studies, illustrating how industrial releases, the long‑term effects of mining activities, agricultural practices and the use of synthetic arsenical compounds may change the arsenic pathways primarily by altering geochemical and biological controls rather than the generalized increase of the total arsenic content. Overall, the topics covered provide an integrated framework for interpreting arsenic dynamics across environmental systems, emphasizing the complex biogeochemical processes governing arsenic cycling.

Abstract: This study characterizes macroalgal assemblage structure in the transitional water system Mar Piccolo of Taranto (eLTER site) from 2012 to 2023, assessing the impact of non-indigenous species (NIS) establishment. Seasonal sampling at three sites evaluated diversity and biomass variation through PERMANOVA, PCoA, PERMDISP and Indicator Value (IndVal) analyses. Results reveal significant spatio-temporal heterogeneity: Site 1 remains dominated by native species (&gt;70% biomass), summer peaks of NIS were recorded at Site 3, whereas Site 4 experienced a substantial NIS expansion, reaching 97% of total biomass by 2021. Statistical clustering identified distinct indicator species for each inlet, such as Amphiroa beauvoisii in the First Inlet and the NIS Hypnea corona in the Second. Water temperature emerged as a primary driver of community shifts. Most species, including both native (Chondracanthus acicularis) and several NIS (Polysiphonia morrowii, Osmundea oederi), exhibited negative correlations with mean thermal values, while Ulva laetevirens showed greater tolerance. These findings highlight the importance of LTER monitoring in demonstrating that the Mar Piccolo’s resistance to NIS pressure is non-uniform across the basin. Under a global warming scenario, thermal forcing is actively reshaping macroalgal assemblages.

Abstract: A three-year (January 2020–December 2022) daily dataset of 16 polycyclic aromatic hydrocarbons (PAHs) co-sampled with PM2.5 and a suite of meteorological variables at a Mediterranean coastal urban site in southern Italy (Pomigliano d’Arco, Campania) is presented and analysed. Raw PAH time series were decomposed into a long-term trend component (LT), a seasonal component (ST), and a residual component (RT) using an iterative missing-value-robust Kolmogorov–Zurbenko (KZ) moving-average filter. Spearman rank correlations between PAH concentrations and four meteorological predictors (mean temperature, relative humidity, mean wind speed, and maximum wind speed) were computed for each congener. Diagnostic molecular ratios — Fluoranthene/(Fluoranthene+Pyrene), BaP/BghiP, Indeno[1,2,3-cd]pyrene/(IcdP+BghiP), and Benz[a]anthracene/(BaA+Chrysene) — were evaluated seasonally and subjected to an information-theoretic Bayesian mixture modelling procedure (SNOB/MML) to estimate the number and nature of prevailing emission source classes. Total PAH concentrations (sum of 16 congeners) ranged from &lt;1 ng m−3 in summer to 46 ng m−3 during winter high-pollution episodes, with BaP peaking at ≈6.7 ng m−3. Pronounced seasonal variability was driven primarily by residential heating emissions, and the incremental lifetime cancer risk (ILCR) for inhalation exposure reached 1.03×10−4 (95% CI: 0.88−1.20×10−4) during the heating season, exceeding standard regulatory thresholds. An anomalous near-background PAH signal during spring 2020 is attributed to the COVID-19 national lockdown, which reduced total PAH concentrations by approximately 85% relative to the seasonal component predicted by the iterative moving-average filter for the same calendar window. Source apportionment via diagnostic ratios identifies residential/biomass combustion as the dominant cold-season source and vehicular emissions as the prevailing warm-season source. These results provide a novel characterisation of PAH pollution dynamics in the undersampled southern Mediterranean and offer insights for targeted abatement policies.