Understanding the spatial variability and environmental drivers of soil organic carbon (SOC) is critical for improving carbon management in fragile karst landscapes. This study collected 110 topsoil samples across county Yangshan, southern China, and applied an interpretable machine learning framework combining Random Forest (RF) and SHapley Additive exPlanations (SHAP) to explore the spatial heterogeneity and key environmental controls of SOC. The measured contents ranged from 3.33 to 44.20 g/kg, with a coefficient of variation of 43.5%, indicating moderate variability of SOC in the study area. The RF-based spatial predictions revealed that higher SOC levels were mainly concentrated in the northern and southern subregions associated with clastic rocks, while lower SOC values clustered in central areas dominated by carbonate bedrocks. SHAP analysis indicated that soil physicochemical properties contributed over 53% to SOC, with total nitrogen and cation exchange capacity exerting the strongest influences, particularly in karst zones. Hydrological, vegetation, and terrain-related factors showed moderate importance, especially in high-elevation areas with natural vegetation and complex topography that promoted SOC accumulation. In contrast, climatic variables had relatively weak impacts, with their influences clustered in lowlands dominated by anthropogenic land uses. These findings revealed spatially heterogeneous controls on SOC between karst and non-karst landscapes, emphasizing the dominant role of soil properties under shallow, erosion-prone conditions and highlighting the role of topography and vegetation in enhancing SOC stocks in mountainous areas. The integrated use of interpretable machine learning approaches improves the understanding of localized SOC dynamics and provides a valuable reference for precision carbon management and ecological restoration in environmentally sensitive regions elsewhere.

The degradation of peatlands across the UK and Ireland has led to rising concentrations of dissolved organic carbon (DOC) in surface waters. This can have implications for the treatment of water supplies. While catchment management strategies to improve water quality, such as peatland restoration, aim to reverse these trends and thereby reduce treatment costs, relationships between DOC generation and land use require further clarification. DOC levels recorded in discharge from relatively intact areas can be used to establish realistic restoration targets. To investigate the relationships between DOC concentrations in streams draining intact areas and adjacent disturbed areas, a year-long integrated hydrological and water quality monitoring program was conducted along a 2.2 km stretch of a first-order stream draining a blanket peat-covered catchment in the Ox Mountains, Co. Sligo, Ireland. Groundwater level monitoring operated continuously at 15 catchment locations in contrasting hydrological settings. Combining the results of continuous stream discharge measurements with groundwater level monitoring permitted event-based water quality sampling at three locations along the stream’s course; this allowed assessment of how changes in DOC concentrations and fluxes varied with land use, moving from the stream’s headwaters, downstream. Data from five water quality sampling events distributed over the course of one-year (December 2023 to November 2024) revealed that DOC fluxes draining the 107 ha catchment of the stream’s headwaters varied between 8.7 mg/m2 and 144 mg/m2. By contrast, DOC fluxes rose consistently going downstream. Results from the final 21 ha interval, (reflecting a substantially more degraded area) showed that fluxes from a formerly afforested peatland ranged from 27 mg/m2 to 907 mg/m2. The results indicated that DOC fluxes are affected by both peatland condition and seasonal variation.

Shipboard training equips early career ocean professionals (ECOPs) with the skills, knowledge, and confidence to tackle the challenges of marine research. Such training helps develop a workforce essential for implementing a truly global ocean observation system and advancing understanding of the ocean and its sustainable use. Working with other organizations and individuals, the Partnership for Observation of the Global Ocean (POGO) offers opportunities to ECOPs, mainly from developing countries, to join research cruises and acquire hands-on experience with real-world oceanographic work. These learning experiences can be organized either as one-on-one training on research cruises with spare berths or collective training on dedicated expeditions designed for larger groups of international students. This article introduces POGO’s shipboard training program by presenting examples from each of the modalities, and it explores the program’s long-term impacts and future directions.

BACKGROUND:Cadmium (Cd) contamination in soils is a pressing environmental issue due to its toxicity and persistence. Given the diverse geological formations and intensive agricultural activities in Ireland, understanding the distribution and sources of soil Cd is particularly important. METHODS:This study used multiple GIS-based and spatial machine learning (SML) techniques to investigate the spatial distribution and controlling factors of Cd in 16,783 topsoil samples across the island of Ireland. Three analytical methods were applied: hot spot analysis to detect clusters of high and low Cd concentrations, Geographically Weighted Pearson Correlation Coefficients (GWPCC) to explore how Cd relationships with other soil properties vary across space, and Random Forest (RF) to rank the contributing factors in Cd accumulation. RESULTS:Hot spot analysis revealed strong spatial overlap between Cd concentrations and key geochemical variables including CIA, Fe, P, pH, SOC, and Zn. GWPCC further highlighted their spatially varying relationships, with significantly strong positive correlations between Cd and pH, Zn, and P in the central midlands. The local correlation coefficients obtained from the GWPCC ranged from negative to the highest values of 0.80, 0.92 and 0.86, respectively, which were significantly higher than the results of traditional Pearson correlation coefficients. These patterns were associated with impure limestones, Zn mineralization, and phosphate fertilizer inputs. Furthermore, the RF model ranked Zn (39.4 %) and P (17.6 %) as the most influential factors, with their importance increasing in limestone-dominated areas (50.9 % and 27.4 %), which emphasized the external contributions from local Zn mineralization and phosphate fertilizers in addition to natural accumulation. CONCLUSION:This study demonstrated the effectiveness of integrating SML techniques with geochemical analysis for identifying Cd sources in the topsoil of Ireland, highlighting the roles of lithology and agricultural activities in Cd accumulation. The results provided valuable insights for contamination management and environmental policy development in Ireland and elsewhere.

The overriding physicochemical controls in seawater discussed here are the chemical composition and the state of master variables including temperature, pressure, salinity, pH and redox status. Dissolved Organic Matter also plays a major role, but since its properties are not sufficiently well quantified it is described as an emergent master variable at this stage. The theoretical basis for the treatment of equilibrium chemistry and kinetics is presented, together with projections of the future development of seawater chemistry resulting from climate change.

Peri-urban agricultural soils are often contaminated by potentially toxic elements (PTEs) due to rapid urbanization, industrial activities, and agricultural practices. In this study, two advanced analytical methods including positive matrix factorization (PMF) model and K-means clustering algorithm were integrated to explore the potential sources and concealed contamination patterns of 8 PTEs in peri-urban soils in county Gaoming, China. Descriptive statistics showed average concentrations of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn) as 19.11, 0.18, 35.69, 20.31, 18.26, 151.7, 67.75, and 0.29 mg/kg, respectively. The PMF model identified three primary sources: geogenic (Cr, Ni), industrial and traffic- related (Pb, Hg, Zn), and agricultural (As, Cd and Cu). The contribution of each source was quantified: geogenic sources contributed 55.6% to Cr and 52.3% to Ni, industrial sources accounted for 41.8% of Pb, 58.4% of Hg, and 41.9% of Zn, while agricultural practices contributed 88.1% of As, 77.9% of Cu, and 70.7% of Cd. Subsequently, K-means clustering classified the soil samples into three distinct clusters based on the derived factor contribution from PMF model, reflecting their clear spatial associations with different types of land use: large-scale agricultural areas (Cluster 1), natural vegetation (Cluster 2), and urbanized zones (Cluster 3). Furthermore, boxplots showed that the highest PTE concentrations were found in the third cluster, confirming the significant impact of human activities, while the lower concentrations in the second cluster indicated more natural conditions. These results underscored the dual influences of agriculture and urbanization on PTE contamination, which highlighted the need for targeted soil management strategies. Moreover, the integration of PMF and K-means clustering effectively reveals potential sources and concealed pollution patterns, providing insights for managing pollution and safeguarding environmental health in rapidly urbanized areas.

Phytoplankton have a high potential for CO2 capture and conversion. Besides being a vital food source at the base of oceanic and freshwater food webs, microalgae provide a critical platform for producing chemicals and consumer products. Enhanced nutrient levels, elevated CO2, and rising temperatures increase the frequency of algal blooms, which often have negative effects such as fish mortalities, loss of flora and fauna, and the production of algal toxins. Harmful algal blooms (HABs) produce toxins that pose major challenges to water quality, ecosystem function, human health, tourism, and the food web. These toxins have complex chemical structures and possess a wide range of biological properties with potential applications as new therapeutics. This review presents a balanced and comprehensive assessment of the roles of algal blooms in generating fixed carbon for the food chain, sequestering carbon, and their unique secondary metabolites. The structural complexity of these metabolites has had an unprecedented impact on structure elucidation technologies and total synthesis, which are highlighted throughout this review. In addition, the influence of biogeochemical environmental perturbations on algal blooms and their influence on biospheric environments is discussed. Lastly, we summarize work on management strategies and technologies for the control and treatment of HABs.

Potentially toxic elements (PTEs) and polycyclic aromatic hydrocarbons (PAHs) harm the ecosystem and human health, especially in urban areas. Identifying and understanding their potential sources and underlying interactions in urban soils are critical for informed management and risk assessment. This study investigated the potential sources and the spatially varying relationships between 9 PTEs and PAHs in the topsoil of Dublin by combining positive matrix factorisation (PMF) and geographically weighted regression (GWR). The PMF model allocated four possible sources based on species concentrations and uncertainties. The factor profiles indicated the associations with high-temperature combustion (PAHs), natural lithologic factors (As, Cd, Co, Cr, Ni), mineralisation and mining (Zn), as well as anthropogenic inputs (Cu, Hg, Pb), respectively. In addition, selected representative elements Cr, Zn, and Pb showed distinct spatial interactions with PAHs in the GWR model. Negative relationships between PAHs and Cr were observed in all samples, suggesting the control of Cr concentrations by natural factors. Negative relationships between PAHs and Zn in the eastern and north-eastern regions were related to mineralisation and anthropogenic Zn-Pb mining. In contrast, the surrounding regions exhibited a natural relationship between these two variables with positive coefficients. Increasing positive coefficients from west to east were observed between PAHs and Pb in the study area. This special pattern was consistent with prevailing south-westerly wind direction in Dublin, highlighting the predominant influences on PAHs and Pb concentrations from vehicle and coal combustion through atmospheric deposition. Our results provided a better understanding of geochemical features for PTEs and PAHs in the topsoil of Dublin, demonstrating the efficiency of combined approaches of receptor models and spatial analysis in environmental studies.

The urgent need for SARS-CoV-2 controls has led to a reassessment of approaches to identify and develop natural product inhibitors of zoonotic, highly virulent, and rapidly emerging viruses. There are yet no clinically approved broad-spectrum antivirals available for beta-coronaviruses. Discovery pipelines for pan-virus medications against a broad range of betacoronaviruses are therefore a priority. A variety of marine natural product (MNP) small molecules have shown inhibitory activity against viral species. Access to large data caches of small molecule structural information is vital to finding new pharmaceuticals. Increasingly, molecular docking simulations are being used to narrow the space of possibilities and generate drug leads. Combining in-silico methods, augmented by metaheuristic optimization and machine learning (ML) allows the generation of hits from within a virtual MNP library to narrow screens for novel targets against coronaviruses. In this review article, we explore current insights and techniques that can be leveraged to generate broad-spectrum antivirals against betacoronaviruses using in-silico optimization and ML. ML approaches are capable of simultaneously evaluating different features for predicting inhibitory activity. Many also provide a semi-quantitative measure of feature relevance and can guide in selecting a subset of features relevant for inhibition of SARS-CoV-2.

The investigation of spatially varying relationships provides a novel and effective way to identify the influencing factors and potential sources of potentially toxic elements (PTEs). The varying relationships for PTEs at different sampling locations can be associated with the different controlling factors from geological units or pollution sources, which benefits environmental studies and management for local government. Generally, the concentrations of lead (Pb) and aluminium (Al) in the soil are reported as being positively correlated under most natural conditions, due to Pb and Al being enriched in clay minerals. However, the original positive relationship can be masked by external influences at the local level, thus the resulting varying relationships can be observed and associated with the potential pollution sources. In this study, Geographically Weighted Pearson Correlation Coefficient (GWPCC) was applied to explore the spatial relationships between Pb and Al in soils. Both positive and negative correlation coefficients were observed from the results of GWPCC at different scales with bandwidths ranging between 43 and 300 nearest neighbours, suggesting the relationships between Pb and Al are spatially varying at the regional scale. Negative correlations were observed in more than 35% of the study area, mainly clustered in the western and north-eastern of Ireland. The positive correlations were found in the central-western and midlands, while comparatively mixed correlations occurred in the eastern coastal areas. The spatial patterns for the majority of negative correlations showed a clear association with blanket peat, which likely can be attributed to long-distance transportation of Pb through atmospheric deposition. Moreover, anthropogenic activities weakened the relationships in the eastern coastal areas. These results demonstrated the efficiency of GWPCC in exploring the spatially varying relationships between environmental variables and identifying associations with related influencing factors, which could be hardly achieved by traditional techniques.

Where atmospheric processing is weak due to low anthropogenic emissions, fertilization of iron-limited oceans by non-volcanic mineral dust aerosols strongly depends on iron solubility at the sources. Southern South America (SSA) is a pristine environment and the main dust supplier to the southern oceans, the most sensitive to iron fertilization. Thus, the present-day lack of SSA dust fertilization of the southern oceans is hypothesized to reflect low source-inherited iron bioavailability. However, a dearth of geochemical studies on SSA dust prevents testing this hypothesis. To remedy this, we conducted the first systematic sampling of SSA dust sources. Iron leaching experiments showed fractional solubilities of close-to-source dust (bulk) and dust-emitting surface sediments (< 63 mu m) in pure water (0.05 +/- 0.05%) , seawater (0.03 +/- 0.04%) and 1% nitric acid (5 +/- 6%) that imply a low mass-normalized fertilization potential of SSA dust compared to dust from other regions. Based on grain size, size-resolved mineralogy, elemental chemistry and iron speciation determinations, we found that variability in labile iron is enhanced by high clay contents, small grain size and higher proportions of paramagnetic versus non-paramagnetic iron, irrespective of oxidation state. The independence of the most labile, water-soluble iron on grain size and its strong negative correlation to the Chemical Index of Alteration may imply that we currently underestimate the role of coarse glaciogenic dust as a supplier of bioavailable iron during drier-than -present ice ages when continental chemical weathering was reduced, and during which enhanced supply of dust-borne bioavailable iron to the southern oceans is observed. (c) 2022 Elsevier Ltd. All rights reserved.

Oligotrophic ocean gyre ecosystems may be expanding due to rising global temperatures [1–5]. Models predicting carbon flow through these changing ecosystems require accurate descriptions of phytoplankton communities and their metabolic activities [6]. We therefore measured distributions and activities of cyanobacteria and small photosynthetic eukaryotes throughout the euphotic zone on a zonal transect through the South Pacific Ocean, focusing on the ultraoligotrophic waters of the South Pacific Gyre (SPG). Bulk rates of CO2 fixation were low (0.1 µmol C l−1 d−1) but pervasive throughout both the surface mixed-layer (upper 150 m), as well as the deep chlorophyll a maximum of the core SPG. Chloroplast 16S rRNA metabarcoding, and single-cell 13CO2 uptake experiments demonstrated niche differentiation among the small eukaryotes and picocyanobacteria. Prochlorococcus abundances, activity, and growth were more closely associated with the rims of the gyre. Small, fast-growing, photosynthetic eukaryotes, likely related to the Pelagophyceae, characterized the deep chlorophyll a maximum. In contrast, a slower growing population of photosynthetic eukaryotes, likely comprised of Dictyochophyceae and Chrysophyceae, dominated the mixed layer that contributed 65–88% of the areal CO2 fixation within the core SPG. Small photosynthetic eukaryotes may thus play an underappreciated role in CO2 fixation in the surface mixed-layer waters of ultraoligotrophic ecosystems.

Seafood is capable of bioaccumulating heavy metals (HM), making it a potentially major dietary source of HM for humans. Presently, little data exists on seafood from the eastern-most boundary of the Mediterranean Sea. This study aims to provide exposure insight of the Israeli population to HM through the consumption of locally caught seafood by assessing the levels of arsenic, mercury, cadmium, and lead in raw tissues of seafood. A wide survey of local fisheries was conducted providing 296 samples from 11 different species, including seven fish, two crustacean, and two cephalopod species. Total arsenic, cadmium, and lead were analyzed by graphite-furnace atomic absorption. Total mercury was measured by cold-vapor mercury analyzer. Arsenic speciation was performed by anion chromatography-inductively coupled plasma sector field mass spectrometry. Results suggested that the total arsenic concentrations were significantly higher in crustaceans and cephalopods than fish. Arsenic speciation revealed two samples that exceed 1 mg/kg of inorganic arsenic, whereas methylated arsenic was below the detection limit. Elevated mercury levels were detected in the commercial benthic species Mullus barbatus (red mullet), cadmium was detected in one-third of the samples, and lead detected in eight samples. Comparing the results to health guidelines, 99.4% of seafood tested in this study abide with acceptable levels of heavy metals in seafood, as defined by both Israeli and European Union guidelines.

During the months of May, June, July and August 2019 the Red Band Difference algorithm was tested over Irish waters to assess its suitability for the Irish harmful algal bloom alert system. Over the 4 weeks of June an extensive localised surface phytoplankton bloom formed in the Celtic Sea, south of Ireland. Satellite imagery from the Sentinel-3a’s Ocean and Land Colour Instrument, processed using the Red Band Difference algorithm detected the bloom in surface shelf waters and helped monitor its movement. Daily satellite images indicated that the bloom appeared at the sea surface on the 2nd June 2019 and peaked in size and surface abundance in offshore shelf waters within 4 weeks, remnants remained at the surface into July. A particle tracking approach was used to replicate oceanic circulation patterns in the vicinity of the observed algal bloom and estimate its trajectory. The initial horizontal distribution of particles in the tracking model were based on a satellite imagery polygon of the bloom when it first appeared in surface waters. Good agreement was observed between satellite imagery of the bloom and the particle tracking model. In situ sampling efforts from a research cruise and the national inshore phytoplankton monitoring programme confirmed that Karenia mikimotoi was the causative organism of the bloom. This pilot study shows great potential to use the Red Band Difference algorithm in the existing Irish harmful algal bloom alert system. In addition, satellite ocean colour data combined with particle tracking model estimates can be a useful tool to monitor high biomass harmful algal bloom forming species, such as Karenia mikimotoi, in surface coastal waters around Ireland and elsewhere.

The pressing need for SARS-CoV-2 controls has led to a reassessment of strategies to identify and develop natural product inhibitors of zoonotic, highly virulent, and rapidly emerging viruses. This review article addresses how contemporary approaches involving computational chemistry, natural product (NP) and protein databases, and mass spectrometry (MS) derived target-ligand interaction analysis can be utilized to expedite the interrogation of NP structures while minimizing the time and expense of extraction, purification, and screening in BioSafety Laboratories (BSL)3 laboratories. The unparalleled structural diversity and complexity of NPs is an extraordinary resource for the discovery and development of broad-spectrum inhibitors of viral genera, including Betacoronavirus, which contains MERS, SARS, SARS-CoV-2, and the common cold. There are two key technological advances that have created unique opportunities for the identification of NP prototypes with greater efficiency: (1) the application of structural databases for NPs and target proteins and (2) the application of modern MS techniques to assess protein-ligand interactions directly from NP extracts. These approaches, developed over years, now allow for the identification and isolation of unique antiviral ligands without the immediate need for BSL3 facilities. Overall, the goal is to improve the success rate of NP-based screening by focusing resources on source materials with a higher likelihood of success, while simultaneously providing opportunities for the discovery of novel ligands to selectively target proteins involved in viral infection.

The understanding of sources and controlling factors of potentially toxic elements (PTEs) in soils plays an important role in the improvement of environmental management. With the rapid growth of data volume, effective methods are required for data analytics for the large geochemical data sets. In recent years, spatial machine learning technologies have been proven to have the potential to reveal hidden spatial patterns in order to extract geochemical information. In this study, two spatial clustering techniques of Getis-Ord Gi* statistic and K-means clustering analysis were performed on 15 PTEs in 6,862 topsoil samples from the Tellus datasets of Northern Ireland to investigate the hidden spatial patterns and association with their controlling factors. The spatial clustering patterns of hot spots (high values) and cold spots (low values) for the 15 PTEs were revealed, showing clear association with geological features, especially peat and basalt. Peat was associated with high concentrations of Bi, Pb, Sb and Sn, while basalt was associated with high concentrations of Co, Cr, Cu, Mn, Ni, V and Zn. The high concentrations of As, Ba, Mo and U were associated with mixture of various lithologies, indicating the complicated influences on them. In addition, three hidden patterns in the 6,862 soil samples were revealed by K-means clustering analysis. The soil samples in the first and second clusters were overlaid on the peatland and basalt formation, respectively, while the samples in the third cluster were overlaid on the mixture of the other lithologies. These hidden patterns of soil samples were consistent with the spatial clustering patterns for PTEs, highlighting the dominant control of peat and basalt in the topsoil of Northern Ireland. This study demonstrates the power of spatial machine learning techniques in identifying hidden spatial patterns, providing evidences to extract geochemical knowledge in environmental studies.

Anthropogenic emissions to the atmosphere have increased the flux of nutrients, especially nitrogen, to the ocean, but they have also altered the acidity of aerosol, cloud water, and precipitation over much of the marine atmosphere. For nitrogen, acidity-driven changes in chemical speciation result in altered partitioning between the gas and particulate phases that subsequently affect long-range transport. Other important nutrients, notably iron and phosphorus, are affected, because their soluble fractions increase upon exposure to acidic environments during atmospheric transport. These changes affect the magnitude, distribution, and deposition mode of individual nutrients supplied to the ocean, the extent to which nutrient deposition interacts with the sea surface microlayer during its passage into bulk seawater, and the relative abundances of soluble nutrients in atmospheric deposition. Atmospheric acidity change therefore affects ecosystem composition, in addition to overall marine productivity, and these effects will continue to evolve with changing anthropogenic emissions in the future.