Spatial Variability Research Articles

Using subtle variations in groundwater geochemistry to identify the proximity of individual geological structures: a case study from the Grimsel Test Site (Switzerland)

Understanding groundwater flow and the evolution of groundwater chemistry in networks of fractures in crystalline rock is of fundamental interest for geothermal projects, nuclear waste disposal, and groundwater resources. Groundwater chemistry at a given location is typically conceived of being of a specific ‘type’ (e.g. meteoric, juvenile, connate, marine), with associated chemical types controlled through water–rock interactions. Minor chemical variations between groundwater sample locations with the same chemical type are generally considered as ‘noise’ in the geochemical data. Here, we argue that this noise contains useful information on the mineral phases encountered by the groundwater as it travels through specific flow pathways. We analyse the spatial variability of groundwater chemistry around the Grimsel Test Site (GTS), Switzerland, where groundwater is hosted in two lithologies: the Central Aar Granite and the Grimsel Granodiorite, where flow occurs predominantly in a fracture network created by brittle reactivation of ductile shear zones. Groundwater chemistry is analysed using principal component and hierarchical cluster analyses, which identify two groundwater types based on their chemistry. The primary control on groundwater type is the host rock lithology (granite/granodiorite). While the spatial variability of groundwater chemistry within each of the two lithologies is small, statistical analysis of the data shows similar groundwater chemistry in borehole intervals that are crosscut by similar geological structures, implying a structural control on groundwater chemistry. Our research shows that subtle chemical variations in groundwater provide information on fracture network connectivity and the proximity of geological features, that specific volumes of groundwater has interacted with.

The Influence of Beachrock Outcrop on Spatial Variability in Beach Morphology

Beachrock is a type of coastal carbonate sedimentary rock developed in the sandy beach intertidal zone, widely distributed along the beach front, and the loss of beach sediment is the main cause of beachrock exposure. Based on the analysis of measured data of different exposure forms of beachrocks in profiles, this paper analyzes the main features and influences of non-dynamic factors, such as the exposure position and morphology, of beachrocks on the dynamic geomorphic processes of beaches. Studies have shown that (1) changes between beach energy dissipation bodies are significant features of coastal geomorphic processes under the influence of beachrocks. The first spatial mode of EOF analysis shows that the erosion position of beach-rock-exposed profiles is mainly concentrated in the protected and real sections of beachrock, and the first temporal mode indicates that the exposure of beachrock results in a lagged response of its profile to dynamic environmental changes. (2) The differences in the exposure forms of beachrocks determine the sand-holding space of the beach, and the differences in the lateral exposure positions of beachrocks determine the main areas where erosion occurs on the profile. Meanwhile, the geomorphic processes of their profiles show different degrees of feedback, and such geomorphic phenomena can serve as reference indicators for the stage-wise evolution process influenced by beachrocks.

Spatial and Temporal Variability of Ocean Thermal Energy Resource of the Pacific Islands

A lack of natural resources drives the oil dependency in Pacific Island Countries and Territories (PICTs), hampering energy security and imposing high electricity tariffs in the region. Nevertheless, the Western Equatorial Pacific is known for its large Sea Surface Temperature (SST) and deep-sea water (DSW) temperature difference favorable for harvesting thermal energy. In this study, we selected 18 PICTs in the western Equatorial Pacific to estimate Annual Energy Production (AEP) for a 1 MW class Ocean Thermal Energy Conversion (OTEC) plant. We combined the DSW temperature from the mean in situ Argo profiles and 1 km resolution satellite SST data to estimate the thermal energy resource resolving the fine features of the island coastline. Furthermore, the twenty-year-long SST dataset was used to analyze the SST variability. The analysis showed that Equatorial islands and Southern islands have the highest inter-annual variability due to El Nino Southern Oscillation (ENSO). The power density varied from 0.26 to 0.32 W/m2 among the islands, with the lowest values found for the southernmost islands near the South Equatorial Countercurrent. Islands within the South Equatorial Current, Equatorial Undercurrent, and North Equatorial Countercurrent showed the highest values for both power density and gross power. Considering a 1 MW class OTEC plant, Annual Energy Production (AEP) in 2022 varied from 7 GWh to 8 GWh, with relatively low variability among islands near the Equator and in low latitudes. Considering the three variables, AEP, SST variability, and distance from the shore, Nauru is a potential candidate for OTEC, with a net power of 1.14 MW within 1 km from the shore.

Lateral holding behaviour of torpedo piles in spatially variable soil: Development of a probabilistic design approach

Offshore torpedo piles are increasingly being utilised in deepwater oil and gas development as part of a taut-leg mooring system. As these piles commonly experience inclined loading in service, accurately determining their lateral holding capacity is crucial. Despite the fact that natural soil characteristics exhibit significant spatial variability, existing studies are mostly deterministic in nature. This paper presents probabilistic analyses of the lateral holding capacity of torpedo piles, explicitly considering the spatial variability of soil shear strength. Large deformation finite element calculations are combined with three-dimensional random field generation in a Monte Carlo framework. The results show that while spatial variability does not apparently alter the failure mechanism of laterally loaded torpedo piles, it significantly influences their holding capacity. The routine deterministic calculation, neglecting spatial variability, would probably yield unconservative estimates. To address this challenge, a probabilistic Broms method is developed, which enables engineers to produce lateral holding capacity estimates with varying confidence levels, using local strength data rather than central tendency. Furthermore, endeavours are also made to calibrate the safety factor, extensively employed in deterministic design practices, from a probabilistic perspective, providing practicing engineers with a viable alternative in scenarios where a local strength profile is unavailable.

Plastic Contamination in Aquatic Ecosystems: A Fisheries Perspective

Plastic contamination poses a dire threat to aquatic ecosystems and fisheries, necessitating a comprehensive exploration of its ecological, economic, and socio-economic dimensions. From the ubiquity of plastics globally to their adverse impacts on fish habitats and the economic viability of fisheries, this analysis delves into the intricate dynamics of plastic pollution. The socio-economic implications for local communities, coupled with the staggering costs of mitigation measures, underscore the urgency of addressing this pervasive issue. The article critically evaluates research quality, emphasising the need for interdisciplinary approaches and robust regulatory frameworks. Mitigation strategies, including sustainable fishing practices and community engagement, are proposed to preserve aquatic ecosystems for future generations. The study also outlines future research directions, emphasising the importance of understanding long-term ecological consequences and addressing spatial and temporal variability in plastic pollution.

Evapotranspiration estimation using high-resolution aerial imagery and pySEBAL for processing tomatoes

AbstractThe use of high-resolution aerial imagery for assessing actual crop evapotranspiration $$ \left({ET}_{a}\right)$$ holds the potential to optimize the use of limited water resources in agriculture. Despite this potential, there is a shortage of information regarding the effectiveness of energy balance algorithms, initially designed for satellite remote sensing in estimating $$ {ET}_{a}$$ using aerial imagery. This study addresses this gap by employing the remote sensing model pySEBAL (Surface Energy Balance Algorithm for Land) in conjunction with high-resolution aerial imagery to estimate $$ {ET}_{a}$$ for processing tomatoes. Throughout the 2021 growing season, an aircraft captured multispectral and thermal imagery over a processing tomato field near Esparto, California, USA. Simultaneously, an eddy covariance flux tower within the field measured high-frequency turbulent fluxes and low-frequency biometeorology variables essential for evaluating the energy balance. The comprehensive assessment of energy balance components, including $$ {ET}_{a}$$, yielded compelling evidence that pySEBAL accurately estimated $$ {ET}_{a}$$ at high spatial resolution. The root mean square error (RMSE) and normalized RMSE for various energy balance components were as follows: 33 W m− 2 (12%) for latent heat flux, 29 W m− 2 (35%) for sensible heat flux, 24 W m− 2 (4%) for net radiation, and 10 W m− 2 (15%) for soil heat flux. Additionally, $$ {ET}_{a}$$ exhibited an RMSE and NRMSE of 0.26 mm d− 1 (6%). Moreover, the spatial mapping of $$ {ET}_{a}$$ across the processing tomato field visually depicted the spatial variability associated with irrigation scheduling, crop development, areas affected by disease, and soil heterogeneity. This research underscores the value of high resolution spatial aerial imagery and pySEBAL algorithm for estimating $$ {ET}_{a}$$ variability in the field, a crucial aspect for guiding precision irrigation management and ensuring the optimal use of limited water resources in agriculture.

Improved constraints on hematite refractive index for estimating climatic effects of dust aerosols

Uncertainty in desert dust composition poses a big challenge to understanding Earth’s climate across different epochs. Of particular concern is hematite, an iron-oxide mineral dominating the solar absorption by dust particles, for which current estimates of absorption capacity vary by over two orders of magnitude. Here, we show that laboratory measurements of dust composition, absorption, and scattering provide valuable constraints on the absorption potential of hematite, substantially narrowing its range of plausible values. The success of this constraint is supported by results from an atmospheric transport model compared with station-based measurements. Additionally, we identify substantial bias in simulating hematite abundance in dust aerosols with current soil mineralogy descriptions, underscoring the necessity for improved data sources. Encouragingly, the next-generation imaging spectroscopy remote sensing data hold promise for capturing the spatial variability of hematite. These insights have implications for enhancing dust modeling, thus contributing to efforts in climate change mitigation and adaptation.

A deep learning-based surrogate model for probabilistic analysis of high-speed railway tunnel crown settlement in spatially variable soil considering construction process

In reality, many high-speed railway tunnels are built by sequential excavation method in spatially variable soil. This study introduces an efficient deep learning-based solution for the probabilistic analysis of tunnel crown settlements considering the spatial variability of surrounding soil. The two-dimensional convolutional neural network (2D-CNN) is employed to uncover the implicit correlation between the soil elastic modulus random field and tunnel crown settlements. The correlation coefficient (R) of the surrogate model is greater than 0.9664. Meanwhile, the mean relative error of the predictions remains within 2.38%, under various scales of fluctuation (SOFs). An additional 10,000 samples were produced to assess the practical performance of the trained model in probabilistic analysis. The relative errors of predictions generally fall within 5%, with a minimum confidence interval of 98.15%. These results demonstrate that the developed 2D-CNN model can effectively substitute the traditional method in predicting tunnel crown settlements considering soil spatial variability.

Microtopographic Variation as a Potential Early Indicator of Ecosystem State Change and Vulnerability in Salt Marshes

AbstractAs global climate change alters the magnitude and rates of environmental stressors, predicting the extent of ecosystem degradation driven by these rapidly changing conditions becomes increasingly urgent. At the landscape scale, disturbances and stressors can increase spatial variability and heterogeneity — indicators that can serve as potential early warnings of declining ecosystem resilience. Increased spatial variability in salt marshes at the landscape scale has been used to quantify the propagation of ponding in salt marsh interiors, but ponding at the landscape scale follows a state change rather than predicts it. Here, we suggest a novel application of commonly collected surface elevation table (SET) data and explore millimeter-scale marsh surface microtopography as a potential early indicator of ecosystem transition. We find an increase in spatial variability using multiple metrics of microtopographic heterogeneity in vulnerable salt marsh communities across the North American Atlantic seaboard. Increasing microtopographic heterogeneity in vulnerable salt marshes mirrored increasing trends in variance when a tipping point is approached in other alternative stable state systems — indicating that early warning signals of marsh drowning and ecosystem transition are observable at small-spatial scales prior to runaway ecosystem degradation. Congruence between traditional and novel metrics of marsh vulnerability suggests that microtopographic metrics can be used to identify hidden vulnerability before widespread marsh degradation. This novel analysis can be easily applied to existing SET records expanding the traditional focus on vertical change to additionally encapsulate lateral processes.

Geometallurgical Simulation of the Work Index in a Porphyry Copper Deposit Using Geostatistical Techniques

The spatial variability in the geometallurgical attributes of the deposits is a crucial parameter from the exploration stage, which conditions and influences the mineral processing. Consequently, the objective of this research is to elaborate the geometallurgical simulation of the Bond Work Index for a porphyry copper deposit. For this purpose, information of primary and response attributes corresponding to ore zones, lithologies and BWi contained in 1,449 samples of exploratory drill holes were used. An exploratory data analysis of this information was carried out, and geometallurgical units were defined based on the geological and processing knowledge that validates the behavior of each one of them within the deposit; then Sequential Gaussian Simulation was applied, running 100 realizations in each GMU, those that best reproduce the statistics of the original samples were chosen. The results show that the lithology of the deposit controls the BWi variability and according to the rock competence the ore zones are classified from the softest to the hardest in oxides, mixed and sulfides.

Estimating Agricultural Irrigation Water Usage in Delaware, USA

Irrigation is an important agricultural management practice and the second largest consumer of freshwater resources in Delaware. As more farmland is converted to irrigated agriculture, it is crucial that water resource managers be able to determine reasonable estimates of irrigation water usage in order to protect the resource. This study used a soil water balance approach to simulate irrigation for corn and soybeans in Delaware (USA). The simulations were divided into four scenarios to determine which irrigation management method best represents agricultural irrigation water usage in Delaware. Two scenarios utilized an evapotranspiration-based (ET-based) approach using meteorological data from the Delaware Environmental Observing System (DEOS) with a soil water availability threshold to determine when and how much to irrigate. A second set of scenarios used a calendar-based approach and a rain gauge trigger to simulate irrigation. Analyses were performed to examine the seasonal and spatial variability of irrigation in Delaware and to compare simulated irrigation data to reported water use data provided by the Delaware Department of Natural Resources and Environmental Control’s Water Allocation Permit program. Seasonal irrigation varied due to environmental as well as irrigation decision making factors. Spatially, irrigation varied primarily as a result of the soil water holding properties. The ET-based scenario with a fixed amount of irrigation had the best agreement with the reported irrigated water use data. This study demonstrated the utility of high-resolution, environmental data with a soil water balance approach to improve estimates of irrigated water usage at the state and regional scale.

Four Decades of Spatiotemporal Variability of Per- and Polyfluoroalkyl Substances (PFASs) in the Baltic Sea.

Temporal and spatial variability of per- and polyfluoroalkyl substances (PFASs) in herring, cod, eelpout, and guillemot covering four decades and more than 1000 km in the Baltic Sea was investigated to evaluate the effect of PFAS regulations and residence times of PFASs. Overall, PFAS concentrations responded rapidly to recent regulations but with some notable basin- and homologue-specific variability. The well-ventilated Kattegat and Bothnian Bay showed a faster log-linear decrease for most PFASs than the Baltic Proper, which lacks a significant loss mechanism. PFOS and FOSA, for example, have decreased with 0-7% y-1 in the Baltic Proper and 6-16% y-1 in other basins. PFNA and partly PFOA are exceptions and continue to show stagnant or increasing concentrations. Further, we found that Bothnian Bay herring contained the highest concentrations of >C12 perfluoroalkyl carboxylic acids (PFCAs), likely from rivers with high loads of dissolved organic carbon. In the Kattegat, low PFAS concentrations, but a high FOSA fraction, could be due to influence from the North Sea inflow below the halocline and possibly a local source of FOSA and/or isomer-specific biotransformation. This study represents the most comprehensive spatial and temporal investigation of PFASs in Baltic wildlife while providing new insights into cycling of PFASs within the Baltic Sea ecosystem.

Forest species diversity and community composition in the northern Western Ghats, India

ABSTRACT Background The northern Western Ghats (NWG) – a part of the Western Ghats and Sri Lanka biodiversity hotspot supports tropical forests of conservation priority, due to anthropogenic intensification. Woody species diversity and ecology in NWG is poorly studied, necessitating the knowledge of spatial variability in species and underlying environmental factors, for effective conservation. Aims We described woody plant community composition and related it to climate data across a latitude of 1.4 degrees to obtain information about spatial and environmental variation to support protected area planning. Methods To estimate abundance, importance value index, diversity indices and rank – abundance, we recorded woody species in 144 plots across 12 sites. Elevation, temperature, rainfall, humidity data were related to diversity descriptors in a canonical correspondence analysis. Results We recorded 10,291 trees representing 187 species of 52 families. Divergent geo-climatic factors were related to distinct plant community structures, elevation and humidity being the most influential drivers of plant diversity and density. Numerous rare species were found restricted to environmentally distinct forests. Conclusion The wide-ranging environment and corresponding variations in forest community composition across the narrow latitudinal range of the study area revealed the importance of including such gradients in planning future protected areas in the NWG.

Spatial variability of clay minerals in a semi-arid region of Turkiye

Clay minerals are the source of many chemical and physical properties that influence the provision of soil-based ecosystem services. This study aimed to identify the most significant soil characteristics contributing to the spatial variability of clay minerals in a semi-arid region of Turkiye. Additionally, the study assessed the predictive capabilities of Classification and Regression Tree (CART), Random Forest Regression (RF) and eXtreme Gradient Boosting Regression (XGBoost) in estimating soil clay mineral content. Smectite+vermiculite (SMVR) was the most abundant clay mineral in the study area, followed by illite and kaolinite. Hyperparameter tuning significantly improved model accuracy, with root mean square error (RMSE) reductions ranging from 2.53% to 97.3%. The machine learning algorithms demonstrated varying performances in spatial prediction accuracy. The RF model achieved the lowest RMSE (8.587%) and the highest R2 values (0.796) for predicting SMVR. The XGBoost outperformed other models for kaolinite (RMSE: 4.814%, R2:0.713) and illite (RMSE:7.368%, R2:0.613). Exchangeable cations, particularly magnesium (Mg) and calcium (Ca), were identified as crucial factors influencing the spatial distribution of clay minerals. Among these, M concentration had the strongest influence on predicting both SMVR (38.1%) and illite (26.3%). Conversely, for kaolinite prediction, Ca concentration played the most significant role (38.7%), followed by Mg (19.93%). In conclusion, this study demonstrates the effectiveness of machine learning models, particularly XGBoost which achieved the lowest RMSE for all clay minerals investigated. These models offer a valuable tool for predicting clay mineral content in the Kazova Plain. The findings highlight the importance of parent material, weathering processes, and specific soil properties, such as exchangeable cations, in shaping clay mineral distribution. This knowledge not only contributes to a deeper understanding of soil formation in semi-arid environments but also practical applications. For instance, by predicting the abundance of SVMR, known for its high cation exchange capacity land managers can develop targeted strategies for optimizing fertilizer application in the Kazova Plain.

High spatial variability in a species-rich assemblage of diadromous fishes in Hong Kong, southern China.

China has experienced substantial coastal reclamation and damming of rivers. These changes have the potential to impact migrations of diadromous fishes between the sea and fresh waters, but the composition of these fishes and the impacts of barriers to their movement in China have received little attention. We inventoried the species composition and distribution of diadromous fishes, and the impacts of barriers on them, in the Hong Kong Special Administrative Region (HKSAR), southern China. Fish assemblages were surveyed using hand-nets, supplemented by cast-netting and single-pass snorkel surveys, in 24 small coastal streams across three regions. Surveys were undertaken on multiple occasions during the wet and dry seasons to account for the monsoonal tropical climate. Twenty-eight diadromous fishes were collected, mostly gobies, amounting to over half (53%) of the total richness of primary freshwater fishes; four additional species are known from literature records. Diadromous richness was 48% greater during the wet season, when all species were encountered. Richness varied substantially among streams, from a maximum of 17 (2 streams that were diversity hot spots) to none (3 streams). The most widespread diadromous fish was Glossogobius giuris (71% frequency of occurrence), followed by Mugil cephalus (58% occurrence) and Eleotris oxycephala (50% occurrence). The remaining 25 diadromous fishes occurred in fewer than half of the streams; 12 species were confined to a single stream and may be locally threatened. There were conspicuous spatial differences in diadromous assemblages across HKSAR, despite its limited extent (1114 km2), the proximity of the surveyed streams, and the broad geographic distribution of most species. Regional species assemblages were influenced by localized habitat characteristics, with a noticeable distinction between areas with and without large, fast-flowing, and highly oxygenated streams. The presence of in-stream barriers (weirs: 0.3-8.7 m high) did not affect spatial patterns in species assemblages, although, on average, diadromous richness was lower in weir-obstructed streams (4.0 vs. 6.9 species in unobstructed streams). In total, 18 species were confined to unobstructed streams or sections below weirs, whereas the remaining 10 species were recorded both above and below weirs. Only the mottled eel (Anguilla marmorata) and a goby (Stiphodon multisquamus) were able to ascend weirs over 2 m. Although at least 400 m of the lower course of each stream was sampled, diadromous fishes were confined to the first 300 m in 12 of the 13 weir-obstructed streams. Remarkably, the tally of 32 diadromous species in HKSAR exceeds the 19 known from mainland China, highlighting the need for further research on composition and conservation status of diadromous fishes.

Exploring Dengue Dynamics: A Multi-Scale Analysis of Spatio-Temporal Trends in Ibagué, Colombia.

Our study examines how dengue fever incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales in the city of Ibagué, located in the Andean region of Colombia. We used the dengue incidence in Ibagué from 2013 to 2018 to examine the associations with climate, socioeconomic, and demographic factors from the national census and satellite imagery at four levels of local spatial aggregation. We used geographically weighted regression (GWR) to identify the relevant socioeconomic and demographic predictors, and we then integrated them with environmental variables into hierarchical models using integrated nested Laplace approximation (INLA) to analyze the spatio-temporal interactions. Our findings show a significant effect of spatial variables across the different levels of aggregation, including human population density, gas and sewage connection, percentage of woman and children, and percentage of population with a higher education degree. Lagged temporal variables displayed consistent patterns across all levels of spatial aggregation, with higher temperatures and lower precipitation at short lags showing an increase in the relative risk (RR). A comparative evaluation of the models at different levels of aggregation revealed that, while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling, and they highlight the potential for targeted public health interventions based on localized risk factor analyses. Notably, the intermediate levels emerged as the most informative, thereby balancing spatial heterogeneity and case distribution density, as well as providing a robust framework for understanding the spatial determinants of dengue.

Quantifying the features of earthquake clusters in north-Central Iran, based on nearest-neighbor distances and network analysis

The statistical features of earthquake clusters in North-Central Iran (Tehran Region) are investigated, with the aim of quantitatively characterizing the properties of earthquake triggering and allow exploring their possible relations with the tectonic setting of the study area.The nearest-neighbor approach is used for the identification of the earthquake clusters in the space-time-energy domain. This approach permits for a data-driven identification of clusters so that, within multi-event clusters, the features of secondary and higher orders dependent events can be explored. The study is based on a revised dataset that is extracted from the catalog compiled by the Iranian Seismological Center (IRSC) for the period of 1996–2022. In order to exclude the effect of non-tectonic events, which turn out quite numerous within the study region, explosions within quarry-rich areas are removed; the identification of non-tectonic events is performed by considering the normalized ratios of daytime to nighttime events in an iterative removal procedure. According to preliminary analysis of the resulting catalog, an area is selected, within which a satisfactory completeness level is assessed for events with magnitude >2.0. Robust values of the scaling parameters, namely the b-value and the fractal dimension of epicenters, are also computed and are used to calculate the nearest-neighbor distances and to identify the earthquake clusters.The nearest-neighbor method also permits to investigate the internal structure of earthquake sequences, and to differentiate the spatial properties of seismicity according to the different topological features of the clusters structure. The obtained results allow us identifying two macro-areas, approximately separated by the 52°E meridian, which are characterized by different clustering features, namely: high complexity indexes, indicating simple (burst-like) structure of clusters, to the East; low complexity index, corresponding to complex multi-level (swarm-like) structure of clusters, to the West. The complexity measures, borrowed from network theory (i.e. the Closeness and Outdegree Centralization indexes), consistently capture the complexity of the identified clusters, and confirm that the cluster structures have distinct preferred geographic locations. The territorial heterogeneity of the examined clustering properties can be related with the spatial variability of tectonic, structural and geophysical features of the Alborz region, in good agreement with findings from the Alps-Dinarides junction (Northeastern Italy), a region also characterized by a contractional structural setting, mainly including reverse and strike-slip faulting systems, and by moderate to high seismic activity.

Environmental Influences on Illex argentinus Trawling Grounds in the Southwest Atlantic High Seas

To understand the spatial temporal distribution characteristics of Illex argentinus caught by trawl fishing vessels in the Southwestern Atlantic Ocean and their relationship with key marine environmental factors, this study analyzed the temporal and spatial changes in the fishing ground center of trawl vessels at the ten-day scale from December 2019 to May 2022, combining Chinese trawl fishing log data marine environmental data with satellite remote sensing marine environmental data. Utilizing the Maxent model, ten-day intervals were used as the temporal scale, and ten marine environmental factors, including sea surface temperature, sea surface height, sea surface salinity, chlorophyll concentration, temperature at 50 m and 100 m depth, and the meridional and zonal velocities of ocean currents were quantitatively analyzed to explore the correlation between the spatial distribution of catch and environmental factors. The study reveals that the trawl fishing grounds for Illex argentinus are divided into southern and northern grounds. The southern grounds first appear near 45°20′ S in December, gradually moving southeastward in February and March. The northern grounds do not appear until April, near 42° S in the high seas. On the ten-day time scale, the central fishing grounds of Illex argentinus show significant spatial variability but minor interannual differences. The Maxent model results indicate that sea surface temperature and chlorophyll a concentration are the key environmental factors influencing the spatial and temporal variability of the high seas trawl fishing grounds for most of the time, with high environmental contribution rates during the fishing season. While the range of suitable habitats with an HSI > 0.6 identified by the Maxent model varies significantly between years, a pattern is observed where the range expands at the start and end of the fishing season and contracts during the peak fishing season. This suggests that a more concentrated range of suitable habitats is conducive to accurate predictions of trawl fishing grounds, enabling efficient fishing operations.

The impact of estuarine flushing on greenhouse gases: A study of the stratified Clyde estuary

Estuaries and coastal waters are sensitive to ecological degradation but receive some of the highest levels of pollutants. One impact of these pollutants is increased greenhouse gas generation, which is significant, but difficult to estimate due to high variability and data paucity. This paper investigates key controls on methane (CH4) and nitrous oxide (N2O) in the urban, mesotidal, stratified Clyde estuary, Scotland, between January 2020 and October 2022. Measurements covered the estuary longitudinally, through tidal cycles and across the river-estuary transition. Dissolved CH4 and N2O were always supersaturated relative to air exhibiting strong spatial and temporal variability. Estuary surface freshwater layer CH4 concentrations were positively correlated with turbidity and exceeded 5.4 μmol l−1. Lower saline layer CH4 concentrations exceeded 10.8 μmol l−1 and were highest after freshwater flushing events. The CH4 concentrations decreased exponentially with salinity persistence (time since last freshwater flushing event), reducing by 50% after 10 days of continuously saline water. Salinity persistence likely provides a tipping point between the dominance of different microbial communities. Considering the persistence of saline conditions can explain much of the previously reported variability in estuarine CH4. In the surface freshwater layer, N2O exceeded 0.15 μmol l−1, while in the lower saline layer N2O exceeded 0.21 μmol l−1, despite lower dissolved nitrogen, increasing the N2O per unit available nitrogen. There was a significant inverse exponential correlation (R2 = 0.96) between N2O and dissolved oxygen in the lower layer, with low oxygen driving elevated N2O concentrations. This study provides unique insights into the conditions that generate CH4 and N2O in a stratified urban-impacted estuary.

Visualising Daily PM10 Pollution in an Open-Cut Mining Valley of New South Wales, Australia—Part II: Classification of Synoptic Circulation Types and Local Meteorological Patterns and Their Relation to Elevated Air Pollution in Spring and Summer

Abstract: The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (air-borne particles with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The present study was initiated to quantitatively examine the spatial and temporal variability of PM10 pollution in the region. An earlier paper of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns were quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology–air pollution relationships were visualised and interpreted on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs. the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs. elevated PM10 pollution in the valley. Two local meteorological patterns, the hot–dry–northwesterly wind conditions and the hot–dry–calm conditions, were found to be the most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of an eastward migrating continental high-pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.