Sustainable Water Research Articles

A Mathematically Optimized Design Solution for Structure of PEMFC Catalyst Layer Based on a Two-Phase Flow Model

Abstract Proton exchange membrane fuel cells (PEMFCs) have emerged as a promising solution as the world is moving toward sustainable energy resources. However, in order to compete economically with existing technologies, further improvements in performance are necessary. Mathematical modeling and optimization are viable tools for designing better PEMFCs. This study aims to provide a framework for topological optimization of the electrode structure, with the ultimate goal of enhancing cell performance. To achieve this, a two-phase flow model of PEMFC is developed to characterize the cell performance. The model is then coupled with a topology optimization technique, which is the main focus of the present work, to seek an optimized constituent distribution in the catalyst layer. Results indicate that an electrode with a heterogeneous structure can enhance the overall cell performance by balancing various transport and rate processes. The optimized designs are investigated for various key factors, including effective diffusivity, effective conductivity, and liquid water management, to demonstrate how an optimized design can be advantageous.

Synthesis of Benzoic Acids from Electrochemically Reduced CO2 Using Heterogeneous Catalysts.

A method for the synthesis of benzoic acids from aryl iodides using two of the most abundant and sustainable feedstocks, carbon dioxide (CO2) and water, is disclosed. Central to this method is an effective and selective electrochemical reduction of CO2 (eCO2RR) to CO, which mitigates unwanted dehalogenation reactions occurring when H2 is produced via the hydrogen evolution reaction (HER). In a 3-compartment set-up, CO2 was reduced to CO electrochemically by using a surface-modified silver electrode in aqueous electrolyte. The ex-situ generated CO further underwent hydroxycarbonylation of aryl iodides by MOF-supported palladium catalyst in excellent yields at room temperature. The method avoids the direct handling of hazardous CO gas and gives a wide range of benzoic acid derivatives. Both components of the tandem system can be recycled for several consecutive runs while keeping a high catalytic activity.

Managing knowledge and sustainability in resource constrained disaster environments

AbstractOrganizations use both tangible and intangible resources for their existence to achieve sustainable development goals. Organizational knowledge mainly comprises of tacit knowledge held by employees. It becomes more critical when the situations are unpredictable, for example in rescue/disaster projects involving human lives at risk. Tacit knowledge utilization has been understudied particularly in such environments bounded by resource constraint and sustainability issues. This qualitative research attempts to fill this gap by collecting data from the knowledge workers in a large public sector non‐profit rescue organization. The study reveals various individual level, group level, organizational level, community level, and situational level factors, which impact the tacit knowledge utilization of rescue workers to properly execute emergency life threatening rescue operations and sustainability issues.

Acorns: From an Ancient Food to a Modern Sustainable Resource

Acorns, the seeds of oak trees, are pivotal in the Mediterranean region, historically serving as vital sustenance for wildlife and humans. Although traditionally used for livestock, in times of scarcity, acorns were also used in human diets together with their use in traditional medicine. In this review, we explore and deepen the multifaceted significance of oak species, consolidating nutritional data while emphasizing sustainable practices for modern dietary integration and potential applications in other areas, promoting long-term ecological balance. In the Mediterranean region, notable species are Quercus ilex, Q. faginea, Q. suber, and Q. coccifera, which differ in flavor, texture, and nutritional content. Rich in carbohydrates, acorns can contain up to 50% starch as well as amino acids, proteins, and lipids. This nutritional composition favors the preparation of flour for use in bread doughs or even diverse types of processing such as roasting, which makes it a substitute for coffee. Furthermore, acorn by-products, containing tannins, find applications in leather processing. This review highlights and summarizes in detail the nutritional potential, culinary adaptability, and diverse applications of acorns, emphasizing the need to preserve and utilize natural resources sustainably. In doing so, it sheds light on the often-overlooked value of these humble seeds.

Advancing water security in Africa with new high-resolution discharge data

VegDischarge v1, which covers over 64,000 river segments in Africa, is a natural river discharge dataset produced by coupled modeling; the agro-hydrologic VegET model and the mizuRoute routing model for the period 2001-2021. Using remote sensing data and hydrological modeling system, the 1-km runoff field simulated by VegET, was routed with mizuRoute. Performance metrics show strong model reliability, with R² of 0.5–0.9, NSE of 0.6–0.9, and KGE of 0.5–0.8 at the continental scale. The total average annual discharge for Africa is quantified at 3271.4 km³·year−1, with contributions to oceanic basins: 1000.0 km³·year−1 to the North Atlantic, primarily from the Senegal, Gambia, Volta, and Niger Rivers; 1327.2 km³·year−1 to the South Atlantic, largely from the Congo River; 214.7 km³·year−1 to the Mediterranean Sea, predominantly from the Nile River; and 729.4 km³·year−1 to the Indian Ocean, with inputs from rivers such as the Zambezi. The dataset is valuable for stakeholders and researchers to understand water availability, its temporal and spatial variations that affect water-related infrastructure planning, sustainable resource allocation, and the development of climate resilience strategies.

Assessment of dew harvesting as a sustainable water source and air quality indicator: a case study of Dhanbad, Jharkhand, India

ABSTRACT Dew formation takes place when atmospheric water vapor condenses on the surface which cools down due to radiation deficit. As it is a local phenomenon, its study gives information about the pollutants present in the atmosphere. Also, since dew formation is a natural phenomenon, and involves passive cooling, this can be a potential method for atmospheric water harvesting. The main intention of this present research work was the collection of dew samples and their analysis for yield and chemical composition. Dew samples were collected on a self-fabricated dew collection set up during the winter season. In total 30 dew samples were collected and analyzed for ionic constituents. The results were then compared to those of rain samples. The pH of dew was found to be in the range of 7-8.7, showing the neutral to alkaline nature of dew. Ionic compositions were higher in dew than in rain. While sulfate largely contributed to the ion composition of dew, nitrate was the least contributing ion. The average concentration for sulfate and nitrate in dew were found to be 0.55 and 0.03 meq/L, respectively. The average yield of dew was found to be 0.13L/m2. This yield value from the simplest of collection set-up ensures that dew can be considered as the potential water source with advanced condensing material and passive/active cooling in arid and semi-arid regions.

FeBTC MOF‐Derived Fe3O4@C Nanocomposite: Controlled Synthesis and Application as Potential Adsorbent for Rhodamine Dye Elimination From Wastewater

ABSTRACTThis study explores the controlled synthesis of a novel Fe3O4@C magnetic nanocomposite derived from FeBTC metal–organic framework (MOF) and its application as an efficient adsorbent for the removal of rhodamine dye from wastewater. The FeBTC MOF was first synthesized and then thermally decomposed in a controlled oxygen atmosphere at 375°C (1 h) to form the Fe3O4@C nanocomposite with a magnetic Fe3O4 core embedded in a porous carbon matrix. A comprehensive characterization of the nanocomposite was performed using x‐ray diffraction (XRD), transmission electron microscopy (TEM), and x‐ray photoelectron spectroscopy (XPS) to confirm the successful formation and to evaluate its structural and morphological properties. The morphological investigation revealed that the particles of Fe3O4 had a spherical shape with diameter of 10–15 nm. The carbon coating appeared as a thin amorphous layer surrounding the Fe3O4 nanoparticles. The adsorption capacity of Fe3O4@C for rhodamine B (RhB) dye was assessed under various conditions, including different pH values, contact times, initial dye concentrations, and temperatures. Complete dye removal was attained in 45 min using 50 mg of the nanocomposite and 50 mL of 5.0 ppm RhB at the optimum pH of 9.1. Under the same experimental conditions, the highest adsorption capacity of 13.0 mg/g was obtained, but using 15.0 mg of the nanocomposite. Fe3O4@C nanocomposite exhibits high adsorption efficiency, with a maximum removal capacity of 100%, which is clearly superior to many conventional adsorbents. This performance can be attributed to the synergistic effects of the magnetic Fe3O4 and the large surface area of the carbon matrix. Kinetic models were employed to understand the adsorption mechanism. The adsorption kinetics followed a pseudo‐second‐order model. The reusability of the adsorbent was tested over multiple cycles and showed a minimal loss of performance (drops to 93.0% after five removal cycles). The study demonstrates that the Fe3O4@C nanocomposite is a promising candidate for the effective removal of organic dyes from wastewater, offering potential benefits for environmental remediation and sustainable water management.

Biophysical and socioeconomic characterization of Agewmariam experimental watershed in Northern Ethiopia: insights and management options.

Watershed characterization is essential for sustainable watershed management and effective resource utilization, particularly in assessing changes resulting from interventions. This study investigates the biophysical and socio-economic conditions of the Agewmariam watershed, aiming to develop land capability and suitability maps while identifying viable management options. Biophysical data, including land slope, soil properties, erosion severity, stoniness/rockiness cover, and vegetation cover, were collected through field surveys and analyzed using overlay analysis in ArcGIS. Socio-economic data, encompassing population demographics, income, and expenditure, were gathered via individual interviews, complete census, and focus group discussions and summarized using descriptive statistics. The study identified 259 households with an average family size of 4 and a demographic dependency ratio of 96.6%. The major sources of income are crop production and livestock rearing supplemented by casual labor and food aid. The average farmland holding size is 0.5ha, with sorghum, barley, teff, and wheat as the dominant crops. The watershed is characterized by six land capability classes (classes II, III, IV, VI, VII, and VIII). The suitability analysis indicated that the watershed is not currently suitable for wheat and teff crops unless physical, chemical, and biological management improvements are implemented. The study revealed that the major limiting factors for land capability and suitability were slope, erosion severity, stoniness, soil organic matter, and soil texture. The socio-economic characterization enhanced awareness about the local socio-economic condition, informing appropriate planning and management strategies. The study recommends intensive soil and water conservation intervention, afforestation on hillsides, changing the land use system, and the addition of organic matter and fertilizers to enhance land capability and suitability for sustainable agricultural practices.

Groundwater Dynamics in the Middle Brahmaputra River Basin: A Case Study of Shallow Aquifers in Inner Guwahati City, Assam, India

This study investigated the hydrogeological characteristics and groundwater dynamics in the shallow aquifer zones of inner Guwahati city, Assam, India. Sixteen dug wells spread across the city, specifically used for domestic purposes, were selected for this study. Additionally, ten wells were selected for trend analysis. The borehole lithology reveals predominant compositions of clay, sand, and granules, with thin clay cappings indicating significant groundwater potential. Depth-to-water level analysis revealed varying water levels across the study area, with shallow levels in the northern and western regions and gradual deepening toward the eastern and southern parts. The groundwater flow directions show nonuniform patterns and reflect the influence of topography and domestic pumping in urban residential zones. The general groundwater flow direction is toward the Brahmaputra River. Trends in groundwater level, assessed using the Mann–Kendall test and Sen’s slope, suggest both falling and rising trends across different locations, indicating complex groundwater dynamics influenced by factors such as recharge, extraction, and topography. However, the long-term rainfall data indicate no significant trend over the studied period, suggesting limited natural influence on groundwater level trends. These findings may contribute to a comprehensive understanding of groundwater dynamics in the study area and are essential for sustainable water resource management and mitigation of groundwater depletion risks.

Mapping Soil Surface Moisture of an Agrophytocenosis via a Neural Network Based on Synchronized Radar and Multispectral Optoelectronic Data of SENTINEL-1,2—Case Study on Test Sites in the Lower Volga Region

In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to enhance precision farming practices, which are increasingly vital in addressing global agricultural challenges such as water scarcity and the need for sustainable resource management. To verify the developed method, data from two experimental plots were utilized. These plots were located on irrigated soybean crops, with the first plot situated on the right bank (plot No. 1) and the second on the left bank (plot No. 2) of the lower Volga River. Two experimental soil moisture geodatasets were created through measurements and geo-referencing points using the gravimetric method (for plot No. 1) and the proximal sensing method (for plot No. 2) employing the Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict the reflection coefficient of an electro-magnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated based on Sentinel-1 data) to the index (calculated based on the data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). The retrieved soil moisture values were compared with in situ measurements, showing a determination coefficient of 0.44–0.65 and a standard deviation of 2.4–4.2% for plot No. 1 and similar metrics for plot No. 2. The conducted research laid the groundwork for developing a new technology for remote sensing of soil moisture content in agrophytocenosis, serving as a crucial component of precision farming systems and agroecology. The integration of this technology promotes sustainable agricultural practices by minimizing water consumption while maximizing crop productivity. This aligns with broader environmental goals of conserving natural resources and reducing agricultural runoff. On a larger scale, data derived from such studies can inform policy decisions related to water resource management, guiding regulations that promote efficient water use in agriculture.

Differentiation of adsorption and degradation in steroid hormone micropollutants removal using electrochemical carbon nanotube membrane

The growing concern over micropollutants in aquatic ecosystems motivates the development of electrochemical membrane reactors (EMRs) as a sustainable water treatment solution. Nevertheless, the intricate interplay among adsorption/desorption, electrochemical reactions, and byproduct formation within EMR complicates the understanding of their mechanisms. Herein, the degradation of micropollutants using an EMR equipped with carbon nanotube membrane are investigated, employing isotope-labeled steroid hormone micropollutant. The integration of high-performance liquid chromatography with a flow scintillator analyzer and liquid scintillation counting techniques allows to differentiate hormone removal by concurrent adsorption and degradation. Pre-adsorption of hormone is found not to limit its subsequent degradation, attributed to the rapid adsorption kinetics and effective mass transfer of EMR. This analytical approach facilitates determining the limiting factors affecting the hormone degradation under variable conditions. Increasing the voltage from 0.6 to 1.2 V causes the degradation dynamics to transition from being controlled by electron transfer rates to an adsorption-rate-limited regime. These findings unravels some underlying mechanisms of EMR, providing valuable insights for designing electrochemical strategies for micropollutant control.

Catalyst Activation of Peroxymonosulfate for Reactive Species Generation and Organic Pollutant Degradation: A Mini Review

This review focuses on the application and mechanisms of peroxymonosulfate (PMS) in advanced oxidation processes (AOPs). It clarifies the significance of PMS in degrading organic pollutants, highlighting its high efficiency in treating persistent contaminants, such as antibiotics. The review details the roles and mechanisms of various catalysts, including single-atom catalysts, metal oxides, non-metal oxides and their composites, as well as metal-organic frameworks (MOFs), in activating PMS. It emphasizes the influence of catalyst surface active sites on both radical and nonradical activation pathways. Key factors affecting PMS activation efficiency, such as PMS concentration, pH value, coexisting ions, and temperature, are examined to underline the importance of optimizing these parameters for effective reaction conditions. By synthesizing existing research, the review not only illustrates the extensive application potential of PMS in AOPs but also identifies future research challenges and directions. This provides a theoretical foundation and technical support for developing efficient, economical, and sustainable water treatment technologies.

Investigating novel thin-film nanocomposite membranes for sustainable water recovery using fertilizer-driven forward osmosis – Experimental and machine learning approaches

Water recovery from municipal wastewater can augment freshwater resources to meet increasing demand. An osmotic-driven membrane separation process- forward osmosis (FO) is currently being explored as a sustainable technology. This work proposes to address the challenges associated with FO namely (i) robust membrane and (ii) draw solute (DS) recovery. Robust thin-film nanocomposite membranes incorporating a) nickel ferrite or b) nitrogen-enriched nanoporous polytriazine (NENP-1) covalent organic framework were fabricated with enhanced hydrophilicity, surface roughness and anti-fouling properties. The optimum membranes M2 and C2 displayed a pure water flux of 4.86 ± 0.11 and 5.4 ± 0.2 L/m2h respectively and a specific reverse solute flux (SRSF) <<1 when using 1 M NaCl solution as the DS (feed solution – DI water). Fertilizer-driven FO (FDFO) was proposed as the diluted DS need not be recovered but could be used directly for irrigation. The pure water flux for both M2 and C2 membranes were in the following order for the fertilizers used as DS (1 M) - (NH4)2SO4 + (NH₄)₂HPO₄ > (NH4)2SO4 > (NH₄)₂HPO₄ which indicates that the mixed ions in the DS enhanced the water permeance. In all cases, the SRSF was < 1 g/L indicating the feasibility of the process. The membranes exhibited a TOC removal of > 90 % from synthetic municipal wastewater containing persistent organic pollutants and personal & pharmaceutical care products and the membranes could be regenerated after washing with a 10 mM solution of SDS for 5 cycles of operation. Further, ANN was used to develop a predictive model for water flux in FO membranes and the optimized architecture of 7–5–50–40–1 displayed a remarkable overall R2 value of 0.98 (MSE= 0.027). The FDFO process using the fabricated thin-film nanocomposite membranes holds enormous potential to be investigated in larger-scale studies.

Unveiling Relationships: Enhancing Sustainability of Golpata (Nipa Palm) Through Socio‐Ecological Systems Management in the Sundarbans Mangrove Forest

ABSTRACTThis study explores the social‐ecological dynamics of common pool resource (CPR) management in the Sundarbans mangrove forest in Bangladesh. We focus on golpata (Nypa fruticans), a vital CPR for local communities. A social‐ecological systems (SESs) framework was employed to assess the relationships between social (actors and governance) and ecological (resource system and resource unit) dimensions and their influence on the sustainability of golpata management. Data on 12 variables and 24 indicators were collected within the Chadpai range of the Sundarbans. Results revealed both positive and negative relationships between the SES dimensions. Positive correlations were found between the resource system and resource unit, and between governance and both resource system and unit (albeit weaker). Conversely, negative correlations emerged between actors and both resource systems and units, suggesting a potential disconnect between community user practices and resource sustainability. Based on these findings, the study recommends four distinct management scenarios tailored to specific SES regions within the Sundarbans. These scenarios emphasize strengthening governance frameworks, enhancing stakeholder engagement, and implementing targeted ecological interventions to improve the sustainability of golpata resources. This research contributes to a deeper understanding of the social‐ecological complexities of golpata management in the Sundarbans. The findings can inform policymakers in developing effective strategies for sustainable resource use and community livelihoods within this critical ecosystem.

Turning Corn Stalk Trashes into a Photothermal Agent for Interfacial Solar Water Evaporation for Sustainable Water Purification

Biomass‐based photothermal materials have a higher photothermal conversion efficiency and contribute significantly to improved solar water evaporation systems. This work aims to transform corn stalk (CS) trash into efficient photothermal materials with centralized and less‐contact area water supply systems to achieve enhanced interfacial heat accumulation. We successfully synthesized cornstalk biochar synthesized via pyrolysis to maintain the environmental concern and is deposited onto a scalable, and cost‐effective (&lt;1$) polyurethane foam (triangle shape, where the tip plays the wick and centralized water supply). The detailed characterizations validate the porous structure of CS biochar‐coated PU foam (CSB@PU), and enhance interfacial heat accumulation up to 47.8 °C under 1 kW m−2 solar irradiation which is endowed via thermal management of polystyrene foam (PS). This reproducible interfacial heat accumulation and sustainable evaporator bestow a water evaporation rate of up to 1.38 kg m−2 h−1, and solar‐to‐vapor conversion efficiency (84%) under one sun which is more efficient than other biomass‐derived evaporators. Inductively coupled plasma‐optical emission spectroscopy analysis validates the reductions of primary (Na+, K+, Mg2+, and Ca2+) and heavy metal ion (Fe3+, Hg2+, Cd2+, and Pb2+) concentrations in condensate, insights the potential of CSB@PU to advance the water treatment technologies.

Sediment Simulation Using Multi Layer Perceptron (MLP), Co-Active Neuro-Fuzzy Inference System (CANFIS) and Multiple Linear Regression Tecniques (MLR) for Hurdag Watershed

Sediment modeling plays a crucial role in sustainable water resources planning, development, and management. Techniques like the Multilayer Perceptron (MLP), Co-active Neuro-Fuzzy Inference System (CANFIS), and Multiple Linear Regression (MLR) have proven effective for sediment modeling and forecasting. This study aimed to develop and assess the applicability of MLP, CANFIS, and MLR models by training and testing them during the monsoon season (June to September) for the Hurdag watershed in the Damodar-Barakar basin, located in Hazaribagh district, Jharkhand, India. Daily rainfall, runoff (or streamflow), and sediment concentration data from 1997 to 2006 were used, with the data split into two sets: training set (1997–2004) and a testing set (2005–2006). The analysis was conducted using NeuroSolution 5.0 software and Microsoft Excel for performance evaluation indices. The best input combinations for sediment yield simulation were identified, and 10 optimal models were selected from 31 different input combinations. These input combinations were applied to the network for training using the back propagation algorithm for MLP and Gaussian and generalized bell membership functions for CANFIS models. Multiple networks were trained individually, with the most accurate predictions during testing being chosen as the best models. The models’ performance was evaluated using statistical indices such as root mean squared error (RMSE), coefficient of efficiency (CE), and correlation coefficient (r). The results showed that MLP and CANFIS models performed best in predicting sediment concentration for the Hurdag watershed, whereas MLR models showed poor performance for the given dataset. Specifically, sediment concentration for the current day could be modeled using current day rainfall and runoff data (MLP-7), while runoff could be simulated using the previous day's rainfall data (MLP-2).

Evaluating the impact of nature-based solutions on the provision of water-related and water-dependant ecosystem services

Water scarcity is a pressing issue in the Mediterranean region, exacerbated by overuse of resources for agriculture and the impacts of climate change. Addressing this challenge requires improved water cycle management and the adoption of Nature-based Solutions (NbS) to enhance infrastructure efficiency and sustainability. With the aim of promoting the implementation and assessment of NbS, we have developed a monitoring framework that integrates the assessment of ecological, socio-economic and cultural aspects under the umbrella of the IUCN Global Standard for NbS. A list of Key Performance Indicators (KPIs) was selected following standard methodologies. We have applied the framework to five case studies in the Mediterranean region to evaluate its efficiency to assess NbS tailored to local challenges and contexts. As part of the monitoring framework, we used the IUCN self-assessment tool for the Global Standard for NbS, demonstrating adherence of 50–75 % across all case studies. Common KPIs were identified, streamlining monitoring efforts and providing guidance from the design phase onwards. Our monitoring framework offers a comprehensive approach to evaluating NbS interventions, ensuring alignment with global standards and enhancing resilience in water management. By integrating the IUCN Global Standard, it provides robust guidance for future execution, contributing to sustainable water resource management in the Mediterranean and beyond.

A Localized Evaluation of Surface Water Quality Using GIS-Based Water Quality Index along Satpara Watershed Skardu Baltistan, Pakistan

Surface water quality in Skardu, Gilgit-Baltistan, Pakistan, is of immense importance because of the city’s dependence on these resources for domestic uses, agriculture, and drinking water. The water quality index (WQI) was integrated with the Geographic Information System (GIS) to spatially envision and examine water quality data to facilitate the identification of pollution hotspots, trend analysis, and knowledge-based decision-making for effective water resource management. This study aims to evaluate the physiochemical and bacteriological parameters of the Satpara watershed and to provide the spatial distribution of these parameters. This study endeavors to achieve Sustainable Development Goal 6 (SDG 6) by identifying localities with excellent and unfit water for drinking, sanitation, and hygiene. A total of fifty-one surface water samples were collected from various parts of the Satpara watershed during the fall season of 2023. Well-established laboratory techniques were used to investigate water for parameters like Electrical Conductivity (EC), pH, turbidity, total dissolved solids (TDSs), major cations (K+, Na+, Mg2+, Ca2+), major anions (Cl−, SO42−, NO3−, HCO3−), and bacteriological contaminants (E. coli). Spatial distribution maps of all these parameters were created using the Inverse Distance Weighted (IDW) technique in a GIS environment. A significant variation in the quality of water was observed along the study area. The level of Escherichia coli (E. coli) contamination is above the permissible limit at various locations along the watershed, making water unsafe for direct human consumption in these areas. Some regions showed low TDS values, which could adversely affect human health and agricultural yield. From the WQI valuation, 58.82% of the collected samples were “Poor”, 31.8% were “Very poor” and 9.8% were found to be “Unfit for drinking”. The research findings emphasize the pressing need for consistent monitoring and adoption of water management strategies in Skardu City to warrant sustainable soil and water use. The spatial maps generated for various parameters and the water quality index WQI offer critical insights for targeted intercessions.

Towards sustainable resource management: Graph modelling insights from Ghana's oil and gas local content ecosystem

A critical aspect of the management of oil and gas resources in Ghana is the effective promotion and development of local content to maximise the participation of local businesses and workforce in the oil and gas industry. This study examined local content drive in Ghana's oil and gas sector, analyzing its functional relations with institutional characteristics, participation, technology transfer, and economic growth via graph modelling framework. A total of 250 key stakeholders were involved in this cross-sectional study. A network analysis revealed the centrality of institutional characteristics in driving local content development, while economic growth was the least integrated domain. Diverse strengths and directionality of connections between domains were observed, which highlight the complexity of areas of influence and potential challenges. Process modelling analysis showed a significant positive direct effect of institutional characteristics on local content drive, emphasizing the critical role of robust and supportive institutions in driving local content initiatives. Conversely, economic growth exhibited a smaller yet significant negative direct effect, indicating a trade-off between economic expansion and local content focus. The broader implications of these findings are discussed, and recommendations are proposed to enhance the effectiveness of local content drive in Ghana's oil and gas sector.

Development of a method for estimating asari clam distribution by combining three-dimensional acoustic coring system and deep neural network

Developing non-contact, non-destructive monitoring methods for marine life is crucial for sustainable resource management. Recent monitoring technologies and machine learning analysis advancements have enhanced underwater image and acoustic data acquisition. Systems to obtain 3D acoustic data from beneath the seafloor are being developed; however, manual analysis of large 3D datasets is challenging. Therefore, an automatic method for analyzing benthic resource distribution is needed. This study developed a system to estimate benthic resource distribution non-destructively by combining high-precision habitat data acquisition using high-frequency ultrasonic waves and prediction models based on a 3D convolutional neural network (3D-CNN). The system estimated the distribution of asari clams (Ruditapes philippinarum) in Lake Hamana, Japan. Clam presence and count were successfully estimated in a voxel with an ROC-AUC of 0.9 and a macro-average ROC-AUC of 0.8, respectively. This system visualized clam distribution and estimated numbers, demonstrating its effectiveness for quantifying marine resources beneath the seafloor.