Human health risks associated to trace elements and metals in commercial fish from the Brazilian Amazon.

Fish constitutes the main protein source for the Amazonian population. However, the impact of different anthropogenic activities on trace element and metal accumulation in fish and their risks for human health at a regional scale remain largely unexplored. Here we assessed exposure levels of 10 trace elements and metals (Cr, Mn, Fe, Ni, Cu, Zn, As, Cd, Pb, and Hg) in 56 samples belonging to 11 different species of fish from the Brazilian Amazon. We studied the relationship between exposure levels, fish origin, and fish feeding habits, and assessed toxicological and carcinogenic risks for the Amazonian population. No significant correlation was found between sampling site and exposure levels to the studied elements, but a significant difference was found between the accumulation of some metals and the position of the fish species in the food chain. The concentrations of Cr and Hg in fish flesh were found to exceed the Brazilian limits for human consumption. This study shows that current fish consumption patterns can lead to estimated daily intakes of Hg, As and Cr that exceed the oral reference dose, thus posing a toxicological concern. Furthermore, carcinogenic risks may be expected due to the continued exposure to Cr and As. The results of this study show that the consumption of wild caught fish in the Amazon region should be controlled. Moreover, continued monitoring of trace element and metal contamination in fish and on the health of the Amazonian population is recommended, particularly for riverine and indigenous communities.

Priority sources identification and risks assessment of heavy metal(loid)s in agricultural soils of a typical antimony mining watershed.

Heavy metal(loid) (HM) pollution in agricultural soils has become an environmental concern in antimony (Sb) mining areas. However, priority pollution sources identification and deep understanding of environmental risks of HMs face great challenges due to multiple and complex pollution sources coexist. Herein, an integrated approach was conducted to distinguish pollution sources and assess human health risk (HHR) and ecological risk (ER) in a typical Sb mining watershed in Southern China. This approach combines absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models with ER and HHR assessments. Four pollution sources were distinguished for both models, and APCS-MLR model was more accurate and plausible. Predominant HM concentration source was natural source (39.1%), followed by industrial and agricultural activities (23.0%), unknown sources (21.5%) and Sb mining and smelting activities (16.4%). Although natural source contributed the most to HM concentrations, it did not pose a significant ER. Industrial and agricultural activities predominantly contributed to ER, and attention should be paid to Cd and Sb. Sb mining and smelting activities were primary anthropogenic sources of HHR, particularly Sb and As contaminations. Considering ER and HHR assessments, Sb mining and smelting, and industrial and agricultural activities are critical sources, causing serious ecological and health threats. This study showed the advantages of multiple receptor model application in obtaining reliable source identification and providing better source-oriented risk assessments. HM pollution management, such as regulating mining and smelting and implementing soil remediation in polluted agricultural soils, is strongly recommended for protecting ecosystems and humans.

Analysis on typical characteristics and causes of coal mine gas explosion accidents in China.

Large-scale coal mine gas explosion (CMGE) accidents have occurred occasionally and exerted a devastating effect on society. Therefore, it is essential to systematically identify the characteristics and association rules of causes of CMGE accidents through analysis on large-scale CMGE accident reports. In this study, 298 large-scale CMGE accidents in China from 2000 to 2021 were taken as the data sample, and mathematical statistical methods were adopted to analyze their general characteristics, coupling cross characteristics, and characteristics of gas accumulation and ignition sources. Moreover, the text mining technology and the Apriori algorithm were used for exploring the formation mechanism of CMGE accidents, during which 46 main causal factors were identified and 59 strong association rules were obtained. Furthermore, an accident causation network was constructed based on the co-occurrence matrix. The key causal items and sets of CMGE accidents were clarified through network centrality analysis. According to the research results, electrical equipment failure, cable short circuit, mine lamp misfire, hot-line work, and blasting spark are the key ignition sources of CMGE. Fan failure, airflow short circuit, and local ventilation fan damage are the main causes of gas accumulation. Besides, the confidence levels of two association rules of "static spark-fan failure" and "blasting spark-airflow short circuit" are higher than 70%, indicating that they are the two dominant risk-coupling paths of gas explosions. In addition, six causes appear frequently in the shortest risk paths of gas explosion and are closely related to other causes, i.e., fan failure, local ventilation fan damage, static sparks, electrical equipment failure, self-heating ignition, and friction impact sparks. This study provides a new perspective on identifying causes of accidents and their complex association mechanisms from accident report data for practical guidance in risk assessment and accident prevention.

Integrated assessment for groundwater quality and flood vulnerability in coal mining regions.

Coal mining activities greatly damage water resources, explicitly concerning water quality. The adverse effects of coal mining and potential routes for contaminants to migrate, either through surface water or infiltration, into the groundwater table. Dealing with pollution from coal mining operations is a significant surface water contamination concern. Consequently, surface water resources get contaminated, harming nearby agricultural areas, drinking water sources, and aquatic habitats. Moreover, the percolation process connected with coal mining could alter groundwater quality. Subsurface water sources can get contaminated by toxins generated during mining activities that infiltrate the soil and reach the groundwater table. The aims of this study are the creation of models and the provision of proposals for corrective measures. Twenty-five scenarios were simulated using MODFLOW; according to the percolation percentage and contamination, 35% of the study area, i.e., the middle of the research area, was the most affected. About 38.08% of the area around the mining zones surrounding Margherita is prone to floods. Agricultural areas, known for applying chemical fertilizers, are particularly vulnerable, generating a risk of pollution to surrounding water bodies during flooding. The outputs of this research contribute to identifying and assessing flood-vulnerable regions, enabling focused measures for flood risk reduction, and strengthening water resource management.

Root traits drive the recovery of soil nematodes during restoration of open mines in a tropical rainforest.

Mining is a major threat to vegetation and soil in the tropical forests. Reforestation of degraded surface mines is critically dependent on the recovery of soil health, where the nematodes play an important role. However, the key determinants of community assembly of soil nematodes during mine-restoration remain unknown in the tropical rainforests. Here, the recovery of taxonomic diversity of nematode communities and their trophic groups during reforestation of an extremely degraded tropical open-mining area is studied. The factors that may impact their recovery, such as root traits (length, area and tissue density), soil properties (pH and soil organic matter content (SOM)), and taxonomic diversities of soil bacterial and fungal communities are investigated. Differences in these parameters were evaluated in the three soil types: (i) mined soil - the erstwhile soil that was removed during mining and stock-piled for 10 years at the foot of an extremely degraded open-mining area; (ii) reforested soil, sampled from a 10-year successful restoration, which used the mined soil for reforestation; and (iii) undisturbed soil, collected from an adjacent undisturbed/not-mined tropical rainforest. A total of 11, 34 and 29 nematode-genera were identified in mined-, undisturbed-, and reforested soils, respectively. The taxonomic diversities of the 5 nematode groups in the mined soil were 1.5-5.2 times lower than in the undisturbed soil, but were similar in the restored and undisturbed soils. Taxonomic diversities of phytophagous and predator nematodes were correlated to restored root traits; whereas of bacterivores, fungivores, and omnivores were correlated to pH, SOM, soil bacterial and fungal communities. Consequently, complete loss of roots during mining likely severely reduced the nematodes, but their recovery after reforestation led to the restoration of taxonomic diversity of nematode communities. The mix-planting fast-growing tree species may be appropriate for recovering soil health, including nematode diversity, during reforestation of open tropical mines.

Network medicine and systems pharmacology approaches to predicting adverse drug effects.

Identifying and understanding the relationships between drug intake and adverse effects that can occur due to inadvertent molecular interactions between drugs and targets is a difficult task, especially considering the numerous variables that can influence the onset of such events. The ability to predict these side effects in advance would help physicians develop strategies to avoid or counteract them. In this article, we review the main computational methods for predicting side effects caused by drug molecules, highlighting their performance, limitations and application cases. Furthermore, we provide an overall view of resources, such as databases and tools, useful for building side effect prediction analyses.

Comparative study of five machine learning algorithms on prediction of the height of the water-conducting fractured zone in undersea mining.

Prediction of water-conducting fractured zone (WCFZ) of mine overburden is the premise for reducing or eliminating water inrush hazards in undersea mining. To obtain a more robust and precise prediction of WCFZ in undersea mining, a WCFZ prediction dataset with 122 cases of fractured zones was constructed. Five machine learning algorithms (linear regression, XGBRegressor, RandomForestRegressor, LineareSVR, and KNeighborsRegressor) were employed to develop five corresponding predictive models, taking multiple factors into account.The optimal parameters for each model are obtained through ten-fold cross-validation (10CV). The model's predictive performance was validated and assessed using two metrics, namely the coefficient of determination (R2) and mean squared error (MSE). A comparison was made with the regression performance of commonly used empirical formulas. The results indicate that the constructed model outperforms reliance solely on theoretical criteria, showing a high R2 value of up to 0.925 and a low MSE value of 3.61. The proposed model was validated in a recently established mining area on Sanshan Island, China. It shows low absolute and relative errors of 0.71 m and 2.01%, respectively, between the predicted value from the model and observation result from the field, demonstrating a high level of consistency with on-site conditions. This paves a path to leveraging machine learning algorithms for predicting the height of WCFZ.

Analysis of child development facts and myths using text mining techniques and classification models.

The rapid dissemination of misinformation on the internet complicates the decision-making process for individuals seeking reliable information, particularly parents researching child development topics. This misinformation can lead to adverse consequences, such as inappropriate treatment of children based on myths. While previous research has utilized text-mining techniques to predict child abuse cases, there has been a gap in the analysis of child development myths and facts. This study addresses this gap by applying text mining techniques and classification models to distinguish between myths and facts about child development, leveraging newly gathered data from publicly available websites. The research methodology involved several stages. First, text mining techniques were employed to pre-process the data, ensuring enhanced accuracy. Subsequently, the structured data was analysed using six robust Machine Learning (ML) classifiers and one Deep Learning (DL) model, with two feature extraction techniques applied to assess their performance across three different training-testing splits. To ensure the reliability of the results, cross-validation was performed using both k-fold and leave-one-out methods. Among the classification models tested, Logistic Regression (LR) demonstrated the highest accuracy, achieving a 90 % accuracy with the Bag-of-Words (BoW) feature extraction technique. LR stands out for its exceptional speed and efficiency, maintaining low testing time per statement (0.97 µs). These findings suggest that LR, when combined with BoW, is effective in accurately classifying child development information, thus providing a valuable tool for combating misinformation and assisting parents in making informed decisions.

Analysis of Guangzhou city image perception based on weibo text data (2019-2023).

With the popularization of smart mobile terminals and social media, a large amount of data containing textual information about the city has been generated on social media platforms, covering all areas of the city. This provides a new way for the study of comprehensive perception of city image. In the Internet era, users express their opinions about cities through social media platforms (e.g., Sina Weibo), and mining this information helps to understand the image of cities on mainstream social media and to target positive images to improve the competitiveness of the city's image. In this paper, 370,000 microblog messages related to "Guangzhou City" between 2019 and 2023 are collected using web crawler technology, and three typical text analysis methods are adopted: Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Sentiment Analysis (SnowNLP), to understand the characteristics of Guangzhou city image. gain an in-depth understanding of Guangzhou's urban image characteristics. The study shows that extensive data analysis methods based on text mining can perceive the dynamics and trends of the city in a timely manner, refine the characteristics of Guangzhou's urban image, and propose communication strategies for Guangzhou's image. This study aims to mine Guangzhou's urban image presented on Weibo, provide data support for relevant departments in China and Guangzhou to formulate communication strategies, and provide references for other cities to manage their urban image.

Formononetin inhibits IgE by huPlasma/PBMCs and mast cells/basophil activation via JAK/STAT/PI3-Akt pathways.

Rationale: Food allergy is a prevalent disease in the U.S., affecting nearly 30 million people. The primary management strategy for this condition is food avoidance, as limited treatment options are available. The elevation of pathologic IgE and over-reactive mast cells/basophils is a central factor in food allergy anaphylaxis. This study aims to comprehensively evaluate the potential therapeutic mechanisms of a small molecule compound called formononetin in regulating IgE and mast cell activation. Methods: In this study, we determined the inhibitory effect of formononetin on the production of human IgE from peripheral blood mononuclear cells of food-allergic patients using ELISA. We also measured formononetin's effect on preventing mast cell degranulation in RBL-2H3 and KU812 cells using beta-hexosaminidase assay. To identify potential targets of formononetin in IgE-mediated diseases, mast cell disorders, and food allergies, we utilized computational modeling to analyze mechanistic targets of formononetin from various databases, including SEA, Swiss Target Prediction, PubChem, Gene Cards, and Mala Cards. We generated a KEGG pathway, Gene Ontology, and Compound Target Pathway Disease Network using these targets. Finally, we used qRT-PCR to measure the gene expression of selected targets in KU812 and U266 cell lines. Results: Formononetin significantly decreased IgE production in IgE-producing human myeloma cells and PBMCs from food-allergic patients in a dose-dependent manner without cytotoxicity. Formononetin decreased beta-hexosaminidase release in RBL-2H3 cells and KU812 cells. Formononetin regulates 25 targets in food allergy, 51 in IgE diseases, and 19 in mast cell diseases. KEGG pathway and gene ontology analysis of targets showed that formononetin regulated disease pathways, primary immunodeficiency, Epstein-Barr Virus, and pathways in cancer. The biological processes regulated by formononetin include B cell proliferation, differentiation, immune response, and activation processes. Compound target pathway disease network identified NFKB1, NFKBIA, STAT1, STAT3, CCND1, TP53, TYK2, and CASP8 as the top targets regulated at a high degree by formononetin. TP53, STAT3, PTPRC, IL2, and CD19 were identified as the proteins mostly targeted by formononetin. qPCR validated genes of Formononetin molecular targets of IgE regulation in U266 cells and KU812 cells. In U266 cells, formononetin was found to significantly increase the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R. In basophils KU812 cells, formononetin significantly increased the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK, TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R. Conclusion: These findings comprehensively present formononetin's mechanisms in regulating IgE production in plasma cells and degranulation in mast cells.

Research on prediction method of coal mining surface subsidence based on MMF optimization model.

Coal seam mining causes fracture and movement of overlying strata in goaf, and endangers the safety of surface structures and underground pipelines. Based on the engineering geological conditions of 22,122 working face in Cuncaota No.2 Coal Mine of China Shenhua Shendong Coal Group Co., Ltd. a similar material model test of mining overburden rock was carried out. The subsidence of overburden rock was obtained through the full-section strain data of distributed optical fiber technology, and the characteristics of mining surface subsidence were studied. The Weibull model was used to adjust the mathematical form of the first half of the surface subsidence curve via the MMF function. On this basis, the prediction model of coal seam mining surface subsidence was established, and the parameters of the prediction model of surface subsidence were determined. The test results show that with the advancement of coal seam mining, the fit goodness of the surface subsidence prediction curve based on the MMF optimization model reaches 0.987. Compared with the measured values, the relative error of the surface subsidence prediction model is reduced to less than 10%. The model displays good prediction accuracy. The time required for settlement stability in the prediction model is positively correlated with parameter a and negatively correlated with parameter b. The research results can be further extended to the prediction of overburden "three zones" subsidence, and provide a scientific basis for the evaluation of surface subsidence compression potential in coal mine goaf.

Spatiotemporal variations and the ecological risks of microplastics in the watersheds of China: Implying the impacts of the COVID-19 pandemic.

China is not only the first reported place of the COVID-19 pandemic but also is the biggest microplastic emitter in the world. Nevertheless, the impact of the COVID-19 pandemic on microplastic pollution in the watersheds of China remains poorly understood. To address this, the present study conducted a data mining and multivariate statistical analysis based on 8898 microplastic samples from 23 Chinese watershed systems before and during the COVID-19 pandemic. The results showed that the COVID-19 pandemic extensively affected the abundance, colors, shapes, polymer types, and particle sizes of microplastic in Chinese watershed systems. Before and during the COVID-19 pandemic, 77.27 % of the Chinese watershed systems observed increased microplastic abundance. Moreover, the COVID-19 pandemic itself, natural conditions (such as altitude and weather), and anthropogenic factors (such as civil aviation throughput) are highly intertwined, jointly impacting the microplastic in the watersheds of China. From the perspective of ecological risks, the COVID-19 pandemic was more likely to aggravate the microplastic pollution in the middle and down reaches of the Yangtze River Watersheds. Overall, whether before or during the COVID-19 pandemic, the main watershed systems of China still stayed at a high pollution level, which rang the alarm bell that watershed systems of China had been at serious ecological risk accused of microplastic contamination.

Chronic effects of metal releases from historical mining on threatened crayfish in Madison County Missouri, USA.

The Little St. Francis River and its tributaries drain metals-contaminated areas of the Madison County Mines National Priority List Superfund site (MCM) which was designated in 2003 to facilitate remediation of metals contamination within the MCM. One concern for natural resource trustees in the MCM is the potential effects of elevated metals concentrations on the federally threatened St. Francis River crayfish, Faxonius quadruncus, which has a geographic range that is limited to the St. Francis River watershed. A survey of riffle-dwelling crayfish, in-situ cage study, and laboratory toxicity tests were conducted to assess the effects of mining-derived metals on F. quadruncus and other crayfish species in the MCM. Crayfish densities were significantly greater at sites upstream of metals releases from historical mining (henceforth mining releases) compared to densities at sites downstream of mining releases, and metals concentrations in whole-body crayfish, surface water, sediments, macroinvertebrates, fish, and plant material were greater at sites downstream of mining releases compared to sites upstream of mining releases. Crayfish densities were also negatively correlated with consensus-based adverse effects indices, expressed as surface-water toxic units and sediment probable effects quotients. Decreased growth and increased mortality during cage and laboratory studies were likely due to exposure to, and subsequently uptake of, elevated concentrations of metals. Crayfish in all studies were found to bioaccumulate metals, which supports their utility as bioindicators of metals contamination. Study results show that elevated metals concentrations associated with mining releases in the MCM continue to adversely affect biota, including the federally threatened F. quadruncus.

Distribution, sources, and risk assessment of polycyclic aromatic hydrocarbons in surface soils and plants from industrial and agricultural areas, Junggar Basin, Xinjiang.

The contamination characteristics of Polycyclic Aromatic Hydrocarbons (PAHs) in different environmental functional areas are different. In this study, the contamination of PAHs in soils and common plants in typical mining and farmland areas in Xinjiang, China, was analyzed. The results showed that the contamination levels of PAHs in mining soils were significantly higher than those in farmland soils, and the mining soils were dominated by 4-5-ring PAHs and farmland soils by 3-4-ring PAHs. Analysis of their sources using a positive definite factor matrix model showed that PAHs in mining soils mainly originated from coal and natural gas combustion, and transportation processes; while farmland soils mainly came from biomass and coal combustion, and fossil fuel volatile spills. The cancer risk of PAHs in soils was evaluated using a combination of the Monte Carlo and the lifetime carcinogenic risk models, and the results showed that the overall level of cancer risk for mining soils was higher than that for farmland soils, and can put some people in high risk of cancer. For plant samples, except for individual crop samples, the contamination levels of mining plants and crops were similar, with 4-5-ring PAHs dominating in desert plants in mining areas and the highest proportion of 3-ring PAHs in crops in agricultural fields, and PAHs in both plants were mainly from biomass and coal combustion. The results of correlation analysis showed that 2-ring PAHs in crop roots were significantly positively correlated with it in corresponding soils, and some high-ring PAHs in crop leaves were significantly negatively correlated with it in corresponding soils. Therefore, there were significant differences in the pollution characteristics of PAHs in soils and common plants in mining and agricultural areas. Human health risks and ecological risks are mainly concentrated in mining areas, and appropriate intervention measures should be taken for pollution remediation.

Niche and interspecific associations of dominant plant species in antimony mining ecological damaged site in Nandan, Guangxi, China.

In order to provide a guide for plant selection of ecological restoration at antimony (Sb) mining ecological damaged sites, species composition, importance value, niche, and interspecific associations of tree, shrub, and herb layers were examined at Sb mining site in Nandan City, Guangxi, China. The results showed that 23 vascular plant species were recorded at the Sb mining ecological damaged site, belonging to 22 genera and 13 families, primarily Gramineae, Cyperaceae, Fabaceae, and Asteraceae. The highest importance values for trees, shrubs, and herbs were observed in Rhus chinensis (56.7%), Coriaria nepalensis (56.3%), and Eremochloa ciliaris (44.0%), which were characterized by fairly large niche widths of 1.58, 1.32 and 1.57, respectively. The highest niche overlap values were found between R. chinensis and Triadica sebifera in the tree layer, and between Thysanolaena latifolia and Bidens pilosa in the herb layer, with the value of 0.68 and 0.99, respectively. Shrub layer exhibited a lower range of niche overlap (0.30-0.42), suggesting significant niche differentiation among different species. In the tree and shrub layers, most species showed insignificantly negative associations, the proportion was 83.3% and 66.7%, respectively, indicating that the plant community was not stable. Herb layer generally exhibited significantly positive correlations, with 52.4% of species pairs showing positive correlation, indicating weak resource competition among species. Overall, plant community at Sb mining ecological damaged site was unstable. In the process of ecological restoration, trees and shrubs that can adapt to the conditions and have positive associations should be prioritized in species selection, such as R. chinensis, C. lanceolata, C. nepalensis, and B. nivea. This will promote vegetation positive succession, rehabilitate the ecosystem and ensure sustainable development at Sb mining ecological damaged sites.

A Pipeline for the Usage of the Core Data Set of the Medical Informatics Initiative for Process Mining - A Technical Case Report.

INTRODUCTION: Process Mining (PM) has emerged as a transformative tool in healthcare, facilitating the enhancement of process models and predicting potential anomalies. However, the widespread application of PM in healthcare is hindered by the lack of structured event logs and specific data privacy regulations. CONCEPT: This paper introduces a pipeline that converts routine healthcare data into PM-compatible event logs, leveraging the newly available permissions under the Health Data Utilization Act to use healthcare data. IMPLEMENTATION: Our system exploits the Core Data Sets (CDS) provided by Data Integration Centers (DICs). It involves converting routine data into Fast Healthcare Interoperable Resources (FHIR), storing it locally, and subsequently transforming it into standardized PM event logs through FHIR queries applicable on any DIC. This facilitates the extraction of detailed, actionable insights across various healthcare settings without altering existing DIC infrastructures. LESSONS LEARNED: Challenges encountered include handling the variability and quality of data, and overcoming network and computational constraints. Our pipeline demonstrates how PM can be applied even in complex systems like healthcare, by allowing for a standardized yet flexible analysis pipeline which is widely applicable.The successful application emphasize the critical role of tailored event log generation and data querying capabilities in enabling effective PM applications, thus enabling evidence-based improvements in healthcare processes.

A Concept for Mining Transitive Sequential Patterns from Pancreatic Cancer Patient Journeys.

Pancreatic cancer, renowned for its aggressive nature and poor prognosis, necessitates the optimization of treatment strategies. The sequence of procedures in clinical trials is critical, such as evaluating the potential benefits of preoperative chemo-radio-therapy for pancreatic cancer. Nevertheless, we might not be aware of other temporal sequences which have an effect on therapy response or the general outcome. Extracting transitive sequential patterns from patients' medical trajectories allows researchers to identify temporal characteristics for complex diseases. We illustrate how such sequential patterns can be discovered and might be utilized in pancreatic cancer research as well as patient care.

A comprehensive analysis of real options in solar photovoltaic projects: A cluster-based approach.

Solar photovoltaic (PV) projects are pivotal in addressing climate change and fostering a sustainable energy future. However, the complex landscape of renewable energy investments, characterized by high upfront costs, market uncertainties, and evolving technologies, demands innovative evaluation methods. The Real Options Approach has emerged as a powerful tool, offering strategic flexibility in decision-making under uncertainty. This paper comprehensively analyzes the application of real options for evaluating solar photovoltaic projects in 2008-2023. Analysis of document descriptors (author keywords, index keywords, and noun phrases extracted from titles and abstracts) reveals that the dominant research topics in the last ten years (2014-2023) include investment optimization, strategic analysis, energy policy, optimization of energy generation and investments in wind energy. These descriptors are used to analyze the evolution of research interests on a two-year basis and reveal the yearly evolution of the research topics. Finally, the concept of emergence is used to unveil emerging research trends, providing valuable insights for researchers and practitioners in the renewable energy sector. Ultimately, this work contributes to a deeper understanding of how real options analysis empowers decision-makers to make informed choices in advancing clean and sustainable energy solutions.

A reliable method to prepare milligram size environmental samples to quantify metal(loid)s by high-resolution graphite furnace atomic absorption spectrometry.

We searched for an extraction method that would allow a precise quantification of metal(loid)s in milligram-size samples using high-resolution graphite furnace atomic absorption spectrometry (HR-GFAAS). We digested biological (DORM-4, DOLT-5 and TORT-3) and sediment (MESS-4) certified reference materials (CRMs) using nitric acid in a drying oven, aqua regia in a drying oven, or nitric acid in a microwave. In addition, we digested MESS-4 using a mixture of nitric and hydrofluoric acids in a drying oven. We also evaluated the effect of sample size (100 and 200 mg) on the extraction efficiency. Nitric acid extraction in a drying oven yielded the greatest recovery rates for all metal(loid)s in all tested CRMs (80.0 %-100.0 %) compared with the other extraction methods tested (67.3 %-99.2 %). In most cases, the sample size did not have a significant effect on the extraction efficiency. Therefore, we conclude that nitric acid digestion in a drying oven is a reliable extraction method for milligram-size samples to quantify metal(loid)s with HR-GFAAS. This validated method could provide substantial benefits to environmental quality monitoring programs by significantly reducing the time and costs required for sample collection, storage, transport and preparation, as well as the amount of hazardous chemicals used during sample extraction and analysis. •Sample digestion with nitric acid in a drying oven yielded the greatest recovery rates of metal(loid)s from biological and sediment certified reference materials.•The recovery rates of metal(loid)s from biological and sediment certified reference materials using nitric acid digestion in a drying oven ranged from 73 % to 100 %.•Digestion with nitric acid in a drying oven is a simple and reliable method to extract small size environmental samples for metal(loid)s quantification by high-resolution graphite furnace atomic absorption spectrometry.

Uncharted depths: Navigating the energy security potential of deep-sea mining.

In the context of a global shift towards low-carbon energy systems, this paper provides an in-depth analysis of deep-sea mining's (DSM) potential role in enhancing global energy security. Addressing the growing demand for critical minerals essential for clean energy technologies, electric vehicles (EVs), and energy storage systems, the paper examines how DSM can diversify the global mineral supply and reduce reliance on geopolitically sensitive sources. It explores DSM's capacity to recalibrate energy prices, influence the competitive landscape of clean energy technologies, and shift geopolitical dynamics. The paper delves into the multi-faceted impacts of DSM on energy security, including geopolitical shifts, supply chain diversification, and environmental trade-offs. By providing a holistic view that links mineral supply security to sustainable energy transitions, this study extends beyond prior research focused mainly on the technical and environmental aspects of DSM. The findings illustrate DSM's intersection with international politics, its effect on energy pricing strategies, and the balance between resource exploitation and environmental stewardship. Strategic policy recommendations are offered to optimize DSM's benefits while minimizing its ecological impacts, aligning the emerging DSM industry with global sustainability goals. In addition to identifying challenges, the paper proposes actionable solutions, contributing a unique perspective to the discourse on DSM and energy security.