Shakeel Ahmed TalpurMuhammad Yousuf Jat BalochChunli SuJaved Iqbal...
998-1009页
查看更多>>摘要：Synthesized iron oxyhydroxide was applied for the adsorptive removal of As(Ⅴ) and As(Ⅲ) from the aquas media. Additionally, this investigation highlighted the synergistic effect of calci-um carbonate in conjunction with iron oxyhydroxide, resulting in enhanced removal efficiency. The ex-periment was conducted under various conditions: concentration, dosage, pH, agitation, and tempera-ture. Material characterizations such as Brunauer Emmett Teller, X-ray diffraction, scanning electron microscopy, and Fourier transform infrared spectroscopy were implied to understand adsorption mechanisms. The Langmuir model revealed optimal concentrations for As(Ⅴ) = 500 μg/L at pH-5 and As(Ⅲ) = 200 μg/L at pH-7, resulting in 95％ and 93％ adsorption efficiencies, respectively. Maximum adsorption capacities "qm" were found to be 1266.943 μg/g for As(Ⅴ) and 1080.241 μg/g for As(Ⅲ). Freundlich model demonstrated favorable adsorption by indicating "n > 1" such as As(Ⅴ) = 2.542 and As(Ⅲ) = 2.707; similarly, the speciation factor "RL < 1" for both species as As(Ⅴ) = 0.1 and As(Ⅲ) =0.5, respectively. The kinetic study presented a pseudo-second-order model as best fitted, indicating throughout chemisorption processes for removing As(Ⅴ) and As(Ⅲ). Furthermore, incorporating calci-um carbonate presented a significant leap in the removal efficiency, indicating As(Ⅴ) from 95％ to 98％and As(Ⅲ) from 93％ to 96％, respectively. Our findings offer profound motivation for developing ef-fective and sustainable solutions to tackle arsenic contamination, underscoring the exceptional promise of iron oxyhydroxide in conjunction with calcium carbonate to achieve maximum removal efficiency.
查看更多>>摘要：Luan River is the main water source in Beijing-Tianjin-Hebei region, northern China, where the groundwater system is vulnerable and pollution issue is serious. It is significant for regional groundwater resources protection to identify the hydrogeochemistry evolution and affecting factors along flow direction occurred in the upper reaches, especially the surface water-groundwater (SW-GW) conversion relationship. In this study, recharge, conversion and geochemistry evolution of SW and GW were elucidated based on physical-hydrochemical indicators and stable isotopes in 36 GW samples and 20 SW samples, which were collected in July 2019 and July 2020. The factor analysis was further utilized to determine the main factors responsible for regional hydrogeochemical evolution. Re-sults indicate that GW recharged SW in plateau area, and SW and GW recharged each other in typical Alpine valley area. The hydrochemical types are HCO3-Ca·Mg and HCO3-Ca, and the hydrochemical evolution is dominated by weathering of silicate and carbonate minerals. The cation exchange adsorp-tion has minor impact on groundwater hydrochemistry. The rise of SO42- and NO3- contents in ground-water is related to industrial and agricultural activities. The main controlling factors of SW hydro-chemical components included recharge from groundwater, industrial and mining activities, explaining 90.04％ of data variance. However, water-rock interaction, agricultural and domestic sewage are re-sponsible for GW quality, accounting for 83.38％.
查看更多>>摘要：A knowledge graph (KG) is a knowledge base that integrates and represents data based on a graph-structured data model or topology. Geoscientists have made efforts to construct geoscience-related KGs to overcome semantic heterogeneity and facilitate knowledge representation, data integra-tion, and text analysis. However, there is currently no comprehensive paleontology KG or data-driven discovery based on it. In this study, we constructed a two-layer model to represent the ordinal hierar-chical structure of the paleontology KG following a top-down construction process. An ontology con-taining 19365 concepts has been defined up to 2023. On this basis, we derived the synonymy list based on the paleontology KG and designed corresponding online functions in the OneStratigraphy database to showcase the use of the KG in paleontological research.
查看更多>>摘要：Geological reports are a significant accomplishment for geologists involved in geological investigations and scientific research as they contain rich data and textual information. With the rapid development of science and technology, a large number of textual reports have accumulated in the field of geology. However, many non-hot topics and non-English speaking regions are neglected in main-stream geoscience databases for geological information mining, making it more challenging for some re-searchers to extract necessary information from these texts. Natural Language Processing (NLP) has obvious advantages in processing large amounts of textual data. The objective of this paper is to identi-fy geological named entities from Chinese geological texts using NLP techniques. We propose the Ro-BERTa-Prompt-Tuning-NER method, which leverages the concept of Prompt Learning and requires only a small amount of annotated data to train superior models for recognizing geological named enti-ties in low-resource dataset configurations. The RoBERTa layer captures context-based information and longer-distance dependencies through dynamic word vectors. Finally, we conducted experiments on the constructed Geological Named Entity Recognition (GNER) dataset. Our experimental results show that the proposed model achieves the highest F1 score of 80.64％ among the four baseline algo-rithms, demonstrating the reliability and robustness of using the model for Named Entity Recognition of geological texts.
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