Physics and Chemistry of the Earth最新文献

{"title":"Quantitative inversion modeling of surface gold abundance based on remote sensing imagery and geochemical Data: An example from Tasiast-Tijirit gold district, Mauritania","authors":"Gong Cheng ,&nbsp;Asad Atta ,&nbsp;Xiaoqing Deng ,&nbsp;Aqil Tariq ,&nbsp;Syed Hussain ,&nbsp;Lingyi Liao ,&nbsp;Mohamed Faisal ,&nbsp;Changliang Gao","doi":"10.1016/j.pce.2025.103991","DOIUrl":"10.1016/j.pce.2025.103991","url":null,"abstract":"<div><div>The Tasiast-Tijirit Terrane in northwestern Mauritania is an important gold mining district that mainly consists of igneous and metamorphic units that are thought to represent the remnants of older greenstone belts. Surface outcrops typically contain a high concentration of economically valuable elements. This study focuses on the quantitative inversion of auriferous soil and rock samples based on remote sensing data, highlighting the significance of using surface geochemical samples to delineate anomaly areas of gold mineralization in desert regions for effective mineral exploration programs. The backpropagation neural network inversion model was used in this work to quantitatively invert the soil and rock samples with spectral band reflectance of Landsat-7 ETM+ and GF-2 satellite imagery at 1:50000 and 1:5000 scale, respectively. Landsat-7 ETM+ was chosen because its spectral bands are almost identical to the GF-2 remote sensing data, allowing for a reasonable correlation between the datasets. Results indicate that the established model achieved R² values of modeling and test sets are 0.65 and 0.63, 0.52 and 0.49 with RMSE values of 0.009 and 0.014, 0.034 and 0.055 for soil and rock samples, respectively, using Landsat-7 ETM+. Similarly, GF-2 imagery R² values of modeling and test sets are 0.73 and 0.69, 0.60 and 0.57, with RMSE values of 0.005 and 0.004, 0.015 and 0.023 for soil and rock samples, respectively. The inversion modeling and predicted anomaly areas are well aligned with the geochemical exploration map and actual mining area. The findings suggest that although Landsat-7 imagery provides an overall distribution of surface gold elements, it is restricted in its ability to delineate high gold-rich zones in desert regions due to relatively coarse resolution besides the geological and environmental conditions such as wind erosion and weathering effects. Conversely, GF-2 imagery enabled precise delineation of the anomaly locations with rock samples, proving to be more effective owing to its higher resolution scale of 1:5000. Overall, the adopted innovative methodology that implements high-resolution satellite data with the bakpropagation neural network model promise to be very effective in enhacing minerals prediction accuracy and lowering the exploration costs.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 103991"},"PeriodicalIF":3.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in human health risk, detection and bioremediation of bacterial contaminants in water: A review","authors":"Venkatesh Anand Iyer ,&nbsp;Praveen Dahiya ,&nbsp;Dharmender Kumar","doi":"10.1016/j.pce.2025.103990","DOIUrl":"10.1016/j.pce.2025.103990","url":null,"abstract":"<div><div>Worldwide, the bacterial pollution in drinking water constitutes a major concern to human health. Bacterial infections by <em>Escherichia coli, Salmonella</em> spp.<em>, Campylobacter</em> spp., and <em>Legionella pneumophila</em>, can cause serious diseases, and their propensity to multiply swiftly in aquatic environments amplifies the risk. The vulnerable populations, including children and the elderly, are particularly prone to waterborne illnesses. Bacteria having pathogenic potential reproduce rapidly and this will increase risk of human health. In addition to this, many bacterial pathogens produce that have negative health effects resulting in severe illness, organ damage, and even lead to the death of a human being. The advances in detection and disinfection technologies, including quantitative microbial risk assessment (QMRA), metagenomics, and molecular diagnostic approaches, have boosted pathogen surveillance. Control techniques, such as membrane filtration, advanced oxidation processes, and bioremediation, offer viable options. This review addresses the entry and survival processes of bacterial pathogens in water, related health risks, and new technological breakthroughs in microbial abatement. Through microbial bioremediation technology, this study delives a comprehensive understanding of bacterial contamination in water and offers useful insights for policymakers, water management authorities, and public health specialists. Therefore, the development of a rapid detection and control strategy for water contaminants might lead to the necessity of coordinated measures to protect water quality for public health concerns.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 103990"},"PeriodicalIF":3.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adsorptive removal of arsenic from water/wastewater using nano-sized metal oxides: A review","authors":"Hemant Kumar Joshi ,&nbsp;Naveen Chandra Joshi","doi":"10.1016/j.pce.2025.103992","DOIUrl":"10.1016/j.pce.2025.103992","url":null,"abstract":"<div><div>The discharge of arsenic metal contamination onto ecological settlements is a significant factor contributing to contemporary environmental concerns. The requirement to prioritize water security has emerged as a relatively recent societal concern. Nano sized oxides of metals like Fe, Al, Ti, Zn, etc. have garnered significant attention and research as promising adsorbents for efficient arsenic removal from wastewater because to their effective surface active sites, abundant availability, porous architectures, wide surface area, cost-effectiveness, environmental friendliness, and chemical stability. This article examines the recent advancements made in the field of eliminating arsenic from wastewater by the utilization of Nano sized metal oxides and their derivatives, with a critical perspective. The comparative study shows that mesoporous aluminium magnesium oxide was found to be the best adsorbent for arsenite As(III) and arsenate As(V) with adsorption capacity of 813 mg/g at pH 7 and 912 mg/g at pH 3, respectively, that has been synthesized and used for arsenic removal to date. This article also provides descriptions of adsorption mechanism, behaviour and regeneration. The enhancement of adsorption efficacy is emphasized by focusing on future prospects and technological challenges. The assessment encompassed the commercial feasibility of real-time applications, as well as their potential utilization on a large-scale industrial level, in addition to the projected outlook for these applications.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 103992"},"PeriodicalIF":3.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Water reservoirs quality management using meta-heuristic Algorithms: Analysis and optimization of water quality considering uncertainties","authors":"Seyedeh Zahra Hassani ,&nbsp;Parisa-Sadat Ashofteh ,&nbsp;Seyed Rohollah Hoseini Vaez","doi":"10.1016/j.pce.2025.103987","DOIUrl":"10.1016/j.pce.2025.103987","url":null,"abstract":"<div><div>Managing reservoir water quality under uncertainty remains a critical challenge in contemporary water resource management. This study introduces a robust simulation–optimization meta-model framework to enhance reservoir outflow quality, focusing on minimizing Total Dissolved Solids (TDS) concentrations. To circumvent the computational limitations of high-fidelity simulators, a Supervised Learning (SL) surrogate model was developed as a substitute for the CE-QUAL-W2 simulator. Achieving a prediction accuracy of 85 %, the SL model effectively captures complex, nonlinear interactions within water quality dynamics. Two hybrid metaheuristic frameworks—Particle Swarm Optimization integrated with SL (PSO-SL) and Enhanced Vibrating Particle System integrated with SL (EVPS-SL)—were implemented to optimize reservoir outflows under uncertainty. Both approaches successfully balanced the competing objectives of meeting downstream water demand and minimizing TDS concentrations, while significantly reducing computational costs and improving convergence behavior. The rigorously calibrated CE-QUAL-W2 model demonstrated high validation scores (<em>NSE</em> = 0.99 for storage volume and 1.00 for water level; PBIAS = −0.05 % and −0.0004 %, respectively), confirming its reliability for surrogate training. Additionally, the study examined uncertainty propagation using two widely adopted sampling techniques: Monte Carlo Simulation and Latin Hypercube Sampling (LHS). Optimization outcomes were assessed using performance metrics—reliability, vulnerability, and resilience. The PSO-SL model, coupled with Monte Carlo sampling, exhibited the most balanced performance, achieving 41 % reliability and 26 % vulnerability. In contrast, EVPS-SL with LHS demonstrated faster convergence (30 % reduction in computational time) but yielded lower reliability (16 %) and higher vulnerability (87 %). This research not only advances reservoir water quality management under uncertainty but also contributes methodologically to the integration of data-driven surrogates and optimization within environmental systems modeling.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 103987"},"PeriodicalIF":3.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated geophysical evaluation of potential gold mineralisation within Nigerian Yashi Sheet 56 (1:100, 000)","authors":"Fahad Abubakar ,&nbsp;Babangida Mohammed Ahmed ,&nbsp;Ibrahim Adeiza Rabiu ,&nbsp;Joseph Omeiza Alao ,&nbsp;Ismail Ahmad Abir ,&nbsp;Aliyu Ohiani Umaru ,&nbsp;Fatihu Kabir Sadiq ,&nbsp;Ahmed Ibrahim Isiaka ,&nbsp;Lukman Adesina Olayinka ,&nbsp;Jose Adoze Usman ,&nbsp;Gomina David Ochu ,&nbsp;Danga Onimisi Abdulmalik","doi":"10.1016/j.pce.2025.103986","DOIUrl":"10.1016/j.pce.2025.103986","url":null,"abstract":"<div><div>This research aims to identify prospective gold mineralisation zones in northern Nigeria, specifically within the Nigerian Yashi Sheet 56 (1:100,000), where artisanal mining activities are increasingly prevalent. The study integrates hydrothermal alteration analysis and structural mapping using high-resolution airborne gamma-ray spectrometry and magnetic datasets. Advanced enhancement techniques, including K/eTh ratio mapping, ternary imaging, and radioelemental distribution, were employed to assess hydrothermal alterations and improve interpretative accuracy. Structural delineation of mineralized zones was achieved through analytic signal processing and Center for Exploration Targeting (CET) Grid analysis. Results indicate that high K/eTh ratio values (0.097–0.118 %/ppm) correlate strongly with hydrothermal alteration zones and known mining sites. The highest amplitude peaks (0.058–0.140 nT/m) and high lineament density zones are identified as potential mineralisation targets. The predominant structural trends are E-W, ENE-WSW, and NE-SW, with mineralisation zones aligning more closely with the NE-SW trend. However, two of the six documented mining sites were not captured by the analytic signal and CET Grid analysis, likely due to intense potassic hydrothermal alteration, as suggested by the K/eTh ratio. A strong correlation among the datasets confirms the effectiveness of this integrated approach in delineating potential gold mineralisation zones, particularly in the southeastern part of the study area, where mining activities are concentrated. Areas characterized by high K/eTh ratios, elevated magnetic amplitudes, and dense lineament distributions are considered prime exploration targets. However, more priority is given to high K/eTh ratio due to the nature of mineralisation in the area. The findings boost the gold exploration strategy.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 103986"},"PeriodicalIF":3.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physicochemical characterisations and basic geotechnical properties of untreated and treated marine stabilised soil with Anadara granosa powder and nano-SiO2 powder","authors":"Ahmad Azizi Harun ,&nbsp;Masyitah Md Nujid ,&nbsp;Muhammad Mukhlisin","doi":"10.1016/j.pce.2025.103973","DOIUrl":"10.1016/j.pce.2025.103973","url":null,"abstract":"<div><div>Marine soil in the Penang State of Malaysia was characterised with respect to its engineering properties as a subgrade pavement layer in road constructions and coastal construction projects. The marine soil samples were collected from the Simpang Ampat region of Penang and underwent comprehensive physicochemical, mineralogical, and basic geotechnical analyses, potentially using them as subgrade pavement in road construction. The study explored the impact of incorporating <em>Anadara granosa</em> or better known as cockle shell powder and nano-SiO₂ or nano-silica powder into marine soil, focusing on its physicochemical and geotechnical properties. It involved a series of laboratory experiments to examine the grain size distribution, pH, and ignition loss of marine soil and determine its physical, chemical, and microstructure characteristics. The findings from ANOVA analysis demonstrated that incorporating cockle shell powder and nano-silica powder significantly affected the basic properties of marine soils, such as clay content, dry density, void ratio, and porosity. This study improved the understanding of the physicochemical properties of both untreated and treated marine soils, laying the groundwork for future research aimed at developing engineering solutions and mitigation strategies for coastal construction. Understanding the unique characteristics of marine soils is essential for building sustainable coastal areas and adapting infrastructure to changing environmental conditions.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 103973"},"PeriodicalIF":3.0,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intercomparison of machine learning models for estimating leaf area index of rice using UAV-based multispectral imagery","authors":"Sumit Kumar Vishwakarma,&nbsp;Benu Bhattarai,&nbsp;Kritika Kothari,&nbsp;Ashish Pandey","doi":"10.1016/j.pce.2025.103977","DOIUrl":"10.1016/j.pce.2025.103977","url":null,"abstract":"<div><div>Leaf Area Index (LAI) serves as a crucial biophysical indicator, providing valuable insights into canopy vigor and water use. Accurate LAI estimation is essential for crop monitoring, and crop yield prediction. The present study assessed the efficacy of different machine learning (ML) algorithms in estimating LAI obtained from field experiment conducted in Roorkee, India, where rice was grown under two irrigation techniques, and three nitrogen levels. LAI was measured using a ceptometer and images were captured from an Unmanned Aerial Vehicle (UAV)-borne multispectral sensor. Nine ML models were built using 20 vegetation indices, which included Multiple Linear Regression (MLR), Ridge Regression, Lasso Regression, Elastic Net Regression, Extreme Gradient Boosting Regression (XGBoosting), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) and Random Forest (RF). Among the vegetation indices used in the study, ENDVI and EG showed the highest positive correlation (r = 0.68) with LAI values. Other vegetation indices such as NDVI, ARVI, OSAVI, and NDI also had a positive correlation (r ≥ 0.60) with LAI values. During model testing, lower R² values were recorded for MLR (0.74), Ridge (0.75), Lasso (0.78), ElasticNet (0.74), and XGBoosting (0.77) models, while KNN (0.82), SVM (0.84), ANN (0.83), and RF (0.80) models performed better. Overall, the SVM outperformed other ML algorithms in predicting the LAI of rice under different treatments. Our study demonstrated that UAV-based multispectral images coupled with ML algorithms are capable of producing LAI of rice with reasonable accuracy.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 103977"},"PeriodicalIF":3.0,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multicriteria evaluation of cropland suitability using multisource datasets of satellite remote sensing and ground observation","authors":"Danish Raza ,&nbsp;Hong Shu ,&nbsp;Sahar Mirza ,&nbsp;Hasnat Aslam ,&nbsp;Aqil Tariq ,&nbsp;Rana Waqar Aslam ,&nbsp;Hafsa Aeman ,&nbsp;Muhsan Ehsan ,&nbsp;Maryam Muhammad Ali","doi":"10.1016/j.pce.2025.103978","DOIUrl":"10.1016/j.pce.2025.103978","url":null,"abstract":"<div><div>The potential of agriculture land monitoring serves as the lifeblood of communities, nourishing populations and fostering economic growth on a global scale. Towards the advancement of the computational approach, this study employed an analytical hierarchal process modeling for agriculture land suitability assessment by integrating a comprehensive array of 4 major criteria with a decision matrix, including 19 influencing parameters. This research incorporates the field data, including soil chemical and physical properties, irrigation water accessibility and irrigation water quality parameters, which are analyzed with cutting-edge remote sensing data layers and climatic variables using an integrated modelling approach. Thorough field observations and integrated methodology improved the conventional practices by considering the close interactions between soil, irrigation water, cropland and topography. The most innovative aspect of this research is based upon the seamless fusion of data layers of different datasets, which improves agriculture's suitability. Pairwise comparisons are systematically conducted to assign weights to each parameter, ensuring a robust decision-support framework with weighted overlay. The finding showed that the 72209.03 acres (9.13 %) cropland is highly suitable, whereas the 717738.48 acres (90.77 %) area is suitable, and the 788.49 acres (0.1 %) area is less suitable for crop cultivation. The study emphasizes the significance of each parameter in influencing suitability, contributing valuable insights into sustainable land management practices. The findings provide meaningful information for policymakers, land use planners and agriculture stakeholders interested in optimizing land management strategies to ensure sustainable agriculture development.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 103978"},"PeriodicalIF":3.0,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering sediment pathways: Novel integrated approaches for sediment source identification and vulnerability prediction by machine learning models in major dam catchments in Chota Nagpur plateau, India","authors":"Sk Asraful Alam,&nbsp;Ramkrishna Maiti","doi":"10.1016/j.pce.2025.103974","DOIUrl":"10.1016/j.pce.2025.103974","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The declining storage capacity of major dam reservoirs in the Chota Nagpur Plateau is primarily attributed to excessive sedimentation from upper catchments. However, identifying sediment source zones and understanding sediment connectivity remains a challenge. This study introduces an integrated ‘RUSLE–IC–SDR-SWAT-SEH-ML’ framework to assess reservoir sedimentation by combining soil erosion hotspots (SEH) with sediment connectivity pathways. The methodology was applied to the Maithon, Panchet, and Tenughat dam catchments to evaluate sediment yield (SY) variations. The results indicate an increasing trend in severe soil erosion (SE) across all catchments, with Maithon exhibiting an increase from 0.74 % to 1.36 % (Δ0.31, R&lt;sup&gt;2&lt;/sup&gt; = 0.65), Panchet from 1.78 % to 3.58 % (Δ0.285, R&lt;sup&gt;2&lt;/sup&gt; = 0.52), and Tenughat from 0.92 % to 1.49 % (Δ0.7, R&lt;sup&gt;2&lt;/sup&gt; = 0.69). The SWAT model estimated mean SY at 9.146 &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;h&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; (Tenughat), 5.871 &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;h&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; (Panchet), and 7.662 &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;h&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; (Maithon). Machine learning analysis showed SVM as the best performer for Maithon and Tenughat, while RF was superior for Panchet (R&lt;sup&gt;2&lt;/sup&gt; = 0.998, 0.994) in predicting SY vulnerability. The extent of well-connected areas increased from 14.45 km&lt;sup&gt;2&lt;/sup&gt; to 17.69 sq.km in Maithon, 21.22 sq.km to 26.64 sq.km in Panchet, and 18.77 sq.km to 22.96 sq.km in Tenughat, indicating an intensifying risk of sediment input into the reservoirs. The Mantel test confirmed that key variables explained 90–97 % of SY variance across the catchments (p &lt; 0.001). ANOVA results showed a statistically significant difference (p &lt; 0.001) within the catchments, while the LSD post-hoc test revealed significant differences (p &lt; 0.05) between Maithon and Panchet, as well as Panchet and Tenughat. The observed differences between the Panchet and Tenughat dams (p &lt; 0.001) are attributed to the regulation of water and sediment flow in the Panchet Dam's upper catchment through the Tenughat Dam. Additionally, the Panchet Dam catchment exhibited the highest sediment yield due to extensive mining activities, leading to significant statistical differences compared to Maithon. This study underscores the importance of integrating sediment connectivity analysis and machine learning models for effective reservoir sediment management. The findings provide","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 103974"},"PeriodicalIF":3.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term simulation of process and hydrogeochemistry of gas field produced water reinjection into a limestone reservoir","authors":"Bo Liu ,&nbsp;Weifeng Jing ,&nbsp;Daixi Long ,&nbsp;Jiahong Li ,&nbsp;Dan Ding ,&nbsp;Kunpeng Cai","doi":"10.1016/j.pce.2025.103976","DOIUrl":"10.1016/j.pce.2025.103976","url":null,"abstract":"<div><div>Large water volumes have been injected to enhance oil and gas recovery. However, the generated oil or gas-produced water (GPW) may contain undesirable and harmful substances. Reinjection of GPW into suitable subsurface formations is considered an effective disposal method. In this study, a numerical model of vertical-radial two dimensional well flow was developed to explore the long-term process and hydrogeochemistry of GPW reinjection into a limestone formation, which is considered as a homogeneous equal thickness reservoir. The obtained results indicated that there was an increase in the reservoir pressure at the reinjection well from 150 to 251.4 Bar at a reinjection rate of 300 m³/day. The pressure propagation range extended up to approiximately 3300 m during the injection. Calcite volume caused a maximum volume fraction change of −0.91, corresponding to a dissolution rate of 0.083/year, which increased the reservoir porosity to over 0.9. Reservoir pH values were altered within 90 m of the reinjection well as a result of water-rock interaction. Additionally, concentrations of Ca²⁺, Mg²⁺, Na⁺, Cl⁻, HCO₃⁻, and SO₄²⁻ in the reservoir were affected within 2000–3000 m from the reinjection well due to injection pressure, water-rock interactions, and diffusion. This study provides insights into assessing the environmental behaviors of GPW in reservoirs, and ensuring safe, and effective long-term GPW reinjection; though accuracy and reliability of the model requires further validation using practical monitoring data in the future.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"139 ","pages":"Article 103976"},"PeriodicalIF":3.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}