Physics and Chemistry of the Earth最新文献

{"title":"Bridging data scarcity in groundwater quality studies: A systematic evaluation of statistical and deep learning-based generators","authors":"C.D. Aju ,&nbsp;Bhupendra Bahadur Singh ,&nbsp;A.L. Achu ,&nbsp;Madhusudan Ingale ,&nbsp;Mangesh M. Goswami ,&nbsp;M.C. Raicy ,&nbsp;L. Elango","doi":"10.1016/j.pce.2026.104327","DOIUrl":"10.1016/j.pce.2026.104327","url":null,"abstract":"<div><div>Large-scale groundwater quality assessments are often hindered by the limited availability of hydrochemical data. Synthetic data generation provides a means to augment small datasets; however, the reliability of these methods and their implications for predictive modeling remain underexplored in environmental studies, particularly in the context of groundwater sustainability. We systematically evaluated six approaches, including bootstrap sampling, Gaussian noise perturbation, Monte Carlo sampling, SMOGN, CTGAN, and TVAE, using a groundwater quality dataset from southern India. Synthetic datasets were evaluated for their similarity to real data using the Kolmogorov–Smirnov test, the Wasserstein distance, moment differences, Pearson correlation, kernel density estimation plots, and principal component analysis. The practical utility of the synthetic data was evaluated by training a Random Forest model to predict total dissolved solids (TDS) from major ions. The model performance on the real dataset was assessed using R², RMSE, and MAE. Bootstrap delivered near-perfect agreement with the real data (R² = 0.999, NSE = 0.999, RMSE = 41.5 mg L⁻¹), with SMOGN being competitive. Gaussian perturbation was acceptable, while TVAE was moderate. Monte Carlo and CTGAN performed poorly, with negative NSE indicating performance worse than predicting the mean. SHAP-based feature importance analysis confirmed that the best-performing synthetic methods preserved the dominant hydrochemical drivers. Overall, traditional resampling approaches (Bootstrap, SMOGN) outperformed complex deep generative models on small-sample groundwater datasets. This methodology can support risk assessments by improving the accuracy of water-quality predictive models, thereby facilitating effective resource management and pollution control. This study provides practical guidance for assessing and managing groundwater quality by recommending synthetic data augmentation strategies tailored to dataset characteristics, particularly in data-limited regions.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"143 ","pages":"Article 104327"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174283","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":"Generative AI-enabled forecasting and green supply chain sustainability assessment: Evidence from China's palm oil trade with ASEAN","authors":"Yuhan Liu ,&nbsp;Xiangzheng Deng ,&nbsp;Yunxiao Gao","doi":"10.1016/j.pce.2026.104341","DOIUrl":"10.1016/j.pce.2026.104341","url":null,"abstract":"<div><div>Food systems and agri-commodity supply chains face increasing pressures from climate change, trade volatility, and environmental degradation, palm oil positioned at the center of sustainability debates. China, as a major importer, and Indonesia, the leading producer, play crucial roles in influencing the environmental footprint and resilience of cross-border palm-oil trade. This study integrates generative AI-based demand forecasting with spatial sustainability assessment to evaluate the alignment between China's future palm-oil demand and Indonesia's sustainability-compliant supply. Using Time-series Generative Adversarial Networks (TimeGAN), we generate scenario-rich forecasts of China's palm-oil imports through 2030. A province-level Green Supply Chain Sustainability Index (GSCI) for Indonesia, incorporating deforestation intensity, land-use efficiency, zero-deforestation commitments, and RSPO certification, supports traceability-based allocation. Results show that more than 60% of China's projected imports can be met by high-GSCI provinces, indicating strong potential for deforestation-free procurement without undermining supply security. The results demonstrate how AI-enabled forecasting combined with spatial sustainability indicators can inform environmentally responsible sourcing strategies and enhance resilience in cross-border palm-oil supply systems.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"143 ","pages":"Article 104341"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174280","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":"Mapping seawater intrusion for sustainable groundwater management in arid regions: A geophysical approach from Al-Hofuf, Eastern Saudi Arabia","authors":"Kamal Abdelrahman ,&nbsp;Alfahad A. Almakrami ,&nbsp;Saad S. Alarifi","doi":"10.1016/j.pce.2026.104294","DOIUrl":"10.1016/j.pce.2026.104294","url":null,"abstract":"<div><div>Seawater intrusion affects the sustainability of coastal aquifers, particularly in arid regions such as Eastern Saudi Arabia, where groundwater is the primary source of water. This study maps and characterizes seawater intrusion in Al-Hofuf area using Vertical Electrical Sounding (VES) with a Schlumberger array at 20 sites along a coastal–inland transect (AB/2 up to 400 <em>m</em>). Resistivity data were inverted through 1D modeling and statistically validated (R² &gt; 0.97), revealing four geoelectric layers. The upper heterogeneous Quaternary sediments exhibited variable resistivity, while a low-resistivity clay-rich layer (3.4–39 Ω m) acted as an aquitard. The underlying fractured and karstified limestone (12.2–132 Ω m) forms the principal freshwater aquifer. Very low resistivity (&lt;10 Ω m) near the coast (VES-20, VES-16, VES-10) indicated active intrusion, whereas inland points (VES-7, VES-8) showed high resistivity, denoting fresh or dry sands. Spatial analysis revealed an intrusion extending up to 35 km inland, along permeable sands and fractured limestone. Structural geology, particularly north–south trending anticlines and synclines, modulates saline migration, acting as partial flow barriers. Integrating geoelectrical results with structural mapping produced the first high-resolution seawater intrusion model for Al-Hofuf, identifying vulnerable zones and potential sites for Managed Aquifer Recharge (MAR). This cost-effective and scalable approach supports sustainable groundwater management in arid coastal environments, aligning with Sustainable Development Goal 6.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"143 ","pages":"Article 104294"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174276","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":"Remote sensing applications for assessing climate change impacts on deciduous forests—A systematic review","authors":"Sude Gül Yel ,&nbsp;Hasan Burak Özmen ,&nbsp;Siğnem Öney Birol ,&nbsp;Esra Tunç Görmüş ,&nbsp;Gordana Kaplan","doi":"10.1016/j.pce.2026.104321","DOIUrl":"10.1016/j.pce.2026.104321","url":null,"abstract":"<div><div>Deciduous forests play a vital role in global carbon sequestration, hydrological regulation, and biodiversity conservation, yet they are increasingly vulnerable to the adverse effects of climate change. This review synthesizes recent advances in remote sensing applications for assessing climate-induced changes in phenology, productivity, and resilience of deciduous forests. A systematic literature analysis was conducted using Web of Science, incorporating 70 peer-reviewed studies that employed satellite-based datasets such as MODIS, Landsat, and Sentinel-2. The review identifies key remote sensing metrics, including NDVI, EVI, LAI, and SIF, which are used to monitor phenological shifts, drought impacts, and changes in forest productivity. Analytical approaches such as time series analysis, regression models, and machine learning (e.g., RF, LMEM) were evaluated for their effectiveness in modeling forest-climate interactions. Results highlight distinct regional patterns in phenological responses, sensor preferences across climate zones, and a growing reliance on multi-sensor data fusion to enhance monitoring precision. Despite advancements in remote sensing technologies, critical research gaps persist, especially regarding the biological interpretation of spectral indices and the assessment of forest resilience mechanisms at finer spatial scales. This review underscores the need for interdisciplinary approaches and advanced remote sensing frameworks to support future forest conservation and climate adaptation strategies.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"143 ","pages":"Article 104321"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174649","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":"Assessing the impact of microplastic polymer wastes on environmental fate and human health risks","authors":"Maria Joseph Angelaa Lincy ,&nbsp;Senthil Kumar Babu ,&nbsp;Saghya Infant Shofia ,&nbsp;Senthilkumar Nangan ,&nbsp;Balamurugan Paneerselvam","doi":"10.1016/j.pce.2025.104246","DOIUrl":"10.1016/j.pce.2025.104246","url":null,"abstract":"<div><div>As plastics keep getting manufactured and wasted in enormous amounts, microplastic pollution is becoming a serious environmental and human health issue. This review consolidates new developments concerning the sources, distribution, detection, and physiological effects of microplastics. Microplastics are derived from the breakdown of large plastic litter and from direct industrial sources like cosmetics, textiles, and tire tread particles, infiltrating aquatic, atmospheric, and terrestrial environments. Recent research illustrates that microplastics have the ability to pass through biological barriers, accumulate in various human organs, and induce oxidative stress, release of inflammatory cytokines, mitochondrial impairment, and changes in lipid metabolism offering novel evidence for physiological causation of toxicity. Parameters controlling environmental transport are particle size, form, density, and ecological factors like wind patterns, ocean currents, and precipitation. Human exposure is mainly via ingestion, inhalation, and dermal uptake, with recent biomonitoring verifying microplastics in blood, placenta, breast milk, and lung tissues. Analytical advancements such as Fourier Transform Infrared (FTIR), Raman, and pyrolysis–GC/MS methods have enhanced detection sensitivity but continue to pose challenges with respect to contamination control, nanoplastic quantification, and standardization of methods. Recent studies also investigate synergistic toxicity between microplastics and co-contaminants (e.g., heavy metals, persistent organic pollutants) and examine potential transgenerational and microbiome-mediated effects. By synthesizing these novel findings, this review presents an updated understanding of microplastic contamination and its mechanistic connections to human physiology, with the goal of informing future research direction and evidence-based environmental policy.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"142 ","pages":"Article 104246"},"PeriodicalIF":4.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840039","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":"Soil-structure interaction and uplift bearing capacity: A study on low-header CEP single pile foundations","authors":"Muhammad Rizwan ,&nbsp;Sobia Naseem ,&nbsp;Muhammad Akhtar Tarar","doi":"10.1016/j.pce.2025.104255","DOIUrl":"10.1016/j.pce.2025.104255","url":null,"abstract":"<div><div>Low-header Concrete Expanded-Plate (CEP) single pile foundations play a critical role in supporting renewable energy structures, transmission line supports, and seismic-resilient infrastructure. Their unique geometry and interaction with surrounding soil make uplift resistance a key design consideration. Despite growing application, research on their uplift behavior remains fragmented, with studies often focusing on isolated mechanisms, simplified analytical models, or site-specific experiments. This critical and state-of-the-art review synthesizes current knowledge on soil–structure interaction (SSI) and uplift bearing capacity of low-header CEP piles, integrating experimental findings, numerical simulations, and analytical models across diverse soil conditions. The review evaluates key mechanisms governing uplift resistance, including plate–soil interlocking, shaft shear transfer, and nonlinear soil behavior, while highlighting inconsistencies, methodological limitations, and gaps in existing research. Furthermore, it examines field validation studies, centrifuge testing, and modern numerical approaches, identifying opportunities for reliability-based design, machine-learning-assisted prediction, and adaptive monitoring of pile performance. By consolidating dispersed evidence and providing a structured evaluation of SSI effects, this review offers guidance for improved design strategies and outlines directions for future research to enhance the reliability and efficiency of CEP pile foundations under uplift-dominated loading.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"142 ","pages":"Article 104255"},"PeriodicalIF":4.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840033","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":"BAMPP: A novel Bayesian network enhanced by average marginal posterior probabilities to identify critical ground truth meteorological stations for drought monitoring","authors":"Rizwan Niaz ,&nbsp;Saman Munir ,&nbsp;Muhammad Ahmad Raza ,&nbsp;Rifat Tur ,&nbsp;Sadegh Partani ,&nbsp;Ali Danandeh Mehr","doi":"10.1016/j.pce.2025.104215","DOIUrl":"10.1016/j.pce.2025.104215","url":null,"abstract":"<div><div>This study introduces a new approach for identifying critical meteorological stations essential for analyzing spatiotemporal dynamics of drought events at regional scale. We propose a Bayesian network enhanced by Average Marginal Posterior Probabilities (AMPP) to evaluate stations based on frequency, severity, and persistence of Standardized Precipitation Index (SPI) at multiple timescales (SPI-3, SPI-6, and SPI-12). The method is demonstrated across the province of Ankara, Türkiye, effectively capturing the probabilistic relationships and interdependencies governing drought propagation among stations. Our analysis revealed distinct spatiotemporal patterns across the region at all time scales. For short-term droughts (SPI-3), critical station identity varied seasonally, indicating localized dynamics; for instance, Esenboga was key station in February, March, July, October and December, while Beypazari was influential in the other months. In contrast, for medium- and long-term droughts (SPI-6, SPI-12), Beypazari was the most critical station across all months, establishing it as the representative station for long-term drought monitoring in the region. This method provides a robust, probabilistic tool for optimizing drought monitoring networks and enhancing regional water resource management.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"142 ","pages":"Article 104215"},"PeriodicalIF":4.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840099","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":"Integrating Conventional and machine learning approaches with seismic inversion for optimized porosity estimation in heterogeneous siliciclastic rocks","authors":"Muyyassar Hussain ,&nbsp;Urooj Shakir ,&nbsp;Zohaib Naseer ,&nbsp;Muhammad Raiees Amjad ,&nbsp;Farooq Arshad ,&nbsp;Muhammad Kamal ,&nbsp;Ahmed E. Radwan","doi":"10.1016/j.pce.2025.104248","DOIUrl":"10.1016/j.pce.2025.104248","url":null,"abstract":"<div><div>Heterogeneous siliciclastic rocks pose significant challenges in reservoir characterization, where such heterogeneity is often not sufficiently captured by conventional petrophysical techniques alone. In this work, an innovative integrative framework for optimizing porosity estimation in complex depositional conditions is presented, combining sophisticated machine learning (ML) methods with traditional seismic inversion, considering the extreme heterogeneity of the Lower Goru Formation (LGF), Tajjal gas field, Gambat area, Lower Indus Basin (LIB), Pakistan. Specifically, a probabilistic neural network (PNN) is employed as a sophisticated deep-learning technique and coupled with well logs and 3D seismic datasets to develop an optimized predictive model for porosity estimation within the C-Interval of the LGF. A three-dimensional variation of porosity, calculated using a seismic inverted PNN map, ranges from -10 % to 13%. The petrophysical analysis began with a conventional approach based on empirical formulas, and the computed properties were then calibrated and cross-matched with log analysis performed using machine learning algorithms. Along with a limited 3D seismic dataset, three wells were used for petrophysical evaluation with the conventional technique, whereas in the supervised ML approach, two wells were used for training while one was kept as a test well. Among the models applied, the Extra Trees Regressor (ETR) provided low Vshl values and high effective porosity (PHIE), achieving over 99% R² with the lowest Mean Squared Error (MSE) score. When compared to conventional interpretation at the test well, the Random Forest Regressor (RFR) provided water saturation (Sw) estimates with approximately 100% accuracy, demonstrating a relatively lower deviation than traditional petrophysical methods. Thus, the porosity values obtained from the integrated approach show strong agreement, reinforcing confidence in their reliability for future exploration endeavors in heterogeneous siliciclastic formations. Consequently, the results of this research can be applied to similar geological settings, particularly those characterized by alternating shale–sandstone layers enriched with hydrocarbons and exhibiting thickness variations with lateral extents.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"142 ","pages":"Article 104248"},"PeriodicalIF":4.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840102","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":"Agro-climatic classification of Türkiye's Agricultural Basins using long-term ERA5-Land temperature data","authors":"Mehmet Ali Çelik","doi":"10.1016/j.pce.2026.104307","DOIUrl":"10.1016/j.pce.2026.104307","url":null,"abstract":"<div><div>The 30 main agricultural basins in Türkiye are categorized in this study using long-term mean, maximum, and minimum temperature indicators that were obtained from ERA5-Land data. Four thermally coherent clusters representing different agro-climatic regimes were formed from the basins using Principal Component Analysis (PCA) and K-means clustering. Mild and steady temperatures in Cluster 1 (coastal and lowland basins) are ideal for intensive, high-value crop production. Wide diurnal ranges and low minimum temperatures are characteristics of Cluster 2 (high-altitude and continental basins), which represents cold and frost-prone agricultural zones. The highest mean and maximum temperatures are found in Cluster 3 (the basins of the Mediterranean, Aegean, and Southeastern Anatolia), which denotes hot, dry climates with significant risks of heat and water stress. Cluster 4, which has mixed agricultural potential and moderate thermal variability, serves as a transitional zone between continental and maritime climates. Temperature trends across all agricultural basins during the 1950–2023 period were positive, with some basins (Coastal Aegean, Coastal Mediterranean, Gediz and GAP basins) experiencing an average temperature increase rate of 2–7 °C per century. Analyses reveal that minimum temperatures are increasing faster than maximum temperatures, and that nocturnal warming is a dominant climate signal across Türkiye. Strong inter-station coherence in all temperature variables was confirmed by correlation analysis, indicating that regional-scale atmospheric dynamics shape Türkiye's temperature patterns more so than local microclimatic effects. These thermal clusters offer a spatially explicit framework for adaptive agricultural planning from an agro-climatological point of view, providing information on heat stress vulnerability, evapotranspiration demand, and frost risk. The findings demonstrate how temperature-based clustering can be combined with other policy tools like crop suitability mapping, irrigation control, and agro-climatic zoning. This study contributes to the development of climate-adapted and sustainable agricultural strategies in Türkiye by identifying agricultural regions with similar temperatures.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"142 ","pages":"Article 104307"},"PeriodicalIF":4.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023083","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":"Numerical simulation of CO2 migration and mineralization reaction characteristics in goaf","authors":"Zheng Dai ,&nbsp;Shugang Li ,&nbsp;Biao Hu ,&nbsp;Xiangguo Kong ,&nbsp;Jingfei Zhang ,&nbsp;Bing Zhu ,&nbsp;Qian Wei","doi":"10.1016/j.pce.2025.104220","DOIUrl":"10.1016/j.pce.2025.104220","url":null,"abstract":"<div><div>Coal mine goafs, as a special type of saline aquifer characterized by multi-fracture structures, remain insufficiently studied as potential CO₂ storage sites. Therefore, this study integrates the fracture evolution of mining overlying strata with the TOUGHREACT numerical simulation to construct a two-dimensional idealized goaf model. A 50-day isothermal CO₂ injection simulation was conducted to systematically assess pressure evolution, multiphase CO₂ migration, and the acidification–mineral dissolution–precipitation processes driven by the fracture network, and to reveal the key differences in sequestration mechanisms between the goaf and a continuous geological body (CGB), clarifying the mineral evolution pathways. The results show that the fracture network significantly enhances CO₂-brine contact efficiency, forming a dissolved CO₂ distribution that envelops the surrounding minerals, and leading a triangular pressure field inside the goaf. In strongly acidic zones, primary minerals such as calcite and k-feldspar undergo rapid dissolution, releasing Ca²⁺, Mg²⁺, and Al³⁺, which promote the formation of secondary minerals such as smectite and quartz. At the same spatial and temporal scale, the CO₂ dissolution storage capacity of the goaf exceeds that of the CGB by 3261 kg, demonstrating superior sequestration performance. These findings provide theoretical support for the engineering deployment of CO₂ sequestration in such unconventional reservoirs.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"142 ","pages":"Article 104220"},"PeriodicalIF":4.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685426","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}