Physics and Chemistry of the Earth最新文献

{"title":"Tree species influence on heavy metals content in degraded mining soils: Environmental impact and remediation strategies","authors":"Pankaj Maurya ,&nbsp;Reginald Ebhin Masto ,&nbsp;Hridesh Agarwalla ,&nbsp;Dane Lamb ,&nbsp;Jorge Paz-Ferreiro","doi":"10.1016/j.pce.2025.104057","DOIUrl":"10.1016/j.pce.2025.104057","url":null,"abstract":"<div><div>Restoring environmental and ecological health in post-mining landscapes requires effective mitigation of heavy metals (HMs) and potentially toxic elements (PTEs). Examining the impact of topography and vegetation, particularly trees, on the spatial distribution of PTEs, can help develop site-specific reclamation strategies aimed at minimizing their toxicity. This research study investigated the impact of topographical locations and the nature of tree species (<em>Leucaena leucocephala, Senna siamea and Azadirachta indica</em>) on the total and bioavailable concentrations of PTEs in soils from a reclaimed overburden dump in the Jharia coalfield region, India. Findings revealed elevated levels of Cr (315 ± 27.90 mg/kg), Cd (1.62 ± 0.13 mg/kg), and Zn (140 ± 32.20 mg/kg) across the mine soil, suggesting potential environmental risks. This was confirmed by a higher geo-accumulation index value (<em>I</em>_<em>geo</em>) and contamination factor value (Cf). Despite these elevated total concentrations, the bioavailable fractions remained relatively low, with Cd ranging from 2.92 to 11.46 %, Cr from 0.10 to 0.22 %, and Zn from 5.15 to 22.4 %. The overall pollution load index (PLI) was significantly affected by the tree species but not by the topography. Specifically, <em>L. Leucocephala</em> (0.955) exhibited a low PLI, followed by <em>S. siamea</em> (1.014) and <em>A. indica</em> (1.074). These findings reveal the influential role of tree species in shaping the total concentration and bioavailability of PTEs in soils impacted by mining activities. To control the off-site movement of PTEs from overburden heaps, peripheral trenches may be established. These trenches can be selectively vegetated with <em>L. leucocephala</em> for phytoremediation due to its tolerance to harsh conditions.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"141 ","pages":"Article 104057"},"PeriodicalIF":4.1,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889928","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":"Comprehensive investigation of the smouldering characteristics of hemic peat under varying moisture conditions and the combined influence of wind velocity and direction","authors":"Nor Azizah Che Azmi ,&nbsp;Ahmad Safuan A Rashid ,&nbsp;Nazirah Mohd Apandi ,&nbsp;Abd Wahid Rasib ,&nbsp;Mohd Nazri Mohd Nasir ,&nbsp;Adnan Zainorabidin ,&nbsp;Mohamad Sofi Mohamed ,&nbsp;Muhammad Amir Mat Shah ,&nbsp;Diana Che Lat ,&nbsp;Afiqah Ismail","doi":"10.1016/j.pce.2025.104054","DOIUrl":"10.1016/j.pce.2025.104054","url":null,"abstract":"<div><div>Frequent peat fires in tropical peatlands release substantial carbon emissions and cause severe ecological damage, which contributes to climate change, biodiversity loss, and ecosystem disruption. Peat smouldering is a slow, low-temperature combustion process significantly influenced by moisture content. This study investigates the combined effects of moisture levels and wind conditions on smouldering dynamics in peat. Experiments were conducted using a controlled combustion box with peat specimens containing peat soil and varying moisture contents, 5 %, 50 %, 100 %, 150 % and 200 %. Results showed that low moisture content (5 %) produced a maximum temperature of 620 °C, while high moisture (200 %) limited combustion to just 38 °C. The rate of peat fire propagation was assessed by estimating the time interval at which the combustion temperature attained the smouldering point of 250 °C. The spread rates became progressively more susceptible to wind influence while decreasing with higher moisture content and greater bulk density. Overall, the smouldering spread rate increased with wind speed but decreased with higher moisture content and deeper burning layers. These findings provide valuable insights into the behaviour of peat combustion under varying environmental conditions and support the development of more effective fire prevention and mitigation strategies in tropical peatlands, especially in Malaysia.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104054"},"PeriodicalIF":4.1,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144826455","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":"Spatiotemporal dynamics of air pollutants and human health burden in Bihar, India: A multi-source assessment for effective air quality management","authors":"Ram Pravesh Kumar ,&nbsp;Arti Choudhary","doi":"10.1016/j.pce.2025.104047","DOIUrl":"10.1016/j.pce.2025.104047","url":null,"abstract":"<div><div>This study presents an integrated assessment of ambient air pollution, meteorological influences, and health risk implications in Bihar, India, from 2021 to 2024 using ground monitoring, geospatial modeling, and machine learning tools. The key pollutants analyzed include PM₂.₅, NO₂, and O₃, together with AOD data obtained from MODIS. The findings reveal alarming air quality deterioration, particularly during winter and post-monsoon seasons, driven by biomass burning, vehicular emissions, and meteorological stagnation. PM₂.₅ consistently emerged as the most hazardous pollutant, with annual average concentrations exceeding WHO guidelines throughout all four years. The highest annual mean was observed in 2022 (91.3 μg/m³), corresponding to the highest average AQI (162) and PM₂.₅ sub-index reaching at 165. Health burden assessments indicated that the Attributable Fraction (AF) due to PM₂.₅ exposure varied between 5.43 % in 2024 and 9.62 % in 2022, corresponding to an estimated 39553 and 70106 Premature Deaths (PD), respectively. Spatial analysis identified pollution hotspots in Patna, Muzaffarpur, Gaya, and Bhagalpur. Seasonal bivariate polar plots and HYSPLIT trajectory models confirmed the regional emission, role of transboundary transport and regional wind patterns, which significantly influenced the air quality. A positive correlation was found between PM₂.₅ and AOD (r = 0.43), while NO₂ and O₃ exhibited inverse seasonal patterns. Despite improvements in 2024, significant health risks remain, highlighting the need for region-specific, data-driven mitigation strategies. These results offer critical insights for evidence-based air quality management and highlight the urgent need for spatiotemporally resolved policy responses under the National Clean Air Programme (NCAP) and SDG 11.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104047"},"PeriodicalIF":4.1,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827149","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":"Hybrid machine learning optimization for solar radiation forecasting","authors":"Bilel Zerouali ,&nbsp;Nadjem Bailek ,&nbsp;Saleh Qaysi ,&nbsp;Salah Difi ,&nbsp;Nassir Alarifi ,&nbsp;Ahmed Elbeltagi ,&nbsp;Celso Augusto Guimarães Santos ,&nbsp;Kai He ,&nbsp;Youssef M. Youssef","doi":"10.1016/j.pce.2025.104052","DOIUrl":"10.1016/j.pce.2025.104052","url":null,"abstract":"<div><div>Accurate solar radiation forecasting is critical for optimizing solar energy systems and enhancing energy resource planning. This study evaluates the performance of five advanced machine learning models—XGBoost, AdaBoost, CatBoost, LightGBM, and Histogram-Based Gradient Boosting—in forecasting hourly solar radiation across three distinct Australian climatic zones: Alice Springs, Darwin, and Tennant Creek. Model performance was improved through hyperparameter tuning using the Nelder–Mead optimization method and feature selection via the Local Interpretable Model-agnostic Explanations (LIME) technique, which enables transparent identification of the most influential predictors. Additionally, SHapley Additive exPlanations (SHAP) was employed to provide further insights into feature importance and individual contributions to model predictions. Both LIME and SHAP analyses consistently identified Global Horizontal Irradiance (GHI) as the most influential predictor across all stations. Air Temperature (TEMP) contributed positively, albeit to a lesser extent, while Relative Humidity (HUM) exhibited minimal impact. Among the models evaluated, CatBoost combined with Nelder–Mead and LIME achieved the lowest RMSE in Alice Springs (62.78 W/m²) and Tennant Creek (52.08 W/m²), whereas AdaBoost demonstrated the best performance in Darwin (RMSE = 22.93 W/m²). The integration of Nelder–Mead and LIME reduced prediction errors by 6 %–82 % across the sites. These findings underscore the importance of climate-specific model selection and demonstrate that hybrid optimization and interpretability frameworks can significantly improve both the accuracy and transparency of solar radiation forecasting. Enhanced predictive performance offers substantial scientific and societal benefits, particularly for advancing renewable energy deployment and energy planning in climate-sensitive regions.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104052"},"PeriodicalIF":4.1,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864579","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":"Comprehensive geotechnical analysis for urban underground construction in Jakarta","authors":"Chairul Salam M ,&nbsp;Hendra Rezkie ,&nbsp;Halim ,&nbsp;Orhan Kural","doi":"10.1016/j.pce.2025.104050","DOIUrl":"10.1016/j.pce.2025.104050","url":null,"abstract":"<div><div>This study advances geotechnical analysis for urban underground construction by introducing a fully integrated hybrid framework that combines empirical methods, Finite Element Method (FEM) simulations, and Artificial Neural Networks (ANN). Focusing on Jakarta's Mass Rapid Transit (MRT) Phase 2 project, the framework evaluates soil bearing capacity and settlement behavior under complex geotechnical conditions. Comprehensive subsurface data were utilized, including stratigraphy, cohesion, internal friction angles, unit weight, silt content, and Standard Penetration Test (SPT) values. Unlike previous studies limited to 2D FEM–ANN couplings or single-parameter AI predictors, this research introduces a novel 3D FEM–ANN–empirical hybrid framework that incorporates lithological heterogeneity and silt content sensitivity, validated against field-monitored settlement data along the Jakarta MRT alignment — a first in Southeast Asian urban geotechnics. The integration enhances predictive accuracy by over 20 % compared to traditional methods, effectively addressing soil heterogeneity and dynamic loading conditions. This hybrid framework offers a transferable, robust solution for safer and more efficient underground construction in tropical alluvial environments. The validated framework offers a transferable predictive model for similar urban tunneling projects in Southeast Asia's tropical alluvial settings, with potential to inform regional geotechnical practice and design guidelines.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"141 ","pages":"Article 104050"},"PeriodicalIF":4.1,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004502","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":"Corrigendum to “The search for small intrusions of the Stepnyak gold-bearing type within the Akmola region of Kazakhstan: A multi-geophysical technique and unmanned aerial vehicle approach” [Phys. Chem. Earth, Parts A/B/C, 140, (October 2025), 103988 https://doi.org/10.1016/j.pce.2025.103988]","authors":"Aliya Maussymbayeva ,&nbsp;Gulzada Umirova ,&nbsp;Strukova Polina ,&nbsp;Aigerim Abdullina ,&nbsp;Tileuberdi Nurbol ,&nbsp;Samuel Nunoo","doi":"10.1016/j.pce.2025.104046","DOIUrl":"10.1016/j.pce.2025.104046","url":null,"abstract":"","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104046"},"PeriodicalIF":4.1,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144924997","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":"Real-time earthquake magnitude estimation utilizing a mixed dataset set of observed and simulated P-wave onsets for the Kumaon Himalaya from Mw3.0–7.5 earthquakes","authors":"Suraj Kumar Pal ,&nbsp;S. Devi ,&nbsp;Sandeep ,&nbsp;P. Kumar ,&nbsp;A. Joshi ,&nbsp;Monika ,&nbsp;R. Kumar","doi":"10.1016/j.pce.2025.104044","DOIUrl":"10.1016/j.pce.2025.104044","url":null,"abstract":"<div><div>We propose new earthquake early warning (EEW) magnitude regression relations by utilizing 254 simulated and observed records for the Kumaon Himalaya. Initially, we validated the modified semi empirical technique (MSET) aimed at P phase simulation for the 2011 Indo Nepal earthquake (M_w5.4) occurred in the Kumaon Himalaya. The close resemblance between observed and simulated vertical records that are generated by MSET at 5 stations with relatively low root mean square error (RMSE) suggests the applicability of MSET. We then simulated 65 strong motion records for 7 future earthquakes (M_w 6.0–7.5) in the study region. Subsequently, we extracted 3 commonly used EEW parameters Average Period (τ_c), Peak Displacement Amplitude (P_d), and Peak Ground Velocity (PGV) from 45 simulated and 209 observed records, utilizing 3s and 5s P-wave time windows (PTWs). The 3s PTW is applied for earthquake magnitudes in the range of 3≤M_w&lt;6, while the 5s PTW is used for magnitudes between 6≤M_w ≤ 7.5. The developed magnitude empirical relations are then used to estimate both the magnitude and the PGV using P-wave onset of 20 additional simulated records. The comparable match between scenario magnitude and predicted magnitudes estimated using τ_c-M_w and P_d-M_w with maximum relative error 0.041 and 0.052 respectively suggests the relevance of the developed regression relations. Similarly, with maximum relative error of 0.37 between scenario PGV and predicted PGV further confirms the validity of P_d-PGV relation. Additionally, the estimation of lead times using proposed relations vary from 6.85 to 90s for 17 metropolis and devotional sites located near to the study region during future scenario earthquakes.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104044"},"PeriodicalIF":4.1,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827148","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":"Heavy metals bioavailability prediction in swine manure composting: Development of ensemble machine learning models","authors":"Gebre Gelete ,&nbsp;Zaher Mundher Yaseen ,&nbsp;Hüseyin Gökçekuş","doi":"10.1016/j.pce.2025.104036","DOIUrl":"10.1016/j.pce.2025.104036","url":null,"abstract":"<div><div>Assessing the bioavailability of heavy metals (HMs) in compost products is crucial for evaluating the related environmental problems. For this, machine learning (ML) models have shown an excellent accuracy in HM fraction prediction and solve the limitations of experimental methods. Thus, this study aimed to predict the bioavailability of different HMs (Cu, Zn, Cd, As, and Cr) in swine manure compost using AdaBoost, random forest (RF), CatBoost, K-nearest neighbors (KNN), and extreme gradient boost (XGB) algorithms. Afterward, three multi-model ensemble techniques, namely AdaBoost ensemble (ABE), Neuro-Fuzzy ensemble (NFE), and weighted average ensemble (WAE), were developed using the outputs of single ML models as input to boost the overall prediction accuracy. The prediction accuracy of the proposed models was evaluated using statistical indices, namely determination coefficient (R²), mean absolute error (MAE), Nash Sutcliffe Efficiency (NSE), and root mean square error (RMSE), and graphical illustrations. The prediction results demonstrated that the best single models for Cd, Cu, Zn, As, and Cr were AdaBoost (NSE = 0.941), XGB (NSE = 0.918), AdaBoost (NSE = 0.959), AdaBoost (NSE = 0.973), and KNN (NSE = 0.859), respectively. The results of multi-model ensemble techniques indicated that the nonlinear NFE gave the highest accuracy, improving the accuracy of individual models for Cu, Cd, As, and Cr by 4.139 %–10.265 %, 5.314 %–26.564 %, 2.78 %–7.197 %, and 4.79 %–24.238 %, respectively based on testing set NSE values.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"141 ","pages":"Article 104036"},"PeriodicalIF":4.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918033","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":"Permeability reduction prediction in low-salinity water flooding of clay-rich sandstones through machine learning","authors":"Yuman Li ,&nbsp;Farag M.A. Altalbawy ,&nbsp;Hardik Doshi ,&nbsp;Anupam Yadav ,&nbsp;R. Roopashree ,&nbsp;Aditya Kashyap ,&nbsp;Karthikeyan Jayabalan ,&nbsp;Subhashree Ray ,&nbsp;Dilsora Abduvalieva ,&nbsp;Ahmad Abumalek ,&nbsp;Guichao Liu ,&nbsp;Mohammad R.K.M. Al-Badkubi","doi":"10.1016/j.pce.2025.104034","DOIUrl":"10.1016/j.pce.2025.104034","url":null,"abstract":"<div><div>This study focuses on developing robust predictive models to estimate permeability reduction throughout low-salinity water injection within clay-rich sandstone reservoirs. Utilizing a comprehensive dataset sourced from experimental studies, the research analyzes key input parameters, including clay content, ionic strength, total dissolved solids (TDS), and flow rate, to identify their impact on permeability variation. A Gradient Boosting Machine (GBM) algorithm, integrated with various optimization strategies, such as Gaussian Process Optimization (GPO), Evolution Strategies (ES), Bayesian Probability Improvement (BBI), Batch Bayesian Optimization (BBO), is employed to refine hyperparameters for improved predictive accuracy. Performance assessments using k-fold cross-validation demonstrate the reliability and generalization ability of the models. Among the optimization approaches, GPO consistently outperforms others, achieving superior R², lower errors, and computational efficiency. SHAP analysis further highlights ionic strength as a dominant feature driving permeability reduction predictions. The results emphasize the importance of accurate permeability forecasting for optimizing production in low-salinity water flooding processes.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104034"},"PeriodicalIF":4.1,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772545","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 pollution indices, health risks model and multivariate statistics to appraise the seasonal and spatial dynamics of trace metals pollution in the arid shallow aquifer, Saudi Arabia","authors":"Radhi Abdulaali Alhadi ,&nbsp;Natarajan Rajmohan ,&nbsp;Hassan M. Albishri ,&nbsp;Nassir Alamri ,&nbsp;Mohammad M. Alahmadi","doi":"10.1016/j.pce.2025.104045","DOIUrl":"10.1016/j.pce.2025.104045","url":null,"abstract":"<div><div>Groundwater metal pollution is a serious concern globally, especially in the arid coastal aquifer, Saudi Arabia due to aridity and high water demand. The study was performed in the Kulais region, Saudi Arabia, to explore the seasonal and spatial dynamics of trace metals (TM) pollution, sources and health risks in an arid shallow aquifer using pollution indices, health risks models and multivariate statistics. Groundwater samples (n = 106) were obtained in March (Post-monsoon (PM), n = 53) and September (Pre-monsoon (PRM), n = 53)) and analysed for TMs (n = 14), Electrical conductivity (EC) and pH. Both EC and groundwater level (GWL) emphasize the role of evaporation in the PRM season. Seasonal dynamics depict high TM accumulation in PRM compared to PM. Groundwater suitability assessment and pollution indices (heavy metal pollution indices, contamination index, heavy metal evaluation index) demonstrate that groundwater is highly polluted (PRM ≫ PM) and unsuitable for drinking irrespective of the season. The hazard index (HI) and carcinogenic risk assessment suggest that all samples cause serious health issues and are inexpedient for oral consumption. Spatial dynamics indicate the enrichment of EC, Sr and B in the downstream coastal wells due to saline sources/evaporation. Further, other parameters also indicate that wells in the upstream and downstream are highly polluted compared to the central region. Multivariate statistics imply that TMs are derived by saline/evaporation (B, Sr), reductive dissolution of Mn–FeO/FeOOH (Fe,Mn,Zn), silicate weathering and farming practices (Cu,Mo). This study recommended efficient water treatment before the water distribution for various applications.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104045"},"PeriodicalIF":4.1,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773069","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}