Physics and Chemistry of the Earth最新文献

{"title":"Comparison of surface and subsurface limestone reservoir properties: key insights derived from the analysis of geological and geophysical datasets","authors":"Muhsan Ehsan ,&nbsp;Gohar Hammayun Khan Raja ,&nbsp;Afifa Tassaduq ,&nbsp;Waqas Naseem ,&nbsp;Muhammad Ali ,&nbsp;Kamal Abdelrahman ,&nbsp;Ali Y. Kahal","doi":"10.1016/j.pce.2025.104027","DOIUrl":"10.1016/j.pce.2025.104027","url":null,"abstract":"<div><div>Fimkassar Oil Field (FOF) is located in the Potwar Sub Basin, having a fractured carbonate supply that includes reservoir formations (Chorgali-Sakesar). The Sakesar Limestone, considered a significant and established reservoir in numerous oil and gas fields in the Potwar Plateau, was evaluated for reservoir potential in the current work. This study conducts a petrophysical analysis to evaluate hydrocarbon potential, as well as an analysis of outcrop and core samples to investigate the porosity and permeability of the Sakesar Limestone. Seismic data structure interpretation was performed to assess the subsurface structure patterns, and seismic attributes were employed to identify fractures in Sakesar Limestone. The reservoir petrophysical properties were compared with the core samples analysis results of the Sakesar Limestone. The porosity and permeability of outcrop and core samples of Sakesar Limestone were determined in the laboratory. Two wells of the FOF have been taken for well log interpretation. The porosity calculated from the outcrop and core samples is 0.68 %–11.65 % and 3.86 %–10.76 %, respectively. The permeability calculated from the outcrop samples ranges from 0.0 to 26.82 mD. The lithological cross-plots were used to identify the lithology, and rock physics analysis provided information on the fluid type present in the formation. The correlation of the reservoir properties indicated that Sakesar Limestone has hydrocarbon potential and is a good reservoir.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104027"},"PeriodicalIF":3.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655813","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 effects of climate and land use/land cover changes on runoff in the Mangrove Forests of the Northern Persian Gulf","authors":"Sharif Joorabian Shooshtari ,&nbsp;Reza Chamani","doi":"10.1016/j.pce.2025.104025","DOIUrl":"10.1016/j.pce.2025.104025","url":null,"abstract":"<div><div>Mangrove forests are increasingly threatened by rapid Land Use/Land Cover (LULC) changes and climate variability. This study analyzes spatio-temporal dynamics of the Nayband mangrove forests in the northern Persian Gulf, Iran, for 1990, 2003, 2022, and a projected 2030, using a Markov chain and Multi-Layer Perceptron Artificial Neural Network. Additionally, maximum 24-h rainfall data (2003–2017) were analyzed using the Weibull method to estimate return periods of 2, 5, 10, 50, and 100 years. To assess climate change impacts, daily rainfall data from ACCESS-ESM1-5, HadGEM3-GC31-LL, and MRI-ESM2-0 models under SSP126, SSP245, and SSP585 scenarios were downscaled using the LARS-WG model. The Soil Conservation Service method estimated runoff height and volume under various climate change and LULC conditions. Under 2030 LULC projections relative to 2022, runoff volumes are expected to decline by 4.88 %, 3.15 %, 2.44 %, 1.09 %, and 0.96 % across the aforementioned return periods under SSP126. SSP245 shows smaller reductions of 1.51 %, 1.02 %, 0.78 %, 0.15 %, and 0.65 %. In contrast, the SSP585 scenario projects an increase in runoff volume, with corresponding rises of 16.28 %, 12.09 %, 21.32 %, 9.59 %, and 9.21 %. The analysis revealed that runoff variability was more significantly affected by climate change than by LULC change. Accordingly, the findings of this study provide a valuable foundation for shaping management strategies focused on the restoration and expansion of mangrove forests, while also supporting informed development planning within this predominantly industrial region, defined by petrochemical, oil, and gas operations.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104025"},"PeriodicalIF":3.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665816","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":"Empirical study on cashew agroforestry adoption in a fragile and resource-scarce environment: The case of smallholder farmers in the west coast region of the Gambia","authors":"Baseedy Bojang ,&nbsp;Diana Emang","doi":"10.1016/j.pce.2025.104014","DOIUrl":"10.1016/j.pce.2025.104014","url":null,"abstract":"<div><div>Understanding the profitability, acceptability, feasibility, and associated factors of cashews agroforestry is crucial for empowering low-income smallholder farmers. It supports their economic survival, risk reduction, and adoption of sustainable agriculture practices. This study examines whether cashews agroforestry adoption is a safe and reliable choice for smallholder farmers in a fragile, resource-scarce environment to enhance soil fertility, reduce degradation and improve overall land health for sustainable agriculture and environmental conservation. Data were collected from 20 smallholder farmers in Kombo East, The Gambia, via a survey. Results showed cashews agroforestry profits were higher than other crops. A 1-ha cashews farm has a net present value of USD1,030.45 when discounted at 20 %, with a cost-benefit ratio of 2.93 and an internal rate of return at 47.2 %, despite poor rainfall and 40 % loss during harvesting. More than half of the farmers expanded their farms, with 70 % motivated by income generation and soil improvement. They adopted multiple cashews agroforestry systems. The study reveals cashew agroforestry as a reliable option for smallholder farmers, especially those with limited land, enhancing earnings and improving soil conditions. Key factors influencing adoption include farm expansion ability, land availability, rainfall variability, gender, firewood and charcoal needs, windbreak and erosion control, and cashews profitability. These findings offer valuable insights for policymakers to promote tree-based agroforestry in The Gambia and for comparable smallholder farmers, as it is financially appealing, reduces labour demands, and presents lower risk than monocropping systems.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104014"},"PeriodicalIF":3.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597065","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":"Enhancing precision in sediment yield Estimation: The synergistic effects of runoff and vegetation dynamics","authors":"S.H.R. Sadeghi ,&nbsp;M. Tavosi ,&nbsp;M. Moradnezhad ,&nbsp;A.R. Pakravan ,&nbsp;R. Yaghooti ,&nbsp;F. Esmaeilzadeh ,&nbsp;H. Fereydoni ,&nbsp;F.Z. Enayati ,&nbsp;R. Alipour ,&nbsp;M. Zabihi Seilabi ,&nbsp;A. Katebikord ,&nbsp;S. Mousavian","doi":"10.1016/j.pce.2025.104016","DOIUrl":"10.1016/j.pce.2025.104016","url":null,"abstract":"<div><div>Severe floods and high sediment production have caused numerous problems in watersheds. More insight studies are yet to be conducted to disclose existing ambiguities in fluvial phenomena. Therefore, refining and updating the parameters and factors of sediment yield estimation models, such as MUSLE, is essential for providing more precise results, leading to more effective watershed management. To achieve this, the role of more dynamic factors, including runoff and vegetation cover, was optimized in sediment yield estimation using 38 rainfall-runoff events recorded in the Galazchai Watershed, Northwestern Iran. Accordingly, the role of runoff, the parameter “m” of the MUSLE model, was optimized, and subsequent modeling was performed using linear, logarithmic, power, inverse, and multivariate inverse regression models. Furthermore, five vegetation cover-based methods were assessed for calculating the C-factor. The results indicated that the parameter “m” was successfully estimated using multivariate inverse regression based on runoff volume and peak discharge. The C-factor was also calculated using NDVI according to Method 1 as outlined in the original MUSLE model. The adapted MUSLE model provided the best sediment yield estimate for the Galazchai Watershed with determination coefficients (R²) of 0.87 and 0.56 for the calibration and validation stages, respectively. If the present adapted approach proves effective in other watersheds, it could enable the accurate and storm-based estimation of parameter “m” and precise sediment yield predictions using only runoff volume and peak discharge data. Therefore, the findings from the current study are highly valuable for enhancing erosion and sedimentation research and optimizing watershed management programs.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104016"},"PeriodicalIF":3.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144587714","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":"Optimizing LSTM for sediment load prediction in the Swat river basin, Pakistan: Evaluation of optimizers and activation functions","authors":"Nauman Gul ,&nbsp;Afed Ullah Khan ,&nbsp;Basir Ullah ,&nbsp;Bakht Niaz Khan ,&nbsp;Hamed M. Almalki ,&nbsp;Abdulbasid S. Banga ,&nbsp;Kailash Kumar","doi":"10.1016/j.pce.2025.104019","DOIUrl":"10.1016/j.pce.2025.104019","url":null,"abstract":"<div><div>Accurately estimating sediment load is essential for sustainable water resources management, as excessive sedimentation can reduce reservoir capacity, degrade water quality, and impair aquatic ecosystems, ultimately affecting long-term planning and infrastructure design. However, predictions can be uncertain due to the choice of optimizers and activation functions in machine learning models. This study investigates the influence of seven optimizers (Adam, RMSprop, Adagrad, Adadelta, Adamax, Nadam, and Ftrl) and eight activation functions (ELU, Sigmoid, Linear, Softplus, Swish, SELU, Tanh, and Softmax) on the performance of a Long Short-Term Memory (LSTM) network for sediment load prediction. A total of 56 optimizer-activation combinations were tested using historical data from the Chakdara station in the Swat River basin, Pakistan. The dataset was normalized using Min-Max scaling, with a 70:30 train-test split. Model performance was evaluated using the Coefficient of Determination (R²), Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Percent Bias (PBIAS). Results showed that the Adam-SELU combination achieved the best performance, with an R² of 0.81, MSE of 1363.61, and RMSE of 36.93 during training, and an R² of 0.80, MSE of 2385.2, and RMSE of 48.84 during testing. This configuration also exhibited minimal bias, with PBIAS values of −1.07 (training) and −1.04 (testing). Other combinations, such as Adam-Nadam and Adam-RMSprop, performed relatively well but with higher error metrics. The findings highlight Adam-SELU as the most effective configuration for sediment load prediction, offering improved accuracy. These insights can guide watershed managers in selecting robust models for sediment forecasting.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104019"},"PeriodicalIF":3.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655812","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":"Artificial intelligence for reservoir modeling and property estimation in petroleum engineering","authors":"Abdulrahman S. Aljehani","doi":"10.1016/j.pce.2025.104015","DOIUrl":"10.1016/j.pce.2025.104015","url":null,"abstract":"<div><div>This study aims to advance the application of artificial intelligence (AI) in reservoir modeling by developing and evaluating machine learning (ML) techniques for estimating key subsurface properties, including permeability, porosity, and relative permeability. While prior research has applied AI methods in this domain, most approaches focus on narrow datasets or lack integration with physical reservoir behavior. In contrast, this work combines multiple AI techniques—artificial neural networks (ANNs), support vector machines (SVMs), fuzzy logic (FL), and evolutionary algorithms—into a hybrid modeling framework that captures complex, nonlinear rock–fluid interactions in multiphase flow environments. The study introduces novel preprocessing and training strategies to address data quality issues and improve generalizability. Case studies using real field data demonstrate that the proposed AI models outperform conventional empirical and statistical methods in prediction accuracy, robustness, and computational efficiency. The findings suggest that AI can significantly reduce reliance on expensive laboratory measurements, support faster reservoir characterization, and enhance decision-making under geological uncertainty. This work contributes a scalable and interpretable ML-based workflow that bridges data-driven insights with engineering principles, offering a step forward in the digital transformation of reservoir management.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104015"},"PeriodicalIF":3.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604857","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":"Urban growth and environmental impact assessment in Malda: A comprehensive study using Shannon's entropy and remote sensing","authors":"Lal Mohammad ,&nbsp;Jatisankar Bandyopadhyay ,&nbsp;Ismail Mondal ,&nbsp;Mohamed Mohamed Ouda ,&nbsp;Nikhilesh Mondal ,&nbsp;Mukhiddin Juliev ,&nbsp;Abdulrazak H. Almaliki","doi":"10.1016/j.pce.2025.104021","DOIUrl":"10.1016/j.pce.2025.104021","url":null,"abstract":"<div><div>Urbanization poses enormous environmental and ecological challenges, making it crucial to strike a balance between urban growth and ecological preservation for the benefit of future generations. This study assesses the spatiotemporal changes and Environmental Impacts of Urbanization (EIU) in the Malda Urban Agglomeration (MUA) areas between 1990 and 2020 using geospatial techniques. Satellite-derived indices, Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST)—were combined with field-based data to evaluate urbanization trends and their ecological consequences. Shannon's entropy model was employed to assess aerial urban growth during the study period. The relative entropy values were 0.751 in 1990 and 0.814, 0.882, and 0.947 in 2000, 2010, and 2020, respectively, indicating rapid urbanization in the MUA region, where the growth rate of urban expansion was found to be more than four times from the base year, 1990. This study took a different perspective to assess the impacts of urban expansion on environmental effects. The linear regression (LR) model demonstrated a substantial association between population growth and an increase in built-up areas in English Bazar, Old Malda, and MUA, with R² values of 0.927, 0.989, and 0.982, respectively. The LR model also revealed that rapid urbanization has significantly impacted environmental conditions (NDBI, LST, and vegetation cover) in the study. Thus, this study demonstrates the impacts of urbanization using Geospatial techniques, which can help policymakers plan for sustainable development (SDGs) and effectively mitigate urban planning and management challenges for a sustainable city environment.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104021"},"PeriodicalIF":3.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581387","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":"A systematic review of sources and pathways of microplastics at higher altitudes in glaciated environments","authors":"Ramesh Kumar ,&nbsp;Prity S. Pippal ,&nbsp;Virendra Kumar Yadav ,&nbsp;Padma Namgyal ,&nbsp;Rajesh Kumar","doi":"10.1016/j.pce.2025.104018","DOIUrl":"10.1016/j.pce.2025.104018","url":null,"abstract":"<div><div>Microplastics have been detected in various parts of cryosphere ecosystems, including snow, hail, sea ice, glaciers, and permafrost. This widespread presence highlights the need to understand their sources and pathways in these sensitive environments. Therefore, this review summarizes the sources and transport of microplastics and the current state of microplastics in the glaciated environment, utilizing the bibliometric method and visual analysis using the PRISMA framework. The study revealed that the global atmospheric transport of microplastics plays a crucial role in contaminating glaciated environments. Additionally, the findings of this review suggest that the presence of microplastics in high-mountain ecosystems may be attributed to deposition via atmospheric precipitation. The findings of the bibliometric analysis suggest that the number of published papers on microplastics in the glaciated environment has grown exponentially, with the USA, UK, and China being the leading research countries. The number of publications produced from China and the United Kingdom accounts for about half (50 %) of all publications from the top 10 countries. Zhang Y, Kang S, and Wang X were highly influential authors in microplastic research in glaciated environments. Additionally, a bibliometric analysis revealed that the percentage of literature addressing microplastics and glaciers in relation to the Sustainable Development Goals (SDGs) increased throughout the study period. The observation suggests that the existing body of literature makes a significant contribution to the Sustainable Development Goals (SDGs) related to clean water and sanitation (SDG 6) and Climate Action (SDG 13).</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104018"},"PeriodicalIF":3.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572212","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":"Global classification of aerosols based on ground observations: A comparison between an empirical method and machine learning algorithms","authors":"Muhammad Awais,&nbsp;Lunche Wang","doi":"10.1016/j.pce.2025.104017","DOIUrl":"10.1016/j.pce.2025.104017","url":null,"abstract":"<div><div>Aerosols are atmospheric particles that remain suspended in both solid and liquid states, significantly influencing climatic processes through the scattering and absorption of sunlight. They possess distinct physical, chemical, optical, and microphysical properties. Moreover, identifying aerosol types is also essential, as it provides crucial insights into their characteristics and behavior. Therefore, the current study investigates the global distribution of aerosol's optical and microphysical properties across 50 AERONET (Aerosol Robotic Network) sites located on six continents. Based on these properties, aerosols were classified using an empirical method into six major types: Dust Aerosols (DA), Continental Aerosols (CA), Highly Absorbing (HA), Low Absorbing (LA), Non-Absorbing (NA), and Uncertain (UC). The dominance of aerosol types, their mean frequency, and their spatial and temporal variations were analyzed across all sites during the overall study period as well as during summer and winter seasons. The findings indicate that DA and UC aerosol types are prevalent in West Africa and the Middle East during both the overall period and summer. The CA type shows relatively uniform distribution globally during the overall and winter seasons, with the exception of the East Asian region. Conversely, HA and LA types are less frequent in West Africa during the overall period and display reduced occurrences in both West Africa and the Middle East during summer. The NA type is predominantly observed in East Asia across all periods—overall, summer, and winter. To complement the empirical classification, three machine learning (ML) algorithms—Support Vector Machines (SVM), Naive Bayes, and Logistic Regression—were applied for aerosol classification and their performance compared with the empirical method. The effectiveness of the models was evaluated using performance metrics, including accuracy, precision, recall, F1-score, and a confusion matrix. Among the ML methods, SVM and Naive Bayes achieved the highest overall accuracy of 98 % and 96 %, respectively, with perfect classification (100 % precision, recall, and F1-score) for the NA type. Logistic Regression also performed well, with an accuracy of 96 %, along with high precision (97 %), recall (100 %), and F1-score (99 %) for the NA type.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104017"},"PeriodicalIF":3.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581386","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":"Beyond conventional treatment: A comprehensive systematic review on the advanced oxidation processes for the removal of malathion pesticide from aqueous environments","authors":"Mohadeseh Gholamzadeh ,&nbsp;Ali Naghizadeh ,&nbsp;Farzaneh Gholizadeh ,&nbsp;Elham Derakhshani","doi":"10.1016/j.pce.2025.104010","DOIUrl":"10.1016/j.pce.2025.104010","url":null,"abstract":"<div><div>This study presents a systematic review of research focused on the removal of malathion from aqueous solutions. Malathion, recognized as a highly toxic pesticide and a strong inhibitor of acetylcholinesterase, is the central subject of this investigation. This review was conducted through comprehensive searches in reputable databases such as Scopus, PubMed, Science Direct, and Web of Science, with data collection extending until May 2024. A variety of parameters were meticulously evaluated, including the type of catalyst and adsorbent, catalyst size, optimal pH, initial malathion concentration, optimal catalyst concentration, optimal contact time, type of irradiation, removal efficiency, adsorbent dosage, maximum adsorption capacity, and optimal temperature. Out of an initial pool of 630 articles, 74 studies met the inclusion and exclusion criteria and were selected for detailed analysis. The results show that malathion removal efficiencies reported in these studies were generally above 70 %, with some studies achieving up to 100 % removal. This systematic review highlights the significant roles that physical, chemical, and biological processes play in the effective removal of malathion from aqueous environments.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104010"},"PeriodicalIF":3.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571171","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}