Environmental Earth Sciences最新文献

{"title":"Un-planned urban growth monitoring from 1991 to 2021 of Aizawl city, north-east India by multi-temporal changes and CA-ANN model","authors":"Imanuel Lawmchullova,&nbsp;Jonathan Lalrinawma,&nbsp;Lal Rinkimi,&nbsp;Joseph Lalngaihawma,&nbsp;Ch. Udaya Bhaskara Rao,&nbsp;Brototi Biswas","doi":"10.1007/s12665-025-12244-x","DOIUrl":"10.1007/s12665-025-12244-x","url":null,"abstract":"<div><p>Monitoring urban landuse and landcover (LULC) change is a crucial element in developing cities like Aizawl to improve land use planning for future smart cities. The objective of the current study is to analyze the lulc changes of Aizawl city between 1991 and 2021 using multi-date Landsat images and a cellular automata-artificial neural network (CA-ANN) model to predict future scenarios. The present study is highly essential for examining the urban expansion in a vertical hill city and the historical influence of settlement patterns along the edges of hill ranges for proper land use planning. The automatic classification of support vector machines (SVM) in-built at Orfeo tool box (OTB) modules was employed for LULC pattern classification. The land cover change method of the semi-automatic classification plugin (SCP) was used to identify the past LULC using Landsat 4, 5, 7, and 8. The future LULC was stimulated using the machine-learning approaches modules for land use change evaluation (Molusce) plugin in QGIS 2.18. Also, we highlight the factors that influence future LULC changes and the impacts of unplanned hill cities from the results of multi-criteria evaluation (MCE) and analytical hierarchical process (AHP). The study reveals that built-up areas are continuously increasing while open forest, agricultural land, and fallow land are diminishing, even in the projected land use land cover thematic layer in 2031. The built-up area has seen the highest change, from 5.98 to 25.8% in 1991 to 2021; the rate of increase has been 0.636 km²/year-1 during the last 30 years. Similarly, dense forest cover also increased from 12.14 to 18.72% from 1991 to 2021, while other landuse landcover patterns like open forest, fallow land, and agricultural land are declining due to urban expansion. The accuracy level of Kappa coefficients was 97.30% in 1991 and 100% in the years 2001, 2011, and 2021, respectively.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative attribution analysis of water and sediment changes in the Lower Yellow River (1950–2022) under the influence of climate change and human activities","authors":"Yihao Wen,&nbsp;Haijue Xu,&nbsp;Jinliang Zhang,&nbsp;Yuchuan Bai","doi":"10.1007/s12665-025-12227-y","DOIUrl":"10.1007/s12665-025-12227-y","url":null,"abstract":"<div><p>Hydrological processes in regional river basins are significantly affected by both global climate change and human activities. This research focuses on analyzing changes in water and sediment dynamics in the lower Yellow River (LYR) while pinpointing the primary factors driving these transformations. The study aims to evaluate the respective contributions of climate change and human interventions to water–sediment interactions, providing insights for optimal water resource and watershed management. Methods such as the Mann–Kendall test and double cumulative curves were employed to explore long-term trends and sudden changes in runoff and sediment transport. The IHA-RVA method was used to conduct a detailed quantitative assessment of water–sediment variability, while Copula functions were employed to model the probability of simultaneous abundance or scarcity of water and sediment. To conduct the attribution analysis, we employed the Budyko framework along with fractal theory to quantify the respective contributions of climate change and human activities to water–sediment variations from 1950 to 2022. The results indicate substantial reductions in annual runoff and sediment transport within the LYR during this time, with overall decreases of 53.03% in runoff and 62.81% in sediment transport. The frequency of synchronous water–sediment events ranged from 56.54% to 67.29%, while asynchronous occurrences varied from 32.71% to 43.46%. Quantitative analysis revealed that human activities accounted for 74.11%–77.02% of the observed changes in runoff and a striking 91.48%–93.63% of sediment transport reductions in the lower Yellow River. These changes were predominantly driven by large-scale initiatives such as the Green for Grain program and the construction of major hydraulic infrastructure, emphasizing the dominant role of anthropogenic interventions over climatic factors in influencing hydrological dynamics. These findings provide important theoretical insights and practical guidance for enhancing soil and water conservation measures and improving regional management strategies in the LYR.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive analysis of hourly extreme precipitation events and timing in Indian urban centres","authors":"Maharnab Kundu,&nbsp;Ujjwal Saha","doi":"10.1007/s12665-025-12273-6","DOIUrl":"10.1007/s12665-025-12273-6","url":null,"abstract":"<div><p>This study presents a comprehensive analysis of extreme hourly precipitation events in Indian urban centres using high-resolution rainfall data spanning 1969–2021. Extreme events were identified using a 99th percentile threshold for each of the 68 urban stations, and GIS tools were employed for geospatial analysis and visualization of spatiotemporal patterns. The findings reveal distinct regional patterns, with higher occurrences of extreme rainfall in the North East and West Coast regions, while certain areas in Central North East and South Peninsular India exhibit exceptions. Seasonal analysis underscores the dominance of extreme events during the summer monsoon, and diurnal patterns indicate that 59% of such events occur from afternoon to late evening. Quantitative results show a significant increase in the frequency of extreme hourly events in over 14% of stations, while 30% of stations experienced a decline. These insights are crucial for understanding the timing, intensity, and trends of extreme rainfall, offering valuable guidance for urban flood management, disaster preparedness, and the development of climate-resilient infrastructure.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Failure parameter inversion of the Baige landslides based on seismic signal analysis","authors":"Gang Fan,&nbsp;Ziyu Lin,&nbsp;Jiawen Zhou","doi":"10.1007/s12665-025-12250-z","DOIUrl":"10.1007/s12665-025-12250-z","url":null,"abstract":"<div><p>Southwest China is prone to landslide disasters due to the complex geographical condition. The failure parameters of two Baige landslides in 2018 were inversed based on seismic signals recorded at adjacent seismograph stations. The global crustal one-dimensional average velocity model provided by Crust 1.0 was adopted to calculate Green’s function. Then, the force‒time functions of the two Baige landslides were inversed. Based on the landslide motion model, the kinetic motion parameters of the two Baige landslides were calculated, and the disaster processes of the two Baige landslides were ultimately revealed in this study. The results showed that the duration of the first Baige landslide was approximately 130 s, including three stages, i.e., the collapse and slide stage of the main sliding mass (last 44 s), the crushing and disintegration stage (last 47 s), and the scattering and accumulation stage (last 39 s). The duration of the second Baige landslide was approximately 130 s, including the collapse and slide stage (last 32 s), the crushing and disintegration stage (last 46 s) and the scattering and accumulation stage (last 58 s). The maximum force is 1.65 × 10¹¹ N in for the first Baige landslide and 1.69 × 10¹¹ N for the second Baige landslide, respectively. The maximum velocity of the centroid reached 65.3 m/s at t = 44 s for the first Baige landslide, while the maximum velocity of the centroid reached 64.9 m/s at t = 32 s for the second Baige landslide, which are larger than the existing simulation results. The calculated displacement matches the actual terrain based on the historical satellite images after the two Baige landslides. The trajectory angles of the centroids of the two landslides ranged from 4°~34° and 1°~51°, respectively, and the sliding friction coefficient ranged from 0.03 ~ 0.95 and 0.05 ~ 1.24, respectively. This study provides an indirect method for measuring landslide parameters, and a seismological basis and references for studying the failure mechanism of large-scale landslides.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate change scenarios and the increasing severity of thermal extremes in the pampas region","authors":"Andrea Soledad Brendel,&nbsp;Federico Ferrelli,&nbsp;María Cintia Piccolo","doi":"10.1007/s12665-025-12264-7","DOIUrl":"10.1007/s12665-025-12264-7","url":null,"abstract":"<div><p>This research aimed to analyze the Spatial and Temporal trends and variations of extreme thermal events in the Pampas region (Argentina) over three periods: the present (2009–2023), the near future (2024–2038), and the Far future (2085–2099) under two greenhouse gas concentration scenarios, RCP 4.5 and RCP 8. Across these periods, 14 extreme thermal indices were calculated using maximum and minimum temperature series recorded in situ by 48 meteorological stations. For future projections, we employed two validated climate models: the CCSM4 model (validation index: 0.91) for the humid region and the CNRM-CM5 model (validation index: 0.91) for the central region, selected based on their high performance in representing regional thermal conditions. Results revealed a significant warming trend, with regional maximum temperature increasing by 1.1 °C during 2009–2023, and projections of up to 1.4 °C increase in the Far future under RCP 8.5. A notable Spatial heterogeneity was observed, with Western and central sectors of the Pampas showing more pronounced warming patterns than Eastern coastal areas. Extreme indicators showed pronounced changes: absolute maximum temperature (TXx) increased by 2.5 °C in the present period, with projections of up to 4.9 °C increase by 2085–2099 under RCP 8.5. Warm days (TX90p) increased by 5 days/15 years in the present, with projections of 6.7 days/15 years in the Far future. Concurrently, cold events decreased significantly, with cool days (TX10p) declining by 6 days/15 years in the present and projected to decrease by 7.1 days/15 years in the Far future. This thermal intensification will adversely affect agricultural production, economic development, infrastructure, biodiversity, and public health, heightening the vulnerability of the region’s socio-ecosystems. These findings are critical for developing Spatial management plans and designing climate adaptation and mitigation measures at local and regional scales.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modified slow sand filter amended magnetic corncob biochar and zero-valent iron for arsenic removal from drinking water","authors":"Taimoor Khan,&nbsp;Qasim Ali,&nbsp;Imdad Ullah,&nbsp;Shams Ali Baig,&nbsp;Dilawar Farhan Shams,&nbsp;Xinhua Xu,&nbsp;Muhammad Danish","doi":"10.1007/s12665-025-12259-4","DOIUrl":"10.1007/s12665-025-12259-4","url":null,"abstract":"<div><p>The toxicity of arsenic (As) in drinking water poses a significant risk to public health, and its effective removal is essential to reduce the associated risks. Modified slow sand filter (SSF) has emerged as a promising decentralized water treatment method in developing countries due to its user friendliness, economic viability, and environment-friendly properties. The present study investigated the total arsenic removal efficiency and turbidity reduction in laboratory-scale SSF columns designed for a 60-day filtration period. For this purpose, SSF columns were modified with magnetic corncob biochar (MCCB) and zero-valent iron (ZVI) layers in different ratios. The characterization tests, including Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and X-ray Diffraction (XRD), were conducted before and after the filtration. Results revealed that the MCCB surface was porous with a honeycomb-like structure before adsorption, containing cave-like holes favourable for arsenic removal. Similarly, the ZVI surface exhibited a tabular and thread structure. The EDS results confirmed the presence of Fe in the MCCB and ZVI, indicating the magnetic properties of both adsorbents. Notably, maximum As removal efficiency of 80% was observed in SSF(b) with a 10 cm MCCB layer after 60 days, whereas SSF(d) with a 10 cm ZVI layer achieved 99% within just 10 days of filtration. In addition, SSF columns containing ZVI layers achieved a maximum turbidity removal of 98% and 99% after 10 days of filtration, while SSF(b) with a 10 cm MCCB layer reached a turbidity removal of 99.9% after 60 days. Statistical analyses indicated that these differences were significant (<i>p</i> &lt; 0.05), demonstrating the superior efficacy of the ZVI-based SSF for arsenic removal and the strong performance of MCCB in turbidity reduction. Overall, SSF-amended MCCB and ZVI demonstrated effective removal of As and turbidity. The study suggests that the designed SSFs are durable and user-friendly filter made of locally avaible low-cost materials for water filtration.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From trash to tap: assessment of microplastics contamination in leachate and groundwater","authors":"Meganathan Raju,&nbsp;Rajan Gandhimathi","doi":"10.1007/s12665-025-12262-9","DOIUrl":"10.1007/s12665-025-12262-9","url":null,"abstract":"<div><p>Microplastic (MP) pollution in groundwater is a growing concern due to its toxic properties and harmful effects. Meanwhile, landfills and dumpsites act as storage areas for plastic materials, which gradually disintegrate into microplastics over time, leading to pollution of the surrounding environment. Knowledge of the presence of MPs in the groundwater is scarce, and it is the need of the hour. This article focuses on the MPs migration from the dumpsite to the surrounding groundwater by analyzing the MPs in leachate generated from the dumpsite and MPs found in the groundwater near the solid waste dumpsite region in Ariyamangalam, Tiruchirappalli, Tamil Nadu, India. In this study, the Nile Red staining method has been used to quantify the microplastics with sizes as small as 3.42 μm. The results indicated that the MPs abundance in groundwater is about 11 to 77 particles/L with an average size of 45.16 μm, and in leachate on average, 102 to 140 particles/L were identified with the average size of 152 μm. Based on appearance, most of the MPs are of a fragment’s nature; some films and fibers were also found in the groundwater. Meanwhile, in leachate, fragments (45%) and fibers (44%) were found to be in equal proportion, along with a smaller number of films (11%). From micro-Raman characterization, polyethylene was the dominating polymer, followed by polypropylene, polyethylene terephthalate, polystyrene, polyvinyl chloride, poly methyl methacrylate, polyamide, and polyvinyl alcohol in the groundwater. The risk assessment reveals that the groundwater near the dumpsite zone comes under risk category IV based on the polymer risk index, which means that there is a high risk due to the certain kind of highly toxic polymer present in the groundwater.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of water pollution sources in the Daluxi River using kernel principal component analysis and gradient boosting decision tree","authors":"Ying Liu,&nbsp;Nairui Zheng,&nbsp;Shuhan Yang,&nbsp;Fangfei Liu,&nbsp;Miaohan Liu,&nbsp;Yu Chen","doi":"10.1007/s12665-025-12241-0","DOIUrl":"10.1007/s12665-025-12241-0","url":null,"abstract":"<div><p>This study focused on the Daluxi River, a small watershed and a primary tributary of the Yangtze River. Based on the nonlinear characteristics of water quality parameters and environmental factors such as meteorological and hydrological influences, a comparative analysis was conducted using Kernel Principal Component Analysis (KPCA) and Principal Component Analysis (PCA). KPCA extracted four potential sources for both the upstream and downstream sections, accounting for 79% of the total variance in each case—an increase of 7% and 6% compared to PCA, respectively. To address the limitation of KPCA in directly revealing the relationship between principal components and the original water quality data, six machine learning algorithms—Extreme Learning Machine (ELM), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT)—were employed to perform regression analysis between the kernel principal components and the original water quality parameters, thereby elucidating source characteristics. The results indicated that GBDT exhibited the best fitting performance (R² = 0.988, MAE = 0.05, RMSE = 7.13%). Based on the extracted KPC, the Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) model was used to calculate the contribution rates of various pollution sources in the Wandang and Siming areas. The results indicate that combining KPCA with GBDT and APCS-MLR can effectively uncover the complex relationships among water quality, meteorological, and hydrological factors, thereby enhancing the accuracy and reliability of pollution source analysis. This study advances research by using KPCA to capture nonlinear relationships and integrating machine learning for enhanced pollution source analysis.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A GIS-based study on groundwater level fluctuation and delineation of potential zones","authors":"Kanwarpreet Singh,&nbsp;Abhishek Sharma,&nbsp;Aditya Kumar Tiwary,&nbsp;Mayank Kaushal,&nbsp;Akhilesh Nautiyal,&nbsp;Sushindra Kumar Gupta,&nbsp;Sashikant Sahoo,&nbsp;Ali Salem,&nbsp;Salah El-Hendawy,&nbsp;Mohamed A. Mattar,&nbsp; Randeep,&nbsp;Ritik B. Kansal","doi":"10.1007/s12665-025-12197-1","DOIUrl":"10.1007/s12665-025-12197-1","url":null,"abstract":"<div><p>The rising demand for water in Punjab, fueled by swift urban growth, industrial development, and intensive agricultural practices, has resulted in significant groundwater depletion. In the state, more than 97% of cultivable land is irrigated, with groundwater accounting for approximately 70–75% of the total irrigation water supply. The present study analyzes fluctuations in groundwater levels within the S.A.S. Nagar district over a span of 26 years, from 1995 to 2021, utilizing comprehensive water level data. The findings indicate a significant decrease, with groundwater levels plummeting from 3.6 m in 1995 to 30.7 m in 2021, reflecting an average decline of over 1 m annually. The rate of depletion increased significantly after 1998, largely as a result of a broad transition from canal irrigation to tube wells, which offered farmers more convenient access to water. The findings indicate that 32% of the area exhibits high groundwater potential, whereas merely 3% shows low potential. Furthermore, 8% of the area is categorized as having a high flood risk, while 7% is identified as having a high drought risk. Despite the introduction of initiatives like underground pipeline systems and enhanced rice farming techniques, the groundwater table persists in its decline. The results underscore the critical necessity for revised irrigation policies, enhanced water conservation strategies, and greater public engagement to secure the enduring sustainability of groundwater resources.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting shear strength of unsaturated soils based on soil–water retention curve","authors":"Xiongdong Lan,&nbsp;YueQin Qiu,&nbsp;Xiao Zhang,&nbsp;Xianghui Li","doi":"10.1007/s12665-025-12226-z","DOIUrl":"10.1007/s12665-025-12226-z","url":null,"abstract":"<div><p>The complexity of unsaturated cohesive soil behavior presents challenges in directly measuring unsaturated shear strength, making it a complex and time-consuming task. Scholars have proposed indirect models to estimate unsaturated strength using soil–water retention curves and saturated shear strength indicators. However, scholars lack consistency in defining parameters to characterize apparent cohesion, resulting in a lack of standardized expressions. To establish a unified model for predicting the strength of different types of unsaturated cohesive soils, existing unsaturated shear strength models based on soil–water retention curves were systematically reviewed. Fifteen sets of experimental data were collected and utilized to analyze and compare the predictive performance of these models. It was observed that existing predictive models partially reflect the strength of unsaturated cohesive soils to some extent. However, they have applicability limitations and fail to predict the unsaturated shear strength of all soil types fully. An improved model for the shear strength of unsaturated cohesive soil was developed to overcome these limitations based on the Khalili and Khabbaz (1998) model. This improvement involved replacing a fixed empirical value in the Khalili and Khabbaz (1998) model with the water loss obtained from the soil–water retention curve. The average relative error (ARE) and normalized sum of square error (SSE) were used to quantitatively evaluate the predictive accuracy of the unsaturated soil strength model, comparing the improved model with existing ones. The analysis revealed that the improved model demonstrated higher prediction accuracy across fifteen types of unsaturated soils. Furthermore, soil–water retention curve tests and unsaturated triaxial tests were performed on two types of test soils, with sand-clay mass ratios of 3:2 and 1:4, respectively. By comparing the test data, the effectiveness of the improved model in predicting shear strength was evaluated, affirming its generalizability and accuracy in estimating the shear strength of unsaturated clay soils.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}