H. Nasiri, M. Asadian Ghahfarokhi, M. Ghodsi Hassanabad, A. Bahmanpour
{"title":"Reducing oil spills created by explosions in seabed pipelines with innovative coatings to protect marine ecosystems","authors":"H. Nasiri, M. Asadian Ghahfarokhi, M. Ghodsi Hassanabad, A. Bahmanpour","doi":"10.1007/s13762-025-06643-7","DOIUrl":"10.1007/s13762-025-06643-7","url":null,"abstract":"<div><p>The present study aims to assess the impact of explosion on coated and uncoated pipes using the finite element method simulation software ABAQUS. In addition, this study seeks to evaluate the effectiveness of protective coatings in reducing the vulnerability of pipelines to explosions, as well as Reducing oil spills and pollution of the marine environment. To this aim, the designed numerical models with outer diameter of 914 mm, thickness of 12.7 mm, and coating thicknesses of 12.8, 38.3, 63.8, and 89.3 mm were placed at distances of 122 and 1122 mm from TNT explosives with masses of 0.3 and 1 kg. The results revealed that utilizing a protective layer with a thickness of 89.3 mm protects the pipe against the blast wave optimally. In this case, the maximum pipe indentation intensity was about 9 mm, which is about 60% less than that in the uncoated pipe (15 mm). However, the damage created by the explosion bubble is among the key points, which has rarely been examined before. Improper design and implementation of the protective coating can lead to stress concentration and increased damage to the pipe due to the formation of the explosion bubble. Thus, the type of coating, as well as its thickness and distance from the main pipe should be selected carefully. Selecting the optimal distance for the protective coating significantly protects the subsea pipeline from the explosion and its bubble, resulting in reducing the risk of oil spills and pollution of the marine environment.</p></div>","PeriodicalId":589,"journal":{"name":"International Journal of Environmental Science and Technology","volume":"22 14","pages":"13631 - 13644"},"PeriodicalIF":3.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028240","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":"Bio-based acrylate crosslinked copolymer from vegetable oil-derived N-octadecyl acrylate and acrylic acid for heavy metal ions adsorption","authors":"M. Mousavi, M. Mansour Lakouraj, M. Ehsani","doi":"10.1007/s13762-025-06686-w","DOIUrl":"10.1007/s13762-025-06686-w","url":null,"abstract":"<div><p>Heavy metal contamination of water resources is one of the most serious problems affecting human health and ecosystems. To remove heavy metal ions from polluted water, a bio-based cross-linked copolymer comprising vegetable oil-derived <i>n</i>-octadecyl acrylate and acrylic acid was synthesized via reversible addition-fragmentation chain transfer polymerization using multifunctional calix[4]resorcin-arene thioester as an initiator. Its amphiphilic structure, featuring both hydrophilic and hydrophobic domains, ensured efficient hazardous adsorption. Several techniques, including nuclear magnetic resonance spectroscopy, fourier transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy, Brunauer–Emmett–Teller analysis, and thermogravimetric analysis, were used to characterize and confirm the formation of the polymeric adsorbent. This copolymer has been evaluated for its ability to remove some common risky metal cations, including cadmium, nickel, and cobalt ions from aqueous solutions, which showed high adsorption efficiency. Optimization studies were conducted to maximize ion adsorption. In addition, pseudo-first-order and pseudo-second-order models were used to analyze adsorption kinetics, and Langmuir and Freundlich models were applied to study adsorption isotherms. The thermodynamics of adsorption was also investigated. As a result, high adsorption efficiency and effective ion desorption capability emphasize its recoverability and reusability.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":589,"journal":{"name":"International Journal of Environmental Science and Technology","volume":"22 14","pages":"13463 - 13482"},"PeriodicalIF":3.4,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028130","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}
T. O. Kolawole, J. O. Olajide-Kayode, L. A. Azeez, K. W. Fomba, M. T. Jimoh, M. O. Raheem, I. A. Oyediran
{"title":"Correction: Health risk assessment of potentially toxic elements in dust and soils of used lead-acid battery shops, Nigeria","authors":"T. O. Kolawole, J. O. Olajide-Kayode, L. A. Azeez, K. W. Fomba, M. T. Jimoh, M. O. Raheem, I. A. Oyediran","doi":"10.1007/s13762-025-06719-4","DOIUrl":"10.1007/s13762-025-06719-4","url":null,"abstract":"","PeriodicalId":589,"journal":{"name":"International Journal of Environmental Science and Technology","volume":"22 14","pages":"13981 - 13981"},"PeriodicalIF":3.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028257","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":"Short-term rainfall forecasting using multi-task learning and Weibull based postprocessing technique","authors":"S. M. Miri, M. R. Kavianpour, M. J. Alizadeh","doi":"10.1007/s13762-025-06690-0","DOIUrl":"10.1007/s13762-025-06690-0","url":null,"abstract":"<div><p>Reliable short-term rainfall forecast plays a key role for flood forecasting which can prevent or mitigate life and financial loses. In this study, a new framework integrating maximum overlap discrete wavelet transforms as a data preprocessing technique, long short-term memory as deep learning algorithm with a multitask learning approach, and a postprocessing technique gaining Weibull distribution are employed to achieve reliable rainfall forecasts from 1-h up to 12-h ahead. The model inputs include real time observations from a synoptic station (Aliabad) in Golestan Province. The multitask learning approach combine continuous rainfall forecasts from regression and rainfall detection from classification. Overall, the proposed framework showed efficiency to improve probability of detection and false alarm ratio as well as correlation between forecasted and real values. The postprocessing technique was applied to improve the model forecasts for extreme events since they were generally underestimated. The results demonstrate that the proposed methodology can be successfully for rainfall forecasts within various time windows accordingly. Furthermore, it only considers real time rainfall observations as the model inputs which is promising for regions with data shortage of other parameters such as temperature and humidity.</p></div>","PeriodicalId":589,"journal":{"name":"International Journal of Environmental Science and Technology","volume":"22 14","pages":"13571 - 13584"},"PeriodicalIF":3.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028256","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}
H. Esmaeili, M. A. Afshar Kazemi, R. Radfar, N. Pilevari
{"title":"From chaotic errors to natural curves: real-coded genetic calibration of wastewater treatment systems","authors":"H. Esmaeili, M. A. Afshar Kazemi, R. Radfar, N. Pilevari","doi":"10.1007/s13762-025-06714-9","DOIUrl":"10.1007/s13762-025-06714-9","url":null,"abstract":"<div><p>Non-normal residuals in rule-based wastewater controllers undermine reliability and hinder statistical monitoring. This study resolves the issue by fusing a zero-order Sugeno fuzzy-inference system with a real-coded genetic algorithm that jointly tunes rule weights and membership functions while steering errors toward Gaussian form. Fuzzy-cognitive mapping reduces the candidate rule set to five dominant rules, which are then optimized on a training–testing split from a full-scale plant. The resulting controller lifts the treated-water-quality index from 51.08 to 69.28, lowers mean-squared error and attains a test RMSE of 0.03; the residual standard deviation is virtually identical, confirming a near-normal error distribution.</p></div>","PeriodicalId":589,"journal":{"name":"International Journal of Environmental Science and Technology","volume":"22 14","pages":"13557 - 13570"},"PeriodicalIF":3.4,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028225","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":"Absorption of carbon dioxide using a deep eutectic solvent in a packed bed column","authors":"M. Kermaninejad, V. Mohebbi","doi":"10.1007/s13762-025-06713-w","DOIUrl":"10.1007/s13762-025-06713-w","url":null,"abstract":"<div><p>The increase in atmospheric carbon dioxide is primarily attributed to the extensive consumption of fossil fuels, raising global concerns about the greenhouse effect and related problems. Separating, transferring, and then storing or consuming carbon dioxide is one of the ways to reduce its emission. In recent years, many activities have focused on surveying new types of solvents, such as ionic liquids and deep eutectic solvents (DES). A DES is generally composed of two or three components, capable of self-association, often through hydrogen-bond interactions, to form a eutectic mixture. A deep eutectic solvent consisting of a choline chloride and diethylene glycol mixture (ratio 1:3) has been used. The experiments were carried out in a packed bed filled with Raschig rings. Results show that the volumetric mass transfer coefficient on the liquid side clearly increased with the liquid flow rate, while exhibiting a marginal increase with the gas flow rate. The measured value of the liquid side volumetric mass transfer coefficient was (0.83 × 10<sup>−2</sup> to 1.37 × 10<sup>−2</sup>) s<sup>−1</sup>. Volumetric mass transfer coefficients were determined and compared with published correlations in the literature. It showed good agreement with the experimental data, with a maximum relative error of less than 15%.</p></div>","PeriodicalId":589,"journal":{"name":"International Journal of Environmental Science and Technology","volume":"22 14","pages":"13545 - 13556"},"PeriodicalIF":3.4,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028224","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":"Graphene quantum dots functionalized dimercaptosuccinic acid-putrescine as a fluorescence probe for the determination of As(III)","authors":"A. Rostamiiranagh, R. Mohammad-Rezaei","doi":"10.1007/s13762-025-06671-3","DOIUrl":"10.1007/s13762-025-06671-3","url":null,"abstract":"<div><p>Here, a novel fluorescence probe based on graphene quantum dots (GQDs) capped with dimercaptosuccinic acid (DMSA) and putrescine (PUT) for fast and selective determination of As(III) was reported. Electron transmission microscopy and Fourier transform infrared spectroscopy have been used to characterize the prepared DMSA-PUT capped GQDs. Due to high affinity of DMSA-PUT functional groups to As(III), the FL intensity of the designed DMSA-PUT capped GQDs was linearly quenched in the presence of As(III) from 0.025 to 150 ppb with suitable reproducibility and repeatability. Fluorescence response of DMSA-PUT capped GQDs in the presence of several ions at various pHs were studied revealing suitable selectivity of the developed sensor for As<sup>3+</sup>. Statistical student’s t-test analysis of the standard NIST SRM 2669 and SRM 1572b confirmed the accuracy of the method against systematic and constant errors (> 95% recovery with < 5% RSD). The prepared fluorescent probe shows a limit of detection of 0.0132 ppb, which is appropriate for detecting hazardous As(III) in drinkable ground waters. The developed sensor was utilized for fluorescence probing of As<sup>3+</sup> in underground water samples with satisfactory analytical results.</p></div>","PeriodicalId":589,"journal":{"name":"International Journal of Environmental Science and Technology","volume":"22 14","pages":"13413 - 13422"},"PeriodicalIF":3.4,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028081","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}
H. Esmaeili, M. A. Afshar Kazemi, R. Radfar, N. Pilevari
{"title":"Integrated strategies for carbon emission control and air quality improvement in Iran","authors":"H. Esmaeili, M. A. Afshar Kazemi, R. Radfar, N. Pilevari","doi":"10.1007/s13762-025-06645-5","DOIUrl":"10.1007/s13762-025-06645-5","url":null,"abstract":"<div><p>Synergistic reduction of carbon emissions and air pollutants is critical to achieving two major strategic objectives: (a) significantly improving Iran’s ecological environment and (b) meeting its national targets for carbon mitigation and pollution control. Strengthening emissions management at the provincial level remains an urgent priority, not only for Iran but also for many fossil-fuel-dependent countries. Drawing on data from a major industrial region in Iran, this study applies environmentally extended input–output analysis combined with structural path analysis to identify the principal contributors to CO<sub>2</sub>, SO<sub>2</sub>, and total particulate matter (TPM) emissions and to map critical supply-chain emission pathways. The results reveal that fossil fuel extraction, nonmetal mineral products, metal smelting, power and heating plants, and the transportation sector dominate direct emissions. On the consumption side, construction activities, equipment manufacturing, and service sectors collectively account for over 45% of embodied CO<sub>2</sub>, SO<sub>2</sub>, and TPM emissions. Among the top 100 emission pathways, 32 are common across all three pollutants, representing 27–51% of Iran’s total emissions. These key pathways primarily involve exports and gross capital formation driving upstream emissions in sectors such as nonmetal mineral products and metal smelting. The findings offer a robust foundation for designing targeted mitigation policies and provide valuable insights for provincial strategies within Iran, as well as for other nations seeking effective approaches to reduce both carbon emissions and air pollution simultaneously.</p></div>","PeriodicalId":589,"journal":{"name":"International Journal of Environmental Science and Technology","volume":"22 13","pages":"12661 - 12676"},"PeriodicalIF":3.4,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814367","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}
A. Kheirdast, S. A. Jozi, M. M. E. Tehrani, S. Rezaian
{"title":"Presenting a conceptual model of spatial resilience in the face of fire in Tehran, Iran","authors":"A. Kheirdast, S. A. Jozi, M. M. E. Tehrani, S. Rezaian","doi":"10.1007/s13762-025-06635-7","DOIUrl":"10.1007/s13762-025-06635-7","url":null,"abstract":"<div><p>Crises are the main reflection of life mismanagement. Therefore, what is important in the occurrence of accidents is resilience of a place that can improve the continuity of the region's environment and provide stability and dynamism in times of crisis. This research aims to calculate the level of <i>spatial resilience</i> in the face of fire in Region 19 of Tehran. This is an applied research and the sampling method is random. The weights of the factors were calculated using Expert Choice software and the inconsistency rate was less than 0.1. The validity of the questionnaire was calculated through the CVR index for the items in the form of a Likert scale and its estimated value was 0.49. The reliability of the questionnaire was calculated by Cronbach's alpha coefficient and SPSS20 software and its value was 0.82. The data were standardized using the hierarchical evaluation model and ArcGIS<sub>10.6</sub> software. The model was designed using the Vensim<sub>PLx32</sub> system. Findings achieved by examining 3 criteria and 12 spatial indexes that identify the main features of resilience in Region 19 showed that Physical Resilience, Operational Resilience, and Infrastructural Resilience were prioritized respectively as the 1st, 2nd, and 3rd priorities with the importance factors of 0.12, 0.58 and 0.28. The results obtained from the spatial resilience zoning map show that 21.67% of the area (2,812,993 m<sup>2</sup>) has low resilience, and 43.79% of the area (5,682,805 m<sup>2</sup>) has medium resilience and 16.59% of the area (21,536,913 m<sup>2</sup>) has high resilience.</p></div>","PeriodicalId":589,"journal":{"name":"International Journal of Environmental Science and Technology","volume":"22 13","pages":"12717 - 12736"},"PeriodicalIF":3.4,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814248","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":"Ground-based data analysis and combined approaches for particulate matter 2.5 prediction","authors":"E. Nourmohammad, Y. Rashidi","doi":"10.1007/s13762-025-06499-x","DOIUrl":"10.1007/s13762-025-06499-x","url":null,"abstract":"<div><p>Effective air quality management requires accurate prediction of particulate matter 2.5 levels, which are influenced by various factors, including weather and human activities. This study explores integrating ground-based meteorological and traffic data with satellite-derived datasets to improve particulate matter 2.5 prediction accuracy in Tehran. The results demonstrate that while ground-based data can offer valuable insights, combining these datasets with satellite information significantly enhances predictive performance. Accurate prediction of particulate matter 2.5, a harmful air pollutant linked to respiratory and cardiovascular diseases, is critical for managing air quality in densely populated cities. This study compares remote sensing data with four configurations of ground data, meteorological and traffic data, and a combination of remote sensing and meteorological data in predicting particulate matter 2.5 concentrations across Tehran’s 22 districts. Ground data included meteorological factors, traffic data, and direct air quality measurements, supplemented by satellite-based aerosol optical depth estimates from NASA’s HD4 archives via Google Earth Engine. Using machine learning, deep learning, and statistical models, study evaluated the predictive accuracy of each dataset. The findings show that remote sensing data consistently outperforms all ground data configurations, offering superior performance and flexibility. This indicates that satellite-based remote sensing is an effective, independent tool for particulate matter 2.5 prediction, particularly in regions lacking ground monitoring infrastructure. These results underscore the potential of satellite-derived particulate matter 2.5 estimates for public health research and air quality management. The study emphasizes the importance of remote sensing in air pollution monitoring and proposes its integration into future air quality forecasting systems.</p></div>","PeriodicalId":589,"journal":{"name":"International Journal of Environmental Science and Technology","volume":"22 13","pages":"12625 - 12636"},"PeriodicalIF":3.4,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814402","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}