https://sustainearth.sbu.ac.ir/ Sustainable Earth Trends en daily 1 Wed, 01 Jul 2026 00:00:00 +0330 Wed, 01 Jul 2026 00:00:00 +0330 https://sustainearth.sbu.ac.ir/article_105950.html This study employs HEC-HMS and HEC-RESSIM software to simulate flood events and optimize reservoir operations in the Dez River watershed, Iran. The HEC-HMS model was calibrated using 21 historical flood events, with key parameters—including rainfall losses, unit hydrograph methods, and flood routing (using the Muskingum method)—adjusted to minimize error between observed and simulated discharge. Results reveal significant variations in hydrological response across sub-basins: Dorood and Dorood 3 exhibit slow runoff due to large drainage areas (3449 km² and 2655 km²) and low infiltration rates (0.91–1.1 mm/hr), while Tang Panj basins show rapid response (lag times of 2.0–2.5 hr) and high infiltration (2.19–4.4 mm/hr), likely due to steep terrain and permeable soils. For reservoir management, HEC-RESSIM was used to simulate Dez Dam operations under current and elevated water level scenarios (352–362 masl), prioritizing downstream demands (irrigation, municipal supply, and ecological flows). Seasonal allocation patterns were identified, with peak diversions in June–July (320–324 MCM) for agriculture and minimal releases in winter (87–96 MCM). Raising the dam height by +8m (360 masl) optimally balanced flood control (22–25% storage increase) and drought resilience, though +10m required costly infrastructure upgrades. The study underscores the trade-offs between storage capacity, operational flexibility, and environmental needs, providing a framework for adaptive water resource management in semi-arid regions. https://sustainearth.sbu.ac.ir/article_106062.html Soil erosion is a major environmental challenge in arid and semi-arid regions, threatening agricultural productivity, water resources, and ecological stability. This study quantified and spatially analyzed soil erosion in the Birjand Plain, eastern Iran, by integrating the Revised Universal Soil Loss Equation (RUSLE) model with satellite-derived data using the Google Earth Engine (GEE) cloud-computing platform. Key erosion factors—including rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and support practices (P)—were extracted from multi-source remote sensing datasets. The results revealed considerable spatial variability in erosion intensity across the plain. Approximately 67.3% of the area experiences very low soil loss rates (0–10 tons/ha/year), while 22.9% is categorized as low erosion (10–20 tons/ha/year). Moderate (20–40 t/ha/year), high (40–60 t/ha/year), and very high (>60 t/ha/year) erosion zones constitute 7.5%, 2.0%, and 0.2% of the area, respectively. These high-risk zones are often associated with steep terrain, sparse vegetation, and poor land management. The use of GEE facilitated fast, large-scale erosion modeling with high spatial resolution and minimal reliance on ground data. This approach proves to be scalable, cost-efficient, and reproducible, particularly for data-scarce regions. The findings provide valuable insights for land managers and policymakers aiming to prioritize soil conservation efforts and develop sustainable land-use strategies. This research underscores the potential of integrating remote sensing and cloud-based tools with empirical models to enhance environmental monitoring and resilience in erosion-prone landscapes. https://sustainearth.sbu.ac.ir/article_106067.html Drought stress is regarded as the primary limiting factor for different crops within agricultural systems located in arid and semi-arid areas. Thus, choosing suitable plant seeds and assessing their growth under drought stress serves as an effective approach to manage these conditions and yield optimal crops. The tomato plant is an essential annual crop that is adversely affected by drought stress, and its germination stage is notably susceptible. The selection of commercial tomato seeds for economic production is crucial. In this regard, this research focused on analyzing the germination parameters influenced by low water potential induced by mannitol, which simulates drought stress, and the growth of the tomato seed Super Strain-B (F1). This research was conducted at room temperature, where seed germination occurred in Petri dishes with varying concentrations of the osmoticum (0, -3, -6, -9, and -12 bars of drought induced by mannitol). Our findings indicate that drought stress by mannitol significantly decreased germination. The findings indicated that in treatments with -9 and -12 bar potential, the germination parameters of seeds, like germination percentage and rate, mean daily germination, hypocotyl and epicotyl length, and fresh and dry weight of epicotyl and hypocotyl, were significantly lower, with some treatments showing zero amount in comparison to others. In contrast, the control treatment exhibited faster germination parameters, so that complete seed germination occurred by the ninth day. The findings of this study have important implications for helping tomato production and sustainable land management, especially in regions with limited water resources. https://sustainearth.sbu.ac.ir/article_106108.html The research highlights the necessity of innovative governance and strategic management to ensure sustainable drinking water infrastructure, crucial for public health and economic growth amid climate change, which is a critical component of the sustainable earth trend. In this study, VOSviewer software was used for bibliometric analysis and visualization of conceptual relationships to examine the conceptual structure and research trends in areas related to strategic management of smart financial markets aimed at providing safe drinking water. The period for the studies being examined was chosen to span from 2020 to 2025. This search yielded 335 documents, and after filtering, 199 documents were analyzed, which included 87 research papers, 63 review papers, and 49 books. Four primary scientific outputs from the software were utilized for data analysis. Climate change, urbanization, and geopolitical dynamics have made access to water resources more complex. Investing in drinking water infrastructure not only helps improve public health and reduce healthcare costs, but also leads to economic growth. Transparent and equitable governance of water can help strengthen social cohesion and reduce international tensions. The use of smart financial markets and blended finance can help improve access to safe and sustainable drinking water. Especially in rural and remote areas, these approaches can help reduce inequalities and increase resilience to climate change. Finally, investment in technology, education and infrastructure development will be key elements for success in providing safe and sustainable earth trend. https://sustainearth.sbu.ac.ir/article_106121.html Riverine systems are of significant morphological importance due to their key role in the management and protection of floodplains. This study aims to evaluate the morphological changes and management of the Kal-e Shur River in Sabzevar using the Rasgen classification system. Initially, morphometric parameters were calculated using HEC-RAS software, and 419 cross-sections across 40 river reaches were extracted from satellite imagery. Sediment samples were collected from the riverbed to characterize the local sediment types. Nineteen geomorphological parameters were analyzed, and the most influential factors controlling river type were identified using the Information Gain Ratio (IGR) index. Results indicated that land use, precipitation, elevation, soil type, drainage density, vegetation cover index (NDVI), lithology, topographic slope, distance from the channel, topographic wetness index (TWI), and sediment transport index (STI) exerted the greatest influence. Segments classified as type D, with gentle slopes and sandy beds, exhibited poor conditions regarding sediment supply and erosion, although vegetation cover in these areas remained relatively preserved. Type C reaches were characterized by significant channel incision and high potential for erosion and flooding. In type B rivers, changes in flow patterns and sediment input contributed to channel instability. Conversely, type A, E, and G segments showed stable beds, lower sensitivity to turbulence, and better-preserved vegetation cover. These findings demonstrate that the Rasgen classification aligns well with geomorphological and morphometric factors, supporting its application in river management, restoration, and the mitigation of erosion, sedimentation, and flood hazards. https://sustainearth.sbu.ac.ir/article_106141.html Securing accurate insights into crop water requirements is crucial, particularly in arid regions and for key strategic crops. This study presents a novel applied methodology to evaluate the efficacy of ensemble modeling in determining the Saffron Water Requirement (SWR). The research aims to generate more reliable insights and enhance the accuracy of water requirement estimations for saffron, a vital crop in eastern Iran. The proposed plan includes establishing a rigorous testing process to evaluate the efficiency of various ensemble methods. Three significant ensemble classes— Ensemble Learning Machine (ELM), combination, and averaging techniques — were addressed to produce the new prediction of SWR. As such, a Decision Tree Regression (DTR) tool and six different experimental methods, serving as base models, were initially applied. The effectiveness of various ensemble methods was evaluated using statistical and qualitative tests. This plan included time series comparisons, key diagnostic indices such as RMSE and NSE, Absolute Error Decomposition (AED) analysis, and the Rate of Improvement (ROI). Results showed that applied ensemble systems are not only of high quality but also capable of presenting a skillful prediction of SWR compared to base models. Results revealed that the boosting procedure had a beneficial effect on DTR simulations, increasing them by more than 74 percent. Additionally, combining methods could enhance the base prediction by more than 75%. https://sustainearth.sbu.ac.ir/article_106151.html Petroleum hydrocarbons are toxic to humans and other organisms, and some are carcinogenic. Various remediation techniques have been developed to address this issue sustainably, including thermal methods. Low-temperature thermal desorption (LTTD) is a treatment technology that offers an environmentally sustainable approach to removing contaminants from solid media by volatilizing them with heat but without combustion of the media. This study is aimed at evaluating the efficiency of LTTD to remove phenanthrene and pyrene, and their mixture from contaminated soils. The soil samples were contaminated with these compounds at three concentration levels, including 2000, 4000, and 8000 mg kg-1 at different temperatures (i.e., 100, 200, and 300 oC) and retention times (i.e., 30, 60, and 90 min). The compounds were extracted using an ultrasonic bath and analyzed with HPLC. Analysis of the findings obtained by the Taguchi method indicated that increased concentration, temperature, and retention time led to a higher efficiency in removing the compounds from contaminated soils. The results indicate that increasing temperature, retention time, and contaminant concentration positively influence removal efficiency, with the highest desorption rates reaching over 88%. These findings suggest that LTTD is a promising solution for addressing soil contamination by petroleum hydrocarbons, offering an efficient and eco-friendly approach for remediation. Therefore, LTTD could be a highly effective and sustainable solution for removing pollutants from contaminated soil, as well as in severe contamination. https://sustainearth.sbu.ac.ir/article_106176.html The accelerated urban growth of Tehran has introduced significant modifications to its underlying geosystems, with cascading effects on ecological patterns and land use dynamics. This research investigated the dynamic relationship between Tehran’s geological evolution and its ecological conditions, focusing on how geosystems influenced the ecological dynamics of western Tehran, particularly regarding land use patterns. The novelty of this work lies in integrating geosystem variables (lithology [L], river proximity [R], and fault distribution [F]) into a fuzzy logic–based a land use (LU) change index, empirically validated through a network of control points. The research was conducted in the foothills of the Alborz Mountains and along the Vardij River. Data collection involved geological mapping, remote sensing, and field studies. The findings emphasized the critical role of geosystems in shaping ecosystems, revealing that significant land use changes occurred within a susceptible LU index range of 4.02 to 4.90, contrary to the belief that high LU indexes predominantly drive urban development. This transformation was influenced by the rugged topography and the optimization and vulnerability of the studied variables, highlighting the need for effective environmental management and conservation strategies in the face of increasing urbanization pressures. Ultimately, this study underscored that understanding the relationships between geosystems and ecosystems was essential for preserving the ecological integrity of western Tehran. By integrating geological factors into land use planning, stakeholders could better manage natural resources and protect the region's ecological heritage. https://sustainearth.sbu.ac.ir/article_105808.html Wheat (Triticum aestivum L.) is a vital global food source, increasingly threatened by climate change, particularly drought conditions that adversely affect key growth parameters, including plant height, branch number, and grain weight. This study aims to assess the growth performance of two local wheat cultivars, Bhouth 22 and IBA 99, under drought stress conditions in Basrah, Iraq. Conducted over an area of 0.25 hectares, the research evaluates key metrics including plant count, branch and leaf numbers, and weight of 1000 grains. Results indicate that IBA 99 exhibits a higher yield per hectare, averaging 4.21 tons under 3-day irrigation compared to 4.09 tons under 6-day irrigation. In contrast, Bhouth 22 shows an average production of 5.21 tons under 6-day irrigation, demonstrating its potential for higher productivity in specific conditions. The weight of 1000 grains for IBA 99 averaged 51.02 grams under 3-day irrigation, while Bhouth 22 averaged 52.91 grams under 6-day irrigation, indicating superior grain quality. Statistical analysis reveals significant differences in growth responses, particularly in grain weight and production metrics, with P-values below 0.05 indicating statistical significance. Additionally, drought conditions resulted in decreased tillering and leaf area, which negatively impacted photosynthetic capacity and overall growth. The findings underscore the importance of developing drought-resistant wheat varieties to enhance agricultural resilience and ensure food security amid escalating climate challenges. This research contributes valuable insights for breeding programs aimed at improving wheat productivity in water-limited environments, essential for sustaining global food supplies. https://sustainearth.sbu.ac.ir/article_105820.html This research conducts a Life Cycle Assessment (LCA) of Top White Liner paper produced by Persia Golestan Paper Factory, utilizing SimaPro software and the ReCiPe method to assess its environmental effects. The functional unit for this assessment was 1 ton of Top White Liner paper. Notably, this marks the first application of this method to assess the effects of paper production in Iran. Findings indicate that calcium carbonate is the primary contributor to adverse environmental impacts, including global warming, particulate matter formation, acidification, ecosystem toxicity, and resource depletion. Furthermore, Alkyl Ketene Dimer (AKD) and sodium hypochlorite significantly influence ozone depletion, marine eutrophication, and water consumption. Prioritizing human health and ecosystem quality, the overall environmental score for the product was determined to be -19.8 Pt, signifying a net environmental benefit. A comparative analysis with similar recycled paper products from global SimaPro databases reveals that while Top White Liner demonstrates superior performance in critical categories such as global warming and particulate matter formation, further optimization is required in toxicity, eutrophication, and resource depletion. These findings underscore the imperative of integrating recycled materials and implementing process improvements to mitigate the environmental footprint of paper production. This study exemplifies the utility of integrated LCA methodologies in guiding sustainable production practices and informing policy development within the paper industry, thereby contributing to broader sustainable development goals. https://sustainearth.sbu.ac.ir/article_106221.html Pirozeh (turquoise) is a supergene mineral that forms from meteoric water along fractures which is associated with copper deposits. Although the Persian Pirozeh (turquoise) has been mined since at least the first millennium BCE, it is not a well-known gemstone for geologists. The aim of this paper is about mineralogy and mineral chemistry of the Pirozeh (turquoise) samples of Baghu (Damghan) and Nishapur areas of Iran by using of the scanning electron microscope (SEM). The samples are also analyzed by Energy-dispersive X-ray spectroscopy (EDS) for measuring the major element concentrations of Pirozeh (turquoise) group minerals as well as other mineral phases accompanying them. The results show that the origin of the Pirozeh (turquoise) samples from these areas can be determined by scanning electron microscopy due to their different mineral assemblages and textures. The EDS results suggest that by increasing the Cu content of Pirozeh (turquoise) group minerals their colors move to deeper blue while in contrast green Pirozeh (turquoise) minerals are of notable concentration of Fe. the data propose that the Cu and Fe concentrations of Pirozeh (turquoise) group minerals are not the only factors affecting the color of this gemstone. The geochemistry of Nishapur Pirozeh (turquoise) is defined by high Cu, high Al, and very low Fe/Zn. This results in its celebrated stable blue color. But the Baghu Pirozeh (turquoise) is geochemically distinct due to its higher Fe content. This acts the color from pure blue into the green-blue and green spectrum. https://sustainearth.sbu.ac.ir/article_106229.html This paper addresses the literature gap in analyzing the impacts of climate change on peri-urban agriculture, with a focus on the Tehran city region, a critical agricultural hub in Iran that is vulnerable to climate variability. The objective was to quantify the impacts of climate change on the yields of key crops—wheat, barley, apples, walnuts, grapes, cucumbers, watermelons, cotton, and alfalfa—using quantile regression models (0.25, 0.50, and 0.75) to capture heterogeneous yield responses. The models, applied to crop yield records from 1994 to 2014 and climatic data from five synoptic stations, are robust to outliers and estimate conditional quantiles of yield distributions. Impacts were projected for three time slices (2030s, 2060s, and 2080s) under IPCC RCP4.5 and RCP8.5 scenarios. Results reveal that climate variability has a significant but non-monotonic impact on major crop yields. The highest positive and negative impacts are on walnuts and apples, respectively. These results have many implications for planners and other stakeholders involved in peri-urban agriculture and can provide important insights for place-specific adaptation and mitigation measures. Several adaptation strategies can be implemented to mitigate negative impacts and capitalize on potential benefits, including modifications to crop cultivars and planting schedules, the development of institutional frameworks and infrastructure to address water scarcity and enhance food security, and the education of farmers on climate-related challenges and the adoption of advanced agricultural technologies. https://sustainearth.sbu.ac.ir/article_105650.html Today, water pollution poses potential risks to human health and the environment. Recent research activities have focused on advanced oxidation processes (AOPs) as innovative technologies for water purification. Cerium oxide (CeO2) is one of the most abundant rare earth oxides, which, when combined with other metals, creates an active surface and enhances photocatalytic properties. This study investigated the antibacterial properties and photocatalytic activity of cerium oxide nanoparticles combined with the semiconductors nickel and copper in the removal of organic pollutants (Amoxicillin) and antibacterial. The results showed that these nanocomposites have significant antibacterial properties at high concentrations. The optimal amount of the nanocomposite was found to be 0.7 mg/L, with a pH of 3 being the optimal condition for this experiment. Additionally, the optimal concentration of the pollutant was 5mg/L, which, in the presence of the nanocomposites CuCo2O4/CeO2 and NiCo2O4/CeO2, successfully removed 81% and 78% of the organic pollutant, respectively, under UV-A light exposure. The results of this study indicate that the synthesis of CuCo2O4/CeO2 and NiCo2O4/CeO2 nanoparticles serves as an effective antibacterial and highly photocatalytic material that can be used for the removal of organic pollutants and antibacterial. Considering the frequency of presence of organic pollutants and pathogens in the water environment, this research aims to strengthen CeO₂ with different morphologies by metals such as Cu and Ni so that they can act as recombination centers and increase its photocatalytic properties. https://sustainearth.sbu.ac.ir/article_106235.html This study was carried out to assess soil moisture variability in relation to climate across different land uses in a tropical region, to evaluate the variations in climate and soil moisture under grassland, forest land, and continuously cultivated land. Soil moisture was measured weekly at two depths (0–20 and 20–40 cm) for the period of January 2024 to December 2024. Meteorological data (rainfall, temperature, sunshine duration, and soil moisture) in this study were obtained by remote sensing from the National Oceanic and Atmospheric Administration (NOAA). Line graph analysis was used to explain the variations of soil moisture and climate under different land use, while regression analysis showed the relationship between soil moisture and climate. The study showed that land use influenced atmospheric temperature, rainfall, and sunshine duration, with continuously cultivated land recording the highest temperature (29.38°C). Forest land received higher rainfall (458.95 mm month¯¹) than grass land and continuously cultivated land. The study also showed that forest land maintained higher soil moisture (1.88 kg kg¯¹) levels than other land uses. Land use systems significantly influenced sunshine duration, with continuously cultivated land recording the highest sunshine duration (5.04 hours day¯¹). The result also showed that soil moisture exhibited a negative relationship with sunlight duration and atmospheric temperature, and had a strong positive relationship with rainfall. The findings of this study can be used as important implications for sustainable land management practices, particularly in regions with limited water resources. https://sustainearth.sbu.ac.ir/article_106327.html Shiraz, recognized as the cultural capital of Iran, has increasingly faced the Surface Heat Island Urban (SHIU) phenomenon due to rapid urbanization and population growth. This study examines SHIU effects in Shiraz by analyzing Land Surface Temperature (LST) derived from MODIS satellite imagery (MOD11A2 and MCD12Q1 products) from 2000 to 2024, using GIS techniques to assess spatial thermal patterns and urban microclimate dynamics. The LinearFit function was applied to evaluate temporal trends in SHIU, while spatial variables—including building density, land cover, green space ratio, public green areas, population metrics, and construction activity—were analyzed across 11 urban zones. Findings revealed that daytime SHIU cores reached up to 32°C in the south, west, and parts of the north, while nighttime cores (13–14°C) expanded toward central and historic districts (Zone 8). The most significant nighttime LST increase (2–3°C) occurred in the northern and western zones (1, 4, 6, and 10), and to a lesser extent in the south (Zone 7), where substantial land cover changes and urban development were observed. These results underscore the critical need for climate-sensitive urban planning strategies. Integrating green infrastructure, regulating building density, and preserving open spaces can help mitigate SHIU effects and enhance urban resilience. The study provides actionable insights for policymakers, urban designers, and planners to promote sustainable development and improve thermal comfort in rapidly growing cities like Shiraz. https://sustainearth.sbu.ac.ir/article_106362.html Architects and planners' concern is to decrease the impact of urbanization on the natural environment and increase human-nature relationships. A biophilic city is a city full of nature, a place where, in the normal course of work and life, residents feel, see, and experience rich nature. The present study aims to analyze the capabilities of Ahvaz city to determine how far this city is from becoming a biophilic city. The current research is descriptive-analytical and applied regarding content and methodology. The statistical population of the research consists of geography and urban planning experts. The result of their viewpoint was the basis of the research analysis. The Entropy and WASPAS methods were used for data analysis. Eventually, the IDW interpolation method has been utilized to display the biophilic city index conditions in the Ahvaz region. The research results found that region 2 has the best quality of biophilic indicators compared to other regions. Region 3 of Ahvaz ranked next with an extremely negligible difference. The region that has given the lowest rank is region 7. The results of the research showed that the majority of the regions of Ahvaz city have a significant weakness in the biophilic city indicators due to climate conditions. Nevertheless, Ahvaz can become a biophilic city due to its river "Karoun", plus the appealing and diverse plant species. However, it requires related groundwork to diminish the impact of the city's climatic features as much as feasible. https://sustainearth.sbu.ac.ir/article_106383.html This paper introduces an advanced structural health monitoring (SHM) framework that integrates Bayesian data fusion with a Modified Teaching–Learning-Based Optimization (MTLBO) algorithm to improve damage identification in space frames and truss structures. The proposed method utilizes multiple vibration-based damage indices, including Modal Strain Energy (MSE), Frequency Response Function Strain Energy (FRFSE), Flexibility Strain Energy (FSE), and Residual Force-Based (RFB) indicators, derived from natural frequencies and mode shapes. These indices collectively enhance damage localization accuracy while reducing the number of false or redundant damaged elements, thereby improving computational efficiency. Through the application of Bayesian data fusion, the approach establishes a robust probabilistic framework that effectively integrates different damage indicators to refine detection results. The incorporation of the MTLBO algorithm further enhances the convergence speed and accuracy of the optimization process, expediting the identification of damage across complex structural systems. Moreover, by employing a probabilistic objective function, the framework demonstrates notable resilience to measurement noise, ensuring reliable performance under uncertain conditions. A key innovation of this study lies in the simultaneous probabilistic treatment of both damage localization and damage severity estimation, enabling more precise, noise-resistant, and computationally efficient damage detection. This hybrid methodology represents a significant advancement in SHM, offering a robust and practical solution for real-world structural assessment and maintenance applications. https://sustainearth.sbu.ac.ir/article_106451.html This study highlights that sustainable groundwater management in arid regions hinges on using vulnerability models to guide protective policies, ensure water security, and advance Sustainable Development Goal (SDG 6) on clean water and sanitation. The research evaluates the vulnerability of the unconfined alluvial aquifer in the Bam–Narmashir Plain, Kerman Province, using two parametric models: DRASTIC and SINTACS. The primary objectives were to compare the performance of these models in identifying pollution-prone zones and to assess the degree of agreement between their outputs and actual groundwater quality data. Seven key parameters were considered: depth to water table, net recharge, soil type, topography, impact of the unsaturated zone, hydraulic conductivity, and lithology. Results indicate that the central and northeastern parts of the aquifer exhibit higher vulnerability due to the high permeability of sediments and shallow groundwater depth. A comparison of the vulnerability maps generated by the two models revealed that the SINTACS model, owing to its greater flexibility in parameter weighting, offers superior discrimination of high-vulnerability zones. Model validation using nitrate concentration data showed that, although both models adequately reflect the general pattern of contamination, their correlation with actual field measurements requires improvement. Overall, the findings underscore the necessity of locally calibrating vulnerability models and implementing continuous groundwater quality monitoring for the sustainable management of water resources in the Bam–Narmashir region. https://sustainearth.sbu.ac.ir/article_106501.html The aim of the present study was to design an urban justice model in Sari City. This study is applied in terms of purpose, exploratory in terms of data collection method, and qualitative and quantitative (mixed) in terms of data nature. The data collection method in the qualitative part was interviews, and the analysis method was the Grounded Theory technique in the MAXQDA 2018 software environment. In the quantitative part, the data were analyzed using the Delphi technique and then structural equations. The statistical population in the qualitative part included 15 senior managers of Sari Municipality and academic elites from the fields of urban management and planning, who were interviewed in-depth using the snowball method until theoretical saturation was reached. In the quantitative part, the statistical population included all employees of Sari Municipality, and to measure the model, a researcher-made questionnaire was distributed among 285 of the samples using the relative cluster sampling method. The research findings showed that the factor loading of all items was more than 0.6, which means the validity of the items. Also, considering the value of the path coefficients obtained among the variables, it can be stated that all the existing relationships between the variables were positive and direct, with the highest relationship being related to the spatial justice variable, which was equal to 0.471, and the lowest factor being the structural justice variable with a path coefficient of 0.177. The findings indicate that the proposed urban justice model for Sari City is valid and reliable, with all item loadings above 0.6. Spatial justice emerged as the most influential factor, while structural justice had the least impact, highlighting the need to prioritize spatial considerations in urban planning for enhancing overall urban justice. https://sustainearth.sbu.ac.ir/article_106503.html Human development and land transformation have turned land use and land cover (LULC) change into one of the major global environmental challenges, affecting essential biogeochemical cycles such as water, carbon, and energy. This study aims to model and predict LULC changes in the Birjand watershed, located in eastern Iran, using an Artificial Neural Network (ANN) within the Land Change Modeler (LCM) framework. Landsat satellite imagery (TM and ETM+) was used to detect historical changes, while eight explanatory variables digital elevation model, slope, soil texture, and distance from roads, rivers, faults, residential areas, and agricultural lands were applied to simulate transition potentials for the 2000–2010 calibration period. A Markov chain analysis was then employed to estimate the probability of transitions between land use classes, and predicted maps were generated for 2020. The results revealed that barren lands were predominantly converted into residential areas, reflecting rapid urban expansion. Validation of the simulated 2020 map against observed data indicated strong model performance, supported by accuracy metrics such as the Kappa coefficient and Overall Accuracy. Based on the validated model, LULC projections for 2040 show a continued decline in agricultural lands alongside the expansion of urban and barren areas. The projected areas amount to approximately 73.82 km² of agricultural land, 68.81 km² of urban land, and 3,282.39 km² of barren land. These projections underscore the urgent need for sustainable land-use planning and management strategies to minimize environmental degradation in arid regions. https://sustainearth.sbu.ac.ir/article_106536.html In this study, a completely randomized design with 9 treatments at three replications was used to investigate the effect of Leonardite on the chemical behavior of phosphorus in a calcareous soil. The experimental treatments included a control group, three levels of phosphorus (100 and 200 mg/kg) supplied as monopotassium phosphate, Leonardite at four levels (0, 1, 3, and 5% w/w), and their combinations (P100L0, P100L1, P100L3, P100L5, P200L0, P200L1, P200L3, and P200L5). The phosphorus treatments were added to one kilogram of soil and maintained under controlled conditions for one month. Subsequently, the Leonardite treatments were applied to the soils, and incubation continued for 90 days. At the end of the incubation period, samples were taken from each treatment, and the amount of released phosphorus was measured at 2, 4, 8, 24, 48, 96, and 120 hours. The results of the kinetic investigations indicated that the addition of different levels of Leonardite increased the phosphorus (P) availability in the soil, which was attributed to the low pH and acidic functional groups present in Leonardite. The Elovich equation, with the highest coefficient of determination and the lowest standard error, was the best equation describing the kinetics of P release in the soil. This study confirmed that Leonardite application serves as an effective organic amendment in calcareous soils, significantly enhancing phosphorus availability while improving phosphorus uptake and utilization in plants. These findings demonstrated Leonardite's potential for advancing sustainable agriculture and supporting environmental conservation in arid and semi-arid regions. https://sustainearth.sbu.ac.ir/article_106549.html This study aims to examine the factors influencing the reduction of fossil energy consumption and the achievement of zero-carbon urban status. The research employs a descriptive–analytical approach and, due to its emphasis on correlations among variables, is classified as correlational research. It is also applied in purpose, cross-sectional in timing, and extensive in scope. DesignBuilder software was used to calculate and analyze the energy performance of a residential building selected as the case study. The building’s initial annual energy consumption was measured at 103.61 kWh/m². After implementing several carbon-reduction strategies, including enhanced insulation, optimized glazing ratios, shading improvements, and passive solar design, the consumption decreased to 56.62 kWh/m², representing a reduction of about 45 percent. Further analysis showed that integrating passive and active measures together generated greater efficiency gains than applying each measure separately. Sensitivity results indicated that envelope improvements and solar-responsive design had the strongest influence on overall energy reduction. The findings reveal that Semnan city possesses significant potential for clean-energy enhancement due to its high solar availability and climate characteristics. The study concludes that applying simulation-based evaluation alongside climate-responsive design can play a critical role in guiding Semnan toward a zero-carbon trajectory. The results also emphasize the importance of developing localized energy guidelines, encouraging efficient building technologies, and supporting urban planning policies aligned with sustainability principles. The main innovation of this research lies in presenting the first comprehensive simulation-based assessment of building energy performance in Semnan using this methodological framework. https://sustainearth.sbu.ac.ir/article_106558.html Underground dams serve as an innovative solution for water resource management in arid and border regions, playing a critical role in aquifer recharge and minimizing water loss. This study evaluates the sustainability of the Karnaweh Shirin underground dam in Kalat County, Razavi Khorasan Province, using structural analysis (MICMAC) and integrating field and documentary data. By identifying 14 indicators across three dimensions—economic (economic yield, income stability), social (community participation, equitable utilization), and environmental (water quality, biodiversity)—the complex relationships among variables were analyzed, and key drivers were identified. The results indicate that income stability, water and food security, equitable utilization, and biodiversity conservation are the primary drivers, while social conflicts and design-implementation inconsistencies pose significant challenges. This research provides an integrated framework for enhancing the sustainability of underground dams and offers a model for similar regions through recommendations such as strengthening community participation, managing conflicts, and implementing engineering improvements. The findings underscore the necessity of systemic approaches in managing water resources in arid border regions.  https://sustainearth.sbu.ac.ir/article_106573.html The Ganges Kobadak (G-K) Irrigation Project is the largest irrigation initiative in South Asia, which has historically met the water demands of this region with minimal electricity consumption. However, its effectiveness has declined in recent years due to both technical and climate-related challenges. This study follows a qualitative approach to explore the project's inception, historical performance, and the impact of climate change on its current issues. Data were collected through in-depth interviews using a semi-structured interview guide, involving a total of 11 respondents, including seven farmers, two residents, and two project officials. The thematic analysis technique was followed to present the findings. This study found that after the construction of the Farakka Barrage, the G-K project has experienced a loss in efficiency, with severe droughts, flash floods, and declining groundwater levels complicating water availability. Increased salinity in soil and water, excessive heat, and mechanical issues have further undermined agricultural production, threatened food security, and prompted migration. The study concludes that urgent intervention is needed to address these issues. Recommendations include revising the water distribution treaty with India, rehabilitating technical infrastructure, modernizing equipment, increasing manpower, and ensuring equitable water distribution. Climate-driven challenges such as extreme weather conditions, water salinity, and inconsistent rainfall require immediate, comprehensive action to restore the project's effectiveness and safeguard the livelihoods of millions of farmers. A fair water-sharing agreement and comprehensive project rehabilitation are vital to ensuring food security and reducing climate change issues in the project. https://sustainearth.sbu.ac.ir/article_106602.html Soil erosion poses a significant threat to environmental stability, land fertility, and food security, necessitating precise assessment and mapping. In this study, three widely used machine learning algorithms, including Random Forest (RF), Generalized Linear Model (GLM), and Multivariate Adaptive Regression Splines (MARS), to predict the sensitivity of various water erosion forms in Gonabad County, an arid and semi-arid region in Iran. Thirty-one environmental variables (physiographic, climatic, hydrological, pedological, and land cover) were initially prepared using remote sensing and GIS data. Following collinearity analysis via the Variance Inflation Factor (VIF), 21 variables were selected as model inputs. Erosion types, including rill, channel, streambank, and gully erosion, were recorded using the BLM method and extensive field surveys.The results showed that the RF model performed better in predicting different forms of water erosion than the GLM and MARS models. Specifically, the RF model achieved superior performance with Overall Accuracy (OA) ranging from 0.866 to 0.994, and Kappa coefficients between 0.723 and 0.988 across various erosion facies. The ROC and TSS indices also confirmed the higher predictive power of the RF algorithm compared to its counterparts. Sensitivity analysis identified geology, geomorphology, land use, and soil properties (litter, gravel, gypsum, clay, acidity, hydrological groups, evaporation) as the primary drivers of erosion. Furthermore, channel erosion was identified as the dominant type, covering 24.85% of the study area. This research underscores that integrating machine learning with GIS and remote sensing provides a robust framework for soil erosion assessment and sustainable natural resource management. https://sustainearth.sbu.ac.ir/article_106603.html Accurate simulation of climatic parameters such as air relative humidity (RH) is essential for understanding regional climate dynamics, particularly in climatically diverse regions. This research evaluates the performance of seven CMIP6 climate models in simulating RH across two cities with distinct climates in Iran: Rasht (humid) and Yazd (arid). For this purpose, historical RH data (1990-2014) were compared with model outputs using the Kling-Gupta Efficiency (KGE) metric, bias correction via Quantile Mapping (QM), Corrected Ratio of the Mean (CRM) and ensemble modeling. In Rasht, only three models yielded positive KGE values, with CanESM5 performing best (KGE=0.21), while the EM achieved a higher accuracy (KGE=0.29) but slightly underestimated RH (CRM=-0.00176). In Yazd, all the models showed acceptable performance, with CanESM5-CanOE leading (KGE=0.72) and the EM outperforming individual models (KGE=0.77 and CRM=0.00024). Future RH projections (2026-2050) under three SSP scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) revealed that Rasht may experience significant RH declines under SSP5-8.5, while the RH trends in Yazd remain relatively stable across these scenarios. These findings highlight the importance of ensemble modeling and bias correction in improving RH simulations, particularly in arid regions. https://sustainearth.sbu.ac.ir/article_106605.html Rainfall patterns are essential for effective water resource management, vegetation planning, and developing climate adaptation strategies. This study aims to delineate dry land regions and seasonal rainfall regimes to better understand vegetation diversity and inform management strategies in Southeast Ethiopia. The analysis includes assessment of annual and seasonal rainfall variability, long-term trends, and extreme events to support effective vegetation planning. Dry lands region were identified using the United Nations Environment Programme (UNEP) criterion based on the precipitation-to-potential evapotranspiration (P/PET) ratio (<0.65). Historical rainfall and temperature (maximum and minimum) data from 1987 to 2020 for 28 meteorological stations were analyzed using DrinC and ArcGIS software. The result revealed that 71.4% of the stations (20 sites) fall within dry land regions, while the remaining eight stations have aridity index values greater than 0.65, indicating non-dryland conditions. Based on spatial and temporal rainfall characteristics, the study area was divided into northern and southern regions. Annual rainfall variability was moderate in the north and high in the south region, while seasonal rainfall was high across the entire area. Most stations in both regions exhibited stable annual and seasonal rainfall trends with no significant variation at 95% significant level, although the localized significant trends in Tedechebila, Adigala, Asebot, and Gode were observed. Asebot recorded the highest annual rainfall (1106 mm), whereas Kebridehar recorded the lowest (30 mm). These findings highlight the need for region-specific water and land management and improved monitoring strategies to sustain vegetation diversity in dryland ecosystems. https://sustainearth.sbu.ac.ir/article_106633.html High-resolution temperature data are essential for climate-sensitive decision-making. This study compares the downscaling performance of ERA5-Land and MSWX reanalysis datasets using three ensemble-based machine learning models—Random Forest, XGBoost, and LightGBM—over the topographically complex Karun and Jarahi Basins in Iran. A two-stage framework involving spatial downscaling and error correction was implemented with auxiliary predictors. To address class imbalance, SMOGN and KNNOR-Reg resampling techniques were tested. Results show that LightGBM consistently achieved the best predictive accuracy (RMSE ≈ 1.85°C–2.31°C), with MSWX yielding superior results overall due to its finer spatial resolution. KNNOR-Reg preserved the physical plausibility of temperature distributions better than SMOGN. Visual assessments confirmed that downscaled outputs effectively captured terrain-driven microclimates. This comprehensive evaluation highlights the importance of dataset choice, model selection, and balancing strategy in downscaling workflows and underscores the potential of machine learning in improving the utility of coarse-resolution climate data for localized applications. These advancements ultimately enhance the reliability of climate information needed to support environmentally sustainable planning, resource management, and climate-resilience strategies in vulnerable regions. https://sustainearth.sbu.ac.ir/article_106637.html Heavy metals are among the most significant types of pollutants entering the environment, making the control of their presence in the environment crucial. This study aims to assess the health risk of iron (Fe), manganese (Mn), cadmium (Cd), and chromium (Cr) metals in aquatic plants and water of Qanats in South Khorasan in eastern Iran. For this purpose, Water samples (n=30) and plant samples (n=120), one sample from each of the four aquatic plant species: Adiantum capillus-vernis, Plantago lanceolata, Chara globularis, and Mentha Pulegium, were collected from 10 different Qanats in 2023 with three replications. The concentration of metals in the samples was measured by atomic absorption spectrometry. The results of this study showed that the average concentration of Fe, Mn, Cd, and Cr in the water samples of the studied aqueducts was 0.29±.01, 0.016±.003, 0.0059±.0001, 0.47±.0021, respectively. The results also showed that the average concentration of Cd in the samples of Adiantum capillus-vernis, Plantago lanceolata, Chara globularis, and Mentha Pulegium was 0.529±0.010, 0.0879±0.021, 0.0645±0.030, 0.717±0.011, respectively. The average values of pollution index (Cd) of Fe, Mn, Cd, and Cr in the ten counties were 1.02±0.03, 0.97±0.01, 4.375±0.02 ,0.34±0.004, respectively, which indicates that the water of the South Khorasan aqueducts is less than the risk threshold determined for these indicators in terms of contamination with these metals. Although the carcinogenic risk index (CR) for chromium and cadmium in adults and children is less than one, there is a possibility of cancer for residents in the long term. https://sustainearth.sbu.ac.ir/article_106641.html This study presents a comparative analysis of the legal rules and judicial practices of Iran and France concerning the expropriation of urban lands and the classification of mawat lands. It addresses the question of similarities and differences between the two systems in regulating the relationship between private property rights and public authority. The research hypothesis holds that France, benefiting from coherent legislation and stable judicial interpretations, provides greater consistency and predictability than Iran, where fragmented regulations and divergent judicial approaches weaken legal certainty. The study adopts a descriptive–analytical and comparative methodology based on documentary research, statutory analysis, and judicial precedents, drawing on principles such as proportionality, public interest, and the balance between public authority and private ownership. The findings indicate that the French legal system applies clearer and more objective criteria for establishing public interest and identifying lands subject to expropriation, while administrative courts actively review the legality and necessity of such measures. In Iran, despite several legislative interventions, ambiguities in defining mawat lands, varying interpretations of public necessity, and the absence of a specialized administrative judiciary have resulted in inconsistent rulings. Compensation mechanisms in France, grounded in the principle of full reparation, operate with greater efficiency and transparency, whereas valuation and compensation procedures in Iran lack uniformity and precision. Nevertheless, both systems seek to reconcile urban development with the protection of property rights. The study concludes that incorporating regulatory clarity and judicial coherence from the French model into Iran’s legal framework could improve expropriation procedures and criteria for lands. https://sustainearth.sbu.ac.ir/article_106649.html Today, plastic pollution has become a very important environmental issue. The excessive and increasing use of plastic products and the unprincipled disposal of plastic waste have led to the spread of nanoplastic and microplastic pollution. This topic threatens the ecosystem and can endangers human health. Several studies have been conducted on the direct and indirect influences of nanoplastics and microplastics on plant. Microplastic or nanoplastic particles may be absorbed and/or adsorbed by leaves and roots and have various effects. The present work is an attempt to investigate the direct effects of nanoplastics and microplastics on vascular plants, including studying the effects of this pollutant on growth and physiological responses such as photosynthesis, germination, cytotoxicity, oxidative stress, and genetics. Microplastics and nanoplastics disrupt plant function and pollute the food chain. It is hoped that by investigating the direct effects of micro- and nanoplastics on plants, a basis will be provided for future research on the complex interactions of plants and plastics. https://sustainearth.sbu.ac.ir/article_106683.html This research evaluates the geochemical potential for Cu–Au mineralization in the Sonajil porphyry deposit, Iran, using statistical geochemistry, Pearson correlation analysis, and Concentration–Area (C–A) fractal modeling. A total of 1,248 geochemical samples were analyzed for major ore-related elements including Au, Ag, Cu, Mo, Pb, and Zn. Statistical analysis reveals strongly skewed and kurtotic distributions for all elements (e.g., Au skewness = 26.16; Cu = 12.30; Mo = 10.05), indicating significant anomalies associated with hydrothermal mineralization. Based on C–A fractal modeling, quantitative geochemical thresholds were determined, with high-anomaly classes identified as Au > 616.60 ppm, Ag > 3.15 ppm, Cu > 9,772.37 ppm, Mo > 66.07 ppm, Pb > 107.89 ppm, and Zn > 288.40 ppm, corresponding to mineralized zones. Spatial analysis shows that Au and Ag anomalies cover approximately 12–15% of the study area, high Cu anomalies occupy ~10% predominantly in the SW to W, and Mo, Pb, and Zn anomalies cover 14–18% in the eastern part, associated with peripheral alteration halos. Correlation analysis reveals significant relationships such as Cu–Ag (r = 0.404), Au–Sb (r = 0.515), Mo–Co (r = 0.692), and Cr–Ag (r = 0.717), suggesting a multi-stage hydrothermal evolution. Mineralization zoning indicates Cu–Ag enrichment in the central zone and Mo–Pb–Zn enrichment in peripheral zones. Integration of quantitative thresholds with spatial mapping confirms that the Sonajil area hosts a robust, multi-phase porphyry system, providing a reliable basis for exploration targeting. https://sustainearth.sbu.ac.ir/article_106726.html Food security under increasing water and energy constraints has become a critical challenge for arid and semi-arid countries. Iran, facing groundwater depletion, energy shortages, climate change, and population growth, requires integrated and feasible policy solutions beyond sectoral approaches. This study develops a national-scale system dynamics model to analyze wheat-based food security in Iran within the Water–Food–Energy (WEF) Nexus framework. The model captures nonlinear feedbacks among wheat production and demand, surface and groundwater dynamics, electricity availability for irrigation pumping, and farmers’ behavioral responses. Using historical data (2006–2024), the model is validated and simulated over the 2025–2040 horizon. Five combined policy scenarios are evaluated, focusing on demand management, post-harvest loss reduction, wheat water productivity improvement, irrigation efficiency enhancement, and pump efficiency optimization. Results indicate that under the baseline scenario, the wheat gap reaches 5.59 million tons by 2040. In contrast, the most effective combined policy—integrating productivity improvement, irrigation efficiency, and pump optimization—eliminates the deficit and generates a surplus of approximately 0.37 million tons. Simultaneously, cultivated area declines by 27.6%, water consumption by 39.3%, and energy consumption by nearly 50%. The findings demonstrate that integrated cross-sectoral policies, supported by system dynamics modeling, can simultaneously improve food security, reduce resource pressure, and enhance long-term sustainability and resilience in resource-constrained countries. https://sustainearth.sbu.ac.ir/article_106730.html Groundwater is highly vulnerable to pollution resulting from human activities. Preparing vulnerability maps is considered as a key tools in groundwater resource management. Asadabad Plain placed in Hamadan Province is a region with significant industrial and agricultural activities. The infiltration of urban and agricultural wastewater into the groundwater of this plain has posed environmental threats to both water and soil resources. The objevtive of this study was to assess the vulnerability of Asadabad Plain aquifer to nitrate pollution using three models: DRASTIC, GOD, and AVI. Ground water samples were collected from 23 shallow and deep wells with appropriate spatial distribution across the plain on October 2020, Hydrochemical analysis was performed for determining of pH, EC and nitrate concentration. Groundwater vulnerability maps were prepared using three models to saticfying high vulnerability regions with elevated nitrate concentrations. The results indicated that, according to DRASTIC model, 46.6% of groundwater resources fell into low vulnerability range, and 53.4% within the moderate range. In the GOD models, spatial zoning map based on GOD model showed that 61.1% of groundwater resources have low vulnerability and 38.9%, have moderate vulnerability. Spatial zoning using of AVI models, indicated that 68.3% and 31.7%,. Of groundwater resources fell into low and moderate vulnerability, respectively.To improve the future outlook of vulnerability maps, it is essential to validate these studies using physical models as well. Since DRASTIC simultaneously considers various indicators such as water table depth, soil permeability, soil/rock texture, and slope, it therefore well reflects the actual state of the aquifer. https://sustainearth.sbu.ac.ir/article_106740.html National parks play a crucial role in preserving biodiversity, natural beauty, and cultural heritage, with the first park established in Yellowstone in 1872. Today, there are nearly 7,000 national parks worldwide, with 27 being publicly accessible and government-protected. In Ethiopia, national parks face significant threats due to anthropogenic activities such as illegal hunting, habitat conversion, invasive species, deforestation, and climate change. These challenges are exacerbated by weak law enforcement and insufficient financial support. To combat these issues, comprehensive management strategies are essential. Implementing community involvement in conservation, raising awareness of ecological importance, sharing benefits derived from national parks, strengthening government enforcement, and engaging in scientific research on land use changes are vital steps. Additionally, providing alternative income sources for local populations can alleviate pressures on these critical ecosystems. Through these measures, the sustainability and integrity of Ethiopia’s national parks can be improved, ultimately benefiting both wildlife and local communities. https://sustainearth.sbu.ac.ir/article_106752.html This study contributes to the sustainability literature by developing an integrated analytical framework that internalizes the hidden and explicit environmental costs of cryptocurrency production and connects them to sustainable development pathways in emerging economies. This study examines cryptocurrencies' duality in emerging economies, financial opportunities versus environmental costs like energy consumption, emissions, e-waste, and resource depletion. The study begins with a systematic literature review (based on 250 articles from Scopus spanning 2021–2025) and including the distribution of articles by country, publication trends, and a keyword co-occurrence map for cryptocurrencies. The costs encompass energy consumption, carbon emissions, electronic waste, and pressure on natural resources. An examination of the hidden economic costs of bitcoin cryptocurrency production, reveals annual energy consumption of approximately 204 TWh/y, with direct electricity generation costs exceeding 10.2 billion dollars at a global average rate of 0.05 dollars per kWh; however, in Iran, with subsidized electricity, this amounts to about 1.2 billion dollars. Emissions are estimated at 65 to 115 Mt of carbon dioxide with removal costs ranging from 23.5 to 41.6 billion dollars, and management of approximately 20 to 30 thousand tons of electronic waste annually with recycling and secondary carbon offset costs of 20 to 150 million dollars. This study, with a focus on green financial mechanisms such as green cryptocurrency bonds, sustainable investment funds, and sustainable development indices, offers actionable strategies to transform cryptocurrencies from an environmental threat into a strategic driver of sustainable development in emerging economies. https://sustainearth.sbu.ac.ir/article_106757.html Mud volcanoes, as active geomorphological structures, reflect a combination of tectonic, geochemical, and hydrodynamic processes occurring deep within the Earth’s crust. Monitoring their spatial and temporal dynamics provides crucial insights into crustal deformation and subsurface fluid migration, especially in geologically complex and earthquake-prone regions such as the Makran coast of southeastern Iran. In this study, multi-temporal satellite datasets from Sentinel-2 and Landsat 8 were systematically processed and analyzed within Google Earth Engine and QGIS environments. Spatiotemporal patterns of activity were extracted for several major mud volcanoes between 2015 and 2025. The results indicate notable variations in vent formation, mud discharge volume, and eruption intensity, closely associated with local seismic events, fault structures, and subsurface pressure changes. The developed remote sensing and GIS-based model provides a robust, cost-effective framework for continuous and indirect monitoring of mud volcano dynamics. Furthermore, it can serve as a reliable decision-support tool in geological hazard assessment, land-use management, and regional environmental planning. This integrative approach enhances understanding of the interactions between tectonics and surface manifestations, offering valuable guidance for mitigating potential risks in active coastal zones. https://sustainearth.sbu.ac.ir/article_106781.html Eucalyptus cultivation faces critical challenges regarding irrigation management and water quality in arid regions. This factorial study, conducted as a randomized complete block design with three replications in Dezful, southwestern Iran, evaluated the effects of three irrigation intervals (3, 6, and 9 days) and two water quality levels (raw wastewater and treated effluent) on growth, biomass yield, and essential oil content of Eucalyptus camaldulensis. Irrigation followed regional practices, totaling 9,061, 4,963, and 3,581 mm for respective intervals—substantially exceeding species' requirements due to sandy soil and arid conditions. Both irrigation interval and water quality significantly affected all parameters (p < 0.01). Increasing irrigation interval consistently reduced tree height, stem diameter, and biomass yields. The highest fresh wood (3.86 kg tree⁻¹), fresh leaf (6.50 kg tree⁻¹), dry wood (1.75 kg tree⁻¹), and dry leaf yields (3.49 kg tree⁻¹) were obtained under 3-day intervals with treated effluent. Raw wastewater reduced biomass yields by 16–74% compared to treated effluent. Notably, essential oil exhibited an opposing trend: the highest concentration (1.888%) occurred under 3-day intervals with raw wastewater, while the lowest (1.22%) under 9-day intervals with treated effluent. These findings demonstrate that while treated effluent at 3-day intervals maximizes biomass production, moderate stress from raw wastewater enhances essential oil concentration. https://sustainearth.sbu.ac.ir/article_106830.html In Nigeria which contains various agro ecological zones the major causes of land degradation and loss of nutrients are surface runoff and soil erosion. The rapid population growth and urbanisation are changing the land cover and altering the hydrological process, and putting pressure on the soil resources. Watersheds need to be managed by understanding interconnections between land cover, rainfall-runoff processes, soil nutrient erosion possibilities and spatial changes. This review is based on the synthesis of evidence found in recent Nigerian case studies (mostly published between 2023 and 2025), as well as in foreign hydrologic literature. It discusses the hydrological mechanisms that govern the surface runoff and infiltration, compares the erosion modelling methods used including the Soil Conservation Service Curve Number (SCS CN) and the Revised Universal Soil Loss Equation (RUSLE) and the effects of Land Use Land Cover (LULC) change on surface runoff, soil erosion and nutrient distribution. The techniques used to measure spatial heterogeneity in the soil nutrients are remote sensing and Geographic Information Systems (GIS) in combination with geostatistical techniques. The paper identifies the impacts of deforestation and urban sprawl on runoff and soil fertility, covers the progress in remote sensing-based LULC mapping, and the pedometric and geostatistical methods of creating soil nutrient maps. The review concludes that land cover change particularly vegetation loss and expansion of impervious surfaces systematically increases runoff and erosion risk while accelerating nutrient redistribution and soil fertility decline. Synthesis further reveals a critical gap in the integration of hydrological modelling, remote sensing outputs and field-based nutrient mapping at multi-watershed and national scales, underscoring the need for coordinated, data-driven land management frameworks. The recommendations are made concerning sustainable land management and research directions. https://sustainearth.sbu.ac.ir/article_106903.html This study aimed to develop Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models to predict Soil Organic Carbon (SOC) using Remote Sensing (RS) data. Therefore, 60 surface soil samples were collected from 0-20 cm depth across the watershed of Qeshlaq Dam in western Iran, and soil properties were analyzed. A total of 11 auxiliary variables were obtained from three Landsat 9 satellite image databases, topographic indices and soil properties. Finally, the predicted data from the aforementioned approaches were used to prepare a spatial distribution map of SOC. The measured SOC values showed good correlation with EC, Color Index, Brightness Temperature Index 10 and 11 indices. The comparative evaluation of the results from the two models showed that the ANN with a higher correlation coefficient (R=0.821) and lower error (RMSE=0.108, MAE=0.002) provided better accuracy than the MLR method. The spatial distribution maps obtained from the two models showed that the ANN produced a more uniform and reliable spatial pattern and more stable spots. In contrast, the map obtained from the MLR had more extreme values and obvious point dispersion, which is a result of the limitation of linear models in representing spatial complexity. Therefore, ANN can be used in other regions and similar studies as a useful and rapid tool and can be effectively used in reducing soil sampling costs with spatial information-based management. The results of the present study highlight the integration of RS datasets and advanced machine learning models for accurate SOC estimation. https://sustainearth.sbu.ac.ir/article_106939.html Urban pollution is a major environmental challenge for developing cities, threatening public health and sustainability. The present study aimed to assess the impact of emerging technologies on the reduction of urban pollutants in the Maragheh. This research was applied in terms of purpose and descriptive-analytical in nature, employing a quantitative approach with a survey strategy. The statistical population comprised the citizens of Maragheh, and the sample size was determined as 196 (Cochran formula). The data collection instrument was a researcher-developed questionnaire based on a five-point Likert scale, and the research indicators were measured based on the perceptions and subjective evaluations of experts rather than objective data obtained from direct pollutant measurements. The validity of the instrument was confirmed through content and construct validity, and its reliability was verified using Cronbach’s alpha and composite reliability. Data analysis was conducted using structural equation modeling (PLS-SEM). The results indicated that emerging technologies exerted a positive, direct, and statistically significant effect on the reduction of urban pollutants in Maragheh. Among the dimensions of emerging technologies, waste management technologies accounted for the highest contribution, while water and air purification and treatment technologies demonstrated the lowest contribution in explaining the emerging technologies construct. Furthermore, air pollutants exhibited the highest and water pollutants the lowest degree of explained variance by the urban pollutant reduction construct. Based on the findings, adopting a differentiated approach to the allocation of technological resources and integrating technological interventions with institutional reforms and public education appeared essential for achieving comprehensive urban pollutant reduction. https://sustainearth.sbu.ac.ir/article_106963.html Global plastic pollution is a crucial environmental challenge of the 21st century. The study aims to illustrate the global plastic pollution scenario, including the present status and future directions. Data published in various books, reports, and peer-reviewed journals within the last decade were considered in this review. The study found that the global plastic production has increased from less than 2 million tonnes in 1950 to over 430 million tonnes in 2024, with nearly 70% of total plastic waste still disposed of in landfills, incinerated, or leaked into the open environment. Polyethylene (26.2%), polypropylene (19%), and polyvinyl chloride (12.8%) dominate global production, while bio-based plastics account for only 0.7%. The global average plastic waste generation is 28 kg per capita per year, with 8.8 kg mismanaged per capita per year. Packaging materials alone contribute 155.9 million tonnes (39%) of total waste annually. It was observed that plastic debris in terrestrial and aquatic ecosystems poses severe ecological, economic, and health hazards. Persistent polymers, such as polyvinyl chloride, Polyurethane, and polyacrylonitrile, exhibit high hazard scores, releasing toxic and carcinogenic compounds. Meanwhile, microplastics have become ubiquitous in water, air, soil, and human body. Developing countries face the highest burden of reducing plastic pollution due to inadequate waste management infrastructure. These findings highlight the need for a transition toward circular economy, emphasizing recycling, material recovery, and biodegradable alternatives. Strong policy interventions, Extended Producer Responsibility (EPR), scientific research, and international cooperation are vital to mitigating plastic pollution and achieving environmental sustainability in the 21st century. https://sustainearth.sbu.ac.ir/article_107157.html Pistachio (Pistacia vera L.) production in arid regions depends critically on sufficient winter chilling to ensure regular budbreak and stable yields. This study assesses current and future winter chill for major pistachio cultivars in Yazd Province, central Iran, under ongoing climate warming. Three chill models were evaluated: the Chilling Hours model (temperatures between 0 and 7.2 °C), the Utah model, and the Positive Utah model. Observed three-hourly and daily minimum and maximum temperatures from seven synoptic stations (2003–2020) were combined with statistically downscaled projections from the CanESM2 global climate model under three emissions scenarios (RCP2.6, RCP4.5, RCP8.5) for 2021–2050. The Chilling Hours model, driven by mean daily temperature, showed the best performance, with the lowest errors and highest correlation with observed chill-related phenology, and mean temperature outperformed minimum and maximum temperature as a predictor of chill accumulation. Projections indicate a general decline in winter chilling across Yazd Province, with reductions in seasonal chilling hours and a tendency toward earlier chill accumulation in autumn. Low-chill cultivars such as ‘Kale Ghochi’ are projected to remain largely suitable, whereas high-chill cultivars, including ‘Akbari’ and ‘Fandoghi’, face increasing chill deficits, threatening their long-term viability. These results highlight the need for adaptation measures, including promotion of low-chill cultivars, site selection in cooler microclimates, and improved phenological modeling for risk management. https://sustainearth.sbu.ac.ir/article_107158.html Pesticides can threaten the health of aquatic organisms. The aim of this study was to investigate the effect of sublethal concentrations of the herbicide Super Galant on antioxidant indices and histological changes in the gills and intestine of Capoeta fusca. It was hypothesized that this exposure would cause oxidative stress and tissue damage. The median lethal concentration (LC50) of the herbicide Super Galant in Capoeta fusca was determined based on OECD guidelines, and antioxidant and histological evaluations were conducted after 28 days of exposure on 40 fish. The data were analyzed using the ANOVA test at a significance level of P<0.05. The results show that the median lethal concentration of the herbicide Super Galant in 96 hours is 1.20 mg/L. Also, the activity of antioxidant enzymes including superoxide dismutase (SOD) and total antioxidant capacity (TAC) levels respectively from 130.47 ± 19.57 U/mL and 1.62 ± 0.22 nmol/mL in the control group in the treatments of 25, 50, and 75 percent of the 96-hour median lethal concentration of the herbicide Super Galant decreased to 112.82 ± 15.79, 106.91 ± 9.62 and 103.11 ± 12.37 U/mL and 1.41 ± 0.19, 0.94 ± 0.11 and 0.67 ± 0.06 nmol/mL. Histopathological damages found in the gills include fusion of secondary lamellae, shortening of secondary lamellae, tissue oedema and curvature. Overall, the results of this study show that exposure to the herbicide Super Galant causes physiological disruption and structural damage in Capoeta fusca and can affect the health of aquatic ecosystems. https://sustainearth.sbu.ac.ir/article_107231.html Considering the importance of vegetation, this study investigated the vulnerability of vegetation to drought using the Vegetation Health Index (VHI) in the northern counties of Kerman. Based on the Enhanced Vegetation Index (EVI), months April, May, June, and July, were selected as the study periods. Using Land Surface Temperature (LST) and EVI data derived respectively from the MOD11A1 and MOD13A2 products for the period 2001–2025, the mean Temperature Condition Index (TCI) and Vegetation Condition Index (VCI) were calculated for the study months in each county. After combining these two indices, the VHI was computed. By classifying VHI, the Probability of Vegetation Vulnerability Index (PVVI) was obtained. Furthermore, by considering the regional weighted mean center of the VHI index, the spatial–temporal distribution pattern of drought was determined using the gravity center shift model. The results indicated that the lowest PVVI in most counties occurred in June, while the highest of it occurred in April and May. The mean PVVI values also showed that in most counties the average values decreased in June compared to the previous months, with the exception of Rigan, where the index increased in June relative to the previous month. The results of the gravity center shift model indicated that the greatest expansion of drought among the study months occurred in June, with a spread of approximately 14 km totally or 0.56 Km per year. Overall, the results of this study can be useful for regional, provincial, and national decision‑making aimed at sustainable vegetation management.