Flash floods rank among the most devastating natural hazards, causing widespread socio-economic, environmental, and infrastructural damage globally. Hence, innovative management approaches are required to mitigate their increasing frequency and intensity, driven by factors such as climate change and urbanization. Accordingly, this study introduced an integrated flood assessment approach (IFAA) for sustainable management of flood risks by integrating the analytical hierarchy process-weighted linear combination (AHP-WLC) and fuzzy-ordered weighted averaging (FOWA) methods. The IFAA was applied in South Khorasan Province, Iran, an arid and flood-prone region. Fifteen controlling factors, including rainfall (RF), slope (SL), land use/land cover (LU/LC), and distance to rivers (DTR), were processed using the collected data. The AHP-WLC method classified the region into flood susceptibility zones: very low (10.23%), low (23.14%), moderate (29.61%), high (17.54%), and very high (19.48%). The FOWA technique ensured these findings by introducing optimistic and pessimistic fuzzy scenarios of flood risk. The most extreme scenario indicated that 98.79% of the area was highly sensitive to flooding, while less than 5% was deemed low-risk under conservative scenarios. Validation of the IFAA approach demonstrated its reliability, with the AHP-WLC method achieving an area under curve (AUC) of 0.83 and an average accuracy of ~75% across all fuzzy scenarios. Findings revealed elevated flood dangers in densely populated and industrialized areas, particularly in the northern and southern regions, which were influenced by proximity to rivers. Therefore, the study also addressed challenges linked to sustainable development goals (SDGs), particularly SDG 13 (climate action), proposing adaptive strategies to meet 60% of its targets. This research can offer a scalable framework for flood risk management, providing actionable insights for hydrologically vulnerable regions worldwide.

Groundwater salinization poses a critical threat to sustainable development in arid and semi-arid rurbanizing regions, exemplified by Kerman Province, Iran. This region experiences groundwater ecosystem degradation as a result of the rapid conversion of rural agricultural land to urban areas under chronic drought conditions. This study aims to enhance Groundwater Pollution Risk (GwPR) mapping by integrating the DRASTIC index with machine learning (ML) models, including Random Forest (RF), Boosted Regression Trees (BRT), Generalized Linear Model (GLM), Support Vector Machine (SVM), and Multivariate Adaptive Regression Splines (MARS), alongside hydrogeochemical investigations, to promote sustainable water management in Kerman Province. The RF model achieved the highest accuracy with an Area Under the Curve (AUC) of 0.995 in predicting GwPR, outperforming BRT (0.988), SVM (0.977), MARS (0.951), and GLM (0.887). The RF-based map identified new high-vulnerability zones in the northeast and northwest and showed an expanded moderate vulnerability zone, covering 48.46% of the study area. Analysis revealed exceedances of WHO standards for total hardness (TH), sodium, sulfates, chlorides, and electrical conductivity (EC) in these high-vulnerability areas, indicating contamination from mineralized aquifers and unsustainable agricultural practices. The findings underscore the RF model’s effectiveness in groundwater prediction and highlight the need for stricter monitoring and management, including regulating groundwater extraction and improving water use efficiency in riverine aquifers.

Understanding the impacts of the spatial non-stationarity of environmental factors on surface soil moisture (SSM) in different seasons is crucial for effective environmental management. Yet, our knowledge of this phenomenon remains limited. This study introduces an interpretable machine learning framework that combines the SHapley Additive exPlanations (SHAP) method with two-step clustering to unravel the spatial drivers of SSM across Iran. Due to the limited availability of in situ SSM data, the performance of three global SSM datasets—SMAP, MERRA-2, and CFSv2—from 2015 to 2023 was evaluated using agrometeorological stations. SMAP outperformed the others, showing the highest median correlation and the lowest Root Mean Square Error (RMSE). Using SMAP, we estimated SSM across 609 catchments employing the Random Forest (RF) algorithm. The RF model yielded R² values of 0.89, 0.83, 0.70, and 0.75 for winter, spring, summer, and autumn, respectively, with corresponding RMSE values of 0.076, 0.081, 0.098, and 0.061 m³/m³. SHAP analysis revealed that climatic factors primarily drive SSM in winter and autumn, while vegetation and soil characteristics are more influential in spring and summer. The clustering results showed that Iran’s catchments can be grouped into five categories based on the SHAP method coefficients, highlighting regional differences in SSM controls.

This study examines the occurrence and ecological risks of phthalate esters (PAEs) and bisphenol A (BPA) in raw urban wastewater from Bushehr, a coastal city in the northern Persian Gulf. PAEs, used as plasticizers, and BPA, found in various consumer products, are endocrine disruptors that pose environmental and health threats. The research evaluates the concentrations, distribution, and sources of these pollutants and assesses their ecological risks. Wastewater samples were collected from six discharge stations along the Bushehr coastline between February and April 2023. Using gas chromatography-mass spectrometry (GC-MS), seven PAE compounds, including dimethyl phthalate (DMP), diethyl phthalate (DEP), diisobutyl phthalate (DIBP), dibutyl phthalate (DBP), benzyl butyl phthalate (BBP), bis(2-ethylhexyl) phthalate (DEHP), and di-octyl phthalate (DOP), alongside BPA were analyzed. DEHP emerged as the dominant PAE, with concentrations ranging from 19.67 to 39.75 µg/L, while BPA levels ranged from 0.10 to 2.50 µg/L, peaking at Rishehr Park. Ecological risk assessment, conducted using the risk quotient (RQ) method, revealed that DEHP posed a high ecological risk (RQ > 1) to sensitive aquatic species. These findings highlight the urgent need for improved wastewater treatment and pollution control to safeguard marine ecosystems and public health. The study contributes valuable insights into the contamination levels and sources of PAEs and BPA in the northern Persian Gulf, advancing the understanding of regional marine pollution and its ecological impacts.

This study critically analyzes the prevailing paradigmatic model in rural development, specifically examining spatial planning projects in Iran. It underscores the necessity of comprehending the intellectual foundations that inform these initiatives, particularly in developing countries where such analyses are often deficient. Employing a mixed-method approach, we evaluated nine spatial planning projects funded by the Planning and Budget Organization in Iran from 2007 to 2019. Our methodology included qualitative content analysis of relevant documents and in-depth interviews with project promoters and academics. We found that the boundaries of rural development models extend beyond conventional perceptions, with projects often comprising a combination of different exogenous, endogenous, and neo-endogenous aspects that may sometimes conflict with one another. This integration reveals broad abstract and practical features of rural development models that have received insufficient attention thus far. Additionally, centralized and ideological planning systems in developing countries such as Iran complicate this situation and diminish the impact of individual and local advocacy initiatives. This study emphasizes the necessity of establishing a coherent paradigmatic model to enhance the effectiveness of rural development projects. This research aims to facilitate the reintegration of rural areas into their natural development cycles by addressing gaps in planning and policy formulation.