Hamide Mahmoodi

Dust storms have emerged as a major environmental and public health challenge in arid and semi-arid urban regions, where heterogeneous urban structures and population distributions intensify spatial variability in vulnerability. This study aimed to spatially model urban particulate-related vulnerability associated with dust-prone conditions using an integrated framework based on exposure, sensitivity, and adaptive capacity, in which green infrastructure was represented as a key adaptive-capacity dimension. The study area included four major Iranian metropolises (Tehran, Mashhad, Isfahan, and Ahvaz), representing diverse climatic and socio-environmental conditions. Multi-source datasets, including remote sensing indicators, ground-based air quality and infrastructure data, and census-based socio-demographic variables, were integrated within a Geographic Information System (GIS) environment. A five-step spatial multi-criteria decision-making approach was applied, in which criteria were weighted using the Analytic Hierarchy Process (AHP), normalized via the Min–Max method, and aggregated using the Weighted Linear Combination (WLC) technique to produce vulnerability maps. The results revealed strong intra-urban heterogeneity and inter-city differences in vulnerability patterns. Isfahan exhibited the highest vulnerability, with 40.3% of its area and 64.4% of its population classified as high-risk, followed by Tehran (32.1% of area; 60.6% of population). Ahvaz showed moderate spatial vulnerability (20.8% of area) but high population exposure (50.6%), indicating population concentration in risk-prone zones. Mashhad demonstrated relatively lower vulnerability, with only 19.5% of its area in high-risk classes and 53.7% in low-risk zones, coinciding with more distributed green space provision. These findings demonstrate that integrating environmental, socio-demographic, and infrastructural factors, including urban green infrastructure, is essential for capturing urban vulnerability to dust hazards. The proposed geospatial framework provides a decision-support tool for identifying priority areas and informing green infrastructure planning and climate-adaptive resilience strategies in arid-region cities.

Ecotourism, as one of the most important forms of sustainable tourism, plays a significant role in the conservation of natural resources, the economic development of local communities, and the achievement of sustainable development goals. However, the sustainable development of this sector requires the accurate identification of suitable areas and the simultaneous assessment of the ecological capacities and limitations arising from anthropogenic activities, an issue that has received less attention at the national scale, particularly in countries with high environmental diversity such as Iran. Therefore, the present study was conducted with the aim of assessing the potential for ecotourism development in Iran based on Geographic Information System (GIS) and Spatial Multi-Criteria Decision Analysis (SMCDA). In line with the scope of the sub-factors evaluated, the assessed construct is referred to throughout this study as Environmental–Anthropogenic Ecotourism Suitability (EAES), reflecting an environmental-quality and anthropogenic-pressure perspective rather than a comprehensive assessment of ecotourism sustainability. The main innovation of this study lies in the simultaneous integration of natural and anthropogenic factors at the national scale and the sensitivity assessment of the results through the design of different development scenarios. In this study, a set of natural sub-factors including protected areas, vegetation cover, slope, precipitation, land use, and natural attraction density, as well as anthropogenic sub-factors including settlements, roads, accommodations, mines, industrial parks, dams, power transmission lines, dust, and the Global Human Modification (GHM) index were used. The weighting of the sub-factors was carried out using the Best–Worst Method (BWM), and spatial suitability maps were subsequently generated through the Weighted Linear Combination (WLC) method in the GIS environment. To evaluate uncertainty and examine the effect of the relative importance of sub-factors, three scenarios including natural factor dominance, anthropogenic factor dominance, and a balanced scenario were designed and analyzed. The results showed that protected areas (0.19), vegetation cover (0.17), and natural attraction density (0.16) were the most important natural sub-factors, while the GHM index (0.16), roads (0.15), and settlements (0.14) were the most important anthropogenic sub-factors affecting ecotourism development. The spatial pattern of the results indicated the concentration of areas with good potential in the Alborz and Zagros mountain ranges, the Hyrcanian forests, and parts of the protected areas of Iran. In the balanced scenario, approximately 24.8% of Iran’s area was classified within the suitable and highly suitable classes, while 46.3% was classified within the moderately suitable class. Furthermore, comparison of the scenarios showed that the use of one-dimensional approaches may lead to unrealistic estimates of ecotourism capacity. Overall, the results of this study indicate that the sustainable development of ecotourism in Iran requires the adoption of an integrated approach in which the conservation of natural assets and the management of anthropogenic interventions are simultaneously considered. The proposed framework can serve as a decision-support tool for spatial planning, investment prioritization, and sustainable ecotourism development policymaking in Iran and other similar regions.

The development of agricultural tourism (agritourism) has gained increasing importance as a tool for improving rural economies and promoting sustainable natural resource management. This study proposes a scenario-based framework for agritourism development assessment using hierarchical principal component analysis in Iran. National spatial and statistical datasets, geographic information, topographic maps, and climatic data were used. Sub-criteria related to five main criteria including accessibility, risk and safety, environmental conditions, tourism attractions, and tourism facilities were prepared and normalized. First-level principal component analysis was independently applied to each criterion to reduce data dimensionality and generate weighted potential maps. Then, second-level principal component analysis was used to produce the final agritourism development potential map. To evaluate model robustness, a weight-based sensitivity analysis was performed. In addition, risk-oriented, development-oriented, and environment-oriented management scenarios were defined to examine the effects of alternative priorities on agritourism potential patterns. Results showed that the first principal component explained approximately 44% of the variance in accessibility, 48% in environmental conditions, 69% in tourism attractions, 90% in tourism facilities, and 38% in risk and safety, while the first three components explained more than 80% of variability for most criteria. At the second level, the first component explained about 64% of the total variance, indicating its dominant role in shaping the spatial pattern of agritourism potential. The proportion of the very high potential class in accessibility, risk and safety, tourism attractions, tourism facilities, and environmental conditions was 25%, 19.7%, 15.8%, 6.6%, and 1.8%, respectively, whereas only about 10% of the country fell into this class in the final map. Sensitivity analysis revealed that accessibility and environmental conditions had the greatest influence on model stability, whereas tourism attractions showed the most stable behavior. Scenario analysis indicated that the very high potential class increased to 13.9% under the development-oriented scenario, while it decreased to 10% and 9.1% under the environment- and risk-oriented scenarios, respectively.

The dynamism of adventure tourism necessitates the precise identification of areas with suitable natural, infrastructural, and service capacities for hosting activities. The aim of this study is to assess the multi-scenario spatial suitability for the sustainable development of adventure tourism camps using a Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) approach. The datasets used included topographic, climatic, environmental, accessibility, natural and cultural attraction, and service infrastructure indicators. The relevant criteria were first standardized, and their weights were determined using the Analytic Hierarchy Process (AHP). Subsequently, the layers were integrated through a Weighted Linear Combination (WLC) model. Four scenarios were designed for sensitivity analysis: the first scenario with balanced weight distribution (S_bal), the second prioritizing accessibility (S_acc), the third focusing on natural attractions (S_att), and the fourth emphasizing services (S_serv). The results indicated that approximately 21% and 9% of Chaharmahal and Bakhtiari province have high and very high potential for adventure activities, respectively, which were selected as initial options for the multi-scenario analysis. In the balanced (S_bal) scenario, 31% and 13% of the area of these options fell into high and very high suitability classes, respectively. The Service-Based Scenario (S_serv) increased the share of high and very high suitability areas to 34% and 19%, while Accessibility-Based Scenario (S_acc) reduced these classes to 27% and 10%. In the Attraction-Based Scenario (S_att), the areas in the high and very high suitability classes were 30% and 12%, respectively. The findings demonstrate that altering the priority of components can significantly change the spatial pattern of suitability, and sustainable planning of adventure tourism activities should be conducted based on management objectives and regional capacities. The proposed framework is generalizable to other regions and can serve as a basis for decision-making in balanced development, optimal infrastructure allocation, and sustainable management of adventure tourism.

This study focuses on assessing the physical growth of cities and the land-cover changes resulting from it, which play a crucial role in understanding the environmental impacts and managing phenomena such as the Daytime Urban Surface Heat Island Intensity (DSUHII). Predicting the trends of these changes for the future provides valuable insights for urban planning and mitigating thermal effects in arid environments. This research aims to evaluate the spatial and temporal changes in the intensity of urban surface heat islands in cities under different climatic conditions, resulting from land-cover changes in the past, and to predict future trends. For this purpose, Landsat satellite data products, including Surface Reflectance with a 30-m resolution and Land Surface Temperature (LST) originally at a 100 (120)-meter resolution for Landsat 8 (Landsat 5) (resampled to 30 m for compatibility), along with a database of underlying criteria affecting urban growth, were used to analyze land-cover and LST changes. The land-cover classification was carried out using the Support Vector Machine (SVM) algorithm, and its accuracy was assessed. Spatial and temporal changes in LST and land-cover classes were quantified using cross-tabulation models and subtraction operators. Subsequently, the impact of land-cover changes on LST in different climates was analyzed, and the trends of land-cover and DUSHII changes were simulated for the future using the CA–Markov model. The results showed that in the humid climate (Babol and Rasht), built-up areas increased by over 100% from 1990 to 2023 and are projected to grow further by 2055, while green spaces significantly decreased. In the cold–dry climate (Mashhad), urban development increased dramatically, and green spaces nearly halved. In the hot–dry climate (Yazd and Kerman), built-up areas tripled, and the reduction of green spaces will continue. Additionally, in cities with hot and dry climates, a significant area of barren land was converted into built-up areas, and this trend is predicted to continue in the future. DSUHII in Babol increased from 2.5 °C in 1990 to 5.4 °C in 2023 and is projected to rise to 7.8 °C by 2055. In Rasht, this value increased from 2.9 °C to 5.5 °C, and is expected to reach 7.6 °C. In Mashhad, the DSUHII was negative, decreasing from −1.1 °C in 1990 to −1.5 °C in 2023, and is projected to decline to −1.9 °C by 2055. In Yazd, DSUHII also remained negative, decreasing from −2.5 °C in 1990 to −3.3 °C in 2023, with an expected drop to −6.4 °C by 2055. Similarly, in Kerman, the intensity of DSUHII decreased from −2.8 °C to −5.1 °C, and it is expected to reach −7.1 °C by 2055. Overall, the conclusions highlight that in humid climates, DSUHII has significantly increased, while green spaces have decreased. In moderate, cold, and dry climates, a gradual reduction in DSUHII is observed. In the hot–dry climate, the most substantial decrease in DSUHII is evident, indicating the varying impacts of land-cover changes on DSUHII across these regions.