Mohammad Farhadi

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.

Atmospheric dust storms are a major source of particulate pollution in arid and semi-arid regions, with far-reaching ecological, infrastructural, and socio-economic consequences. Beyond their documented impacts on air quality and human health, dust events increasingly threaten tourism-related socio-ecological systems by degrading environmental quality, damaging infrastructure, and increasing exposure risks in sensitive regions. This study develops a national-scale, spatially explicit framework to model vulnerability of tourism destinations to dust storm pollution. Iran was selected as a representative dust-prone region due to its high frequency of atmospheric dust events and its dense and diverse distribution of tourism destinations. Multi-source satellite data, including aerosol optical depth, precipitation, and vegetation indices, were combined with spatial data on infrastructure, land use, and ecosystem services within a Geographical Information System (GIS)-based multi-criteria decision analysis framework. Exposure, sensitivity, and adaptive capacity components were quantified and integrated to generate a national vulnerability map to dust storm pollution. Results indicate that approximately 34% of Iran falls within high and very high vulnerability classes, primarily concentrated in central, eastern, and southern regions characterized by intense dust pollution, low vegetation cover, and limited environmental buffering capacity. A substantial proportion of tourism-related infrastructures are located within high-risk dust pollution zones. The spatial patterns reveal pronounced north-south and west-east gradients in dust-related vulnerability, driven by climatic aridity, pollutant intensity, and uneven ecological resilience. By explicitly linking atmospheric dust pollution processes with spatial vulnerability patterns, this study provides a transferable modelling framework for assessing pollution-driven risks to socio-ecological systems. The findings support regional-scale environmental management, climate adaptation planning, and pollution mitigation strategies in dust-affected regions worldwide.