Like many developing countries, Iran faces air pollution, especially in its metropolises and industrial cities. Nitrogen dioxide (NO₂) is one of the significant air pollutants; therefore, this study aims to investigate the spatiotemporal variability of NO₂ using Tropospheric Monitoring Instrument (TROPOMI) sensor mounted on the Sentinel-5P (S5P) satellite and the Google Earth Engine (GEE) platform over Iran. In addition, we used ground truth data to assess the correlation between data acquired by this sensor and ground stations. The results show that there is a strong correlation between products of the TROPOMI sensor and data provided by the Air Quality Monitoring Organization of Iran. The results also display that the correlation coefficient (R) of NO₂ between ground truth data and the TROPOMI sensor varies in the range of 0.4 to 0.92, over three years. Over an annual period (2018 to 2021) and wide area, these data can become valuable points of reference for NO₂ monitoring. In addition, this study proved that the tropospheric NO₂ concentrations are generally located over the northern part of Iran. According to the time and season, the concentration of the tropospheric NO₂ column shows higher values during winter than in the summertime. The results show that a higher concentration of the tropospheric NO₂ column is in winter while in some southern and central parts of the country more NO₂ concentration can be seen in the summertime. This study indicates that these urban areas are highly polluted, which proves the impact of pollutants such as NO₂ on the people living there. In other words, small parts of Iran are classified as high and very highly polluted areas, but these areas are the primary location of air pollution in Iran. We provide a code repository that allows spatiotemporal analysis of NO₂ estimation using TROPOMI time-series images within GEE. This method can be applied to other regions of interest for NO₂ mapping.

The aim of this study was the spatial and temporal monitoring of soil erosion patterns in Iran. The G2 model was used, as an appropriate tool to provide the required month-time step country-wide soil loss maps and statistical outputs. The input parameters were derived with field surveys and remote sensing imagery (MODIS, SRTM, SPOT, and Sentinel-2 scenes). An innovation for G2 was the consideration of the snow cover effect on the rainfall erosivity. It was also the first time G2 was used to map such a big country like Iran on a country scale. The mean annual soil erosion in Iran was found to be 16.5 t ha⁻¹, which is equal to about 2.7 billion tons of soil loss. In spatial terms, the highest soil loss values were found in the north, west and southwest part of Iran, in the steep slopes in Alborz and Zagros mountains. In temporal terms, the highest and lowest monthly erosion values of 3.2 and 0.09 t ha⁻¹ correspond to January and July, respectively.

Rapid and timely evaluation and monitoring of the urban environment has gained significant importance in understanding the state of urban sustainability in metropolises. Multi-source remote sensing (RS) data are a valuable source for a comprehensive understanding of urban environmental changes in developing countries. However, in the Middle East, a region with several developing countries, limited study has been conducted to understand urban environmental changes. In this study, to evaluate the changes in the urban environment, 32 metropolises in the Middle East were studied between 2000 and 2019. For this purpose, a comprehensive environmental index (CEI) integrated with Google Earth Engine (GEE) platform for processing and analysis is introduced. The results show degraded environmental conditions in 19 metropolises based on a significant increasing trend in the time series of the CEI index. The highest increasing trend in the value of the CEI was observed in the cities of Makkah, Jeddah, Basra, Riyadh, and Sana’a. The results also show that the percentage of urban areas in all 32 cities that falls into the degraded class varies from 5% to 75% between 2005 and 2018. The results of CEI changes in megacities, such as Ajman, Tehran, Jeddah, Makkah, Riyadh, Karaj, and Sana’a show that these cities have increasingly suffered from the degradation of environmental conditions since 2001. According to the results, it is recommended to pay more attention to environmental issues regarding the future of urban development in these cities. The proposed approach in this study can be implemented for environmental assessment in other regions.

Appropriate spatial accessibility to healthcare facilities is an important component of the efficient delivery of healthcare. This study aims to measure spatial accessibility to healthcare facilities in Isfahan Metropolitan Area, a rapidly growing megacity in Iran. We used two methods of population-weighted fuzzy analytic hierarchy process and the two-step floating catchment area (2SFCA) to measure spatial accessibility to urban healthcare facilities, including hospitals, pharmacies, clinics, and laboratories. The results of these two methods were compared. Not surprisingly, the center of Isfahan has the highest accessibility scores for healthcare facilities, and the peripheral areas of the city have the lowest levels. Despite the existence of higher numbers of healthcare facilities in northern parts of Isfahan, accessibility to healthcare facilities is low because of the large population. Both methods show that healthcare services are not evenly distributed in Isfahan. Some areas with a high population have low accessibility to health facilities. Ideally, these areas would be prioritized for future health infrastructure investment. The methods used here can help urban healthcare policymakers identify spatial inequalities in access to care and thus target the areas in need.

Earthquakes pose significant natural hazards and impact populations worldwide. Iran is among the most susceptible countries to seismic activity, making comprehensive earthquake risk assessment crucial. This study employs geospatial methods, including integrating satellite, ground-based, and auxiliary data to model earthquake risk across this country. A Fuzzy Inference System (FIS) is used to generate earthquake hazard probability and vulnerability layers, considering factors such as slope, elevation, fault density, building density, proximity to main roads, proximity to buildings, population density, and earthquake epicenter, magnitude, proximity to the epicenter, depth density, peak ground acceleration (PGA). The results highlight high-risk areas in the Alborz and Zagros Mountain ranges and coastal regions. Moreover, the findings indicate that 39.7% (approximately 31.7 million people) of Iran’s population resides in high-risk zones, with 9.6% (approximately 7.7 million) located in coastal areas vulnerable to earthquakes. These findings offer valuable insights for crisis management and urban planning initiatives.