This research enhances air quality predictions in Abu Dhabi by employing Autoregressive Integrated Moving Average (ARIMA) models on comprehensive air quality data collected from 2015 to 2023. We collected hourly data on nitrogen dioxide (NO2), particulate matter (PM10), and fine particulate matter (PM2.5) from 19 well-placed ground monitoring stations. Our approach utilized ARIMA models to forecast future pollutant levels, with extensive data preparation and exploratory analysis conducted in R. Our results found a significant drop in NO2 levels after 2020 and the highest levels of particulate matter observed in 2022. The findings of our research confirm the effectiveness of the models, indicated by Mean Absolute Percentage Error (MAPE) values ranging from 7.71 to 8.59. Additionally, our study provides valuable spatiotemporal insights into air pollution historical evolution, identifying key times and areas of heightened pollution, which can help in devising focused air quality management strategies. This research demonstrates the potential of ARIMA models in precise air quality forecasting, aiding in proactive public health initiatives and environmental policy development, consistent with Abu Dhabi’s Vision 2030.

This research presents a GIS-based approach to evaluate land suitability for the urbanization of Saudi Arabia’s NEOM region, an ambitious initiative for sustainable development within a desert setting. By employing a multi-criteria decision-making framework, we have synthesized data on land resources, stability, accessibility, construction costs, and proximity to conservation areas using the Analytical Hierarchy Process (AHP) to prioritize these factors based on their importance for sustainable growth. Our analysis reveals that areas designated with grid values of 9 and 8, totaling 6312.33 square kilometers or 28.23% of the area studied, are most favorable for urban development. These sectors span the northern, central, and southern parts of NEOM, which are notable for their strategic location near future infrastructure and natural assets conducive to sustainable practices. This finding supports NEOM’s key initiatives such as “The Line,” “Oxagon,” and “TROJENA,” showcasing the model’s utility in directing urban development strategies. The study discusses the broader socio-economic and environmental ramifications of pinpointing areas suited and unsuited for development, underlining the model’s role in facilitating decision-making that aligns urban expansion with environmental stewardship. Furthermore, it advocates for leveraging development-restricted zones in conservation efforts and renewable energy ventures. Nevertheless, the study concedes certain limitations, such as the dependence on static datasets and inherent challenges related to the AHP method, including the presumption of criteria independence. Future research avenues are proposed, emphasizing the integration of dynamic data sources, comprehensive socio-cultural impact evaluations, and adopting flexible urban planning methodologies to refine the model’s precision and practicality. Overall, this detailed examination showcases NEOM’s blueprint as an exemplar for future urban environments, illustrating a systematic strategy for developing desert regions that integrate innovation, ecological sustainability, and the preservation of cultural heritage.

Our study meticulously pursued its objectives through a strategic data-gathering approach and a thorough analytical evaluation. In the data collection phase, we undertook an exhaustive accumulation of geographical and environmental information, utilizing high-resolution satellite images and diverse thematic maps. We employed GIS-based models and the Analytical Hierarchy Process (AHP) to effectively determine the most suitable areas for urban development. These models pinpointed zones along the coastal areas and nearby plains, spanning 198.89 square kilometers or 8.25% of the total study area, as highly appropriate for development due to their potential for desalination and tourism, and proximity to vital infrastructure such as the forthcoming NEOM airport and high-speed railway connections. The findings from the Southwestern NEOM model corroborate with NEOM’s strategic goals, especially validating the chosen location for The Line. This location is considered ideal due to its accessibility, terrain suitability, and capability to incorporate renewable energy and sustainable water management, thereby supporting the strategic aims and placement of the initiative. This not only highlights the strategic positioning of these areas but also aligns with the Saudi Vision 2030, fostering economic diversification and sustainable development. Together, these actions have established a comprehensive framework for urban planning that respects environmental conservation and promotes sustainable development objectives. By incorporating socio-economic and cultural factors, our research offers a well-rounded approach to urban development, ensuring that new infrastructure is both environmentally considerate and strategically situated to promote enduring growth and community wellbeing in Southwestern NEOM.

Urban expansion and land cover changes significantly influence land surface temperature (LST), especially in arid environments. This study investigates spatial and temporal patterns of land cover and LST across Abu Dhabi, UAE, for the benchmark years 2017, 2020, and 2024, using Sentinel-2 imagery and MODIS thermal data via Google Earth Engine (GEE). Four dominant land cover classes were mapped: bare desert, urban areas, vegetation, and water bodies. Between 2017 and 2024, bare desert coverage declined from 92.8% to 90.9%, while urban land grew from 3.0% to 4.5%, vegetation increased from 3.3% to 4.1%, and water decreased from 0.9% to 0.5%. Thermal analysis based on MODIS summer composites revealed that 2020 was the hottest year, with an average LST of 53.14 °C , higher than 2017 (52.40 °C) despite COVID-19 mobility restrictions, likely due to extreme heat and atmospheric conditions. These values reflect emirate-wide averages, aggregated across all land cover types. By 2024, average LST declined to 48.76 °C, coinciding with expanded vegetation and milder summer temperatures. The observed 3.6 °C reduction may reflect both climatic moderation and land cover transformations. LST comparisons across land cover types showed a consistent thermal hierarchy: bare desert exhibited the highest surface temperatures, followed by urban areas, vegetation, and water bodies. These results highlight the cooling role of green infrastructure in hyper-arid cities. The findings contribute to Sustainable Development Goals (SDGs) 11, 13, and 15 by providing geospatial insights for sustainable land management and urban climate resilience.

Introduction: Identifying optimal locations for constructing emergency water reservoirs for Fire Following Earthquake (FFE) suppression is recognized as a key factor in reducing financial and human losses within FFE crisis management strategies; however, previous studies have not provided a comprehensive model for this purpose.Methods: This study proposes a method that integrates potential post-earthquake damages, accessibility conditions, and economic considerations to identify the most suitable initial locations for these reservoirs. Based on operational needs in post-earthquake scenarios and relevant literature, the main influential criteria and distance classes were determined. These criteria and distance classes were then weighted and integrated using the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) methods to generate an optimal location map.Results: Implementation of the proposed method in Tehran resulted in an optimal location map for FFE-specific emergency water reservoirs. Results indicated that locating reservoirs within 4,000 m of major routes, fire stations, and commercial, industrial, and therapeutic centers simultaneously ensures fire engine access within 10 mins even under worst-case conditions.Discussion: Furthermore, optimally siting reservoirs within 800 m of demand points reduces access time to below 2 mins.