Environmental and Earth Sciences Proceedings

Aircraft emissions are a growing environmental concern due to their contribution to local air pollution and potential health risks, particularly around rapidly expanding airports. In Egypt, rapid urban growth and tourism have driven the construction of new airports, underscoring the need to assess their environmental impacts, particularly those related to aircraft emissions in the surrounding areas. Few studies have assessed aircraft emissions across multiple Egyptian airports, particularly under future capacity and climate scenarios, using dispersion models. This study evaluates the environmental impact of aircraft emissions at four Egyptian airports using the Graz Lagrangian Dispersion Model (GRAL). The analysis accounts for projected increases in airport capacity through 2030 and 2035 and examines how climate change may influence pollutant dispersion. Emissions from 2021 served as a baseline, while future meteorological conditions were simulated with the RegCM4 regional climate model under the RCP4.5 scenario. Results show that maximum daily average carbon monoxide concentrations at Administrative Capital Airport increased from ~24.5 µg/m³ in 2021 to ~100.3 µg/m³ in 2035, while nitrogen dioxide concentrations at El-Meliz Airport rose from ~20.3 to ~47.6 µg/m³. Similar upward trends were observed for sulfur dioxide and particulate matter (PM₁₀), although all simulated values remained below the thresholds established by Egyptian Environmental Law. These findings highlight that continued growth in aviation activity, even without breaching national standards, may contribute to long-term health risks for nearby communities.

We present an example of how a doctoral network can bring together multidisciplinary expertise and novel scientific advances in atmospheric dust. This network (Dust-DN) has started operations and is a strategic alliance of high-profile partners, able to leverage unique facilities for atmospheric research and innovative space missions. The network aims to improve our understandings of dust processes and microphysics, identify the signature of source regions, address the socio-economic impacts of dust transport and improve the quantification of the role of dust in the climate system. The first results have already been achieved and are shown here, and many more are expected to follow.

The field of climate modeling is undergoing a significant transformation, moving away from the traditional General Circulation Models (GCMs) and toward the use of sophisticated artificial intelligence (AI)-based prediction systems. Research has shown that AI has the potential to improve climate modeling’s regional accuracy and computing efficiency. Machine learning downscaling better captures local precipitation extremes than GCMs, while hybrid AI–physics models cut ensemble costs by reducing computational demand without sacrificing accuracy. Nevertheless, these investigations have frequently functioned in discrete settings and oversimplified situations without a thorough connection with basic physical concepts. This drawback emphasizes the necessity of a more comprehensive strategy that can handle the intricacies of climatic variability and guarantee reliable model validation. In order to assess the possibilities and challenges of hybrid models in comparison to conventional GCMs, highlighting that AI complements GCMs in regional downscaling and extremes, while GCMs provide stronger global consistency, this study synthesizes proven climate models, AI methodologies, and their accuracy in climate predictions and analyzes existing climate models to evaluate the potential and limitations of hybrid models compared to traditional GCMs. Integrated AI-driven models show notable improvements in predicting regional variations in climate and accelerating simulation processes, especially when dealing with the growing presence of extreme weather occurrences. However, it is important to have consistent datasets and open evaluation procedures in order to guarantee accuracy and deal with the difficulties that come with model benchmarking. This research highlights how crucial it is to maintain interdisciplinary cooperation in order to properly utilize what AI has to offer in climate modeling. This partnership is essential to creating more accurate and useful climate projections, which will eventually guide successful mitigation and adaptation plans for a changing global environment. In order to have a greater understanding of our climate’s future, we must keep pushing the limits of the existing modeling tools.