Groundbreaking tools and models to reduce air pollution in urban areas

MODELAIR提出了理论、实验、数值和数据驱动科学的结合，将为未来可持续城市模拟、控制和设计新的颠覆性技术，并将为10名博士候选人（dc）提供专业培训，使这些新技术可用于市议会和相关工业部门。MODELAIR有助于欧盟使命：气候中和和智慧城市，减少气体排放，为公民提供更清洁的空气。MODELAIR将开发一种基于人工智能（AI）的工具，以帮助做出明智的决策，控制城市地区的空气污染。为此，MODELAIR将开发基于非侵入式传感和创新数据源的新型分析工具和新的降阶模型（ROMs）。由布里斯托尔、布鲁塞尔和马德里的工业和市议会评估，MODELAIR将提高目前最先进的建模能力，考虑到建筑物、道路和其他结构对空气污染流动和扩散的影响。新的基于人工智能的工具的适用性限制将在三个与城市空气污染相关的具体问题中进行测试：(i)研究城市拓扑结构对空气污染的影响；（ii）描述伊克塞尔区（布鲁塞尔- be）的特征，以开发实时决策工具，评估传感器网络的配置和维护，以提供高质量的空气污染监测服务；（iii）调查污染物排放源（强度和位置）的影响，以优化交通路线，减少空气污染。最终目标是利用新的工具和方法方法（解决三种不同的挑战）获得可转移的产出，以改善空气质量分散模型，这些模型将有助于市议会和工业界制定控制空气污染的新法规。

MODELAIR proposes a combination of theoretical, experimental, numerical, and data-driven science that will simulate, control and design new disruptive technologies for future sustainable cities and will provide specialized training to 10 doctoral candidates (DCs) to make these new technologies available to city Councils and relevant industrial sectors. MODELAIR contributes to EU Mission: Climate-neutral and smart cities, reducing gas emissions and offering cleaner air to citizens. MODELAIR will develop an Artificial Intelligence (AI) - based tool to help make informed and sensible decisions to control air pollution in urban areas. For such aim, MODELAIR will develop novel analysis tools and new Reduced Order Models (ROMs) based on both non-intrusive sensing and innovative data sources. Assessed by the industry and city councils from Bristol, Brussels and Madrid, MODELAIR will improve the current state-of-the-art modeling capability, taking into account the influence that buildings, roadways and other structures have on the flow and dispersion of air pollution . The limits of applicability of the novel AI-based tool will be tested in three specific problems related to air pollution in urban areas by: (i) studying the influence of the urban topology on air pollution, (ii) characterizing the Ixelles District (Brussels-BE) to develop a real-time decision-making tool that will assess about the disposition and maintenance of the sensor network to provide a high-quality air pollution monitoring service, and (iii) investigating the influence of the pollutant emission source (strength and location) to optimize traffic routes to reduce air pollution. The final aim is to obtain transferable outputs using novel tools and methodology approaches (addressing three different challenges) to improve air quality dispersion models that will serve to city councils and industry to develop new regulations to control air pollution.