Groundbreaking tools and models to reduce air pollution in urban areas

MODELAIR建议将理论、实验、数值和数据驱动的科学相结合，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名博士生(DC)提供专门培训，使这些新技术可用于市议会和相关工业部门。MODELAIR为欧盟使命做出贡献：气候中立和智慧城市，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，以帮助做出明智和明智的决定，以控制城市地区的空气污染。为此，MODELAIR将开发新的分析工具和基于非侵入性传感和创新数据源的新降阶模型(ROM)。经过布里斯托尔、布鲁塞尔和马德里的行业和市议会的评估，MODELAIR将改进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范围将在与城市地区空气污染有关的三个具体问题中进行测试：(I)研究城市拓扑对空气污染的影响，(Ii)确定Ixelle区(布鲁塞尔-BE)的特征，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，以及(Iii)调查污染物排放源(强度和位置)的影响，以优化交通路线，以减少空气污染。最终目标是使用新的工具和方法方法(应对三个不同的挑战)获得可转让的产出，以改进空气质量扩散模型，这将有助于市议会和行业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.