Hybrid AI and multiscale physical modelling for optimal urban decarbonisation combating climate change

该提案所阐述的挑战是如何(1)准确评估城市地区的碳排放；(2)帮助设计和管理城市，减少碳足迹；(3)量化城市碳排放对全球气候变化的影响——实现1.5℃的目标。减少温室气体排放是应对全球变暖的关键。据估计，到2020年，英国温室气体净排放量的24%来自交通运输部门，21%来自能源供应，18%来自商业，16%来自住宅部门，11%来自农业。准确的城市碳排放评估将有助于决策者在决策过程中以及公共和私人空间的管理者优化能源使用、碳减排和经济效益。模型是理解碳生命周期和大气过程、进行预测、不确定性量化和脱碳优化控制/设计的有力工具。然而，对环境和人类发展的综合评价可以说是联合国面临的最困难和最重要的“系统”问题。复杂的碳循环和大气物理过程在广泛的空间尺度（从米到度）和时间尺度（从小时、天到几十年）上起作用。目前，还没有一个跨社区、城市和全球尺度的综合模型可以用于探索人类活动相关碳排放与全球气候变化之间的复杂关系。在这里，我的目标是开发一个混合AI（人工智能）-多尺度物理-知情的最佳管理框架，以准确评估和减缓城市地区的二氧化碳。有效的碳评估和管理需要多尺度碳模型的实施，这些模型可以充分捕捉城市碳排放和分散模式的时空变异性。当前的模型要么在计算上过于昂贵，要么无法捕捉到此类问题的详细可变性。拟议的工作将通过开发先进的多尺度碳模型（基于我们最近开发的流动性-城市模型）来推进科学现状，其中使用动态适应网格使我们能够解决复杂的城市湍流和碳分散过程。在不同尺度上考虑了城市基础设施对碳分散过程的影响。然后，基于人工智能的建模将用于城市基础设施的优化设计和布局，以减少碳排放。城市的能源效率和碳基能源使用是根据选定城市伦敦现有基础设施的详细数据集进行测量的。建模框架将包括新的城市基础设施/布局的碳参数化方案，从而能够更准确地评估城市碳排放及其对气候变化的影响。现有城市基础设施的潜在改进，以及新城市发展的优化设计，将通过本文提出的基于人工智能的最优控制工具提供，以减少碳排放和提高能源效率。最后，将开发一个基于人工智能的温室气体参数化模块，用于将高分辨率网格计算的二氧化碳通量与现有的地球系统建模相结合。城市碳排放对全球气候的影响可以根据现有和改进的城市基础设施和布局进行准确评估。这一创新框架将允许对现有和新的脱碳政策选择进行批判性评估，从而改善当地和全球气候。该工具有可能改变未来城市中用于脱碳的城市基础设施设计、GI和BI的方式，并为准确量化城市碳排放对全球变暖的影响铺平道路贝,N . .2020年英国温室气体排放，最终数据Navarro et al. 2018。地球系统。力学。， 9, 1045

The challenges articulated in this proposal are how to (1) accurately assess carbon emissions in urban areas; (2) help design and manage cities so that the carbon footprint is reduced; and (3) quantify the impact of urban carbon emissions on global climate change-towards the 1.5 degree climate goal.Greenhouse gas emission reduction is key to tackling global warming. In 2020, 24% of net greenhouse gas emissions in the UK were estimated to be from the transport sector, 21% from energy supply, 18% from business, 16% from the residential sector and 11% from agriculture [1]. Accurate assessment of urban carbon emissions will help policy makers in their decision-making processes and managers of public and private spaces to optimise energy use, carbon reduction and economic benefit. Models are powerful tools in understanding carbon life cycle and atmospheric processes, making predictions, uncertainty quantification and optimal control/design for decarbonisation. However, integrated assessment of the environment and human development is arguably the most difficult and important "systems" problem faced [2]. The complex carbon cycle and atmospheric physical processes act over a wide range of spatial (from meters to degrees) and temporal (from hours, days to decades) scales. Currently, there is no integrated modelling across neighbourhood, city and global scales which can be used for exploring the complex relationship between carbon emissions associated with human activities and global climate change. Here I aim to develop a hybrid AI (Artificial Intelligence)-multiscale physics-informed optimal management framework for accurate assessment and mitigation of CO2 in urban areas. Effective carbon assessment and management necessitate the implementation of multiscale carbon models that can capture adequate spatial and temporal variability of urban carbon emissions & dispersion patterns. Current models are either excessively computationally expensive, or fail to capture the detailed variability of such problems. The proposed work will advance the status of science by developing an advanced multiscale carbon model (based on our recently developed Fluidity-Urban model) where, the use of dynamically adapted meshes enables us to resolve complex urban turbulent flows and carbon dispersion processes. The effect of city infrastructures on carbon dispersion processes is considered at different scales. AI-based modelling will then be used for the optimal design of urban infrastructures and layout for mitigation of carbon emissions. Energy efficiency and carbon-based energy usage in cities are measured based on detailed datasets of existing infrastructures in the selected city-London. The modelling framework will include new carbon parameterisation schemes for urban infrastructures/layout, enabling more accurate assessment of urban carbon emissions, and their impact on climate change. Potential improvements to existing urban infrastructures, and optimal designs for new urban developments will be provided through the AI-based optimal control tool proposed here for carbon reduction and energy efficiency. Finally, an AI-based GHG parameterisation module will be developed for coupling the calculated CO2 fluxes at high resolution grids with existing Earth System modelling. The impact of carbon emissions in cities on global climate can then be evaluated accurately based on existing and improved city infrastructure and layouts.This innovative framework will allow the critical assessment of existing and new policy options on decarbonisation to be carried out, thus improving local and global climate. The tool could potentially change the way in which city infrastructure design, GI and BI for decarbonisation are used in our future cities and pave the way for accurate quantification of the impact of urban carbon emissions on global warming.[1] BEIS, N.. 2020 UK Greenhouse Gas Emissions, Final Figures.[2] Navarro et al. 2018. Earth Syst. Dynam., 9, 1045