Climate change and the rapid growth of urban populations are intensifying environmental stresses within cities, making the behavior of urban atmospheric flows a critical factor in public health, energy use, and overall livability. This study targets to develop fast and accurate models of urban pollutant dispersion to support decision-makers, enabling them to implement mitigation measures in a timely and cost-effective manner. To reach this goal, an end-to-end data-driven approach is proposed to model and predict the airflow and pollutant dispersion in a street canyon in skimming flow regime. A series of time-resolved snapshots obtained from large eddy simulation (LES) serves as the database. The proposed framework is based on four fundamental steps. Firstly, a reduced basis is obtained by spectral proper orthogonal decomposition (SPOD) of the database. The projection of the time series snapshot data onto the SPOD modes (time-domain approach) provides the temporal coefficients of the dynamics. Secondly, a nonlinear compression of the temporal coefficients is performed by autoencoder to reduce further the dimensionality of the problem. Thirdly, a reduced-order model (ROM) is learned in the latent space using Long Short-Term Memory (LSTM) netowrks. Finally, the pollutant dispersion is estimated from the predicted velocity field through convolutional neural network that maps both fields. The results demonstrate the efficacy of the model in predicting the instantaneous as well as statistically stationary fields over long time horizon.
Generative Urban Flow Modeling: From Geometry to Airflow with Graph Diffusion
Applied Mathematics Department, ETSIAE-School of Aeronautics, Universidad Politécnica de Madrid（应用数学系、ETSIAE航空学院、马德里理工大学）；Microflown Technologies（Microflown技术公司）；Computational Science and Engineering Laboratory, Harvard University（计算科学与工程实验室、哈佛大学）
CityAQVis: Integrated ML-Visualization Sandbox Tool for Pollutant Estimation in Urban Regions Using Multi-Source Data (Software Article)
Graphics-Visualization-Computing Lab, International Institute of Information Technology Bangalore, Karnataka 560100, India（图形可视化计算实验室，国际信息学院班加罗尔，卡纳塔克邦560100，印度）
FuXi-Air: Urban Air Quality Forecasting Based on Emission-Meteorology-Pollutant multimodal Machine Learning
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention（上海大气颗粒污染与防治重点实验室）；National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary（长江入海口湿地生态系统观测与研究站）；Department of Environmental Science and Engineering（环境科学与工程学院）；Shanghai Academy of AI for Science (SAIS)（上海人工智能科学研究院）；MOE Laboratory for National Development and Intelligent Governance（教育部国家发展与智能治理实验室）；Shanghai Institute for Energy and Carbon Neutrality Strategy（上海能源与碳中和战略研究院）；IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health（国际减灾与发展研究院风险互联与治理国际联合实验室）；Shanghai Institute of Eco-Chongming (SIEC)（上海生态崇明研究院）；Shanghai Environmental Monitoring Center (SEMC)（上海市环境监测中心）
Universidad Nacional de Educación a Distancia — UNED（西班牙国立远程教育大学）；Inverence
Air quality has become one of the main issues in public health and urban planning management, due to the proven adverse effects of high pollutant concentrations. Considering the mitigation measures that cities all over the world are taking in order to face frequent low air quality episodes, the capability of foreseeing future pollutant concentrations is of great importance. Through this paper, we present SOCAIRE, an operational tool based on a Bayesian and spatiotemporal ensemble of neural and statistical nested models. SOCAIRE integrates endogenous and exogenous information in order to predic…
Low-Cost High-Order Singular Value Decomposition for Tensor-Based Reconstruction from Sparse Sensor Measurements: Urban Flow and Air-Quality Applications
ETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid（马德里理工大学航空与航天工程学院）；Ove Arup（奥雅纳）；Department of Aerospace Engineering, University of Michigan（密歇根大学航空航天工程系）
Air pollution is a major global environmental health threat, in particular for people who live or work near pollution sources. Areas adjacent to pollution sources often have high ambient pollution concentrations, and those areas are commonly referred to as air pollution hotspots. Detecting and characterizing pollution hotspots are of great importance for air quality management, but are challenging due to the high spatial and temporal variability of air pollutants. In this work, we explore the use of mobile sensing data (i.e., air quality sensors installed on vehicles) to detect pollution hotsp…
Fast localized urban microclimate emulator: wind and temperatures in unseen urban morphologies
Department of Building, Civil and Environmental Engineering, Cetner Zero Energy Building Studies, Concordia University（建筑、土木与环境工程系，零能耗建筑研究中心，康科迪亚大学）；School of Computer Science, McGill University（计算机科学系，麦吉尔大学）；Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates（人工智能Mohamed bin Zayed大学，阿布扎比，阿拉伯联合酋长国）
Operator-based machine learning framework for generalizable prediction of unsteady treatment dynamics in stormwater infrastructure
Water Infrastructure Laboratory, Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA（水基础设施实验室，土木与环境工程系，田纳西大学， Knoxville，田纳西州 37996，美国）
Interpretable PM2.5 Forecasting for Urban Air Quality: A Comparative Study of Adaptive Time-Series Models
School of Computing, National University of Computer & Emerging Sciences (FAST)（国家计算机与新兴科学大学（FAST）计算学院）；College of Computing and Informatics, Saudi Electronic University（沙特电子大学计算与信息学院）
WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data
NIT Meghalaya（梅加拉亚国家理工学院）；Sorbonne University（索邦大学）；Sorbonne Center for Artificial Intelligence（索邦人工智能中心）；Universiti Kebangsaan（马来西亚国民大学）；University of Philippines（菲律宾大学）；Institute of Environmental Science and Meteorology（环境科学与气象研究所）
Zendrive Inc.（Zendrive公司）；Stony Brook University（石溪大学）；University of Toronto（多伦多大学）
Air pollution poses a serious threat to sustainable environmental conditions in the 21st century. Its importance in determining the health and living standards in urban settings is only expected to increase with time. Various factors ranging from artificial emissions to natural phenomena are known to be primary causal agents or influencers behind rising air pollution levels. However, the lack of large scale data involving the major artificial and natural factors has hindered the research on the causes and relations governing the variability of the different air pollutants. Through this work, w…
Establishing a real-time traffic alarm in the city of Valencia with Deep Learning
Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universidad Politécnica de Valencia（瓦伦西亚理工大学信息技术与通信大学研究所（ITACA））；Instituto de Física Corpuscular (IFIC), Universidad de Valencia-CSIC（瓦伦西亚大学-西班牙国家研究委员会粒子物理研究所（IFIC））；University of Sussex（萨塞克斯大学）；Centro de Investigaciones Sobre Desertificación (CIDE), CSIC - Universidad de Valencia - Generalitat Valenciana（西班牙国家研究委员会-瓦伦西亚大学-瓦伦西亚自治区荒漠化研究中心（CIDE））
Tackling air pollution is an imperative problem in South Korea, especially in urban areas, over the last few years. More specially, South Korea has joined the ranks of the world's most polluted countries alongside with other Asian capitals, such as Beijing or Delhi. Much research is being conducted in environmental science to evaluate the dangerous impact of particulate matters on public health. Besides that, deterministic models of air pollutant behavior are also generated; however, this is both complex and often inaccurate. On the contrary, deep recurrent neural network reveals potent potent…
Institute for Applied Sustainability to the Built Environment, University of Applied Sciences and Arts of Southern Switzerland（瑞士南部应用科学与艺术大学建筑环境应用可持续性研究所）
The ability to forecast the concentration of air pollutants in an urban region is crucial for decision-makers wishing to reduce the impact of pollution on public health through active measures (e.g. temporary traffic closures). In this study, we present a machine learning approach applied to the forecast of the day-ahead maximum value of the ozone concentration for several geographical locations in southern Switzerland. Due to the low density of measurement stations and to the complex orography of the use case terrain, we adopted feature selection methods instead of explicitly restricting rele…
