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, we introduce a large scale city-wise dataset for exploring the relationships among these agents over a long period of time. We also introduce a transformer based model - cosSquareFormer, for the problem of pollutant level estimation and forecasting. Our model outperforms most of the benchmark models for this task. We also analyze and explore the dataset through our model and other methodologies to bring out important inferences which enable us to understand the dynamics of the causal agents at a deeper level. Through our paper, we seek to provide a great set of foundations for further research into this domain that will demand critical attention of ours in the near future.
Recently, air pollution is one of the most concerns for big cities. Predicting air quality for any regions and at any time is a critical requirement of urban citizens. However, air pollution prediction for the whole city is a challenging problem. The reason is, there are many spatiotemporal factors affecting air pollution throughout the city. Collecting as many of them could help us to forecast air pollution better. In this research, we present many spatiotemporal datasets collected over Seoul city in Korea, which is currently much suffered by air pollution problem as well. These datasets incl…
Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
Real estate markets depend on various methods to predict housing prices, including models that have been trained on datasets of residential or commercial properties. Most studies endeavor to create more accurate machine learning models by utilizing data such as basic property characteristics as well as urban features like distances from amenities and road accessibility. Even though environmental factors like noise pollution can potentially affect prices, the research around this topic is limited. One of the reasons is the lack of data. In this paper, we reconstruct and make publicly available …
Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de Valencia（瓦伦西亚理工大学纯数学与应用数学大学研究所）；FLOW, Engineering Mechanics, KTH Royal Institute of Technology（瑞典皇家理工学院工程力学系FLOW实验室）
The sustainability of urban environments is an increasingly relevant problem. Air pollution plays a key role in the degradation of the environment as well as the health of the citizens exposed to it. In this chapter we provide a review of the methods available to model air pollution, focusing on the application of machine-learning methods. In fact, machine-learning methods have proved to importantly increase the accuracy of traditional air-pollution approaches while limiting the development cost of the models. Machine-learning tools have opened new approaches to study air pollution, such as fl…
Zhejiang University（浙江大学）；Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）；Wuhan University（武汉大学）
The problem of air pollution threatens public health. Air quality forecasting can provide the air quality index hours or even days later, which can help the public to prevent air pollution in advance. Previous works focus on citywide air quality forecasting and cannot solve nationwide city forecasting problem, whose difficulties lie in capturing the latent dependencies between geographically distant but highly correlated cities. In this paper, we propose the group-aware graph neural network (GAGNN), a hierarchical model for nationwide city air quality forecasting. The model constructs a city g…
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）
This is a relevant problem because the design of most cities prioritizes the use of motorized vehicles, which has degraded air quality in recent years, having a negative effect on urban health. Modeling, predicting, and forecasting ambient air pollution is an important way to deal with this issue because it would be helpful for decision-makers and urban city planners to understand the phenomena and to take solutions. In general, data-driven methods for modeling, predicting, and forecasting outdoor pollution requires an important amount of data, which may limit their accuracy. In order to deal …
Emory University（埃默里大学）；Pomona College（波莫纳学院）；Rollins School of Public Health（罗林斯公共卫生学院）
Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks (ANNs). Most of the prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone, oxides of nitrogen, and PM2.5. Given that traditional, highly sophisticated air quality monitors are expensive and are not universally available, these models cannot adequately s…
Air pollution has long been a serious environmental health challenge, especially in metropolitan cities, where air pollutant concentrations are exacerbated by the street canyon effect and high building density. Whilst accurately monitoring and forecasting air pollution are highly crucial, existing data-driven models fail to fully address the complex interaction between air pollution and urban dynamics. Our Deep-AIR, a novel hybrid deep learning framework that combines a convolutional neural network with a long short-term memory network, aims to address this gap to provide fine-grained city-wid…
Auto-Configured Explainable Graph Neural Networks for Multi-Site Pollution Prediction
University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB)（奥兰科技大学-穆罕默德·布迪亚夫）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
Deep Reinforcement Learning for Urban Air Quality Management: Multi-Objective Optimization of Pollution Mitigation Booth Placement in Metropolitan Environments
School of Computer Science（计算机科学学院）；Engineering, Vellore Institute of Technology（工程学院，维洛雷理工学院）；Chennai Campus（钦奈校区）；Chennai, Tamil Nadu 600127, India（钦奈，泰米尔纳德邦600127，印度）
Collaborative Imputation of Urban Time Series through Cross-city Meta-learning
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University（香港理工大学土木与环境工程系）；Department of Traffic Engineering, Tongji University（同济大学交通工程系）；Centre for Learning, Teaching, and Technology, The Education University of Hong Kong（香港教育大学学习、教学与科技中心）；Research Institute for Artificial Intelligence of Things, Department of Computing, The Hong Kong Polytechnic University（香港理工大学人工智能物联网研究所）
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））
Department of Physics and Astronomy, Queen Mary University of London（量子力学与天文学系，伦敦女王学院）；ESFM, Instituto Politécnico Nacional（电子系统与机械工程系，国立理工学院）；CIC, Instituto Politécnico Nacional（研究中心，国立理工学院）；UPIITA, Instituto Politécnico Nacional（电子工程系，国立理工学院）
Cycling can reduce greenhouse gas emissions and air pollution and increase public health. With this in mind, policy-makers in cities worldwide seek to improve the bicycle mode-share. However, they often struggle against the fear and the perceived riskiness of cycling. Efforts to increase the bicycle's mode-share involve many measures, one of them being the improvement of cycling safety. This requires the analysis of the factors surrounding accidents and the outcome. However, meaningful analysis of cycling safety requires accurate bicycle flow data that is generally sparse or not even available…
Missouri University of Science and Technology（密苏里科技大学）
In this paper, we present a novel community sensing paradigm -- {C}ommunity {S}ensing {W}ithout {A}ggregation}. CSWA is designed to obtain the environment information (e.g., air pollution or temperature) in each subarea of the target area, without aggregating sensor and location data collected by community members. CSWA operates on top of a secured peer-to-peer network over the community members and proposes a novel \emph{Decentralized Spatial-Temporal Compressive Sensing} framework based on \emph{Parallelized Stochastic Gradient Descent}. Through learning the \emph{low-rank structure} via dis…
HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Computer Science, Zhejiang University of Technology（浙江工业大学计算机科学学院）；Alibaba Group（阿里巴巴集团）
Car sharing is one the pillars of a smart transportation infrastructure, as it is expected to reduce traffic congestion, parking demands and pollution in our cities. From the point of view of demand modelling, car sharing is a weak signal in the city landscape: only a small percentage of the population uses it, and thus it is difficult to study reliably with traditional techniques such as households travel diaries. In this work, we depart from these traditional approaches and we rely on web-based, digital records about vehicle availability in 10 European cities for one of the major active car …
The Institute for Computer Science and Control of the Hungarian Academy of Sciences（匈牙利科学院计算机科学与控制研究所）；General Electric (GE) Hungary Ltd.（通用电气（GE）匈牙利有限公司）
As urbanization proceeds at an astonishing rate, cities have to continuously improve their solutions that affect the safety, health and overall wellbeing of their residents. Smart city projects worldwide build on advanced sensor, information and communication technologies to help dealing with issues like air pollution, waste management, traffic optimization, and energy efficiency. The paper reports about the prototype of a smart city initiative in Budapest which applies various sensors installed on the public lighting system and a cloud-based analytical module. While the installed wireless mul…
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）
