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 hotspots. One major challenge with mobile sensing data is uneven sampling, i.e., data collection can vary by both space and time. To address this challenge, we propose a two-step approach to detect hotspots from mobile sensing data, which includes local spike detection and sample-weighted clustering. Essentially, this approach tackles the uneven sampling issue by weighting samples based on their spatial frequency and temporal hit rate, so as to identify robust and persistent hotspots. To contextualize the hotspots and discover potential pollution source characteristics, we explore a variety of cross-domain urban data and extract features from them. As a soft-validation of the extracted features, we build hotspot inference models for cities with and without mobile sensing data. Evaluation results using real-world mobile sensing air quality data as well as cross-domain urban data demonstrate the effectiveness of our approach in detecting and inferring pollution hotspots. Furthermore, the empirical analysis of hotspots and source features yields useful insights regarding neighborhood pollution sources.
Comprehensive Monitoring of Air Pollution Hotspots Using Sparse Sensor Networks
New York University（纽约大学）；Google Research（谷歌研究）；Toyota InfoTechnology Center（丰田信息技术中心）；Kaiterra Inc（Kaiterra公司）；University of Warwick（沃里克大学）；Yale University（耶鲁大学）
Deep-MAPS: Machine Learning based Mobile Air Pollution Sensing
Imperial College London（帝国理工学院）；Southwest Jiaotong University（西南交通大学）；Department of Civil and Environmental Engineering, Imperial College London, UK（帝国理工学院土木与环境工程系）；School of Transportation and Logistics, Southwest Jiaotong University, China（西南交通大学交通运输与物流学院）
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations
Data Science Institute（数据科学研究所）；Imperial College London（伦敦帝国理工学院）；Leonardo Centre, Imperial Business School（帝国理工商学院莱昂纳多中心）；Department of Earth Science & Engineering（地球科学与工程系）
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）
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）
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）
Mining Citywide Dengue Spread Patterns in Singapore Through Hotspot Dynamics from Open Web Data
Agency for Science, Technology and Research (A*STAR)（科技研究局）；Nanyang Technological University（南洋理工大学）；Jilin University（吉林大学）；Tsinghua University（清华大学）；University of New South Wales（新南威尔士大学）
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）
Modelling spatio-temporal trends of air pollution in Africa
African Institute for Mathematical Sciences（非洲数学科学研究所）；Institute of Applied Statistics, University of Burundi（布隆迪大学应用统计研究所）；Ecole Normale Superieur de Bujumbura（布琼布拉高等师范学院）
GraphSVR: A Graph Convolutional Support Vector Regression Framework for Robust Spatiotemporal Air Pollution Forecasting
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology（维杰雷理工学院数学系）；SAFIR, Sorbonne University（索邦大学SAFIR中心）；Statistics Centre Abu Dhabi（阿布扎希统计中心）
AirCast: Improving Air Pollution Forecasting Through Multi-Variable Data Alignment
Wageningen University and Research（瓦赫宁根大学与研究中心）；Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）；Weizmann Institute of Science（魏茨曼科学研究所）
Using construction waste hauling trucks' GPS data to classify earthwork-related locations: A Chengdu case study
School of Transportation and Logistics at Southwest Jiaotong University（西南交通大学交通运输与物流学院）；School of Economics and Management at Southwest Jiaotong University（西南交通大学经济管理学院）
Synergistic Neural Forecasting of Air Pollution with Stochastic Sampling
Mohamed bin Zayed University of Artificial Intelligence（莫德尔·本·扎耶德人工智能大学）；Weizmann Institute of Science（魏兹曼科学研究所）；Linköping University（林雪平大学）；Australian National University（澳大利亚国立大学）
STeP-Diff: Spatio-Temporal Physics-Informed Diffusion Models for Mobile Fine-Grained Pollution Forecasting
Shenzhen International Graduate School, Tsinghua University, China（清华大学深圳国际研究生院）；Department of Electronic Engineering, Beijing National Research Center for Information Science and Tech- nology (BNRist), Tsinghua University, China（电子工程系，北京信息科学与技术国家研究中心（BNRist），清华大学）；Shenzhen Smartcity Communication, Shenzhen, China（深圳智慧城市通信）；College of Soil and Water Conservation, Southwest Forestry University, China（水土保持学院，西南林业大学）；College of Civil Engineering, Southwest Forestry University, China（土木工程学院，西南林业大学）
