CityAQVis：基于多源数据的城市区域污染物估算集成机器学习-可视化沙盒工具（软件文章）
CityAQVis: Integrated ML-Visualization Sandbox Tool for Pollutant Estimation in Urban Regions Using Multi-Source Data (Software Article)
AI总结：
本文介绍CityAQVis，一个集成机器学习与可视化的交互式沙盒工具，利用多源数据预测并可视化地面污染物浓度，支持模型比较，助力空气质量管理决策。
摘要：
城市空气污染对公众健康、环境可持续性和政策规划构成重大风险。有效的空气质量管理需要能够整合多样数据集并传达复杂时空污染模式的预测工具。目前，在交互式工具中，将空气污染物浓度的空间分布预测与可视化无缝集成方面存在空白。我们提出了CityAQVis，一个交互式机器学习（ML）沙盒工具，旨在利用多源数据（包括卫星观测、气象参数、人口密度、海拔和夜间灯光）预测并可视化地面水平的污染物浓度。虽然传统的空气质量可视化工具往往缺乏预测能力，但CityAQVis使用户能够构建和比较预测模型，可视化模型输出，并提供对地面污染动态的洞察。该工具的试点实施通过预测大都市区域二氧化氮（NO2）浓度的案例研究进行了测试，突显了其对各种污染物的适应性。通过直观的图形用户界面（GUI），用户可以在两种不同的城市情景下对地表污染物浓度的空间分布进行比较可视化。我们的结果强调了机器学习驱动的视觉分析在提高态势感知和支持空气质量管理中数据驱动决策方面的潜力。
英文摘要：
Urban air pollution poses significant risks to public health, environmental sustainability, and policy planning. Effective air quality management requires predictive tools that can integrate diverse datasets and communicate complex spatial and temporal pollution patterns. There is a gap in interactive tools with seamless integration of forecasting and visualization of spatial distributions of air pollutant concentrations. We present CityAQVis, an interactive machine learning ML sandbox tool designed to predict and visualize pollutant concentrations at the ground level using multi-source data, which includes satellite observations, meteorological parameters, population density, elevation, and nighttime lights. While traditional air quality visualization tools often lack forecasting capabilities, CityAQVis enables users to build and compare predictive models, visualizing the model outputs and offering insights into pollution dynamics at the ground level. The pilot implementation of the tool is tested through case studies predicting nitrogen dioxide (NO2) concentrations in metropolitan regions, highlighting its adaptability to various pollutants. Through an intuitive graphical user interface (GUI), the user can perform comparative visualizations of the spatial distribution of surface-level pollutant concentration in two different urban scenarios. Our results highlight the potential of ML-driven visual analytics to improve situational awareness and support data-driven decision-making in air quality management.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
