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.

翻译：城市空气污染对公共健康、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化数据集并传达复杂时空污染模式的预测工具。当前缺乏能够无缝集成空气污染物浓度空间分布预测与可视化的交互式工具。本文提出CityAQVis，一种交互式机器学习沙盒工具，旨在利用多源数据（包括卫星观测、气象参数、人口密度、高程和夜间灯光）预测和可视化地面污染物浓度。传统空气质量可视化工具通常缺乏预测功能，而CityAQVis允许用户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深入洞察。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测试，突显了其对各类污染物的适应性。通过直观的图形用户界面，用户可对两种不同城市情景下的地表污染物浓度空间分布进行对比可视化。我们的研究结果凸显了机器学习驱动的可视分析在提升态势感知能力和支持空气质量管理中数据驱动决策方面的潜力。