CityAQVis: Integrated ML-Visualization Sandbox Tool for Pollutant Estimation in Urban Regions Using Multi-Source Data (Software Article)

2025-09-13

AI Summary

Long-standing fragmentation between urban air pollution forecasting and visualization tools impedes fine-grained environmental governance. Method: This paper proposes a sandbox system integrating machine learning with interactive visualization, fusing multi-source data—including satellite remote sensing, meteorological variables, population density, nighttime light intensity, and elevation—to enable high-accuracy spatiotemporal prediction and dynamic spatial mapping of ground-level pollutants (e.g., NOₓ). The system features a configurable modeling interface allowing users to design, compare, and evaluate models, as well as simulate pollution patterns under diverse urban scenarios. Contribution/Results: Empirical validation across multiple metropolitan regions demonstrates strong generalizability across pollutants and robust cross-regional adaptability. The system significantly enhances the scientific rigor and decision-making responsiveness of air quality management.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Scalability of ML Systems
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

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.

Problem

• Predicting urban air pollution concentrations using multi-source data integration
• Bridging the gap between forecasting and interactive visualization tools
• Enabling comparative analysis of spatial pollutant distributions across scenarios

Innovation

• Interactive ML sandbox tool for pollutant prediction
• Integrates multi-source data including satellite observations
• Provides comparative visualization of spatial pollutant distributions