CityAQVis: Integrated ML-Visualization Sandbox Tool for Pollutant Estimation in Urban Regions Using Multi-Source Data (Software Article)
Brij Bidhin Desai, Yukta Arvind Rajapur, Aswathi Mundayatt, Jaya Sreevalsan-Nair
TL;DR
CityAQVis addresses the need for interactive, forecasting-enabled visualization in urban air quality by integrating multi-source data (satellite TVCD, meteorology, population, elevation, and nighttime lights) into a configurable ML sandbox. The framework supports training, comparing, and visualizing ground-level NO2 predictions across cities and time within a single GUI, demonstrated through intra- and inter-city case studies in Indian cities. A pollutant-agnostic design, compatibility with Google Earth Engine, and a Streamlit-based interface enable scalable exploration and data-driven decision making for urban planners and researchers. The work highlights the value of comparative visual analytics in understanding urban pollution dynamics and outlines concrete steps for extending to additional data sources and pollutants.
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.