Tackling air quality with SAPIENS

2026-01-30

AI Summary

This study addresses the urgent need for high spatiotemporal resolution monitoring and forecasting in urban air pollution management by overcoming the coarse granularity of existing approaches. Leveraging fine-grained real-time traffic data and pollution sensor observations from Mexico City, the authors innovatively transform color-coded traffic maps into concentric ring structures to represent traffic intensity. Integrating image processing techniques with partial least squares (PLS) regression, they develop a dynamic predictive model that effectively captures the relationship between traffic intensity and pollutant concentrations. The proposed framework demonstrates robust performance across diverse training conditions, achieving improved prediction accuracy while offering a transferable architecture for air quality forecasting applicable to other urban environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Time-Series/Data Streams
• Computer Vision: Multi-modal Vision

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 traffic and log analysis
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Air pollution is a chronic problem in large cities worldwide and awareness is rising as the long-term health implications become clearer. Vehicular traffic has been identified as a major contributor to poor air quality. In a lot of cities the publicly available air quality measurements and forecasts are coarse-grained both in space and time. However, in general, real-time traffic intensity data is openly available in various forms and is fine-grained. In this paper, we present an in-depth study of pollution sensor measurements combined with traffic data from Mexico City. We analyse and model the relationship between traffic intensity and air quality with the aim to provide hyper-local, dynamic air quality forecasts. We developed an innovative method to represent traffic intensities by transforming simple colour-coded traffic maps into concentric ring-based descriptions, enabling improved characterisation of traffic conditions. Using Partial Least Squares Regression, we predict pollution levels based on these newly defined traffic intensities. The model was optimised with various training samples to achieve the best predictive performance and gain insights into the relationship between pollutants and traffic. The workflow we have designed is straightforward and adaptable to other contexts, like other cities beyond the specifics of our dataset.

Problem

• air quality
• traffic intensity
• hyper-local forecasting
• real-time data
• urban pollution

Innovation

• traffic intensity representation
• hyper-local air quality forecasting
• concentric ring-based traffic modeling
• Partial Least Squares Regression
• data-driven environmental modeling