Tackling air quality with SAPIENS

TL;DR

This paper presents a novel approach to predict hyper-local air quality in Mexico City by transforming traffic maps into concentric ring descriptions and applying regression models to link traffic intensity with pollution levels.

Contribution

It introduces an innovative traffic representation method and demonstrates its effectiveness in modeling and forecasting air pollution using real-world data.

Findings

The new traffic representation improves pollution prediction accuracy.

The model effectively captures the relationship between traffic and air quality.

The workflow is adaptable to other urban contexts.

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.