Tackling air quality with SAPIENS
This work addresses the need for hyper-local, dynamic air quality forecasts in cities, though it is incremental as it applies an existing statistical method (Partial Least Squares Regression) to a new dataset with a novel traffic representation.
The researchers tackled the problem of coarse-grained air quality forecasts by developing a method to predict pollution levels using traffic intensity data from Mexico City, achieving improved characterization of traffic conditions through a novel concentric ring-based representation of traffic maps.
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.