Air in Your Neighborhood: Fine-Grained AQI Forecasting Using Mobile Sensor Data

TL;DR

This paper introduces a novel spatio-temporal graph neural network approach to accurately forecast fine-grained air quality index (AQI) in neighborhoods using sparse mobile sensor data, significantly outperforming existing methods.

Contribution

It presents a new spatio-temporal GNN model for neighborhood-level AQI prediction, achieving substantial error reduction and uncovering new insights into AQI patterns.

Findings

71.654 MSE reduction over previous methods

Discovered strong short-term AQI patterns

Identified dynamic spatial relations in AQI data

Abstract

Air pollution has become a significant health risk in developing countries. While governments routinely publish air-quality index (AQI) data to track pollution, these values fail to capture the local reality, as sensors are often very sparse. In this paper, we address this gap by predicting AQI in 1 km^2 neighborhoods, using the example of AirDelhi dataset. Using Spatio-temporal GNNs we surpass existing works by 71.654 MSE a 79% reduction, even on unseen coordinates. New insights about AQI such as the existence of strong repetitive short-term patterns and changing spatial relations are also discovered. The code is available on GitHub.