AIREX: Neural Network-based Approach for Air Quality Inference in Unmonitored Cities

TL;DR

This paper introduces AIREX, a neural network model that uses a mixture-of-experts and attention mechanisms to accurately infer air quality in unmonitored cities, addressing a gap in existing spatial inference methods.

Contribution

AIREX is the first neural network approach to infer air quality in unmonitored cities by modeling inter-city correlations with a mixture-of-experts and attention mechanisms.

Findings

AIREX outperforms existing methods in accuracy on real-world data.

The mixture-of-experts approach effectively captures inter-city air quality correlations.

Attention mechanisms improve the impact estimation from monitored to unmonitored cities.

Abstract

Urban air pollution is a major environmental problem affecting human health and quality of life. Monitoring stations have been established to continuously obtain air quality information, but they do not cover all areas. Thus, there are numerous methods for spatially fine-grained air quality inference. Since existing methods aim to infer air quality of locations only in monitored cities, they do not assume inferring air quality in unmonitored cities. In this paper, we first study the air quality inference in unmonitored cities. To accurately infer air quality in unmonitored cities, we propose a neural network-based approach AIREX. The novelty of AIREX is employing a mixture-of-experts approach, which is a machine learning technique based on the divide-and-conquer principle, to learn correlations of air quality between multiple cities. To further boost the performance, it employs attention mechanisms to compute impacts of air quality inference from the monitored cities to the locations in the unmonitored city. We show, through experiments on a real-world air quality dataset, that AIREX achieves higher accuracy than state-of-the-art methods.