Unsupervised Air Quality Interpolation with Attentive Graph Neural Network

Rapid industrial expansion, urbanization, and traffic growth have led to a decline in air quality that significantly impacts human health and environmental sustainability, particularly in developing nations. Due to the limited number of monitoring stations, the air quality index is not gathered at numerous locations. To address the difficulty of predicting the air quality value at an arbitrary place, several studies, including statistical and machine learning approaches, have been proposed. The majority of existing research employs classic distance-based interpolation techniques. In this paper, we propose a novel attentive neural-based approach for estimating unmonitored air quality values. This method follows the encoder-decoder paradigm, with the encoder and decoder being learned independently utilizing distinct processes. In the encoder, we propose AGE, an inductive unsupervised learning methodology that integrates attention mechanisms. AGE learns a set of functions that generate spatial embeddings by aggregating features from the surrounding region. For the decoder, we utilize the Gated Recurrent Unit and a fully-connected layer to estimate the air quality index at the targeted location. We conduct extensive experiments to evaluate the performance of our proposed method and compare it to the state-of-the-art (SOTA). The experimental results show that our approach reduces the estimation error from 8.07% to 37.04% compared to the SOTA.