Deep Learning for Air Quality Forecasts: a Review

Abstract

Air pollution is one of major environmental issues in the twenty-first century due to global industrialization and urbanization. Its mitigation necessitates accurate air quality forecasts. However, current state-of-the-art air quality forecasts are limited from highly uncertain chemistry-transport models (CTMs), shallow statistical methods, and heterogeneous and incomplete observing networks. Recently, deep learning has emerged as a general-purpose technology to extract complex knowledge using massive amount of data and very large networks of neurons and thus has the potential to break the limits of air quality forecasts. Here, we provide a brief review of recent attempts on using deep learning techniques in air quality forecasts. We first introduce architectures of deep networks (e.g., convolutional neural networks, recurrent neural networks, long short-term memory neural networks, and spatiotemporal deep network) and their relevance to explore the nonlinear spatiotemporal features across multiple scales of air pollution. We then examine the potential of deep learning techniques for air quality forecasts in diverse aspects, namely, data gap filling, prediction algorithms, improvements of CTMs, estimations with satellite data, and source estimations for atmospheric dispersion forecasts. Finally, we point out some prospects and challenges for future attempts on improving air quality forecasts using deep learning techniques.