FuXi-Air: Urban Air Quality Forecasting Based on Emission-Meteorology-Pollutant multimodal Machine Learning
Journal:
arXiv
Published Date:
Jun 9, 2025
Abstract
Air pollution has emerged as a major public health challenge in megacities.
Numerical simulations and single-site machine learning approaches have been
widely applied in air quality forecasting tasks. However, these methods face
multiple limitations, including high computational costs, low operational
efficiency, and limited integration with observational data. With the rapid
advancement of artificial intelligence, there is an urgent need to develop a
low-cost, efficient air quality forecasting model for smart urban management.
An air quality forecasting model, named FuXi-Air, has been constructed in this
study based on multimodal data fusion to support high-precision air quality
forecasting and operated in typical megacities. The model integrates
meteorological forecasts, emission inventories, and pollutant monitoring data
under the guidance of air pollution mechanism. By combining an autoregressive
prediction framework with a frame interpolation strategy, the model
successfully completes 72-hour forecasts for six major air pollutants at an
hourly resolution across multiple monitoring sites within 25-30 seconds. In
terms of both computational efficiency and forecasting accuracy, it outperforms
the mainstream numerical air quality models in operational forecasting work.
Ablation experiments concerning key influencing factors show that although
meteorological data contribute more to model accuracy than emission inventories
do, the integration of multimodal data significantly improves forecasting
precision and ensures that reliable predictions are obtained under differing
pollution mechanisms across megacities. This study provides both a technical
reference and a practical example for applying multimodal data-driven models to
air quality forecasting and offers new insights into building hybrid
forecasting systems to support air pollution risk warning in smart city
management.
