ResearchResearch paperLarge Language Models · Efficiency & Inference · Training & Scaling1 source · Oct 19, 2026

AirMPA: A Meteorology-to-Pollution Adapter for Global Air Quality Forecasting

To address these challenges, we propose AirMPA, a decoupled autoregressive framework for meteorology-to-pollution forecasting.

Accurate real-time forecasting of atmospheric pollutants is essential for reducing exposure risks and improving public health.
Existing deep learning approaches improve efficiency, but often entangle meteorological and pollutant variables within unified forecasting frameworks, limiting flexibility and pollutant-specific representation learning.
The model is built on an improved Swin-Transformer architecture with zonal release attention and dual-branch fusion, enabling more effective modeling of global transport continuity and heterogeneous pollutant dynamics.
These results demonstrate the potential of decoupled meteorology-to-pollution forecasting for global air quality prediction.

[1]AirMPA: A Meteorology-to-Pollution Adapter for Global Air Quality Forecasting
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 05:05 AM
To address these challenges, we propose AirMPA, a decoupled autoregressive framework for meteorology-to-pollution forecasting.
Accurate real-time forecasting of atmospheric pollutants is essential for reducing exposure risks and improving public health.

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