Offline Meteorology-Pollution Coupling Global Air Pollution Forecasting Model with Bilinear Pooling

2025-03-24

AI Summary

Existing global air pollution forecasting models—both physics-based and online deep learning (DL) approaches—suffer from high computational costs and poor real-time performance, hindering practical pollution control applications. To address this, we propose the first DL-driven offline meteorology–pollution coupled forecasting framework, introducing the novel “offline coupling” paradigm that decouples meteorological field modeling from pollutant dynamics, drastically reducing computational dependency. Methodologically, our approach integrates bilinear pooling, lightweight neural networks, and a multi-source spatiotemporal alignment mechanism. We further provide the first empirical evidence of cross-task transferability of meteorological representations in DL-based pollution prediction. The model uses only 13% of the parameters of typical online DL models, achieves a 15% reduction in global RMSE, outperforms the Copernicus Atmosphere Monitoring Service (CAMS) on 63% of evaluated variables, and maintains superiority on 85% of variables for lead times beyond 48 hours—enabling efficient, real-time global atmospheric early warning.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Air pollution has become a major threat to human health, making accurate forecasting crucial for pollution control. Traditional physics-based models forecast global air pollution by coupling meteorology and pollution processes, using either online or offline methods depending on whether fully integrated with meteorological models and run simultaneously. However, the high computational demands of both methods severely limit real-time prediction efficiency. Existing deep learning (DL) solutions employ online coupling strategies for global air pollution forecasting, which finetune pollution forecasting based on pretrained atmospheric models, requiring substantial training resources. This study pioneers a DL-based offline coupling framework that utilizes bilinear pooling to achieve offline coupling between meteorological fields and pollutants. The proposed model requires only 13% of the parameters of DL-based online coupling models while achieving competitive performance. Compared with the state-of-the-art global air pollution forecasting model CAMS, our approach demonstrates superiority in 63% variables across all forecast time steps and 85% variables in predictions exceeding 48 hours. This work pioneers experimental validation of the effectiveness of meteorological fields in DL-based global air pollution forecasting, demonstrating that offline coupling meteorological fields with pollutants can achieve a 15% relative reduction in RMSE across all pollution variables. The research establishes a new paradigm for real-time global air pollution warning systems and delivers critical technical support for developing more efficient and comprehensive AI-powered global atmospheric forecasting frameworks.

Problem

• Develops offline coupling model for global air pollution forecasting
• Reduces computational demands compared to online coupling methods
• Improves prediction accuracy and efficiency using bilinear pooling

Innovation

• DL-based offline coupling with bilinear pooling
• Reduces parameters by 87% vs online models
• Achieves 15% RMSE reduction in pollution variables