AeroMosaic:Transport-Aware Multimodal Evidence Fusion for Atmospheric Pollution Risk Inference

Atmospheric pollution risk inference is not merely generic time-series extrapolation: target-day risk depends jointly on numerically preserved pollutant state, exogenous meteorological regime, and directed cross-city transport. Existing time-series models are strong sequence learners, graph-based air-quality models often rely on static or weakly directed spatial priors, and multimodal methods commonly merge heterogeneous evidence without specifying its functional role. We present \ourmodel{}, a transport-aware multimodal evidence-fusion framework that assembles an atmospheric evidence mosaic from numerical pollutant/time states, compact precipitation text, and wind-aligned graph structure. The model preserves historical PM$_{2.5}$ and time features as numerical prefix tokens, compresses recent precipitation into prompt-side context, constructs a daily wind-driven DAG to organize upwind-to-downwind evidence, and refines city embeddings through post-pooling DAG residual propagation with root-sensitive mechanisms. We analyze \ourmodel{} on six PM$_{2.5}$ risk-inference benchmarks under a unified year-based split. The results provide protocol-bounded evidence that transport-aligned structure and role-separated interfaces yield useful risk-inference signals, while leaving no-oracle evaluation as future work.