AtmosFlux-NetX: A Multimodal Spatio-Temporal Fusion Model for Ultra-Fine Air Pollution Reconstruction

Proper reconstruction of air quality is still a difficult task because of the high sparseness of sensors and the non-linear pollutant movements as well as the dynamism of environmental factors. This work presents a proposal of GreenSphere-GAN, a two-domain adversarial model that is built to produce ultra-fine spatial-temporal air quality maps and at the same time detects sources of pollution. The approach incorporates four new algorithms, including a hyper-spatial encoder, which captures micro-scale interactions of pollutants, a temporal-flux predictor, which captures dynamic propagation patterns, a cross-domain fusion attention module which integrates meteorological data with emission data and a residual adversarial refinement network which makes the reconstruction more real-world. All these elements enable GreenSphere-GAN to determine with high accuracy the concentrations of pollutants and pinpoint the likely emission hotspots. Experimental assessment proves that the framework has a considerable enhancement of reconstruction precision and the reliability of localization of the source. The results demonstrate that GreenSphere-GAN is a robust instrument in environmental control because it will allow implementing more efficient policymaking and actively controlling urban pollution.