A Dual-Layer Aggregation Graph Neural Network for Rapid Indoor Pollutant Dispersion Prediction

Rapid urbanization and the increasing proportion of time spent indoors have intensified concerns regarding indoor air quality and public health. Fast and reliable prediction of indoor pollutant dispersion is essential for building design optimization and risk-informed emergency response. To enable efficient indoor pollutant dispersion prediction, this study proposes a graph neural network framework with dual-layer aggregation and graph augmentation to address several limitations of traditional GNNs, including restricted receptive fields, inefficient long-range information propagation, and limited ability to capture long-range dependencies. Graph data augmentation is employed to enrich the diversity of concentration field distributions, while a dual-layer aggregation mechanism is introduced to expand the receptive field and enhance message-passing efficiency. Specifically, node features are first aggregated from adjacent edges to capture local information and are then further aggregated across nodes to incorporate global contextual features, enabling the modeling of long-range physical dependencies. Validation across multiple building configurations demonstrates that the proposed model effectively captures physical characteristics of pollutant dispersion in distant rooms and accurately predicts toxic gas dispersion under different layouts, achieving a coefficient of determination R2 of up to 0.97, while delivering computational speeds approximately one order of magnitude faster than conventional CFD solvers.