Work

Air pollutant prediction by spatial-temporal information reconstruction and fusion

Year: 2025

Type: article

Abstract: Accurate prediction of air pollutant concentrations is crucial for urban public health and environmental management. While previous studies have explored the influence of various factors and spatial-temporal relationships on air pollution, they often fall short in precisely capturing the complexity of influencing variables and in thoroughly understanding the spatial-temporal evolution patterns of pollutant concentrations. To address this issue, we develop a novel Multi-spatial-temporal Dynamic Information Fusion (MSTDIF) model based on a parallel learning mechanism. Specifically, we select information on pollutant-related influencing factors within and among cities and reconstruct them into Intra-city Spatial-Temporal (Intra-ST) and Inter-cities Spatial-Temporal (Inter-ST) information patterns, respectively. Furthermore, we design Intra-city Spatial-temporal Multi-factors Interaction (Intra-SMI) module to extract multivariate features from Intra-ST pattern and establish the Inter-cities Spatial-temporal Pollutant Synergy diffusion (Inter-SPS) module to extract spatial-temporal synergistic diffusion features from Inter-ST pattern. By fusion operation integrating cutting-edge spatial-temporal mining techniques and decision networks, these two types of features are effectively fused to achieve accurate prediction. Extensive experiments conducted on real-world datasets covering six major air pollutants demonstrate that the MSTDIF model significantly outperforms nine state-of-the-art benchmark models in terms of both prediction accuracy and interpretability of pollutant spatial-temporal dynamics.

Source: Results in Engineering

DOI: 10.1016/j.rineng.2025.107393