A Spatiotemporal Interpolation Graph Convolutional Network for Estimating PM₂.₅ Concentrations Based on Urban Functional Zones

Urban functional zones (UFZs) contain abundant landscape information that can be adopted to better understand the surroundings. Various landscape compositions and configurations reflect different human activities, which may affect the particulate matter (PM2.5) concentrations. The very high-resolution (VHR) image features can reflect the physical and spatial structures of the UFZs. However, the existing PM2.5 estimation methods neither have been based on the scale of UFZs, nor have the VHR image features of UFZs as independent variables. Hence, this article proposes a spatiotemporal interpolation graph convolutional network (STI-GCN) model and introduces VHR image features to achieve PM2.5 estimation in UFZs. First, UFZs are split, and VHR image features are extracted by the visual geometry group 16 (VGG16). Subsequently, meteorological factors, aerosol optical depth (AOD), and VHR image features are used to estimate the PM2.5 concentrations at the scale of the UFZs. The two metropolises, Beijing and Shanghai, are chosen to assess the validity of the STI-GCN model. As for Beijing and Shanghai, the overall accuracy ${R^{2}}$ of the STI-GCN model can reach 0.96 and 0.89, the root-mean-square errors (RMSEs) are 8.15 and 6.40 $\mu \text {g}/{\text {m}^{3}}$ , the mean absolute errors (MAEs) are 5.51 and 4.78 $\mu \text {g}/{\text {m}^{3}}$ , and the relative prediction errors (RPEs) are 18.53% and 17.38%, respectively. Experiments show that the STI-GCN consistently outperforms other models. What’s more, the PM2.5 values are relatively high in commercial and official zones (COZs) and relatively low in urban green zones (UGZs).