Interpretable distance adaptive GCN-autoencoder for soft sensor validation and remote reconstruction in urban air quality monitoring networks

The air quality monitoring system (AQMS) has attracted considerable attention due to its environmental significance and impact on human health. AQMS are critical for facilitating early-warning mechanisms to implement policies and protect urban communities. However, existing frameworks rely on physical sensors compromised by degradation, leading to unreliable decision-making. To overcome this limitation, this study introduces a region-wide soft sensor validation using a memory-integrated graph convolutional autoencoder (LSTM-GCN-AE). Results indicate that the relevance-embedded LSTM-GCN-AE outperforms the traditional GCN, achieving a 43.4 % improvement in reconstruction accuracy under precision faults and a 50.2 % enhancement in imputation performance for PM_2.5sensor, identified through interpretability analysis of relevant nodes in the GCN. Moreover, the proposed framework successfully maintained consistency between predicted and actual environmental conditions, thereby enhancing the reliability of real-time AQMS data, health risk assessment, and early-warning mechanisms for urban air quality management.