ScholarWorks@동국대학교

초록

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<inf>2.5</inf>sensor, 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. © 2025 Elsevier B.V., All rights reserved.

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