A holistic air monitoring dataset with complaints and POIs for anomaly detection and interpretability tracing
Xiliang Liu, Xiaoying Zhi, Tao Zhou, Liyou Zhao, Li Tian, Ruoyun Gao, Jiashuo Luo, Wenqiong Cui, Qi Wang · Scientific Data · 2025
Urban air pollution poses a global health risk. This study presents the Airware-Haikou dataset, a robust resource for urban air pollution research, integrating multivariate time-series air quality monitoring data (MTSAM), Point of Interest (POI) data, and a public complaint corpus. The MTSAM, collected from 95 monitoring stations in Haikou, China, includes hourly measurements of six air pollutants and five meteorological factors. The data underwent rigorous pre-processing, including spatial-temporal interpolation and rebalancing, to ensure consistency and reliability. Using POI data and monitoring station coordinates, the MTSAM was segmented into four spatial-temporal subsets via cluster analysis, enabling detailed characterization of air quality dynamics. The public complaint corpus, extracted from the UIE model, serves as a baseline for post hoc interpretation of deep learning models, linking public sentiment with empirical air quality data. The Airware-Haikou dataset offers a comprehensive foundation for urban air pollution studies, while its validation model, DsRL-Net, significantly enhances the accuracy and reliability of pollution detection, advancing research in this critical field.