Fine-grained air quality (AQ) mapping is made possible by the proliferation of cheap AQ micro-stations (MSs). However, their measurements are often inaccurate and sensitive to local disturbances, in contrast to standardized stations (SSs) that provide accurate readings but fall short in number. To simultaneously address the issues of low data quality (MSs) and high label sparsity (SSs), a multi-task spatio-temporal network (MTSTN) is proposed, which employs self-supervised learning to utilize massive unlabeled data, aided by seasonal and trend decomposition of MS data offering reliable information as features. The MTSTN is applied to infer NO$_2$, O$_3$ and PM$_{2.5}$ concentrations in a 250 km$^2$ area in Chengdu, China, at a resolution of 500m$\times$500m$\times$1hr. Data from 55 SSs and 323 MSs were used, along with meteorological, traffic, geographic and timestamp data as features. The MTSTN excels in accuracy compared to several benchmarks, and its performance is greatly enhanced by utilizing low-quality MS data. A series of ablation and pressure tests demonstrate the results' robustness and interpretability, showcasing the MTSTN's practical value for accurate and affordable AQ inference.

本研究针对低质量空气质量微型站（MSs）测量不精确和标准化站（SSs）标签稀缺的问题，提出了一种多任务时空网络（MTSTN），利用自监督学习来处理大量未标记数据，并通过季节性和趋势分解获取可靠特征。研究表明，MTSTN在成都市250平方公里区域内对NO$_2$、O$_3$和PM$_{2.5$的推断精度显著优于现有基准，体现了在准确性和经济性方面的重要应用价值。

基于低质量传感数据的细粒度空气质量推断：自监督学习方法