Air quality forecasting has garnered significant attention recently, with data-driven models taking center stage due to advancements in machine learning and deep learning models. However, researchers face challenges with complex data acquisition and the lack of open-sourced datasets, hindering efficient model validation. This paper introduces PurpleAirSF, a comprehensive and easily accessible dataset collected from the PurpleAir network. With its high temporal resolution, various air quality measures, and diverse geographical coverage, this dataset serves as a useful tool for researchers aiming to develop novel forecasting models, study air pollution patterns, and investigate their impacts on health and the environment. We present a detailed account of the data collection and processing methods employed to build PurpleAirSF. Furthermore, we conduct preliminary experiments using both classic and modern spatio-temporal forecasting models, thereby establishing a benchmark for future air quality forecasting tasks.

本文介绍PurpleAirSF数据集，以其高时间分辨率、各种空气质量测量指标和多样化的地理覆盖为特点，成为开展新型预测模型、研究空气污染模式及其对健康和环境产生影响研究的有用工具；同时，建立了一个基准未来空气质量预测任务。

释放逼真的空气质量预报：引入即用的PurpleAirSF数据集