Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset

• URL: http://arxiv.org/abs/2306.13948v2
• Date: Mon, 13 Nov 2023 12:08:31 GMT
• Title: Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset
• Authors: Jingwei Zuo, Wenbin Li, Michele Baldo and Hakim Hacid
• Abstract summary: This paper introduces PurpleAirSF, a comprehensive and easily accessible dataset from the PurpleAir network. We present a detailed account of the data collection and processing methods employed to build PurpleAirSF. We conduct preliminary experiments using both classic and modern-temporal forecasting models, thereby establishing a benchmark for future air quality forecasting tasks.
• Score: 4.190243190157989
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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