Eclipse Dataset: Advancing Urban Sensing Research with Hyperlocal Environmental Data from Chicago

The growth of urban centers, the impacts of climate change, and the need for information to inform city planning and community organizing have intensified the need for monitoring solutions in cities. The advancement of low-cost sensor technologies and digital twin frameworks presents an opportunity for cities to deploy extensive, real-time monitoring systems. However, few examples of long-term, large-scale, publicly available urban sensor datasets exist, limiting the ability of researchers and planners to explore important topics such as hyperlocal environmental variations. In this paper, we introduce a comprehensive dataset from a 118-node LTE-M connected, solar-powered air quality sensor network deployed across Chicago, Illinois, from April 2021 to April 2023. This dataset comprises 94,915,745 readings of United States EPA criteria air pollutants, in two parts: 1) 75,932,596 gas sensor readings for carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2), and 2) 18,983,149 particulate matter (PM) readings. This open-access dataset uniquely enables researchers to examine critical aspects of smart city sensing, including environmental equity, sensor network deployment dynamics, and interactions between urban infrastructure, natural environments, and residents. Via integration with open datasets from the City of Chicago and other sources, this dataset serves as a foundational tool for advancing research in environmental justice, public health, and urban planning, empowering researchers to build and test ideas before going to deployment to move closer to achieving smart, sustainable urban environments.