Urban environmental monitoring campaigns depend on expertise from city agencies, residents, and researchers. Deployment efforts rarely include all three stakeholders, typically leading to initiatives that struggle to produce credible, actionable data. We describe the implementation of a large-scale, long-term air quality sensing network in Chicago Illinois; detail stakeholder interviews and meetings; and present three interfaces—–a website accessible via in-situ QR codes, APIs, and a mobile, mixed-media experience. We show how a collaborative approach created a more equitable sensor distribution compared to crowdsourced or regulatory designs. We highlight shared goals of education, engagement, and empowerment despite the diversity of tool and analytics needs across stakeholder groups. Reflecting on our work, we develop a “three-legged stool” framework representing the criticality of balanced participation from three key stakeholder groups—city, community, and research—in deploying novel urban technologies. This approach can help HCI researchers facilitate more democratic technology deployments in urban spaces.

This paper presents Eclipse, a platform for low-cost urban environmental sensing using solar-powered and cellular-connected devices. Dense sensor networks promise to monitor pollution at fine spatial and temporal resolutions, yet few cities have actually implemented such networks due to high costs and limited accuracy. We address these barriers by developing an end-to-end framework for urban air quality sensing with minimal infrastructure requirements. We designed an unobtrusive device that collects data on fine particulate matter (PM2.5), temperature, relative humidity, and barometric pres-sure. A modular design further includes four low-cost gas sensors - Ozone (03), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), and Carbon Monoxide (CO) - selected based on local priorities. We deployed 115 devices across Chicago, reliably collecting data for over 90% of expected sensor-hours from July 2 - September 30, 2021. We further developed a calibration strategy that reduced errors by 41.2 – 98.8%, improving accuracy to levels recommended for hotspot detection (PM2.5and 03) or education (NO2and SO2). Through this work, we offer insights on the real-world deployment of a replicable, large-scale, end-to-end platform for hyperlocal urban environmental sensing.