Co-creating Public Air Quality Displays for the AWAIR Project by Leveraging Artistic Boundary Objects
Year: 2025
Type: preprint
Abstract: Despite the widespread health risks associated with environmental hazards, the public display of real-time environmental health information remains relatively sparse. Existing displays tend to rely on static, number-heavy formats and scientific frameworks that may be inaccessible to diverse audiences. This paper presents findings from the AWAIR project, which employed participatory and artistic co-design methods to reimagine the display of public air pollution information in three neighborhoods across London, UK. Through collaborative workshops with residents, the study explored how individuals articulate informational needs and emotional responses to air pollution, how these evolve through dialogue, and how this can inform the public display of environmental phenomena. This research demonstrates how artistic artifacts, employed as ‘boundary objects’, created space for participants to express and negotiate diverse values, experiences, and concerns that ranged from health protection to aesthetic and symbolic resonance. Rather than seeking consensus, the co-design process embraced productive friction among science, diverse lived experiences, and personal approaches to sense-making. Participants generated a range of display concepts, including smiley face icons, color codes, and heart imagery, illustrating the potential for affective, accessible, and context-sensitive communication. However, the process also surfaced tensions tied to the technological infrastructures conventionally relied upon to define air pollution, as well as assumptions about scientific and digital literacy. This work underscores the need for ongoing, iterative, and inclusive design practices that foreground creativity and co-creation. By centering diverse publics in environmental communication design, such approaches can render air quality information exchange more meaningful, democratic, and just.
Authors: Kayla Schulte, Echo Wan, Andrew Grieve
Institution: Imperial College London
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7084050701
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