A three-dimensional visualization tool that uses custom environmental IoT sensors to capture and visualize ground-level ozone and other atmospheric gasses in real-time. Through new forms of immersive representation and data collection techniques, the project seeks to employ a new approach to understanding the dynamic relationship between the ‘human', the ‘non-human’ and 'nature' that recognizes their deep interdependence and co-constitutive existence. We propose an alternative to tools developed to represent a world of static weight and solid substances and thus, are incapable of expressing the experiences of a world of dynamic lightness and relations that shape our immediate air milieu and is shaped by it. Hence, paving a shift in attitude that favors the conception of ourselves as atmosphere designers. The system is tested within three different site typologies, a neighborhood, a nature-infrastructure edge, and an urban center.
Final Project @ Harvard University GSD A Visualization Medium for the Immediate (atmospheric) Milieu
Collaboratively created by Aria Xiying Bao, Ibrahim Ibrahim
Produced in Unreal Engine
(designing) Atmospheric Matter
We developed a simulation system within the Unreal Engine that models four key gases present in the atmosphere: Carbon Dioxide, Carbon Monoxide, Nitrogen Dioxide, and Ozone. These gas digital twins are created through a combination of physical simulations and impact models. This system was designed to create connections between different simulations, including the gas simulation within the Unreal Engine, digital assets such as environment models and textures, and databases containing information on gas detection within a selected site area.
Sensing Artifacts
The information regarding air pollution and its correlation with mortality rates due to direct or indirect exposure underlines the intricacy of monitoring air pollution, specifically in terms of connecting standard measurement techniques with personal exposure. Since stationary sensors are not capable of accurately gauging individual exposure levels to nearby pollutants, an alternative solution is to create a network of inexpensive IoT sensors positioned throughout various sites. The development of the hardware component is aimed at addressing the dynamic challenges that arise when sensors are placed in a mobile context.
