Dust Sensor Close-up

Pollution Visualization Project

Pollution Data Visualizers

Project Summary
This research explores how pollution data can be used to create art that visualizes information. The goal is to make pollution data easy for spectators to understand at a glance. The research was initially funded by McMaster University’s USRA program. 

This visualizer uses compressed particulate data plus, temperature and humidity data to create generative art using p5.js programming. This project is a collaboration with developer Daven Bigelow.

The ecoMirror is based on a concept adapted from the project titled Carnival Glass. In the case of the ecoMirror, a monitor reflects an image of the environment where it is located. As the pollution index of the local area fluctuates it produces real-time distortions based on the live data captured from the environment.

The ecoTree is a robotic art piece. The tree responds to compressed data to make it move dynamically. Data compression allows for more intense movements echoing the previous months of air quality. The ecoTree is the collaborative work between Meta Pleb artist, Karin Fish and robotics engineer, Colin Gagich.

Inventory list for ecoTree: Aurdinos, pollution sensors, protective boxes, 18 gauge wire, computer, vinyl siding, fluorescent light lens panels, monofilament, 2 motors, 3/4” plywood. 20cm PVC plumbing tube, steel pole, 2 pullies, zip ties

Software: Arduino Software, Serial

Dimensions: ecoTree 284x46cm (closed) 284x152cm (opened)


Pollution Visualization ecoTree Plans


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