Indoor Air Quality Forecast in Shared Spaces– Predictive Models and Adaptive Design Proposals

Abstract

The high concentration of air pollutants in indoor environments can have a remarkable adverse impact on health and well-being, cognitive performance and productivity. Indoor air pollutants are especially problematic in naturally ventilated shared spaces such as classrooms and meeting rooms, where human-generated pollutants can rise rapidly. When the inhabitants are exposed to indoor air pollution, recovering from its ramifications takes time and harms their well-being in the long run. In our approach, we seek to predict and prevent such hazardous situations instead of rectifying them after they happen. The prediction and prevention are accomplished through algorithms that can learn from the evolution of air pollutants and other variables to indicate whether or not a high level of pollution is forecast. We present two AI-enabled methods, one providing the forecast for the concentration level of carbon dioxide in the next 5 and 20 minutes with 86% and 92% accuracy. The second algorithm provides predictive indicators about how the CO2 level will evolve during the upcoming session (meeting or a course) before the session starts. We will discuss design implications and present design proposals on how these methods can inform interactive solutions for preventing high concentrations of indoor air pollutants.