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Can we improve building management and reduce carbon footprint through a mesh network of indoor air quality sensors?

Project: Research


The World Health Organization (WHO) has called air pollution the “single largest environmental health risk” and has estimated it to be linked to more than 6 million premature deaths in 2012. In the UK outdoor air quality tends to be the focus, despite, on average people spending ~90% of their time indoors, where concentrations of certain pollutants can be on average 2-5 times greater. Energy efficiency improvements have driven the design of new buildings, resulting in high levels of air tightness to prevent heat loss leading to a significant unintended consequence: poor indoor air quality (IAQ). Given the recent shock of COVID-19, a renewed emphasis on air flow may dramatically increase ventilation rates, undermining even these efficiency gains.

The objective of this project is to gain a much better understanding of the dichotomies between building ecology and sustainability in relation to energy usage, indoor air quality, and the potential for airborne transmission of viruses. The project will generate a significant database of indoor air quality measurements using the ‘V2000 lite’. In a set of experiments we will create a unique multi-method dataset comprising building specific parameters to facilitate the cross-correlation between air quality, energy usage, and the potential for airborne transmission of viruses, which we will use to understand and inform the practices and usage of these spaces.

Informing the design of future eco-innovation IoT products, these studies will provide significant new knowledge and insights into building ecology, and will supplement the current understanding of the health effects of indoor air pollution by providing reliable data to epidemiologists. Moreover, it will facilitate the development of micro- and macro-models for assisting building design, usage optimization, and low-energy pathways. It is envisaged, through the development of micro- and macro- models, the building owner through education and minor modifications (behaviour and possible instrumentation based feedback loops) will be able to generate a significant reduction in carbon-footprint whilst also improving indoor air quality and reducing the risk of airborne transmission of viruses.
Effective start/end date1/01/211/01/24

Research outputs