Computational Sustainability as a Framework for a Bioelectromagnetically Conscious Urban Microenvironment

The concept of computational sustainability in urban environments emphasizes integrating data engineering, artificial intelligence (AI), and computational techniques to advance the United Nations (UN) Sustainable Development Goals (SDGs). A bioelectromagnetically conscious microenvironment represents an architectural and spatial design approach that incorporates various disciplines to align with environmental well-being and regulate electromagnetic (EM) fields. As cities and high-density urban regions are increasingly saturated with EM fields from various sources, the integration of computational sustainability and bioelectromagnetics becomes essential to enhance the occupant experience when interacting with buildings and spaces. This study addresses EM anthropocene influence by presenting an EM exposure mapping (EMEM) framework that involves data acquisition (DAQ), interpolation to produce heatmaps, and machine learning (ML) algorithms for validation and prediction. An exploratory case study is conducted on a microenvironment comprising three buildings on the University of Ottawa campus in Canada. Although the results were limited to selected buildings, the proposed framework supports EMEM investigations across diverse components of the urban landscape. Upon additional validation across a diverse range of environmental conditions, this characterization method may serve as a blueprint for architects, engineers, regulators, and building certification systems to promote designs that are mindful of the visualization and management of ambient EM influences.