Enhancing Indoor Air Quality Estimation: A Spatially Aware Interpolation Scheme

The comprehensive and accurate assessment of the indoor air quality (IAQ) in large spaces, such as offices or multipurpose facilities, is essential for IAQ management. It widely recognized that various factors affect well-being, health, productivity occupants. In environments, it important to assess places where difficult install sensors due space constraints. Spatial interpolation a technique uses sample values known points predict other unknown points. Unlike outdoor spatial spaces constraints, being separated into rooms by walls having facilities conditioners heaters installed. Therefore, necessary identify independent related regions utilize them interpolation. this paper, we propose groups with similar characteristics utilizes these We integrated data collected from multiple locations within an office subsequently conducted comparative experiment accuracy our proposed method comparison commonly used approaches, inverse distance weighting (IDW), kriging, natural neighbor interpolation, radial basis function (RBF). Additionally, performed experiments using publicly available Intel Lab dataset. experimental results demonstrate scheme outperformed existing methods. show was able obtain better predictions reflecting space.