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Overview

What is a Smellscape?

In 1985, Porteous introduced the smellscape concept [1], which can be described as the smell environment perceived and understood by a person in a place. Distinguishing between "smell" (i.e., a sensation detected by inhaling airborne molecules of a substance) and "odour" (i.e., the combined substances in the air causing olfactory sensations), the former emphasizes the human experience as a perceptual construct, making it a user-centred approach within the built environment community. This definition originated and is used in outdoor settings and it was later adapted to indoor environments as the smell environment perceived and understood by a person in an indoor context [2]. The term "context", as opposed to the original term "place", also accounts for social, cultural, and historical aspects [2].

The Circumplex Model of Indoor Smellscape Perception

Research suggested that indoor smellscape perception can be measured using eight Perceived Affective Qualities (PAQ): Pleasant, Present, Light, Engaging, Unpleasant, Absent, Overpowering, Detached. The eight PAQs can be organized into a circular structure called a "circumplex model". This model suggests that indoor smellscape perception can be organized in a two-dimensional space defined by two orthogonal axes — Pleasantness and Presence — with two additional axes, Engagement and Power, rotated 45° within the same plane [3].

Circumplex Model

What is SmellscaPy?

SmellscaPy is a Python library for analysing and representing indoor smellscape perceptual data.
It provides tools for data validation, calculation of perceptual indices, visualization, descriptive statistics and modelling to support reproducible research in smellscape studies.

Key Features

  • Data validation & preprocessing of smellscape survey datasets
  • Computation of perceptual indicators (i.e., pleasantness, presence)
  • Visualizations: scatter plots, density plots, simplified density plots, dynamic plots
  • Integration with the Python scientific stack (Pandas, NumPy, Matplotlib)
  • Ready-to-use example datasets for tutorials and testing
  • Analysis: descriptive statistics and modelling

Use Cases

SmellscaPy can be used to analyse and interpret perceptual data related to olfactory experiences across a wide range of research and application domains:

  • Post-occupancy evaluation (POE) and Indoor Environmental Quality (IEQ) research: to analyse and visualise subjective data on the impact of odours in built environments, supporting advanced analyses in post-occupancy studies and indoor comfort research.
  • Material development and smell-oriented design: to analyse perceptual feedback collected during the testing of materials with olfactory properties or during the evaluation of smell-oriented architectural solutions, facilitating research and development of sensory-integrated products.
  • Cognitive science and environmental psychology: to investigate affective responses to odours.
  • Cultural heritage and olfactory identity: to document smellscapes associated with places, practices, and traditions, contributing to the protection and enhancement of cultural identity through the sensory dimension.
  • Virtual reality and immersive simulations: to integrate olfactory perception into multisensory experimental setups and virtual environments, enabling the study of smell-related responses in controlled and interactive scenarios.

References

  1. J.D. Porteous, Smellscape, Progress in Physical Geography: Earth and Environment 9 (1985) 356–378. https://doi.org/10.1177/030913338500900303.
  2. G. Torriani, S. Torresin, I. Lara-Ibeas, R. Albatici, F. Babich, Perceived air quality (PAQ) assessment methods in office buildings: a systematic review towards an indoor smellscape approach, Build Environ (2024) 111645. https://doi.org/10.1016/j.buildenv.2024.111645.
  3. G. Torriani, R. Albatici, F. Babich, M. Vescovi, M. Zampini, S. Torresin, Developing a principal components model of indoor smellscape perception in office buildings, Build Environ 279 (2025) 113044. https://doi.org/10.1016/j.buildenv.2025.113044.