Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales

Shawhin Talebi, David J. Lary, Lakitha O.H. Wijeratne, Bharana Fernando, Tatiana Lary, Matthew Lary, John Sadler, Arjun Sridhar, John Waczak, Adam Aker, Yichao Zhang

Research output: Contribution to journalArticlepeer-review


The human body is an incredible and complex sensing system. Environmental factors trigger a wide range of automatic neurophysiological responses. Biometric sensors can capture these responses in real time, providing clues about the underlying biophysical mechanisms. In this prototype study, we demonstrate an experimental paradigm to holistically capture and evaluate the interactions between an environmental context and physiological markers of an individual operating that environment. A cyclist equipped with a biometric sensing suite is followed by an environmental survey vehicle during outdoor bike rides. The interactions between environment and physiology are then evaluated though the development of empirical machine learning models, which estimate particulate matter concentrations from biometric variables alone. Here, we show biometric variables can be used to accurately estimate particulate matter concentrations at ultra-fine spatial scales with high fidelity (r2 = 0.91) and that smaller particles are better estimated than larger ones. Inferring environmental conditions solely from biometric measurements allows us to disentangle key interactions between the environment and the body. This work sets the stage for future investigations of these interactions for a larger number of factors, e.g., black carbon, CO2, NO/NO2/NOx, and ozone. By tapping into our body’s ‘built-in’ sensing abilities, we can gain insights into how our environment influences our physical health and cognitive performance.

Original languageEnglish (US)
Article number4240
Issue number11
StatePublished - Jun 1 2022


  • holistic sensing
  • machine learning
  • particulate matter
  • physiology

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering


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