Our instruments went out for a spin when researchers at the University of Texas loaded up the back of a hatchback for a study of the physiological effects of air pollution. The study was published in 2022 in the journal Sensors.
Included were three instruments from 2B Tech to measure O3 (Model 205), NO2 and NO (Model 405 nm), and black carbon (Black Carbon Photometer). Other instruments measured particulate matter (PM), CO2, water vapor, and meteorological parameters. A bicyclist was outfitted with biometric sensors as the hatchback followed and gathered air quality data.
Researchers developed empirical machine learning models to investigate the interaction between environmental variables (in this paper, particulate matter concentrations) and physiological parameters of the bicyclist. Those biometric parameters included body temperature, galvanic skin response, heart rate variability, blood oxygen saturation, eye pupil measurements, and others. The top nine important biometric values were used in the model development.
A tantalizing finding from the study: the biometric measurements themselves were in some cases good “detectors” of PM. The relationship was particularly strong (r2=0.91) at smaller sizes of PM (see figure below for PM1 results).
The researchers suggest that the smaller particles might be more accurately predicted because they are more well mixed in the atmosphere, so that the values measured by the instruments in the follow-vehicle are more representative of the concentrations experienced by the bicyclist ahead. In addition, the smaller particles penetrate the respiratory system more deeply and therefore may have a greater effect on physiology. They plan to investigate the other environmental variables that were collected (ozone, black carbon, NOx) to better understand the interactions of air quality with the human body.
Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales, S. Talebi, D.J. Lary, L.O.H. Wijeratne, B. Fernando, T. Lary, M. Lary, J. Sadler, A. Sridhar, J. Warzak, A. Aker and Y. Zhang, Sensors (2022), 22, 11, 4240.