Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning

Shisir Ruwali
,
Shawhin Talebi
ORCID logo ,
Bharana Fernando
,
Lakitha O. H. Wijeratne
ORCID logo ,
John Waczak
ORCID logo ,
Prabuddha Madusanka Hathurusinghe Dewage
,
David J. Lary
* ORCID logo ,
John Sadler
,
Tatiana Lary
,
Matthew Lary
,
Adam Aker
Version 1 : Received: 9 February 2024 / Approved: 12 February 2024 / Online: 14 February 2024 (03:38:19 CET)

A peer-reviewed article of this Preprint also exists.

Ruwali, S.; Talebi, S.; Fernando, A.; Wijeratne, L.O.H.; Waczak, J.; Dewage, P.M.H.; Lary, D.J.; Sadler, J.; Lary, T.; Lary, M.; Aker, A. Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning. BioMedInformatics 2024, 4, 1019-1046. Ruwali, S.; Talebi, S.; Fernando, A.; Wijeratne, L.O.H.; Waczak, J.; Dewage, P.M.H.; Lary, D.J.; Sadler, J.; Lary, T.; Lary, M.; Aker, A. Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning. BioMedInformatics 2024, 4, 1019-1046.

Abstract

In this study, we adopt a unique approach by utilizing the responses of human autonomic systems to gauge the abundance of pollutants in inhaled air. Air pollution has numerous impacts on human health on a variety of time scales. This study uses biometric observations of the human autonomic response on the second timescale to investigate how the human body autonomically responds to inhaled pollutants in microenvironments, including particulate matter. PM1 and PM2.5, carbon dioxide (CO2), nitrogen dioxide (NO2), and nitric oxide (NO) on small temporal and spatial scales. These pollutants are exemplars of the wider human exposome. We compare two experimental approaches that use a similar methodology, employing a biometric suite to capture the physiological responses of cyclists and sensors to monitor the pollutants in the air surrounding them. We employ machine learning algorithms to estimate the levels of these pollutants and decipher the body’s automatic reactions to them. We observed high precision in predicting PM1, PM2.5 and CO2 using a limited set of biometrics from participants. Although the predictions for NO2 and NO were reliable at lower concentrations, the precision varied throughout the data range. This discrepancy suggests the potential to improve our models with more comprehensive data collection or advanced machine learning techniques.

Keywords

Autonomic Response; Exposome; Micoenvironment; Air Quality; Biometric Observations; Machine Learning; Particulate Matter; CO2; NO2; NO

Subject

Public Health and Healthcare, Public, Environmental and Occupational Health

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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