Introduction: Air pollution has numerous impacts on human health on a variety of time scales. Pollutants such as particulate matter—PM₁ and PM_2.5, carbon dioxide (CO₂), nitrogen dioxide (NO₂), and nitric oxide (NO) are exemplars of the wider human exposome. In this study, we adopted a unique approach by utilizing the responses of human autonomic systems to gauge the abundance of pollutants in inhaled air. Objective: To investigate how the human body autonomically responds to inhaled pollutants in microenvironments, including PM₁, PM_2.5, CO₂, NO₂, and NO, on small temporal and spatial scales by making use of biometric observations of the human autonomic response. To test the accuracy in predicting the concentrations of these pollutants using biological measurements of the participants. Methodology: Two experimental approaches having a similar methodology that employs a biometric suite to capture the physiological responses of cyclists were compared, and multiple sensors were used to measure the pollutants in the air surrounding them. Machine learning algorithms were used to estimate the levels of these pollutants and decipher the body’s automatic reactions to them. Results: We observed high precision in predicting PM₁, PM_2.5, and CO₂ using a limited set of biometrics measured from the participants, as indicated with the coefficient of determination (R²) between the estimated and true values of these pollutants of 0.99, 0.96, and 0.98, respectively. Although the predictions for NO₂ and NO were reliable at lower concentrations, which was observed qualitatively, the precision varied throughout the data range. Skin temperature, heart rate, and respiration rate were the common physiological responses that were the most influential in predicting the concentration of these pollutants. Conclusion: Biometric measurements can be used to estimate air quality components such as PM₁, PM_2.5, and CO₂ with high degrees of accuracy and can also be used to decipher the effect of these pollutants on the human body using machine learning techniques. The results for NO₂ and NO suggest a requirement to improve our models with more comprehensive data collection or advanced machine learning techniques to improve the results for these two pollutants.

Breathing clean air is crucial for maintaining good human health. The air we inhale can significantly impact our physical and mental well-being, influenced by parameters such as particulate matter and gases (e.g. carbon dioxide, carbon monoxide, and nitrogen dioxide). Building on previous research that explored the effects of particulate matter (PM) in specific environments, analyzed using biometric indicators and machine learning models; this work focuses on the effects and estimation of inhaled nitrogen dioxide (NO2). This study involved a cyclist equipped with sensors to monitor various biometric parameters. In addition, an electric car following the cyclist measured the ambient NO2 levels using an onboard sensor. A total of 329 biometric variables have been taken into account, of which 320 biometric variables are cognitive responses extracted using an electroencephalogram (EEG) and 9 biometric variables are physiological responses extracted using several sensors. Inhaled NO2 levels are first estimated initially by making use of all 329 variables, then using 9 physiological responses and finally using only 6 of the 9 physiological responses. The study also uses a ranking method to pinpoint which biometric variables most significantly estimate inhaled NO2 levels. Furthermore, it investigates the linear and non-linear relationship between certain variables and inhaled NO2. The general precision of the prediction for the data set was moderate, as indicated by the coefficient of determination (R2) and the root mean square error (RMSE) between the true and estimated values of NO2 to be 0.35 and 5.41 ppb, respectively, in the test set. A higher accuracy in the prediction of lower values of NO2 levels was qualitatively observed using a scatter diagram and a Quantile-Quantile plot where the data were more plentiful. For more robust conclusions, additional data and refined machine learning models are necessary.

Life expectancy in the United States varies significantly by region, a gap often explained by socioeconomic factors like income and education. However, the relative contribution of atmospheric exposures is less understood. We identify formaldehyde exposure and wet-bulb temperature as leading predictors of county-level life expectancy. Our analysis of over 24,000 county-year observations (2012–2019) reveals that formaldehyde consistently ranks as the second-strongest predictor of life expectancy, surpassed only by educational attainment. Wet-bulb temperature, a physiological measure of heat stress, emerges as a top-five predictor. We identified these patterns using XGBoost with SHAP analysis, integrating atmospheric, livestock, and socioeconomic data within an external exposome framework. These results suggest that air pollutants and heat stress capture health-relevant information beyond traditional socioeconomic indicators.

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.

The air we breathe contains contaminants such as particulate matter (PM), carbon dioxide (${\mathrm{CO}}_2$), nitrogen dioxide (${\mathrm{NO}}_2$), and nitric oxide (NO), which, when inhaled, bring about several changes in the autonomous responses of our body. Our previous work showed that we can use the human body as a sensor by making use of autonomous responses (or biometrics), such as changes in electrical activity in the brain, measured via electroencephalogram (EEG) and physiological changes, including skin temperature, galvanic skin response (GSR), and blood oxygen saturation (${\mathrm{SpO}}_2$). These biometrics can be used to estimate pollutants, in particularly ${\mathrm{PM}}_1$ and ${\mathrm{CO}}_2$, with high degree of accuracy using machine learning. Our previous work made use of the Welch method (WM) to obtain a power spectral density (PSD) from the time series of EEG data. In this study, we introduce a novel approach for obtaining a PSD from the EEG time series, developed by Astrapi, called the Astrapi Spectrum Analyzer (ASA). The physiological responses of a participant cycling outdoors were measured using a biometric suite, and ambient ${\mathrm{CO}}_2$, ${\mathrm{NO}}_2$, and NO were measured simultaneously. We combined physiological responses with the PSD from the EEG time series using both the WM and the ASA to estimate the inhaled concentrations of ${\mathrm{CO}}_2$, ${\mathrm{NO}}_2$, and NO. This work shows that the PSD obtained from the ASA, when combined with other physiological responses, provides much better results (RMSE = 9.28 ppm in an independent test set) in estimating inhaled ${\mathrm{CO}}_2$ compared to making use of the same physiological responses and the PSD obtained by the WM (RMSE = 17.55 ppm in an independent test set). Small improvements were also seen in the estimation of ${\mathrm{NO}}_2$ and NO when using physiological responses and the PSD from the ASA, which can be further confirmed with a large number of dataset.

Life expectancy in the United States varies significantly by region, a gap often explained by socioeconomic factors like income and education. However, the relative contribution of atmospheric exposures is less understood. We identify formaldehyde exposure and wet-bulb temperature as leading predictors of county-level life expectancy. Our analysis of 22,540 county-year observations (2012–2019) shows that formaldehyde ranked as the second-strongest predictor, surpassed only by educational attainment. Wet-bulb temperature, a physiological measure of heat stress, ranked sixth and was the leading meteorological predictor. We identified these patterns using XGBoost with SHAP analysis, integrating atmospheric exposures, livestock density, socioeconomic conditions, and smoking prevalence within an external exposome framework. These results suggest that air pollutants and heat stress provide predictive information beyond traditional socioeconomic indicators.