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

Abstract

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.

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

“…The time series plot of PM 1 and PM 2.5 in Figure 17a,b shows that their true values are very close to the estimated values for the majority of the data set without any significant differences, which explains their smallest RMSE among all pollutants. This supports the conclusion made previously [46] that two of the possible reasons why these estimates are highly accurate and precise could be (a) that these particulate matter are abundant and mix well with the ambient environment, thus having a higher probability of being inhaled by the participant and entering the sensors placed nearby and (b), with the minute size of PM 2.5 , that these particulates, when inhaled, can reach deep into the lungs and bloodstream, creating many negative health effects [3,5,6], thereby impacting the human body to a large extent.…”

“…The concentration of PM 1 particles during these trials was between 0.708 and 7.655 µg/m 3 . As mentioned before, since these minute particles, when inhaled, can pass through the nose and reach deep into the lungs and bloodstream, the immediate changes in the body were, thus, most likely dominated by these PM 1 particles for which the estimation of PM 1 using biometric variables was very high with the R 2 between the true and estimated values being 0.91 [46].…”

“…The human body is a sensing system in itself, and it reacts to environmental variables and changes in them such as temperature, humidity, and air quality. It was previously shown that autonomous physiological and cognitive responses that result from the inhalation of particulate matter on a small temporal and spatial scale can be used to estimate PM 1 and PM 2.5 using machine learning models with very high accuracy [46] in a study that was limited to a single participant. The inclusion of multiple participants in the experimental static bike ride paradigm in which the measurement of PM 1 and PM 2.5 was performed shows that the methodology that was implemented on a single participant can be extended to multiple participants as well, producing even better results for PM 1 and PM 2.5 with an R 2 value of nearly 1 and a very low RMSE, as shown in Table 9.…”

“…The SHAP value beeswarm plot in Figure 8a shows that skin temperature, pupil distance, and heart rate were among the main features that were the most influential in estimating PM 1 . Skin temperature and the distance of the pupil were also important variables in the estimation of PM 1 when a single participant was used for the study [46]. The distance of the pupils, which indicates the vergence of the eyes, has been associated with the attention load [59].…”

“…Since the relations between and among variables are not always linear or functional, we made use of machine learning algorithms to perform regression for nonlinear, nonparametric, multidimensional data. The use of machine learning models has been shown to estimate ambient PM with high degrees of precision, especially PM 2.5 [44][45][46]. By simultaneously measuring biological parameters and air quality components, we examined the interaction between the body and the environment while also testing the accuracy of estimating pollutants using machine learning techniques.…”