Construction of a minute ventilation model to address inter-individual inhaled dose variability within identical exposure scenarios using wearable devices

Pengpeng Wu; Qian Guo; Yuchen Zhao; Mengyao Bian; Gang Wang; Wei Wu; Jing Shao; Qirong Wang; Xiaoli Duan; Junfeng Zhang

doi:10.1016/j.scitotenv.2024.176415

Construction of a minute ventilation model to address inter-individual inhaled dose variability within identical exposure scenarios using wearable devices

Sci Total Environ. 2024 Sep 21:176415. doi: 10.1016/j.scitotenv.2024.176415. Online ahead of print.

Authors

Pengpeng Wu¹, Qian Guo², Yuchen Zhao¹, Mengyao Bian¹, Gang Wang³, Wei Wu³, Jing Shao⁴, Qirong Wang⁴, Xiaoli Duan⁵, Junfeng Zhang⁶

Affiliations

¹ School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
² China North Artificial Intelligence & Innovation Research Institute, Beijing 100072. China; Collective Intelligence & Collaboration Laboratory, Beijing 100072, China.
³ Department of Otolaryngology-Head and Neck Surgery, PLA Strategic Support Force Characteristic Medical Center, Beijing 100101, China.
⁴ National Institute of Sports Medicine, General Administration of Sport of China, Beijing 100029, China.
⁵ School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China. Electronic address: jasmine@ustb.edu.cn.
⁶ Nicholas School of the Environment and Global Health Institute, Duke University, Durham, NC 27708, USA; Duke Kunshan University, Kunshan 215316, China.

PMID: 39312972
DOI: 10.1016/j.scitotenv.2024.176415

Abstract

Inhaled dose is crucial for accurately assessing exposure to air pollution, determined by pollutant concentration and minute ventilation (VE). However, the VE predictive models and its application to assess the health effects of air pollution are still lacking. In this study, we developed VE predictive models using machine learning techniques, utilizing data obtained from eighty participants who underwent a laboratory cardiopulmonary exercise test (CPET). VE predictive models were developed using generalized additive model (GAM), random forest model (RF) and extreme gradient boosting (XGBoost) and analyzed for explanation of input variables. The Random Forest model, cross-validated, exhibited outstanding performance with an R² of 0.986 and a MAE of 1.816 L/min. The median difference between the measured VE and the predicted VE was 0.18 L/min, and the median difference between the black carbon (BC) inhaled dose based on predicted VE and measured VE was 0.02 ng. Employing explainable machine learning, the results showed that metabolic equivalent (METs), heart rate, and body weight are the three top important variables, emphasizing the significance of incorporating METs variables when constructing VE models. Through multiple linear regression models and an adjusted stratified analysis model, the significant adverse association between BC concentration and inhaled dose on diastolic blood pressure (DBP) was only observed in female. The disparity in the effect of BC inhaled dose compared to BC concentration on DBP reached up to 115 %. This study is the first to explore the ability of different machine learning algorithms to construct VE prediction models and directly apply the models to assess health effects of an example pollutant. This study contributes to the accurate assessment of air pollution exposure leveraging wearable devices, an approach useful for environmental epidemiology studies.

Keywords: Environmental epidemiology; Heart rate; Machine learning; Minute ventilation; Random Forest.