A dynamic inventory database for assessing age-, gender-, and route-specific chronic internal exposure to chemicals in support of human exposome research

Li, Zijian; Xiong, Jie

doi:10.1016/j.jenvman.2023.117867

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…This approach was adopted in our previous modeling studies. 44,65 Although the primary purpose of pulmonary circulation is to facilitate gas exchange (such as oxygen and carbon dioxide) between the human body and the external environment, it also has the ability to transport chemicals to the lung tissue. 35 Therefore, conducting a comprehensive investigation into the roles of both bronchial and pulmonary circulation in the biotransfer modeling of chemicals within the lung tissue can be valuable in accurately describing the fate and distribution of these chemicals in the human lung.…”

Section: Limitations and Additional Considerationsmentioning

confidence: 99%

Co-PBK: a computational biomonitoring tool for assessing chronic internal exposure to chemicals and metabolites

Zhang,

2023

Environ. Sci.: Processes Impacts

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Section: Limitations and Additional Considerationsmentioning

confidence: 99%

Co-PBK: a computational biomonitoring tool for assessing chronic internal exposure to chemicals and metabolites

Zhang,

2023

Environ. Sci.: Processes Impacts

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“…With the small size of PM 2.5 , these particulates can penetrate deeply into the lungs and bloodstream, creating adverse health effects related to the respiratory system [3], increased mortality [4], heart disease [5], inflammatory responses, and adverse birth-related effects [6]. The pollutants we considered are exemplars of the wider human exposome [7][8][9], which refers to the comprehensive accumulation of all environmental exposures that an individual encounters throughout their lifetime, including chemicals and biological agents. The exposome encompasses exposures to both gases and particulates, and appropriate care should be taken to include the often ignored ultrafine particulates [10].…”

Section: Introductionmentioning

confidence: 99%

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

Ruwali,

Talebi,

Fernando

et al. 2024

BioMedInformatics

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Introduction: Air pollution has numerous impacts on human health on a variety of time scales. Pollutants such as particulate matter—PM1 and PM2.5, 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, PM2.5, 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, PM2.5, 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, PM2.5, 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.

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Modeling Environmental Fate, Transport, and Transformation of Pesticides: First-Order Kinetic Models for Regional and Global Applications

2023

Reviews Env.Contamination (formerly:Residue Reviews)

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A dynamic inventory database for assessing age-, gender-, and route-specific chronic internal exposure to chemicals in support of human exposome research

Cited by 6 publications

References 31 publications

Co-PBK: a computational biomonitoring tool for assessing chronic internal exposure to chemicals and metabolites

Co-PBK: a computational biomonitoring tool for assessing chronic internal exposure to chemicals and metabolites

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

Modeling Environmental Fate, Transport, and Transformation of Pesticides: First-Order Kinetic Models for Regional and Global Applications

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