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Understanding “Atmosome”, the Personal Atmospheric Exposome: Comprehensive Approach

暴露的 环境资源管理 环境科学 医学 环境卫生
作者
Hari Bhimaraju,Nitish Nag,Vaibhav Pandey,Ramesh Jain
出处
期刊:JMIR biomedical engineering [JMIR Publications Inc.]
卷期号:6 (4): e28920-e28920
标识
DOI:10.2196/28920
摘要

Background Modern environmental health research extensively focuses on outdoor air pollutants and their effects on public health. However, research on monitoring and enhancing individual indoor air quality is lacking. The field of exposomics encompasses the totality of human environmental exposures and its effects on health. A subset of this exposome deals with atmospheric exposure, termed the “atmosome.” The atmosome plays a pivotal role in health and has significant effects on DNA, metabolism, skin integrity, and lung health. Objective The aim of this work is to develop a low-cost, comprehensive measurement system for collecting and analyzing atmosomic factors. The research explores the significance of the atmosome in personalized and preventive care for public health. Methods An internet of things microcontroller-based system is introduced and demonstrated. The system collects real-time indoor air quality data and posts it to the cloud for immediate access. Results The experimental results yield air quality measurements with an accuracy of 90% when compared with precalibrated commercial devices and demonstrate a direct correlation between lifestyle and air quality. Conclusions Quantifying the individual atmosome is a monumental step in advancing personalized health, medical research, and epidemiological research. The 2 main goals in this work are to present the atmosome as a measurable concept and to demonstrate how to implement it using low-cost electronics. By enabling atmosome measurements at a communal scale, this work also opens up potential new directions for public health research. Researchers will now have the data to model the impact of indoor air pollutants on the health of individuals, communities, and specific demographics, leading to novel approaches for predicting and preventing diseases.

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