Analysis of Carbon Dioxide Concentration Prediction Model and Diffusion Tendency of Expiratory by Simultaneous Multipoint Sensing

Motokazu Moritani,Kensuke Miyamoto,Kota Itoda,Junya Imani,Norifumi Watanabe,Yoshiyasu Takefuji,Hiroyuki Aoyama

doi:10.3390/app10134631

Motokazu Moritani, Kensuke Miyamoto + Show 5 more

Open Access

https://doi.org/10.3390/app10134631

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Abstract

Recent indoor air quality studies show that even 1000 parts per million (ppm) concentration of Carbon Dioxide (CO2) has an adverse effect on human intellectual activities. Therefore, it is required to keep the CO 2 concentration below a certain value in a room. In this study, in order to analyze the diffusion tendency of carbon dioxide by breathing, we constructed a simultaneous multi-point sensing system equipped with a carbon dioxide concentration sensor to measure indoor environment. Furthermore, it was evaluated whether the prediction model can be effectively used by comparing the prediction value by the model and the actually measured value from the sensor. The experimental results showed that CO 2 by exhaled breathing diffuses evenly throughout the room regardless of the sensor’s relative positions to the human test subjects. The existing model is sufficiently accurate in a room which has above at least a 0.67 cycle/h ventilation cycle. However, there is a large gap between the measured and the model’s predicted values in a room with a low ventilation cycle, and that suggests a measurement with a sensor still is necessary to precisely monitor the indoor air quality.

Highlights

With the spread and development of the Internet of Things (IoT), highly accurate and compact sensor devices for environmental measurement have been developed and can be used in a variety of situations
It is clear that the prediction model deviates from the measured values in the environment with low ventilation cycle
Under the condition that there is a certain error between the accuracy of the CO2 concentration sensor in a room and the average CO2 emissions of a typical user, we examined how much the deviation between the prediction model and the measured values by the measurement device occurs

Summary

Introduction

With the spread and development of the Internet of Things (IoT), highly accurate and compact sensor devices for environmental measurement have been developed and can be used in a variety of situations. These sensor devices are used for specific facilities, and for measuring and adjusting the environment of places where people normally live, such as smart homes and smart offices [1,2]. There are many factors that should be evaluated in a person’s living environment, and it is difficult to manage all of them [3], such as tobacco smoking and ambient particulate matter pollution. We focus on the concentration of carbon dioxide (CO2 ) in a room

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