Abstract
Carbon dioxide is an indicator of indoor air quality. A number of factors influence its concentration. Due to the fact that they all present time variability, CO2 concentration indoors considerably varies over time. In this work we focus on the dynamics of indoor CO2 concentration changes. We examine the dynamics of CO2 variation and use it as a source of information on the character of collective impact of factors on indoor air. The proposed method is based on mean square displacement analysis (MSD) applied to the segments of the time series of CO2 monitoring data. The segments are determined based on the introduced criterion for optimal sample size selection. The method was validated by showing that it reproduces the known stochastic dynamics of the simulated data set properly. From the real data analysis, we found that indoors the stochastic dynamics of CO2 concentration in time was mainly nonlinear. Moreover, it exhibited a cycle of change which could be associated with the daily variation of the collective influence of factors on indoor air. We intend to apply the method to other parameters of indoor air, aiming at developing a capability of describing the dynamics of indoor air as a complex system.
Highlights
It is widely recognized that the concentration of CO2 is an indicator of indoor air quality
In this work we proposed a method which allows to recognize the character of stochastic temporal dynamics of CO2 concentration in indoor air
The method focuses on the time series of measured CO2 concentrations and it utilities mean square displacement analysis (MSD) analysis as a tool to characterize the dynamics
Summary
It is widely recognized that the concentration of CO2 is an indicator of indoor air quality. While the variability of CO2 concentration in time is able to reflect the influence of factors on indoor air, the dynamics of changes may provide the information on the character of their impact This statement is fundamental for our work. The proposal to study the dynamics of CO2 variation, and use it as a source of information on the type of collective impact of factors on indoor air, is a major novelty in this work. The size of intervals as well as their position in time domain is unknown prior to the analysis For this reason, the method shall allow to identify segments of the time series reflecting one kind of dynamics of CO2 concentration. It may become very useful in diagnosing the microclimate as well as designing a proper one
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