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Dynamics of carbon dioxide concentration in indoor air

机译:室内空气中二氧化碳浓度的动态

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摘要

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.
机译:二氧化碳是室内空气质量的指标。许多因素影响其浓度。由于它们都具有时间变化性,因此室内的CO2浓度会随时间变化很大。在这项工作中,我们专注于室内CO2浓度变化的动态变化。我们研究了CO2变化的动态并将其用作有关因素对室内空气的集体影响特征的信息来源。所提出的方法基于应用于二氧化碳监测数据时间序列各部分的均方位移分析(MSD)。根据引入的标准选择最佳样本大小,确定片段。通过显示该方法正确再现了模拟数据集的已知随机动力学,从而验证了该方法的有效性。通过实际数据分析,我们发现室内CO2浓度随时间的随机动态主要是非线性的。而且,它表现出一个变化的周期,可能与因素对室内空气的集体影响的每日变化有关。我们打算将该方法应用于室内空气的其他参数,以开发将室内空气动力学描述为复杂系统的能力。

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