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Comparison of the modelling and the experimental results on concentrations of ultra-fine particles indoors

机译:室内超细颗粒浓度的建模与实验结果比较

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摘要

This paper describes a dynamic method for estimating particle number concentrations indoors. In order to predict the variations of concentrations, a number balance model has been developed. In the model, the total number concentration of particles with sizes from 0.01 to larger than 1 μm was used. The model was validated with experimental data obtained at different measurement locations in Sweden, and the results revealed very good agreement between predicted and measured concentrations. The study showed that this method is a good tool for predicting the number of concentrations of ultra-fine particles (NCUFPs) indoors. If combined with the measurement results even weak indoor sources can be identified, which might be very difficult with a purely experimental technique. The method can also be used to analyze particle removal from the indoor air by mechanisms other than ventilation (sink effects). In addition, the model simulations suggest that at relatively high outdoor concentrations, temporarily lower air change rates could be employed in order to decrease the concentrations of ultra-fine particles indoors. The study revealed that such ventilation strategy might be applicable in buildings without strong indoor sources (e.g. offices).
机译:本文介绍了一种估算室内粒子数浓度的动态方法。为了预测浓度的变化,已经开发了数字平衡模型。在该模型中,使用了从0.01到大于1μm的颗粒总数。该模型已通过在瑞典不同测量地点获得的实验数据进行了验证,结果表明预测浓度和测量浓度之间具有很好的一致性。研究表明,此方法是预测室内超细颗粒(NCUFP)浓度的好工具。如果与测量结果结合使用,甚至可以识别出室内微弱的信号源,这对于纯实验技术而言可能非常困难。该方法还可用于分析除通风以外的其他机制(室内效果)从室内空气中去除颗粒的方法。此外,模型模拟表明,在相对较高的室外浓度下,可以采用暂时较低的换气速率,以降低室内的超细颗粒浓度。该研究表明,这种通风策略可能适用于没有强大室内源(例如办公室)的建筑物。

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