Information is lacking on the degree and causes of temporal variability of indoor air concentrations in industrial buildings. Vapor intrusion (VI) is controlled by multiple variables that interact on different time scales. Indoor concentrations are expected to display multiple periodicities—diurnal, seasonal, and others. An extensive dataset was analyzed using 6-h time resolution for 6 continuous months including trichloroethylene (TCE), radon, differential pressure, barometric pressure, differential temperature, wind speed, and precipitation. The samples were collected in a sampling zone or compartment within a large industrial building near a point of volatile organic compound (VOC) release. The objectives for this project included assessing VOC temporal variability in an industrial building and evaluating whether using VI indicators/tracers, which are less costly and time intensive to measure, may be able to predict VOC concentrations. This paper reports descriptive, time series, and machine learning data analyses. At this location, radon was proven by far the most effective indicator or tracer for TCE VI. Both the time series and machine learning ranked barometric pressure as a more important influence on VI than temperature. Substantial autocorrelation and diurnal periodicity were observed in TCE concentrations.

中文翻译：

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工业建筑中的时间变化—时间序列和机器学习分析

缺乏有关工业建筑中室内空气浓度随时间变化的程度和原因的信息。蒸汽入侵（VI）由在不同时间尺度上相互作用的多个变量控制。预计室内浓度会表现出多种周期性-昼夜，季节等。使用6小时的时间分辨率连续6个月分析了一个广泛的数据集，包括三氯乙烯（TCE），ra，压差，大气压力，压差温度，风速和降水。样品收集在大型工业建筑内挥发性有机化合物（VOC）释放点附近的采样区或隔室中。该项目的目标包括评估工业建筑中VOC的时间变异性，以及评估是否使用VI指标/示踪剂，这些方法成本较低且测量时间比较短，因此可以预测VOC浓度。本文报告了描述性，时间序列和机器学习数据分析。在此地点，ra已被证明是TCE VI最有效的指示剂或示踪剂。时间序列和机器学习都将大气压力列为对VI的重要影响，而不是温度。在TCE浓度中观察到大量的自相关和昼夜周期性。时间序列和机器学习都将大气压力列为对VI的重要影响，而不是温度。在TCE浓度中观察到大量的自相关和昼夜周期性。时间序列和机器学习都将大气压力列为对VI的重要影响，而不是温度。在TCE浓度中观察到大量的自相关和昼夜周期性。