Influence of temperature and relative humidity on subjective and objective air quality data in shopping centers
Keywords:Indoor air quality, shopping center, total volatile organic compounds, E-Nose, VOC
Germany has 493 shopping centers mostly located in urban cities. According to STASTICA, the number of shopping centers was doubled in the last two decades .For consumers, good indoor air quality (IAQ) is a basic requirement for their shopping experience.
This leads to very high air exchange rates for current operation of HVAC systems in shopping centers. Accordingly, achieving good IAQ in combination with increasing energy efficiency is a main issue for operation. Thus, previous studies were done to evaluate the intensity of shopping product emissions. Trained subject panels evaluated the emissions from retail products by using an intensity comparative scale. In this paper, we analyze the IAQ parameters not only by trained human panel, but also by analyzing the volatile organic compounds (VOC) through objective tests. In a first step, we cluster five different product groups: books, clothing, shoes, coffee and perfume. For these groups, we measure the emissions through a multi VOC sensor system and a trained human panel, depending on two main parameters: temperature and relative humidity. The multi VOC sensor system consists of electrochemical sensors which resistivity changes according to the oxidation reactions that happens on the surface of the sensors at high temperatures. We use the results to investigate the correlation between the intensity of VOCs in respect to the two main parameters. Finally, we used the subjective data along with the objective data, to evaluate the perceived odor intensity and correlate the evaluations with the measured VOC concentrations by the multi VOC sensor system. The evaluation and visualization is done in
principle of statistical data analysis methods such as Friedman test. The results show the potential for the metal oxide semiconductor sensors technology for detection of VOCs and for prediction of perceived intensity based on objective data. Moreover, a product specific regression leads to better prediction results, which shows that different limit values are required for different shop types.
Furthermore, the results show an influence of air temperature and humidity on subjective perception. Thus, for all products investigated, the perceived intensity and the percentage of dissatisfied people increases with rising temperature and relative humidity.
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