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Empirical cumulative distribution function of CO2 concentration increments as a descriptor of indoor air variability

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Indoor air quality broadly refers to the environmental characteristics inside buildings and characterizes the physical, chemical and biological state of indoor air at some place and time. Usually it is characterized by physicochemical properties of indoor environment such as temperature, relative humidity, airflows and concentration of characteristic pollutants, e.g. carbon dioxide. They are highly variable in time. In the paper, changes of empirical cumulative distribution function (ECDF) of CO2 concentration were considered as a descriptor of indoor air variability. Using this measure, there may be performed the analysis of CO2 variation in a short as well as long term. The approach utilizes the idea of classification. A class of variation of CO2 concentration has its individual ECDF. It indicates a particular case of balance/imbalance between CO2 delivery and removal processes, including gas dispersion. The analysis is applicable for evaluating the stability of indoor environment and it may provide support for the diagnosis of the performance of the air exchange system. Exemplary results were provided for an open space office and a lecture theater.
Rocznik
Strony
173--187
Opis fizyczny
Bibliogr. 17 poz., rys.
Twórcy
autor
  • Laboratory of Sensor Technique and Indoor Air Quality Studies, Faculty of Environmental Engineering, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław
  • Laboratory of Sensor Technique and Indoor Air Quality Studies, Faculty of Environmental Engineering, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław
autor
  • Laboratory of Sensor Technique and Indoor Air Quality Studies, Faculty of Environmental Engineering, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław
Bibliografia
  • [1] LOGUE J.M., MCKONE T.E., SHERMAN M.H., SINGER B.C., Hazard assessment of chemical air contaminants measured in residences, Indoor Air, 2011, 21 (2), 92.
  • [2] JAAKKOLA J.J.K., The office environment model. A conceptual analysis of the sick building syndrome, Indoor Air, 1998, 8 (S4), 7.
  • [3] ZHANG X., ZHAO Z., NORDQUIST T., NORBACK D., The prevalence and incidence of sick building syndrome in Chinese pupils in relation to the school environment: a two-year follow-up study, Indoor Air, 2011, 21 (6), 462.
  • [4] FANGER P.O., What is IAQ?, Indoor Air, 2006, 16, 328.
  • [5] HAVERINEN-SHAUGHNESSY U., MOSCHANDREAS D.J., SHAUGHNESSY R.J., Association between substandard classroom ventilation rates and students’ academic achievement, Indoor Air, 2011, 21, 121. Increments of CO2 concentration as a descriptor of indoor air variability 187
  • [6] BLUYSSEN P.M., Towards an integrative approach of improving indoor air quality, Build. Environ., 2009, 44, 1980.
  • [7] APTE M.G., FISK W.J., DAISEY J.M., Associations between indoor CO2 concentrations and sick building syndrome symptoms in U.S. office buildings: an analysis of the 1994–1996 BASE study data, Indoor Air, 2000, 10 (4), 246.
  • [8] SATISH U., MENDELL M.J., SHEKHAR K., HOTCHI T., SULLIVAN D., STREUFERT S., FISK W.J., Impact of CO2 on human decision making and productivity, Environ. Health Persp., 2012, 120, 1671.
  • [9] SEPPANEN O.A., FISK W.J., MENDELL M.J., Association of ventilation rates and CO2 concentrations with health and other responses in commercial and institutional buildings, Indoor Air, 1999, 9 (4), 226.
  • [10] SZCZUREK A., MACIEJEWSKA M., POŁOCZAŃSKI R., WYŁOMAŃSKA A., Dynamics of carbon dioxide concentration in indoor air, Stoch. Environ. Res. Risk Assess., 2015, paper accepted.
  • [11] MACIEJEWSKA M., SZCZUREK A., Representativeness of shorter measurement sessions in long-term indoor air monitoring, Environ. Sci.: Processes Impacts, DOI: 10.1039/C4EM00409D.
  • [12] SZCZUREK A., MACIEJEWSKA M., TEUERLE M., WYŁOMAŃSKA A., Method to characterize collective impact of factors on indoor air, Physica A, 2015, 420, 190.
  • [13] MACIEJEWSKA M., SZCZUREK A., SIKORA G., WYŁOMAŃSKA A., Diffusive and subdiffusive dynamics of indoor microclimate: A time series modeling, Phys. Rev. E, 2012, 86, 031128.
  • [14] XIAO Y., GORDON A., A C++ program for the Cramér–von Mises two-sample test, J. Stat. Softw., 2007, 17 (8), 1.
  • [15] ANDERSON T.W., On the distribution of the two-sample Cramer–von Mises criterion, Ann. Math. Statist., 1962, 33 (3), 1148.
  • [16] SCHMID F., TREDE M., A distribution free test for the two sample problem for general alternatives, Comput. Stat. Data Ann., 1995, 20, 409.
  • [17] http://www.mathworks.com/matlabcentral/fileexchange/13407-two-sample-cramer-von-mises-hypotesis-test
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-679d7c5a-4e6e-4b8a-9a1d-eac7735e80e3
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