Classification of buildings mold threat using electronic nose

Mold is considered to be one of the most important features of Sick Building Syndrome and is an important problem in current building industry. In many cases it is caused by the rising moisture of building envelopes surface and exaggerated humidity of indoor air. Concerning historical buildings it is mostly caused by outdated raising techniques among that is absence of horizontal isolation against moisture and hygroscopic materials applied for construction. Recent buildings also suffer problem of mold risk which is caused in many cases by hermetization leading to improper performance of gravitational ventilation systems that make suitable conditions for mold development. Basing on our research there is proposed a method of buildings mold threat classification using electronic nose, based on a gas sensors array which consists of MOS sensors (metal oxide semiconductor). Used device is frequently applied for air quality assessment in environmental engineering branches. Presented results show the interpretation of e-nose readouts of indoor air sampled in rooms threatened with mold development in comparison with clean reference rooms and synthetic air. Obtained multivariate data were processed, visualized and classified using a PCA (Principal Component Analysis) and ANN (Artificial Neural Network) methods. Described investigation confirmed that electronic nose – gas sensors array supported with data processing enables to classify air samples taken from different rooms affected with mold.Mold is considered to be one of the most important features of Sick Building Syndrome and is an important problem in current building industry. In many cases it is caused by the rising moisture of building envelopes surface and exaggerated humidity of indoor air. Concerning historical buildings it is mostly caused by outdated raising techniques among that is absence of horizontal isolation against moisture and hygroscopic materials applied for construction. Recent buildings also suffer problem of mold risk which is caused in many cases by hermetization leading to improper performance of gravitational ventilation systems that make suitable conditions for mold development. Basing on our research there is proposed a method of buildings mold threat classification using electronic nose, based on a gas sensors array which consists of MOS sensors (metal oxide semiconductor). Used device is frequently applied for air quality assessment in environmental engineering branches. Presented results show the interpretati...

[1]  H. T. Nagle,et al.  Effectiveness of an Electronic Nose for Monitoring Bacterial and Fungal Growth , 2000 .

[2]  Magdalena Frąc,et al.  High-Resolution Continuum Source Atomic Absorption Spectrometry with Microwave-Assisted Extraction for the Determination of Metals in Vegetable Sprouts , 2015 .

[3]  Naresh Magan,et al.  Electronic Nose for the Early Detection of Moulds in Libraries and Archives , 2004 .

[4]  Brian Everitt,et al.  Principles of Multivariate Analysis , 2001 .

[5]  Robert A. Samson,et al.  Health Implications of Fungi in Indoor Environments , 1994 .

[6]  Agata Gryta,et al.  Fast and Accurate Microplate Method (Biolog MT2) for Detection of Fusarium Fungicides Resistance/Sensitivity , 2016, Front. Microbiol..

[7]  A. Romain,et al.  Microbial volatile organic compounds as indicators of fungi. Can an electronic nose detect fungi in indoor environments , 2005 .

[8]  Janusz Pawliszyn,et al.  Methyl benzoate as a marker for the detection of mold in indoor building materials. , 2005, Journal of separation science.

[9]  Zbigniew Suchorab,et al.  Application of Gas Sensor Arrays in Assessment of Wastewater Purification Effects , 2014, Sensors.

[10]  Anne-Claude Romain,et al.  Detection of diverse mould species growing on building materials by gas sensor arrays and pattern recognition , 2006 .

[11]  Guang Li,et al.  A pattern recognition method for electronic noses based on an olfactory neural network , 2007 .

[12]  Grzegorz Łagód,et al.  Gas sensors array as a device to classify mold threat of the buildings , 2016 .

[13]  Danuta Barnat-Hunek,et al.  Utilization of sewage sludge in the manufacture of lightweight aggregate , 2015, Environmental Monitoring and Assessment.

[14]  Waldemar Wójcik,et al.  Advanced diagnostics of industrial pulverized coal burner using optical methods and artificial intelligence , 2012 .

[15]  Koichi Makimura,et al.  Assessment of Airborne Particles in Indoor Environments: Applicability of Particle Counting for Prediction of Bioaerosol Concentrations , 2016 .

[16]  Zbigniew Suchorab,et al.  Free of Volatile Organic Compounds Protection against Moisture in Building Materials/Zabezpieczenia Przegród Budowlanych Przed Wilgocią Wolne Od Lotnych Związków Organicznych , 2014 .