Chemical Engineering / Kimya Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/14
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Article Citation - WoS: 8Citation - Scopus: 9Indoor Air Quality in Chemical Laboratories(Elsevier Ltd., 2016) Ugranlı, Tuğba; Güngörmüş, Elif; Sofuoğlu, Sait Cemil; Sofuoğlu, Sait Cemil; Sofuoğlu, Aysun; 03.02. Department of Chemical Engineering; 03.07. Department of Environmental Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyChemical laboratories are special microenvironments, in which many pollutants may be found because of the large range and number of chemicals that can be used, while concentrations of some specific ones may relatively be elevated due to high source strengths depending on the type and the number of experiments conducted and the number of people working in the laboratory. Laboratories can be considered as public places for the students whereas they are occupational microenvironments for their staff (technicians, specialists and teaching/research assistants). Hence, laboratory indoor air quality (IAQ) is of importance due to chronic, toxic and carcinogenic health risks for the staff in addition to possible acute effects for both staff and students. This chapter presents background information regarding pertinent indoor air pollutants, factors that determine their concentrations, indoor environmental comfort, a review of the literature on indoor environmental quality in chemical laboratories and measures of IAQ management.Article Citation - WoS: 23Citation - Scopus: 22Exposure To Particulate Matter in a Mosque(Elsevier Ltd., 2012) Ocak, Yılmaz; Kılıçvuran, Akın; Sofuoğlu, Sait Cemil; Sofuoğlu, Aysun; Sofuoğlu, Sait Cemil; Sofuoğlu, Aysun; 03.02. Department of Chemical Engineering; 03.07. Department of Environmental Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyIndoor air quality in mosques during prayers may be of concern for sensitive/susceptible sub-groups of the population. However, no indoor air pollutant levels of potentially toxic agents in mosques have been reported in the literature. This study measured PM concentrations in a mosque on Friday when the mid-day prayer always receives high attendance. Particle number and CO 2 concentrations were measured on nine sampling days in three different campaigns before, during, and after prayer under three different cleaning schedules: vacuuming a week before, a day before, and on the morning of the prayer. In addition, daily PM 2.5 concentrations were measured. Number concentrations in 0.5-1.0, 1.0-5.0, and>5.0μm diameter size ranges were monitored. In all campaigns the maximum number concentrations were observed on the most crowded days. The lowest number concentrations occurred when vacuuming was performed a day before the prayer day in two of the three size ranges considered. PM 2.5 concentrations (four-hour samples that integrated before, during, and after the prayer) were comparable to the other indoor environments reported in the literature. CO 2 concentrations suggested that ventilation was not sufficient in the mosque during the prayers. The results showed that better ventilation, a preventive cleaning strategy, and a more detailed study are needed.Article Citation - WoS: 31Citation - Scopus: 37Application of Artificial Neural Networks To Predict Prevalence of Building-Related Symptoms in Office Buildings(Elsevier Ltd., 2008) Sofuoğlu, Sait Cemil; Sofuoğlu, Sait Cemil; 03.07. Department of Environmental Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyArtificial neural networks (ANN) were constructed to predict prevalence of building-related symptoms (BRS) of office building occupants. Six indoor air pollutants and four indoor comfort variables were used as input variables to the networks. A symptom metric was used as the measure of BRS prevalence, and employed as the output variable. Pollutant concentration, comfort variable, and occupant symptom data were obtained from the Building Assessment and Survey Evaluation study conducted by the US Environmental Protection Agency, in which all were measured concurrently. Feed-forward networks that employ back-propagation algorithm with momentum term and variable learning rate were used in ANN modeling. Root mean square error and R2 value of the simple linear regression between observed and predicted output were used as performance measures. Among the constructed networks, the best prediction performance was observed in a one-hidden-layered network with an R2 value of 0.56 for the test set. All constructed networks except one showed a better performance than the multiple linear regression analysis.