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, Aysun; Sofuoğlu, Sait CemilChemical 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: 12Citation - Scopus: 15Assessment and Improvement of Indoor Environmental Quality in a Primary School(Taylor and Francis Ltd., 2017) Ekren, Orhan; Karadeniz, Ziya Haktan; Atmaca, İbrahim; Ugranlı Çiçek, Tuğba; Sofuoğlu, Sait Cemil; Toksoy, MacitThis study reports levels of indoor environmental quality variables before and after installation of heat recovery ventilation in a primary school located in an urban area in Izmir, Turkey. A CO2-based modeling was performed to determine the required flow rates that would comply with an international ventilation standard, followed by computational fluid dynamics modeling for best airflow distribution in a classroom. Temperature, CO2, PM2.5, and total volatile organic compounds were found at undesired levels, among which relative humidity, CO2, and PM2.5 were improved after the intervention. Reductions in the mean and maximum concentrations were 29% and 68% for CO2 and 29% and 46% for PM2.5. This intervention study was a part of the city-wide main project that aimed to increase awareness of the students and their families, teachers, and staff regarding importance of indoor environmental quality in both at school and home due to its possible effects on children's health and academic performance, one of the major challenges of today's societies all around the globe.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 CemilArtificial 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.