Environmental Engineering / Çevre Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/4321

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End date: 2019Publisher: search.filters.publisher.Taylor and Francis Ltd.

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  • Article
    Citation - WoS: 12
    Citation - Scopus: 15
    Assessment 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, Macit
    This 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: 14
    Citation - Scopus: 22
    Forecasting Ambient Air So2 Concentrations Using Artificial Neural Networks
    (Taylor and Francis Ltd., 2006) Sofuoğlu, Sait Cemil; Sofuoğlu, Aysun; Birgili, Savaş; Tayfur, Gökmen
    An Artificial Neural Networks (ANNs) model is constructed to forecast SO 2 concentrations in Izmir air. The model uses meteorological variables (wind speed and temperature) and measured particulate matter concentrations as input variables. The correlation coefficient between observed and forecasted concentrations is 0.94 for the network that uses all three variables as input parameters. The root mean square error value of the model is 3.60 g/mt 3 . Considering the limited number of available input variables, model performances show that ANNs are a promising method of modeling to forecast ambient air SO 2 concentrations in Izmir.