Prediction of Indoor Environmental Quality Using a Regression Model for Educational Buildings in Hot Arid Climate: A Case Study in the Al-Najaf Technical Institute – Iraq
Keywords:acoustic comfort, educational building, indoor environmental quality (IEQ), regression model, thermal comfort
In hot climates, achieving a good indoor environmental quality (IEQ) in existing buildings is important especially with climate change challenges as future heat waves will increase in frequency, duration, and intensity. In educational buildings, there is much more focus on the IEQ parameters and the interactions among them that need to be in line with the continuously changing learning environment. This study assesses the IEQ parameters (represented by noise, temperature and humidity) at three selected campus areas (lecture rooms of an administrative department building (LR), main hall of a management department building (MH) and a central library building (CL)) at the Al-Najaf Technical Institute (NTI), Al-Najaf City, Iraq, for the period from May to December 2019. A statistical analysis using a multi-linear regression model was performed to determine the relationship between the selected IEQ parameters and explain the noise level behavior as a function of the temperature and relative humidity. The research indicated that the noise levels and temperature values exceeded the maximum standard limits in all buildings reflecting the displeasing sound and heating quality within the studied areas, while the readings for relative humidity within each building environment complied with standards. Moreover, for both LR and MH buildings (R2 ≥ 0.8, significance F ≤ 0.01), the noise values were satisfactorily modeled by temperature and relative humidity highlighting the interactions between temperature, humidity and noise under consistent conditions. However, the results for the CL building (R2 = 0.6, significance F = 0.1) showed no relationship between the IEQ parameters, highlighting the fact that this building is exposed to unsteady conditions (an irregular number of people using this building during the daytime) resulting in a high variation of data measurements. The current results demonstrate that detailed modeling can be helpful to predict IEQ parameters depending on other known parameters in buildings. The results of the predictive model aligned with the directly measured data. Therefore, its performance is equally effective, but with a significant reduction in cost and time consumed.
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