Maher Elbayoumi

Multivariate methods to predict ground level ozone during daytime, nighttime, and critical conversion time in urban areas

Ground–level ozone (O3) is known to exhibit strong daily variations that lead to complexity of the pollutants’ analysis and predictions. This study aimed to introduce and explore the variations in O3 concentrations during daytime (DT), nighttime (NT), and critical conversion time (CCT) using multiple linear regression (MLR) and principal component regression (PCR) analyses. The original variables and principal component analysis (PCA) results were used as the input for MLR analysis. Hourly averages of six air pollutants and four meteorological parameters at Shah Alam during 1999–2009 were selected for this study. The monitoring records in 2010 were used to assess the developed models using several performance indicators. Results showed that the MLR model during DT exhibited optimal performance in terms of normalized absolute error, index of agreement, prediction accuracy, and coefficient of determination (R2) with values of 0.2762, 0.9211, 0.8581, and 0.7354, respectively. PCR during CCT also showed significantly higher performance than that during DT and NT. This result was evidenced by higher percentage of total variances, which could be explained by the selected variables in PCA during CCT.

Seasonal Variation in Schools’ Indoor Air Environments and Health Symptoms among Students in an Eastern Mediterranean Climate

ABSTRACT The indoor air quality (IAQ) in classrooms highly affects the health and productivity of students. This article aims to clarify seasonal variation in indoor environment and sick building syndromes (SBS) symptoms in an Eastern Mediterranean climate. A series of field measurements were conducted during the fall and winter seasons from October 2011 to March 2012 in 12 naturally ventilated schools located in the Gaza Strip. Data on environmental perception and health symptoms were obtained from 724 students by using a validated questionnaire. The results showed that indoor PM10 and PM2.5 concentrations were 426.3 ± 187.6 μg/m3 and 126.6 ± 94.8 μg/m3, respectively. The CO2 concentrations and ventilation rate widely exceeded their reference values during the winter season. The prevalence rates of general symptoms were relatively high at baseline assessment in the fall season and increased significantly during follow-up in the winter season. Significant increases in disease symptoms such as mucosal irritation and pre-existing asthma symptoms among students could be related to poor indoor air quality. Five distinct groups of SBS symptoms from factor analysis of students’ related symptoms were significantly correlated with PM10 and PM2.5, CO2, ventilation rate, and indoor temperature. As vulnerable children, this situation negatively affects their school performance and health.

Sustainable GIS Based-ANN's Solution for Landfill Suitability Analysis

Sustainable suitability analysis for landfill sites is an important and necessary issue for authorities of solid waste planning in the fast growing zones, due to the increasing complexity coming from dealing with various disciplines and requirement and the needy of satisfaction. A combination of geographic information systems including spatial analysis, and artificial neural network ANNs were employed in this study for decision-makers in the sustainable suitability analysis problems in Malaysia and GIS was used to manipulate and present spatial data. The GIS analysis reveals three distinct groupings based on actual conditions of the case study area, environmental factors, economic factors and social factors which are reflection of different factors contributing to the sustainable development. The result shown that ANNs has good information extraction and evaluation functions of the suitability value based on the exact relationship between the input criteria and the output landfill site data with high coefficient of determination (R2) which help decision-makers to analysis sustainable suitability for landfill sites.