Controlling energy consumption in residential buildings using air infiltration in humid climates

Environment, Development and Sustainability - Given the ever-increasing people’s need for housing, affordable housing and environmental comfort are important, and the use of inactive design...     

An attempt to develop ecotourism in an unknown area: the case of Nehbandan County,South Khorasan Province,Iran

Environment, Development and Sustainability - This study is an attempt to develop ecotourism in Nehbandan County, in the South Khorasan Province, Iran, by representing unknown areas that have...     

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R² were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R² confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.     

Graphene and its derivatives: synthesis,modifications, and applications in wastewater treatment

Graphene was discovered in 2004 and has attracted intensive interests because of its unique mechanical, electric, thermal, optical, and structural properties, which makes graphene a potential candidate for various applications. Graphene is being used as a composite or filler material with metals, metal oxides, and polymers for potential advanced applications in solar cells, lithium-ion batteries, photocatalysis and sensing. These applications depend upon the distinctive properties of graphene, which in turn depend on the adopted synthetic approach. This article reviews the recent developments in synthesis of graphene and related composite materials. The synthesis of graphene through exfoliation, epitaxial growth and direct growth via carbon source, and modification approaches by covalent and noncovalent methodologies are discussed. Graphene-based metal and metal oxide composites for the purification of wastewater using photolytic process are also presented.     

Isotherm,kinetic, and thermodynamic studies for dynamic adsorption of toluene in gas phase onto porous Fe-MIL-101/OAC composite

Environmental Science and Pollution Research - In the present paper, micro-mesoporous Fe-MIL-101/OAC composite using in situ incorporation of Fe-MIL-101 into oxidized activated carbon was...     

Household waste behaviours among a community sample in Iran: An application of the theory of planned behaviour

Understanding the factors influencing recycling behaviour can lead to better and more effective recycling programs in a community. The goal of this study was to examine factors associated with household waste behaviours in the context of the theory of planned behaviour (TPB) among a community sample of Iranians that included data collection at time 1 and at follow-up one year later at time 2. Study participants were sampled from households under the coverage of eight urban health centers in the city of Qazvin. Of 2000 invited households, 1782 agreed to participate in the study. A self-reported questionnaire was used for assessing socio-demographic factors and the TPB constructs (i.e. attitude, subjective norms, perceived behavioural control, and intention). Furthermore, questions regarding moral obligation, self-identity, action planning, and past recycling behaviour were asked, creating an extended TPB. At time 2, participants were asked to complete a follow-up questionnaire on self-reported recycling behaviours. All TPB constructs had positive and significant correlations with each other. Recycling behaviour at time 1 (past behaviour) significantly related to household waste behaviour at time 2. The extended TPB explained 47% of the variance in household waste behaviour at time 2. Attitude, perceived behavioural control, intention, moral obligation, self-identity, action planning, and past recycling behaviour were significant predictors of household waste behaviour at time 2 in all models. The fact that the expanded TPB constructs significantly predicted household waste behaviours holds great promise for developing effective public campaigns and behaviour-changing interventions in a region where overall rates of household waste reduction behaviours are low. Our results indicate that educational materials which target moral obligation and action planning may be particularly effective.