Water conservation behavior: Exploring the role of social, psychological, and behavioral determinants.

Water conservation is vital to safeguard future water availability when natural resources like water become extremely scarce. It is fundamental to understand the significant determinants of water conservation activities which can also facilitate the implementation of appropriate policies for water demand management. Thus, the goal of this study was to determine the important social, psychological, and behavioral factors of water conservation behavior. A questionnaire survey was used to collect the data from 625 international students and employees from different universities in Japan. The structural equation modeling demonstrated that the proposed model explained 46% of the variation in water conservation behavior. Awareness of water issues was highly related to attitude, responsibility, and culture. Except for culture, attitude and responsibility were significantly connected with emotion. Finally, emotion, habit, culture and involvement were significantly and positively associated with water conservation behavior. These factors are incorporated for the first time in this study into a single model to better understand individual water conservation behavior. The sequential regression model showed that all determinants including demographic factors raised the variation's proportion by 53% in water conservation. Female participants had a significantly higher positive attitude, emotion, and water conservation behavior than male participants. Older participants exhibited higher levels of awareness, habit, culture, and water conservation behavior when compared to younger people. Lastly, participants believed that the most dominant component in water conservation behavior was the awareness of water issues. These findings could assist policymakers in raising household awareness, accountability, and involvement towards water conservation efforts.

Response surface methodology for strontium removal process optimization from contaminated water using zeolite nanocomposites.

The effective removal of strontium from polluted water is an emerging issue worldwide, especially in Japan, after the destruction of Fukushima's Daiichi Nuclear Power Plant. In the strontium removal process, statistical optimization of associated factors is needed to reduce the quantity of chemicals and the number of experimental trials. In this study, response surface methodology based on the central composite design was employed for assessing the influence of different factors and their interaction effects on the efficiency of strontium removal. We have considered nanoscale zero-valent iron-zeolite (nZVI-Z) and nano-Fe/Cu zeolite (nFe/Cu-Z) as adsorbents for the effective removal of strontium. The results suggested that the studied three factors such as pH, contact time, and concentration are positively related to the adsorption of strontium. That is, the maximum strontium removal occurred at pH, initial concentration, and contact time of 12, 200 mg L-1, and 30 min, respectively. The experimental maximum strontium adsorption capacity of nZVI-Z and nFe/Cu-Z adsorbents is 32.5 mg/g and 34 mg/g, respectively. The present study also showed that the most statistically significant potential contributor was initial concentration, followed by contact time in the removal process. The study indicated that the interaction effect between contact time and initial concentration was statistically important, suggesting the need for a multi-mechanism technique in the removal phase of strontium. Tόth, Langmuir, Dubinin-Astakhov (D-A), Freundlich, and Hill isotherm models were also fitted with the experimental strontium adsorption data, in which the Tόth model fitted best compared to the other models based on the RMSD and R2.

Statistical techniques for the optimization of cesium removal from aqueous solutions onto iron-based nanoparticle-zeolite composites.

Statistical optimization of performance determining factors is essential for the development of a cesium removal system from aqueous solutions. Therefore, factorial experimental design and multiple regression techniques were employed to assess the primary and interaction effects of the pH, initial concentration, and contact time in the cesium removal process using nanoscale zero-valent iron-zeolite (nZVI-Z) and nano-Fe/Cu-zeolite (nFe/Cu-Z) as an adsorbent. The optimum region of cesium removal was identified by constructing a contour plot. The study revealed that initial concentration was the most significant factor followed by contact time. The study also suggested that maximum cesium removal occurred at pH, initial concentration, and contact time of 6, 200 mg/L, and 30 min, respectively. Moreover, the statistically significant interaction effect was observed between contact time and initial concentration. The experimental data were also fitted with Tόth, Langmuir, Dubinin-Astakhov (D-A), Freundlich, and Hill models and found that the Tόth model fitted better compared with the other four models based on Akaike information criterion (AIC) and root-mean-square deviation (RMSD). The findings of this paper can undoubtedly contribute to constructing the optimum statistical process of removing hazardous pollutants from the water, which significantly impacts on human health and the environment. Graphical abstract.