ORCID Profile
0000-0001-8326-026X
Current Organisations
King Faisal University
,
University of Haifa
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Publisher: Springer Science and Business Media LLC
Date: 12-2015
Publisher: MDPI AG
Date: 13-06-2023
DOI: 10.3390/APP13127086
Abstract: It is challenging to use conventional one-variable-at-time (OVAT) batch experiments to evaluate multivariate/inter-parametric interactions between physico-chemical variables that contribute to the adsorptive removal of contaminants. Thus, chemometric prediction approaches for multivariate calibration and analysis reveal the impact of multi-parametric variation on the process of concern. Hence, we aim to develop an artificial neural network (ANN), and stepwise regression (SR) models for multivariate calibration and analysis utilizing OVAT data prepared through experimentation. After comparing the models’ performance, ANN was the superior model for this application in our work. The standard deviations (SD) between the observed and ANN-predicted values were very close. The average correlation coefficient (R2) between observed and ANN-predicted values for the training dataset was 96.9%. This confirms the ability of our developed ANN model to forecast lindane removal accurately. The testing dataset correlation coefficients (89.9% for ANN and 67.75% for SR) demonstrated a better correlation between observed and predicted ANN values. The ANN model training and testing dataset RMSE values were 1.482 and 2.402, lower than the SR values of 4.035 and 3.890. The MAPE values for the ANN model’s training and testing datasets, 0.018 and 0.031, were lower than those for the SR model. The training and testing datasets have low RSR and PBIAS values, implying model strength. The R2 and WIA values are above 0.90 for both datasets, proving the ANN model’s accuracy. Applying our developed ANN model will reduce the cost of removing inorganic and organic impurities, including lindane, and optimize chemical utilization.
Publisher: MDPI AG
Date: 02-03-2023
DOI: 10.3390/SU15054470
Abstract: Heavy metals in the environment cause adverse effects on living organisms. Agro-wastes have the potential to remove heavy metals from aqueous solutions. In this study, the orange peel cellulose (OPC) beads were utilized as adsorbents to remove metals from wastewater. The surface of the adsorbent was studied by Fourier transform infrared spectroscopy (FT-IR) and scanning electron microscopy coupled with energy dispersive x-ray spectroscopy (SEM-EDS). The concentrations of the metals before and after adsorption were measured using inductively coupled plasma mass spectrometry. The removal of the metal ions (i.e., Cr6+, Cd2+, and Pb2+) using the OPC was investigated by varying the pH, contact time, and adsorbent dosages parameters. The maximum removal efficiency obtained for the metal ions occurred at pHs 4–8. The use of the Langmuir isotherm and Freundlich isotherm models demonstrated the statistical significance of the heavy metal adsorption processes (R2 0.96). At a neutral pH, the OPC adsorption order was Pb2+ Cd2+ Cr6+ with % removal values of 98.33, 93.91, and 33.50, respectively. The adsorption equilibrium for Cr6+ was reached after 36 h. For Cd2+ and Pb2+, equilibrium was reached after 8 and 12 h, respectively. The FT-IR and SEM-EDS confirmed the presence of many functional groups and elements on the adsorbent. The adsorption of heavy metals using the OPC is a low-cost, eco-friendly, and innovative method for the removal of metals in aquatic environments. The findings of this study will be highly significant for the public in the affected areas worldwide that have credible health concerns due to water contamination with heavy metals.
Publisher: Elsevier BV
Date: 06-2019
Publisher: CRC Press
Date: 07-04-2017
Publisher: MDPI AG
Date: 04-11-2022
DOI: 10.3390/SU142114482
Abstract: Climate change poses a challenge to the security and long-term viability of the global food supply chain. Climate unpredictability and extreme weather events have significant impacts on Saudi Arabia’s vulnerable food system, which is already under stress. The Kingdom of Saudi Arabia faces distinct challenges in comparison to other dry locations across the world. Here, the per capita water demand is high, the population is growing, the water resources are extremely limited, and there is little information on the existing groundwater supplies. Consequently, it is anticipated that there will be formidable obstacles in the future. In order to make data-driven decisions, policymakers should be aware of causal links. The complex concerns pertaining to the Saudi Arabian food system were analyzed and rationally explained in the current study. A causality analysis examined different driving factors, including temperature, greenhouse gas (GHG) emission, population, and gross domestic product (GDP) that cause vulnerabilities in the country’s food system. The results of the long-run causality test show that GDP has a positive causal relationship with the demand for food, which implies that the demand for food will increase in the long run with an increase in GDP. The result also shows that Saudi Arabia’s GDP and population growth are contributing to the increase in their total GHG emissions. Although the Kingdom has made some efforts to combat climate change, there are still plenty of opportunities for it to implement some of the greatest strategies to guarantee the nation’s food security. This study also highlights the development of appropriate policy approaches to ersify its import sources to ensure future food security.
Publisher: Wiley
Date: 24-08-2023
DOI: 10.1111/SPC3.12878
Abstract: Using cross‐sectional data from N = 4274 young adults across 16 countries during the COVID‐19 pandemic, we examined the cross‐cultural measurement invariance of the perceived vulnerability to disease (PVD) scale and tested the hypothesis that the association between PVD and fear of COVID‐19 is stronger under high disease threat [that is, absence of COVID‐19 vaccination, living in a country with lower Human Development Index (HDI) or higher COVID‐19 mortality]. Results supported a bi‐factor Exploratory Structural Equation Modeling model where items loaded on a global PVD factor, and on the sub‐factors of Perceived Infectability and Germ Aversion . However, cross‐national invariance could only be obtained on the configural level with a reduced version of the PVD scale (PVD‐r), suggesting that the concept of PVD may vary across nations. Moreover, higher PVD‐r was consistently associated with greater fear of COVID‐19 across all levels of disease threat, but this association was especially pronounced among in iduals with a COVID‐19 vaccine, and in contexts where COVID‐19 mortality was high. The present research brought clarity into the dimensionality of the PVD measure, discussed its suitability and limitations for cross‐cultural research, and highlighted the pandemic‐related conditions under which higher PVD is most likely to go along with psychologically maladaptive outcomes, such as fear of COVID‐19.
No related grants have been discovered for Aminur Rahman.