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Fulltext Screening of Metal Oxides and Hydroxides for Arsenic Removal from Water Using Molecular Dynamics Simulations

Hira NE, Lock SSM, Arshad U, Asif K, Ullah F, Farooqi AS, et al.

ACS Omega, 2023 Dec 19;8(50):48130-48144.
PMID: 38144150 DOI: 10.1021/acsomega.3c07014

Arsenic in groundwater is a harmful and hazardous substance that must be removed to protect human health and safety. Adsorption, particularly using metal oxides, is a cost-effective way to treat contaminated water. These metal oxides must be selected systematically to identify the best material and optimal operating conditions for the removal of arsenic from water. Experimental research has been the primary emphasis of prior work, which is time-consuming and costly. The previous simulation studies have been limited to specific adsorbents such as iron oxides. It is necessary to study other metal oxides to determine which ones are the most effective at removing arsenic from water. In this work, a molecular simulation computational framework using molecular dynamics and Monte Carlo simulations was developed to investigate the adsorption of arsenic using various potential metal oxides. The molecular structures have been optimized and proceeded with sorption calculations to observe the adsorption capabilities of metal oxides. In this study, 15 selected metal oxides were screened at a pressure of 100 kPa and a temperature of 298 K for As(V) in the form of HAsO4 at pH 7. Based on adsorption capacity calculations for selected metal oxides/hydroxides, aluminum hydroxide (Al(OH)3), ferric hydroxide (FeOOH), lanthanum hydroxide La(OH)3, and stannic oxide (SnO2) were the most effective adsorbents with adsorption capacities of 197, 73.6, 151, and 42.7 mg/g, respectively, suggesting that metal hydroxides are more effective in treating arsenic-contaminated water than metal oxides. The computational results were comparable with previously published literature with a percentage error of 1%. Additionally, SnO2, which is rather unconventional to be used in this application, demonstrates potential for arsenic removal and could be further explored. The effects of pH from 1 to 13, temperature from 281.15 to 331.15 K, and pressure from 100 to 350 kPa were studied. Results revealed that adsorption capacity decreased for the high-temperature applications while experiencing an increase in pressure-promoted adsorption. Furthermore, response surface methodology (RSM) has been employed to develop a regression model to describe the effect of operating variables on the adsorption capacity of screened adsorbents for arsenic removal. The RSM models utilizing CCD (central composite design) were developed for Al(OH)3, La(OH)3, and FeOOH, having R2 values 0.92, 0.67, and 0.95, respectively, suggesting that the models developed were correct.
Fulltext Profile and Predictors of Maternal Quality of Life During Physiological Pregnancy: A Cross-Sectional Analysis

Ishaq R, Shoaib M, Baloch NS, Sadiq A, Raziq A, Huma ZE, et al.

Front Public Health, 2021;9:801035.
PMID: 35111720 DOI: 10.3389/fpubh.2021.801035

Background: Quality of Life (QoL) and its determinants are significant in all stages of life, including pregnancy. The physical and emotional changes during pregnancy affect the QoL of pregnant women, affecting both maternal and infant health. Hence, assessing the QoL of pregnant women is gaining interest in literature. We, therefore, aimed to describe the QoL of pregnant women during physiological pregnancy and to identify its associated predictors in women attending a public healthcare institute of Quetta city, Pakistan.
Methods: A cross-sectional study was conducted at the Obstetrics and Gynecology Department of Sandeman Provincial Hospital Quetta city, Pakistan. The respondents were asked to answer the Urdu (lingua franca of Pakistan) version of the Quality of Life Questionnaire for Physiological Pregnancy. Data were coded and analyzed by SPPS v 21. The Kolmogorov-Smirnov test was used to establish normality of the data and non-parametric tests were used accordingly. Quality of Life was assessed as proposed by the developers. The Chi-square test was used to identify significant associations and linear regression was used to identify the predictors of QoL. For all analyses, p < 0.05 was taken significantly.
Results: Four hundred and three pregnant women participated in the study with a response rate of 98%. The mean QoL score was 19.85 ± 4.89 indicating very good QoL in the current cohort. The Chi-Square analysis reported a significant association between age, education, occupation, income, marital status, and trimester. Education was reported as a positive predictor for QoL (p = 0.006, β = 2.157). On the other hand, trimester was reported as a negative predictor of QoL (p = 0.013, β = -1.123).
Conclusion: Improving the QoL among pregnant women requires better identification of their difficulties and guidance. The current study highlighted educational status and trimester as the predictors of QoL in pregnant women. Health care professionals and policymakers should consider the identified factors while designing therapeutic plans and interventions for pregnant women.

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