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

A Critical Review of the Removal of Radionuclides from Wastewater Employing Activated Carbon as an Adsorbent

Radionuclide-contaminated water is carcinogenic and poses numerous severe health risks and environmental dangers. The activated carbon (AC)-based adsorption technique has great potential for treating radionuclide-contaminated water due to its simple design, high efficiency, wide pH range, quickness, low cost and environmental friendliness. This critical review first provides a brief overview of the concerned radionuclides with their associated health hazards as well as different removal techniques and their efficacy of removing them. Following this overview, this study summarizes the surface characteristics and adsorption capabilities of AC derived from different biomass precursors. It compares the adsorption performance of AC to other adsorbents, such as zeolite, graphene, carbon nano-tubes and metal-organic frameworks. Furthermore, this study highlights the different factors that influence the physical characteristics of AC and adsorption capacity, including contact time, solution pH, initial concentration of radionuclides, the initial dosage of the adsorbent, and adsorption temperature. The theoretical models of adsorption isotherm and kinetics, along with their fitting parameter values for AC/radionuclide pairs, are also reviewed. Finally, the modification procedures of pristine AC, factors determining AC characteristics and the impact of modifying agents on the adsorption ability of AC are elucidated in this study; therefore, further research and development can be promoted for designing a highly efficient and practical adsorption-based radionuclide removal system.

Ecological risk and machine learning based source analyses of trace metals in typical surface water

Surface water is threatened by trace metal pollution due to increasing anthropogenic activities. Therefore, an appropriate source identification was essential to reduce the ecological risk posed by the given pollutants. In this study, shallow and deep learning approaches trained by a registered environmental dataset of discharge sources were employed to classify the potential emission sources of trace metals in the Elbe River, Germany. The results showed that the overall concentration rank of the given metals was Zn (226.5 ± 526.5 μg·L^-1) > Ni (5.6 ± 4.7 μg·L^-1) > Cu (5.3 ± 5.8 μg·L^-1) > As (3.3 ± 3.7 μg·L^-1) > Pb (2.9 ± 5.2 μg·L^-1) > Cr (1.8 ± 2.5 μg·L^-1) > Cd (1.3 ± 3.1 μg·L^-1) in seven tributaries and the mainstream of the Elbe River, among which the tributary Triebisch had the highest risk quotient over 86. Random Forest outperformed other algorithms with the highest Kappa median values of 0.59 and the lowest Hamming-loss values of 0.22 in extraction of the majority voted class. Then, the source apportionment conducted by random forest suggested that wastewater disposal and metal industrial emissions were the source contributors in the tributary Triebisch (probabilities: 0.39, 0.3), upstream segment (0.45, 0.25), and downstream segment (0.32, 0.23) of the given river. Additional sources of mineral industry emissions were found in the upstream segment (0.21) and downstream segment (0.22). The data provided herein suggest that random forest would be an effective approach to identify pollutants in aquatic environments and could assist source-oriented adaptive management.

Effect of strontium on nutrient uptake, physiological parameters, and strontium localization in lettuce

Human activities increase the risk of stable and radioactive strontium (Sr) isotopes entering the environment and food chain. In this study, the effects of Sr on the nutrient uptake and physiological responses of lettuce under different "Sr treatment" concentrations (0, control, 1, 2, 3, 4, and 5 mM) and "times" (7, 14, and 21 day) were studied in a hydroponic system. In addition, the distribution of Sr on the surfaces and cross-sections of lettuce leaves was revealed by scanning electron microscopy with energy-dispersive X-ray (SEM-EDX) analysis. A two-way analysis of variance (ANOVA) method was used to analyze the significance of "Sr treatment," "time," and their "interaction." The results showed that an increase in Sr uptake in lettuce could significantly reduce the uptake of calcium (Ca). The contents of sulfur (S), potassium (K), and iron (Fe) in lettuce leaves showed significant differences with the sampling day. Similarly, the fresh weight of lettuce leaves and roots as well as the photosynthetic pigment contents of lettuce leaves was also significantly different with the sampling day. The activities of antioxidant enzymes (superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD)) showed significant differences with the sampling day. The activities of SOD and CAT decreased significantly with the sampling day, while POD increased significantly. The MDA content increased significantly with increasing hydroponic Sr concentration on the 21st day. SEM-EDX analysis showed that the weight percentage of Sr in the vascular bundle sheath in the cross-section of lettuce leaves was relatively higher than that in the mesophyll. This study aids our understanding of the distribution of Sr in lettuce leaf tissues and the effect of Sr on lettuce physiology.

Study on Optimum IUPAC Adsorption Isotherm Models Employing Sensitivity of Parameters for Rigorous Adsorption System Performance Evaluation

Adsorption cooling technologies driven by low-grade thermal or solar power are used as an energy-efficient alternative to conventional refrigeration and air conditioning systems. Explicit understanding of the adsorption cycles requires precise determination of the performance parameters, replication of the experimental data, and the rigorous study of the adsorption heat transformation method. Hence, the optimum adsorption isotherms model must be identified. Scientists often face difficulties in selecting the suitable isotherm model as there are many models for a particular form of adsorption isotherm. The present study introduces a novel approach for choosing the optimal models for each type of International Union of Pure and Applied Chemistry (IUPAC) classified adsorption isotherm using robust statistical methods. First, the box-and-whisker plots of error identification are employed. Tóth for Type-I(a) and Type-I(b), modified BET for Type-II, GAB for Type-III, Universal for Type-IV(a), and Type-IV(b), Sun Chakrabarty for Type-V, and Yahia et al. for Type-VI were found lower than the other candidate models in box-and-whisker plot. The optimality of our selected models was further verified using analysis of variance (ANOVA), pairwise Tukey honest significant difference (HSD) test, Kruskal–Wallis rank-sum test, and pairwise Wilcoxon rank-sum test. In short, rigorous statistical analysis was performed to identify the best model for each type of isotherm by minimizing error. Moreover, specific cooling effect (SCE) of Maxsorb III/ethanol and silica gel/water pairs were determined. Results showed that Tóth is the optimal isotherm model for the studied pairs, and the SCE values obtained from the model agree well with experimental data. The optimum isotherm model is indispensable for the precise designing of the next generation adsorption cooling cycles.

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.