Influence of Wooden Sawdust Treatments on Cu(II) and Zn(II) Removal from Water

Organic waste materials and semi-products containing cellulose are used as low-cost adsorbents that are able to compete with conventional sorbents. In addition, their capacity to bind heavy metal ions can be intensified by chemical treatments using mineral and organic acids, bases, oxidizing agents, and organic compounds. In this paper, we studied the biosorption capacity of natural and modified wooden sawdust of poplar, cherry, spruce, and hornbeam in order to remove heavy metals from acidic model solutions. The Fourier transform infrared spectroscopy (FTIR) spectra showed changes of the functional groups due to the alkaline modification of sawdust, which manifested in the considerably increased intensity of the hydroxyl peaks. The adsorption isotherm models clearly indicated that the adsorptive behavior of metal ions in treated sawdust satisfied not only the Langmuir model, but also the Freundlich model. The adsorption data obtained for studied sorbents were better fitted by the Langmuir isotherm model for both metals, except for spruce sawdust. Surface complexation and ion exchange are the major mechanisms involved in metal ion removal. We investigated the efficiency of the alkaline modified sawdust for metal removal under various initial concentrations of Cu(II) and Zn(II) from model solutions. The highest adsorption efficiency values (copper 94.3% at pH 6.8 and zinc 98.2% at pH 7.3) were obtained for poplar modified by KOH. For all types of sawdust, we found that the sorption efficiency of modified sorbents was higher in comparison to untreated sawdust. The value of the pH initially increased more in the case of modified sawdust (8.2 for zinc removal with spruce NaOH) and then slowly decreased (7.0 for Zn(II) with spruce NaOH).This research was funded by Scientific Grant Agency of the Ministry of Education, Science, Research, and Sport of the Slovak Republic and the Slovak Academy of Sciences, grant number 1/0419/19 and grant number 1/0326/18

A batch study of Ni(II) sorption on natural Slovak zeolite

The adsorption mechanism of nickel on natural Slovak zeolite, a regional low-cost material, was studied in a batch system. The effect of various initial concentrations of metal ions in the adsorption process was tested. The highest adsorption efficiency (almost 70%) was reached at a concentration of 50 mg/L. The lowest efficiency was determined at a concentration of 350 mg/L (approx. 19%). Sorption data have been interpreted in terms of Langmuir and Freundlich isotherms. The Freundlich model with regression coefficient 0.83 better fitted for the batch experiments. The change of pH in equilibrium showed to ion-exchange reaction. The conductivity increases in all cases. Due to relatively low efficiency in higher concentration of aquatic solutions, further research and modification of zeolite is needed.This work has been supported by the Slovak Grant Agency for Science (Grant No. 1/0419/19)

Influence of Wooden Sawdust Treatments on Cu(II) and Zn(II) Removal from Water

Organic waste materials and semi-products containing cellulose are used as low-cost adsorbents that are able to compete with conventional sorbents. In addition, their capacity to bind heavy metal ions can be intensified by chemical treatments using mineral and organic acids, bases, oxidizing agents, and organic compounds. In this paper, we studied the biosorption capacity of natural and modified wooden sawdust of poplar, cherry, spruce, and hornbeam in order to remove heavy metals from acidic model solutions. The Fourier transform infrared spectroscopy (FTIR) spectra showed changes of the functional groups due to the alkaline modification of sawdust, which manifested in the considerably increased intensity of the hydroxyl peaks. The adsorption isotherm models clearly indicated that the adsorptive behavior of metal ions in treated sawdust satisfied not only the Langmuir model, but also the Freundlich model. The adsorption data obtained for studied sorbents were better fitted by the Langmuir isotherm model for both metals, except for spruce sawdust. Surface complexation and ion exchange are the major mechanisms involved in metal ion removal. We investigated the efficiency of the alkaline modified sawdust for metal removal under various initial concentrations of Cu(II) and Zn(II) from model solutions. The highest adsorption efficiency values (copper 94.3% at pH 6.8 and zinc 98.2% at pH 7.3) were obtained for poplar modified by KOH. For all types of sawdust, we found that the sorption efficiency of modified sorbents was higher in comparison to untreated sawdust. The value of the pH initially increased more in the case of modified sawdust (8.2 for zinc removal with spruce NaOH) and then slowly decreased (7.0 for Zn(II) with spruce NaOH)

Evaluation of Zeolite Adsorption Properties for Cu(II) Removal from Acidic Aqueous Solutions in Fixed-Bed Column System

The development of human society after 18th century is associated with metals. Technology of extraction and processing of heavy metals is essential for many areas of industry. Naturally, the extraction, processing and cleaning of impurities give the metals not only a new form, but also cause their intensive distribution in the environment, which represents a huge threat. Countries of the middle Europe, where extraction of mineral resources takes place a long period, have to solve the problems of mine wastewater. Finding of the new and cheap ways of these wastewater treatment can increase the quality of the environment in the affected areas. Sorption techniques belong to an effective and cost acceptable methods for remove of heavy metals from aqueous environment. The presented paper describes the adsorption behavior of Slovak natural zeolite in fixed-bed column system. In order to determine its applicability for mine drainage treatment, copper removal from model sulfuric acid solutions (pH 4) was studied

Removal of copper, zinc and iron from water solutions by spruce sawdust adsorption

The water pollution by toxic elements is one of the major problems threatening human health as well as the quality of the environment. Sorption is considered a cost-effective method that is able to effectively remove heavy metals. During past few years, researches have been researching usage of low-cost adsorbents like bark, lignin, chitosan peat moss and sawdust. This paper deals with the study of copper, zinc and iron adsorption by adsorption of spruce sawdust obtained as a by-product from locally used wood. Raw spruce sawdust was used to remove heavy metal ions from the model solutions with ion concentration of 10 mg/L during 24 hours or 5, 10, 15, 30, 45, 60, 120 min, respectively. Fourier-transform infrared spectroscopy was applied to determine functional groups of sawdust. Sorption efficiency was higher than 67% in short-time experiments and higher than 75% for one day experiments in all tested cations

Removal of Copper from Water Solutions by Adsorption on Spruce Sawdust

Pollution of water by toxic elements is one of the major factors of concern for human health, as well as for environmental quality, and draws a large amount of scientific attention. New and cheaper methods of wastewater treatment are increasing the quality of the environment and reducing the negative impacts on fauna, flora, and human beings. The sorption technique is considered a cost effective method for effectively removing heavy metals. During the past few years, there have been increasing studies dedicated to using low-cost adsorbents like bark, tannin-rich materials, lignin, chitosan peat moss, and sawdust. The presented paper describes the adsorption behavior of spruce wood sawdust. In order to determine its applicability for wastewater treatment, copper removal from model solutions was studied

Effectiveness of two lightweight aggregates for the removal of heavy metals from contaminated urban stormwater

Contaminated runoff stormwater from urban environments carries several contaminants to water bodies, thereby affecting the health of living beings and ecological systems. Among all the contaminants, heavy metals possess high toxicity and impact water quality. The stormwater management through green infrastructures composed by adequate materials can provide an excellent solution, simultaneously ensuring the appropriate hydraulic performance and contaminant removal rate. The proposed research aims at the elimination of heavy metals (i.e. Ni, Cu, Zn, Cd and Pb) through column experiments by selecting four possible and novel treatments for urban stormwaters. Two lightweight aggregates (Arlita and Filtralite) were tested separately and in combination with CaCO3. The study determines the efficiency and lifetime of each treatment by varying the interaction time between the filter materials and contaminated water and the type of filter. The observed removal mechanisms were closely related to the changes in pH due to the interactions between water and different materials. The reductions in heavy metal concentrations depend on the type of heavy metal, interaction time and type of filter material. Results indicate that the combined use of CaCO3, Arlita and Filtralite did not improve the removal rates of heavy metals. However, it decreased the efficiency of the decontamination process. The significance of this study lies on the removal efficiency of Arlita and Filtralite as decontamination treatments. Both the tested lightweight aggregates led to a considerable decrease in the heavy metal concentrations in urban runoff stormwater although Filtralite was particularly efficient. After 4 weeks, the treatments were still successfully reducing and stabilising 99% of the heavy metals in the contaminated stormwater. These results confirm that the lifetime of the tested lightweight aggregates is adequate and emphasise, as a novel application of these materials, on their feasibility for the improvement of urban stormwater quality.This work was funded by the University of Alicante [project GRE17-12], by Generalitat Valenciana [project GV/2020/059] and by the Slovak Grant Agency for Science [Grant No. 1/0419/19]. Additional acknowledge to the Technical Research Services of the University of Alicante (SSTTI-UA) for the analyses performed using the equipment held at this institution, which was financed by the EU, MINECO and Generalitat Valenciana [State Programme for Knowledge Generation and Scientific and Technological Strengthening of the R+D+i System and P.O. FEDER 2007-2013 funds]

Sorption isotherm study of manganese removal from aqueous solutions by natural and MnO2-coated zeolite

The applicability of the natural and MnO2-coated zeolite as sorbent for the removal of Mn(II) from synthetic solutions has been investigated. Batch experiments were carried out to determine the influence of pH and Mn(II) concentration on the sorption process. A maximum removal efficiency (98.9%) was observed for modified zeolite with the concentration of 10 mg/dm3 of manganese in solution. The equilibrium data showed a very good correlation for both Langmuir and Freundlich sorption models and this suggests both monolayer adsorption and a heterogeneous surface existence. Maximum sorption capacity calculated from the Langmuir model constituted 5.57 mg/g for natural zeolite and 13.41 mg/g for modified zeolite

The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses. Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation. Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR. Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients