The objective of this study was to investigate the performance of different weight of Salvinia molesta plants in biological treatment of domestic wastewater. Three treatment systems containing 280g (GS1), 140g (GS2) and 70g (GS3) of S. molesta plants were used for the phytoremediation process. Physicochemical analysis such as pH, colour, chemical oxygen demand (COD), and biological oxygen demand (BOD5) of the influent and effluent water samples were performed according to spectrophotometric methods. The outcome of the study demonstrated that the different weight of S. molesta plants played a significant role in improving the quality of the wastewater samples, in which GS1 removed 96.8% (colour), 91% (BOD5), and 82.6% (COD). While up to 88.6% (colour), 87.1% (BOD5), and 81.1% (COD) reduction was observed for GS2 treatment systems, and GS3 was efficient in removing 85.5% (colour), 86.1% (BOD5), and 68.3% (COD). Also, a pH value of 6.29-7.19, 5.97-7.07, and 6.17-7.42 was obtained from GS1, GS2 and GS3 treatment systems, respectively. Thus, the treatment system with the highest quantity of S. molesta (GS1) demonstrated better performance compared to the other two systems (GS2 and GS3). The findings of this research can be applied in addressing the goals of sustainable development through the use of green technology to reduce the threat of water pollution in natural water bodies. Perhaps existing and future water scarcity can be resolved through the use of phytoremediation technology.
Addressing inaccuracies in review articles is essential to prevent the proliferation of misinformation. This communication is dedicated to rectifying factual errors identified in a recent review article featured in this journal, with a specific emphasis on addressing errors related to the Temkin, Flory-Huggins, Sips, and Baudu isotherm models. By elucidating and clarifying these inaccuracies, we aim to uphold the integrity of scientific discourse and ensure the accurate dissemination of information within the scholarly community.
Clean air is critical component for health and survival of human and wildlife, as atmospheric pollution is associated with a number of significant diseases including cancer. However, due to rapid industrialization and population growth, activities such as transportation, household, agricultural, and industrial processes contribute to air pollution. As a result, air pollution has become a significant problem in many cities, especially in emerging countries like India. To maintain ambient air quality, regular monitoring and forecasting of air pollution is necessary. For that purpose, machine learning has emerged as a promising technique for predicting the Air Quality Index (AQI) compared to conventional methods. Here we apply the AQI to the city of Visakhapatnam, Andhra Pradesh, India, focusing on 12 contaminants and 10 meteorological parameters from July 2017 to September 2022. For this purpose, we employed several machine learning models, including LightGBM, Random Forest, Catboost, Adaboost, and XGBoost. The results show that the Catboost model outperformed other models with an R2 correlation coefficient of 0.9998, a mean absolute error (MAE) of 0.60, a mean square error (MSE) of 0.58, and a root mean square error (RMSE) of 0.76. The Adaboost model had the least effective prediction with an R2 correlation coefficient of 0.9753. In summary, machine learning is a promising technique for predicting AQI with Catboost being the best-performing model for AQI prediction. Moreover, by leveraging historical data and machine learning algorithms enables accurate predictions of future urban air quality levels on a global scale.
This correspondence critically examines and rectifies modeling deficiencies identified in a recent article published in this journal. Our analysis covers a range of models and issues, including the Temkin isotherm, the Flory-Huggins isotherm, the pseudo-first-order kinetic model, the pseudo-second-order kinetic model, the intraparticle diffusion model, the Elovich kinetic model, and the computation of thermodynamic parameters. The elucidation and correction of these modeling issues contribute to a more accurate and reliable understanding of the studied phenomena, thereby enhancing the scientific rigor of the subject paper.
Customer relationship management (CRM) is an innovative technology that seeks to improve customer satisfaction, loyalty, and profitability by acquiring, developing, and maintaining effective customer relationships and interactions with stakeholders. Numerous researches on CRM have made significant progress in several areas such as telecommunications, banking, and manufacturing, but research specific to the healthcare environment is very limited. This systematic review aims to categorise, summarise, synthesise, and appraise the research on CRM in the healthcare environment, considering the absence of coherent and comprehensive scholarship of disparate data on CRM. Various databases were used to conduct a comprehensive search of studies that examine CRM in the healthcare environment (including hospitals, clinics, medical centres, and nursing homes). Analysis and evaluation of 19 carefully selected studies revealed three main research categories: (i) social CRM 'eCRM'; (ii) implementing CRMS; and (iii) adopting CRMS; with positive outcomes for CRM both in terms of patients relationship/communication with hospital, satisfaction, medical treatment/outcomes and empowerment and hospitals medical operation, productivity, cost, performance, efficiency and service quality. This is the first systematic review to comprehensively synthesise and summarise empirical evidence from disparate CRM research data (quantitative, qualitative, and mixed) in the healthcare environment. Our results revealed that substantial gaps exist in the knowledge of using CRM in the healthcare environment. Future research should focus on exploring: (i) other potential factors, such as patient characteristics, culture (of both the patient and hospital), knowledge management, trust, security, and privacy for implementing and adopting CRMS and (ii) other CRM categories, such as mobile CRM (mCRM) and data mining CRM.
The present study investigated the sustainable approach for wastewater treatment using waste algal blooms. The current study investigated the removal of toxic metals namely chromium (Cr), nickel (Ni), and zinc (Zn) from aqueous solutions in batch and column studies using biochar produced by the marine algae Ulva reticulata. SEM/EDX, FTIR, and XRD were used to examine the adsorbents' properties and stability. The removal efficiency of toxic metals in batch operations was investigated by varying the parameters, which included pH, biochar dose, initial metal ion concentration, and contact time. Similarly, in the column study, the removal efficiency of heavy metal ions was investigated by varying bed height, flow rate, and initial metal ion concentration. Response Surface Methodology (Central Composite Design (CCD)) was used to confirm the linearity between the observed and estimated values of the adsorption quantity. The packed bed column demonstrated successful removal rates of 90.38% for Cr, 91.23% for Ni, and 89.92% for Zn heavy metals from aqueous solutions, under a controlled environment. The breakthrough analysis also shows that the Thomas and Adams-Bohart models best fit the regression values, allowing prior breakthroughs in the packed bed column to be predicted. Desorption studies were conducted to understand sorption and elution during different regeneration cycles. Adding 0.3 N sulfuric acid over 40 min resulted in the highest desorption rate of the column and adsorbent used for all three metal ions.
The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms.
A comprehensive understanding of physiochemical properties, thermal degradation behavior and chemical composition is significant for biomass residues before their thermochemical conversion for energy production. In this investigation, teff straw (TS), coffee husk (CH), corn cob (CC), and sweet sorghum stalk (SSS) residues were characterized to assess their potential applications as value-added bioenergy and chemical products. The thermal degradation behavior of CC, CH, TS and SSS samples is calculated using four different heating rates. The activation energy values ranged from 81.919 to 262.238 and 85.737-212.349 kJ mol-1 and were generated by the KAS and FWO models and aided in understanding the biomass conversion process into bio-products. The cellulose, hemicellulose, and lignin contents of CC, CH, TS, and SSS were found to be in the ranges of 31.56-41.15%, 23.9-32.02%, and 19.85-25.07%, respectively. The calorific values of the residues ranged from 17.3 to 19.7 MJ/kg, comparable to crude biomass. Scanning electron micrographs revealed agglomerated, irregular, and rough textures, with parallel lines providing nutrient and water transport pathways in all biomass samples. Energy Dispersive X-ray spectra and X-ray diffraction analysis indicated the presence of high carbonaceous material and crystalline nature. FTIR analysis identified prominent band peaks at specific wave numbers. Based on these findings, it can be concluded that these residues hold potential as energy sources for various applications, such as the textile, plastics, paints, automobile, and food additive industries.
Metallic nanoparticles (NPs) are of particular interest as antimicrobial agents in water and wastewater treatment due to their broad suppressive range against bacteria, viruses, and fungi commonly found in these environments. This review explores the potential of different types of metallic NPs, including zinc oxide, gold, copper oxide, and titanium oxide, for use as effective antimicrobial agents in water and wastewater treatment. This is due to the fact that metallic NPs possess a broad suppressive range against bacteria, viruses, as well as fungus. In addition to that, NPs are becoming an increasingly popular alternative to antibiotics for treating bacterial infections. Despite the fact that most research has been focused on silver NPs because of the antibacterial qualities that are known to be associated with them, curiosity about other metallic NPs as potential antimicrobial agents has been growing. Zinc oxide, gold, copper oxide, and titanium oxide NPs are included in this category since it has been demonstrated that these elements have antibacterial properties. Inducing oxidative stress, damage to the cellular membranes, and breakdowns throughout the protein and DNA chains are some of the ways that metallic NPs can have an influence on microbial cells. The purpose of this review was to engage in an in-depth conversation about the current state of the art regarding the utilization of the most important categories of metallic NPs that are used as antimicrobial agents. Several approaches for the synthesis of metal-based NPs were reviewed, including physical and chemical methods as well as "green synthesis" approaches, which are synthesis procedures that do not involve the employment of any chemical agents. Moreover, additional pharmacokinetics, physicochemical properties, and the toxicological hazard associated with the application of silver NPs as antimicrobial agents were discussed.
Several agro-waste materials have been utilized for sustainable engineering and environmental application over the past decades, showing different degrees of effectiveness. However, information concerning the wider use of palm oil clinker (POC) and its performance is still lacking. Therefore, as a solid waste byproduct produced in one of the oil palm processing stages, generating a huge quantity of waste mostly dumped into the landfill, the waste-to-resource potential of POC should be thoroughly discussed in a review. Thus, this paper provides a systematic review of the current research articles on the several advances made from 2005 to 2021 regarding palm oil clinker physical properties and performances, with a particular emphasis on their commitments to cost savings during environmental and engineering applications. The review begins by identifying the potential of POC application in conventional and geopolymer structural elements such as beams, slabs, and columns made of concrete, mortar, or paste for coarse aggregates, sand, and cement replacement. Aspects such as performance of POC in wastewater treatment processes, fine aggregate and cement replacement in asphaltic and bituminous mixtures during highway construction, a bio-filler in coatings for steel manufacturing processes, and a catalyst during energy generation are also discussed. This review further describes the effectiveness of POC in soil stabilization and the effect of POC pretreatment for performance enhancement. The present review can inspire researchers to find research gaps that will aid the sustainable use of agroindustry wastes. The fundamental knowledge contained in this review can also serve as a wake-up call for researchers that will motivate them to explore the high potential of utilizing POC for greater environmental benefits associated with less cost when compared with conventional materials.
Cymodocea serrulata mediated titanium dioxide nanoparticles (TiO2 NPs) were successfully synthesized. The XRD pattern and FTIR spectra demonstrated the crystalline structure of TiO2 NPs and the presence of phenols, flavonoids and alkaloids in the extract. Further SEM revealed that TiO2 NPs has uniform structure and spherical in shape with their size ranged from 58 to 117 nm. Antibacterial activity of TiO2 NPs against methicillin-resistant Staphylococcus aureus (MRSA) and Vibrio cholerae (V. cholerae), provided the zone of inhibition of 33.9 ± 1.7 and 36.3 ± 1.9 mm, respectively at 100 μg/mL concentration. MIC of TiO2 NPs against MRSA and V. cholerae showed 84% and 87% inhibition at 180 μg/mL and 160 μg/mL respectively. Subsequently, the sub-MIC of V. cholerae demonstrated minimal or no impact on bacterial growth at concentration of 42.5 μg/mL concentration. In addition, TiO2 NPs exhibited their ability to inhibit the biofilm forming V. cholerae which caused distinct morphological and intercellular damages analysed using CLSM and TEM. The antioxidant properties of TiO2 NPs were demonstrated through TAA and DPPH assays and exposed its scavenging activity with IC50 value of 36.42 and 68.85 μg/mL which denotes its valuable antioxidant properties with potential health benefits. Importantly, the brine shrimp based lethality experiment yielded a low cytotoxic effect with 13% mortality at 100 μg/mL. In conclusion, the multifaceted attributes of C. serrulata mediated TiO2 NPs encompassed the antibacterial, antioxidant and anti-biofilm inhibition effects with low cytotoxicity in nature were highlighted in this study and proved the bioderived TiO2 NPs could be used as a promising agent for biomedical applications.