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.
The widespread use of synthetic pesticides has resulted in a number of issues, including a rise in insecticide-resistant organisms, environmental degradation, and a hazard to human health. As a result, new microbial derived insecticides that are safe for human health and the environment are urgently needed. In this study, rhamnolipid biosurfactants produced from Enterobacter cloacae SJ2 was used to evaluate the toxicity towards mosquito larvae (Culex quinquefasciatus) and termites (Odontotermes obesus). Results showed dose dependent mortality rate was observed between the treatments. The 48 h LC50 (median lethal concentration) values of the biosurfactant were determined for termite and mosquito larvae following the non-linear regression curve fit method. Results showed larvicidal activity and anti-termite activity of biosurfactants with 48 h LC50 value (95% confidence interval) of 26.49 mg/L (25.40 to 27.57) and 33.43 mg/L (31.09 to 35.68), respectively. According to a histopathological investigation, the biosurfactant treatment caused substantial tissue damage in cellular organelles of larvae and termites. The findings of this study suggest that the microbial biosurfactant produced by E. cloacae SJ2 is an excellent and potentially effective agent for controlling Cx. quinquefasciatus and O. obesus.
The considerable increase in world energy consumption owing to rising global population, intercontinental transportation and industrialization has posed numerous environmental concerns. Particularly, in order to meet the required electricity supply, thermal power plants for electricity generation are widely used in many countries. However, an annually excessive quantity of waste fly ash up to 1 billion tones was globally discarded from the combustion of various carbon-containing feedstocks in thermoelectricity plants. About half of the industrially generated fly ash is dumped into landfills and hence causing soil and water contamination. Nonetheless, fly ash still contains many valuable components and possesses outstanding physicochemical properties. Utilizing waste fly ash for producing value-added products has gained significant interests. Therefore, in this work, we reviewed the current implementation of fly ash-derived materials, namely, zeolite and geopolymer as efficient adsorbents for the environmental treatment of flue gas and polluted water. Additionally, the usage of fly ash as a catalyst support for the photodegradation of organic pollutants and reforming processes for the corresponding wastewater remediation and H2 energy generation is thoroughly covered. In comparison with conventional carbon-based adsorbents, fly ash-derived geopolymer and zeolite materials reportedly exhibited greater heavy metal ions removal and reached the maximum adsorption capacity of about 150 mg g-1. As a support for biogas reforming process, fly ash could enhance the activity of Ni catalyst with 96% and 97% of CO2 and CH4 conversions, respectively.