Conversion of seaweed waste to biochar for the removal of heavy metal ions from aqueous solution: A sustainable method to address eutrophication problem in water bodies.

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.

Impact of air pollutants on climate change and prediction of air quality index using machine learning models.

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.

Emerging waste-to-wealth applications of fly ash for environmental remediation: A review.

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.

Exploring Modified Rice Straw Biochar as a Sustainable Solution for Simultaneous Cr(VI) and Pb(II) Removal from Wastewater: Characterization, Mechanism Insights, and Application Feasibility.

This study aimed to investigate the efficacy of a rice straw biosorbent in batch adsorption for the removal of chromium (Cr(VI)) and lead (Pb(II)) heavy-metal ions from wastewater. The biosorbent was chemically synthesized and activated by using concentrated sulfuric acid. The produced biosorbent was then characterized by using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD) analyses, which provided insights into surface morphology and functional groups. The study examined the effects of pH, rice straw dose, ion concentration, and contact time on metal ion adsorption. Optimal conditions for efficient removal (95.57% for Cr(VI) and 85.68% for Pb(II)) were achieved at a pH of 2.0, a biosorbent dose of 2 g/L, an initial concentration of 20 mg/L, and a contact time of 50 min in synthetic solutions. The isotherms and kinetics model fitting results found that both metal ion adsorption processes were multilayer on the hetero surface of rice straw biosorbent via rate diffusion kinetics. Thermodynamic investigations were conducted, and the results strongly indicate that the adsorption process is endothermic and spontaneous. Notably, the results indicated that the highest desorption rate was achieved by adding 0.3 N HCl to the system.