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.