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.

Impact of climate change and anthropogenic activities on aquatic ecosystem - A review.

All living things depend on their natural environment, either directly or indirectly, for their high quality of life, growth, nutrition, and development. Due to the fast emissions of greenhouse gases (GHGs), the Earth's climate system is being negatively impacted by global warming. Stresses caused by climate change, such as rising and hotter seas, increased droughts and floods, and acrid waters, threaten the world's most populated areas and aquatic ecosystems. As a result, the aquatic ecosystems of the globe are quickly reaching hazardous conditions. Marine ecosystems are essential parts of the world's environment and provide several benefits to the human population, such as water for drinking and irrigation, leisure activities, and habitat for commercially significant fisheries. Although local human activities have influenced coastal zones for millennia, it is still unclear how these impacts and stresses from climate change may combine to endanger coastal ecosystems. Recent studies have shown that rising levels of greenhouse gases are causing ocean systems to experience conditions not seen in several million years, which may cause profound and irreversible ecological shifts. Ocean productivity has declined, food web dynamics have changed, habitat-forming species are less common, species ranges have changed, and disease prevalence has increased due to human climate change. We provide an outline of the interaction between global warming and the influence of humans along the coastline. This review aims to demonstrate the significance of long-term monitoring, the creation of ecological indicators, and the applications of understanding how aquatic biodiversity and ecosystem functioning respond to global warming. This review discusses the effects of current climate change on marine biological processes both now and in the future, describes present climate change concerning historical change, and considers the potential roles aquatic systems could play in mitigating the effects of global climate change.

Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam.

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.

Soft computing-based models and decolorization of Reactive Yellow 81 using Ulva Prolifera biochar.

The present research explores the application of optimization tools namely Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the decolorization of Reactive Yellow 81 (RY81) from an aqueous solution. The characterization of the biochar was carried out using FTIR, elemental analysis, proximate analysis, BET analysis and Thermogravimetric analysis. Five independent variables namely solution pH, biochar dose, contact time, initial dye concentration and temperature were analyzed using RSM, ANN and ANFIS models. The maximum removal efficiency of 86.4% was obtained and the statistical error analysis was calculated. The correlation coefficient of 0.9665, 0.9998 and 0.9999 was obtained for RSM, ANN and ANFIS models, respectively. Adsorption Isotherm models and kinetic models were used to understand the adsorption mechanism. Maximum monolayer adsorption of 225 mg g-1 was predicted by Hill isotherm model. A partition coefficient of 4.09 L g-1 was obtained at an initial dye concentration of 250 mg L-1. It was revealed from the thermodynamic studies that reactions are endothermic and spontaneous. Further, to check the potential of the biochar, regeneration cycle was studied. The desorption efficiency of 99.5% was achieved at an S/L ratio of 3, regeneration cycles of 2, and sodium hydroxide was found as the best elutant for the desorption.

Towards sustainable biodiesel production by solar intensification of waste cooking oil and engine parameter assessment studies.

Renewable energy sources for harnessing biofuels are the viable solution to substitute fossil fuels and reduce production cost. In this study, waste cooking oil was converted into biodiesel via a customized solar reactor. The solar reactor was customized using copper tubes and black surface to trap solar energy for conversion of waste cooking oil into biodiesel. The main experimental parameters studied are temperature (30 to 50 °C), stirring speed (100 to 500 rpm), catalyst loading (0.25 to 1.25 wt%), flow rate (3 to 15 LPH), and methanol to oil ratio (3:1 to 15:1), respectively. The uppermost conversion of 82% was achieved at catalyst load of 0.75 wt%, stirring speed of 300 rpm, flow rate of 3 LPH and methanol/oil ratio of 12:1. Performance of biodiesel blend (D80 + BD20) in CI engine showed a decrease in ignition delay (10.5 deg. CA) and brake thermal efficiency (32.7%) at maximum load (100%). Smoke emission was also decreased with an increase in biodiesel blend at lower brake power, but an increase in brake power increased the smoke emission.