A study on the effects of sugarcane bagasse hydrochar as an environmentally friendly fertilizer on bean plant and sandy loam soil characteristics

The present study investigated the effects of engineered sugarcane bagasse hydrochar on the soil properties of sandy loam and the growth parameters of bean plants. After preparing the optimal hydrochar, its physicochemical properties were determined through various analyses. The effects of different rates of hydrochar (0%, 1%, 2%, and 5% w/w) were then investigated on the bulk density, porosity, pH, organic carbon, nitrogen, and phosphorus content of the soil, as well as on plant height, lateral branch number, leaf number, and dry weight of aerial parts and roots. The results show that the addition of engineered sugarcane bagasse hydrochar at all levels improved the soil properties of sandy loam. However, an inverse effect was observed for the electrical conductivity (EC) parameter. The 5% hydrochar treatment resulted in a significant increase of 78.4% in organic carbon, while a minimal decrease of 0.4% was observed in pH. Regarding the growth parameters of bean plants, only the 1% engineered hydrochar treatment showed a positive effect on growth parameters

Prediction of nitrate leaching from soil amended with biosolids by machine learning algorithms

This study focused on employing machine learning algorithms to forecast nitrate leaching from soils treated with biochar and vermicompost derived from sugarcane bagasse. input variables including bulk density, porosity, organic carbon, nitrogen, phosphorus, anion exchange capacity, cation exchange capacity, pH, and electrical conductivity, while nitrate leaching was the target variable for prediction. A comparative analysis of machine learning models indicated that Random Forest Regression outperformed linear regression in the prediction of nitrate leaching. Additionally, among the input variables, anion exchange capacity, cation exchange capacity, bulk density, and EC showed the most significant influence in utilizing these models as predictive tools for nitrate leaching from soils treated with slow-release fertilizers

Barium/Cobalt@Polyethylene Glycol Nanocomposites for Dye Removal from Aqueous Solutions

Dyes are known as one of the most dangerous industrial pollutants which can cause skin diseases, allergy, and provoke cancer and mutation in humans. Therefore, one of the important environmental issues is the effective removal of dyes from industrial wastewater. In the current work, BaFe12O19/CoFe2O4@polyethylene glycol (abbreviated as BFO/CFO@PEG) nanocomposite was synthesized and evaluated regarding its capacity for adsorptive removal of a model dye Acid Blue 92 (denoted as AB92) from aqueous solutions. The characteristics of the prepared nanocomposite was determined by tests such as X-ray diffraction (XRD), scanning electron microscope (SEM), vibration sample magnetization (VSM), and Fourier transform infrared spectroscopy (FTIR). The effects of conditional parameters including pH (2–12), initial concentration of dye (20–100 mg/L), adsorbent dosage (0.02–0.1 g/L) and contact time (0-180 min) on the adsorption of dye were investigated and then optimized. The results indicated that with the increase of the adsorbent dosage from 0.02 to 0.1 g/L, the removal efficiency increased from 74.1% to 78.6%, and the adsorbed amount decreased from 148.25 to 31.44 mg/g. The maximum removal efficiency (77.54%) and adsorption capacity (31.02 mg/g) were observed at pH 2. Therefore, the general optimization conditions revealed that the maximum adsorption efficiency of dye was obtained in condition of initial concentration of 20 mg/L, contact time of 1 h and pH of solution equal 2. The adsorption isotherm and kinetic data were evaluated using a series of models. The pseudo-second order kinetic model and Freundlich isotherm model show the best fitting with experimental data with R2∼0.999