The 2,4-dichlorophenoxyacetic acid (2,4-D) herbicide, as an aromatic hydrocarbon, is a dangerous and toxic organic pollutant among the agricultural pesticides. In this research, the performance of the biochar made from rice husk (BRH), granular activated carbon (GAC), and multi-walled carbon nanotubes (MWCNTs) was investigated for adsorption of 2,4-D in a fixed-bed column system. The influence of pH (2, 5, 7, 9), flow rate (0.5, 1, 1.5 mL min−1), bed depth (3, 6, 9 cm), and influent 2,4-D concentration (50, 100, 150, 300 mg L−1) on the adsorption process was evaluated. The resulting breakthrough curves indicated that the higher removal efficiency of 2,4-D took place at the lower flow rate, lower influent 2,4-D concentration, higher bed depth, and lower pH. While in most cases the removal ability of GAC was better than other adsorbents, generally, this study confirmed that the BRH, as a cheap and sustainable material, can be a viable alternative to GAC and MWCNTs for remediation and treatment scenarios, particularly in developing countries.

The mathematical model's usage in water quality prediction has received more interest recently. In this research, the potential of random forest regression (RFR), Bayesian multiple linear regression (BMLR), and multiple linear regression (MLR) were examined to predict the amount of 2,4-dichlorophenoxy acetic acid (2,4-D) elimination by rice husk biochar from synthetic wastewater, using five input operating parameters including initial 2,4-D concentration, adsorbent dosage, pH, reaction time, and temperature. The equilibrium and kinetic adsorption data were fitted best to the Freundlich and pseudo-first-order models. The thermodynamic parameters also indicated the exothermic and spontaneous nature of adsorption. The modeling results indicated an R2 of 0.994, 0.992, and 0.945 and RMSE of 1.92, 6.17, and 2.10 for the relationship between the model-estimated and measured values of 2,4-D removal for RFR, BMLR, and MLR, respectively. Overall performances indicated more proficiency of RFR than the BMLR and MLR models due to its capability in capturing the non-linear relationships between input data and their associated removal capacities. The sensitivity analysis demonstrated that the 2,4-D adsorption process is more sensitive to initial 2,4-D concentration and adsorbent dosage. Thus, it is possible to permanently monitor waters more cost-effectively with the suggested model application.

The potential of a granular activated carbon (GAC), a rice husk biochar (BRH) and multiwalled carbon nanotubes (MWCNTs) for removing 2,4-dichlorophenoxyacetic acid (2,4-D) from simulated wastewater and drainage water has been evaluated. In this regard, a response surface methodology (RSM) with a central composite design (CCD) (CCD-RSM design) was used to optimize the removal of 2,4-D from simulated wastewater under different operational parameters. The maximum adsorption capacities followed the order GAC > BRH > MWCNTs, whereas the equilibrium time increased in the order MWCNTs < GAC < BRH. In the case of GAC and BRH, the 2,4-D removal percentage increased significantly upon increasing the adsorbent dosage and temperature and decreased upon increasing the initial 2,4-D concentration and pH. The results showed that the contact time and temperature were not important as regards the adsorption efficiency of 2,4-D by MWCNTs, whereas rapid removal of 2,4-D from simulated wastewater was achieved within the first 5 min of contact with the MWCNTs. The results confirmed that the Freundlich isotherm model with the highest coefficient of determination (R 2 ) and the lowest standard error of the estimate (SEE) satisfactorily fitted the 2,4-D experimental data. In addition, successful usage of the three adsorbents investigated was observed for removal of 2,4-D from drainage water from an agricultural drainage system. An economic analysis with a rate of return (ROR) method indicated that BRH could be used as an eco-friendly, low-cost, versatile and high adsorption capacity alternative to GAC and MWCNTs for the removal of 2,4-D.