The study examined the adsorption of hexavalent chromium [Cr(VI)] from aqueous solution by acidically prepared rice husk carbon (APRHC). APRHC was characterized in terms of surface area, micropore area, micropore volume, average pore diameter and surface morphology. The effects of pH, contact time, initial Cr(VI) concentration and adsorbent dose on the adsorption of Cr(VI) from aqueous solution were investigated. Batch adsorption tests showed that Cr(VI) adsorption depends on initial concentration, contact time and pH. Equilibrium adsorption was achieved in 120 min, while maximum Cr(VI) adsorption occurred at pH 2. An artificial neural network (ANN) was used to model Cr(VI) adsorption. The Levenberg–Marquardt (LM) training algorithm was found to be the best among the 11 backpropagation (BP) algorithms tested, with a lowest mean square error (MSE) of 8.8876 and highest coefficient of determination (R²) of 0.987. Adsorption of Cr(VI) by APRHC followed pseudo-second order kinetics. Langmuir and Freundlich isotherm equations were fitted to the equilibrium adsorption data; the former isotherm yielded a better fit. The thermodynamic results indicate that the process of Cr(VI) adsorption by APRHC was endothermic in nature. Desorption of Cr(VI) was very low, i.e. in the range from 0.1 to 9%. Cr(VI) adsorption capacity by APRHC was compared with that of various adsorbents. APRHC showed a high capacity for adsorption of Cr(VI). APRHC can be employed as an effective adsorbent and substitute for commercially available activated carbon for the removal of Cr(VI) from aqueous solutions and wastewater systems.

The interest in the use of mathematical models for the simulation of hydrological processes has largely increased especially in the prediction of runoff. It is the subject of extreme research among engineers and hydrologists. This study attempts to develop a simple conceptual model that reflects the features of the arid environment where the availability of hydrological data is scarce. The model simulates an hourly streamflow hydrograph and the peak flow rate for any given storm. Hourly rainfall, potential evapotranspiration, and streamflow record are the significant input prerequisites for this model. The proposed model applied two (2) different hydrologic routing techniques: the time area curve method (wetted area of the catchment) and the Muskingum method (catchment main channel). The model was calibrated and analyzed based on the data collected from arid catchment in the center of Jordan. The model performance was evaluated via goodness of fit. The simulation of the proposed model fits both (a) observed and simulated streamflow and (b) observed and simulated peak flow rate. The model has the potential to be used for peak discharges’ prediction during a storm period. The modeling approach described in this study has to be tested in additional catchments with appropriate data length in order to attain reliable model parameters.

The application of coal ash (CA) in construction industries has grown rapidly, posing risk to the environment due to heavy metals leaching from the material. This research presents a simulation of ecological risk assessment and model risk indicators (ERI) of leached heavy metals (lead (Pb), copper (Cu), zinc (Zn) and arsenic (As)) from CA (FA: fly ash and BA: bottom ash) via response surface methodology (RSM). The ERI values were based on quantified leached heavy metals from the toxicity characteristic leaching procedure (TCLP-1311) and synthetic precipitation leaching procedure (SPLP-1312). The ecological risk index ($R_I$ ) values for TCLP were 10.27 × 10⁰ (FA), 9.91 × 10⁰ (BA) and 12.58 × 10⁰ (FA + BA); whereas $R_I$ for SPLP were 10.34 × 10⁰ (FA), 9.90 × 10⁰ (BA) and 12.61 × 10⁰ (FA + BA). Twenty-nine combinations of operations were evaluated based on Box-Behnken design with ERI as the response variable. The established model risk indicator (i.e., coded and actual factors) of Pb, Cu, Zn and ‘As’ showed significant model terms that describe their relationship very well, perfectly fit to the corresponding ERI (sum of squares = 0.4160, F value = 682,375.55) with probability of 0.01% for an F-value could occur due to noise. The optimized models were validated with error percentage of less than 5%. The established ERI models showed significant model terms and will be useful for ecological monitoring of CA application in construction industries.