This study observed the influence of temperature, initial Cu(II) ion concentration, and sorbent dosage on the Cu(II) removal from the water matrix using surface-oxidized cellulose nanowhiskers (CNWs) bearing carboxylate functionalities. In addition, this study focused on the actual conditions in a wastewater treatment plant. Conductometric titration of CNWs suspensions showed a surface charge of 54 and 410 mmol/kg for the unmodified and modified CNWs, respectively, which indicated that the modified CNWs provide a relatively high surface area per unit mass than the unmodified CNWs. In addition, the stability of the modified CNWs was tested under different conditions and proved that the functional groups were permanent and not degraded. Response surface methodology (RSM) and artificial neural network (ANN) models were employed in order to optimize the system and to create a predictive model to evaluate the Cu(II) removal performance of the modified CNWs. The performance of the ANN and RSM models were statistically evaluated in terms of the coefficient of determination (R 2 ), 2 absolute average deviation (AAD), and the root mean squared error (RMSE) on predicted experiment outcomes. Moreover, to confirm the model suitability, unseen experiments were conducted for 14 new trials not belonging to the training data set and located both inside and outside of the training set boundaries. Result showed that the ANN model (R 2 =0.9925, AAD = 1.15%, RMSE = 1.66) outperformed the RSM model (R 2 =0.9541, AAD = 7.07%, RMSE = 3.99) in terms of the R 2 , AAD, and RMSE when predicting the Cu (II) removal and is thus more reliable. The Langmuir and Freundlich isotherm models were applied to the equilibrium data and the results revealed that Langmuir isotherm (R 2 = 0.9998) had better correlation than the Freundlich isotherm (R 2 = 0.9461). Experimental data were also tested in terms of kinetics studies using pseudo-first order and pseudo-second order kinetic models. The results showed that the pseudo-second-order model accurately described the kinetics of adsorption.
Climate change is considered to be one of the biggest threats faced by nature and humanity today. The goal of this study is to predict future climate change for rainfall in the Upper Kurau Basin. In this research, the applicability of statistical downscaling model (SDSM) in downscaling rainfall in the Upper Kurau River basin, Perak, Malaysia was investigated. The investigation includes calibration of the SDSM model by using large-scale atmospheric variables encompassing the National Centers for Environmental Prediction (NCEP) reanalysis data. Rainfall data were derived for three 30-year time slices, 2020s, 2050s and 2080s, with A2 and B2 scenarios. A2 is considered among the "worst" case scenarios, projecting high emissions for the future. Unlikely, B2 projected a lower emission for the future and it is considered as "environmental" case scenarios. Results from simulation showed that during the calibration and validation stage, the SDSM model was well acceptable in regards to its performance in downscaling of daily and annual rainfalls. Under both scenarios A2 and B2, during the prediction period of 2010-2099, changes of annual mean rainfall in the Upper Kurau River basin would present a trend of increased rainfall in 2020s; insignificant changes in the 2050s; and a surplus of rainfall in the 2080s, as compared to the mean values of the base period. Annual mean rainfall would increase by about 33.7% under scenario A2 and increase by 27.9% under scenario B2 in the 2080s. Most of the areas of the Upper Kurau River Basin were dominated by increasing trend of rainfall and will become wetter in the future.
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