Enhanced photocatalytic regeneration of carboxymethyl cellulose/lignin/ZnO complex hydrogel after methylene blue adsorption.

The biodegradable, nontoxic, and renewable carboxymethyl cellulose (CMC) hydrogel has been developed into a green adsorbent. However, the weak chemical interaction limits its adsorption capability and reusability. This work incorporated lignin with complex structure and ZnO nanoparticles with photocatalytic properties into CMC hydrogel beads to improve the removal of methylene blue (MB) through chemical interaction. Scanning electron microscopic images and Fourier-transform infrared spectra confirmed the compatibility between lignin and ZnO nanoparticles as well as the increment of active sites for dye removal. The MB adsorption on CMC hydrogel beads was more significantly affected by the temperate and initial concentration compared to contact time, pH, and adsorbent dosage. The MB adsorption capacity of CMC hydrogel was improved to 276.79 mg/g after incorporating lignin and ZnO nanoparticles. The adsorption followed the pseudo-second-order kinetic model and Langmuir isotherm model, indicating chemical adsorption. After 6 cycles, the adsorption capacity was reduced by about 15 %. The UV irradiation could recover and improve MB adsorption capacity of CMC hydrogel beads containing ZnO nanoparticles due to the introduction of reactive oxygen species.

Carboxymethyl cellulose/sodium alginate beads incorporated with calcium carbonate nanoparticles and bentonite for phosphate recovery.

Although anionic polyelectrolyte hydrogel beads offer attractive adsorption of cationic dyes, phosphate adsorption is limited by electrostatic interactions. In this work, carboxymethyl cellulose (CMC)/sodium alginate (SA) hydrogel beads were modified with calcium carbonate (CaCO3) and/or bentonite (Be). The compatibility between CaCO3 and Be was proven by the homogeneous surface, as shown in the scanning electron microscopic images. Fourier-transform infrared and X-ray diffraction spectra further confirmed the existence of inorganic filler in the hydrogel beads. Although CMC/SA/Be/CaCO3 hydrogel beads attained the highest methylene blue and phosphate adsorption capacities (142.15 MB mg/g, 90.31 P mg/g), phosphate adsorption was significantly improved once CaCO3 nanoparticles were incorporated into CMC/SA/CaCO3 hydrogel beads. The kinetics of MB adsorption by CMC/SA hydrogel beads with or without inorganic fillers could be described by the pseudo-second-order model under chemical interactions. The phosphate adsorption by CMC/SA/Be/CaCO3 hydrogel beads could be explained by the Elovich model due to heterogeneous properties. The incorporation of Be and CaCO3 also improved the phosphate adsorption through chemical interaction since Langmuir isotherm fitted the phosphate adsorption by CMC/SA/Be/CaCO3 hydrogel beads. Unlike MB adsorption, the reusability of these hydrogel beads in phosphate adsorption reduced slightly after 5 cycles.

A review of bioretention components and nutrient removal under different climates-future directions for tropics.

Bioretention systems have been implemented as stormwater best management practices (BMPs) worldwide to treat non-point sources pollution. Due to insufficient research, the design guidelines for bioretention systems in tropical countries are modeled after those of temperate countries. However, climatic factors and stormwater runoff characteristics are the two key factors affecting the capacity of bioretention system. This paper reviews and compares the stormwater runoff characteristics, bioretention components, pollutant removal requirements, and applications of bioretention systems in temperate and tropical countries. Suggestions are given for bioretention components in the tropics, including elimination of mulch layer and submerged zone. More research is required to identify suitable additives for filter media, study tropical shrubs application while avoiding using grass and sedges, explore function of soil faunas, and adopt final discharged pollutants concentration (mg/L) on top of percentage removal (%) in bioretention design guidelines.

Prediction of water quality index in constructed wetlands using support vector machine.

Poor water quality is a serious problem in the world which threatens human health, ecosystems, and plant/animal life. Prediction of surface water quality is a main concern in water resource and environmental systems. In this research, the support vector machine and two methods of artificial neural networks (ANNs), namely feed forward back propagation (FFBP) and radial basis function (RBF), were used to predict the water quality index (WQI) in a free constructed wetland. Seventeen points of the wetland were monitored twice a month over a period of 14 months, and an extensive dataset was collected for 11 water quality variables. A detailed comparison of the overall performance showed that prediction of the support vector machine (SVM) model with coefficient of correlation (R(2)) = 0.9984 and mean absolute error (MAE) = 0.0052 was either better or comparable with neural networks. This research highlights that the SVM and FFBP can be successfully employed for the prediction of water quality in a free surface constructed wetland environment. These methods simplify the calculation of the WQI and reduce substantial efforts and time by optimizing the computations.

Spatial pattern analysis for water quality in free-surface constructed wetland.

Free-surface constructed wetlands are known as a low-energy green technique to highly decrease a wide range of pollutants in wastewater and stormwater before discharge into natural water. In this study, two spatial analyses, principal factor analysis and hierarchical cluster analysis (HACA), were employed to interpret the effect of wetland on the water quality variables (WQVs) and to classify the wetland into groups with similar characteristics. Eleven WQVs were collected at the 17 sampling stations twice a month for 13 months. All sampling stations were classified by HACA into three clusters, with high, moderate, and low pollution areas. To improve the water quality, the performance of Cluster-III (micropool) is more significant than Cluster-I and Cluster-II. Implications of this study include potential savings of time and cost for long-term data monitoring purposes in the free-constructed wetland.

Gene expression programming for total bed material load estimation--a case study.

This paper presents Gene-Expression Programming (GEP), which is an extension to the genetic programming (GP) approach to predict the total bed material load for three Malaysian rivers. The GEP is employed without any restriction to an extensive database compiled from measurements in the Muda, Langat, and Kurau rivers. The GEP approach demonstrated a superior performance compared to other traditional sediment load methods. The coefficient of determination, R(2) (=0.97) and the mean square error, MSE (=0.057) of the GEP method are higher than those of the traditional method. The performance of the GEP method demonstrates its predictive capability and the possibility of the generalization of the model to nonlinear problems for river engineering applications.