In this study the removal of Chromium (III) and Chromium (VI) ions are investigated via polymer enhanced ultrafiltration under important process parameters. This study proposes the use of unmodified starch as a novel polymer in the ultrafiltration process and its performance on the removal of chromium ions was compared with a commonly used polymer, polyethylene glycol.
The assessment of surface water quality is often laborious, expensive and tedious, as well as impractical, especially for the developing and middle-income countries in the ASEAN region. The application of the water quality index (WQI), which depends on several independent key parameters, has great potential and is a useful tool in this region. Therefore, this study aims to find out the spatial variability of various water quality parameters in geographical information system (GIS) environment and perform a comparative study among the ASEAN WQI systems. At present, there are four ASEAN countries which have implemented the WQI system to evaluate their surface water quality, which are (i) Own WQI system-Malaysia, Thailand and Vietnam-and (ii) Adopted WQI system: Indonesia. A spatial distribution of 12 water quality parameters in the Selangor river basin, Malaysia, was plotted and then applied into the different ASEAN WQI systems. The WQI values obtained from the different WQI systems have an appreciable difference, even for the same water samples due to the disparity in the parameter selection and the standards among them. WQI systems which consider all biophysicochemical parameters provide a consistent evaluation (Very Poor), but the system which either considers physicochemical or biochemical parameters gives a relatively lenient evaluation (Fair-Poor). The Selangor river basin is stressed and impacted by all physical, biological and chemical parameters caused by both the aridity of the climate and anthropogenic activities. Therefore, it is crucial to include all these aspects into the evaluation and corresponding actions should be taken.
Polychlorinated biphenyls (PCBs) were monitored in surface water collected in the Selangor River basin, Malaysia, to identify the occurrence, distribution, and dechlorination process as well as to assess the potential adverse effects to the Malaysian population. Ten PCB homologs (i.e., mono-CBs to deca-CBs) were quantitated by using gas chromatography-mass spectrometry (GC/MS). The total concentration of PCBs in the 10 sampling sites ranged from limit of detection to 7.67 ng L(-1). The higher chlorinated biphenyls (tetra-CBs to deca-CBs) were almost not detected in most of the sampling sites, whereas lower chlorinated biphenyls (mono-CBs, di-CBs, and tri-CBs) dominated more than 90 % of the 10 homologs in all the sampling sites. Therefore, the PCB load was estimated to be negligible during the sampling period because PCBs have an extremely long half-life. The PCBs, particularly higher chlorinated biphenyls, could be thoroughly dechlorinated to mono-CBs to tri-CBs by microbial decomposition in sediment or could still be accumulated in the sediment. The lower chlorinated biphenyls, however, could be resuspended or desorbed from the sediment because they have faster desorption rates and higher solubility, compared to the higher chlorinated biphenyls. The health risk for the Malaysia population by PCB intake that was estimated from the local fish consumption (7.2 ng kg(-1) bw day(-1)) and tap water consumption (1.5 × 10(-3)-3.1 × 10(-3) ng kg(-1) bw day(-1)) based on the detected PCB levels in the surface water was considered to be minimal. The hazard quotient based on the tolerable daily intake (20 ng kg(-1) bw day(-1)) was estimated at 0.36.
Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). Due to its tropical climate, the impact of seasonal monsoons on river quality is significant, with the increased occurrence of extreme precipitation events; however, there has been little discussion on the application of artificial intelligence models for monsoonal river classification. In light of these, this study had applied artificial neural network (ANN) and support vector machine (SVM) models for monsoonal (dry and wet seasons) river classification using three of the water quality parameters to minimise the cost of river monitoring and associated errors in WQI computation. A structured trial-and-error approach was applied on input parameter selection and hyperparameter optimisation for both models. Accuracy, sensitivity, and precision were selected as the performance criteria. For dry season, BOD-DO-pH was selected as the optimum input combination by both ANN and SVM models, with testing accuracy of 88.7% and 82.1%, respectively. As for wet season, the optimum input combinations of ANN and SVM models were BOD-pH-SS and BOD-DO-pH with testing accuracy of 89.5% and 88.0%, respectively. As a result, both optimised ANN and SVM models have proven their prediction capacities for river classification, which may be deployed as effective and reliable tools in tropical regions. Notably, better learning and higher capacity of the ANN model for dataset characteristics extraction generated better predictability and generalisability than SVM model under imbalanced dataset.