In Malaysia, most colored wastewater from dyeing factories is discharged to the environment causing serious problems. In this paper the influence of several reacting conditions, i.e. H2O2, pH, Ultraviolet (UV) intensity and dye concentration, on the performance of the immobilized system is discussed. The pH of the solution was varied from 3 to 11 while H2O2 concentration tested was from 10(-4) M to 5 x 10(-2) M. UV was tested at 365 nm and 254 nm, while dye concentration ranged from 2.5 x 10(-4) M to 10(-3) M. The influence of the reacting conditions was assessed based on absorbance. Using an OG concentration of 10(-3) M, the degradation increases from 17.8% to 49.7%. Optimum concentration of H2O2 was found to be 5 x 10(-3) M for degradation. Increasing the intensity of the UV light via shorter light wavelength also improves the performance of the system. Increasing the concentration of the dye reduces the overall performance of the system. Using the dye concentration of 2.5 x 10(-4) M (H2O2 = 10(-2) M, lambda = 254 nm, pH = 11), gives a degradation of 93.2%. At dye concentration of 10(-3) M, the performance was reduced to 53.1%.
Approximately 230 million children under 5 years old of age suffer from malnutrition and over half of the children below 5 years old deaths are due to malnutrition nowadays. To gain a better understanding of this problem, the application of spatial analysis has risen exponentially in recent years. In this review, the present state of information on the use of spatial analysis in childhood malnutrition studies was evaluated using four databases of digital scientific journals: ScienceDirect, Scopus, PubMed and CINAHL. We chose 2,278 articles from the search results and a total of 27 articles met our criteria for review. The following information was extracted from each article: objective of study, study area, types of malnutrition, subject, data sources, computer software packages, spatial analysis and factors associated with childhood malnutrition. A total of 10 spatial analysis methods were reported in the reviewed articles and the Bayesian geoadditive regression model was the most common method applied in childhood malnutrition studies. This review highlights the importance of the application of spatial analysis in determining the geographic distribution of malnutrition cases, hotspot areas and risk factors correlated with childhood malnutrition. It also provides implications for strategic initiatives to eradicate all forms of malnutrition.