The ambient of air quality in Malaysia is described in terms of Air Pollutant Index (API) which is calculated based on the maximum value of sub-index values of each of the six air pollutants (Sulphur Dioxide (SO 2 ), Nitrogen Dioxide (NO 2 ), Carbon Monoxide (CO), Ozone (O 3 ), particulate matter with aerodynamic diameter less than 10 micrometres (PM 10 ) and particulate matter with aerodynamic diameter less than 2.5 micrometres (PM 2.5 ). It is argued that the existing API cannot give the true picture of how harmful the pollution is towards humans' health because the hazardous levels of these pollutants are not considered in the API development. Therefore, the aim of this study is to determine the weights of the air pollutants according to their levels of hazardous in three selected cities in Malaysia; Putrajaya (PJ), Tawau (TW) and Alor Setar (AS). The study applies Shannon entropy method to weigh the air pollutants where the entropy values of the pollutants are the proxy measure of the pollutants' weights. The pollutants with higher weights would contribute more to air pollution and vice-versa. The study found different weights of the pollutants according to different cities where PJ shows SO 2 at the top ranking with 48.16% of the air pollutants' weight. Meanwhile, TW and AS show PM 2.5 at the first rank with 36.31% and 31.79% respectively. However, the analysis on the overall data shows that PM 2.5 and PM 10 are at the most and second most hazardous pollutants respectively that contribute to the air pollution. The effect of these two particulate matters is said to be more serious since SO 2 that is associated with both particulate matters are difficult to breakdown and bring harmful effects in human life as compared to the other air pollutants. This study signifies a new perspective in measuring the hazardous levels of the air pollutants and the results could be used to improve the existing API in monitoring the air quality in Malaysia.
The issue of age difference in hospital admission should be given special attention since it affects the structure of hospital care and treatments. Patients
A key assumption in traditional statistical process control (SPC) technique is based on the requirement that observations or time series data are normally and independently distributed. The presences of a serial autocorrelation results in a number of problems, including an increase in the type I error rate and thereby increase the expected number of false alarm in the process observation. However, the independency assumption is often violated in practice due to the influence of serial correlation in the observation. Therefore, the aim of this paper is to demonstrate with the hospital admission data, the influence of serial correlation on the statistical control charts. The trend free pre-whitening (TFPW) method has been used and applied as an alternative method to obtain residuals series which are statistically uncorrelated to each other. In this study, a data set of daily hospital admission for respiratory and cardiovascular diseases was used from the period of 1 January 2009 to 31 December 2009 (365 days). Result showed that TFPW method is an easy and useful method in removing the influence of serial correlation from the hospital admission data. It can be concluded that statistical control chart based on residual series perform better compared to original hospital admission series which influenced by the effects of serial correlation data.
This paper aims to demonstrate the use of pre-whitening (PW) technique to handle the presence of autocorrelation on the statistical control charts, for 3-year (2008-2010) daily pediatrics (less than 4 years old) hospital admission. The PW technique has been implemented as an alternative procedure to obtain residuals series which are statistically uncorrelated to each other. Results showed that there is a reduction in the number of out-of-control signals in residual series control chart as compared to the amount of the out-of-control signals on traditional statistical control chart before the use of PW technique. Thus, it is suggested that statistical control chart using residual series performs better when the original pediatric hospital admission series are auto-correlated. In addition, it can be concluded that the Phase II (monitoring period) performance process is likely to follow the similar pattern of the Phase I (baseline period) process except for only one day on the 13th October 2009 that exceeds the upper control limit. This means that the pediatrics hospital admission on that particular day has not improved and fundamentally changed from what are expected in stable process.
The ambient air quality measurement in Malaysia is described as Air Pollution Index (API). Basically the existing API for a given period of time is defined as the maximum value of the sub-index values of six pollutants. Although research has shown that long and short term exposure to air suspended toxicants has a different toxicological impact on human, the API still considers these pollutants as having equal hazardous impacts on human. Hence, this paper aims to propose a new API that includes weights representing different hazardous levels of these pollutants in its calculation. Based on secondary data of six pollutants’ readings for sixteen states of Malaysia for year 2018, the aggregated weights were computed by combining both weights obtained from the subjective experts’ opinions and the objective data-driven methods, which balanced both perspectives of evaluations. The results show that the particulate matter with aerodynamic diameter less than 2.5 micrometre (PM2.5) found to be the most hazardous pollutant since its aggregated weight value is the highest and the distributions of the API readings for all sixteen states were found to be normal. The highest and lowest API readings took place on the 14th of August and 10 of March 2018 respectively. It is argued that the new API readings are more accurate and give a better picture about the occurrence of air pollution in Malaysia in particular. This study provides a new insight in constructing API specifically and contributes a more comprehensive and precise air quality measurements to be analysed by the responsible authorities in their efforts towards healthy environment.
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