Index name

Malaysian Index or Department of Environment

Scientist who Developed Index: Name, Institute; Year; First Reference;

Department of Environment (Malaysia)
1974
DOE (Department of Environment Malaysia) (2002). Malaysia environmental quality report 2001. Putrajaya, Malaysia:Department of Environment, Ministry of Science, Technology and Environment.

Abstract (Summary):

Malaysian Index – Four heavy metals (Copper, Cadmium, Lead and Zinc) are monitored in drinking water at twelve important residential areas using DPASV Technique. The results indicate the water to free of heavy metal pollution. The data monitored have been used to compute Heavy metal pollution index (HPI) using weight arithmetic mean method and the proposed pollution index (HPI) seems to be applicable in the assessment of overall water quality with respect to heavy metal pollution.

Keywords: Water quality, CCMEWQI

Introduction

Malaysian Index

In 1974, the Department of Environment (Malaysian Index) adopted the so-called ‘Opinion Poll WQI (OP WQI),’ also known as the Department of Environment WQI (DoE-WQI), as the index of choice for assessing the WQ statuses of rivers in Malaysia. This WQI was developed in three steps. The first step entailed parameter selection. A panel of experts was consulted on the priority WQ parameters to select and on the weight to be assigned to each parameter. Those experts identified DO, BOD, COD, pH, NH3-N, and SS as the WQVs of utmost concern (Khuan et al., 2002; Norhayati et al., 1997). The second step involved determination of quality function (curve), i.e., sub-index, for each selected parameter. Subindices are calculated by converting the value of each selected WQV into non-dimensional, scaled value through sub-index rating curve where each variable has its own rating curve on a scale of improving WQ, mostly from 0 to 100 (Kaurish and Younos, 2007; Liou et al., 2004). After computing the sub-index for each WQV using the related rating curve, the resultant sub-indices are averaged to give an overall WQI value. (Department of Environment, 2005). The third step corresponds to sub-index aggregation. Based on the calculated WQI, a river may be classified into any of several classes, each reflecting the beneficial use(s) to which this river can be put. These classes are often based on standards or permissible limits of the selected pollution parameters. To this end, the Department of Environment (Malaysian Index) set values of the indicator WQVs and WQI that characterize each water quality class and defined the uses and water treatment requirements of each class.

Uses and Limitation:

It is used as a tool to assess overall water quality with respect to the heavy metals pollution level in ground and surface water for drinking and other aquatic purposes. The index is highly useful to get rightful conclusion of overall quality of water with a systematic rating. It is also used for comparative purposes of quality characteristics at different stations and also to discuss the quality criteria of particular area in detail.

Categorization Table

Standards Required

None of the standards are required for the calculation of this model. It depends on the concentration of individual parameters.v

Variables Selection

Six water quality parameters i.e., DO, BOD, COD, TSS, ammonia-N and pH were used in the calculation of DOE-Water Quality Index (DOE-WQI) as described by Norhayati, Goh, Tong, Wang, and Abdul Halim (1997).

Calculation of Malaysian Index:

The Malaysian Index is based on DOE opinion poll WQI and computed from the equation:

Where SI calculation will be as follows:

Case Studies based on Malaysian Index (The Talar River- Iran)

A study of the water quality changes of Chini Lake was conducted for 12 months, which began in May 2004 and ended in April 2005. Fifteen sampling stations were selected representing the open water body in the lake. A total of 14 water quality parameters were measured and Malaysian Department of Environment Water Quality Index (DOE-WQI) was calculated and classified according to the Interim National Water Quality Standard, Malaysia (INWQS). The physical and chemical variables were temperature, dissolved oxygen (DO), conductivity, pH, total dissolved solid (TDS), turbidity, chlorophyll-a, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solid (TSS), Ammonia-N, nitrate, phosphate and sulphate. Results show that base on Malaysian WQI, the water in Chini Lake is classified as class II, which is suitable for recreational activities and allows body contact. With respect to the Interim National Water Quality Standard (INWQS), temperature was within the normal range, conductivity, TSS, nitrate, sulphate and TDS are categorized under class I. Parameters for DO, pH, turbidity, BOD, COD and Ammonia-N are categorized under class II. Comparison with eutrophic status indicates that chlorophyll-a concentration in the lake was in mesotrophic condition. In general water quality in Chini Lake varied temporally and spatially, and the most affected water quality parameters were TSS, turbidity, chlorophyll-a, sulphate, DO, Ammonia-N, pH and conductivity.

Another study describes design and application of feed-forward, fully-connected, three-layer perceptron neural network model for computing the water quality index (WQI)1 for Kinta River (Malaysia). The modeling efforts showed that the optimal network architecture was 23-34-1 and that the best WQI predictions were associated with the quick propagation (QP) training algorithm; a learning rate of 0.06; and a QP coefficient of 1.75. The WQI predictions of this model had significant, positive, very high correlation (r = 0.977, p < 0.01) with the measured WQI values, implying that the model predictions explain around 95.4% of the variation in the measured WQI values.

The approach presented in this article offers useful and powerful alternative to WQI computation and prediction, especially in the case of WQI calculation methods which involve lengthy computations and use of various sub-index formulae for each value, or range of values, of the constituent water quality variables.

References

Shuhaimi-Othman, M., Lim, E., & Mushrifah, I. (2007). Water quality changes in Chini Lake, Pahang, West Malaysia. Environmental Monitoring and Assessment, 131(1-3), 279–292. doi:10.1007/s10661-006-9475-3.

Gazzaz, N.M., Yusoff,M. K., Aris, A. Z., Juahir, H., & Ramli, M. F. (2012). Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Marine Pollution Bulletin, 64(11), 2409–2420. doi: 10.1016/j.marpolbul.2012.08.005.