The study of interrelationship between raw water quality
parameters, chlorine demand and the formation of disinfection
by-products
Md. Pauzi Abdullah1*, Lim Fang Yee1, Sadia Ata1, Abass Abdullah2,
Basar Ishak2 and Khairul Nidzham Zainal Abidin2
1
Centre for Water Research and Analysis, Faculty of Science and Technology,
Universiti Kebangsaan Malaysia,43600 Bangi, Selangor, Malaysia.
2
Semenyih River Water Treatment Plant, P.O. Box 27, 43807 Dengkil. Selangor, Malaysia.
Abstract: Disinfection is the most crucial process in the treatment of drinking water supply and is the final
barrier against bacteriological impurities in drinking water. Chlorine is the primary disinfectant used in the
drinking water treatment process throughout Malaysia. However, the occurrence of various disinfection byproducts such as trihalomethanes (THM) and haloacetic acids created a major issue on the potential health
hazards which may pose adverse health effects in both human and animals. To simulate real water
treatment conditions and to represent the conditions inherent in a tropical country, this study was performed
at an urbanized Water Treatment Plant with a daily production of about 145 million gallons of treated water.
The purpose of this work is to examine the relationship between the water quality parameters in the raw
water with chlorine demand and the formation of disinfection by-products. This study also investigated the
possibility of the statistical model applications for the prediction of chlorine demand and the THM
formation. Two models were developed to estimate the chlorine demand and the THM formation. For the
statistical evaluation, correlation and simple linear regression analysis were conducted using SPSS. The
results of Kolmogorov-Smirnov test for the estimation of goodness-of-fit of the dependent variables of the
models to the normal distribution showed that all the dependent variables followed the normal distribution
at significance level of 0.05. Good linear correlations were observed between the independent parameters
and formation of THM and the chlorine demand. This study also revealed that ammonia and the specific
ultraviolet absorbent (SUVA) were the function of chlorine consumption in the treatment process. Chlorine
dosage and SUVA increase the yield of THM. Chlorine demand and THM formation was moderately
sensitive, but significant to the pH. Statistical significance (p>0.05) was utilized for both regression
equations. A better understanding of these relationships will help the water utilities or plant operators to
minimize the THM formation, providing a healthier and better drinking water quality as well as optimizing
the chlorine dosage in the disinfection process.
Keywords: chlorine demand, disinfection by-products, drinking water quality
1. Introduction
Chlorine has been successfully used to disinfect drinking water for more than a century. This disinfectant is
used by more than 99 percent of Malaysia water treatment plants today. It is because chlorine can be
technically and economically feasible for drinking water application. Chlorination is widely practice in
Malaysia due to its strong oxidizing potential, effective against a broad spectrum of pathogens, and it
provides chlorine residual throughout the distribution system (LeChevallier, 1999; Propato and Uber, 2004;
Sadiq and Rodssiguez, 2004). However, when surface or raw water is chlorinated, chlorine reacts actively
with organic and inorganic compounds to form disinfection by-products (DBP). Halogenated organic
contaminants originating from the reaction of chlorine with chemicals species and organic components
have been found in Malaysian drinking water supplies. The occurrence of trihalomethanes (THM),
haloacetic acids (HAA) and other volatile organic compounds (VOC) have been found to be associated
with contrary health effect (Loper, 1980; Langvik and Holmbom, 1994; Smeds et al., 1997; Zavaleta et al.,
1999; Milles et al., 2002). Although the benefit of chlorination which provides residual combined chlorine
during the distribution systems is acknowledged, the formation of DBP is not intended since they are
teratogents, mutagens, carcinogens and toxic.
2
Concerns have been expressed over the perceived DBP in the drinking water risk to human health
beginning the early 1970s. Some researchers discovered the identity, formation and potential health effect
of THM after the use of chlorine as disinfectant (Bellar et al., 1974; Rook, 1974). Accordingly, in 1976, the
US Environmental Protection Agency (USEPA) conducted a survey and found that THM is formed
ordinarily in the chlorinated drinking water. These finding sparked the concern for human health, which led
to more comprehensive studies on monitoring and investigating the formation of DBP in chlorinated water.
Due to the adverse health effect by THM, USEPA has set limits on acceptable total THM (TTHM)
concentration of 100 μg/l. In the meantime the World Health Organization has set a drinking water
guidelines for chloroform (0.2 mg/l), bromodichloromethane (0.06 mg/l), chlorodibromomethane (0.1 mg/l)
and bromoform (0.1 mg/l) (WHO, 1993). With stricter regulation and guideline of THM and other DBP in
stage 2 D/DBP rules, many water treatment plants are changing the disinfectant from chlorine to ozone,
chloramines, membrane technologies and etc (Susan, 2003). Malaysia has created a guideline for total
THM concentration in line with USEPA (for maximum allowable level of total THM compounds) of 100
μg/l.
The formation of DBPs in drinking water is mainly caused by the reaction between the organic compound
and chlorine. To better regulate and eliminate the THMs, the understanding of the chemical reaction
between natural organic matter (NOM) and chlorine is important (Peters et al., 1980; Reckhow et al., 1990).
During chlorination, chlorine reacts with NOM present in water. The reaction creates diverse DBP such as
THMs, haloacetic acids (HAAs) and haloacetonitriles (HANs) as reflected in the following equation:
Chlorine + NOM → THMs + HAAs + HANs + Chloral hydrate + halopropanones
+ Cyanogen halides + Chloropicirin + other DBPs
Total organic carbon is an aggregate parameter of different fraction of NOM. Since, the USEPA has
proposed the total TOC parameter as a measure of DBP precursors (USEPA, 1989), several researchers
have investigated NOM in more details by the isolation and fractionation the NOM into minor fractions
(Leenherr, 1981; Aiken et al., 1992; Malcolm and MacCarthy, 1992; Marhaba et al., 2003). Generally,
3
water sample with higher content of NOM was found to form higher level of THM during the chlorination
process. Organic compounds such as orcinol, 1,3-dihydoxybenzoic acids and resorcinol were reported to
create high level of THM (Boyee and Hornig, 1983).
Factors affecting the formation of THM during the chlorination of drinking water have been extensively
studied. In general, the level of THMs formed depends on pH, temperature, contact time, concentrations
and speciation of disinfectant and its residuals, concentration and characterization of NOM and bromide
ions (Trussell and Umphres, 1978; Kohei et al., 1983; Qi et al., 2004). The rate of THM formation
increases with pH (Steven et al., 1976). This can be explained by the concentration of hypochlorous acid.
Chlorine hydrolyzes rapidly in water but the distribution of hyphchlorous acids and hypochlorite ion is pH
dependent. The low pH and the high concentration of HClO result in a shift to higher concentration of
humics. Formation of THM depends on the last step of THM reaction pathway, which is base-catalysed as
with the haloform reaction. Generally, the greater contact time and chlorine dosage will result in production
of THM significantly (Rajan et al., 1990). The study of THMs formation and its relation to chlorine
consumption during the disinfection process is crucial because they are classified as possible carcinogens to
human. The modeling effort usually involves the establishing of empirical and mechanistic relationships
between the THM concentration in the treated water and the water quality and treatment plant operational
control parameters. Several models have been developed for the formation of THMs in the treated water.
Comparison of several types of model is needed in order to predict and simulate the formation of THM in
chlorination process because of the differences in characteristic and composition of the raw water.
An improved understanding is important to the development of effective strategies to regulate THM
formation and the chlorine consumptions. The objective of this work is to develop statistical model
application for the prediction of THM formation and the chlorine demand of the treated water. These
statistical models were developed to provide a better understanding on the formation of THM in water
treatment plant and the chlorine demand of treated water. The result of this work may be useful in
predicting the occurrence of THM precursors, controlling the formation of THM, and capable of optimizing
the chlorine consumption during the chlorination in the water treatment plant.
4
2. Experimental
2.1 Description of water treatment plant
In this study, Semenyih River Water Treatment Plant (SRWTP), which supplies the water needs of more
than 1 million inhabitants and operated by Konsortium Abass Sdn Bhd was selected as the sampling source.
The SRWTP, located in Precint 19, Purajaya, Malaysia, receives raw water from the Semenyih basin,
which is the sub-basin of Langat river basin. It is one of the largest water treatment plant in Malaysia and
has an average treated water production of about 140-145 million gallons per day (mgd). The development
of new township in the basin such as Dengkil, Semenyih, Nilai and Rinching towns attract people to the
basin from other parts of the country to take the advantage of infrastructure, social and industrial
employment. The SRWTP was design to produce quality water complying with drinking water standard.
The water treatment processes performed in SRWTP are coagulation, flocculation, sedimentation, filtration,
fluoridation and chlorination as shown in the Figure 1.
.
Figure 1
Schematic of SRWTP water treatment processes
5
2.2 Sampling points and frequency
In this study, there were only two main sampling points along the processes of the water treatment plant.
These were filtered water and treated water, which are the sample before and after the chlorination process.
A 9-month sampling programme was carried out from January to September 2006. THMs were analyzed in
the treated water after the chlorination. Samples were collected at least twice per week for the purpose of
characterizing variations of THM and the chlorine demand during the sampling period.
3. Reagents and standards
All chemical reagents were of the highest purity grade. Methanol (J.T. Baker) obtained from Fisher
Scientific and deionized water, prepared using Arium 611 DI from Satorius AG, were used to prepare the
THM standard. Six calibration standards ranging from 10 μg/l to 100 μg/l were prepared by adding the
standard solution of THM mixture (Supelco) and 2 μl of a spiked 4-bromofluorobenzene (AccuStandard
Inc). Mineral stabilizer, nessler reagent, polyvinyl alcohol dispersing agent and N,N-diethyl-pphenylenediamine were purchased from HACH. All glassware were cleaned with concentrated chlorine (1
mM) and kept in oven (200 °C) prior to use.
3.1 Analytical procedures
Due to the rapid chemical changes during transportation and storage, pH was measured onsite, immediately
after the sample was taken. Chlorine dosage applied as the sum of pre-chlorination and post-chlorination
was obtained from SRWTP. Chlorine residuals in the water samples were determined with N,N-diethyl-pphenylenediamine (DPD) colorimetric method (APHA, 1998). In this method, DPD is used as an indicator
with ferrous ammonium sulphate (FAS). The wavelength used was at 515 nm.
6
Ammonia nitrogen, cyanide, sulfide, nitrite and nitrate were analysed by means of Nessler method,
pyridine-pyrazalone method, methylene blue method, ferrous sulfate method and cadmium reduction
method, respectively using HACH spectrophotometer DR 2000 (HACH, 1992). TOC was determined by
AnaTOC analyzer, purchased from Anatel, USA employing the photocatalytic oxidation process.
Absorbance of ultraviolet at walvelength of 254 nm or UV-254 is often used to characterize organic
compounds in water. UV-254 was reported to represent aromatic compound of natural organic matter
(NOM) as they imparted an ability to absorb light in the UV range. Specific ultraviolet absorbent, SUVA is
taken as a parameter that indicates the level of aromatic organics in this study. This was achieved using
Shimadzu 1601 spectrophotometer with a 1 cm quartz cell.
THMs were chromatographically separated using Tekmar Purge and Trap Concentrator 3100 and Varian
CP-3800 gas chromatography system with electron capture detector (GC-ECD). THMs mixture was freshly
prepared in methanol for each test. CP-Select 624 CB (30 m × 0.53 mm, df = 3.0 μm) capillary column was
used in the separation. Duplicates analysis were done to check the variation of the result. As for the
analytical conditions of the P &T systems, the sample was purged with a stream of N2 at 35 °C for 11 min
and desorbed at 220 °C for another 4 min. The sorbent tube was heated and backflushed with nitrogen gas
to desorb trapped sample components onto GC-ECD for analysis. Separation of THM species was achieved
under a temperature program namely initial temperature 35 °C; rate 6 °C/min, final temperature 180 °C,
injector and detector temperature were 230°C and 250°C respectively. The carrier gas, nitrogen was set in
constant flow mode at 60 psi to the GC column.
3.2 Modeling effort
In order to predict the chlorine demand and the formation of THM in the treated water, two statistical
models were developed. Pearson correlation was conducted to evaluate the statistically significant variables
of the system at the 95% significance level. For the model development, multiple regression analysis was
performed using SPSS. The THM and chlorine demand were designated as the dependent variable and the
7
raw water quality parameter and other chlorination conditions were defined as independent variables.
Multiple forward stepwise regression method was applied for the development of the model. Validation of
the method was conducted by analyzing the different set of independent data from the same source. The
results from the experimental were compared with the predicted value for the validation process. For
validation, a plot of predicted value versus measured value was evaluated and the best fit line was
determined for Person coefficient and the slope.
4. Result and Discussion
4.1 Model I (chlorine demand model)
In Malaysia, the drinking water treatment plant is required to keep a sufficient level of free chlorine in the
water after the disinfection process before it is distributed. The distribution system rules require a
detectable level of chlorine of 0.2 mg/l throughout the system. However, the increase in chlorine dosage
can lead to the odor and taste problems. Therefore, the dose of chlorine is crucial, not only to ensure the
microbiological water quality, but the cost of chlorination and the odor and taste problem arising with the
increase of chlorine dose.
Chlorine demand is the main factor in determining the chlorine dosage during chlorination. Generally,
chlorine demand is influenced by NOM, ammonia, pH, SUVA, iron, manganese and others inorganic
species. There is a great deal of interest in understanding the factors that affect the chlorine demand and the
dose of chlorine during chlorination. Modeling of chlorine demand in treated water is aimed at creating a
better understanding of the effect of raw water quality on the chlorine consumption and can serve as a
decision-making tool for water quality control cost effectively.
Ammonia (as ammonia and ammonium ion) in this study was measured as ammonium-nitrogen using
HACH spectrophotometer. It occurs naturally in water and is a component of manure, sewage effluents,
domestic waste and fertilizers in the Semenyih basin. Nitrogen could appear in many state of organic and
8
inorganic form, such as amino acids, protein, nitrites and etc. Chlorination of water containing ammonia
leads to the formation of chloramines. Besides chlorine demand, ammonia also creates oxygen demand due
to nitrification. Using the Pearson test, a strong relationship (p=0.684, r=0.000) was found between
ammonia and chlorine demand. It demonstrated that the presence of ammonia in raw water enhances the
chlorine dosage as the chlorine-ammonia reaction is known to produce chloramines and plays an important
role to yield iodinated THMs if the iodine is present in the water.
The pH of the raw water profoundly affects the chlorination process. This is because the nutrient and the
speciation of the metals and other important water quality constituents are mainly determined by pH. Thus,
the chlorine demand of water is pH-dependent. Pearson method of correlation showed that a moderate and
definite relationship (p=0.554, r=0.000) was obtained between the chlorine demand and pH. The speciation
of chlorine species (HOCl and OCl-) is depended on the sample pH. The formation of HOCl dominates in
the acidic pH whereas, the OCl- ion is predominant in basic conditions. The distribution of different
principal oxidizing agents is a function of pH. As pH varies in a system so does the concentration of
hypochlorous acid versus the concentration of hypochlorite ion. Both HOCl and OCl - are good disinfecting
agents, but HOCl is more effective.
SUVA is a spectroscopic parameter that reveals the concentration of aromatic compounds in the water.
Samples with high level of SUVA have a relatively high content of aromatic compounds. The level of
SUVA in water could provide some information concerning the chemical composition and the
characteristic of dissolved organic matter (DOM). The Pearson correlation was used to illustrate the
relationship of SUVA and chlorine demand. Table 1 illustrates that SUVA was linearly related to chlorine
demand (p=0.843, r=0.000). This strong relationship is obvious and it showed that samples with high
aromatic organics will consume more chlorine during disinfection.
9
Table 1
Pearson matrix for the chlorine demand model
log ClDemand
log pH
log SUVA
log NH3
log ClDemand
1.000
0.544
0.843
0.864
log pH
0.544
1.000
0.447
0.486
log SUVA
0.843
0.447
1.000
0.143
log NH3
0.864
0.486
0.699
1.000
In this study, a statistical analysis was applied to the chlorine demand and the independent variables. The
Pearson correlation was used to compare the relationship between the chlorine demand and each of the
independent variables. In order to understand the relationship between all the independent variables and the
chlorine demand, the regression analysis was conducted. A general regression equation which has three
independent variables is expressed as:
Chlorine demand = a(pH)b(SUVA)c(Ammonia)d
Where ammonia and chlorine demand are expressed in mg/l; SUVA in l/mg m and pH has no unit. The
coefficients are found to be:
a= 101.614 ; b= 0.216; c= 0.237; d= 0.576
The statistical determination coefficients of the model, standard error of the estimates, t and p-value are
shown in table 2.
10
Table 2
Coefficient and statistics for chlorine demand model
Variables
β
Std Error
t
p
Constant
1.614
0.142
11.333
0.000
log ClDemand
0.576
0.068
8.507
0.000
log pH
0.237
0.031
7.727
0.000
log SUVA
0.216
0.102
2.111
0.038
The result obtained from statistical analysis showed that ammonia was the main factor causing the chlorine
demand in water. The regression results of SUVA and pH showed the equivalent characteristic and the
level of relation between them and chlorine demand were moderate. The statistical evaluation of the
chlorine demand model is shown in table 3.
Table 3
Chlorine demand model summary
R
0.930
R2
0.866
Adjusted R2
0.861
Std error of estimate
0.516
F
173.843
Figure 2 shows the normal probability plot of measured and the predicted values of Chlorine demand. In
general, the statistical determination coefficients of the model appeared satisfactory. The r 2 (0.866),
standard error of the estimate (0.516) and the F value (173.843) and p (0.000) of the model were found to
be statistically significant.
11
1.0
Expected Cum Prob
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Observed Cum Prob
Figure 2 Goodness of fit for measured and predicted values of the chlorine demand model
4.2 Model II (THM model)
The second model was attempted to investigate the factors affecting the formation of THM. The
development of model for the formation of THM was made at the water treatment plant after the
chlorination. Modeling of THM at water treatment plant allows a better understanding of the effect of the
raw water quality parameter on the THM formation, especially after the chlorination has taken place. This
model was based on the use of pH, SUVA and chlorine dosage as independent parameters. These
parameters were measured at the treated water in the plant. Regression analysis using stepwise procedure
was used to develop the model. The predictive model for the THM formation was formulated. Table 4
shows the Person correlation test for the examination of the relationship between THM level and the
independent variables. It was noted that the relationship between THM and all the independent variables
were significantly correlated.
12
Table 4
Pearson matrix for the THM formation model
log THM
log SUVA
log pH
log Cl dos
log THM
1.000
0.600
0.285
0.689
log SUVA
0.600
1.000
0.151
0.402
log pH
0.285
0.151
1.000
0.143
log Cl dos
0.689
0.402
0.143
1.000
Using the Pearson test, a medium relationship (P=0.689, r=0.000) was obtain between the formation of
THM and the chlorine dosage. Several studies have found that there was a positive and linear relationship
between chlorine dosage and the THM formation as the case with our present study (Uyak and Toroz,
2006). Higher chlorine dose during chlorination provided higher oxidizing species in the reaction with
other compounds in the formation of THM.
It was also found that a high pH value could increase the THM level in treated water. Using Pearson
correlation method, a low, but significant relationship (P=0.285, r=0.004) was found between pH and the
formation of THM. High pH level might increase the hydrolysis rate of the THM precursors such as
propanones, acetaldehydes and increase the rate of reaction with chlorine. Consequently, the THM level is
increased. Previous researchers have shown that lowering the pH resulting in the low-level of THM
formation (Golfinopoulos and Arhondistsis, 2002; Nikalaou et al., 2004).
SUVA has been shown to increase the formation of THM in treated water. The higher level of organic
compounds in water will increase the THM level during the chlorination process. As shown in Table 4,
THM was increased with the increasing of SUVA. However, the influence of SUVA on THM and other
DBPs formation is complicated because SUVA consists of complex mixture of humic and organic
compounds with unknown structure and chemical properties. Many researchers found that THM formation
rose with increasing humic and fulvic material content in water (Marhaba et al., 2000). Pearson correlation
13
showed there was a medium correlation between THM formation and SUVA content (P=0.600, r=0.000) in
this study.
The relationship between dose of chlorine, pH and SUVA were further studied by analysis of regression.
The model for predicting of THM formation, based on these parameters, is as shown below.
THM = a(chlorine dosage)b(SUVA)c(pH)d
where a = 10-1.783, b = 0.663, c = 0.227 and d = 0.478. THM and chlorine dosage are expressed in mg/l;
SUVA in l/mg m and pH has no unit. The coefficients of the model and their significant level are shown in
Table 5.
Table 5
Coefficient and statistics for THM formation model
Variables
β
Std Error
t
p
Constant
-1.783
0.213
-23.160
0.000
log Cl dos
0.663
0.096
6.940
0.000
log SUVA
0.227
0.046
4.910
0.000
log pH
0.478
0.231
2.244
0.028
Based on the model developed, chlorine dosage, pH and SUVA were found to be statistically significant.
Chlorine dose during chlorination was found to create the most important influence on the formation of
THM, followed by pH and SUVA. Figure 3 shows the normal probability plot of measured and the
predicted values of THM. In general, the statistical determination coefficients of the model appeared
satisfactory. The r2 (0.623), standard error of the estimate (0.058) and the F value (44.601) and p (0.000) of
the model were found to be statistically significant.
14
1.0
Expected Cum Prob
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Observed Cum Prob
Figure 3 Goodness of fit for measured and predicted values of the THM formation model
5. Model validation
The purpose of the validation is to measure how well the model fit the experimental data. Model validation
will show the stability and the reasonableness of the THM formation and the chlorine demand models.
Model validation is a fundamental element to the modeling process and can often lead to the model being
reformulated. In order to validate both of the models, analysis of independent set of data is performed. This
involved collecting the new set of data and then comparing the data with the model predictions.
Using independent set of data obtain from the same source, the validation is performed through the
examination of statistical test, as shown in Table 6. Validation of these models involved the observational
studies of the appropriateness of the variables selected, as well as the magnitudes of the model’s coefficient
and the predictive ability of the model.
15
Table 6 Validation statistics of the models
Validation statistics
Chlorine demand model
THM model
Degree of freedom
41
41
Average of prediction bias
1.7126
0.7500
Std deviation of prediction errors
9.7926
5.9367
Std error of estimated mean bias
1.5110
0.9161
Mean square error of prediction
96.5438
34.9390
Error of prediction (%)
20.21
12.18
t value
1.133
0.819
t critical
2.021
2.021
Mean square error of prediction is a parameter of measuring the actual predictive capability of both the
model developed by using a new set of data. It was found that the chlorine demand model have a higher of
prediction error compared to THM model. Finally t-test was conducted for both models to determine the
difference between the experimental and the prediction data. There are 41 degrees of freedom (42 samples)
that represent the validation data set used in the calculation. The critical value of t for a two-tailed test at
0.05 level of significance with df=41 was 2.021. The t-test results showed that all the t value were less than
the t critical value and that both the p values (chlorine demand = 0.364, THM = 0.264) were greater than
0.05. This indicated that there was no significant difference between the experimental and predicted data
for both models. The results of validation showed the practical predictions of chlorine demand and THM
formation. The plots were shown in figure 4 and 5 which indicated reasonable r2 value.
16
Predicted Cl Demand (ppm)
7
6
5
4
3
2
1
0
0
1
2
3
4
5
6
7
Measured Cl Demand (ppm)
Figure 4 Correlation between the experimental and prediction model result (r2 = 0.7848)
Predicted THM (ppb)
120
100
80
60
40
20
0
0
20
40
60
80
100
120
Measured THM (ppb)
Figure 5 Correlation between the experimental and prediction model result (r 2 = 0.8420)
17
6. Conclusion
This study was conducted to evaluate the relationships between the raw water quality and the chlorine
demand as well as the THM formation resulting from of chlorination at a water treatment plant. Empirical
models for chlorine demand and THM formation were developed using real samples. The statistical
regression analysis using stepwise procedure was used to develop the two models. Only variables with
statistically significant were considered in the models. For chlorine demand, it was based on pH, SUVA
and ammonia, whilst the THM formation model was based on chlorine dosage, SUVA and pH. The
correlation and regression analysis for examining the relationship between the independent variables and
the chlorine demand and the THM models showed promise and the relation appeared to be good. To test
the validity of these models, new set of data was analyzed and compared to the predictive values.
Validation results showed that there were no significant differences and the errors of prediction were low
between the observed and the predicted data (< 21%). Both chlorine demand and THM formation models
could be useful typically in tropical countries for the purpose of drinking water quality management and
treatment plant operational management. In addition the models could be used as guidelines in decision
making-tools in choosing appropriate strategies to reduce THM and chlorine consumption and improving
the disinfection process.
Acknowledgements
This work was funded by Ministry of Science, Technology and Inovation (under Grant # 04-01-02-SF0109)
and Konsortium Abass Sdn. Bhd. Technical assistance provided by Semenih River Water Treatment Plant
and Centre for Water Research and Analysis, Universiti Kebangsaan Malaysia are kindly acknowledged.
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