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Stormwater Runoff Quality Characterization at Tropical Urban Catchment
M.F. Chow1*
, Z. Yusop1,& ,& M.E. Toriman
1Institute of Environmental & Water Resources Management (IPASA),
University of Technology Malaysia (UTM), 81310 Johor Bahru, Johor Darul Tazim, Malaysia.
Tel: +60 75531578; Fax: +60 75531575;*E-mail: [email protected],
School of Social Development and Environmental Studies, FSSK. Universiti Kebangsaan Malaysia, 43600 BangiSelangor
Abstract
A comprehensive stormwater monitoring program was conducted in tropical urban land uses to
evaluate the quality of stormwater runoff, and to determine the impact of land uses on the level of
runoff contamination. Stormwater samples and flow rate data were collected at residential,
commercial and industrial sites over 52 storm events from year 2008 to 2009 stataed where is the
location. Samples were analyzed for ten water quality constituents including total suspended
solids, 5-day biochemical oxygen demand, chemical oxygen demand, oil and grease, nitrate
nitrogen, nitrite nitrogen, ammonia nitrogen, soluble phosphorus, total phosphorus and zinc. The
results indicate that land use has a significant influence on the major water quality constituents. A
negative correlation coefficient was observed between rainfall and runoff characteristics with
most which paraemeter? event mean concentration (EMC) of storm quality constituents. In
contrast, all storm water loads were well correlated well with rainfall and runoff variables except
previous storm mean intensity. BOD, COD and NH3-N showed consistently strong first flush
effects at residential, commercial and industrial catchments.
Key words: correlation; event mean concentration; first flush; land use; stormwater; tropical
1.0 Introduction
Urbanization and industrialization are rapidly taking place in many developing countries including
Malaysia. Urbanization associated with anthropogenic and transportation activities will generate
considerable amount of organic matters, nutrients and heavy metals (i.e. Cd, Cu, Pb, Zn) on the catchment
surfaces, which couldwill cause serious environmental impacts (U.S. EPA, 2005; Goonetilleke and
Thomas, 2004, ; Gan et al., 2007, ; Wang et al., 2007;, Zhang et al., 2009,; Jin et al., 2009; Wei et al.,
2010). Recently, water quality impairment associated with The significance ofurban stormwater runoffin
degrading the quality of receiving waters has received been confirmed by many studies in many countries,
including China (Ballo et al., 2009), Canada (Mcleod, 2006); Kuwait (Al-Jaralla and Al-Fares, 2009),
Iran (Taebi and Droste, 2004), Japan (Yamada, 2007) and USA (USEPA, 2005; Atasoy et al., 2006).
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Generally, major pollutants in urban stormwater include suspended solids, nutrients, oxygen demanding
substances, heavy metals and organic substances (Novotny, 2003).
Normally, assessment of urban runoff quality requires the consideration of different type of land use, as
this will affect the amount of runoff and the pollutant loadings of water quality constituents discharged
from a catchment (Mcleod, 2006; Shen et al., 2010). This article of the stormwater runoff quality is
carried out in order to(list the objective[s] here
1.1 Earlier StudiesFor journals format, literature review or earlier studies can beomitted.
Or just blend in the results and discussion.
Previous studies have indicated that there are significant differences in stormwater quality for
different land use categories such as residential, commercial, industrial, street and lawn (USEPA, 1983;
Driver et al., 1985; Smullen and Cave, 2002; Pitt et al, 2004, Al-Jaralla and Al-Fares, 2009; Waschbusch
and Selbig, 1995; Line et al., 1997; Maetrae and Pitt, 2006; Miller and Mattraw, 2007). Assessment of
stormwater quality from urban catchment is usually estimated by using Event Mean Concentration (EMC)
(Charbeneau and Barrett, 1998). The EMC is a flow-weighted average of a constituent concentration.
Generally, the variation of EMCs are wide because of event and site characteristics, such as rainfall
intensity, area, runoff coefficient, and antecedent dry periods (Kim et al., 2005; Luo et al., 2009).
Moreover, the heterogeneous composition of land use for a urbanizing catchment also will result in
significant spatial variations of storm runoff quality (Qin et al., 2010).
Even though numerous studies on stormwater runoff pollution have been conducted worldwide in
urban areas, related researches are comparatively rare in tropical countries like Malaysia. One of the
earliest studies (Mokhtar, 1998), stated that more than 60% of the Malaysian rivers are failing to meet the
quality standards due to pollution contributed from the non point sources or storm-generated activities,
particularly in urban areas. Studies of urban stormwater runoff pollution are important in Malaysia
because the country is subject to high annual rainfall, more than 2000 mm. It is known that the greatest
increase in pollutant loadings generally occurs for these frequent storm events, due to the highly
significant increase in runoff volumes under these conditions. Furthermore, the variability from one
location to another and from storm to storm indicates the need for local data in assessing the quality of
urban stormwater runoff.
The principal goal of this study is to provide an accurate, updated quantification of urban runoff
pollutant loadings specific to local receiving waters. These results will help in the assessment of the
relative contributions of urban runoff to pollutant loadings within the region and are aimed to be used in
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planning urban runoff pollution control strategies. Our study has two objectives: (1) to characterize urban
storm runoff loads using event mean concentrations (EMCs) of 10 constituents for tropical urban
catchments, and (2) to determine relationships between loads and EMCs with storm and runoff
characteristics. Bring to above
2.0 Methodology2.1 Study Area and Climate
This study was carried out at three urban catchments in Skudai, Johor, Malaysia (Figure 1). Each
catchment was chosen on the basis of the type or nature of the land use that it represented, namely,
residential, commercial and industrial. The studied catchments are typical urban land use areas in
Malaysia. The catchment characteristics are summarized in Table 1. The commercial and industrial sites
represent the old urban development which utilizes the old separated open channel drainage design
system to convey the surface runoff from the catchments. The studied residential site utilizes the new
separated underground drainage design system which recommended in the Stormwater Management
Manual for Malaysia (MASMA) since 2000.
The average annual rainfall in Skudai river basin is between 2000 and 2500 mm. The rainfall is
highly localized and dominated by convective type of storms. The monthly rainfall pattern is quite
uniform with the highest usually recorded in December. Averages daily temperature rangeds from 25oC
to 33oC and a mean relative humidity of 87%. Figure 2 shows the average monthly rainfall for 20 years
from 1990 to 2009 at Ladang Gunung Pulai station (provide the coordinate and elevation m.s.l). Explain
why we select this station-closest, complete rainfall data, why not senai meteorological station instead
An average annual rainfall of 2481mm was obtained from this station. The monthly rainfall pattern
(Figure 2) shows two maxima separated by two periods of minimum rainfall (i.e., between the two
monsoons). The first wet season occurs in October to December while March to May are also wet
months. As noted in Figure 2, relatively dry months occur in January and February, and June to
September which coincided with inter-monsoon periods. The driest month usually occur in February.
The rainfall amount for this month is less than 150 mm.
Table 1 Characteristics of study catchment.
Characteristics C1 C2 C3
Land UseArea (ha)
Commercial34.21
Residential32.77
Industrial4.38
No. of shops/houses 597 473 25
Sewer type separated separated separated
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Figure 2. Monthly rainfall distributions in Gunung Pulai, Skudai between 1990 and 2009
Source?
2.2 Water Quantity and Quality MeasurementsStorm runoff was measured and sampled at the main outlet of the drainage area which has a straight
alignment. The sampling point was preferred for developing a good stage-discharges curve. The flow
depth in the storm drain was determined manually by using a stage gauge. A stagedischarge curve was
developed to convert the recorded flow depth to volumetric flow rate. Using the recorded flow depth
versus time data in conjunction with the stage-discharge curve, a hydrograph was developed for each
runoff event. Subsequent integration of the area under the hydrograph yielded the total volume of water
discharged during the runoff event.
Stormwater s samples were grabbed sampled during the storm events. This technique is more
reliable for a small urban catchment, compared to an automatic water sampler, due to the very rapid rise
in water level once the storm has started. In addition, grab sampling can minimize the possibility of oil
and grease from attaching to the inner surfaces of the sampling tube and the containers in the automatic
sampler. The required number of samples for a representative event sampling followed the sampling
protocol recommended by Caltrans (2000). Normally, eight to fifteen samples were collected for every
storm event depending on the storm size.
Depending on the rain intensity and discharge, the sampling intervals were between 1 and 10
minutes on the rising limb of the hydrograph and 10 and 20 minutes on the falling limb. Frequent
sampling on the rising limb of the hydrograph is crucial for assessing the first flush occurrence. For each
sample, about 1 liter of stormwater was collected (Nazahiyah et al, 2007). Furtherhermore, hourly based
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50
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150
200
250
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Rainyday(day)
Rainfalldepth(mm)
Month
Rainfall rainy day
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flow samples were collected manually continuously for twenty-four hours during one working day, the
weekend and a holiday (Wednesday, Saturday and Sunday). A total of seventy-two samples were
collected from each site to establish an average baseflow concentration of the various water quality
constituents, i.e. the same parameters as for the rainfall-runoff event samples. Flow rate for every sample
was measured in order to calculate the base flow pollutant loading. Besides that, rain samples were
collected manually at an open field to evaluate the quality of rainwater at the site. A total of three rainfall
samples from different storm events were collected for rainwater quality analysis. The stormwater
samples were analyzed according to the standard method for water (APHA, 2005). The analytical number
used are TSS (2540D), BOD (5210B), COD (5220B), oil and grease (O&G) (5520B), NO3-N (4500-NO3
B), NO2-N (4500-NO2 B), NH3-N (4500-NH3 F), soluble P (4500-P E), total P (4500-P B) and Zinc
(3120 B).
2.3 Data AnalysisPollutant loadings were estimated using Event Mean Concentration (EMC) which is defined as the total
constituent massMdischarged during an event divided by the total runoff volume, Vduring the event
(Huber, 1993), expressed as:
()()
()(1)
whereMis total mass of pollutant during the entire runoff (kg); Vis total volume of runoff (m3); C(t) is
time varying pollutant concentration (mg/L); Q(t) is time variable flow (L/s); and t is total duration of
runoff (s).
Generally, estimation of annual discharged loads in order to evaluate long-term impacts of urban
wet weather discharges requires Site Mean Concentration (SMC). The SMC value is calculated as the
average of EMC values for a particular catchment.
In this analysis, annual pollutant loadings were calculated using the method defined by
Schueler (1987) as:L =P. Pj . Rv. C (2)
where, L is the normalized annual pollutant load (kg/ha/yr), P is the annual precipitation (mm/yr), Pj is
the dimensionless correction factor that adjusts for storms without runoff, Rv is the dimensionless average
runoff coefficient, C is the flow-weighted average concentration (mg/L).
The annual rainfall depth, P was determined from rainfall records collected at the study
catchments. A mean annual rainfall of 2523 mm was recorded from year 2008 to 2009 and thisvalue was
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used for the annual precipitation (P). A rainfall correction factor (Pj) of 0.9 was used as recommended by
Schueler (1987). This value is supported by rainfall-runoff analysis for all catchments whereby runoff
makes up about 90% of the annual rainfall. The runoff coefficient, Rv, was calculated using Equation as
below (Schueler, 1987) :
Rv = 0.05 + 0.009 (I) (3)
where Rv is runoff coefficient; and I is the percent of the catchments imperviousness. The baseflow
annual loading of pollutant from each catchment was calculated as follow (McPherson et al., 2005):
(4)
where, Wis the total load during the sampling period, n, Cm(mg/L) is the median concentration, and Q
(m3
/s) is the dry weather flow on day j. Similar approach was used by Mukhopadhyay and Smith (2000)for estimating annual pollutant loadings from dry weather flow.
A quantifiable value for first flush strength is calculated by using a log transformed power
function of L = Fb. The power function was log transformed to yield a linear regression equation as
follow:
ln L=b ln F (5)where, the b coefficient becomes the slope term from the linear regression model. The y- intercept was
fixed to zero to ensure that100% of the pollutant load equals 100% of the runoff volume. A value of 1
corresponds to the 45bisector line representing uniform pollutant loading throughout the event. A lower
slope (smaller b-value) represents a stronger first flush effect while a steeper slope (larger b-value)
represents a weaker first flush effect. Slope values above 1 represent a dilution effect.
3.0 Results and Discussion3.1 Rainfall Pattern
To achieve reliable estimation of SMC at each catchment, 17 independent storm events were
collected for each residential, commercial and industrial catchment from year 2008 to 2009. On site
rainfall data such as total depth (Rd), duration (Rdur), mean intensity (I), Max 5 min intensity (I max5) and
antecedent dry day (ADD) are measured for each monitored event at every catchment. A 5-min time step
was used as it is widely used and accepted in tropical environment and therefore provides a basis for
comparison. Judging from the analysis of rainfall-runoff data at Skudai area, storm event is defined as
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rainfall depth greater than 0.8 mm which starts to initiate the surface runoff over the catchment. The
monitored rainfall depths are ranging from 1.8 mm to 107.4 mm while the intensities varies vary from 2.7
mm/hr to 99.5 mm/hr. Most of the storm duration is less than 2 hour and the antecedent dry days are
range from 0.03 to 16.5 day. Intensities of these 51 events are plotted along with intensity-duration-
frequency (IDF) curves for Johor Bahru in Fig. 3. This figure shows that the return period of all these
events ranges from a few days to a few months, and nearly all of them have a return period significantly
less than one year for Johor Bahru (53 mm/hr for a 1-hour duration storm). The monitored storm events
are the typical small and frequent storms that common in Malaysia. The EMCs derived from these storms
are important in predicting the annual pollutant loading from different land uses at the urban catchment.
Figure 3. Intensity-Duration Frequancy (IDF) curves for monitored storm events
3.2 Characteristics of Stormwater Runoff QualityThe EMC value of each stormwater constituent was calculated for every storm event. The SMC
value was determined for every constituent at residential, commercial and industrial catchment. The
results of rainfall, base flow and storm runoff at residential, commercial and industrial site are shown in
Table 2. It can be seen that most of constituent levels in rainwater are very low or under the equipment
detection limits. Therefore, the rainwater quality is not likely to influence the pollutant contents in
the stormwater runoff except possibly for NO3N. This may imply that rainfall is a considerable
source of NO3-N at the urban catchment. Base flow mean concentration of various pollutants
generally exceeded the mean and median values of stormwaters EMC for all land uses exceptTSS and O&G from the commercial and industrial catchments. This may suggest that TSS and
O&G were temporary deposited in the channel or stick to the drains wall until the next storm with
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sufficiently large energy to transport these pollutants again (Chow and Yusop, 2011). Francey et al. (2010)
also showed similar conclusion that TSS concentration in storm flow is higher than in base flow while
nitrogen is always higher in base flow. The practical implication of this is that in many cases small events
and base flow cannot be bypassed but must be treated.
Box plots of EMC were plotted for every constituent and comparison was made between land uses
as shown in Figure 4. The commercial areas exhibit the highest median EMC value for BOD, COD, TSS,
NH3-N and TP, followed by the industrial and the residential areas. Since there are many restaurants
within the commercial catchment, it exports a lot of organic matters during the storm events. In contrast,
industrial areas exhibit the highest median value of EMC for O&G, NO3-N, and Zinc. SMC for zinc at
industrial site is far greater than residential and commercial site. This may explained that type of
roofing, factories and transportation activities at the industrial site are the main source for
contributing the pollutant loading of zinc. Variation of EMC from storm to storm as well as type of
land use may suggest that a long term monitoring program is needed in order to accurately predict the site
mean concentration (SMC).
Table 3 shows the SMCs comparison with urban runoff SMC reported in the Literature. The SMCs
of TSS at the present residential catchment is at least 3 times lower than other studies. Earlier, Nazahiyah
(2005) recorded high SMC of TSS (EMC = 364 mg/L) from a residential catchment that has bare area
near the catchment outlet. It is possible that these sections contribute TSS through erosion processes.
Nazahiyah et al. (2005) in a smaller commercial catchment, closeby to the present site found higher
SMCs for all pollutants. The storm sizes studied by Nazahiyah et al. (2005) were generally small (3.0 to
31.3 mm) compared with 2.0 to 107.4 mm in this study. Therefore, it can be argued that a larger storm
with higher runoff volume tends to dilute pollutants at a faster rate especially in a large catchment. This
was also observed by Brezonik and Stadelmann (2002) in Minnesota, USA who found larger EMCs in a
smaller suburban residential catchment compared to a larger one. He ascribed this to the ability of a large
catchment to redeposit or hold water and constituents in depressions. The site mean EMCs of stormwater
quality constituents reported in other countries is also compared with this study as shown in Table 3.
According to Table 3, almost all the residential SMCs of the listed constituents in this study are lower
than the results of Singapore, Canada and Australia. In contrast, the present SMCs for all constituents at
the commercial catchment areas are higher than elsewhere with an exception for SP. The differences in
the storm runoff quality between the present site and other sites in the developed countries may reflect
different management and maintenance regimes of the urban site. Qin et al. (2010) stated that high
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population density, lack of environmental consciousness and poor litter management in urban areas are
the reasons for poor stormwater quality.
Table 2. Rainfall, wet weather and dry weather quality for all catchments
Concentration (mg/L)
BOD COD TSS O&G NO3-N NO2-N NH3-N Soluble P Total P Zinc
Rainwater 3.1 5.0 2.0 ND 0.9 0.004 0.37 0.03 0.08 -
a)Wet weather
n 17 18 17 17 18 17 18 17 14 12Residential Mean (SMC) 6.5 36 21 2.32 0.90 0.011 0.19 0.07 0.38 0.04
Median 6.5 39 26 2.28 0.94 0.008 0.17 0.07 0.41 0.05
SD 5.3 37 17 1.35 0.49 0.021 0.60 0.07 0.41 0.04Commercial n 16 17 17 17 17 17 17 17 17 8
Mean (SMC) 81.1 225 167 3.66 0.93 0.006 0.71 0.11 0.69 0.08
Median 58.1 196 124 3.89 0.80 0.006 0.70 0.08 0.73 0.05
SD 116 231 267 1.61 0.56 0.003 1.72 0.36 0.57 0.21Industrial n 13 15 17 16 17 16 17 16 14 11
Mean (SMC) 42.6 117 91 4.47 1.20 0.009 0.58 0.08 0.59 0.24
Median 44.8 97 91 4.52 1.14 0.009 0.46 0.08 0.62 0.29
SD 52.2 79 133 1.84 0.74 0.030 2.85 0.08 0.63 0.18b) Dry Weather
Residential Mean 21.2 55 15 3.14 1.35 0.010 0.19 0.31 0.62 -
Median 15.5 47 12 2.9 1.05 0.004 0.10 0.27 0.44 -SD 15.6 34 11 2.3 1.10 0.016 0.30 0.23 0.53
Commercial Mean 68.1 342 59 2.72 3.10 0.018 5.22 0.87 1.82 -
Median 72.5 340 51 2.3 1.20 0.011 3.23 0.71 1.68SD 16.1 117 40 1.7 4.05 0.020 4.96 0.56 0.84
Industrial Mean 62.6 294 49 3.78 2.86 0.013 2.96 0.65 1.55 -
Median 70.8 172 31 3.0 1.35 0.007 2.37 0.46 1.26SD 19.5 303 63 3.0 4.38 0.015 3.36 0.55 1.05
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Figure 4. Box plots of EMC for residential, commercial and industrial
Table 3: SMCs comparison with Urban Runoff SMCs Reported in the LiteratureConstituent
Site TSS O&G BOD COD NO3-N NO2-N NH3-N T P S P Zinc
C1 Residential 21 2.32 6.5 36 0.90 0.011 0.19 0.38 0.07 0.04
Malaysiaa Old Residential 364 - 95.0 311 2.4 0.10 3.50 - 3.0 -
Australiab High density 101.8 - - - - - - 0.35 - 1.24
Canadac Old residential 190 - - 100 - - - 0.53 - -
Singapored High density 66 - - - 0 .70 - - 0.08 - -
C2 Commercial 167 3.66 81.1 225 0.93 0.006 0.71 0.69 0.11 0.08
Malaysiaa Commercial 195 - 135 487 2.8 0.43 3.80 - - -
Australiab Commercial 71.4 - - - - - - 0.15 - -
Canadac Commercial 210 - - 75 - - - 0.45 - -
C3 Light Industrial 91 4.47 42.6 117 1.20 0.009 0.58 0.59 0.08 0.24
Canadac Light Industrial 3.0 - - 41.8 - - - 0.13 - -
USAe Industrial 231 - - - 0.46 - 0.25 0.27 - -- No Dataa Nazahiyah (2005)b Francey et al (2010)c McLeod et al (2006)
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d Chua et al (2009)e Line et al (2002)
3.3 Seasonal Analysis of Storm Runoff
Seasonal differences in EMCs were examined for each constituent at all studied site. The Mann
Whitney test was used to test for significance (p < 5%) in differences between the median EMCs of the
pollutants. Results of the Mann-Whitney test showed that significant different (p < 0.05) was found for
TSS, O&G, NH3-N and TP but not for BOD, COD, NO3-N, NO2-N, SP and Zn. According to Figure 2,
the monthly frequent of rainy day is not much different except for February. However, the storm is less
intense and short in duration for dry season period. These flow limited event will flush away the available
pollutant loading on the impervious surface into the drainage system. Since EMC is defined as total mass
loading divided by total runoff volume, thus a high EMC value is obtained for flow limited event.
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Figure 5. Box plots of EMC for dry and wet season
3.4 Correlation between Hydrological Parameters with EMC and LoadingI would suggest if this section can be pulled out from the result discussion mainly due
to the fact that the pages are exceeded the max required by normal journal (10-15
pages single spacing), this section in fact can be submitted as a separate issue in
separate article.
The relationship between EMCs and loading of various constituents with storm characteristics was
analyzed by using Pearson correlation analysis (Table 4). The storm characteristics analyzed included
rainfall depth (RD), rainfall duration (RDur), mean intensity (I), max 5 minutes intensity (I max5), antecedent
dry day (ADD), runoff volume (Vol) and peak flow (Qpeak). Correlation coefficients were determined
between storm parameters and EMCs for the residential, commercial and industrial catchments. Most of
the storm variables showed negative correlation with EMC. The strongest correlations were found for
ADD with TSS, BOD, NH3-N and SP in the industrial catchment. Strong correlations were also observed
between COD and NH3-N at the residential catchment, and TSS and NO2-N in the commercial catchment.
This finding show that pollutant build-up tend to increase with the length of ADD periods. Elsewhere,
the ADD was also found to be positively correlated with EMCs of TSS ( Chui, 1997; Brezonik and
Stadelmann, 2002; Gan et al., 2007, Kim et al., 2007). On the contrary, no significant relationship was
found between TSS and ADD at the residential catchment. Similar observation was obtained by Deletic
and Maksimovic (1998) in Lund, Sweden, Charbeneau and Barrett (1998) in Texas, USA and He et al.
(2010) in Calgary, Alberta. Gnecco et al. (2006) stated that the build-up process seems to be affected by
the specific site activities rather than antecedent dry day, which generally plays an important role.
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EMC values at the residential catchment are moderately correlated with rainfall and runoff
variables. Negative correlation was observed only between RD vs SP, RDur vs TP and Imax5 vs SP.
Rainfall depth, duration and runoff volume are negatively correlated with TSS, BOD, NH3-N, SP and TP
at the commercial catchment. Meanwhile, rainfall duration at the industrial catchment shows negative
correlation with O&G but was positively correlated with NO 3-N. Kim (2002) found negative correlations
between EMCs of various pollutants against total rainfall, storm duration, average rainfall intensity and
total runoff volume. Larger storms tend to produce lower EMCs due to dilution effects or exhaustion of
pollutant mass (Yusop et al., 2005; Gan etal., 2007). Similarly, Brezonik and Stadelmann (2002) in
Minnesota, USA found negative correlations between precipitation amount and EMCs of dissolved P
(DP), COD, total Kjeldahl nitrogen (TKN), NO3-N plus NO2-N and TN. Brezonik and Stadelmann (2002)
and He et al. (2010) studied that constituents such as TSS, TP and COD showed negative correlation with
rainfall duration. They explained that prolonged storms tend to produce more runoff volume that dilute
the concentrations of these constituents. This finding reinforced NURP results that EMCs are not
linearly correlated with runoff volume (USEPA, 1983).
Imax5 and mean intensity at the commercial catchment are negatively correlated with NO3-N, NH3-
N, SP and NO3-N. Imax5 and mean rainfall intensity at the industrial catchment, both are negatively
correlated with NH3-N, TP and SP. There is no positive correlation between rainfall intensity and EMC
of constituents in all the studied catchments. Although some researchers found that rainfall intensity is an
important parameter for washing off suspended solids (Chui, 1997; Deletic and Maksimovic, 1998;Vazeand Chiew, 2003; Li et al. 2005; Chua et al., 2009; He et al., 2010), this was not observed by Gnecco et al.
(2006), Gan etal. (2007) and Kim et al. (2007). This difference in results indicates the need for local
studies when evaluating the behaviour of EMC with rainfall and flow.
Based on the Pearson correlation analysis (Table 4), the most important parameters that influence
the loading of various pollutants are RD, Imax5, Vol and Qpeak. Mean intensity only correlates positively
with TSS, O&G, NO3-N, NO2-N, TP and Zn in the residential catchment. Similar findings were observed
by Chui (1997), Brezonik and Stadelmann (2002), Chua et al. (2009) and Francey et al. (2010).
Interestingly, rainfall duration is significantly correlated (positive) with the loadings of O&G, COD, NO 3-
N, NO2-N and TP in the commercial catchment. Meanwhile, rainfall depth and runoff volume correlate
positively with the loadings of most constituents except for TSS, BOD and NH 3-N. Rainfall duration
only correlates with NO3-N and SP loadings while mean intensity correlates with O&G, BOD and COD
loadings in industrial catchment. Max 5 min intensity and peak flow are correlated positively with the
loadings of O&G, BOD, COD, NO3-N, NO2-N and Zn.
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Antecedent dry day correlates well (p
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Correlations significant at p < 0.01 were expressed in bold, - No significant correlation at p < 0.05.
3.5 Annual Pollutant Loading
Figure 6 shows the proportions of annual pollutant loads carried by storm runoff and base flow
from residential, commercial and industrial catchments. As noted before, annual pollutant load of Zn is
not discussed here because its baseflow data was not measured. In overall, a large portion of the annual
load of various pollutants was transported in stormflow than in baseflow especially for TSS, BOD, CODand O&G. More than 70% of the total annual load of TSS and O&G were transported in stormwater
runoff. Since the average number of rain days in this area is high (175 days per year), the bulk of annual
pollutant loading must have been discharged by more frequent but with shorter duration storms
(give an example of storm hydrograph here) . Conversely, annual loadings of NH3-N and SP are
mainly evacuated by baseflow from the commercial and industrial catchments. This suggests that daily
activities in these two catchments generate large amount of NH 3-N and SP. Problems of sullage from
urban land use should be addressed more seriously in order to control the pollution into the receiving
waters. Malik (2003) showed similar observation that dry weather flow in storm channels are significant
sources of ortho-P and NH3-N.
On the other hand, stormwater runoff showed greater loadings of N and P in the residential
catchment. This may be associated with fertilizer application on gardens and lawns. Malik (2003) also
b)Commercial b)Commercial
TSS -0.50 -0.56 - -0.62
-0.49 - TSS 0.59 - 0.550.70
0.50 0.580.66
O&G O&G 0.84 0.74 0.54 0.86 - 0.83 0.88
BOD -0.54 -0.62 - - - -0.54 -0.51 BOD 0.55 - 0.64 0.73 - 0.54 0.61
COD COD 0.68 0.55 0.57 0.77 - 0.68 0.71
NO3-N - - -0.49 -0.53 - - - NO3-N 0.84 0.75 0.49 0.80 - 0.83 0.87
NH3-N -0.62 -0.54 - -0.67 - -0.62 -0.69 NH3-N
NO2-N - - - - 0.60 - - NO2-N 0.81 0.72 - 0.80 - 0.80 0.85
SP -0.67 -0.69 - -0.66 - -0.67 -0.68 SP - - - - 0.59 - -
TP -0.50 -0.55 - - - -0.49 - TP 0.76 0.59 0.58 0.79 - 0.75 0.83
c)Industrial c)Industrial
TSS - - - - 0.52 - - TSS
O&G - -0.51 - - - - - O&G 0.78 - 0.76 0.77 - 0.77 0.79
BOD - - - - 0.69 - - BOD - - 0.60 0.58 - - 0.60
COD COD 0.85 - 0.73 0.82 - 0.85 0.80
NO3-N - 0.50 - - - - - NO3-N 0.90 0.61 - 0.72 - 0.90 0.82
NH3-N - - - -0.59 0.59 - - NH3-N - - - - 0.56 - -NO2-N NO2-N 0.54 - - 0.61 - 0.53 0.66
SP - - -0.64 - 0.55 - - SP 0.53 0.55 - - - 0.53 0.58
TP - - - -0.55 - - - TP 0.56 - - - - 0.56 -
Zinc 0.70 - - 0.84 - 0.71 0.79
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concluded that wet weather flow at residential catchment was the major contributor of nutrients into the
receiving waters. Interestingly, the commercial and industrial catchments showed almost equal
proportions of annual NO3-N loading in storm runoff and baseflow.
Figure 6. Proportion of annual pollutant loadings carried by storm runoff and baseflow for different catchment
3.5 First FlushThe statistical results of first flush coefficient (b-value) for constituents at all catchments are shown
in Table 5. Constituents such as BOD, COD and NH3-N showed a consistent strong first flush effect at all
catchments. TP has the greatest magnitude of first flush effect at the residential catchment whereas TSS
and NH3-N had the strongest first flush strength at the commercial and industrial catchment, respectively.
Constituents such as O&G, NO3-N, NO2-N and Zn showed dilution effect (b value > 1.0) during storm
events at all catchments. Generally, all pollutants have first flush effects in all catchments but with
varying strengths. The relative strength of first flush for the residential catchment is TP > NH3-N > BOD >
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COD > TSS > O&G > SP > NO2-N > NO3-N > Zn; commercial catchment TSS > SP > NH3-N > COD >
BOD > TP > Zn > NO2-N > NO3-N > O&G and industrial catchment NH3-N > SP > TP > BOD > COD >
NO3-N>TSS > NO2-N > O&G > Zn.
Table 5. Statistical summary for first flush coefficient (b-value) for the residential, commercial and
industrial catchments
b - value
TSS O&G BOD COD NO3-N NH3-N NO2-N SP TP Zna) ResidentialMin 0.34 0.49 0.60 0.73 0.86 0.39 0.62 0.70 0.45 0.79
Max 1.20 1.21 1.18 1.05 1.24 1.17 1.37 1.33 1.08 1.31
Mean 0.87 0.96 0.84 0.86 1.01 0.84 1.00 0.97 0.82 1.01
Median 0.93 0.99 0.86 0.86 1.02 0.82 0.97 0.94 0.87 1.02
Std.Dev 0.209 0.162 0.159 0.094 0.101 0.220 0.179 0.179 0.204 0.160
b) CommercialMin 0.47 0.69 0.44 0.48 0.70 0.50 0.70 0.45 0.66 0.61
Max 0.96 1.26 1.08 1.07 1.19 1.10 1.20 1.54 0.99 1.48Mean 0.70 1.00 0.81 0.76 0.99 0.73 0.97 0.72 0.84 0.96
Median 0.69 0.99 0.82 0.76 1.02 0.68 1.03 0.63 0.85 0.93
Std.Dev 0.134 0.128 0.188 0.158 0.141 0.185 0.140 0.278 0.103 0.253
c) IndustrialMin 0.72 0.75 0.49 0.39 0.70 0.50 0.70 0.38 0.55 0.77
Max 1.64 1.60 1.22 1.29 1.24 1.19 1.65 1.23 1.31 2.03
Mean 1.03 1.04 0.94 0.96 1.01 0.84 1.03 0.86 0.91 1.22
Median 1.09 1.01 0.96 1.06 1.04 0.82 0.99 0.90 0.96 1.10
Std.Dev 0.234 0.201 0.197 0.231 0.141 0.201 0.240 0.260 0.191 0.352
Figure 7. Mean FF30 values for storm runoff constituents at all catchments
As shown in Figure 7, the average pollutant mass loadings transported by the FF30 are range from
28% to 60%. More mass loading of BOD, COD, TSS, SP and Zn are transported in the first 30% of
runoff volume at the commercial site, if compared to the industrial and residential site. The mean of FF30
values of BOD, COD, TSS, SP and Zn are 53%, 53%, 60%, 51% and 38%, respectively. However, the
FF30 values for O&G, NO3-N, NH3-N and TP are the lowest among all catchments. Bertrand et al. (1998)
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proposed a stricter threshold loading for first flush; at least 80% of the pollutant mass must be transported
in the first 30% of the runoff volume. However, this first flush criterion is seldom met at the present
catchments. Only 5% of the total storm events can meet this criterion for TSS and NH3-N. Some
constituents such as O&G, NO3-N, NO2-N and Zn didnt show a strong first flush effect. The FF30 values
for O&G, NO3-N, NO2-N and Zn are range from 33%-40%, 36%-38%, 34%-40% and 28%-38%,
respectively. Residential site exhibited the highest value of FF30 for O&G and NO2-N. Meanwhile, the
highest FF30 of NO3-N, NH3-N and TP are registered for industrial site.
3.6 Influence of Hydrological Variables on the Strength of First Flush
The strength of a first flush is defined by the coefficient (b value) between the dimensionless
cumulative pollutant mass M(t) from the dimensionless cumulative runoff volume, V(t). A smaller b
value represents a stronger first flush effect and vice versa. The hydrological parameters used to assess
the affect on first flush are rainfall depth, rainfall duration, mean rainfall intensity, maximum 5 min
rainfall intensity, antecedent dry day (ADD), runoff volume, and peak flow (Qpeak). The correlation
analysis results are summarized in Table 6. ADD correlates well with b value of TSS (r = -0.72) and
O&G (r = -0.64) in the residential catchment. The strong negative correlation between ADD and b values
indicated that a longer ADD will result in smaller b value which represents a stronger first flush. This
concurs well with the findings by Gupta and Saul (1996), Deletic and Maksimovic, (1998), Soller et al.
(2005); Nazahiyah et al. (2007); Li et al. (2007) and Huang et al. (2007). There is no significant
correlation between first flush strength of constituent with storm and runoff characteristics.
As shown in Table 6, the first flush strengths of TSS, BOD, COD and NH 3-N in the commercial
catchment are strongly correlated with almost all the storm variables (total rainfall, rainfall duration, max
5 min intensity, runoff volume and peak flow). Therefore, any increase in the magnitude of these storm
variables would result in a greater first flush effect. However, there is no significant correlation between
mean rainfall intensity with TSS in all catchments. He et al. (2010) explained that the TSS loading is
largely determined by the flow magnitude even in the initial phases of the storm. On the other hand, TP
loadings were negatively correlated with rainfall depth, mean rainfall intensity, max 5 min intensity,
runoff volume and peak flow at the commercial catchment. Strong first flush effects for TP occurred
during large storms. The first flush strength of Soluble P increased with increasing rainfall depth, runoff
volume and peak flow at the commercial catchment. These findings concur with the earlier studies thatthe magnitude of first flush has strong positive correlation with maximum rainfall intensity (Gupta and
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Saul, 1996; Deletic, 1998; Taebi and Droste, 2004; Huang et al. 2007; Li et al., 2007) and runoff duration
(Swietlik et al. 1995; Gupta and Saul, 1996; Taebi and Droste, 2004; He et al. 2010).
Table 6 : Correlation coefficients between b-values and hydrological variablesHydrological parameters
Rd RDur I Imax5 ADD Vol Qpeak
a)Residential
TSS -0.72**
O&G -0.64**
b)Commercial
TSS -0.61** -0.70** -0.60* -0.60* -0.64**
BOD -0.76**
-0.75**
-0.77**
-0.77**
-0.81**
COD -0.64**
-0.57*
-0.74**
-0.62**
-0.76**
NH3-N -0.66** -0.54* -0.57* -0.65** -0.66**
SP -0.51*
-0.50*
-0.55*
TP -0.57*
-0.59*
-0.65**
-0.57*
-0.69**
** Correlation is significant at 0.01 level (2-tailed).* Correlation is significant at 0.05 level (2-tailed).
4.0 Conclusions
This study makes significant contributions by answering some key questions especially on the
mechanism of NPS pollutant transport and the influence of hydrologic regime on the pollutant loading.
The findings are useful as basis for improving design criteria and strategies for controlling NPS pollution
in urban areas. This issue is also extremely relevant in tropical environment because its rainfall and the
runoff generation processes are so different from the temperate regions where most of the studies on NPS
pollutant have been carried out. From this study, the following conclusions are made:
1. The EMC values for BOD, COD, TSS, NH3-N, Total P and Soluble P from the commercial
catchment are generally higher than those from industrial and residential catchments. In contrast,
the industrial catchment exhibits the highest medians EMC for O&G, NO3-N, and Zinc. This
finding suggests that the level of NPS pollutant is strongly governed by the major anthropogenic
activities in the catchment. The present study provides a lower mean for the residential catchment
and higher mean for the commercial and industrial catchment. As expected, the EMC values for
stormwater tend to be site specific. Therefore it is important to carry out local stormwater
monitoring program especially in urban areas.
2. On an annual basis, stormwater has transported much greater loads than baseflow for TSS,
BOD, COD and O&G in all the three catchments. In contrast, the annual loadings of NH 3-N and
SP are mainly evacuated by baseflow from the commercial and industrial catchments. On the
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other hand, stormwater runoff showed greater loadings of N and P in the residential catchment.
These findings reinforced the importance role of stormwater runoff in transporting significant
amount of pollutant into the receiving waters.
3. A better understanding of hydrological regime affecting the quality and loadings for major
stormwater constituents was achieved. Except for the antecedent dry days (ADD), the other
rainfall and runoff variables were negatively correlated with EMCs of most pollutants. This study
reinforced the earlier findings on the importance of ADD for causing greater EMC values with
exceptions for O&G, NO3-N, TP and Zinc. The pollutant loadings are influenced primarily by the
rainfall and runoff characteristics. Rainfall depth, mean intensity, max 5 minute intensity, runoff
volume and peak flow were positively correlated with the loadings of most of the constituents.
ADD seemed to be less important for estimating the pollutant loadings.
4. BOD, COD and NH3-N showed consistently strong first flush effects at residential,
commercial and industrial catchments. On the other hand, O&G, NO 3-N, NO2-N and Zn showed
dilution effect during storm events. No significant influence was found between the magnitude of
first flush and storm parameters at the residential and industrial catchments but it was for the
commercial catchment. Rainfall depth, rainfall duration, maximum 5 min intensity, runoff
volume and peak flow had intensified the magnitudes of first flush for TSS, BOD, COD, NH3-N,
SP and TP in the commercial catchment. In short, the influences of storm variables on stormwater
quality were unique for each constituent and the catchment land use.
Acknowledgements
We would like to thank the Ministry of Science, Technology and Innovation (MOSTI) and University
Teknologi Malaysia for supporting this research.
REFERENCES (Pls cite our paper in WST )
Abdullah A M, 2005. Nonpoint source pollution from a tropical urban residential area. Ph.D. Thesis. Universiti Putra
Malaysia, Malaysia.
Al-Jaralla R, Al-Fares R, 2009. Quality of stormwater runoff in the State of Kuwait, Al-Asema
Governorate.International Journal of Environmental Studies, 66 (2): 227239.
APHA (American Public Health Association), 2005. Standard methods for the examination of water and
waste water (19th ed.). Washington DC, USA.
Atasoy M, Palmquist R, Phaneuf D, 2006. Estimating the effects of urban residential development on
water quality using micro data.Journal of Environmental Management, 79: 399-408.
http://www.ingentaconnect.com/content/routledg/genv;jsessionid=35hi4p214a0nf.alicehttp://www.ingentaconnect.com/content/routledg/genv;jsessionid=35hi4p214a0nf.alicehttp://www.ingentaconnect.com/content/routledg/genv;jsessionid=35hi4p214a0nf.alicehttp://www.sciencedirect.com/science/journal/03014797http://www.sciencedirect.com/science/journal/03014797http://www.sciencedirect.com/science/journal/03014797http://www.sciencedirect.com/science/journal/03014797http://www.ingentaconnect.com/content/routledg/genv;jsessionid=35hi4p214a0nf.alice8/3/2019 Artikel Jurnal Zul Yusop Edit by Dr Wan
22/25
22
Baird F C, Dybala T J, Jennings M E, Okerman D J, 1996. Characterization of nonpoint sources and
loadings to the Corpus Christi Bay National Estuary Program study area. Texas Natural Resource
Conservation Commission, Texas, USA. Report No. CCBNEP-05.
Ballo S, Liu M, Hou L, Chang J, 2009. Pollutants in stormwater runoffin Shanghai (China):
implications for management of urban runoffpollution. Progress in Natural Science, 19 (7): 873-880.
Bertrand-Krajewski J L, Chebbo G, Saget A, 1998. Distribution of pollutant mass vs. volume in
stormwater discharges and the first flush phenomenon. Water Research, 32: 23412356.
Brezonik P L, Stadelmann T H, 2002. Analysis and predictive models of stormwater runo ff volumes,
loads, and pollutant concentrations from watersheds in the Twin Cities metropolitan area, Minnesota,
USA. Water Research, 36: 17431757.
Brown J N, Peake B M, 2006. Sources of heavy metals and polycyclic aromatic hydrocarbons in urban
stormwater runoff. Science of the Total Environment, 359: 145155
Caltrans, 2000. Guidance Manual: Stormwater Monitoring Protocols. Department of Transportation,
California. Report No. CTSW-RT-00-005.
Charbeneau, R. J. and Barrett, M. (1998). Evaluation of Methods for Estimating Stormwater Pollutants
Loads. Water Environmental Research. 70: 1295-1302.
Choe J S, Bang K W, Lee J H, 2002. Characterization of Surface Runoff in Urban Areas. Water Science
& Technology; 45(9) 249-254.
Chua L H C, Lo E Y M, Shuy E B, Tan S B K, 2009. Nutrients and suspended solids in dry weather and
storm flows from a tropical catchment with various proportions of rural and urban land use. Journal
of Environmental Management, 90: 3635-3642.
Chui P C, 1997. Characteristics of storm water quality from two urban watersheds in Singapore.
Environmental Monitoring & Assessment, 44: 173-181.
DBKL, 2003. Study and preparation of drainage and stormwater management masterplan for wilayah persekutuan
Kuala Lumpur. Dewan Bandaraya Kuala Lumpur (DBKL), Malaysia. Interim Report.
Deletic A, 1998. The first flush load of urban surface runoff. Water Research, 32(8): 24622470.
DOE, 2003. The study of pollution prevention and water quality improvement of Sungai Tebrau and
Sungai Segget. Department of Environment Malaysia, Malaysia. Final Report.
DOE, 2004. The study on pollution prevention and water quality improvement of Sg. Melaka.
Department of Environment Malaysia, Malaysia. Final Report.
Driver N E, Mustard M H, Rhinesmith R B, Middleburg R F, 1985. U.S. Geological Survey urban
stormwater database for 22 metropolitan areas throughout the United States. U.S. Geological Survey,
USA. Open File Report 85- 337.
8/3/2019 Artikel Jurnal Zul Yusop Edit by Dr Wan
23/25
23
Gan H, Zhuo M, Li D, Zhou Y, 2008. Quality characterization and impact assessment of highway runoff
in urban and rural area of GuangZhou, China.Environmental Monitoring & Assessment, 140(1):
147-159.
Gnecco I, Berretta C, Lanza L G, Barbera P La, 2006. Quality of stormwater runoff from paved surfaces
of two production sites. Water Science & Technology, 54(6-7):177184.
Goonetilleke A, Thomas E C, 2004. Water quality impacts of urbanization: relating water quality to urban
form. Centre for Built Environment and Engineering Research, Queensland University of
Technology, Brisbane, Australia. Technical report.
Huber W C, 1993. Contaminant transport in surface water. In: Handbook of Hydrology (Maidment D R,
editor). New York: McGraw Hill.
Jin S Q, Lu J, Chen D J, Shen Y, Shi Y M, 2009. Relationship between catchment characteristics and
nitrogen forms in Cao-E River Basin, Eastern China.Journal of Environmental Sciences, 21: 429
433.
Kim L H, 2002. Monitoring and modeling of pollutant mass in urban runoff: washoff, buildup and litter.
Ph.D. Thesis. University of California, Los Angeles, USA.
Kim L H, Kayhanian M, Zoh K D, Stenstrom M K, 2005. Modeling of highway stormwater runoff.
Science of the Total Environment, 348:1-18.
Larsen T, Broch K, Andersen M R, 1998. First flush effects in an urban catchment area in Aalborg. Water
Science & Technology, 37(1): 251257.
Lee J H, Bang K W, Ketchum L H, Choe J S, Yu M J, 2002. First flush analysis of urban storm runoff.
Science of the Total Environment, 293: 163175.
Li Y, Lau S L, Kayhanian M, Stenstrom M K, 2005. Particle size distribution in highway runoff.Journal
of Environmental Engineering, 131: 12671276.
Line D E, Wu J, Arnold J A, Jennings G D, Rubin A R, 1997. Water quality of first flush runoff from 20
industrial sites.Water Environment Research, 69(3): 305-310.
Line D E, White N M, Osmond D L, Jennings G D, Mojonnier C B, 2002. Pollutant export from various
land uses in the Upper Neuse River Basin.Water Environment Research, 74(1): 100-108.
Luo H B, Luo L, Huang G, Liu P, Li J X, Hu S, Wang F X, Xu R, Huang X X, 2009. Total pollution
effect of urban surface runoff.Journal of Environmental Sciences, 21: 11861193.
Maestre A, Pitt R, 2006. Identification of significant factors affecting stormwater quality using the
NSQD. In: Stormwater and Urban Water Systems Modeling, Monograph 14 (James W, Irvine K N,
McBean E A, Pitt R E, eds.). Guelph, Ontario: CHI. p. 287326.
http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://www.ingentaconnect.com/content/wef/werhttp://www.ingentaconnect.com/content/wef/werhttp://www.ingentaconnect.com/content/wef/werhttp://www.ingentaconnect.com/content/wef/werhttp://www.ingentaconnect.com/content/wef/werhttp://www.ingentaconnect.com/content/wef/werhttp://www.ingentaconnect.com/content/wef/werhttp://www.ingentaconnect.com/content/wef/werhttp://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://cedb.asce.org/cgi/WWWdisplay.cgi?1685698/3/2019 Artikel Jurnal Zul Yusop Edit by Dr Wan
24/25
24
McConnell R G, Araj E G, Jones D T, 1999. Developing non point source water quality level of service
for Hillsborough County, Florida. In: Proceeding of Sixth Biennial Stormwater Research and
Watershed Management Conference. Florida, USA.
McLeod M S, Kells A J, Putz J G, 2006. Urban runoff quality characterization and load estimation in
Saskatoon, Canada.Journal of Environmental Engineering, 132(11): 14701482.
Miller R A, Mattraw H C, 2007. Stormwater runoff quality from three land-use areas in South Florida.
Journal of the American Water Resources Association, 18 (3): 513519.
Mohamed M, Rahmat S N, Zulkifli Y, Chan C H, 2004. Non-point source pollution loadings from
residential and commercial area in Johor, Malaysia. In: Proceedings of the 8th
International
Conference on Diffuse Pollution. Kyoto, Japan.
Nazahiyah R, 2005. Modeling of non point source pollution from residential and commercial catchments
in Skudai, Johor. Master Thesis. Universiti Teknologi Malaysia, Malaysia.
Nazahiyah R, Yusop Z, Abustan I, 2007. Stormwater quality and pollution load estimation from an urban
residential catchment in Skudai, Johor, Malaysia. Water Science & Technology, 56(7): 1-9.
Novotny V, 2003. Water quality: diffuse pollution and watershed management. New Jersey: John Willey
& Sons.
Pitt R, Maestre A, Morquecho R, Williamson D, 2004. Collection and examination of a municipal
separate storm sewer system database. In: Innovative Modeling of Urban Water Systems,
Monograph 12 (James W, eds.). Guelph, Ontario: CHI.
Qin H P, Khu S T, Yu X Y, 2010. Spatial variations of storm runoff pollution and their correlation with
land-use in a rapidly urbanizing catchment in China. Science of the Total Environment, 408: 4613
4623.
Sansalone J J, Cristina C M, 2004. First flush concepts for suspended and dissolved solids in small
impervious watersheds.Journal of Environmental Engineering, 130(11): 13011314.
Shen Z Y, Hong Q, Yu H, Niu J F, 2010. Parameter uncertainty analysis of non-point source pollution
from different land use types. Science of the Total Environment, 408: 19711978
Smullen J T, Cave K A, 2002. National stormwater runoff pollution database. In: Wet-Weather Flow in
the Urban Watershed (Field R, Sullivan D. eds.). Boca Raton: Lewis Publishers.
SWMA, 2003. Masterplan study on integrated catchment management and urban stormwater
management for Sg. Damansara. Selangor Water Management AuthoritySWMA, Malaysia. Final
Report.
Taebi A, Droste R L, 2004. First flush pollution load of urban stormwater runoff. Journal of
Environmental Engineering, 3: 301309.
http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://www3.interscience.wiley.com/journal/119565979/issuehttp://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://cedb.asce.org/cgi/WWWdisplay.cgi?168569http://www3.interscience.wiley.com/journal/119565979/issuehttp://cedb.asce.org/cgi/WWWdisplay.cgi?1685698/3/2019 Artikel Jurnal Zul Yusop Edit by Dr Wan
25/25
25
Toran L, Grandstaff D, 2007. Variation of nitrogen concentrations in stormpipe discharge in a residential
watershed.Journal of the American Water Resources Association, 43(3): 630-641.
US EPA, 2005. National Management Measures to Control Nonpoint Source Pollution from Urban Areas.
U. S. Environmental Protection Agency, Washington, DC, USA. EPA-841-B-05-004.
US EPA, 1983. Results of the Nationwide Urban Runoff Program (NURP). Water Planning Division,
Washington, DC, USA. PB 84-185552.
Vaze J, Chiew F H S, 2003. Study of pollutant washoff from small impervious experimental plots. Water
Resources Research, 39(6): 1160.
Wang X L, Lu Y L, Han J Y, He G Z, Wang T Y, 2007. Identification of anthropogenic influences on
water quality of rivers in Taihu watershed.Journal of Environmental Sciences, 19: 475481.
Waschbusch R J, Selbig W R, Bannerman R T, 1999. Sources of phosphorus in stormwater and street
dirt from two urban residential basins in Madison, Wisconsin, 1994-95. City of Madison, Wisconsin.
U.S. Geological Survey Water Resources Investigations Report 99-4021.
Wei Q S, Zhu G F, Wu P, Cui L, Zhang K S, Zhou J J, Zhang W R, 2010. Distributions of typical
contaminant species in urban short-term storm runoff and their fates during rain events: a case of
Xiamen City.Journal of Environmental Sciences, 22(4): 533539.
Yamada K, 2007. Diffuse pollution in Japan: issues and perspectives. Water Science & Technology,
56(1): 1120.
Zhang M K, Wang H, 2009. Concentrations and chemical forms of potentially toxic metals in road-
deposited sediments from different zones of Hangzhou, China.Journal of Environmental Sciences,
21: 625631.
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