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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

0

2

4

6

8

10

12

1416

0

50

100

150

200

250

300

1 2 3 4 5 6 7 8 9 10 11 12

Rainyday(day)

Rainfalldepth(mm)

Month

Rainfall rainy day

Formatted: Strikethrough


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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.

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