i
EVALUATION AND PERFORMANCE OF GROSS POLLUTANT TRAPS
(GPTs) FOR OPEN CHANNEL FLOW
NOOR SURAYA BINTI ROMALI
A project report submitted in partial fulfilment of the
requirement for the award of the degree of
Master of Engineering (Civil – Hydrology and Water Resources)
Faculty of Civil Engineering
Universiti Teknologi Malaysia
NOVEMBER, 2008
iii
DEDICATION
To my beloved mother and father,
Fatimah Binti Mohamad and Romali Bin Jusoh,
my sisters and brothers,
my special friend,
not forgotten my supervisors,
Assoc. Prof Dr. Norhan Bin Abd Rahman,
Pn Noraliani Binti Alias.
Thank you for your special supports.
iv
ACKNOWLEDGEMENT
In the name of Allah S.W.T, the Almighty and Merciful, I am so grateful for
the chances, strength and patience in me in the accomplishment of this study. My
appreciation goes much to my supervisor, Assoc. Prof Dr. Norhan Bin Abd Rahman
and my co-supervisor, Pn. Noraliani Binti Alias for their guidance, expertise,
knowledge, and opinion that help me to complete this study.
My thanks go to Muhammad Fuad Bin Shukor, Irma Norazurah Binti
Mohamad, Nur Hanim Binti Abd Ghani and all technicians that are giving full
commitment and cooperation during this study. My thanks also dedicated for my
friends and lecturers for their helps, advices, and supports.
Lastly, a special compliments for the most important person in my life, my
parents, family, and my special friend for their supports. Their loves give me strength
to accomplish this study.
v
ABSTRACT
Nowadays, water has become a scarce resource due to the water
contamination problem. Pollution carried by urban stormwater is considered as one
of the significant contributor to the degradation of receiving waters. One way to
minimize water pollution is by constructing Gross Pollutant Traps (GPTs) at the
point source to treat polluted water prior to being discharged into the river. This
study emphasizes on the utilization of GPTs in removing pollutants during dry and
wet weather conditions especially during stormwater events in open channel system.
The GPTs system is consist of rubbish trap, oil and grease trap, and biofilter which
located at Block L50, UTM Skudai. The results indicated that the GPTs system is
effective in improving water quality during storm event where the effluent of
discharge water of the GPTs system are comply with parameter limit as stated in
Standard A and Standard B of Environmental Quality Act (1974). First flush analysis
shows that the concentration of pollutants in first flush runoff is found more polluted
than the remainder while the values of EMC for TSS, COD, and BOD are higher
compared to other pollutants. Despite of functioned for water quality control, the
GPTs is also benefit for water quantity control where it provide detention time,
storage, and decrease the peak flow of the water flowing through the system.
vi
ABSTRAK
Air telah menjadi satu sumber yang terhad ekoran daripada masalah
pencemaran air yang berlaku pada masa kini. Bahan cemar dari air larian permukaan
yang dibawa oleh hujan lebat telah dikenalpasti sebagai antara faktor yang
merendahkan kualiti air. Satu cara untuk mengurangkan masalah pencemaran air
ialah dengan menggunakan Gross Pollutant Traps (GPTs) untuk merawat air dari
punca pencemaran sebelum dilepaskan ke sungai. Kajian ini menekankan
keberkesanan sistem GPTs menyingkirkan bahan cemar semasa cuaca kering dan
basah terutama sewaktu hujan lebat di dalam sistem saluran terbuka. Lokasi kajian
terletak di sistem GPTs di Blok L50 UTM, Skudai. Sistem GPTs tersebut
merangkumi perangkap sampah, perangkap minyak dan gris, dan penapis air biologi.
Keputusan menunjukkan bahawa sistem GPTs ini efektif dalam meningkatkan kualiti
air di mana ‘effluent’ dari sistem ini mematuhi had yang ditetapkan oleh Standard A
dan Standard B Akta Kualiti Alam Sekitar (1974). Analisis air curahan pertama (first
flush) mendapati tahap kepekatan bahan cemar adalah lebih tinggi dalam sampel air
curahan pertama berbanding sampel air di akhir aliran air manakala nilai EMC untuk
parameter TSS, COD, dan BOD adalah lebih tinggi berbanding parameter lain.
Selain berkeupayaan untuk pengawalan kualiti air, GPTs juga berfungsi dalam
pengawalan kuantiti air dengan menyediakan waktu tahanan air, penyimpanan air,
dan dapat mengurangkan kadar puncak aliran air.
vii
TABLE OF CONTENTS
CONTENTS
ABSTRACT
ABSTRAK
TABLE OF CONTENTS
LIST OF TABLES
LIST OF FIGURES
LIST OF SYMBOLS
LIST OF ABBREVIATION
LIST OF APPENDIX
CHAPTER 1
1.0 INTRODUCTION
1.1 General
1.2 Problem Statement
1.3 Study Objectives
1.4 Scope of Study
1.5 Location of Study
1.6 Significance of Study
CHAPTER 2
2.0 LITERATURE REVIEW
2.1 General
2.2 Water Pollution
2.2.1 Stormwater pollutants
2.3 First Flush
2.4 Event Mean Concentration (EMC)
2.5 Gross Pollutant Traps (GPTs)
2.5.1 Design Criteria and Mechanism of GPTs system
2.5.1.1 Rubbish Trap
2.5.1.2 Filtration
2.5.1.2.1 Biofiltration Swale and Vegetated
Filter Strip
PAGE
v
vi
vii
x
xii
xvi
xvii
xviii
1
2
3
3
5
7
8
9
10
12
13
19
19
20
22
22
viii
2.6
2.5.1.2.2 Media Filtration
2.5.1.3 Oil Separators
2.5.2 The effectiveness of GPTs as water pollution treatment
devices
2.5.2.1 Gross Pollutants Traps
2.5.2.2 Filtration
2.5.2.3 Oil and Grease Traps
2.5.2.4 GPTs system at L50 Block, UTM Skudai
Critical Appraisal
23
23
25
25
30
38
40
44
CHAPTER 3
3.0 METHODOLOGY
3.1 Introduction
3.2 Flow Chart of the Study Methodology
3.3 Experimental Site
3.3.1 Catchment Area
3.4 Experimental System
3.4.1 Rubbish Trap
3.4.2 Oil and Grease Trap
3.4.3 Biofilters
3.4.3.1 Modification of Biofilter
3.4.3.2 Filtration Materials
3.5 Data Collection
3.5.1 Rubbish and Sediment Data
3.5.2 Storm Events
3.5.3 Rainfall and Flow Measurement
3.5.3.1 Calibration
3.5.4 Water Sampling
3.5.4.1 Water Quality Sampling
3.5.4.2 Analytical Method
3.5.4.2.1 First Flush Sample
3.5.4.2.2 Water Quality Measurement
3.5.4.3 Data Analysis
3.6 Design Criteria Of GPTs
3.6.1 Rubbish Trap
3.7 Maintenance
45
46
47
49
50
50
51
52
55
55
56
56
57
58
59
61
61
62
62
62
63
64
65
66
CHAPTER 4
4.0 RESULTS AND ANALYSIS
4.1 Introduction
4.2 Performance of GPTs System during Dry Weather
4.3 Rubbish and Sediment Collection
4.3.1 Amount of Rubbish and Sediment
4.3.1.1 Block L50 Rubbish Trap
68
69
69
69
69
ix
4.4
4.5
4.6
4.7
4.8
4.3.1.1.1 Daily Collection
4.3.1.1.2 Three-day Period Collection
4.3.1.2 Block L52 Rubbish Trap
4.3.2 Rainfall Analysis
4.3.3 Classification of Rubbish
4.3.3.1 Daily Collection
4.3.3.2 Three-day Period Collection
Water Quality Results
4.4.1 Block L50 GPTs system
4.4.1.1 Water Quality Evaluation
4.3.1.1.1 GPTs Evaluation Results
4.3.1.1.2 Rubbish Trap Evaluation Results
4.3.1.1.3 Biofilter Evaluation Results
4.4.2 Block L52 Rubbish Trap
Water Quantity Control
First Flush Analysis
4.6.1 Pollutograph Evaluation
4.6.1.1 Event 04/08/2008
4.6.1.2 Event 07/08/2008
4.6.1.3 Event 12/08/2008
4.6.1.4 Event 04/09/2008
4.6.2 Occurrence of First Flush
Event Mean Concentration (EMC)
Evaluation of Design Criteria
69
72
74
76
78
79
79
81
81
87
87
90
94
97
99
100
101
103
104
105
108
108
109
CHAPTER 5
5.0 CONCLUSION AND RECOMMENDATION
5.1 Conclusion
5.2 Recommendation
112
114
REFERENCES
APPENDIX
115
121
x
LIST OF TABLES
TABLE
TITLE
PAGE
2.1
General urban runoff EMCs of US EPA, NURP, MOE (Ontario),
USGS and NPDES and MASMA
15
2.2
Typical EMCs values of residential land use from various storm
water runoff quality studies
17
2.3
Typical EMCs values of commercial land use from various storm
water runoff quality studies
18
2.4
Overall Classification of Gross Pollutant Traps
20
2.5
Recommended Treatment Trains
30
2.6
Different types of materials used for modification
40
2.7
Mechanism of development used in oil & grease trap
41
3.1
Different materials used for modifications
55
3.2
Roles of the Filtration Media
56
3.3
Characteristics of monitored storm events at Block L50
57
3.4
Characteristics of monitored events at Block L52
58
3.5
Calibration results for IWK flow meters
60
3.6
Calibration results for TPI flow meter
61
3.7
Analytical Methods for Water Quality Measurement
63
3.8
Description of Water Quality Evaluation
64
4.1
Data Collection Procedure for rubbish trap at Block L50 and
70
xi
rubbish trap at Block L52
4.2
Daily rubbish and sediment data collection at Block L50 Rubbish
Trap
71
4.3
Three-day period rubbish and sediment data collection at Block
L50 Rubbish Trap
73
4.4
Three-day period rubbish and sediment data collection at Block
L52 Rubbish Trap
75
4.5
Description of rubbish types
79
4.6
Water Quality Events
82
4.7
Water Quality Results for 21/03/2008
84
4.8
Water Quality Results for 04/08/2008
84
4.9
Water Quality Results for 07/08/2008
85
4.10
Water Quality Results for 12/08/2008
86
4.11
Water Quality Results for Overall GPTs System
87
4.12
Percentages of Removal for GPTs
90
4.13
Evaluation Results for Rubbish Trap
91
4.14
Percentages of Removal for Rubbish Trap
93
4.15
Evaluation Results for Biofilter
94
4.16
Percentages of Removal for Biofilter
94
4.17
Water Quality Results for Block L52 Rubbish Trap
98
4.18
Water Quantity Results of GPTs system
99
4.19
Occurrence of First Flush
108
4.20
Event Mean Concentration (EMC) values
109
4.21
Typical Event Mean Concentration (EMC) values in mg/L
109
4.22
Design Criteria Results
110
xii
LIST OF FIGURES
FIGURES
TITLE
PAGE
1.1
Location of study area
5
1.2
Location of study area at UTM Skudai
6
2.1
Continuous Deflective System (CDS) trap
21
2.2
Schematic of API Separator
24
2.3
Schematic of Plate Separator
24
2.4
Schematic Plan View Representation of the CDS System
26
2.5
Isometric Representation of the CDS unit
27
2.6
Summary of Efficiency data used in DSS
27
2.7
Structure of the GPTs decision-support-system (DSS) for
evaluating gross Pollutant trapping strategies
29
2.8
Schematic diagram of the experimental facility in St. A and
St. B
32
2.9
Relationship reduction ratio of fish flush runoff by soil
infiltration and total rainfall amount
33
2.10
Hardwood (HW) and western cedar (WC) media
34
2.11
Cross section of the grass-plate
35
2.12
Hydroponics botanical filter
36
2.13
Recirculating system
36
2.14
Floating system
36
xiii
2.15
Schematic diagram of an upflow packed bioreactor
38
2.16
Comparison results for Biofilter in terms of percentages of
removal of TSS, COD and BOD
41
2.17
Comparison results of oil and grease trap in terms of
percentages of removal of oil and grease and TSS
42
2.18
Graph of relationship between amounts of rubbish and rainfall
for rubbish trap at L50
43
2.19
Suspended solid (SS) at point before and after rubbish trap
44
3.1
Flow chart of the study methodology
46
3.2
Location of Study (L50 UTM Skudai)
47
3.3
Road and Drainage Layout Plan of L50 Block, UTM Skudai
48
3.4
Catchment area for the GPTs system
49
3.5
Arrangement of GPTs system and dimensions in mm
50
3.6
Rubbish Trap located prior the sump and at the beginning of
the system
51
3.7
Oil and Grease Trap
52
3.8
Biofilter system
53
3.9
Details of Biofilter system
53
3.10
Flow meter equipment at the inlet point of GPTs system(IWK)
58
3.11
Flow meter equipment at the inlet point of GPTs system(TPI)
59
3.12
Calibration of flow meter
60
3.13
Water Quality and Flow Assessment Sampling Point
62
3.14
Conditions of rubbish trap, oil and grease trap, and biofilter
during stormwater event
67
4.1
Relationship between Rubbish and Sediment Amount with
Rainfall at Block L50 Rubbish Trap (Daily Collection)
72
4.2
Relationship between Rubbish and Sediment amount with
Rainfall at Block L50 Rubbish Trap (Three-day period
collection)
74
xiv
4.3
Relationship between Rubbish and Sediment amount with
Rainfall at Block L52 Rubbish Trap (Three-day period
collection)
76
4.4
Correlation between Rubbish and Sediment amount with
Rainfall at Block L50 Rubbish Trap for Daily collection and
Three-day period collection
77
4.5
Correlation between Rubbish and Sediment amount with
Rainfall at Block L52 Rubbish Trap for three-day period
collection
78
4.6
Classification of rubbish according to types for daily collection
for total collection and maximum day collection (Block L50
rubbish trap)
80
4.7
Classification of rubbish according to types for three-day
period collection for total collection and maximum day
collection (Block L50 rubbish trap)
80
4.8
Flow hydrograph and sampling time for event on 04/08/08,
07/08/08, and 12/08/08.
82
4.9
Water Quality Results for GPTs for parameters pH, SS, BOD,
COD, AN, and DO
88
4.10
Water Quality Results for Rubbish Trap for parameters pH,
SS, BOD, COD, AN, and DO
92
4.11
Water Quality Results for biofilter for parameters pH, SS,
BOD, COD, AN, and DO
95
4.12
Water Quality Results for Block L52 during storm event and
dry weather
97
4.13
Flow Hydrograph for event 04/08/2008, 07/08/2008, and
12/08/2008
99
4.14
Flow Hydrograph for event 04/09/2008
101
4.15
Pollutograph for event 04/08/2008 for parameters BOD,
COD,SS, Cu, Zinc, and AN
102
4.16
Pollutograph for event 07/08/2008 for parameters BOD,
COD,SS, Cu, Zinc, and AN
103
4.17
Pollutograph for event 12/08/2008 for parameters BOD,
COD,SS, Cu, Zinc, and AN
104
xv
4.18
Pollutograph for event 04/09/2008 for parameters BOD, COD,
SS, Cu, Zinc, AN, TP, Pb, Nitrite, and Nitrate
106
xvi
LIST OF SYMBOLS
m
- Meter
Q
- Flow in
Q
in
out
- Flow out
s
- Second
M
- Pollutant mass
V
- Runoff volume
t
- Time
C
- Concentration
>
- Greater than
<
- Smaller than
η
- Pollutant removal efficiency
xvii
LIST OF ABBREVIATION
AN
Ammonia Nitrogen
API
American Petroleum Institute
BMPs
Best Management Practices
BOD
Biochemical Oxygen Demand
CDS
Continuous Deflective Separation
COD
Chemical Oxygen Demand
CS
Collector System
DO
Dissolved Oxygen
EMC
Event Mean Concentration
GPTs
Gross Pollutant Traps
HW
Hardwood
MASMA
Urban Stormwater Management Manual for Malaysia
mg/L
Milligram per liter
mm
Millimeter
TSS
Total Suspended Solid
UTM
Universiti Teknologi Malaysia
WC
Western Cedar
DID
Department of Irrigation and Drainage Malaysia
ha
Hectare
xviii
LIST OF APPENDIX
APPENDIX
A
TITLE
Average Recurrence Interval (ARI) for existing drainage
PAGE
121
system
B
Invert Level (I.L) – GPTs System
127
C
Slope – GPTs System
128
D
Details Plan – GPTs System
129
E
CALIBRATION FOR FLOW METER
134
F
LABORATORY TEST RESULTS FOR EVENT
138
04/09/2008
G
FIRST FLUSH CALCULATION
141
H
EMC CALCULATION
169
I
DESIGN CRITERIA CALCULATION
186
1
CHAPTER 1
INTRODUCTION
1.1
General
Water is the basic element of life; without it life would not exist. It is one of
the most important resources for man, and yet it is taken for granted because water is
everywhere and it flows freely when we turn on the tap. The usage for water
increases as population grows until the demand sometimes overshoots the supply or
availability. Although the quantity of water on Earth is same all the time, the quality
of the water that is available has drastically changed. Every watershed is affected by
what takes place on the land. Once used, water flows out as quickly as it comes,
down into the drain and into our rivers. The gunk and grease that is flushed down
into the drain unthinkingly every day will ultimately find their way to a nearby river.
In other words, we are poisoning the very resource that gives us life.
Many ways have been practiced to reduce the water pollution. One of the ways
is to treat wastewater at the source points. This can be accomplished by constructing
Gross Pollutants Traps (GPTs) at the source point to treat water prior to discharge
into the river. Generally, GPTs are devices that collect large pollutants from
waterways, before they enter wetlands and marine waters. They are used in urban
water infrastructure such as stormwater drains, urban wetlands, beach fronts, and
airports. They generally collect larger items from the water, such as take away
containers, leaves, bottles and plastic bags. Smaller pollutants, such as dirt,
chemicals, heavy metals and bacteria are not collected directly by the GPTs;
2
however, some small particles are caught up in the larger items in the trap and thus
prevented from reaching the waterway (Hughes, 2004).
1.2
Problem Statement
Stormwater pollutants are generated from urban land-use activities and are
transported from street surfaces by stormwater runoff before discharging into
receiving waters. Community awareness of the environmental effects of urban
stormwater pollution and their expectation that urban aquatic ecosystems are
protected from environmental degradation has resulted in an increased emphasis on
urban stormwater quality. Many local authorities have implemented stormwater
management strategies for the protection of receiving waters. These include major
public awareness campaigns to encourage environmental sensitivity and structural
methods to physically remove pollutants from stormwater. Such initiatives are
essentially focused on visible pollutant impacts and concerned with reducing gross
pollutants, particularly litter. However, urban stormwater transports a variety of
material ranging from large gross pollutants to fine particulates, all of which impact
urban receiving waters and therefore require a waste water treatment device that are
capable of removing the various types of the pollutants (Walker et. al., 1999).
Pollution carried by urban stormwater is considered a significant contributor
to the degradation of receiving waters. Urban stormwater pollutants include gross
pollutants, trace metals and nutrients that are associated with sediments, and
dissolved pollutants (Walker et. al., 1999). The generation and transport of pollution
in urban systems during a storm event is multifaceted as it concerns many media,
space and time scales (Ahyerre et. al., 1998). During the storm event, the
concentration of pollutants in first flush runoff is believed more polluted than the
remainder due to the washout of deposited pollutants by rainfall.
To preserve the good quality of water resources, it is essential to control the
water pollution in river by treating the waste water especially the first flush during
3
storm event which carries with it concentrations of pollutants that have accumulated
during the period of dry weather between storms. An effective system of waste water
treatment, such as GPTs is important to cater the various types and size of pollutants.
1.3
Study Objectives
The objectives of the study are summarized as follows:
1. To evaluate the effectiveness of GPTs system in removing pollutants during
storm event and dry weather conditions.
2. To investigate the occurrence and the influence of first flush to the
concentration of pollutants entering GPTs system during storm events.
3. To obtain hydrologic data and Event Mean Concentration (EMC) for the
purpose of the evaluation of GPTs system.
1.4
Scope of Study
This Gross Pollutant Traps (GPTs) system consists of rubbish trap, oil and
grease trap, and biofilter. The experimental site will operate at L50 where the
stormwater come from nearby catchment area (parking lot and the nearby building).
The scopes of this study are;
i.
An open drainage system at L50 Block, Universiti Teknologi Malaysia,
Skudai.
ii.
To improve the design criteria of the existing GPTs system in order to
provide a better quality of surface water runoff for the system.
iii.
To study about the first flush runoff phenomenon, in terms of its
concentration of pollution load compared to normal runoff pollution load.
4
iv.
To investigate the capability of the GPTs system in treating the first flush
pollution load.
v.
To determine the relationship between the reductions of the first flush runoff
pollution load by the GPTs system and the total rainfall amount.
vi.
To come out with hydrograph for stormwater events.
vii.
Determination of water quality parameters such as pH, Suspended Solid (SS),
Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD),
Dissolved Oxygen (DO), and Ammonia Nitrogen (AN).
viii.
The maintenances of the GPTs system to ensure the cleanliness of the
experimental site and that there are no overflow of the system occur as the
result of any blockage by the rubbish.
5
1.5
Location of study
The Gross Pollutant Traps (GPTs) system is located at L50 Block, Universiti
Teknologi Malaysia (UTM), Skudai. The compartmentalized GPTs consist of several
compartments of Rubbish Trap, Oil and Grease, and Biofiltration. The UTM river in
this study is the tributaries of the Sungai Skudai, as shown in Figure 1.1. Figure
1.2(a) and 1.2 (b) illustrated the location of study area at UTM Skudai.
Mukim
Kulai
Seelong
Study
Area
Sg Skudai
Kangkar
Pulai
UTM,
Skudai
Taman
Universiti
Figure 1.1: Location of study area
Mukim
Tebrau
6
K TD I
Experimental Site
KTC
L50
Lingkaran Ilmu
KRP
(a)
(b)
Figure 1.2: Location of study area (a) plan and (b) Google Image plan at UTM
Skudai
7
1.6
Significance of Study
GPTs is a device that has a good potential in removing water pollutants.
Nowadays, there are various types of GPTs available in market, such as Baramy
Trap, Continuous Deflective Separation (CDS) Trap, HumeCeptor, and Cleansall
Trap. However, most of the GPTs systems are only concentrated in removing large
pollutants mainly rubbish. There are still lack of GPTs systems that include the
function of removing small size pollutants such as oil, grease, and bacteria available
in market. Hence, a development of GPTs system that consists of compartments that
will function in removing both large and small size of pollutants is essential in order
to produce an effective water pollution treatment system. Moreover, most of the
available products of GPTs are from overseas, mainly Australia. The study on GPTs
in Malaysia is significant in order to produce our local GPTs’ product.
8
CHAPTER 2
LITERATURE REVIEW
2.1
General
Water covers over 70% of the Earth's surface and is a very important resource
for people and the environment. Water pollution affects drinking water, rivers, lakes
and oceans all over the world. This consequently harms human health and the natural
environment. Gross Pollutant Traps (GPTs) is a water treatment device that can be
used to reduce water pollution by treating the water at point source. The design of
GPTs is an important criterion that should be taken into account to obtain an
effective result of water treatment.
The aims of this study are, thus, to investigate the effectiveness of GPTs as a
water pollution treatment device and to evaluate its performance during dry and wet
weather condition especially on storm water event. In the following articles the
related literature are briefly presented. This chapter is mainly divided into several
parts. The first part contains the information about water pollution problems and the
others will discuss about GPTs in terms of its types, design criteria and literature of
previous studies on the role of GPTs in reducing water pollutions.
9
2.2
Water Pollution
Water is one of the most important commodities which Man has exploited
than any other resource for sustenance of his life. Most of the water on this planet is
stored in oceans and ice caps which is difficult to be recovered for our diverse needs.
Most of our demand for water is fulfilled by rain water which gets deposited in
surface and ground water resources. The quantity of this utilizable water is very
much limited on the earth. Though, water is continuously purified by evaporation
and precipitation, yet pollution of water has emerged as one of the most significant
environmental problems of the recent times. Not only there is an increasing concern
for rapidly deteriorating supply of water but the quantity of utilizable water is also
fast diminishing. The causes of such a situation may be many, but gross pollution of
water has its origin mainly in urbanization, industrialization, agriculture and increase
in human population observed in past one and a half century.
The unique properties of water which make it universal solvent and a
renewable resource also make it a substance which by virtue of these properties has
got a much tendency to get polluted. Water can be regarded polluted when it gets
changed in its quality or composition either naturally or as a result of human
activities so as to become less suitable for drinking, domestic, agricultural, industrial,
recreational, wildlife and other uses for which it would have been otherwise suitable
in its natural or unmodified state (Goel, 2006).
Water pollution can come from a number of different sources. If the pollution
comes from a single source, such as an oil spill, it is called point-source pollution. If
the pollution comes from many sources, it is called nonpoint-source pollution. The
causes of water pollution include sewage and waste water, industrial waste and oil
pollution. Pollution carried by urban stormwater is also considered a significant
contributor to the degradation of receiving waters. Stormwater runoff typically
contains significant amounts of anthropogenic pollutants as well as naturally
occurring materials.
10
2.2.1 Stormwater pollutants
Stormwater is a term used to describe water that originates during
precipitation events. It may also be used to apply to water that originates with
snowmelt or runoff water from overwatering that enters the stormwater system.
Stormwater that does not soak into the ground becomes surface runoff, which either
flows into surface waterways or is channeled into storm sewers.
Stormwater is of concern for two main issues - one related to the volume and
timing of runoff water i.e. flood control and water supplies and the other related to
potential contaminants that the water is carrying i.e. water pollution. Because
impervious surfaces such as parking lots, roads, buildings, compacted soil do not
allow rain to infiltrate into the ground, more runoff is generated than in the
undeveloped condition. This additional runoff can erode watercourses, as well as will
cause flooding when the stormwater collection system is overwhelmed by the
additional flow (Wikipedia, 2008).
Pollutants’ entering surface waters during precipitation events is termed
polluted runoff, and may also be labeled as nonpoint source pollution. Pollutants
carried by urban stormwater runoff are considered a significant contributor to the
degradation of receiving waters.
Urban stormwater pollutants include gross
pollutants, trace metals and nutrients that are associated with sediments, and
dissolved pollutants. It is well recognized that a significant amount of pollutants are
transported by stormwater as sediment-bound contaminants. Results from an
investigation by Mann and Hammerschmid (1989) on urban runoff from two
Australian catchments in the Hawkesbury/Nepean basin showed high correlations
between total suspended solids (TSS) and total phosphorus (TP), total Kjeldahl
Nitrogen (TKN) and Chemical Oxygen Demand (COD). Ball et al. (1995) similarly
demonstrated a high correlation between TSS and TP. These Australian studies are
consistent with numerous overseas studies showing similar correlations and
characteristics of road and street runoff.
11
Many investigations have found the concentration of sediment-bound
contaminants in street dirt to be associated with the fine particle size fraction. Pitt
and Amy (1973), NCDNRCD (1993) and Woodward-Clyde (1994) have all shown
that higher concentrations of pollutants such as heavy metals are associated with the
smallest particle size fraction of urban dust and dirt. These data indicate that almost
half of the heavy metals (represented by copper, lead and zinc) found on street
sediments are associated with particles of 60 to 200 µm in size and 75% are
associated with particles finer than 500 µm in size. Dempsey et al. (1993) undertook
a particle size distribution analysis for urban dust and dirt, and partitioned
contaminants into a number of size fractions to determine the concentrations of
contaminants in each particle size range. Results show that the highest recorded
concentrations of Copper, Zinc and Phosphorous are associated with particles
between 74 µm and 250 µm in size.
Colwill et al. (1994) found 70% of oil and approximately 85% of polycyclic
aromatic hydrocarbon (PAH) to be associated with solids in the stormwater. That
study subsequently demonstrated that, over a period of dry weather conditions,
increasing concentrations of oil become associated with particulates with the highest
oil content found in the sediment range of 200 µm to 400 µm.
Coarse sediments transported from urban areas have a physical impact on the
receiving aquatic environment by smothering aquatic habitats and silting waterways
leading to a reduction in the waterway discharge capacity. Fine suspended particles
carried by stormwater flows may not necessarily be considered to have significant
physical impacts on the environment. However, they lead to elevated turbidity levels
and it is generally understood that the highest concentrations of pollutants (such as
nutrients, heavy metals, and organics) are attached to the finer fractions of suspended
sediments in urban stormwater (Walker et al., 1999).
12
2.3
First Flush
The early runoff in a storm event is often more contaminated than the later
part of runoff, which may be due to several factors, including the mobilization of
material accumulated during antecedent dry periods, a lack of dilution flow and a
disproportionate runoff volume from the impervious surfaces, where pollutants may
accumulate. Strong first flushes are usually associated with small impervious
catchments such as highways and parking lots (Ma et al., 2002), and specific rainfall
patterns (Kang et al., 2006).
In general the term first flush has been used to indicate that the mass emission
rate is higher during the initial portions of runoff than during the last portion of the
runoff. The concept of the first flush phenomenon was first advanced in the early
1970s and it continues to be debated by many researchers. Thornton and Saul (1987)
defined the first flush as the initial period of storm water runoff during which the
concentration of pollutants is substantially higher than those observed during the
later stages of the storm event. Geiger (1987) defined the phenomenon as occurring
when the slope of normalized cumulative mass emission plotted against normalized
cumulative volume is greater than 45°. Saget et al. (1995) and Bertrand-Krajewski et
al. (1998) defined the first flush as occurring when at least 80% of the pollutant mass
is discharged in the first 30% of the runoff volume. According to Chapter 30,
MASMA (DID, 2000b), for the purpose of stormwater quality monitoring and
sampling, the first-flush grab sample should be obtained within the first 30 minutes
(min) of discharge. This is generally the most polluted portion of the discharge,
because it may contain pollutants that lie on the surface of the drainage area.
The existence of a first flush depends on the type of pollutant, size of the
catchment as well as the surfaces. Forster (1996) and He et al. (2001) observed a first
flush of heavy metals from rooftops surfaces. Fam et al. (1987) and Ma et al. (2002)
observed the first flush in oil, grease, TSS, COD and total organic carbon from
highways surfaces. Artina et al. (2006) observed the first flush in a commercialindustrial site near Bologna Italy. Barco et al. (2008) examined the pollutant first
flush in an urban catchment with area of 12.7 ha and drained by a combined sewer
13
system located in northern Italy. The results show that treating the maximum amount
of the early part of the runoff is a better strategy than treating a constant flow rate.
Soller et al (2005) had conducted a study on the evaluation of seasonal scale
first flush pollutant loading and implications for urban runoff management. From the
study they had come out with a conclusion that a first flush phenomenon did not
occur consistently for total metals, dissolved metals or anions. There do however
seem to be specific combinations of site and storm circumstances that result in a first
flush effect of dissolved metals and total mercury. They also indicated that there was
no relation between metal concentrations in storm runoff and land use.
Deletic (1997) has analyzed the evidence for the existence and nature of the
first flush load of pollution input into drainage systems, using data on suspended
solid, conductivity, pH, and temperature of storm surface runoff. The study has
found out only slight first flush effects for suspended solids and conductivity of
storm runoff were observed at the catchments. No flush effects have been detected
for pH and temperature. The rainfall and runoff characteristic which influence the
first flush phenomenon are different at each catchment studied, although the
catchments have a very similar characteristics. The study has concluded that the first
flush that strongly present at the end of a drainage system, is not generated by the
first flush of pollution input and the regression curves are not very reliable for
prediction of the first flush load of pollution input into drainage system.
2.4
Event Mean Concentration (EMC)
DID (2000a) in MASMA (Volume 5, Chapter 15) has defined Event Mean
Concentration (EMC) as the flow-weighted mean concentration of a pollutant. The
EMC is computed as the total storm load (mass) divided by the total runoff volume,
although EMC estimates are usually obtained from a flow-weighted composite of
concentration samples taken during a storm. Mathematically:
14
EMC =
M ∫ C (t )Q(t )dt
=
V
∫ Q(t )dt
(2.1)
where C(t) and Q(t) are the time-variable concentration and flow measured during
the runoff event, and M and V are pollutant mass and runoff volume. When EMC is
multiplied by the runoff volume, an estimate of the event loading to the receiving is
obtained. The instantaneous concentration during a storm can be higher or lower than
the EMC, but the use of the EMC as an event characterization replaces the actual
time variation of C versus t in a storm with a pulse of constant concentration having
equal mass and duration as the actual event. This ensures that mass loadings from
storms will be correctly represented.
Kim et al (2006) have conducted a study on diffusion of pollution loading
from urban stormwater runoff in Daejeon city, Korea. In the study, they
characterized stormwater runoff from an urban watershed with combined sewer
systems to measure the stormwater runoff discharge rates and pollutant
concentrations. As the results, they observed the averaged event mean concentrations
(EMCs) of combined sewer overflows (CSO) were 536.1mg TSS/L, 467.7 mg
TCODcr/L, 142.7 mg TBOD/L, 16.5 mg TN/L, and 13.5 mg TP/L.
Chow and Yusop (2008) have conducted a review of the EMC values for
urban storm water runoff. From the study, they have come out with several EMCs
values from agencies and previous studies. The Nationwide Urban Runoff Program
(NURP) study was one of the earliest studies of storm water runoff quality in the
United States (Smullen et al. 1999). Ten water quality pollutants were measured at
more than 2,300 stations at 81 urban sites in 28 metropolitan areas. The EMCs values
for urban runoff in the Unites States which are referenced from US EPA, NURP, and
USGS and NPDES sources are shown in Table 2.1. From Table 2.1, it can be seen
that the total suspended solids (TSS) of US EPA mean are about 15% higher than
that of NURP and approximately 150% higher than that of USGS and NPDES. EMC
for biochemical oxygen demand (BODs) is similar among the sources, but chemical
oxygen demand (COD) varies among the sources by a factor of 2.
15
Table 2.1: General urban runoff EMCs of US EPA, NURP, MOE (Ontario), USGS
and NPDES and MASMA (Chow and Yusop, 2008).
Event Mean Concentration (mg/l)
Pollutant
US EPA(1)
NURP(2)
USGS and
MOE
MASMA(5)
NPDES(3)
(Ontario)(4)
TSS
200.0
174.0
78.4
133.0
85
BOD
12.0
10.4
14.1
COD
103.0
66.1
52.8
809
Total P
0.52
0.34
0.32
0.62
0.13
PO4P
0.17
0.10
0.13
TKN
2.40
1.67
1.73
2.84
NO2 & NO3
1.22
0.84
0.66
2.55
(1) United States Environmental Protection Agency (US EPA, 1983)
(2) Updated Urban Runoff data from Nationwide Urban Runoff Programs (NURP,
1999)
(3) United States Geological Survey (USGS) and National Pollutant Discharge
Elimination System (NPDES, 1999)
(4) Metropolitan Toronto Waterfront Wet Weather Outfall Study, Phase I (1992) and
Phase II (1995), report to Ontario Ministry of the Environment, Toronto, Ontario,
Canada.
(5) Urban Stormwater Management Manual for Malaysia.
One limitation for the EMC values in Table 2.1 is that they do not distinguish
between different urban land use types. However, one conclusion of the NURP study
was that due to the amount of site-to-site variability, any differences in EMCs
between different urban land uses (residential, commercial and mixed) or geographic
regions were for the most part not significant. Similar studies were also conducted in
Canada, for example, the Toronto Area Watershed Management Strategy Study was
conducted by the Ontario Ministry of the Environment to develop a comprehensive
water quality management plan for Toronto area watersheds. The EMCs values for
urban runoff in Toronto are considerably higher than that of US especially the
chemical oxygen demands (COD). While the typical EMC values stated in the urban
stormwater management manual for Malaysia (MASMA) which compiled from
different sources also shown a vary among the US sources especially the total
phosphorus vary among the US data by a factor of 3.
Table 2.2 and 2.3 show the EMC values of residential and commercial land
use from various storm water runoff quality studies. Each study has different
catchment and climate characteristics, therefore a high coefficient of variation is
found among the sources. There is questionable as to the applicability of applying
16
event mean concentrations for different land uses developed in one part of the
country to another region. As seen in the EMCs values presented in Table 2.2 and
Table 2.3, wide variation can exist not only regionally, but on a local scale as well.
Furthermore, national mean concentrations obtained by averaging numbers from a
variety of geographically disbursed studies can still yield differing results. For
example, it can be seen that a large variation exist among the studies from ChongJu,
Central and South Florida and Los Angeles Cities, USA for the EMCs of singlefamily and multi-family residential area. These results may indicate that the event
mean concentration value is site specific and vary from site to site. EMC of Total
suspended Solids (TSS) reported by Nazahiyah (2005) was higher than that among
the US sources for residential area while is similar among the US EPA and NURP
for commercial land use. The size of catchments also shows that have a significant
effect on the event mean concentration of storm water runoff. Study from Brezonik
and Stadelmann (2001), at Twin Cities Metropolitan Area, Minnesota, reported that
the EMCs for urban residential area ≥ 40 ha is greater than the urban residential area
≤ 40 ha by a factor of 2 (Chow and Yusop, 2008).
17
Table 2.2: Typical EMCs values of residential land use from various storm
water runoff quality studies (Chow and Yusop, 2008)
Site
Line et al. (2002)
Upper Neuse River
Basin, North Corolina,
USA.
Baldys et al. (1998)
Dallas-Fort Worth
Area, Texas,(19921993) USA.
Guerard and Weiss
(1995)
Colorado Springs,
Colorado
Los Angeles County
Stormwater Monitoring
Report: 1998-1999
(1999)
Los Angeles Cities
Harper, H. H. (1998)
Central and South
Florida, USA.
Brezonik and
Stadelmann (2001).
Twin Cities
Metropolitan Area, ,
USA.
Centennial Park
Catchment, Australia.
Siti Nazahiyah (2005).
Skudai, Johor,
Malaysia.
Choe, J.S., Bang,
K.W. and Lee, J.H.
(2002)
ChongJu, Korea.
Event Mean Concentration (mg/l)
Land Use
TN
TKN
TP
TSS
Residential
5.92
0.59
73
NO3-N
0.79
NH3-N
0.55
BOD
-
Residential
2.1
1.5
0.38
127
-
-
-
Residential
a
-
3.8
0.75
229
-
0.49
-
Residential
b
Residential
c
Residential
d
Residential
e
Residential
f
Residential
c
Residential
g
Residential
h
-
2.27
1.5
2.23
0.28
0.13
0.25
81.54
30.90
65.18
-
0.29
0.39
0.46
-
1.77
2.29
2.42
-
0.18
0.30
0.49
19.10
27.00
71.70
-
-
4.40
7.40
11.00
0.448.79
0.5219.4
1.2-5.4
1.126.99
0.051.84
0.033.81
16636
31570
0.011.20
0.071.90
-
-
Residential
-
-
-
-
-
-
20 278
364
-
Residential
0.21 0.84
3.0
2.4
-
95
Residential
c
Residential
f
-
4.46
6.81
1.21
2.85
145.8
414.1
-
-
76.2
125.3
a Actual catchment land use: 79.4% residential, 9.3% commercial, 8.3% public,
3.0% undeveloped.
b High Density Residential
c Multi-family Residential
d mixed Residential
e Low-density Residential
f Single-family Residential
g Urban Residential ≤ 40 ha
h Urban Residential ≥ 40 ha
18
Table 2.3: Typical EMCs values of commercial land use from various storm water
runoff quality studies (Chow and Yusop, 2008).
Site
Baldys et al. (1998)
Dallas-Fort Worth
Area,
Texas,(1992-1993)
USA.
Guerard and Weiss
(1995)
Colorado Springs,
Colorado
Los Angeles County
Stormwater Monitoring
Report: 1998-1999
(1999)
Los Angeles Cities
Harper, H. H. (1998)
Central and South
Florida, USA.
Brezonik and
Stadelmann (2001).
Twin Cities
Metropolitan Area,
Minnesota, USA.
Siti Nazahiyah (2005).
Skudai, Johor,
Malaysia.
Choe, J.S., Bang,
K.W.
and Lee, J.H.
(2002)
ChongJu, Korea.
Z. Yusop (2005)
Sg. Pandan
Catchment,
Johor, Malaysia.
Event Mean Concentration (mg/l)
Land Use
TN
TKN
TP
TSS
Commercial
1.5
0.96
0.18
60
NO3-N
-
NH3-N
-
BOD
-
Commercial
a
-
1.8
0.28
284
-
-
-
Commercial
b
-
1.67
0.40
48.80
-
0.30
-
Commercial
c
Commercial
d
Commercial
e
1.18
2.83
-
0.15
0.43
81.00
94.30
-
-
8.20
7.20
1.80
8.18
1.38 7.39
0.22 0.77
42 418
0.18 0.86
-
-
Commercial
-
-
-
195
0.001
-
135
Commercial
-
107.6
14.4
2111
-
-
168.8
Mix land
use*
-
-
-
184
0.5
2.27
39
a Actual catchment land use: 61.1% commercial, 23.0% undeveloped, 15.9%
residential.
b Retail/Commercial
c Low-intensity Commercial
d High-intensity Commercial
e Commercial/Industrial
* Mixed land use: 30.3% Residential, 27.3 % Agricultural, 27.9 % Open Space, 8.1
% Industrial, 6.4% Commercial.
19
2.5
Gross Pollutant Traps (GPTs)
Gross Pollutants Trap (GPTs) can be used to reduce water pollution at the
point source. GPTs remove litter, debris and coarse sediment from stormwater. GPTs
may be used as the pretreatment for the polluted water before flow into a pond or
wetland to confine the area of deposition of coarse sediments. This facilitates the
eventual removal of finer sediments. Traps may also be used to keep coarse sediment
out ponds, protecting the vegetation at the head of the pond from the smothering
effects of sediment. Traps may also be used to remove coarse sediment before the
flows enters an infiltration device or filtration device, which would otherwise clog up
prematurely. GPTs may also serve the purpose of capturing floatable oil, provided
that they are designed appropriately.
Most GPTs will also provide some reduction in other pollutants. For example,
instead of trapping coarse sediment, some GPTs may also remove the particulate
nutrients, trace metal, oil and grease, bacteria, and reduce the dissolved oxygen
demanding substances. All of the above substances can be partly bound to sediments,
and will be removed along with the trapped sediment (DID, 2000c). For the purpose
of this study, the compartmentalized GPTs consist of several compartments of
rubbish trap, oil and grease trap, and biofilters will be adapted.
2.5.1 Design Criteria and Mechanism of GPTs system
Drainage and Irrigation Department (DID, 2000c) has listed the guidance of
designing GPTs in Chapter 34, Urban Stormwater Management Manual for Malaysia
(MASMA). The information of GPTs is including the Planning Considerations,
Classification of GPTs, General Considerations, Design of SBTR Traps, and the
Maintenances of the GPTs. The hydraulics characteristics is an important criteria that
must take into considerations as the GPTs must be designed so as to prevent any
additional surcharge or overflow in the stormwater event of partial or complete
blockage. For this purpose, Manning’s Equation is recommended to determine the
20
average velocity of the flow. For hydrology purposes, the Rational Method is
recommended for the computation of the peak flows.
2.5.1.1 Rubbish Trap
There is a very wide range of devices for the treatment of gross solids.
Selection of suitable devices depends on many factors including catchment size,
pollutant load, the type of drainage system and cost (DID, 2000c). Table 2.4 provides
an overall classification of the types of GPTs that could be used in Malaysia, and the
range of catchment areas for which they are suitable.
Table 2.4: Overall Classification of Gross Pollutant Traps
Group
Floating Debris
Traps (booms)
In-pit devices
Trash Racks &
Litter Control
Devices
Description and
Function
Litter capture on
permanent waterbodies
Litter and sediment
capture in existing pit
Hard or soft litter capture
devices on drains
Sediment removal only,
Sediment Traps
on drains
Sediment and litter
‘SBTR’ Traps
capture for drains or pipes
Proprietary devices Range of devices, mainly
for pipes
Source: MASMA (Chapter 34), 2000
Catchment
Area Range
Purpose-built or
Proprietary
> 200 ha
Proprietary
0.1 – 1 ha
Proprietary
Usually purpose
built from modular
components
2 – 400 ha
> 200 ha
Purpose built
5 – 2000 ha
Purpose built
2 – 40 ha
Propriety
Nowadays, there are several proprietary GPTs in the market such as
HumeguardTM, CleansallTM Trap, and Continuous Deflective SeparationTM (CDS)
(DID, 2000c). The HumeguardTM Trap is marketed by CSR Humes in Australia. It
comprises a specially shaped (floating) boom which diverts material entrained in
stormwater flows from the separator into an adjacent holding chamber which can be
installed in piped drainage systems. The chamber is baffled to ensure that litter and
floating debris is retained and not escapes with the outflow from the chamber. While
21
it is particularly suited to retro-fitting within existing piped drainage system, there is
limitation on the maximum size of pipe on which the device can be installed.
Cleansall
TM
Trap is installed using pre-cast elements marketed by Rocla
Australia. A diversion weir deflects the treatable flow into a circular chamber in
which are seated four quadrant baskets. Litter and other debris are captured by the
baskets as stormwater flows through the mesh baskets and out a depressed outlet at
the base of the chamber. A sediment sump is located immediately downstream of the
chamber where the stormwater wells up to re-join the stormwater conduit. Features
of this system are that it can be installed underground and in such a way as to
minimize head loss in flood flows and that high trapping efficiencies are predicted
from laboratory tests.
The Continuous Deflective SeparationTM (CDS) trap consists of an on-line
stainless steel perforated separation plate placed in a hydraulically balanced chamber.
Solid pollutants are retained in a central chamber under a mild vortex action, and
drop into a basket for later removal and/or for removal using a grab bucket or using
eduction. Features of this system are that it can be installed underground and in such
a way as to minimize head loss in flood flows and that high trapping efficiencies are
predicted from laboratory tests. An illustration of CDSTM trap is shown in Figure 2.1.
STORMWATER DRAIN
HIGH FLOWS
BYPASS OVER
DIVERSION WEIR
Figure 2.1: Continuous Deflective System (CDS) trap (Lariyah et al., 2006)
22
2.5.1.2 Filtration
There are two types of filtration methods that are implemented in Malaysia.
The two Best Management Practices (BMPs) are biofiltration or plant/vegetation
based runoff treatment methods while the latter, typically sand, is used to remove
pollutants through media retention or adsorption process. In Japan, biofilter
technologies have been successfully practiced, based on Shimanto-Gawa system
which is introduced to treat rivers and lakes as a result of eutrophication and
increased in BOD. Biofilter is one technologies used in the control of water pollution
that use microorganisms to treat polluted water before it is being discharged.
Biofilter is a filtration method that uses a combination of physio-chemical adsorption
and in-situ bioremediation in the filtration process. Biofiltration is a pollution control
technique using living material to capture and biologically degrade the pollutants
process. Common uses include processing waste water, capturing harmful chemicals
or silt from surface runoff, and microbiotic oxidation of contaminants in air. Natural
materials are used in the biologically active filter (BAF) as its filtering media which
consists of granular activated carbon, granite, zeolite, lime stones, coarse and fine
sand, fiber sponges, leaves and peanut shells. The filtering material functioned as a
media which enable the adsorption of organic matters and pollutant onto its surface
(Norhan et al., 2008).
2.5.1.2.1
Biofiltration Swale and Vegetated Filter Strip
Biofiltration swales and vegetated filter strips are two practices, which have
been used for some years in most developed countries. Only fairly recently they have
been studied to determine their effectiveness at treating pollution from stormwater
runoff and to assess their abilities to reduce on site peak flow rates. At this time these
two practices are assumed to provide runoff quality treatment only (DID, 2000b).
Biofiltration swale is a vegetated channel that is sloped like a standard storm
drain channel; stormwater enters at one end and exits at the other with treatment
provided as the runoff passes through the channel. With the vegetated filter strips, the
flow is distributed broadly along the width of the vegetated area, and the treatment is
23
provided as runoff travel (as sheet flow) through the vegetation. Methods to be used
are depend on the drainage patterns of the site.
2.5.1.2.2
Media Filtration
Sand filtration basins are open impoundments, which filter runoff through a
layer of sand into an underdrain system. Sand filtration provides runoff treatment,
but not quantity control and these basins are to be located off-line from the primary
conveyance/detention system. While effective at treating conventional pollutants,
sand infiltration is not effective at removing nutrients. Its use for treating oil is being
allowed on an interim basis and sand infiltration may substitute for oil/water
separators.
2.5.1.3 Oil Separators
Oil separation devices are applicable for stormwater runoff from areas where
hydrocarbon products are handled or where small spills routinely fall on paved
surface exposed to rain. The objective of the oil separators is to treat most of the flow
(90 to 95%) from a potentially contaminated catchment to an acceptable degree (10 20 mg/l oil and grease) and to remove free floating oil. In addition to collecting oil,
which rises to the surface, separators collect sediment, which falls to the bottom of
the devices. In fact, the sediment is likely to contain petroleum products, which are
attached to fine sediments.
Oil in general urban stormwater runoff is likely to be in the form of an
emulsion, which is intimately mixed with the water. Such oil will not readily float
out, so oil separators are not used for treatment of general urban runoff. Oil
separators need to treat only the stormwater runoff from oily areas or areas which
may become oily. General stormwater runoff from other areas should be routed away
from the separator and treated for sediment removal in the usual fashion. Treatment
should be as close to the source of the oil-generating activities as possible.
24
Two types of more efficient oil separators are API tanks and plate separators.
API tanks were originally designed by the American Petroleum Institute (API) for
use in refinery applications, but modifications of the design can be used for
stormwater treatment. Figure 2.2 shows the schematic of API Separator. Plate
separators (as shown in Figure 2.3) contain packs of plates, typically spaced at 10
mm to 20mm centers. The plates increase the effective surface of the device. The
close spacing of the plates reduces the height that an oil droplet must rise before it
reaches a collecting surface (DID, 2000b).
Figure 2.2: Schematic of API Separator (DID, 2000b)
Figure 2.3: Schematic of Plate Separator (Meyers, 1980)
25
2.5.2 The effectiveness of GPTs as water pollution treatment devices
A number of previous studies had proved the capability of GPTs in reducing
water pollution problems. The following literature reviews below briefly describe the
design criteria, the mechanism of the trap system and their findings.
2.5.2.1 Gross Pollutants Traps
Walker et al., (1999) have conducted a study to investigate the efficiency of
CDS Gross Pollutants Traps in removal of suspended solids and associated
pollutants. The performance of a continuous deflective separation (CDS) unit in
removing suspended particles, particularly sediment and associated contaminants
from a suburban catchment in Coburg, Melbourne was assessed during stormwater
runoff events and dry weather flow conditions. The results indicate that the CDS unit
can remove nearly all gross pollutants and a significant proportion of finer pollutants,
particularly during storms. An annual removal efficiency of 65% and 21% for TSS
and TP respectively were estimated by assuming typical pollutant concentrations
during different flow conditions and using removal efficiencies estimated using data
collected in the study.
The mechanism of the CDS used is illustrated in Figure 2.4. The CDS
separates and retains gross pollutants by first, diverting the flow and associated
pollutants in a stormwater drainage system away from the main flow stream of the
pipe or channel into a pollutant separation chamber. The separation chamber consists
of a containment sump in the lower section and an upper separation section as shown
in Figure 2.5. Gross pollutants are retained within the chamber by a perforated plate
that allows water to pass through to the outlet pipe. The water and associated
pollutants contained within the separation chamber are kept in continuous motion by
the energy generated by the incoming flow. This has the effect of preventing the
separation plate from becoming blocked by the gross solids retained from the inflow.
Heavier solids settle into the containment sump and much of the neutrally buoyant
material eventually sinks while floating material accumulates at the water surface.
26
Diverting stormwater and associated pollutants into a separation chamber
overcomes problems associated with the direct filtration systems used in
conventional gross pollutant traps. The diversion weir is designed to divert all flows
below the crest level of the diversion weir. During above-design flow conditions,
when water levels exceed the crest of the diversion weir, some flow would by-pass
the CDS system carrying pollutants downstream. The selection of the design crest
level of the diversion weir can vary for different installations depending on site
conditions and the capacity of the CDS unit. The by-pass system is designed to
minimize upstream flood afflux for above design flow conditions. If the unit
discharges directly to a watercourse, some type of outlet scour protection will be
appropriate, such as gabions or riprap.
DETAILS OF SEPARATION CHAMBER
FROM INLET
TO OUTLET
Figure 2.4: Schematic Plan View Representation of the CDS System
(Walker et al., 1999)
The solid separation system consists of a perforated stainless steel partition,
which acts as a filter screen with a perimeter volute outlet passage. The perforations
in the separation screen are typically elongated in shape and are aligned with the
longer axis in the vertical direction. The size of the elliptical perforations can be
specified according to performance requirements and typical perforation size for use
in urban stormwater systems is 4.7 mm by 10 mm however finer screens have been
used. The separation screen is installed with the leading edge of each perforation
extending into the flow.
27
Separation
Chamber
Containment
Sump
Figure 2.5: Isometric Representation of the CDS unit (CDS Technologies, 1998)
A
research
by
Cooperative
Research
Centre
for
CATCHMENT
HYDROLOGY on ‘Decision Support System for Determining Effective Trapping
Strategies for Gross Pollutants’ found that CDS is the most effective devices in
removing litter and pollutants. Figure 2.6 shows the percentages of efficiencies for
litters and total load for different types of trapping system, where CDS give the
highest results for both litter and total load efficiencies.
120
SUMMARY OF EFFIENCY DATA USED IN DSS
100
%
80
Efficiencies for
Litter (%)
60
40
Efficiencies for
Total Load (%)
20
0
Side entry Trash Litter
CDS GPTs Floating
pit traps racks Control devices
debris trap
devices
Figure 2.6: Summary of Efficiency data used in DSS (Allison, et al, 1999)
28
Rawson et al, (2002) were designed a study to examine the impact of the
structure on physico-chemical and nutrient variables. A GPT system was installed on
Brookvale Creek, just downstream of the Warringah Mall shopping centre as part of
the Brookvale Creek rehabilitation Program in 2000. It was found that the GPT does
change some quality parameters of the waters in Brookvale Ck. In dry weather
periods, there was an increase in the turbidity and suspended solids of the waters
within the GPT. There was a general increase in temperature, which was correlated
with a decrease in dissolved oxygen between sites above and below the GPT. The
GPT had an insignificant effect on conductivity and pH. Of the chemical variables,
ammonia-N, nitrate-N and phosphorus all decreased slightly, although not
significantly. In wet weather conditions, the water quality variables that were
strongly affected include conductivity, suspended solids, turbidity, nitrate-N and
phosphorus. As part of the Brookvale Creek Rehabilitation Program wetlands were
constructed along the creek below the GPT. These wetlands appeared to significantly
affect pH, temperature, dissolved oxygen and suspended solids which all decreased
between the GPT and the sites downstream. In general, while the pollutant trap
appears to affect the water quality, it is not to such a level as to necessitate further
remediation.
Lariyah, et al (2006) have conducted a study on developing a Decision
Support System (DSS) for GPTs that can be used to assist engineers and authorities
to select and design an appropriate gross pollutants system for particular urban area.
The software developed takes into account the urban drainage layout, trapping
locations, the predominant land-use type and funding limitations in assessing the
benefits and costs of proposed strategy. The developed software was based on results
obtained from a gross pollutant field monitoring study (gross pollutant movement
and trapping), as well as a review of GPTs devices commonly used in Malaysia. The
structure of the DSS is shown in Figure 2.7, showing the steps involved from the
model inputs to the range of outputs. As the result, the study has indicated that a
tremendous reduction in the maintenance cost and gross pollution of urban drainage
systems and rivers can be achieved, and the gross pollutant trap will not only perform
as a device to recover hydraulic efficiency of drainage facilities, but also enhance the
environment.
29
Catchment’s
Characteristic
Rainfall
Street
Cleanings
Typical
load
Inputs
Actual
load
Conventional
& Proprietary
Clean-up
Technique
Trapped Load
Installation &
Maintenance
Outputs
Computation
Design of GPTs
Receiving Water
Figure 2.7: Structure of the GPTs decision-support-system (DSS) for evaluating
gross pollutant trapping strategies (Lariyah, et al, 2006)
Water Sensitive Urban Design (WSUD) is a new philosophy of water
management being developed within Australia that embraces sustainability principles
to reduce the impacts of development on the total water cycle. a number of WSUD
systems have been installed in Australia that integrate various controls into
“treatment train systems”. Some of these WSUD systems have been very successful.
However, many have been poorly designed, primarily due to the lack of an integrated
approach in their design, operation and management and a failure to identify the
expected pollutants and site constraints at the beginning of the process. Extensive
studies have been undertaken to better understand these treatment systems and the
situations in which they are most effective. In both New South Wales and South
Australia rainwater tanks, stormwater infiltration systems, and wetlands have been
studied extensively (Coombes, 2002, Beecham, 2001, and Argue, 2001). In Victoria,
the Cooperative Research Centre for Catchment Hydrology has been researching bioretention systems, swales, wetlands and ponds (Lloyd, 2002, Fletcher, 2002, and
Wong, 2000). Dallmer Roach and Beecham (2004) have documented effective
treatment trains using pre-filtering and other good design practices based on these
30
and other case studies. They have come out with the suggested treatment trains that
can be applied for certain controls and end users, as shown in Table 2.5.
Table 2.5: Recommended Treatment Trains (Dallmer Roach and Beecham, 2004)
Control and Use
RAINWATER TANKS
Outdoor, Toilet and Hot
Water
Indoor (excluding drinking
water)
Drinking Water
INFILTRATION TRENCHES
Rainwater from Roofs
Stormwater Runoff
VELOCITY REDUCTION
Residential Conveyance
DISCHARGE REDUCTION
Urban Development
Treatment Train
First Flush Filter + Rainwater Tank
Screened Roof Gutter + First Flush Filter + Rainwater Tank
Screened Roof Gutter + First Flush Filter + Rainwater Tank
+ Point of Use Filter (UV or micro filter)
First Flush Filter + Infiltration Trench
Grass Filter Strip or Pre-filter Pit + Infiltration Trench
First Flush Filter + Rainwater Tank + Grass Swale
Grass Swale + Detention Basin + Pre-filtering Pit +
Infiltration Basin
Limited Pervious Area
Porous Pavement + Bio-filtration Trench
HIGH QUALITY DISCHARGE
Natural System Replication or Litter Rack or Boom + Sediment Trap + Wetland
Open Channel
Urban Development
Grass Swale + Bio-filtration Trench + Sedimentation Pond
+ Wetland
Reuse
Litter Baskets or GPT or Pre-filter Pit + Detention + Sand
Filter
FINE PARTICULATE REMOVAL
Residential Areas
Grass Swale + Bio-retention Trench
Commercial Areas
Pre-filter Pit + Bio-retention Trench
2.5.2.2 Filtration
A filtration method based on soil infiltration has been developed in Lake
Biwa, Japan as a way to reduce pollutant loading of organic matter and nutrients
from non-point sources in urban areas (Wada and Fujii, 2007). Two kinds of pilot
plants were installed in roadside, and their performance was examined in several
rainfall events. These plants were designed to remove pollutants in the first flush,
which corresponded to the cumulative runoff volume of 2mm of stormwater, by
percolating the water through soil layer. There are two experimental sites involved in
this study. One is the Kunobe over the bridge of the Otsu-Notogawa-Nagahama Line
31
in Shiga prefecture (St. A: catchment area is 285 m2). The other is the RittoShinanaka Line (St. B: catchment area is 72 m2). Figure 2.8(a) and 2.8(b) show the
schematic diagram of the experimental facility in St. A and St.B. Stormwater runoff
from the road is transferred from the drainage area to this facility under the bridge.
This facility is designed to selectively collect the first flush runoff (cumulative runoff
volume: 2.8 mm∼3.9 mm) and to percolate it through soil, based on the foregoing
and other study results (Wada and Fujii, 2006). This soil is made up of decomposed
granite that is low cost and has a good capability of water purification (Tomioka et
al., 1998). The treated flow rate is controlled o.4 L/m as the retention time is 24
hours (Wada et al., 2001b).
This study investigated the removal characteristic by soil infiltration in terms
of the concentration of each water item, the relationship between the first flush
runoff pollution load and treated effluent load, and the relationship (reduction ratio)
of the first flush runoff pollution load by soil infiltration and total rainfall amount. As
the result, it was found out that the plants were able to remove COD (77%),
TOC(78%), T-N (51%), and T-P (89%). However, the rate of reduction varies from
different configurations between particle substances and dissolved substances, that of
the particle was more than 90% and of the dissolved substance was approximately
40%. In terms of the reduction effect by soil infiltration, it was found that the treated
effluent load is proportional to influent load. Figure 2.9 shows the relationship
reduction ratio of the first flush runoff pollution load by soil infiltration and total
rainfall amount. The reduction ratio showed a decreasing tendency in particular PCOD, POC, and P-TP amount, with an increase in the total rainfall amount. That
suggests the possibility that particle substances are affected by the total rainfall
amount and rainfall intensity.
32
(a)
60cm
Soil Area: 75cm(L) x 75cm(W) x 25cm(D)
Retention Time: 24 hour
40cm
Street
Sidewalk
75cm
12cm
25cm
First Flush
runoff: Cumulative
runoff volume: 2mm
15cm
Treated Effluent
JP patent No.3768186
(b)
Figure 2.8: Schematic diagram of the experimental facility in (a)St. A and (b)St. B
(Wada and Fujii, 2007)
As a way to reduce unpleasant odors, biofiltration has been used as a versatile
odor and gas treatment technology and has gained much acceptance in agriculture.
Several research studies using compost-based biofilters have been conducted with
significant reductions in odor and specific gases reported. Nicolai and Janni (1997)
reported a compost/bean straw biofilter that achieved average odor and H2S removal
efficiencies of 75% to 90%, respectively. Sun et al. (2000) observed an average H2S
removal efficiency between 92.8% and 94.2%, and an average NH3 removal
efficiency between 90.3% and 75.8% with 50% media moisture content and 20 s gas
33
retention time. Martinec et al. (2001) also found from several biofilter research
experiments on odor reduction efficiency up to 95%. The mixture of wood chips and
compost (70:30% to 50:50% by weight) has been recommended as biofilter media
(Nicolai and Janni, 2001a).
(a)
(b)
(c)
(d)
Figure 2.9: Relationship reduction ratio of first flush runoff by soil infiltration and
total rainfall amount for (a) COD, (b) POC, (c) TN, and (d) TP
(Wada and Fujii, 2007)
Chen et al. (2008) have developed a pilot-scale mobile biofilter, where two
types of wood chips; western cedar and 2 in. hardwood (as shown in Figure 2.10)
were examined to treat odor emissions from a deep-pit swine finishing facility in
central Iowa. The biofilters were operated continuously for 13 weeks at different air
flow rates resulting in a variable empty bed residence time (EBRT) from 1.6 to 7.3 s.
During this test period, solid-phase microextraction (SPME) PDMS/DVB 65 lm
fibers were used to extract volatile organic compounds (VOCs) from both the control
plenum and biofilter treatments. Analyses of VOCs were carried out using a
multidimentional gas chromatography–mass spectrometry–olfactometry (MDGC–
MS–O) system. Results indicated that both types of chips achieved significant
reductions in p-cresol, phenol, indole and skatole which represent some of the most
34
odorous and odor-defining compounds known for swine facilities. The results also
showed that maintaining proper moisture content is critical to the success of woodchip based biofilters and that this factor is more important than media depth and
residence time.
Figure 2.10: Hardwood (HW) and western cedar (WC) media (Chen et al., 2008)
Nduwimana Andr´e et al. (2007) have designed a model system for farm
aquaculture water treatment using Lolium perenne Lam as plant biofilter for
improving wastewater quality prior to use for irrigation and the removal of TAN
(total ammonia nitrogen) from the discharge water. The L. perenne was chose
because of its tolerance to a wide range of physiological stress conditions.
Additionally, this grass has an extensive and deep root system that would help
maintain the hydraulic conductivity and contribute to oxygen transport into the bed.
In the study, two aquaculture wastewater treatment systems; recirculation
system and a floating plant bed system were designed to improve the quality of
irrigation water in local communities with low income. In both systems the grass
species Lolium perenne Lam was used as a plant biofilter while vegetable specie
Amaranthus viridis was used to evaluate the performance of the system and the
suitability of the phyto-treated water for irrigation. It was found that the harmful
material removal rate for recirculating system was 88.9% for TAN (total ammonia
nitrogen), 90% for NO2--N, 64.8% for NO3--N while for floating plant bed system
82.7% for TAN, 82% for NO2--N and 60.5% for NO3--N. Comparative analysis of
the efficiency of waste element removal between the two systems revealed that both
35
systems performed well, however, plant growth was not robust for floating plant bed
system while recirculating system is energy consuming.
They conducted a laboratory test where seedlings were cultured (5 g/plate) on
plates with 30% of their bottom perforated, laid on four layers of moist unwoven
fabrics for 60 d (Figure 2.11). Unwoven fabric was used as a growing medium.
During the culture period, a commercial solution was used as nutrient solution. Each
concrete trough consist of 40 plates arranged horizontally (Figure 2.12). The grass
was clipped at 10 cm height before each series of experiments.
The first pilot experiment was designed to compare the efficiency of L.
perenne to remove nutrients in recirculating and in the floating system. In the
recirculating system, six plates (60 days after sow: DAS) were placed on a bench
elevated to a gentle slope (5%) that allows the flow of wastewater from a closed
turtle pond regularly pumped from a large container (80 L). The flow rate was 128
ml/s. The system was equipped with a timer set at 15 s On and 5 min Off. This
regime helped to maintain a high oxygen level in the root zone and as well allows
assimilation of pollutants and back to the large container (Figure 2.13). The setting of
the floating system consisted of a series of concrete trough compartments in which
the same volume (80 L) of the turtle aquaculture farm discharge was put. Six grass
plates were then made to float on the surface during the trial (Figure 2.14). Each
experimental unit was replicated three times, the duration of each trial was 15 days,
and the wastewater was replaced between trials.
Figure 2.11: Cross section of the grass-plate. (1) grass leaves and stems; (2)
unwoven fabric layer(s); (3) hollowed plate bottom; (4) grass roots (Nduwimana
Andr´e et al, 2007).
36
7 1
2
8
3
a
4
4
b
3
7
1
2
8
Figure 2.12: Hydroponics botanical filter (a) top view (b) cross section view. (1)
trough; (2) hydroponics grass-plate; (3) wastewater tank; (4) inlet pipe; (5) pump; (6)
timer; (7) tap; (8) discharge pipe (Nduwimana Andr´e et al, 2007).
Figure 2.13: Recirculating system (Nduwimana Andr´e et al, 2007).
Fig. 2.14: Floating system (Nduwimana Andr´e et al, 2007).
Saliling et al. (2007) have conducted a study evaluated wood chips and wheat
straw as inexpensive and readily available alternatives to more expensive plastic
media for denitrification processes in treating aquaculture wastewaters or other high
nitrate waters. Nine 3.8-L laboratory scale reactors (40 cm packed height × 10 cm
diameter) were used to compare the performance of wood chips, wheat straw, and
Kaldnes plastic media in the removal of nitrate from synthetic aquaculture
wastewater. The upflow bioreactors were loaded at a constant flow rate and three
influent NO3–N concentrations of 50, 120, and 200 mg/L each for at least 4 weeks, in
sequence.
37
The experiments showed that both wood chips and wheat straw produced
comparable denitrification rates to the Kaldnes plastic media. As much as 99% of
nitrate was removed from the wastewater of 200 mg NO3–N/L influent
concentration. Pseudo-steady state denitrification rates for 200 mg NO3–N/L influent
concentrations averaged (1360 ± 40) g N/(m3 d) for wood chips, (1360 ± 80) g N/(m3
d) for wheat straw, and (1330 ± 70) g N/(m3 d) for Kaldnes media. These values were
not the maximum potential of the reactors as nitrate profiles up through the reactors
indicated that nitrate reductions in the lower half of the reactors were more than
double the averages for the whole reactor. COD consumption per unit of NO3–N
removed was highest with the Kaldnes media (3.41–3.95) compared to wood chips
(3.34–3.64) and wheat straw (3.26–3.46). Effluent ammonia concentrations were
near zero while nitrites were around 2.0 mg NO2–N/L for all reactor types and
loading rates. During the denitrification process, alkalinity and pH increased while
the oxidation–reduction potential decreased with nitrate removal.
The laboratory set up is shown in Figure 2.15 where nine 3.8-L bench-scale
upflow biofilters were fed with wastewater synthesized from a formula based on the
chemical characterization of recirculating aquaculture systems. Three biofilters each
were packed with one of the three types of media; hard wood chips, wheat straw, and
Kaldnes plastic media (Kaldnes Miljøteknologi AS, Tønsberg, Norway). Kaldnes
media is made of high-density polyethylene with an average diameter of 10 mm and
height of 7 mm and an approximate effective specific surface area (SSA) of 500
m2/m3 (Ødegaard et al., 2000).
The dimensions of the wood chips particles were approximately 8–50 mm on
a side and 2–15 mm in thickness while the wheat straws were chopped to lengths of
approximately 20–35 mm. The 3.8-L packed column reactors were made of clear
10.16 cm (4 in. i.d.) diameter PVC pipe and were 40 cm in length. The ends of the
PVC column were covered with plastic screens to hold the biofilter media and were
fitted with 10.16 cm × 2.54 cm (4 in. × 1 in., insert) PVC reducer tees. The 2.54 cm
outlets of the reducer tees were fitted with 2.54 cm × 0.635 cm (1 in. × 1/4 in., insert
by barbed) reducer fittings. These reducer fittings coupled with clear vinyl tubing
delivered influent to and effluent from the reactors. Three holes were bored along the
38
column at different heights (10, 20, and 30 cm) and 0.635 cm (1/4 in. barbed by
NPT) adapters were threaded into these holes. These openings were used to extract
liquid samples from these locations.
10cm
10cm
10cm
10cm
Figure 2.15: Schematic diagram of an upflow packed bioreactor used (Saliling et al.,
2007).
2.5.2.3 Oil and Grease Traps
Grease traps are widely used by most restaurants and food processing
industries in Hong Kong to reduce oil and grease to an acceptable level before it can
be discharged to public sewers. To meet demanding effluent standards in the future,
it is necessary to polish the effluent by upgrading the conventional trap design. Chu
and Ng (2000) have conducted a study to evaluate the possibility of upgrading
traditional grease traps by installing tube settlers inside the trap.
Cooking oil made from peanuts was used to make up the synthetic
wastewater in all experiments. The pilot plant used consists of three chambers and
was made from 1-cm-thick acrylic plates. The first chamber served as an equalization
tank to mix and store raw wastewater. Here, a motor-driven peddler with a long span
39
was provided to agitate the mixture so that the interface of oil and water could be
broken. the second chamber was the main grease trap, where the tube settler could be
installed or removed. The tube settler module was made by polypropylene (PP) with
many 45 mm × 60 mm openings on the top and bottom of the module; the dimension
of each module was 600 mm (L) × 250 mm (W) × 325 mm (H). the third chamber
was used to collect treated effluent and was marked at the 20 L, 40 L, and 60 L levels
so that the hydraulic detention time could be determined. A Rio-1700 water pump
(maximum rate 1820 L/hr) was used for pumping the wastewater from the chamber
one to chamber two. A plug valve was installed in the pipeline to adjust the flow rate,
so that suitable detention times could be reached.
The efficiency of the grease trap in removing chemical oxygen demand
(COD) and oil/grease was examined to justify the performance. It was found that
installing a tube settler is a feasible and cheap way to upgrade the conventional
grease trap since it improved oil/grease removal efficiency by 8-10% compared to
the conventional design. In addition, a remarkable improvement in COD removal
was observed following a very short hydraulic detention time after the installation of
tube settlers. This ensured acceptable effluent quality under peak flow rates.
Fast-food restaurants generate grease-containing wastewaters for which there
is, currently, no acceptable treatment technology. The development of microbial
cultures for use in a bioreactor could, therefore, provide effective treatment of these
wastewaters. Wakelin and Forster, (1997) have performed an investigation into
microbial removal of fats, oils and grease by examined the growth of a range of pure
and mixed cultures by using vegetable oils, lard and 'grease' from a fast-food
restaurant grease-trap. The pure cultures were Acinetobacter sp., Rhodococcus rubra,
Nocardia amarae and Microthrix parvicella and these were compared with a mixed
culture isolated from a greasetrap, MC1, and with activated sludge. The effectiveness
of these cultures was assessed in terms of their grease removal efficiency, the
biomass production and yield coefficients. The results found out that Acinetobacter
was the most effective of the pure cultures, typically removing 60-65% of the fatty
material whose initial concentration had been 8 g/l. The effectiveness of the mixed
culture, MC1, was variable, with the removal efficiency ranging from 29% for
rapeseed oil to 73% for the restaurant grease. The activated sludge gave a more
40
consistent removal, which was generally better than 90%. However, there was a lag
phase of about 1 day in every case. Acclimatised activated-sludge did not exhibit a
lag phase and also achieved a high (> 90%) removal efficiency. The absence of a lag
phase resulted in faster growth and fat removal.
2.5.2.4 GPTs system at L50 Block, UTM Skudai
The study of the GPTs system at L50 Block, UTM Skudai has been
conducted since 2005 until present. The GPTs system consists of rubbish trap,
biofilter, and oil and grease trap. Since 2005, a few modifications have been made to
biofilter and oil and grease traps to improve their performance in removing pollutants
(as shown in Table 2.6 and 2.7).
Previous studies from Faradiella (2006), Nur Eezani (2007), and Muhammad
Ashraff (2008) show that the biofilter system is capable in removing pollutants in
water and the most significant parameters in determining biofilter efficiency were
SS, BOD and COD. The various filtration media contained in the biofilters were
proven effective in removing pollutants in water. The comparisons of the results are
shown in Figure 2.16(a) to 2.16(c) where the latest modification in year 2008 gives
the most effective results for the Total Suspended Solid (TSS) and Chemical Oxygen
Demand removal in terms of percentages. While for Biochemical Oxygen Demand
parameters, modification in year 2006 gives the best results.
Table 2.6: Different types of materials used for modification (Muhammad Ashraff,
2008)
2006
• Coir
• Sand
• Gravel
• Charcoal
• Sponge
2007
• Oil Palm Fiber
• Coir
• Gravel
• Bioball
• Charcoal
• Sponge
2008
• Oil Palm Fiber
• Gravel
• Bioball
• EBB
• Charcoal
41
Table 2.7: Mechanism of development used in oil & grease trap (Munzir, 2008)
2005
2006
2008
Detention Time Detention Time
+
Baffles
Detention Time
+
Baffles
+
Banana treetrunks Fibre
Lim (2005), Norizan (2006), and Munzir (2008) have studied the
effectiveness of oil and grease trap and the results are presented in Figure 2.17(a) and
2.17(b) where the modification in 2006 give the best results of oil and grease and
TSS removal.
COMPARISON OF PERCENTAGES OF REMOVAL
FOR TSS
75.20
80.00
%
60.00
40.00
54.15
28.00
31.76
43.75
37.50
2006
36.17
20.00
22.20
20.00
2007
2008
0.00
Average
Maximum
Minimum
(a)
%
COMPARISON OF PERCENTAGES OF REMOVAL
FOR COD
70.00
60.00
50.00
40.00
30.00
20.00
10.00
0.00
56.36
36.48
32.88
25.53
Average
58.06
2006
27.80
Maximum
24.30
9.68
13.16
2007
2008
Minimum
(b)
Figure 2.16: Comparison results for Biofilter in terms of percentages of removal of
(a) TSS and (b) COD
42
(Continued Figure 2.16)
%
COMPARISON OF PERCENTAGES OF REMOVAL
FOR BOD
70.00
60.00
50.00
40.00
30.00
20.00
10.00
0.00
58.69
56.30
50.28
49.97
52.94
41.94
44.90
29.38
2007
12.34
Average
2006
Maximum
2008
Minimum
(c)
Figure 2.16: Comparison results for Biofilter in terms of percentages of removal of
(c) BOD
COMPARISON OF PERCENTAGES OF OIL &
GREASE REMOVAL
120
98.82
98.85
100
%
80
71.63
2005
60
2006
40
2008
20
0
1
(a)
COMPARISON OF PERCENTAGES OF TSS
REMOVAL
120
98.02
100
2005
%
80
60
87.30
2006
45.67
2008
40
20
0
1
(b)
Figure 2.17: Comparison results of oil and grease trap in terms of percentages of
removal of (a) oil and grease and (b) TSS
43
A study to determine the efficiency of rubbish trap in terms of the trapping
rubbish and water quality improvement was conducted by Sarizah (2006) in L50
Block, UTM Skudai. The relationship between amount of rubbish and total rainfall
was investigated. From Figure 2.18 it was found out that the amount of rubbish that
flow into the trap is influenced by the total of rainfall for 10 days observation.
Flowing water in channel will bring along rubbish that present in the channel into the
trap. The faster water flow causing the amount of rubbish that trapped will increase.
During wet season, the amount of rubbish in the trap is between 5 to 18 kg per day.
0
20
30
40
Rainfall (m m )
10
Rainfall
Rubbish
50
60
24
/1
25 2 /2
/1 00
26 2 /2 5
/1 00
27 2 /2 5
/1 00
28 2 /2 5
/1 00
29 2 /2 5
/1 00
30 2 /2 5
/1 00
31 2 /2 5
/1 005
2/
2
1/ 0 0
1/ 5
2
2/ 00
1/ 6
20
06
Rubbish (kg/day)
40
35
30
25
20
15
10
5
0
Date
Figure 2.18: Graph of relationship between amounts of rubbish and rainfall for
rubbish trap at L50 (Sarizah, 2006)
Figure 2.19 shows the amount of suspended solid between the point before
and after the trap. Four set of samples was taken and each set shows decreasing of
suspended solid (SS) except for final samples where the amount of suspended solid is
not achieving either Standard A or Standard B. The present of oil in large quantity
had caused this result where the oil will cover the small particles to form large
particles. These large particles will float at surface of water causing the increasing in
amount of suspended solid in water.
44
Suspended Solid (mg/L)
700
600
500
SS (before)
400
SS (after)
300
Standard A
Standard B
200
100
0
9/9/2005
28/12/2005
11/1/2006
2/2/2006
Date
Figure 2.19: Suspended solid (SS) at point before and after rubbish trap (Sarizah,
2006)
2.6
Critical Appraisal
It appears from the review of the literatures that a significant volume of
experimental works and research have been done on GPTs by previous people. From
the studies, it can be concluded that GPTs has the potential to be an effective water
pollution treatment device. As such, a study on GPTs, mainly on the design criteria is
very essential in order to produce GPTs with better performances that are capable to
overcome the water pollution problems.
45
CHAPTER 3
METHODOLOGY
3.1
Introduction
This study is conducted for the purpose of observing the compatibility of
Gross Pollutants Traps (GPTs) as a wastewater treatment system for surface runoff in
open channel flow. With aim to investigate the feasibility of the GPTs system, a few
methodologies as shown in flow chart (Figure 3.1) have been practiced;
•
Observation at the study site to identify any problems arises
•
Maintenance will be perform in order to ensure the cleanliness of the site
and no channel is being blocked with rubbish or sediments
•
Redesign the existing GPTs system to increase the effectiveness of the
system
•
Field experiments will be conducted with the intention of looking at the
first flush pollutants in the stormwater runoff from catchment areas’
surface
•
Water samples will be taken and will be tested with a few parameters
such as; COD, BOD, SS, AN, DO, and pH for water quality checking
46
3.2
Flow Chart of the Study Methodology
Objectives and Scope
of Study
Observation
• to identify any problems arises
Maintenances
• Removal of rubbish & sediment
• On frequent basis & when
necessary (after stormwater event)
Conclusion &
Recommendation
Modification of GPTs system
• To improve the system’s performance
• Only on biofilter and rubbish trap
Data Collection
• Rubbish & Sediment data collection
-L50: Daily (20/06/08-01/08/08)
3-day period (04/08/08-26/09/08)
-L52: 3-day period (08/08/08-26/09/08)
• Water Quality
L50:
-4 Stormwater Events (Influent&Effluent)17 samples (21/03/08 & 4, 8, 12/08/08)
- First Flush-EMC (10 samples) (04/09/08)
L52:
-1 Stormwater Event (19/08/08) (2 samples)
-1 dry day (20/8/08) (2 samples)
• Hydrological Data:
-Flow, rainfall (June-September 2008)-Daily
Collection
Results and Analysis
• Rubbish and Sediment Collection
Results
• Water Quality:
-Improvement in quality of water
• Water Quantity:
-Provide storage and detention
time
Data Analysis
• Flow hydrograph
• Water Quality Analysis :
- pH, DO, BOD,COD,TSS,AN(Standard A & B)
• EMC (MASMA)
Figure 3.1: Flow chart of the study methodology
47
The methodologies of this study can be viewed from Figure 3.1 where it
started with the identification of objectives and scopes of study as elaborated in
Section 1.3 and Section 1.4 respectively. The next stage is observation,
maintenances, and modification of the GPTs system as mention in Section 3.1. Data
collection are divided into 3 parts i.e. rubbish and sediment collection, water quality,
and hydrological data such as flow and rainfall data. The results then are analyzed to
come out with discussion, conclusion and recommendation.
3.3
Experimental Site
This study is conducted at Block L50, Universiti Teknologi Malaysia, Skudai
for the purpose of observing the effectiveness of GPTs system. Figure 3.2 show the
experimental site investigated in this study where the system is located near a lecture
hall building, L50 parking lot and Co-Curriculum Centre. The detail of the drainage
system is shown in Road and Drainage Plan in Figure 3.3.
GPTs
Co-Curriculum
Centre
Building
Parking Lot
Experimental Site
Figure 3.2: Location of Study (L50 UTM Skudai)
Lingkaran
Ilmu
48
I.L 19.80
Building
I.L 31.6
I.L 31.3
I.L 31.55
I.L 31.54
I.L 31.6
Parking Lot
I.L 31.1
I.L 31.6
Jalan Cengal
I.L 31.0
Figure 3.3: Road and Drainage Layout Plan of L50 Block, UTM Skuda
49
3.3.1
Catchment Area
The catchment area of the system is determined from the drainage and layout
plan of L50 Block, UTM Skudai as shown in Figure 3.4. The catchment area covers 0.16
ha with 90% of the catchments are impervious area, with 70% of it is consist of parking
lot and the rest is the lecture building. The peak flow and the Average Recurrence
Interval (ARI) for existing drainage system are calculated as 0.212 m3/s and 10 years
respectively. The GPTs is design for 10 years ARI and three (3) months ARI for flow
and water quality respectively. (Refer Appendix A for the details of calculation)
GPTs SYSTEM
CATCHMENTS
AREA
Figure 3.4: Catchment area for the GPTs system
50
3.4
Experimental System
The compartmentalized GPTs consist of several compartments of rubbish trap,
oil and grease trap, and biofilter as shown in Figure 3.5. (Refer Appendix B and C for
the Invert Level and slope of the GPTs system respectively).
1500
1700
1470
1270
2000
1550
2500
1700
2760
750
4400
1500
2950
800
Figure 3.5: Arrangement of GPTs system and dimensions in mm
3.4.1
Rubbish Trap
Rubbish trap will control pollution by preventing large pollutants items and
rubbish from being carried into rivers or lakes. The type of rubbish trap used in this
system is Trash Rack. There are two rubbish trap installed at the system; one prior the
sump, and one at the beginning of the system (as shown in Figure 3.6(a) and 3.6(b)
respectively). However, in this study the first one have been removed leaving only one
rubbish trap that will operated in this system.
51
(a)
(b)
Figure 3.6: Rubbish Trap located (a) prior the sump (b) at the beginning of the
GPTs system (used in this study)
3.4.2
Oil and Grease Trap
Oil and grease trap accumulates oil and increases the quality of effluent before
discharge into the environment. Base on the previous study by Lim (2005), the oil and
grease trap is potentially decreased the oil pollution in the experimental site during 2005,
52
since there was a cafeteria operated at the L50 Block. However, the cafeteria is now
closed and there are no oil pollutants that are contributing to the river anymore. Hence,
this study will not including the oil and grease system in the evaluation of the
performance of the GPTs system.
Figure 3.7: Oil and Grease Trap
3.4.3
Biofilter
Biofilter is one of the technologies used in the control of water pollution that use
microorganisms to treat polluted water before it is being discharged. Biofilter is a
filtration method that uses a combination of physio-chemical adsorption and in-situ
bioremediation in the filtration process. Biofiltration is a pollution control technique
using living material to capture and biologically degrade the pollutants process.
Common uses include processing waste water, capturing harmful chemicals or silt from
surface runoff, and microbiotic oxidation of contaminants in air. Natural materials are
used in the biologically active filter (BAF) as its filtering media which consists of
granular activated carbon, granite, zeolite, lime stones, coarse and fine sand, fiber
sponges, leaves and peanut shells. The filtering material functioned as a media which
53
enable the adsorption of organic matters and pollutant onto its surface. Biofilter system
in this study is described in Figure 3.8 and 3.9.
Figure 3.8: Biofilter system.
Oil Palm
Fibre
EBB
Activated
Carbon
Charcoal
(a)
Figure 3.9: Details of Biofilter System: (a) Arrangement of the Biofilter system
54
(Continued Figure 3.9)
800
300
900
300
650
153
Charcoal
Oil Palm
Fibre
Inlet
EBB
Activated Carbon
300
X
Outlet
X
(b)
800
300
900
300
650
60
74
80
300
(c)
Figure 3.9: Details of Biofilter System: (b) Plan view of Biofilter (in mm)
and (c) Details of section X-X (in mm).
55
3.4.3.1 Modification of Biofilter
In order to improve the effectiveness of the biofilter system, a few modification
on its filtration materials have been made since 2006 as shown in Table 3.1. From the
previous study it can be seen that different filtration materials are suitable for different
pollutants removal. For this study, activated carbon has been added to replace bioball.
The activated carbon is produced from coconut shell where the coconut shell is burn
until producing a fine porous material.
3.4.3.2 Filtration Materials
Natural materials are used in the biofilter system as the materials can be found
naturally, easy to obtain and are relatively cheap. Table 3.2 shows the role of each
material in treated the polluted water. The mechanisms of oil palm fibre and activated
carbon are by absorbing the pollutants into their surface while charcoal works by
absorbing gas and liquids. EBB (Eco Bio Blocks) is functioned by treating the
wastewater with “fermented-soybeans bacillus” microorganism that exist in the block.
The details of the filtration materials’ mechanisms and functions are shown in Table 3.2.
Table 3.1: Different materials used for modifications
2006
2007
2008
• Coir
• Oil Palm Fiber
• Oil Palm Fiber
• Oil Palm Fiber
• Sand
• Coir
• Gravel
• Gravel
• Gravel
• Gravel
• Bioball
• Activated Carbon
• Charcoal
• Bioball
• EBB
• EBB
• Sponge
• Charcoal
• Charcoal
• Charcoal
• Sponge
July (2008)
56
Table 3.2: Roles of the Filtration Media
Filtering
Materials
Oil Palm
Fiber
Mechanism
Function
Pollutant will adsorb onto its surface
during the treatment
Capable of removing
suspended matter
contained in water
turbidity Improve turbidity
Gravel
Provides for maximum
entrapment
Activated
The porous material will absorb the
Carbon
contaminants. The pollutants will mix
(from
with the activated carbon particles and
coconut shell) removed later by filtration.
Eco BioThe “fermented-soybeans bacillus”
Block (EBB) microorganism in the block will treat the
wastewater by the decomposition of
organic/inorganic process
Charcoal
3.5
The porosity of activated charcoal is
functioned to adsorb gases and liquids.
Remove hydrocarbons,
oils, phenols, and low
concentrations of metals
Capable in reducing
BOD,COD, ammonia
(NH3), Suspended Solid
(SS), turbidity level in
polluted water
Removing unpleasant
odour in treated water
Data Collection
The data collection for this study involved the collection of rubbish and sediment
amount, water quality sampling, and flow and rainfall data measurement. The
observation and data collection is conducted during dry and wet weather condition.
3.5.1
Rubbish and Sediment Data
Daily data collection for Block L50 rubbish trap was performed for 39 days from
20/06/2008 until 01/08/2008. Because of the small amount of daily rubbish and sediment
57
collection, the daily collection has been change to a three-day period collection started
from 04/08/2008 until 26/09/08. For the purpose of the comparison of the performance,
the three-day period collection also has been started at Block L52 rubbish trap since
10/08/2008 until 26/09/08.
3.5.2
Storm Events
A total of 11 storm events and one event during dry weather condition are
sampled from March 2008 to September 2008. The characteristics of the events are
shown in Table 3.3 and Table 3.4 for Block L50 and Block L52 respectively. The
hydrologic and water quality data are used to produce hydrograph, pollutograph,
observing the concentration of water quality parameters, and for the purpose of detention
time and storage determination.
Table 3.3: Characteristics of monitored storm events at Block L50
Storm
Date
Event
1
21/03/08
2
03/07/08
3
19/07/08
4
21/07/08
5
25/07/08
6
04/08/08
7
07/08/08
12/08/08
8
9
16/08/08
10
04/09/08
Rainfall Rainfall Rainfall
Depth Duration Intensity
(mm)
(hr)
(mm/hr)
NA
28.6
16.6
24.6
20.2
8.2
45.2
57.2
11.4
12.2
NA
0.42
1
1.75
1.08
1.33
2
1.75
1.58
1
NA
68.1
16.6
14.05
18.7
6.2
22.6
32.7
7.2
12.2
Flow
(l/s)
NA
27
2.3
115
0.41
0.7
0.9
0.1
7.6
34.4
Water
Storage Quality EMC
(m3)
Sample
X
√
NA
NA
X
X
NA
X
X
NA
X
X
NA
X
X
0.525
√
√
√
√
0.638
NA
√
√
8.19
X
X
NA
√
√
58
Table 3.4: Characteristics of monitored events at Block L52
Event
Date
1
19/08/08
2
20/08/08
3.5.3
Condition
Storm Event
Dry Weather
Rainfall
Depth
(mm)
9.4
0.0
Flow
(l/s)
382.5
0.54
Water
Quality
Sample EMC
X
√
X
√
Rainfall and Flow Measurement
Rain Gauge ISCO, 0.1 mm TIP has been installed at Meteorology Park, UTM for
the purpose of rainfall data collection. While for flow data collection, two flow meters
have been used, both from ISCO brand but labeled as IWK and TPI respectively. IWK is
installed at the inlet point (Figure 3.10) and TPI is located at the outlet point of the GPTs
system (Figure 3.11).
(a)
(b)
Figure 3.10: Flow meter equipment at the inlet point of GPTs system with (a)
IWK flow meter (b) Plat detector in the rectangular channel
59
(a)
(b)
Figure 3.11: Flow meter equipment at the outlet point of GPTs system with (a)
TPI flow meter (b) Plat detector in the rectangular channel
3.5.3.1 Calibration of Flow Meter
Calibration has been performed at Hydraulics Laboratory, Faculty of Civil
Engineering, UTM Skudai to the flow meters that have been used in this study to
measure the discharge (Q) of the water that flow in and out of the GPTs system. The
height (H), velocity (V), and flow (Q) of the water are measured in laboratory test in
rectangular channel equipment as shown in Figure 3.12(a). The height of water is
measured by ruler. In determining the velocity and flow, a small paper is released to the
flowing water in the rectangular channel (Figure 3.12(b)), and then the time taken by the
paper to travel for a distance of 1 m is taken. The velocity and flow are calculated using
the following formulas:
Velocity (V) =
Dis tan ce(m)
Time( s)
(3.1)
60
Flow (Q)
=
Heigth × Width × Dis tan ce(m3 )
Time( s)
(a)
(3.2)
(b)
Figure 3.12: Calibration (a) rectangular channel equipment (b) determining
velocity and flow
The results then are compared with the results obtained from the flow meter
equipment. The results and the percentages of error of the equipments are shown in
Table 3.5 and Table 3.6. The details of the calculation are shown in Appendix E.
Table 3.5: Calibration results for IWK flow meters
LABORATORY TEST
Reading Height
(m)
1
0.090
2
0.080
3
0.070
4
0.050
5
0.030
Flow
(m3/s)
0.0263
0.0247
0.0208
0.0136
0.0064
Velocity
(m2/s)
0.97
1.03
0.99
0.91
0.71
AUTOMATIC FLOW
METER
Height Flow Velocity
(m)
(m3/s)
(m2/s)
0.0906 0.0275
1.00
0.0876 0.0269
0.99
0.0759 0.0218
0.97
0.0525 0.0129
0.83
0.0358 0.0068
0.61
PERCENTAGES OF
ERRORS
Height Flow Velocity
(%)
(%)
(%)
0.67
4.57
2.67
9.50
9.09
3.64
8.43
4.85
2.03
5.00
5.40
8.70
19.33 5.78
14.60
61
Table 3.6: Calibration results for TPI flow meter
LABORATORY TEST
Reading Height
(m)
1
0.090
2
0.080
3
0.070
4
0.055
5
0.045
3.5.4
Flow
(m3/s)
0.0268
0.0232
0.0201
0.0145
0.0117
Velocity
(m2/s)
0.99
0.96
0.96
0.88
0.87
AUTOMATIC FLOW
METER
Height Flow Velocity
(m)
(m3/s)
(m2/s)
0.0928 0.0284
1.03
0.0877 0.0267
1.01
0.0765 0.0213
0.93
0.0598 0.0154
0.85
0.0501 0.0117
0.84
PERCENTAGES OF
ERRORS
Height Flow Velocity
(%)
(%)
(%)
3.11
5.89
3.69
9.63 15.33
4.70
9.29
6.16
2.66
8.73
6.40
3.10
11.33 0.00
3.40
Water Sampling
3.5.4.1 Water Quality Sampling
The aim of this study is to evaluate the effectiveness of the GPTs system during
dry and wet weather condition. However, there is no flow in the drainage system during
dry weather as the GPTs system is design to functioned only during wet weather (during
stormwater event) and furthermore, the cafeteria at Block L50 which is predicted to
contribute oil waste to the drainage system has stopped its operation since 2006. Hence,
the water sampling only conducted during stormwater event. Figure 3.13 shows the
water quality and flow assessment sampling point for this study where sampling points
1, 2, and 3 represent the influent, after rubbish trap and the effluent of the GPTs system
respectively.
62
1
2
3
Water Quality Sampling Point
Flow Assessment
Figure 3.13: Water Quality and Flow Assessment Sampling Point
3.5.4.2 Analytical Method
3.5.4.2.1
First Flush Sample
With the aim to investigate the occurrence and the influences of first flush to the
concentration of pollutants entering GPTs system during storm event, the first-flush grab
sample method are applied where the water samples are obtained within the first 30
minutes of the discharge (DID, 2000a).
3.5.4.2.2
Water Quality Measurement
The testing was conducted by Spectrum Laboratories (Johore) Sdn. Bhd. The
water quality checked are according to Standard A and Standard B of Environmental
Quality Act (1974) parameters, that are pH, BOD, COD, Lead, Manganese, Zinc,
Phenol, Boron, Suspended Solid, Mercury, Cadmium, Tri-Chromium, Nickel, Oil and
Grease, Free Chlorine, Ammonia Nitrogen, Hexa-Chromium, Arsenic, Cyanide, Copper,
Tin, Iron, and Sulphide.
63
For Event Mean Concentration (EMC) samples, the testing has been conducted
at Environmental Laboratory, Faculty of Civil Engineering, UTM Skudai. The following
water quality parameters are chose based on Urban Stormwater Management Manual for
Malaysia (MASMA), Chapter 15, Volume 5; Suspended Solid, BOD, COD, Lead,
Copper, Zinc, Oil and Grease, Nitrate, Nitrite, Total Phosphorus, and Ammonia
Nitrogen (DID, 2000a). The analytical method used is shown in Table 3.7 for both water
quality and EMC determination.
Table 3.7: Analytical Methods for Water Quality Measurement
pH
ANALYTICAL
METHODS
APHA 4500-H+B
NICKEL
ANALYTICAL
METHOD
APHA 3030-E,3111-B
BOD*5 days
APHA 5210-B
TIN
APHA 3114-C
COD
SUSPENDED SOLID
MERCURY
CADMIUM
HEXA-CHROMIUM
ARSENIC
CYANIDE
LEAD
TRI-CHROMIUM
COPPER
MANGANESE
APHA 5220-C
APHA 2540-D
APHA 3112-B
APHA 3111-B
APHA 3500-Cr D
APHA3114-C
OSRMA P-456
APHA3111-B
APHA3500-Cr D
APHA 3030-E,3111-B
APHA 3030-E,3111-B
ZINC
BORON
IRON
PHENOL
FREE CHLORINE
SULPHIDE
OIL&GREASE
AMMONIA NITROGEN
TOTAL PHOSPHORUS
NITRITE
NITRATE
APHA 3030-E,3111-B
APHA 4500-B C
APHA 3030-E,3111-B
APHA 5530-B C
APHA 4500-Cl F
APHA 4500-S2- F
APAHA 5520-B
APHA 4500 NH3 B C
APHA 4500-P B
APHA 4500-NO2 B
APHA 4500-NO3 B
PARAMETERS
PARAMETERS
3.5.4.3 Data Analysis
Data obtained from the experiment will be analyzed based on their performance.
The effluent discharge is checked with concentration limits stated in Standard A and
Standard B in Third Schedule, Environmental Quality (Sewage and Industrial Effluents),
Regulation 1978, Environmental Quality Act 1974. The source of water located at the
upstream of water supply intake must be compliance with standard A as it is used as a
drinking water. On the other hand, any kind of effluents must be in compliance with
Standard B to ensure safe water for aquatic life.
64
The evaluation is categorized into three stages; that are evaluation for overall
GPTs system, for rubbish trap, and for biofilter. The evaluation is performed by
comparing the concentration of influent and effluent of the discharge as presented in
Table 3.8. Six parameters i.e. pH, Suspended Solid (SS), Biological Oxygen Demand
(BOD), Chemical Oxygen Demand (COD), Ammonia Nitrogen (AN), and Dissolved
Oxygen (DO) have been selected for the evaluation purpose and the effectiveness is
expressed in terms of percentages of removal efficiency. The formula of the percentages
of removal efficiency:
Percentages of removal efficiency (%) =
Influent − Effluent
× 100
Influent
(3.3)
Table 3.8: Description of Water Quality Evaluation
Sampling Point
3.6
No.
Types of Evaluation
Influent
Effluent
1
Overall GPTs System
1
3
2
Rubbish Trap
1
2
3
Biofilter
2
3
Design Criteria of GPTs
Design criteria of GPTs are checked according to
MASMA, Chapter 34,
Volume 13. For hydrology purposes, peak inflows are computed using the Rational
Method;
y
C . I t. A
Qy =
360
(3.4)
where Qy = y year ARI peak flow (m3/s), C = dimensionless runoff coefficient, yIt = y
year ARI average rainfall intensity over time of concentration, tc,(mm/hr), and A =
drainage area (ha) (DID, 2000c).
65
The GPT must be designed so as to prevent any additional surcharge in the
stormwater system in the event of partial or complete blockage. The time stormwater
takes to flow along an open channel may be determined by dividing the length of the
channel by the average velocity of the flow. The average velocity of the flow is
calculated using the hydraulic characteristics of the open channel. The Manning’s
Equation is recommended for this purpose:
1 2/ 3 1/ 2
V= R S
n
(3.5)
where V = average velocity (m/s), n = Manning’s roughness coefficient, R = hydraulic
radius (m), and S = friction slope (m/m) (DID, 2000a).
3.6.1
Rubbish Trap
For the purpose of this study, the selected type of rubbish trap that will be used is
‘Trash Rack’. For the sizing of trash rack, the height is determined based on the rack not
being overtopped in the water quality design storm when the rack is 50% blocked. The
presence of a downstream hydraulic control can lead to the downstream submergence of
the trash rack and an increase in the pool level upstream of the trash rack. Under these
conditions the trash rack height should be sized by a hydraulic analysis of the site and
the trash rack. The sizing method for a standard vertical-bar trash rack is as follows
(Willing & Partners, 1992):
Under unsubmerged conditions, the required height of the trash rack [Hr] is twice
the depth at critical flow [yc] through the unblocked trash rack.
66
H r = 2 yc
1/ 3
⎛ Q0.25 2 ⎞
⎟
= 2⎜⎜
2 ⎟
g
.
L
e ⎠
⎝
(3.6)
where Hr= required height of trash rack (m), Q0.25 = the design flow (m3/s), g =
gravitational acceleration = 9.8 m/s2 , and Le = the effective length of flow through an
unblocked trash rack (m). Using a standard design of vertical 10mm galvanized flat steel
bars at 60 mm centers and a coefficient [Cc]of 0.8 to account for contraction of flow
through the trash rack, gives:
⎛Q ⎞
Hr = 1.22⎜⎜ 0.25 ⎟⎟
⎝ Lr ⎠
(3.7)
where Hr= required height of trash rack (m), Q0.25 = the design flow (m3/s), and Lr =
actual length of the trash rack (m).
3.7
Maintenance
Maintenance must be conducted to ensure the cleanliness of the experimental site
and to ensure that there will be no overflow occur on the system due to any blockage.
Figure 3.14 (a) to 3.14 (d) show the conditions of the GPTs’ system during stormwater
event where the rubbish trap are blocked by the rubbish, mainly dry leaves. This rubbish
should be removed to avoid any blockage for the water to flow. The maintenance should
be conducted at least twice a week and especially after stormwater event.
67
(a)
(b)
(c)
(d)
Figure 3.14: Conditions of (a) rubbish trap, (b) rubbish trap, (c) oil and grease trap and
(d) biofilter during stormwater event.
68
CHAPTER 4
RESULTS AND ANALYSIS
4.1
Introduction
The performance of the GPTs system is evaluated in the following aspects;
water quality, water quantity, first flush analysis, and the determination of EMC
values. For water quality, the effectiveness of GPTs in improving the quality of water
is investigated by observing the amount of rubbish trapped by the rubbish trap before
entering water bodies, the concentration of suspended solid after rubbish trap, the
capability of the biofilter’s filtering materials in removing pollutants and the effluent
of discharge water from the whole GPTs system must comply with parameter limit as
stated in to Standard A and Standard B of Environmental Quality Act (1974).
Despite of functioned for water quality control, the GPTs is also functioned
for water quantity control which it can be a detention basin to provide sufficient
detention time for the sedimentation of pollutant loads. The Event Mean
Concentration (EMC) values and the first flush analysis are appropriate for the
purpose of the evaluation of the GPTs system. All data are well analyzed and are
presented in respective figures and tables.
69
4.2
Performance of GPTs System during Dry Weather
The results of this study shows that the GPTs system is not working during
dry weather. No data collection was recorded during dry weather and no flow is
detected for water quality sampling. There is no flow entering the drainage system
during dry weather as the cafeteria at the nearby building had been closed and was
not operated. Hence, the sources of water for the drainage and GPTs system are
mainly from surface runoff.
4.3
Rubbish and Sediment Collection
Results and data analysis for rubbish trap are presented in two parts. The first
part is for rubbish trap located at Block L50. In order to evaluate its performance, the
results then will be compared with the results obtained from rubbish trap located at
Block L52. The details of data collection procedure for rubbish trap at Block L50 and
rubbish trap at Block L52 are shown in Table 4.1.
4.3.1
Amount of Rubbish and Sediment
4.3.1.1 Block L50 Rubbish Trap
The daily rubbish and sediment collection for L50 rubbish trap has been
started from 20/06/2008 until 01/08/2008. Because of the small amount of daily
rubbish and sediment collection, the daily collection has been change to a three-day
period collection started from 04/08/2008 until 24/09/08.
4.3.1.1.1
Daily Collection
Daily rubbish and sediment collection had been performed for 39 days from
20th June 2008 until 1st August 2008. However, there was no data collection from 4th
70
July until 7th July because of unavoidable cause. From the result shown in Table 4.2,
it can be seen that the rubbish data are only available during wet weather and no
rubbish collected during dry weather. The wet leaves collected during rainy days
were dried to obtain the dry weight of the rubbish. The relationship between the
weather condition and amount of rubbish and sediment collected are shown in Figure
4.1. The amount of rubbish collected was high during wet weather while during dry
weather there was no rubbish data recorded. However, the highest amount of rubbish
i.e. 19.2 kg/day was recorded during dry weather on 20/6/2008 which was the start of
the measurement. The rubbish trap was not cleaned and maintained for almost 2
months, thus generated the high amount of rubbish. The same trend of result is
followed by sediment where the amount of sediment was only recorded during wet
weather condition and the amount of sediment recorded at the start of the
measurement was also very high i.e. 13.9 kg/day.
Table 4.1: Data Collection Procedure for rubbish trap at Block L50 and
rubbish trap at Block L52
Types of Data
1. Rubbish and
Sediment Amount
2. Flow and Velocity
3. Rainfall
4. Water Quality
Site
L50
L52
Frequency
Daily (From 20/6/2008 until 1/8/2008)
3 days time (From 4/8/2008 until 26/9/2008)
Weekly (From 20/7/2008 until 29/7//2008)
3 days time (From 4/8/2008 until 26/9/2008)
L50
Daily (Starting June 2008)
L50
Daily (Starting June 2008)
L52
Daily (Starting June 2008)
L50
Four (4) storm water events
L52
One (1) each for dry and rainy day
71
Table 4.2: Daily rubbish and sediment data collection at Block L50 Rubbish Trap
Amount (kg)
Date
20/6/2008
21/6/2008
22/6/2008
23/6/2008
24/6/2008
25/6/2008
26/6/2008
27/6/2008
28/6/2008
29/6/2008
30/6/2008
1/7/2008
2/7/2008
3/7/2008
8/7/2008
9/7/2008
10/7/2008
11/7/2008
12/7/2008
13/7/2008
14/7/2008
15/7/2008
16/7/2008
17/7/2008
18/7/2008
19/7/2008
20/7/2008
21/7/2008
22/7/2008
23/7/2008
24/7/2008
25/7/2008
26/7/2008
27/7/2008
28/7/2008
29/7/2008
30/7/2008
31/7/2008
1/8/2008
Vegetative
Wet Dry
0
19.1
0
0
0
0
0
1.2
0
0.3
0
0
0
0
0
0
1
0.7
16.9 15.2
0
0
0
0
0
0
2.9
2
4
2.8
0
0
8.5
7
5.5
4.2
3.4
2.8
4.8
3.4
1.6
1.1
0
0
0
0
0
0
0.7
0.4
4
3.6
1.9
1.8
3.3
2.9
0
0
0.15 0.1
0
0
1
0.7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Plastic
Paper
Metal
0.1
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0.1
0
0.5
0.3
0
0
0
0
0
0
0
0.2
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0.1
0
0
0
0.2
0.2
0
0.1
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.005
0
0.001
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Total
Rubbish
19.2
0
0
1.2
0.3
0
0
0
1
17.1
0
0
0
3
4.105
0
9.001
6
3.6
4.8
1.7
0
0
0
0.7
4.2
2.1
3.3
0
0.15
0
1
0
0
0
0
0
0
0
Sediment
13.9
0
0
8
0
0
0
0
0
10.2
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0
0
0
0
0
0
0
0
Weather
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Wet
Wet
Dry
Dry
Dry
Wet
Wet
Dry
Wet
Wet
Wet
Wet
Wet
Dry
Dry
Dry
Wet
Wet
Wet
Wet
Dry
Wet
Dry
Wet
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Rainfall
Depth
(mm)
0
0
0
0
0
0
0
0
0.4
63.8
0
0
0
28.6
13
0
16
0.4
0.2
0.2
2
0
0
0
0.2
16.6
3.8
24.6
0
4.6
0
17.6
0
0
0
0
0
0
0
72
Figure 4.1: Relationship between Rubbish and Sediment Amount with Rainfall at
Block L50 Rubbish Trap (Daily Collection)
4.3.1.1.2
Three-days Period Collection
Three-day period collection had been started from 4/8/2008 until 24/09/2008
and the results are presented in Table 4.3. The relationship between the weather
condition and amount of rubbish and sediment collected are illustrated in Figure 4.2
which shown that the amount of rubbish collected was high during wet weather while
during dry weather there was almost no rubbish and sediment collected. The
maximum rubbish and sediment collection was 12.9kg/3days and 7.5/3 days
respectively.
73
Table 4.3: Three-day period rubbish and sediment data collection at Block L50
Rubbish Trap
Date
3/8/2008
4/8/2008
5/8/2008
6/8/2008
7/8/2008
8/8/2008
9/8/2008
10/8/2008
11/8/2008
12/8/2008
13/8/2008
14/8/2008
15/8/2008
16/8/2008
17/8/2008
18/8/2008
19/8/2008
20/8/2008
21/8/2008
22/8/2008
23/8/2008
24/8/2008
25/8/2008
26/8/2008
27/8/2008
28/8/2008
29/8/2008
30/8/2008
31/8/2008
1/9/2008
2/9/2008
3/9/2008
4/9/2008
5/9/2008
6/9/2008
7/9/2008
8/9/2008
9/9/2008
10/9/2008
11/9/2008
12/9/2008
13/9/2008
14/9/2008
Vegetative
Wet Dry
Plastic
Amount (kg/3 days)
Total
Paper
Metal
Rubbish
Sediment
9
6.3
0
0
1.7
10.7
3.7
3.8
3
0
0
0.2
4
3.8
1.9
1
0
0
0
1.9
1
1.4
1.0
0
0
0
1.4
1.2
2.5
2
0
0
0
2.5
3.2
6
4.1
0
0
0.2
6.2
5.3
4
3.5
0
0
0
4
3.4
12.5
11
0
0
0.2
12.7
4
12.7
10
0
0
0.2
12.9
7.5
5.2
3
0
0
0
5.2
1.4
12
8
0
0
0
12
3
5.5
4.3
0
0
0.4
5.9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Weather
Dry
dry
wet
Dry
Dry
Wet
Wet
Wet
Wet
Dry
Wet
Dry
Dry
Dry
Wet
Wet
Dry
Wet
Dry
Dry
Wet
Wet
Wet
Wet
Wet
Wet
Dry
Dry
Wet
Wet
Wet
Wet
Wet
Wet
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Rainfall
Depth
(mm)
0
0
12.4
0
0
45.2
0.8
7.2
1.2
0
57.2
0
0
0
11.4
5.6
0
9.4
0
0
1.6
0
0
85
0
30
0
0
5.2
1.3
10.2
37.2
34.1
12.2
0
0
0
0
0
0
0
0
0
0
74
(Continued Table 4.3)
Date
15/9/2008
16/9/2008
17/9/2008
18/9/2008
19/9/2008
20/9/2008
21/9/2008
22/9/2008
23/9/2008
24/9/2008
Vegetative
Wet Dry
0.7
0.4
Plastic
0
Amount (kg/3 days)
Total
Paper
Metal
Rubbish
0
0
0.7
Sediment
0.3
0.5
0.3
0
0
0
0.5
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Weather
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Rainfall
Depth
(mm)
0
0
0
0
0
0
0
0
0
0
Figure 4.2: Relationship between Rubbish and Sediment amount with Rainfall at
Block L50 Rubbish Trap (Three-day period collection)
4.3.1.2 Block L52 Rubbish Trap
For the purpose of the performance comparison, the three-day period rubbish
and sediment collection also has been conducted at Block L52 rubbish trap since
10/08/2008 until 24/09/08. The results from Table 4.4 show that the data are
available during dry and wet weather condition. The rubbish trap is functioned
during both weather conditions as the catchments area is consist of food stalls which
produce daily waste water, food and oil to the drainage system. The relationship of
75
amount of rubbish and sediment collected illustrated in Figure 4.3 shows the amount
of collection was high during rainy days.
Table 4.4: Three-day period rubbish and sediment data collection at Block L52
Rubbish Trap
Date
Amount (kg/3 days)
Rubbish
8/8/2008
9/8/2008
10/8/2008
11/8/2008
12/8/2008
13/8/2008
14/8/2008
15/8/2008
16/8/2008
17/8/2008
18/8/2008
19/8/2008
20/8/2008
21/8/2008
22/8/2008
23/8/2008
24/8/2008
25/8/2008
26/8/2008
27/8/2008
28/8/2008
29/8/2008
30/8/2008
31/8/2008
1/9/2008
2/9/2008
3/9/2008
4/9/2008
5/9/2008
6/9/2008
7/9/2008
8/9/2008
9/9/2008
10/9/2008
11/9/2008
12/9/2008
13/9/2008
14/9/2008
Sediment
17
60
18.1
76.4
39
11.2
49.1
14
47
11.5
80
17
28
37.5
12.3
21
24.4
30
2
2
35
0
40
0
Weather
Wet
Wet
Wet
Dry
Wet
Dry
Dry
Dry
Wet
Wet
Dry
Wet
Dry
Dry
Wet
Wet
Wet
Wet
Wet
Wet
Dry
Dry
Wet
Wet
Wet
Wet
Wet
Wet
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Rainfall
Depth
(mm)
0.8
7.2
1.2
0
57.2
0
0
0
11.4
5.6
0
9.4
0
0
1.6
0
0
85
0
30
0
0
5.2
1.3
10.2
37.2
34.1
12.2
0
0
0
0
0
0 0
0
0
0
76
(Continued Table 4.4)
Date
15/9/2008
16/9/2008
17/9/2008
18/9/2008
19/9/2008
20/9/2008
21/9/2008
22/9/2008
23/9/2008
24/9/2008
Amount (kg/3 days)
Rubbish
Sediment
Weather
45
0
Dry
Rainfall
Depth
(mm)
0
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
0
0
0
0
0
0
0
0
0
39
0
21
0
30
0
Figure 4.3: Relationship between Rubbish and Sediment amount with Rainfall at
Block L52 Rubbish Trap (Three-day period collection)
4.3.2 Rainfall Analysis
The relationship between rainfall and rubbish and sediment amount can be
illustrated from Figure 4.1, Figure 4.2 and Figure 4.3. The relationship shows that for
both collection at Block L50 and Block L52, the amount of rubbish and sediment
collected are high when the rainfall value is high.
77
(a)
(b)
Figure 4.4: Correlation between Rubbish and Sediment amount with Rainfall at
Block L50 Rubbish Trap for (a) Daily collection and (b) Three-day period collection
The correlation between rainfall and rubbish and sediment amount for Block
L50 rubbish trap are shown in Figure 4.4(a) and 4.4(b) for daily and three-day period
collection respectively. The amount of rubbish and sediment collected is increase
with the increasing rainfall amount. The same trend of correlation is observed for
rubbish and sediment collection at Block L52 rubbish trap where the collection is
high when the rainfall amount is high (Figure 4.5). These results indicate that rain
influences the amount of rubbish and sediment trapped and the rubbish trap proved
its efficiency in preventing large pollutants from reaching water bodies during storm
event.
78
Figure 4.5: Correlation between Rubbish and Sediment amount with Rainfall at
Block L52 Rubbish Trap for three-day period collection
4.3.3 Classification of Rubbish
The rubbish collected at Block L50 is categorized into plastic, paper, metal,
and vegetative. The categories are selected from the observation made to the rubbish
trap system from the previous studies and the analysis of the catchments area.
Previous study by Sarizah (2006) found that the trapped rubbish at Block L50 was
only from leaf type. Siti Norazela (2007) reported that the rubbish trapped by
Continuous Deflective Separation (CDS) system was classified as plastic, paper,
metal, vegetative and others. The catchment area of Block L50 is consists of parking
lots, a lecture building and co-curriculum centre which may contribute to the plastic
and cans litter by public. There are also a few trees nearby that may falls dry leaves
that will move to the drainage system by wind and runoff. The descriptions of each
category are explained in Table 4.5.
79
Table 4.5: Description of rubbish types
No
Types
Description
1
Plastic
From Personal : Food wrapper, cigarettes, polystyrene wrapper
2
Paper
From Personal : Food wrapper, newspaper, writing paper, tissue
3
Metal
From Personal : Drinks can, food can
4
Vegetative From Nature : Leaf, small wood (from trees)
4.3.3.1 Daily Collection
Figure 4.6(a) and Figure 4.6(b) illustrate the classification of rubbish
according to types for daily data collection from 20/06/08 until 01/08/08. The total
rubbish collected is 82.456 kg for 39 days collection with average of 2.11kg/day. The
highest rubbish type is from vegetative (97.32%), 1.7% from plastic, 0.97% from
paper, and 0.01% from metal types (Figure 4.6(a)).
The maximum rubbish collection was recorded on 20/06/2008, i.e.
19.2kg/day. Figure 4.6(b) illustrated that almost all the rubbish is from vegetative
type with only 0.52% is from plastic. None of the rubbish is paper or metal. The
result is reasonable as the catchment area is covered with trees. The dry leaves may
drop during dry day and will be swept away to the drainage system during storm
events.
4.3.3.2 Three-day Period Collection
A total of 80.6 kg rubbish had been collected during three-day period
collection from 10/08/2008 until 24/09/2008 with average 1.49 kg/day. From the
amount, 96.4% is from vegetative types and 3.6% is metal types. None of plastic and
paper types were collected (Figure 4.7(a)). Maximum rubbish collection i.e. 12.9
kg/3 days was collected on 28/08/2008. Figure 4.7(b) shows that almost 100% of the
collection is from vegetative type.
80
(a)
(b)
Figure 4.6: Classification of rubbish according to types for daily collection for (a)
total collection and (b) maximum day collection (Block L50 rubbish trap)
(a)
Figure 4.7: Classification of rubbish according to types for three-day period
collection for (a) total collection (Block L50 rubbish trap)
81
(Continued Figure 4.7)
(b)
Figure 4.7: Classification of rubbish according to types for three-day period
collection for (b) maximum day collection (Block L50 rubbish trap)
4.4
Water Quality Results
4.4.1
Block L50 GPTs system
A total of six events had been monitored at Block L50 GPTs system as shown
in Table 4.6. Figures 4.8(a) to Figure 4.8(c) are the flow hydrographs for event
04/08/2008, 07/08/2008 and 12/08/2008. The figures show that for all events, the
first sample is taken during the first 30 minutes of the discharge which are
considered as first flush samples. The later samples are taken at the end of the
discharge for the purpose of first flush evaluation.
The water quality results are displayed in Table 4.7, 4.8, 4.9, and 4.10 for
event on 21/03/2008, 04/08/2008, 07/08/2008, and 12/08/2008 respectively. Table
4.7 shows that all effluent is comply with Standard A and Standard B of
Environmental Quality Act 1974 for event 23/03/2008.
82
Table 4.6: Water Quality Events
Event
Date
1
21/03/2008
2
04/08/2008
3
07/08/2008 (First Flush)
4
07/08/2008 (End of Storm)
5
12/08/2008 (First Flush)
6
12/08/2008 (End of Storm)
(a)
(b)
Figure 4.8: Flow hydrograph and sampling time for event on (a) 04/08/08 and (b)
07/08/08
83
(Continued Figure 4.8)
(c)
Figure 4.8: Flow hydrograph and sampling time for event on, and (c) 12/08/08.
For event on 21/03/2008, all effluent is fulfill Standard A while for Standard
B requirement, all effluent is fulfill Standard B except for Ammonia Nitrogen (Table
4.7). For event on 04/08/2008, all effluent is fulfill Standard A except for COD, Hg,
Phenol, and Oil and Grease. While for Standard B requirement, all effluent is fulfill
Standard B except for Oil and Grease, and Ammonia Nitrogen (Table 4.8).
From the 07/08/2008 results in Table 4.9, all the effluent of first flush
samples is fulfill Standard A. For the samples taken at the end of the storm, all
effluent fulfill standard A except for Cu and Phenol. While for Standard B
requirement, both samples for first flush and end storm effluent are fulfill Standard B
except for Ammonia Nitrogen.
The water quality result of 12/08/2008 is shown in Table 4.10 where all the
effluent of first flush samples is fulfill Standard A except for Hg and Zinc. For the
samples taken after 1 hour storm, all effluent fulfills standard A except for Hg and
Phenol. While for Standard B requirement, both samples are fulfill Standard B
except for Ammonia Nitrogen.
84
Table 4.7: Water Quality Results for 21/03/2008
Standard
Parameters
Units Influent Effluent
A
pH
6.4
6.7
6.0-9.0
BOD*5 days at 20⁰C mg/L
20
5
4
COD
mg/L
17
7
50
Suspended solid
mg/L
10
15
50
Mercury as Hg
mg/L <0.001
<0.001
0.005
Cadmium as Cd
mg/L <0.004
<0.004
0.01
Hexa-Chromium as
0.05
Cr6+
mg/L
0.03
<0.02
Arsenic as As
mg/L <0.001
<0.001
0.05
Cyanide as CN
mg/L
<0.02
<0.02
0.05
Lead as Pb
mg/L
<0.02
<0.02
0.1
Tri-Chromium as
0.20
Cr3+
mg/L
<0.02
<0.02
Copper as Cu
mg/L
<0.01
<0.01
0.20
Manganese as Mn
mg/L <0.004
<0.004
0.20
Nickel as Ni
mg/L
<0.02
<0.02
0.20
Tin as Sn
mg/L
<0.01
<0.01
0.20
Zinc as Zn
mg/L
0.06
0.09
1
Boron
mg/L
0.57
<0.01
1
Iron as Fe
mg/L
0.17
0.09
1
Phenol
mg/L <0.001
0.003
0.001
Free Chlorine as Cl2
mg/L
<0.02
<0.02
1
Sulphide as S2mg/L
0.23
0.23
0.50
Not
Oil&Grease
mg/L
<10
<10
detectable
Ammonia Nitrogen
as N
mg/L
1.5
1.7
Standard
B
5.5-9.0
50
100
100
0.05
0.02
0.05
0.100
0.10
0.50
1.00
1.00
1.00
1.00
1.00
2.00
4.00
5.00
1.00
2.00
0.50
10
0.07
Table 4.8: Water Quality Results for 04/08/2008
Results
Standard Standard
Parameters
Units
Point 1
Point 2
Point 3
A
B
pH
5.9
5.6
5.7
6.0-9.0
5.5-9.0
BOD*5 days at 20⁰C
20
mg/L
10
11
17
50
COD
mg/L
38
66
75
50
100
Suspended solid
mg/L
44
21
38
50
100
Mercury as Hg
mg/L
<0.001
0.022
0.022
0.005
0.05
0.01
Cadmium as Cd
mg/L
<0.004
<0.004
<0.004
0.02
Hexa-Chromium as
0.05
Cr6+
mg/L
<0.02
<0.02
<0.02
0.05
Arsenic as As
mg/L
<0.01
<0.01
<0.01
0.05
0.100
Cyanide as CN
mg/L
<0.02
<0.02
<0.02
0.05
0.10
Lead as Pb
mg/L
<0.02
<0.02
<0.02
0.1
0.50
85
(Continued Table 4.8)
Results
Parameters
Tri-Chromium as
Cr3+
Copper as Cu
Manganese as Mn
Nickel as Ni
Tin as Sn
Zinc as Zn
Boron
Iron as Fe
Phenol
Free Chlorine as Cl2
Sulphide as S2-
Units
Point 1
Point 2
Point 3
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
0.02
0.1
<0.004
0.02
<0.01
0.55
0.12
0.04
0.001
<0.02
0.19
<0.02
0.09
0.01
<0.02
<0.01
0.98
0.30
0.42
0.034
<0.02
0.31
<0.02
0.06
<0.004
0.03
<0.01
0.91
0.32
0.06
0.059
<0.02
<0.05
Oil&Grease
Ammonia Nitrogen
as N
mg/L
<10(2)
<10(DA)
13
mg/L
1.5
0.9
0.8
Standard Standard
A
B
0.20
1.00
1.00
1.00
1.00
1.00
2.00
4.00
5.00
1.00
2.00
0.50
0.20
0.20
0.20
0.20
1
1
1
0.001
1
0.50
Not
detectable
10
-
0.07
Table 4.9: Water Quality Results for 07/08/2008
FIRST FLUSH
Parameters
Units
pH
BOD*5 days at
20⁰C
mg/L
COD
mg/L
Suspended solid
mg/L
Mercury as Hg
mg/L
Cadmium as Cd
mg/L
Hexa-Chromium
as Cr6+
mg/L
Arsenic as As
mg/L
Cyanide as CN
mg/L
Lead as Pb
mg/L
Tri-Chromium as
Cr3+
mg/L
Copper as Cu
mg/L
Manganese as Mn mg/L
Nickel as Ni
mg/L
Tin as Sn
mg/L
Zinc as Zn
mg/L
Boron
mg/L
Iron as Fe
mg/L
Phenol
mg/L
Free Chlorine Cl2 mg/L
END OF STORM
Point 1
5.7
Point 2
6
Point 3
5.9
Point 1
5.9
Point 2
5.8
Point 3
6.1
4
17
12
0.001
<0.004
4
20
2
0.06
<0.004
5
20
15
0.002
<0.004
6
20
9
0.002
<0.004
4
23
14
0.003
<0.004
4
20
9
0.003
<0.004
<0.02
<0.01
0.03
<0.02
<0.02
<0.01
<0.02
<0.02
<0.02
<0.01
<0.02
<0.02
<0.02
<0.01
<0.02
<0.02
<0.02
<0.01
0.07
<0.02
<0.02
<0.01
<0.02
<0.02
<0.02
0.81
0.04
<0.02
<0.01
0.42
0.28
0.26
0.072
<0.02
<0.02
0.69
0.03
<0.02
<0.01
0.40
0.35
0.27
<0.001
<0.02
0.03
0.08
0.03
<0.02
<0.01
0.34
0.63
0.12
<0.001
<0.02
<0.02
0.07
0.03
<0.02
<0.01
0.19
0.42
0.15
<0.001
<0.02
<0.02
0.03
0.01
<0.02
<0.01
0.40
0.63
0.20
<0.001
<0.02
<0.02
0.52
0.02
<0.02
<0.01
0.35
1.23
0.23
0.002
<0.02
Standard
A
6.0-9.0
20
50
50
0.005
0.01
0.05
0.05
0.05
0.1
0.20
0.20
0.20
0.20
0.20
1
1
1
0.001
1
Standard
B
5.5-9.0
50
100
100
0.05
0.02
0.05
0.100
0.10
0.50
1.00
1.00
1.00
1.00
1.00
2.00
4.00
5.00
1.00
2.00
86
(Continued Table 4.9)
Table 4.9: Water Quality Results for 07/08/2008
FIRST FLUSH
END OF STORM
Parameters
Sulphide as S2-
Units
mg/L
Point 1
<0.05
Point 2
0.07
Point 3
<0.05
Point 1
<0.05
Point 2
0.07
Point 3
<0.05
Oil&Grease
Ammonia
Nitrogen as N
mg/L
<10
<10
<10
<10
<10
<10
mg/L
0.9
1.4
0.9
0.5
0.5
0.4
Standard Standard
A
B
0.50
0.50
Not
detectable
10
-
0.07
Table 4.10: Water Quality Results for 12/08/2008
FIRST FLUSH
END OF STORM
Standard
A
6.0-9.0
Standard
B
5.5-9.0
Parameters
pH
BOD*5 days at
20⁰C COD
Suspended
solid
Mercury as Hg
Cadmium as
Cd
HexaChromium Cr6+
Arsenic as As
Cyanide as CN
Lead as Pb
Tri-Chromium
as Cr3+
Copper as Cu
Manganese as
Mn
Nickel as Ni
Tin as Sn
Zinc as Zn
Boron
Iron as Fe
Phenol
Free Chlorine
as Cl2
Sulphide as S2-
Units
Point 1
6.1
Point 2
6.1
Point 3
6
Point 1
6.4
Point 2
7
Point 3
6.6
mg/L
mg/L
7
33
8
39
7
39
5
16
4
10
5
16
mg/L
mg/L
23
0.008
62
0.011
29
0.01
9
0.008
4
0.012
9
0.008
0.005
100
0.05
mg/L
<0.004
<0.004
<0.004
<0.004
<0.004
<0.004
0.01
0.02
mg/L
mg/L
mg/L
mg/L
0.03
<0.001
0.03
<0.02
<0.02
<0.001
<0.02
<0.02
<0.02
<0.001
<0.02
<0.02
<0.02
<0.001
0.05
<0.02
<0.02
<0.001
<0.02
<0.02
0.02
<0.001
<0.02
<0.02
0.05
0.05
0.05
0.1
0.05
0.100
0.10
0.50
mg/L
mg/L
<0.02
0.04
0.02
0.04
0.03
0.03
<0.02
0.04
0.06
0.03
0.08
0.04
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
0.04
<0.02
<0.01
2.29
<0.01
0.14
<0.001
0.06
<0.02
<0.01
1.15
0.03
0.31
0.002
0.04
<0.02
<0.01
1.14
<0.01
0.18
<0.001
0.01
<0.02
<0.01
0.42
0.17
0.17
0.008
0.02
<0.02
<0.01
0.32
<0.01
0.19
0.01
0.02
<0.02
<0.01
0.27
0.25
0.15
0.007
mg/L
mg/L
<0.02
<0.05
<0.02
<0.05
<0.02
<0.05
<0.02
<0.05
<0.02
0.07
<0.02
<0.05
Oil&Grease
Ammonia
Nitrogen as N
mg/L
<10
<10
<10
<10
<10
<10
mg/L
0.6
0.9
2.1
0.8
0.8
0.6
20
50
50
0.20
0.20
0.20
0.20
0.20
1
1
1
0.001
1
0.50
Not
detectable
-
50
100
1.00
1.00
1.00
1.00
1.00
2.00
4.00
5.00
1.00
2.00
0.50
10
0.07
87
4.4.1.1 Water Quality Evaluation
The water quality evaluation involved three types of evaluations; evaluation
of overall GPTs system, Rubbish Trap evaluation, and the evaluation of Biofilter as
described in Section 3.5.2.3.
4.4.1.1.1
GPTs Evaluation Results
Table 4.11 presented the overall results for GPTs system evaluation. The
results can best be illustrated by Figure 4.9(a) to 4.9(f) for pH, SS, BOD, COD, AN,
and DO respectively.
Table 4.11: Water Quality Results for Overall GPTs System
pH
SS (mg/L)
BOD (mg/L)
Event Influent Effluent Influent Effluent Influent Effluent
6.4
6.7
10
15
5
4
1
5.9
5.7
44
38
10
17
2
5.7
5.9
12
15
4
5
3
5.9
6.1
9
9
6
4
4
6.1
6
23
29
7
7
5
6.4
6.6
9
9
5
5
6
COD (mg/L)
AN (mg/L)
DO (mg/L)
Event Influent Effluent Influent Effluent Influent Effluent
17
7
1.5
1.7
1
38
75
1.5
0.8
7.4
7.57
2
3
17
20
0.9
0.9
7.57
7.7
20
20
0.5
0.4
7.8
7.4
4
33
39
0.6
2.1
8.8
7.6
5
16
16
0.8
0.6
9
7.8
6
From Figure 4.9(a), it can be noticed that the pH values for all event are in
the range 5.5 to 9 which under the limit of Standard A and Standard B, of
Environmental Quality Act 1974. The improvement of SS concentration is only
detected in Event 2 with 13.64% of removal (Figure 4.9(b)). The percentages of
removal for BOD are detected in Event 1 and Event 4 with 20% and 33.33% removal
respectively. The removal for COD is only detected in Event 1 by 59%. AN shows a
good water quality improvement with 46.67%, 20%, and 25% removal in Event 2,
Event 4, and Event 6 respectively. The concentration of DO is increased for all
88
events with the highest improvement is in Event 6 with 13.33% concentration
increment. The summary of the percentages of removal efficiency are displayed in
Table 4.12.
(a)
(b)
Figure 4.9: Water Quality Results for GPTs for parameters (a) pH and
(b) SS
89
(Continued Figure 4.9)
(c)
(d)
(e)
Figure 4.9: Water Quality Results for GPTs for parameters (c) BOD,
(d) COD and (e) AN
90
(Continued Figure 4.9)
(f)
Figure 4.9: Water Quality Results for GPTs for parameters (f) DO
Table 4.12: Percentages of Removal for GPTs
Percentages of Removal (%)
Event
SS
BOD
COD
AN
DO
20.00
58.82
2.30
1
13.64
46.67
1.72
2
3
33.33
20.00
4
5
4.4.1.1.2
Rubbish Trap Evaluation Results
The evaluation results for rubbish trap are shown in Table 4.13 and the results
are also depicted in Figure 4.10(a) to 4.10(f) for pH, SS, BOD, COD, AN, and DO
respectively.
91
Event
2
3
4
5
6
Event
2
3
4
5
6
Table 4.13: Evaluation Results for Rubbish Trap
pH
SS (mg/L)
BOD (mg/L)
Influent Effluent Influent Effluent Influent Effluent
5.9
5.6
44
21
10
11
5.7
6
12
2
4
4
5.9
5.8
9
14
6
4
6.1
6.1
23
62
7
8
6.4
7
9
4
5
4
COD (mg/L)
AN (mg/L)
DO (mg/L)
Influent Effluent Influent Effluent Influent Effluent
38
66
1.5
0.9
7.4
7.4
17
20
0.9
1.4
7.57
7.8
20
23
0.5
0.5
7.8
7.5
33
39
0.6
0.9
8.8
9.0
16
10
0.8
0.8
9.0
8.6
From Figure 4.10(a), it can be seen that pH values are in a good range of 5.5
to 7 while the percentages of SS removal are in the range of 52% to 83% (Figure
4.10(b). The low performance results can be observed from other parameters;
COD,BOD, and AN where the improvement were detected in only one event. The
poor performance was also detected in DO with percentages of improvement less
than 3%. For overall, it can be concluded that the rubbish trap is very effective in
improving SS concentration compared to other parameters. The percentages of
removal efficiency results are summarized in Table 4.14.
(a)
Figure 4.10: Water Quality Results for Rubbish Trap for parameters (a) pH
92
(Continued Figure 4.10)
(b)
(c)
(d)
Figure 4.10: Water Quality Results for Rubbish Trap for parameters (b) SS, (c)
BOD, and (d) COD
93
(Continued Figure 4.10)
(e)
(f)
Figure 4.10: Water Quality Results for Rubbish Trap for parameters (e) AN
and (f) DO
Table 4.14: Percentages of Removal for Rubbish Trap
Percentages of Removal (%)
Event
SS
BOD
COD
AN
DO
2
52.27
40.00
83.33
3.04
3
33.33
4
37.50
2.27
5
94
4.4.1.1.3
Biofilter Evaluation Results
Table 4.15 illustrated the overall results for biofilter system evaluation. The
comparison of concentration values between the events are displayed in Figure
4.11(a) to 4.11(f) for pH, SS, BOD, COD, AN, and DO respectively. The summary
of the percentages of removal efficiency results for biofilter are shown in Table 4.16.
Event
2
3
4
5
6
Event
2
3
4
5
6
Table 4.15: Evaluation Results for Biofilter
pH
SS (mg/L)
BOD (mg/L)
Influent Effluent Influent Effluent Influent Effluent
5.6
5.7
21
38
11
17
6
5.9
2
15
4
5
5.8
6.1
14
9
4
4
6.1
6
62
29
8
7
7
6.6
4
9
4
5
COD (mg/L)
AN (mg/L)
DO (mg/L)
Influent Effluent Influent Effluent Influent Effluent
66
75
0.9
0.8
7.4
7.4
20
20
1.4
0.9
7.57
7.8
23
20
0.5
0.4
7.8
7.5
39
39
0.9
2.1
8.8
9.0
10
16
0.8
0.6
9.0
8.6
Table 4.16: Percentages of Removal for Biofilter
Percentages of Removal (%)
SS
BOD
COD
AN
DO
Event
11.11
2
35.71
3.04
3
35.71
13.04
20.00
4
53.23
12.50
2.27
5
It can be seen in Figure 4.11(a) that the pH value was only improved in Event
4 from 5.8 to 6.1. In Event 4, the concentration of SS decreases by 35.71% removal
and increases by 53.23% removal in Event 5 (Figure 4.11(b)). The improvement of
BOD can only be seen in Event 5 with 12.5% removal (Figure 4.11(c)). The
concentration of DO increase for all events in the range 1% to maximum value of
10% improvement while AN improve for all Events except event 5 with percentages
of removal from 11% to 35%. From the results, it can be concluded that the biofilter
is very effective in improving AN and DO.
95
(a)
(b)
(c)
Figure 4.11: Water Quality Results for Biofilter for parameters (a) pH, (b) SS, and
(c) BOD
96
(Continued Figure 4.11)
(d)
(e)
(f)
Figure 4.11: Water Quality Results for Biofilter for parameters (d) COD, (e) AN,
and (f) DO
97
4.4.2
Block L52 Rubbish Trap
The water quality at Block L52 rubbish trap is checked to compare with the
water quality results of Block L50. The influent and effluent samples are taken on
two events, i.e., stormwater event on 19/08/2008 and dry weather on 20/08/2008.
The results are presented in Table 4.17 where it shows that all the effluent for storm
event samples are complies with Standard A and Standard B. The effluent for
samples taken during dry weather are complies with Standard A and Standard B
except for SS, BOD, and COD.
Figure 4.12(a) and 4.12(b) presented the comparison of concentration for six
parameters; pH, Biological Oxygen Demand (BOD), Chemical Oxygen Demand
(COD), Suspended Solid (SS), and Ammonia Nitrogen (AN). The effluent samples
taken during dry weather show the excellent improvement with almost 100%
removal for BOD, COD, and SS. During storm event, the percentages of removal for
COD, BOD, and SS are only 35.3%, 56%, and 31.5% respectively. This result
indicates that this L52 rubbish trap is capable in improving water quality in both dry
and wet weather, and more effective during dry day.
(a)
Figure 4.12: Water Quality Results for Block L52 during (a) storm event
98
(Continued Figure 4.12)
(b)
Figure 4.12: Water Quality Results for Block L52 during (b) dry weather
Table 4.17: Water Quality Results for Block L52 Rubbish Trap
Parameters
pH
BOD*5 days at 20⁰C COD
Suspended solid
Mercury as Hg
Cadmium as Cd
Hexa-Chromium as
Cr6+
Arsenic as As
Cyanide as CN
Lead as Pb
Tri-Chromium as
Cr3+
Copper as Cu
Manganese as Mn
Nickel as Ni
Tin as Sn
Zinc as Zn
Boron
Iron as Fe
Phenol
Free Chlorine as Cl2
Sulphide as S
2-
Oil&Grease
Ammonia Nitrogen
as N
Units
mg/L
mg/L
mg/L
mg/L
mg/L
Storm Event
Influent
Effluent
6
6
17
11
75
33
54
37
<0.001
<0.001
<0.004
<0.004
Dry Weather
Influent
Effluent
5.2
6
381
99
1077
255
1937
72
<0.001
<0.001
<0.004
<0.004
Standard
A
6.0-9.0
20
50
50
0.005
0.01
0.05
Standard
B
5.5-9.0
50
100
100
0.05
0.02
mg/L
mg/L
mg/L
mg/L
0.04
<0.001
<0.02
<0.02
<0.02
<0.001
<0.02
<0.02
0.03
0.006
0.17
<0.02
<0.02
0.005
<0.02
<0.02
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
<0.02
<0.01
0.09
<0.02
<0.01
0.04
1.58
0.29
0.014
<0.02
<0.01
0.07
<0.02
<0.01
0.05
0.27
0.65
0.006
0.02
<0.01
0.1
<0.02
<0.01
0.08
0.49
1.87
0.024
<0.02
<0.01
0.1
<0.02
<0.01
0.05
1.01
2.44
0.021
mg/L
<0.02
<0.02
<0.02
<0.02
0.20
0.20
0.20
0.20
1
1
1
0.001
1
<0.05
1.58
1.34
0.50
0.50
Not
detectable
10
mg/L
0.23
mg/L
17
<10
48
<10
mg/L
<0.07
0.8
10.1
8.6
0.05
0.05
0.1
0.20
-
0.05
0.100
0.10
0.50
1.00
1.00
1.00
1.00
1.00
2.00
4.00
5.00
1.00
2.00
0.07
99
4.5
Water Quantity Control
Despite functioned for water quality improvement, the GPTs system can also
benefit for the purpose of water quantity control. Figures 4.13(a) to 4.13(c) are the
flow hydrograph for event 04/08/2008, 07/08/2008, and 12/08/2008 respectively.
From the figures, it can be seen that the values of peak flow out is lower than the
peak flow in. That means that the GPTs system is capable in reducing the peak flow
of the discharge entering the water bodies, thus preventing overflows occurs to the
system. The GPTs also provide storage and detention time for the sedimentation of
pollutant loads in the system. Table 4.18 below shows the detention time, storage
capacity, and the values of peak flow in and out of the GPTs system of three
stormwater events; 04/08/2008, 07/08/2008, and 16/08/2008.
Table 4.18: Water Quantity Results of GPTs system
Qpeak
Storm
Detention Storage Qpeak
in
out
Event
Time
(m3)
(l/s)
(l/s)
(min)
04/08/08*
10
0.525
0.7
0.7
07/08/08
10
0.638
0.9
0.7
16/08/08
10
8.190
7.6
4.6
*Notes: Problem occurs to flow meter during sampling
(a)
Figure 4.13: Flow Hydrograph for event (a) 04/08/2008
100
(Continued Figure 4.13)
(b)
(c)
Figure 4.13: Flow Hydrograph for event (b) 07/08/2008, and (c) 12/08/2008
4.6
First Flush Analysis
First flush analysis results are presented in two parts. In first part, the
pollutograph for four monitored events (04/08/2008, 07/08/2008, 12/08/2008, and
04/09/2008) are depicted to investigate the influences of first flush to the
concentration of pollutants entering the GPTs system. The samples are then checked
for the occurrence of first flush and the results are explained in Section 4.6.2. Flow
hydrographs and sampling time for event 04/08/2008, 07/08/2008, and 12/08/2008
101
are shown in Figure 4.8(a) to 4.8(c) respectively and hydrograph and sampling time
for event 04/09/2008 is displayed in Figure 4.14.
Figure 4.14: Flow Hydrograph for event 04/09/2008
4.6.1 Pollutograph Evaluation
4.6.1.1 Event 04/08/2008
The relationship between the concentration of pollutants with time can be
observed from Figure 4.15(a) to 4.15(f) which represent the pollutograph for BOD,
COD, SS, Cu, Zinc, and AN respectively for event 04/08/2008. From the results, it
can be seen that the concentration of BOD, COD, and Zn is increasing within 20
minutes of the discharge to the end of the discharge. For SS, Zinc, and AN, the
concentration of pollutants is high at the early discharge and low at the end of the
discharge.
102
(a)
(b)
(c)
(d)
(e)
(f)
Figure 4.15: Pollutograph for event 04/08/2008 for parameters (a) BOD, (b) COD,
(c)SS, (d)Cu, (e)Zinc, and (f) AN
103
4.6.1.2 Event 07/08/2008
The pollutograph for event 07/08/2008 are illustrated in Figure 4.16(a) to
4.16(f) for BOD, COD, SS, Cu, Zinc, and AN respectively. Figure 4.15(a) to 4.15(c)
show that the concentration of BOD, COD, and SS is low at the early of the
discharge, become higher at the middle, and the concentration is decreased at the end
of the discharge. For Cu, Zinc, and AN, the concentration of pollutants is high at the
early discharge and low at the end of the discharge.
(a)
(b)
(c)
(d)
Figure 4.16: Pollutograph for event 07/08/2008 for parameters (a) BOD, (b) COD,
(c)SS, and (d)Cu
104
(Continued Figure 4.16)
(e)
(f)
Figure 4.16: Pollutograph for event 07/08/2008 for parameters (e)Zinc, and (f) AN
4.6.1.3 Event 12/08/2008
The pollutograph for event 12/08/2008 are depicted in Figure 4.17(a) to
4.17(f) for BOD, COD, SS, Cu, Zinc, and AN respectively. All the pollutograph
show that the concentration of the six parameters is high at the early of the discharge,
then decrease at the end of the discharge.
(a)
(b)
Figure 4.17: Pollutograph for event 12/08/2008 for parameters (a) BOD and (b)
COD
105
(Continued Figure 4.17)
(c)
(d)
(e)
(f)
Figure 4.17: Pollutograph for event 12/08/2008 for parameters (a) BOD, (b) COD,
(c)SS, (d)Cu, (e)Zinc, and (f) AN
4.6.1.4 Event 04/09/2008
The pollutograph for event 04/09/2008 are depicted in Figure 4.18(a) to
4.18(j) for BOD, COD, SS, Cu, Zinc, AN, TP, Pb, Nitrite, and Nitrate respectively.
From the results obtained, it can be noticed that the concentration of the pollutants is
higher at first sample which was taken at the early of the storm event compared to
the samples taken at time 10 minutes discharge until the end of discharge. The trend
is observed for all the ten parameters. (Refer to Appendix F for laboratory test
result)
106
(a)
(b)
(c)
(d)
(e)
(f)
Figure 4.18: Pollutograph for event 04/09/2008 for parameters (a) BOD, (b) COD,
(c)SS, (d)Cu, (e)Zinc, and (f) AN
107
(Continued Figure 4.18)
(g)
(h)
(i)
(j)
Figure 4.18: Pollutograph for event 04/09/2008 for parameters (g) TP, (h) Pb,
(i) Nitrite, and (j) Nitrate
For overall, the observation from all the four events have found out that the
concentration of pollutants for most event is high at the beginning of the discharge,
i.e. within the 30 minutes of the discharge then decrease throughout the time until at
the end of the discharge. It shows that the concentration of pollutants in first flush
runoff is more polluted than the remainder due to the washout of deposited pollutants
by rainfall.
108
4.6.2 Occurrence of First Flush
The dimensionless normalized mass and flow volumes are used to
characterize the occurrence of first flush. A first flush is exist if the dimensionless
cumulative pollutant mass M exceeds the dimensionless cumulative runoff volume V
at all instances during the storm events or falls above the 45 line when plotting M
versus V graph. The results of first flush analysis for all storm events 04/08/2008,
07/08/2008, 12/08/2008, and 04/09/2008 are presented in Table 4.19. The occurrence
of first flush is only detected for TSS and COD, showing that only the two pollutants
occur in the stormwater runoff for every storm event. The details of first flush
occurrence calculation and determination can be view at Appendix G.
Table 4.19: Occurrence of First Flush
Event
TSS
4/8/08
7/8/08
12/8/08
4/9/08
YES
YES
YES
YES
4.7
BOD5 COD
NO
NO
NO
NO
YES
YES
YES
YES
Cu
Pb
Zn
O&G
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NH3N
NO
NO
NO
NO
NO3N
NA
NA
NA
NO
NO2N
NA
NA
NA
NO
Event Mean Concentration (EMC)
Event Mean Concentration (EMC) is the flow-weighted mean concentration
of a pollutant. EMC is one of the numbers of empirical approaches available for
determining pollutant load. This pollutant load is essential for GPTs design purpose.
EMC values for the four events (04/08/2008, 07/08/2008, 12/08/2008, and
04/09/2008) have been calculated and the results are displayed in Table 4.20. The
detail of the EMC calculation is shown in Appendix H.
PO4
NA
NA
NA
NO
109
Table 4.20: Event Mean Concentration (EMC) values
Date
TSS
BOD5 COD
Cu
Pb
Zn
O&G
4/8/08
7/8/08
12/8/08
4/9/08
31.03
10.91
19.98
3.90
12.22
5.10
6.00
3.65
0.09
0.20
0.04
0.06
<0.02
<0.02
<0.02
0.04
0.85
0.30
0.79
0.24
9.98
<10
<10
0.00
61.08
19.98
27.18
10.98
NH3N
1.30
0.79
1.38
0.09
NO3N
NA
NA
NA
0.11
NO2N
NA
NA
NA
0.003
PO4
NA
NA
NA
0.52
By comparing the results with the typical EMC values for urban area
abstracted from (DID, 2000) in Table 4.21, it was found that all the parameters value
are below the typical EMC limit except for PO4 and Cu. For event 4/9/08, the
concentration of PO4 is 0.52 mg/L which exceeding the limit of 0.13 mg/L while the
value of 0.20 mg/L Cu concentration is also exceeding the limit of 0.03-0.09 mg/L
(for event 7/8/08).
Table 4.21: Typical Event Mean Concentration (EMC) values in mg/L (DID, 2000)
Landuse
Pollutants
Sediment
50 to 200
Urban
4.8
SS
85
TN
1.2
TP Ammonia
0.13
0.01 to
9.8
Faecal
Coliforms
4,000200,000
Cu
0.030.09
Pb
0.20.5
Evaluation of Design Criteria
The sizing of the rubbish trap is checked according to Urban Stormwater
Management Manual for Malaysia (MASMA). The determination of design criteria
for the rubbish trap is based on general design parameters stated in Section 34.5.2,
Chapter 34, MASMA (DID, 2000c). The design criteria checking for rubbish trap is
considering the following criterion; trap area, trap rack size (height), velocity, and
bar spacing. The summarized of design criteria results are presented in Table 4.22.
Zn
0.271.1
110
Table 4.22: Design Criteria Results
Criteria
Requirement
Actual Value
Check
Trap Area, At
Minimum requirement
5.1225 m2
= 0.036 m2
Actual > Min Req.
-OK!
Trap Rack
height
Minimum requirement
50cm
= 26.5cm
Actual > Min Req.
-OK!
Flow Velocity, V
V should not exceed
1.0 m/s
0.25m/s
Actual < 1.0m/s
-OK!
Bar Spacing
Maximum clear
spacing 50mm
30mm bar
spacing
Actual < Max Req.
-OK!
The actual value of the trap area is calculated as 5.1225 m2 while the
minimum requirement of trap area obtained from Design Chart 34.A1, Chapter 34
MASMA is 0.036 m2. As the trash rack area is larger than the minimum requirement
values, hence the size adopted is appropriate. Same goes for the trap rack height
where the actual value 50 cm exceeding the minimum requirement of 26.5 cm. The
bar spacing i.e. 30 cm is under the limit of maximum requirement of 50 mm clear
spacing. From observation at site, the 30 cm clear spacing between bars of the trash
rack is found capable in retaining small bottle and aluminum can. In order to
minimize re-suspension, the flow velocity through the track must not exceed 1 m/s.
the results show that the checking for velocity is appropriate where the calculated
velocity for the track rack is 0.25 m/s. The details of the design criteria calculation
can be viewed in Appendix I.
Annual sediment data is required to calculate the average annual pollutant
retention value for the purpose of sediment trap depth calculation. However, the
sediment data collection in this study is only available for three months (data
collection from 2006/2008 until 26/09/2008). The Event Mean Concentration (EMC)
111
values obtained from this study are also limited where the data are only available for
four stormwater events. Annual EMC values are needed to calculate the average
annual load. From the average annual load and annual volumetric runoff values, the
Annual Mean Concentration (AMC) can be determined. Then, the pollutant removal
efficiency, η of the rubbish trap can be determined from Equation 4.1:
⎛ AMC
⎞
⎝
⎠
proposed ⎟
η = ⎜⎜
AMCexisting ⎟
where AMC = annual mean concentration.
(4.1)
112
CHAPTER 5
CONCLUSION AND RECOMMENDATION
5.1
Conclusion
In the present study, an attempt has been made to evaluate and investigate the
performance of GPTs system for surface runoff in open channel flow. The collection
of rubbish and sediment amount, hydrologic data collection, water quality
assessment, first flush analysis and EMC values determination have been performed
for the purpose of achieving the study objectives. A few conclusions have been made
from the series of field work and laboratory investigation carried out.
In terms of the efficiency of GPTs system in removing pollutants during
storm and dry weather condition, the following conclusions can be made:
i.
The GPTs system is effective in removing pollutants during wet weather
condition. However, the system is not working during dry weather as there is
no flow entering the system during that time.
ii.
For overall, it can be concluded that the GPTs system is effective in
improving water quality during storm event where the effluent of discharge
water of the GPTs system are comply with parameter limit as stated in
Standard A and Standard B of Environmental Quality Act (1974). However
in terms of the pollutant removal efficiency, the results are not encouraging
113
where the percentages of pollutants removal for most water quality
parameters are low.
iii.
The performance of rubbish trap is excellent where it manage to decrease the
concentration of Suspended Solid in the range of 52% to 83% removal
efficiency and is capable in preventing as high as 19.2kg rubbish and 13.9kg
sediment from entering water bodies. Rainfall influence the amount of
rubbish and sediment collected, where the rubbish and sediment amount
increase with the increasing rainfall value.
iv.
Despite of functioned for water quality control, the GPTs is also benefit for
water quantity control where it provide detention time, storage, and decrease
the peak flow of the water flowing through the system.
The investigation of first flush analysis on the water samples and the
determination of EMC values from the hydrologic data have come out with the
conclusions stated that:
v.
For first flush analysis, the concentration of pollutants in first flush runoff
within the early 30 minutes of the runoff is found more polluted than the
remainder. Analysis for occurrence of first flush shows that only TSS and
COD occur in the stormwater runoff for every storm event.
vi.
The values of EMC for all the monitored storm event are higher for TSS,
COD, and BOD compared to other pollutants, i.e. Cu, Pb, Zn, Oil and Grease,
NH3-N, NO3-N, and PO4.
114
5.2
Recommendation
There are some recommendations suggested in order to improve the
performance of the GPTs system;
i.
It is recommended that the maintenance for the GPTs system is conducted for
three-day period to avoid overflows occurred to the system. Maintenance
work is also suggested during storm events to ensure large pollutants that
block the rubbish trap can be removed to avoid overflows.
ii.
The backwater and overflow problems during storm event are affecting the
results of water quality. Thus, providing solution for this problem will
enhance a better water quality results.
iii.
A lot of improvement need to be done to the design criteria of the GPTs to
suit the weather condition in Malaysia as the design implemented before is
based on MASMA which are adopted from the Australia weather conditions.
iv.
New effective filtration materials should be suggested to improve the
workability of the biofilter system. The materials should be economical,
durable, and the most important criteria is it has a good mechanism in
improving the quality of water.
v.
In the future study, the determination of water quality parameters at each
compartments of biofilter system is suggested in order to identify the suitable
filtration materials for specific pollutants.
vi.
Annual sediment data collection is suggested to be conducted to obtained the
Annual Sediment Load value for design purpose.
115
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APPENDIX A
Average Recurrence Interval (ARI) for existing drainage system
35 cm
A1
10 cm
A2
40 cm
1
0.3125
15 cm
50 cm
Manning’s Equation (for Composite drain Section): Q =
1 m ⎛ Ai 5 / 3 ⎞ 1 / 2
⎟S
∑ ⎜
n * i = 1 ⎜⎝ Pi 2 / 3 ⎟⎠
(1)
Where;
Area,A15/3
= (b × y)5/3
= (0.35 m × 0.50 m)5/3
= 0.055
Area,A2
5/3
= ((b+zy)y)5/3
= ((0.15+0.3125×0.40)0.40)5/3
= 0.025
Wetted Perimeter, P12/3
= (2y+b)2/3
= ((2×0.35)+0.50)2/3
= 1.129
Wetted Perimeter, P2
2/3
= (b+2y√(1+z2))2/3
= (0.15+2*0.40*√(1+0.31252))2/3
= 0.992
122
n, Manning’s roughness
= 0.011 (From Table 14.3, MASMA (Chapter 14, for
coefficient
Concrete/Asphalt))
n*, Manning’s roughness
=
coefficient
m
ni Ai
i =1
Pi
∑
m
5/3
2/3
A5 / 3
∑P
i =1
i
2/3
i
= (0.011 × 0.055 / 1.129) + (0.011 × 0.025 / 0.992)
(0.055 / 1.129) + (0.025 / 0.992)
= 0.011
S, friction slope
= 1:1000 or 0.001 (recommended)
From Equation (1):
∴Flow, Q =
1
× ((0.055 / 1.129) + (0.025 / 0.992)) × 0.0011 / 2
0.011
= 0.212 m 3 /s
= 212 l/s
Assume :
QManning ' s
From Rational Method,
=
Q =
0.212m 3 /s =
QRationalMe thod
C×I ×A
360
(2)
C×I × A
360
Where;
C, Dimensionless Runoff Coefficient = 0.9 (From Design Chart 14.3, MASMA)
A, Drainage Area
= 1600m 2 or 0.16 ha
From Equation (2):
∴ y It
=
0.212m3 / s × 360
0.9 × 0.16ha
= 531 mm/hr
Where;
y
It
= y year ARI average rainfall intensity over time of concentration, tc ,
(mm/hr)
123
From Equation 13.2 (MASMA Chapter 13);
ln( RI t ) = a + b ln(t ) + c (ln(t )) 2 + d (ln(t ))3
Try with t=10 mins
ln(532.5) = a + b ln(10) + c (ln(10)) 2 + d (ln(10))3
6.28 = a + 2.3025b + 5.302c + 12.21d
From Table 13.A1, for Johor Bharu,
(Try ARI 5 years)
a = 4.3251
b = 1.0147
c = -0.3308
d = 0.0205
From Equation (3):
6.28 =
a + 2.3025b + 5.302c + 12.21d
6.28 =
4.3251 + (2.3025 × 1.0147) + (5.302 × −0.3308) + (12.21 × 0.0205)
6.28 ≠ 5.16
(Try ARI 10 years)
a = 4.4896
b = 0.9971
c = -0.3279
d = 0.0205
From Equation (3):
6.28 =
a + 2.3025b + 5.302c + 12.21d
6.28 =
4.4896 + (2.3025 × 0.9971) + (5.302 × −0.3279) + (12.21 × 0.0205)
6.28 ≈ 5.30
∴ARI for the drainage system = 10 years
(3)
124
Average Recurrence Interval (ARI) for GPTs system
60cm
30cm
b = 0.30 m
y = 0.60 m
From Manning’s Equation : Q =
AR 2 / 3 S 1 / 2
n
(1)
Where;
A, cross-sectional area
= by
= 0.30m × 0.60m
= 0.180m 2
R, Hydraulic Radius
=
by
b + 2y
=
0.180m2
0.30m + (2 × 0.60m)
= 0.120m
S, friction slope
= 1:1000 or 0.001 (recommended)
n, Manning’s roughness
= 0.011 (From Table 14.3, MASMA (Chapter 14, for
coefficient
Concrete/Asphalt))
From Equation (1):
2
∴Flow, Q =
1
0.180m 2 × 0.120m 3 × 0.0012
0.011
= 0.126 m 3 /s
= 126 l/s
125
Assume :
=
QManning ' s
From Rational Method,
QRationalMe thod
C×I × A
360
Q =
0.126m 3 /s =
(2)
C×I × A
360
Where;
C, Dimensionless Runoff Coefficient = 0.9 (From Design Chart 14.3, MASMA)
A, Drainage Area
= 1600m 2 or 0.16 ha
From Equation (2):
∴ y It
=
0.126m3 / s × 360
0.9 × 0.16ha
= 315 mm/hr
Where;
y
It
= y year ARI average rainfall intensity over time of concentration, tc ,
(mm/hr)
From Equation 13.2 (MASMA Chapter 13);
ln( RI t ) = a + b ln(t ) + c (ln(t )) 2 + d (ln(t ))3
Try with t=10 mins
ln(315) = a + b ln(10) + c(ln(10)) 2 + d (ln(10))3
5.75 = a + 2.3025b + 5.302c + 12.21d
From Table 13.A1, for Johor Bharu,
(Try ARI 5 years)
a = 4.3251
b = 1.0147
c = -0.3308
d = 0.0205
(3)
126
From Equation (3):
5.75 =
a + 2.3025b + 5.302c + 12.21d
5.75 =
4.3251 + (2.3025 × 1.0147) + (5.302 × −0.3308) + (12.21 × 0.0205)
5.75 ≠ 5.16
Try ARI 10 years)
a = 4.4896
b = 0.9971
c = -0.3279
d = 0.0205
From Equation (3):
5.75 =
a + 2.3025b + 5.302c + 12.21d
5.75 =
4.4896 + (2.3025 × 0.9971) + (5.302 × −0.3279) + (12.21 × 0.0205)
5.75 ≈ 5.30
∴ARI for the GPTs system = 10 years
127
APPENDIX B
Invert Level (I.L) – GPTs System
Rubbish
Trap
3
I.L 29.519
Existing Invert Level: 29.700
2
I.L 29.70
4
5
I.L 28.176 I.L 28.013
I.L 27.532
Oil & Grease Trap
Biofilter
I.L 29.70
1
Sump
6
Drainage
7
I.L 25.342
8
I.L 26.514
To UTM
River
Point
1
2
3
4
5
6
7
8
Different
Height
(m)
0.000
0.000
-0.181
-1.343
-0.163
-0.481
-1.018
-1.172
I.L
29.700
29.700
29.519
28.176
28.013
27.532
26.514
25.342
128
APPENDIX C
Slope – GPTs System
Rubbish
Trap
Oil & Grease Trap
1
Biofilter
1:4
2
Point
1
2
Horizontal
Distance
(m)
0
15.168
Different
Heigth
(m)
0
-3.849
Slope
1:4
129
APPENDIX D
Details Plan – GPTs System
C
1470
A
2760
Drainage
800
Plan view of the GPTs system (in mm)
Biofilter
2500
Oil & Grease
Trap
4400
750
Sump
D
1450
1050
1100
65
1500
1830
Rubbish
Trap
2000
950
B
1550
1700
1270
1700
A
1500
2950
To UTM
River
130
Details of Section A-A (in mm)
950
1050
Details of Section B (Rubbish Trap) (in mm)
50
2100
131
Details of Section C (Oil and Grease Trap)
125
1300
125
Inlet
1130
X
X
150
250
300
1270
Y
1130
Y
250
1470
Y
Y
Outlet
Plan view of Oil & Grease Trap (in mm)
132
1300
250
1380
1120
250
250
1550
250
Details of section X-X (in mm)
560
1380
270
φ270
250
1550
Details of section Y-Y (in mm)
250
133
Details of Section D (Biofilter)
800
300
900
300
650
153
Charcoal
Oil Palm
Fibre
EBB
Inlet
Activated Carbon
300
X
X
Plan view of Biofilter (in mm)
800
300
900
300
650
Details of section X-X (in mm)
60
74
300
80
Outlet
134
APPENDIX E
CALIBRATION FOR FLOW METER (IWK)
LABORATORY TEST
Reading
1
2
3
4
5
Velocity
Height
(m)
0.090
0.080
0.070
0.050
0.030
Width
(m)
0.30
0.30
0.30
0.30
0.30
Area
(m2)
0.027
0.024
0.021
0.015
0.009
Distance(m) Time(s)
3.00
3.00
3.00
3.00
3.00
3.08
2.92
3.03
3.30
4.20
V (m/s)
0.97
1.03
0.99
0.91
0.71
Flow
Q
(m3/s)
0.0263
0.0247
0.0208
0.0136
0.0064
EFFICIENCY
HEIGHT
Reading
1
2
3
4
5
0.001
0.008
0.006
0.003
0.006
Average
/
/
/
/
/
Percentages of Error
=
0.090
=
0.080
=
0.070
=
0.050
=
0.030
=
0.67
9.50
8.43
5.00
19.33
8.59
%
%
%
%
%
%
AUTOMATIC FLOWMETER
Velocity
Flow
Height
Q (m3/s)
(m)
V (m2/s)
0.0906
1.00
0.0275
0.0876
0.99
0.0269
0.0759
0.97
0.0218
0.0525
0.83
0.0129
0.0358
0.61
0.0068
135
FLOW
Reading
1
2
3
4
5
0.0012
0.0022
0.0010
0.0007
0.0004
Average
/
/
/
/
/
Efficiency
0.0263
0.0247
0.0208
0.0136
0.0064
0.03
0.04
0.02
0.08
0.10
Average
/
/
/
/
/
Efficiency
0.97
1.03
0.99
0.91
0.71
=
=
=
=
=
=
4.57
9.09
4.85
5.40
5.78
5.94
%
%
%
%
%
%
=
=
=
=
=
=
2.67
3.64
2.03
8.70
14.60
6.33
%
%
%
%
%
%
VELOCITY
Reading
1
2
3
4
5
136
CALIBRATION FOR FLOW METER (TPI)
LABORATORY TEST
Reading
1
2
3
4
5
Velocity
Height
(m)
0.090
0.080
0.070
0.055
0.045
Width
(m)
0.30
0.30
0.30
0.30
0.30
Area
(m2)
0.0270
0.0240
0.0210
0.0165
0.0135
Distance(m) Time(s)
3.00
3.00
3.00
3.00
3.00
3.02
3.11
3.14
3.42
3.45
V (m/s)
0.99
0.96
0.96
0.88
0.87
=
=
=
=
=
=
3.11
9.63
9.29
8.73
11.33
8.42
Flow
Q
(m3/s)
0.0268
0.0232
0.0201
0.0145
0.0117
EFFICIENCY
HEIGHT
Reading
1
2
3
4
5
0.003
0.008
0.006
0.005
0.005
Average
/
/
/
/
/
Efficiency
0.090
0.080
0.070
0.055
0.045
%
%
%
%
%
%
AUTOMATIC FLOWMETER
Velocity
Flow
Height
Q (m3/s)
(m)
V (m2/s)
0.0928
1.03
0.0284
0.0877
1.01
0.0267
0.0765
0.93
0.0213
0.0598
0.85
0.0154
0.0501
0.84
0.0117
137
FLOW
Reading
1
2
3
4
5
0.0016
0.0035
0.0012
0.0009
0.0000
Average
/
/
/
/
/
Efficiency
0.0268
0.0232
0.0201
0.0145
0.0117
0.04
0.05
0.03
0.03
0.03
Average
/
/
/
/
/
Efficiency
0.99
0.96
0.96
0.88
0.87
=
=
=
=
=
=
5.89
15.33
6.16
6.40
0.00
6.76
%
%
%
%
%
%
=
=
=
=
=
=
3.69
4.70
2.66
3.10
3.40
3.51
%
%
%
%
%
%
VELOCITY
Reading
1
2
3
4
5
138
APPENDIX F
LABORATORY TEST RESULTS FOR EVENT 04/09/2008 (EMC)
SS
Sample
1
2
3
4
5
6
7
8
9
10
Wt. of Filter
Wt. of Filter
Vol. of
SS
SS
Paper (mg)
84.21
83.64
84.32
84.41
84.37
84.1
84.31
84.02
84.18
84.06
Paper+SS (mg)
90.22
83.77
84.69
84.67
84.50
84.27
84.62
84.05
84.40
84.06
sample (ml)
100
100
100
100
100
100
100
100
100
100
(mg/ml)
0.0601
0.0013
0.0037
0.0026
0.0013
0.0017
0.0031
0.0003
0.0022
0
(mg/L)
60
1
4
3
1
2
3
0
2
0
Time
(min)
0
10
15
20
25
30
40
45
55
65
BOD
Sample
1
2
3
4
5
6
7
8
9
10
Time (min)
0
10
15
20
25
30
40
45
55
65
DO1(ppm) DO5(ppm)
14.95
5.60
7.98
6.16
8.22
6.06
7.18
5.74
8.5
6.17
9.25
5.90
8.18
5.88
9.43
6.30
7.93
6.20
8.44
6.80
Nutrient
Water
(ml)
175
175
175
175
175
175
175
175
175
175
Sample(ml)
125
125
125
125
125
125
125
125
125
125
BOD5(mg/L)
13
3
3
2
3
5
3
4
2
2
139
COD
Sample
1
2
3
4
5
6
7
8
9
10
NH3-N
Time
(min)
0
10
15
20
25
30
40
45
55
65
COD
(mg/L)
128
0
14
15
1
15
0
0
0
0
Cu
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
NH3-N
(mg/L)
0.39
0.25
0.17
0.06
0.04
0.03
0.03
0.2
0.19
0.07
Time
(min)
0
10
15
20
25
30
40
45
55
65
NITRATE
(mg/L)
0.22
0.04
0.08
0.11
0.1
0.13
0.11
0.13
0.13
0.09
Time
(min)
0
10
15
20
25
30
40
45
55
65
NITRITE
(mg/L)
0.008
0.001
0.003
0.004
0.002
0.003
0.003
0.002
0.001
0
NO3-N
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
Cu (mg/L)
0.16
0
0
0.08
0
0.05
0.13
0.11
0.13
0
Pb
Sample
1
2
3
4
5
6
7
8
9
10
NO2-N
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
Pb (mg/L)
0.061
0.057
0.054
0.04
0.046
0.036
0.04
0.04
0.038
0.032
Sample
1
2
3
4
5
6
7
8
9
10
140
PO4
Sample
1
2
3
4
5
6
7
8
9
10
Time (min)
0
10
15
20
25
30
40
45
55
65
PO4 (mg/L)
0.58
0.22
0
0.22
0.09
1.58
0.11
0.33
1.72
0.02
Oil & Grease
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
Wt. of
Beaker +
beaker (g)
49.1874
87.7020
55.5878
86.1999
56.4375
52.3234
47.8652
61.4478
55.6800
54.4224
O&G (g)
49.1774
87.6872
55.5811
86.1986
56.4320
52.3188
47.8588
61.4471
55.6776
54.4177
Vol. of
sample
(ml)
350
350
350
350
350
350
350
350
350
350
O&G
(mg/L)
0
0
0
0
0
0
0
0
0
0
141
APPENDIX G
FIRST FLUSH CALCULATION FOR EVENT 04/08/2008
SS
Time
Sample (min)
1
0
2
2
3
5
ΣQ
=
Q
(m3/s)
0.0007
0.0007
0.0002
0.0250
Zn
(mg/L)
44
21
38
Cum Normalized
∆t(min) Time
Time
0
0
0.00
2
2
0.40
3
5
1.00
Σ Cum
T
5
OCCURRENCE OF FIRST FLUSH: YES
Avg Q
(m3/s)
0.0000
0.0007
0.00045
Avg V
(m2)
0
0.084
0.081
Σ Avg V
0.165
Avg
Conc
Cum
Cum/ΣV (mg/L)
V
0
0.00
0
0.084
0.51
32.5
0.165
1.00
29.5
Σ Mass
=
Mass
(mg)
0
2730
2389.5
5119.5
Cum Cum/
Mass ΣMass
0
0.00
2730
1.35
5119.5 2.54
142
BOD
Sample
1
2
3
Time
(min)
0
2
5
Q
(m3/s)
0.0007
0.0007
0.0002
ΣQ=
0.0250
Zn
(mg/L)
10
11
17
∆t(min)
0
2
3
Σ Cum
T
OCCURRENCE OF FIRST FLUSH: NO
Cum
Time
0
2
5
5
Normalized Avg Q
Time
(m3/s)
0.00
0.0000
0.40
0.0007
1.00
0.00045
Σ Avg
V
Avg V
(m2)
0
0.084
0.081
0.165
Avg
Conc
Cum V Cum/ΣV (mg/L)
0
0.00
0
0.084
0.51
10.5
0.165
1.00
14
Σ Mass
=
Mass
(mg)
0
882
1134
2016
Cum
Mass
0
882
2016
Cum/
ΣMass
0.00
0.44
1.00
143
COD
Sample
1
2
3
Time
(min)
0
2
5
ΣQ
=
Q
(m3/s)
0.0007
0.0007
0.0002
0.0250
Zn
(mg/L)
38
66
75
∆t
Cum Normalized Avg Q
(min) Time
Time
(m3/s)
0
0
0.00
0.0000
2
2
0.40
0.0007
3
5
1.00
0.00045
Σ Cum
Σ Avg
T
5
V
OCCURRENCE OF FIRST FLUSH: YES
Avg V
(m2)
0
0.084
0.081
0.165
Cum
V
0
0.084
0.165
Avg
Cum/
Conc
Mass
Cum
Cum/ΣV (mg/L)
(mg)
Mass ΣMass
0.00
0
0
0
0.00
0.51
52
4368
4368
2.17
1.00
70.5
5710.5 10078.5 5.00
Σ Mass
=
10078.5
144
CU
Sample
1
2
3
Time
(min)
0
2
5
Q
Zn
(m3/s) (mg/L)
0.0007
0.1
0.0007
0.09
0.0002
0.06
ΣQ=
0.0250
∆t
(min)
0
2
3
Σ Cum
T
OCCURRENCE OF FIRST FLUSH: NO
Cum
Time
0
2
5
5
Normalized Avg Q
Time
(m3/s)
0.00
0.0000
0.40
0.0007
1.00
0.00045
Σ Avg
V
Avg V
(m2)
0
0.084
0.081
0.165
Avg
Conc
Cum V Cum/ΣV (mg/L)
0
0.00
0
0.084
0.51
0.095
0.165
1.00
0.075
Σ Mass
=
Mass
(mg)
0
7.98
6.075
14.055
Cum
Mass
0
7.98
14.055
Cum/
ΣMass
0.00
0.00
0.01
145
ZINC
Time
Sample (min)
1
0
2
2
3
5
ΣQ
=
Q
Zn
(m3/s) (mg/L)
0.0007
0.55
0.0007
0.98
0.0002
0.91
0.0250
∆t
(min)
0
2
3
Σ Cum
T
OCCURRENCE OF FIRST FLUSH: NO
Cum Normalized Avg Q Avg V
(m2)
Time
Time
(m3/s)
0
0.00
0.0000
0
2
0.40
0.0007 0.084
5
1.00
0.00045 0.081
Σ Avg
5
V
0.165
Cum
V
0
0.084
0.165
Cum/ΣV
0.00
0.51
1.00
Avg
Conc
(mg/L)
0
0.765
0.945
Mass
(mg)
0
64.26
76.545
Σ Mass =
140.805
Cum/
Cum
Mass ΣMass
0
0.00
64.26
0.03
140.805 0.07
146
OIL & GREASE
Time
Sample (min)
1
0
2
2
3
5
ΣQ
=
Q
Zn
3
(m /s) (mg/L)
0.0007
9
0.0007
9
0.0002
13
0.0250
∆t
(min)
0
2
3
Σ Cum
T
OCCURRENCE OF FIRST FLUSH: NO
Cum
Time
0
2
5
5
Normalized Avg Q
Time
(m3/s)
0.00
0.0000
0.40
0.0007
1.00
0.00045
Σ Avg
V
Avg V
(m2)
0
0.084
0.081
0.165
Avg
Conc
Mass
Cum V Cum/ΣV (mg/L) (mg)
0
0.00
0
0
0.084
0.51
0.095
9
0.165
1.00
0.075
11
Σ Mass
=
20
Cum
Mass
0
756
891
Cum/
ΣMass
0.00
0.38
0.44
147
NH3-N
Sample
1
2
3
Time
(min)
0
2
5
Q
Zn
(m3/s) (mg/L)
0.0007
1.5
0.0007
0.9
0.0002
0.8
ΣQ=
0.0250
∆t
(min)
0
2
3
Σ Cum
T
Cum
Time
0
2
5
5
Normalized Avg Q
Time
(m3/s)
0.00
0.0097
0.40
0.0007
1.00
0.00045
Σ Avg
V
Avg V
(m2)
0
0.084
0.081
0.165
Avg
Conc
Cum V Cum/ΣV (mg/L)
0
0.00
0
0.084
0.51
32.5
0.165
1.00
29.5
Σ Mass
=
Mass
(mg)
0
1.2
0.85
Cum
Mass
0
100.8
68.85
Cum/
ΣMass
0.00
0.05
0.03
2.05
OCCURRENCE OF FIRST FLUSH: NO
*NOTE: No first flush calculation is performed for Pb, NO3-N, NO2-N, and PO4 as no or very small amount of the pollutant is
water sample.
detected in the
148
FIRST FLUSH CALCULATION FOR EVENT 07/08/2008
SS
Time
Q
Zn
Sample (min) (m3/s) (mg/L)
0.0009
12
1
0
2
0.00085
2
2
5
0.0008
15
3
25
0.0003
9
4
27
0.0003
14
5
30
0.0003
9
6
ΣQ
=
0.0035
∆t
(min)
0
2
3
20
2
3
Σ Cum
T
Cum
Time
0
2
5
25
27
30
30
Normalized Avg Q
Time
(m3/s)
0.00
0.0000
0.07
0.000875
0.17
0.000825
0.83
0.00055
0.90
0.0003
1.00
0.0003
Avg V
(m2)
0
0.105
0.1485
0.66
0.036
0.054
Σ Avg V
1.0035
Avg
Cum/
Conc
Mass
Cum
ΣMass
(mg)
Mass
Cum V Cum/ΣV (mg/L)
0.00
0.00
0
0
0
0.00
0.11
0.10
7
735
735
0.14
0.25
0.25
8.5
1262.25 1997.25
0.39
0.91
0.91
12
7920
9917.25
1.94
0.95
0.95
11.5
414
10331.25 2.02
1.00
1.00
11.5
621
10952.25 2.14
Σ Mass
=
10952.25
OCCURRENCE OF FIRST FLUSH: YES
149
BOD
Sample
1
2
3
4
5
6
Time
(min)
0
2
5
25
27
30
Q
Zn
(m3/s) (mg/L)
0.0009
4
0.00085
4
0.0008
5
0.0003
6
0.0003
4
0.0003
4
ΣQ=
0.0035
∆t
(min)
0
2
3
20
2
3
Σ Cum
T
Cum
Time
0
2
5
25
27
30
30
Normalized Avg Q
Time
(m3/s)
0.00
0.0000
0.07
0.000875
0.17
0.000825
0.83
0.00055
0.90
0.0003
1.00
0.0003
Avg V
(m2)
0
0.105
0.1485
0.66
0.036
0.054
Σ Avg V
1.0035
Avg
Conc
Mass
(mg)
Cum V Cum/ΣV (mg/L)
0.00
0.00
0
0
0.11
0.10
4
420
0.25
0.25
4.5
668.25
0.91
0.91
5.5
3630
0.95
0.95
5
180
1.00
1.00
4
216
Σ Mass
=
5114.25
OCCURRENCE OF FIRST FLUSH: NO
Cum
Mass
0
420
1088.25
4718.25
4898.25
5114.25
Cum/
ΣMass
0.00
0.08
0.21
0.92
0.96
1.00
150
COD
Time
Q
Sample (min) (m3/s)
0.0009
1
0
2
0.00085
2
5
0.0008
3
25
0.0003
4
27
0.0003
5
30
0.0003
6
ΣQ
=
0.0035
Zn
(mg/L)
17
20
20
20
23
20
∆t
(min)
0
2
3
20
2
3
Σ Cum
T
Cum
Time
0
2
5
25
27
30
30
Normalized Avg Q Avg V
(m2)
Time
(m3/s)
0.00
0.0000
0
0.07
0.000875 0.105
0.17
0.000825 0.1485
0.83
0.00055
0.66
0.90
0.0003
0.036
1.00
0.0003
0.054
Σ Avg V
1.0035
Cum V
0.00
0.11
0.25
0.91
0.95
1.00
Avg
Conc
Cum/ΣV (mg/L)
0.00
0
0.10
18.5
0.25
20
0.91
20
0.95
21.5
1.00
21.5
Σ Mass
=
OCCURRENCE OF FIRST FLUSH: YES
Mass
(mg)
0
1942.5
2970
13200
774
1161
20047.5
Cum/
Cum
Mass ΣMass
0
0.00
1942.5
0.38
4912.5
0.96
18112.5 3.54
18886.5 3.69
20047.5 3.92
151
CU
Sample
1
2
3
4
5
6
Time
(min)
0
2
5
25
27
30
Q
Zn
(m3/s) (mg/L)
0.0009
0.81
0.00085
0.69
0.0008
0.08
0.0003
0.07
0.0003
0.03
0.0003
0.52
ΣQ=
0.0035
∆t
(min)
0
2
3
20
2
3
Σ Cum
T
Cum
Time
0
2
5
25
27
30
30
Normalized Avg Q
Time
(m3/s)
0.00
0.0000
0.07
0.000875
0.17
0.000825
0.83
0.00055
0.90
0.0003
1.00
0.0003
Avg V
(m2)
0
0.105
0.1485
0.66
0.036
0.054
Σ Avg V
1.0035
Avg
Conc
Mass
(mg)
Cum V Cum/ΣV (mg/L)
0.00
0.00
0
0
0.11
0.10
0.75
78.75
0.25
0.25
0.385
57.1725
0.91
0.91
0.075
49.5
0.95
0.95
0.05
1.8
1.00
1.00
0.275
14.85
Σ Mass
=
202.0725
OCCURRENCE OF FIRST FLUSH: NO
Cum
Mass
0
78.75
135.9225
185.4225
187.2225
202.0725
Cum/
ΣMass
0.00
0.02
0.03
0.04
0.04
0.04
152
Zn
Sample
1
2
3
4
5
6
Time
(min)
0
2
5
25
27
30
Q
Zn
(m3/s) (mg/L)
0.0009
0.42
0.00085
0.4
0.0008
0.34
0.0003
0.19
0.0003
0.4
0.0003
0.35
ΣQ=
0.0035
∆t
(min)
0
2
3
20
2
3
Σ Cum
T
Cum
Time
0
2
5
25
27
30
30
Normalized Avg Q
Time
(m3/s)
0.00
0.0000
0.07
0.000875
0.17
0.000825
0.83
0.00055
0.90
0.0003
1.00
0.0003
Avg V
(m2)
0
0.105
0.1485
0.66
0.036
0.054
Σ Avg V
1.0035
Avg
Conc
Mass
Cum
(mg)
Mass
Cum V Cum/ΣV (mg/L)
0.00
0.00
0
0
0
0.11
0.10
0.41
43.05
43.05
0.25
0.25
0.37
54.945 97.995
0.91
0.91
0.265
174.9 272.895
0.95
0.95
0.295
10.62 283.515
1.00
1.00
0.375
20.25 303.765
Σ Mass
=
303.765
OCCURRENCE OF FIRST FLUSH: NO
Cum/
ΣMass
0.00
0.01
0.02
0.05
0.06
0.06
153
NH3-N
Sample
1
2
3
4
5
6
Time
(min)
0
2
5
25
27
30
Q
Zn
(m3/s) (mg/L)
0.0009
0.9
0.00085
1.4
0.0008
0.9
0.0003
0.5
0.0003
0.5
0.0003
0.4
ΣQ=
0.0035
∆t
(min)
0
2
3
20
2
3
Σ Cum
T
Cum
Time
0
2
5
25
27
30
30
Normalized Avg Q
Time
(m3/s)
0.00
0.0000
0.07
0.000875
0.17
0.000825
0.83
0.00055
0.90
0.0003
1.00
0.0003
Avg V
(m2)
0
0.105
0.1485
0.66
0.036
0.054
Σ Avg V
1.0035
Avg
Conc
Mass
Cum
(mg)
Mass
Cum V Cum/ΣV (mg/L)
0.00
0.00
0
0
0
0.11
0.10
1.15
120.75 120.75
0.25
0.25
1.15
170.775 291.525
0.91
0.91
0.7
462
753.525
0.95
0.95
0.5
18
771.525
1.00
1.00
0.45
24.3
795.825
Σ Mass
=
795.825
Cum/
ΣMass
0.00
0.02
0.06
0.15
0.15
0.16
OCCURRENCE OF FIRST FLUSH: NO
*NOTE: No first flush calculation is performed for Pb, Oil and Grease, NO3-N, NO2-N, and PO4 as no or very small amount of the pollutant is
detected in the water sample.
154
FIRST FLUSH CALCULATION FOR EVENT 12/08/2008
SS
Sample
1
2
3
4
5
6
Time
(min)
0
2
5
60
62
65
Q
(m3/s)
0.1
0.1
0.1
0.1
0.1
0.1
ΣQ=
0.6000
Zn
(mg/L)
23
62
29
9
4
9
∆t
(min)
0
2
3
55
2
3
Σ Cum
T
Cum
Time
0
2
5
60
62
65
65
Normalized
Time
0.00
0.03
0.08
0.92
0.95
1.00
Avg Q
(m3/s)
0.0000
0.1
0.1
0.1
0.1
0.1
Σ Avg
V
Avg V
(m2)
0
12
18
330
12
18
390
Avg
Conc
Mass
Cum
(mg)
Mass
Cum V Cum/ΣV (mg/L)
0.00
0.00
0
0
0
12.00
0.03
42.5
510000 510000
30.00
0.08
45.5
819000 1329000
360.00
0.92
19
6270000 7599000
372.00
0.95
6.5
78000 7677000
390.00
1.00
6.5
117000 7794000
Σ Mass
=
7794000
OCCURRENCE OF FIRST FLUSH: YES
Cum/
ΣMass
0.00
0.22
0.57
3.25
3.28
3.33
155
BOD
Sample
1
2
3
4
5
6
Time
(min)
0
2
5
60
62
65
Q
(m3/s)
0.1
0.1
0.1
0.1
0.1
0.1
ΣQ=
0.6000
Zn
(mg/L)
7
8
7
5
4
5
∆t
(min)
0
2
3
55
2
3
Σ Cum
T
Cum
Time
0
2
5
60
62
65
65
Normalized
Time
0.00
0.03
0.08
0.92
0.95
1.00
Avg Q
(m3/s)
0.0000
0.1
0.1
0.1
0.1
0.1
Σ Avg
V
Avg V
(m2)
0
12
18
330
12
18
390
Avg
Conc
Mass
Cum
(mg)
Mass
Cum V Cum/ΣV (mg/L)
0.00
0.00
0
0
0
12.00
0.03
7.5
90000
90000
30.00
0.08
7.5
135000 225000
360.00
0.92
6
1980000 2205000
372.00
0.95
4.5
54000 2259000
390.00
1.00
4.5
81000 2340000
Σ Mass
=
2340000
OCCURRENCE OF FIRST FLUSH: NO
Cum/
ΣMass
0.00
0.04
0.10
0.94
0.97
1.00
156
COD
Sample
1
2
3
4
5
6
Time
(min)
0
2
5
60
62
65
Q
(m3/s)
0.1
0.1
0.1
0.1
0.1
0.1
ΣQ=
0.6000
Zn
(mg/L)
33
39
39
16
10
16
∆t
(min)
0
2
3
55
2
3
Σ Cum
T
Cum
Time
0
2
5
60
62
65
65
Normalized
Time
0.00
0.03
0.08
0.92
0.95
1.00
Avg Q
(m3/s)
0.0000
0.1
0.1
0.1
0.1
0.1
Σ Avg
V
Avg V
(m2)
0
12
18
330
12
18
390
Avg
Conc
Mass
Cum
(mg)
Mass
Cum V Cum/ΣV (mg/L)
0.00
0.00
0
0
0
12.00
0.03
36
432000
432000
30.00
0.08
39
702000
1134000
360.00
0.92
27.5
9075000 10209000
372.00
0.95
13
156000 10365000
390.00
1.00
13
234000 10599000
Σ Mass
=
10599000
OCCURRENCE OF FIRST FLUSH: YES
Cum/
ΣMass
0.00
0.18
0.48
4.36
4.43
4.53
157
Cu
Sample
1
2
3
4
5
6
Time
(min)
0
2
5
60
62
65
Q
(m3/s)
0.1
0.1
0.1
0.1
0.1
0.1
ΣQ=
0.6000
Zn
(mg/L)
0.04
0.04
0.03
0.04
0.03
0.04
∆t
(min)
0
2
3
55
2
3
Σ Cum
T
Cum
Time
0
2
5
60
62
65
65
Normalized
Time
0.00
0.03
0.08
0.92
0.95
1.00
Avg Q
(m3/s)
0.0000
0.1
0.1
0.1
0.1
0.1
Σ Avg
V
Avg V
(m2)
0
12
18
330
12
18
390
Avg
Conc
Cum V Cum/ΣV (mg/L)
0.00
0.00
0
12.00
0.03
0.04
30.00
0.08
0.035
360.00
0.92
0.035
372.00
0.95
0.035
390.00
1.00
0.035
Σ Mass
=
OCCURRENCE OF FIRST FLUSH: NO
Mass
(mg)
0
480
630
11550
420
630
13710
Cum
Mass
0
480
1110
12660
13080
13710
Cum/
ΣMass
0.00
0.00
0.00
0.01
0.01
0.01
158
Zn
Sample
1
2
3
4
5
6
Time
(min)
0
2
5
60
62
65
ΣQ=
Q
(m3/s)
Zn
∆t(min)
(mg/L)
0.1
2.29
0
0.1
1.15
2
0.1
1.14
3
0.1
0.42
55
0.1
0.32
2
0.1
0.27
3
Σ Cum
0.6000
T
Cum
Time
0
2
5
60
62
65
65
Normalized
Time
0.00
0.03
0.08
0.92
0.95
1.00
Avg Q
(m3/s)
0.0000
0.1
0.1
0.1
0.1
0.1
Σ Avg
V
Avg V
(m2)
0
12
18
330
12
18
390
Avg
Conc
Cum V Cum/ΣV (mg/L)
0.00
0.00
0
12.00
0.03
1.72
30.00
0.08
1.145
360.00
0.92
0.78
372.00
0.95
0.37
390.00
1.00
0.295
Σ Mass
=
OCCURRENCE OF FIRST FLUSH: NO
Mass
Cum
(mg)
Mass Cum/ΣMass
0
0
0.00
20640 20640
0.01
20610 41250
0.02
257400 298650
0.13
4440 303090
0.13
5310 308400
0.13
308400
159
NH3-N
Sample
Time
(min)
Sample
1
2
3
4
5
6
(min)
0
2
5
60
62
65
ΣQ=
Q
(m3/s)
(m3/s)
0.1
0.1
0.1
0.1
0.1
0.1
0.6000
Zn
(mg/L)
(mg/L)
0.6
0.9
2.1
0.8
0.8
0.6
∆t
(min)
∆t(min)
0
2
3
55
2
3
Σ Cum
T
Cum
Time
Normalized
Time
Avg Q
(m3/s)
Avg V
(m2)
Time
0
2
5
60
62
65
Time
0.00
0.03
0.08
0.92
0.95
1.00
(m3/s)
0.0000
0.1
0.1
0.1
0.1
0.1
Σ Avg
V
(m2)
0
12
18
330
12
18
65
390
Avg
Conc
Cum V Cum/ΣV (mg/L)
Cum V Cum/ΣV (mg/L)
0.00
0.00
0
12.00
0.03
0.75
30.00
0.08
1.5
360.00
0.92
1.45
372.00
0.95
0.8
390.00
1.00
0.7
Σ Mass
=
Mass
(mg)
Cum
Mass
(mg)
Mass
0
0
9000
9000
27000 36000
478500 514500
9600 524100
12600 536700
Cum/
ΣMass
ΣMass
0.00
0.00
0.02
0.22
0.22
0.23
536700
OCCURRENCE OF FIRST FLUSH: NO
*NOTE: No first flush calculation is performed for Pb, Oil and Grease, NO3-N, NO2-N, and PO4 as no or very small amount of the pollutant is
detected in the water sample.
160
FIRST FLUSH CALCULATION FOR EVENT 04/09/2008
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
ΣQ=
Q
Zn
(m3/s) (mg/L)
0.0000
60
0.0097
1
0.0153
4
0.0200
3
0.0344
1
0.0227
2
0.0149
3
0.0061
0
0.0020
2
0.0003
0
0.1254
∆t
(min)
0
10
5
5
5
5
10
5
10
10
Σ Cum
T
Cum
Time
0
10
15
20
25
30
40
45
55
65
65
Normalized
Time
0.00
0.15
0.23
0.31
0.38
0.46
0.62
0.69
0.85
1.00
Avg Q
(m3/s)
0.0000
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Σ Avg
V
Avg V
(m2)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
46.23
Avg
Conc
Cum V Cum/ΣV (mg/L)
0
0.00
0
2.91
0.06
30.7
6.66
0.14
2.5
11.955
0.26
3.15
20.115
0.44
1.95
28.68
0.62
1.5
39.96
0.86
2.4
43.11
0.93
1.7
45.54
0.99
1.25
46.23
1.00
1.1
Σ Mass
=
OCCURRENCE OF FIRST FLUSH: YES
Mass
(mg)
0
89337
9375
16679.25
15912
12847.5
27072
5355
3037.5
759
180374.3
Cum
Mass
0
89337
98712
115391.3
131303.3
144150.8
171222.8
176577.8
179615.3
180374.3
Cum/
ΣMass
0.00
0.53
0.59
0.68
0.78
0.85
1.02
1.05
1.07
1.07
161
BOD
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
Q
(m3/s)
0.0000
0.0097
0.0153
0.0200
0.0344
0.0227
0.0149
0.0061
0.0020
0.0003
ΣQ=
0.1254
Zn
(mg/L)
13
3
3
2
3
5
3
4
2
2
∆t
(min)
0
10
5
5
5
5
10
5
10
10
Σ Cum
T
Cum
Time
0
10
15
20
25
30
40
45
55
65
65
Normalized
Time
0.00
0.15
0.23
0.31
0.38
0.46
0.62
0.69
0.85
1.00
Avg Q
(m3/s)
0.0000
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Σ Avg
V
Avg
V
(m2)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
46.23
Cum
V
0
2.91
6.66
11.955
20.115
28.68
39.96
43.11
45.54
46.23
Cum/ΣV
0.00
0.06
0.14
0.26
0.44
0.62
0.86
0.93
0.99
1.00
Avg
Conc
(mg/L)
0
7.819
2.786
2.52
2.639
3.976
3.955
3.801
3.402
2.359
Σ Mass
=
OCCURRENCE OF FIRST FLUSH: NO
Mass
(mg)
0
22753
10448
13343
21534
34054
44612
11973
8267
1628
168613
Cum
Mass
0
22753.29
33200.79
46544.19
68078.43
102132.9
146745.3
158718.4
166985.3
168613
Cum/
ΣMass
0.00
0.13
0.20
0.28
0.40
0.61
0.87
0.94
0.99
1.00
162
COD
Time
Sample (min)
1
0
2
10
3
15
4
20
5
25
6
30
7
40
8
45
9
55
10
65
ΣQ
=
Q
COD
(m3/s) (mg/L)
0.0000
128
0.0097
0
0.0153
14
0.0200
15
0.0344
1
0.0227
15
0.0149
0
0.0061
0
0.0020
0
0.0003
0
∆t(min)
0
10
5
5
5
5
10
5
10
10
0.1254
Σ Cum T
Cum Normalized Avg Q
Time
Time
(m3/s)
0
0.00
0.0000
10
0.15
0.00485
15
0.23
0.0125
20
0.31
0.01765
25
0.38
0.0272
30
0.46
0.02855
40
0.62
0.0188
45
0.69
0.0105
55
0.85
0.00405
65
1.00
0.00115
Σ Avg
65
V
Avg V
(m2)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
46.23
Cum V
0
2.91
6.66
11.955
20.115
28.68
39.96
43.11
45.54
46.23
Cum/ΣV
0.00
0.06
0.14
0.26
0.44
0.62
0.86
0.93
0.99
1.00
Avg
Conc
(mg/L)
0
64
7
14.5
8
8
7.5
0
0
0
Mass
(mg)
0
186240
26250
76777.5
65280
68520
84600
0
0
0
ΣM
507667.5
OCCURRENCE OF FIRST FLUSH: YES
Cum
Cum/
ΣMass
Mass
0
0.00
186240
1.10
212490
1.26
289267.5 1.72
354547.5 2.10
423067.5 2.51
507667.5 3.01
507667.5 3.01
507667.5 3.01
507667.5 3.01
163
Cu
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
Q
Zn
(m3/s) (mg/L)
0.0000
0.16
0.0097
0
0.0153
0
0.0200
0.08
0.0344
0
0.0227
0.05
0.0149
0.13
0.0061
0.11
0.0020
0.13
0.0003
0
∆t
(min)
0
10
5
5
5
5
10
5
10
10
Cum
Time
0
10
15
20
25
30
40
45
55
65
Normalized
Time
0.00
0.15
0.23
0.31
0.38
0.46
0.62
0.69
0.85
1.00
Avg Q
(m3/s)
0.0000
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Avg V
(m2)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
Avg
Conc
Cum V Cum/ΣV (mg/L)
0
0.00
0
2.91
0.06
0.08
6.66
0.14
0
11.955
0.26
0.04
20.115
0.44
0.04
28.68
0.62
0.025
39.96
0.86
0.09
43.11
0.93
0.12
45.54
0.99
0.12
46.23
1.00
0.065
OCCURRENCE OF FIRST FLUSH: NO
Mass
(mg)
0
232.8
0
211.8
326.4
214.125
1015.2
378
291.6
44.85
Cum
Mass
0
232.8
232.8
444.6
771
985.125
2000.325
2378.325
2669.925
2714.775
Cum/
ΣMass
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.01
0.02
0.02
164
Pb
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
Q
Zn
(m3/s) (mg/L)
0.0000 0.061
0.0097 0.057
0.0153 0.054
0.0200
0.04
0.0344 0.046
0.0227 0.036
0.0149
0.04
0.0061
0.04
0.0020 0.038
0.0003 0.032
ΣQ=
0.1254
∆t
(min)
0
10
5
5
5
5
10
5
10
10
Σ Cum
T
Cum
Time
0
10
15
20
25
30
40
45
55
65
65
Normalized
Time
0.00
0.15
0.23
0.31
0.38
0.46
0.62
0.69
0.85
1.00
Avg Q
(m3/s)
0.0000
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Σ Avg
V
Avg V
(m2)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
46.23
Avg
Conc
Cum V Cum/ΣV (mg/L)
0
0.00
0
2.91
0.06
0.059
6.66
0.14
0.0555
11.955
0.26
0.047
20.115
0.44
0.043
28.68
0.62
0.041
39.96
0.86
0.038
43.11
0.93
0.04
45.54
0.99
0.039
46.23
1.00
0.035
ΣM
OCCURRENCE OF FIRST FLUSH: NO
Mass
(mg)
0
171.69
208.125
248.865
350.88
351.165
428.64
126
94.77
24.15
2004.285
Cum
Mass
0
171.69
379.815
628.68
979.56
1330.725
1759.365
1885.365
1980.135
2004.285
Cum/
ΣMass
0.00
0.00
0.00
0.00
0.01
0.01
0.01
0.01
0.01
0.01
165
Zn
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
ΣQ=
Q
Zn
(m3/s) (mg/L)
0.0000
1.34
0.0097
0.21
0.0153
0.2
0.0200
0.2
0.0344
0.21
0.0227
0.16
0.0149
0.26
0.0061
0.21
0.0020
0.26
0.0003
0.25
0.1254
∆t
(min)
0
10
5
5
5
5
10
5
10
10
Σ Cum
T
Cum
Time
0
10
15
20
25
30
40
45
55
65
65
Normalized
Time
0.00
0.15
0.23
0.31
0.38
0.46
0.62
0.69
0.85
1.00
Avg Q
(m3/s)
0.0000
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Σ Avg
V
Avg V
(m2)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
Avg
Conc
Cum V Cum/ΣV (mg/L)
0
0.00
0
2.91
0.06
0.775
6.66
0.14
0.205
11.955
0.26
0.2
20.115
0.44
0.205
28.68
0.62
0.185
39.96
0.86
0.21
43.11
0.93
0.235
45.54
0.99
0.235
46.23
1.00
0.255
46.23
OCCURRENCE OF FIRST FLUSH: NO
ΣM
Mass
(mg)
0
2255.25
768.75
1059
1672.8
1584.525
2368.8
740.25
571.05
175.95
11196.38
Cum
Mass
0
2255.25
3024
4083
5755.8
7340.325
9709.125
10449.38
11020.43
11196.38
Cum/
ΣMass
0.00
0.01
0.02
0.02
0.03
0.04
0.06
0.06
0.07
0.07
166
NH3-N
Time
Sample (min)
1
0
2
10
3
15
4
20
5
25
6
30
7
40
8
45
9
55
10
65
ΣQ
=
Q
Zn
(m3/s) (mg/L)
0.0000
0.39
0.0097
0.25
0.0153
0.17
0.0200
0.06
0.0344
0.04
0.0227
0.03
0.0149
0.03
0.0061
0.2
0.0020
0.19
0.0003
0.07
0.1254
∆t
(min)
0
10
5
5
5
5
10
5
10
10
Σ Cum
T
Cum Normalized
Time
Time
0
0.00
10
0.15
15
0.23
20
0.31
25
0.38
30
0.46
40
0.62
45
0.69
55
0.85
65
1.00
65
Avg Q
(m3/s)
0.0000
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Σ Avg
V
Avg
Avg
Conc
Mass
Cum
V
Cum
Cum/ΣV (mg/L)
(mg)
Mass
(m2)
V
0
0
0.00
0
0
0
2.91
2.91
0.06
0.32
931.2
931.2
3.75
6.66
0.14
0.21
787.5
1718.7
5.295 11.955
0.26
0.115 608.925 2327.625
8.16 20.115
0.44
0.05
408
2735.625
8.565 28.68
0.62
0.035 299.775 3035.4
11.28 39.96
0.86
0.03
338.4
3373.8
3.15 43.11
0.93
0.115
362.25 3736.05
2.43 45.54
0.99
0.195
473.85
4209.9
0.69 46.23
1.00
0.13
89.7
4299.6
Σ Mass
46.23
=
4299.6
OCCURRENCE OF FIRST FLUSH: NO
Cum/
ΣMass
0.00
0.01
0.01
0.01
0.02
0.02
0.02
0.02
0.02
0.03
167
Tp
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
ΣQ=
Q
Zn
(m3/s) (mg/L)
0.0000
0.58
0.0097
0.22
0.0153
0
0.0200
0.22
0.0344
0.09
0.0227
1.58
0.0149
0.11
0.0061
0.33
0.0020
1.72
0.0003
0.02
0.1254
∆t
(min)
0
10
5
5
5
5
10
5
10
10
Σ Cum
T
Cum
Time
0
10
15
20
25
30
40
45
55
65
65
Normalized
Time
0.00
0.15
0.23
0.31
0.38
0.46
0.62
0.69
0.85
1.00
Avg Q
(m3/s)
0.0000
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Σ Avg
V
Avg V
(m2)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
46.23
Avg
Conc
Cum V Cum/ΣV (mg/L)
0
0.00
0
2.91
0.06
0.4
6.66
0.14
0.11
11.955
0.26
0.11
20.115
0.44
0.155
28.68
0.62
0.835
39.96
0.86
0.845
43.11
0.93
0.22
45.54
0.99
1.025
46.23
1.00
0.87
ΣM
OCCURRENCE OF FIRST FLUSH: NO
Mass
(mg)
0
1164
412.5
582.45
1264.8
7151.775
9531.6
693
2490.75
600.3
23891.18
Cum/
Cum
ΣMass
Mass
0
0.00
1164
0.01
1576.5
0.01
2158.95
0.01
3423.75
0.02
10575.53 0.06
20107.13 0.12
20800.13 0.12
23290.88 0.14
23891.18 0.14
168
NO3-N
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
ΣQ=
Q
NO3-N
(m3/s) (mg/L) ∆t(min)
0.0000
0.22
0
0.0097
0.04
10
0.0153
0.08
5
0.0200
0.11
5
0.0344
0.1
5
0.0227
0.13
5
0.0149
0.11
10
0.0061
0.13
5
0.0020
0.13
10
0.0003
0.09
10
Σ Cum
0.1254
T
Cum
Time
0
10
15
20
25
30
40
45
55
65
65
Normalized
Time
0.00
0.15
0.23
0.31
0.38
0.46
0.62
0.69
0.85
1.00
Avg Q
(m3/s)
0.0000
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Σ Avg
V
Avg V
(m2)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
Avg
Conc
Cum V Cum/ΣV (mg/L)
0
0.00
0
2.91
0.06
0.13
6.66
0.14
0.06
11.955
0.26
0.095
20.115
0.44
0.105
28.68
0.62
0.115
39.96
0.86
0.12
43.11
0.93
0.12
45.54
0.99
0.13
46.23
1.00
0.11
46.23
ΣM
Mass
(mg)
0
378.3
225
503.025
856.8
984.975
1353.6
378
315.9
75.9
Cum
Mass
0
378.3
603.3
1106.325
1963.125
2948.1
4301.7
4679.7
4995.6
5071.5
23891.18
OCCURRENCE OF FIRST FLUSH: NO
*NOTE: No first flush calculation is performed for Oil and Grease and NO2-N as no amount of the pollutant is detected in the water sample.
Cum/
ΣMass
0.00
0.00
0.00
0.01
0.01
0.02
0.03
0.03
0.03
0.03
169
APPENDIX H
EMC CALCULATION FOR EVENT 04/08/2008
Suspended Solid (SS)
No
Time
Q
(m3/s)
SS
∆t(min)
Avg
Q(m3/s)
1
2
3
12:15
12:17
12:20
0.0007
0.0007
0.0002
44
21
38
2
3
0
0.0007
0.00045
0.0001
Avg
Vol
(m3)
0.084
0.081
Σ
Avg
Vol (L)
Avg Conc
(mg/L)
Mass(mg)
84
81
32.5
29.5
2730
2389.5
165
Σ
5119.5
Avg
Vol (L)
Avg Conc
(mg/L)
Mass(mg)
84
81
10.5
14
882
1134
EMC
31.03
BOD
No
Time
Q
(m3/s)
BOD5
∆t(min)
Avg
Q(m3/s)
1
2
3
12:15
12:17
12:20
0.0007
0.0007
0.0002
10
11
17
2
3
0
0.0007
0.00045
0.0001
Avg
Vol
(m3/s)
0.084
0.081
Σ
Σ
165
2016
EMC
12.22
COD
No
Time
1
2
3
12:15
12:17
12:20
Q
(m3/s)
0.0007
0.0007
0.0002
COD
Avg
∆t(min)
(mg/L)
Q(m3/s)
38
2
0.0007
66
3
0.00045
75
0
0.0001
Avg Vol
(m3)
0.084
0.081
Avg
Vol (L)
84
81
Σ
165
Avg Conc
Mass(mg)
(mg/L)
52
4368
70.5
5710.5
Σ
10078.5
EMC
61.08
170
Cu
No
Time
1
2
3
12:15
12:17
12:20
Q
(m3/s)
0.0007
0.0007
0.0002
Cu
(mg/L)
0.1
0.09
0.06
Dt(min)
2
3
0
Avg
Q(m3/s)
0.0007
0.00045
0.0001
Avg Vol
(m3/s)
0.084
0.081
S
Avg
Vol (L)
84
81
165
Avg Conc
Mass(mg)
(mg/L)
0.095
7.98
0.075
6.075
S
14.055
EMC
0.09
Pb
No
Time
1
2
3
12:15
12:17
12:20
No
Time
1
2
3
12:15
12:17
12:20
Q
(m3/s)
0.0007
0.0007
0.0002
Pb
(mg/L)
<0.02
<0.02
<0.02
The concentration values for each time are <0.02mg/L. Hence, the
EMC value for Pb is <0.02 mg/L.
Zn
Q
(m3/s)
0.0007
0.0007
0.0002
Zn
(mg/L)
0.55
0.98
0.91
∆t(min)
2
3
0
Avg
Q(m3/s)
0.0007
0.00045
0.0001
Avg Vol
(m3/s)
0.084
0.081
Avg
Vol (L)
84
81
Avg Conc
(mg/L)
0.765
0.945
Σ
165
Σ
Mass(mg)
64.26
76.545
140.805
EMC
0.85
171
Oil & Grease
No
Time
1
2
3
12:15
12:17
12:20
Q
(m3/s)
0.0007
0.0007
0.0002
Avg
Oil&Grease
∆t(min)
Q(m3/s)
(mg/L)
9
2
0.0007
9
3
0.00045
13
0
0.0001
Avg Vol
(m3/s)
0.084
0.081
Avg Vol
(L)
84
81
Avg Conc
(mg/L)
9
11
Σ
165
Σ
Mass(mg)
756
891
1647
NH3-N
No
Time
1
2
3
12:15
12:17
12:20
Q
(m3/s)
0.0007
0.0007
0.0002
AN
∆t(min)
1.5
0.9
0.8
2
3
0
Avg
Q(m3/s)
0.0007
0.00045
0.0001
Avg Vol
(m3)
0.084
0.081
Avg Vol
(L)
84
81
Σ
165
Avg Conc
Mass(mg)
(mg/L)
1.2
100.8
0.85
68.85
Σ
169.65
EMC
1.03
EMC
9.98
172
EMC CALCULATION FOR EVENT 07/08/2008
Suspended Solid (SS)
No
Time
Q (m3/s)
1
2
3
4
5
6
11:05
11:07
11:10
11:30
11:32
11:35
0.0009
0.00085
0.0008
0.0003
0.0003
0.0003
SS
(mg/L)
12
2
15
9
14
9
∆t(min)
2
3
20
2
3
Avg
Q(m3/s)
0.000875
0.000825
0.00055
0.0003
0.0003
Avg Vol (m3)
0.105
0.1485
0.66
0.036
0.054
Avg Vol
(L)
105
148.5
660
36
54
Avg Conc
(mg/L)
7
8.5
12
11.5
11.5
Σ
1003.5
Σ
Avg Vol
(m3/s)
0.105
0.1485
0.66
0.036
0.054
Avg Vol
(L)
105
148.5
660
36
54
Avg Conc
(mg/L)
4
4.5
5.5
5
4
Σ
1003.5
Σ
Mass(mg)
735
1262.25
7920
414
621
10952.25
EMC
10.91
BOD
No
Time
Q (m3/s)
1
2
3
4
5
6
11:05
11:07
11:10
11:30
11:32
11:35
0.0009
0.00085
0.0008
0.0003
0.0003
0.0003
BOD5
(mg/L)
4
4
5
6
4
4
∆t(min)
2
3
20
2
3
Avg
Q(m3/s)
0.000875
0.000825
0.00055
0.0003
0.0003
Mass(mg)
420
668.25
3630
180
216
5114.25
EMC
5.10
173
COD
No
Time
Q (m3/s)
1
2
3
4
5
6
11:05
11:07
11:10
11:30
11:32
11:35
0.0009
0.00085
0.0008
0.0003
0.0003
0.0003
COD
(mg/L)
17
20
20
20
23
20
∆t(min)
2
3
20
2
3
Avg
Q(m3/s)
0.000875
0.000825
0.00055
0.0003
0.0003
Avg Vol (m3)
0.105
0.1485
0.66
0.036
0.054
Avg Vol
(L)
105
148.5
660
36
54
Σ
Avg Conc
(mg/L)
18.5
20
20
21.5
21.5
Σ
1003.5
Mass(mg)
1942.5
2970
13200
774
1161
20047.5
Cu
No
Time
1
2
3
4
5
6
11:05
11:07
11:10
11:30
11:32
11:35
Avg
Q
Cu
∆t(min)
Q(m3/s)
(m3/s) (mg/L)
0.0009
0.81
2
0.000875
0.00085
0.69
3
0.000825
0.0008
0.08
20
0.00055
0.0003
0.07
2
0.0003
0.0003
0.03
3
0.0003
0.0003
0.52
Avg Vol
(m3/s)
0.105
0.1485
0.66
0.036
0.054
Σ
Avg Vol
(L)
105
148.5
660
36
54
1003.5
Avg Conc
(mg/L)
0.75
0.385
0.075
0.05
0.275
Σ
Mass(mg)
78.75
57.1725
49.5
1.8
14.85
202.0725
EMC
0.20
EMC
19.98
174
Pb
No
Time
Q (m3/s)
1
2
3
4
5
6
11:05
11:07
11:10
11:30
11:32
11:35
0.0009
0.00085
0.0008
0.0003
0.0003
0.0003
No
Time
1
2
3
4
5
6
11:05
11:07
11:10
11:30
11:32
11:35
Pb
(mg/L)
<0.02
<0.02
<0.02
<0.02
<0.02
<0.02
The concentration values for each time are <0.02mg/L. Hence, the
EMC value for Pb is <0.02 mg/L.
Zn
Q
(m3/s)
0.0009
0.00085
0.0008
0.0003
0.0003
0.0003
Zn
∆t(min)
0.42
0.4
0.34
0.19
0.4
0.35
2
3
20
2
3
Avg
Q(m3/s)
0.000875
0.000825
0.00055
0.0003
0.0003
Avg Vol
(m3/s)
0.105
0.1485
0.66
0.036
0.054
Avg Vol
(L)
105
148.5
660
36
54
Avg Conc
(mg/L)
0.41
0.37
0.265
0.295
0.375
Σ
1003.5
Σ
Mass(mg)
43.05
54.945
174.9
10.62
20.25
303.765
EMC
0.30
175
Oil & Grease
No
Time
1
2
3
4
5
6
11:05
11:07
11:10
11:30
11:32
11:35
Q
Oil&Grease
3
(m /s)
(mg/L)
0.0009
<10
0.00085
<10
0.0008
<10
0.0003
<10
0.0003
<10
0.0003
<10
The concentration values for each time are <0.02mg/L. Hence, the
EMC value for Oil & Grease is <0.02 mg/L.
NH3-N
No
Time
1
2
3
4
5
6
11:05
11:07
11:10
11:30
11:32
11:35
Avg
Q
NH3-N
∆t(min)
3
Q(m3/s)
(m /s) (mg/L)
0.0009
0.9
2
0.000875
0.00085
1.4
3
0.000825
0.0008
0.9
20
0.00055
0.0003
0.5
2
0.0003
0.0003
0.5
3
0.0003
0.0003
0.4
Avg Vol
(m3)
0.105
0.1485
0.66
0.036
0.054
Avg Vol
(L)
105
148.5
660
36
54
Avg Conc
(mg/L)
1.15
1.15
0.7
0.5
0.45
Σ
1003.5
Σ
Mass(mg)
120.75
170.775
462
18
24.3
795.825
EMC
0.79
176
EMC CALCULATION FOR EVENT 12/08/2008
Suspended Solid (SS)
No
Time
1
2
3
4
5
6
16:30
16:32
16:35
17:30
17:32
17:35
Q
(m3/s)
0.1
0.1
0.1
0.1
0.1
0.1
SS
∆t(min)
23
62
29
9
4
9
2
3
55
2
3
Avg
Q(m3/s)
0.1
0.1
0.1
0.1
0.1
Avg Vol
(m3)
12
18
330
12
18
Avg Vol
(L)
12000
18000
330000
12000
18000
Σ
390000
Avg Vol
(m3/s)
12
18
330
12
18
Avg Vol
(L)
12000
18000
330000
12000
18000
Avg Conc
(mg/L)
42.5
45.5
19
6.5
6.5
Σ
Mass(mg)
510000
819000
6270000
78000
117000
7794000
EMC
19.98
BOD
No
Time
1
2
3
4
5
6
16:30
16:32
16:35
17:30
17:32
17:35
Q
(m3/s)
0.1
0.1
0.1
0.1
0.1
0.1
BOD5
∆t(min)
7
8
7
5
4
5
2
3
55
2
3
Avg
Q(m3/s)
0.1
0.1
0.1
0.1
0.1
Σ
390000
Avg Conc
Mass(mg)
(mg/L)
7.5
90000
7.5
135000
6
1980000
4.5
54000
4.5
81000
Σ
2340000
EMC
6.00
177
COD
No
Time
1
2
3
4
5
6
16:30
16:32
16:35
17:30
17:32
17:35
Q
(m3/s)
0.1
0.1
0.1
0.1
0.1
0.1
COD
∆t(min)
33
39
39
16
10
16
2
3
55
2
3
Avg
Q(m3/s)
0.1
0.1
0.1
0.1
0.1
Avg Vol
(m3)
12
18
330
12
18
Avg Vol
(L)
12000
18000
330000
12000
18000
Avg Conc
(mg/L)
36
39
27.5
13
13
Σ
390000
Σ
Avg Vol
(m3/s)
12
18
330
12
18
Avg Vol
(L)
12000
18000
330000
12000
18000
Avg Conc
(mg/L)
0.04
0.035
0.035
0.035
0.035
Mass(mg)
432000
702000
9075000
156000
234000
10599000
EMC
27.18
Cu
No
Time
1
2
3
4
5
6
16:30
16:32
16:35
17:30
17:32
17:35
Q
(m3/s)
0.1
0.1
0.1
0.1
0.1
0.1
Cu
∆t(min)
0.04
0.04
0.03
0.04
0.03
0.04
2
3
55
2
3
Avg
Q(m3/s)
0.1
0.1
0.1
0.1
0.1
Σ
390000
Σ
Mass(mg)
480
630
11550
420
630
13710
EMC
0.04
178
Pb
No
Time
1
2
3
4
5
6
16:30
16:32
16:35
17:30
17:32
17:35
No
Time
1
2
3
4
5
6
16:30
16:32
16:35
17:30
17:32
17:35
Q
(m3/s)
0.1
0.1
0.1
0.1
0.1
0.1
Lead
<0.02
<0.02
<0.02
<0.02
<0.02
<0.02
The concentration values for each time are <0.02mg/L. Hence, the
EMC value for Pb is <0.02 mg/L.
Zn
Q
(m3/s)
0.1
0.1
0.1
0.1
0.1
0.1
Zn
∆t(min)
2.29
1.15
1.14
0.42
0.32
0.27
2
3
55
2
3
Avg
Q(m3/s)
0.1
0.1
0.1
0.1
0.1
Avg Vol
(m3/s)
12
18
330
12
18
Avg Vol
(L)
12000
18000
330000
12000
18000
Avg Conc
(mg/L)
1.72
1.145
0.78
0.37
0.295
Σ
390000
Σ
Mass(mg)
20640
20610
257400
4440
5310
308400
EMC
0.79
179
Oil & Grease
No
Time(a.m)
1
2
3
4
5
6
16:30
16:32
16:35
17:30
17:32
17:35
Q
(m3/s)
0.1
0.1
0.1
0.1
0.1
0.1
Oil&Grease
<10
<10
<10
<10
<10
<10
The concentration values for each time are <0.02mg/L. Hence, the
EMC value for Oil & Grease is <0.02 mg/L.
NH3-N
No
Time(a.m)
1
16:30
Q
(m3/s)
0.1
2
3
4
5
6
16:32
16:35
17:30
17:32
17:35
0.1
0.1
0.1
0.1
0.1
2
Avg
Q(m3/s)
0.1
Avg Vol
(m3/s)
12
Avg Vol
(L)
12000
Avg Conc
(mg/L)
0.75
3
55
2
3
0.1
0.1
0.1
0.1
18
330
12
18
18000
330000
12000
18000
1.5
1.45
0.8
0.7
27000
478500
9600
12600
Σ
390000
Σ
536700
AN
∆t(min)
0.6
0.9
2.1
0.8
0.8
0.6
Mass(mg)
9000
EMC
1.38
180
EMC CALCULATION FOR EVENT 04/09/2008
Suspended Solid (SS)
Sample
Time (min)
Q (m3/s)
SS
∆t(min)
Avg Q(m3/s)
Avg Vol (m3)
Avg Vol (L)
Avg Conc(mg/L)
Mass(mg)
1
2
3
4
5
6
7
8
9
10
0
10
15
20
25
30
40
45
55
65
0.0000
0.0097
0.0153
0.0200
0.0344
0.0227
0.0149
0.0061
0.0020
0.0003
60
1
4
3
1
2
3
0
2
0
0
10
5
5
5
5
10
5
10
10
0
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
Σ
0
2910
3750
5295
8160
8565
11280
3150
2430
690
46230
0
30.7
2.5
3.15
1.95
1.5
2.4
1.7
1.25
1.1
Σ
0
89337
9375
16679.25
15912
12847.5
27072
5355
3037.5
759
180374.3
Time (min)
0
10
15
20
25
30
40
45
55
65
Q (m3/s)
0.0000
0.0097
0.0153
0.0200
0.0344
0.0227
0.0149
0.0061
0.0020
0.0003
BOD
13
3
3
2
3
5
3
4
2
2
Avg Q(m3/s)
0
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Avg Vol (m3)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
Σ
Avg Vol (L)
0
2910
3750
5295
8160
8565
11280
3150
2430
690
46230
Avg Conc(mg/L)
0
7.819
2.786
2.52
2.639
3.976
3.955
3.801
3.402
2.359
Σ
Mass(mg)
0
22753.29
10447.5
13343.4
21534.24
34054.44
44612.4
11973.15
8266.86
1627.71
168612.99
EMC
3.90
BOD
Sample
1
2
3
4
5
6
7
8
9
10
∆t(min)
0
10
5
5
5
5
10
5
10
10
EMC
3.65
181
COD
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
Q (m3/s)
COD
0.0000
0.0097
0.0153
0.0200
0.0344
0.0227
0.0149
0.0061
0.0020
0.0003
128
0
14
15
1
15
0
0
0
0
Time
(min)
0
10
15
20
25
30
40
45
55
65
Q (m3/s)
Cu
0.0000
0.0097
0.0153
0.0200
0.0344
0.0227
0.0149
0.0061
0.0020
0.0003
0.16
0
0
0.08
0
0.05
0.13
0.11
0.13
0
∆t
(min)
0
10
5
5
5
5
10
5
10
10
Avg
Q(m3/s)
0
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Avg Vol
(m3)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
Σ
∆t
(min)
0
10
5
5
5
5
10
5
10
10
Avg Q
(m3/s)
0
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Avg Vol
(m3)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
Σ
Avg Vol (L)
0
2910
3750
5295
8160
8565
11280
3150
2430
690
46230
Avg
Conc(mg/L)
0
64
7
14.5
8
8
7.5
0
0
0
Σ
Mass
(mg)
0
186240
26250
76777.5
65280
68520
84600
0
0
0
507667.5
EMC
10.98
Mass
(mg)
0
232.8
0
211.8
326.4
214.125
1015.2
378
291.6
44.85
2714.775
EMC
0.06
Cu
Sample
1
2
3
4
5
6
7
8
9
10
Avg Vol (L)
Avg Conc(mg/L)
0
2910
3750
5295
8160
8565
11280
3150
2430
690
46230
0
0.08
0
0.04
0.04
0.025
0.09
0.12
0.12
0.065
Σ
182
Pb
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
Q
(m3/s)
0.0000
0.0097
0.0153
0.0200
0.0344
0.0227
0.0149
0.0061
0.0020
0.0003
Pb
∆t(min)
0.061
0.057
0.054
0.04
0.046
0.036
0.04
0.04
0.038
0.032
0
10
5
5
5
5
10
5
10
10
Avg
Q(m3/s)
0
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Avg Vol
(m3)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
Σ
Avg Vol
(L)
0
2910
3750
5295
8160
8565
11280
3150
2430
690
46230
Avg
Conc(mg/L)
0
0.059
0.0555
0.047
0.043
0.041
0.038
0.04
0.039
0.035
Σ
Mass(mg)
0
171.69
208.125
248.865
350.88
351.165
428.64
126
94.77
24.15
2004.285
EMC
0.043
Zn
Sample
1
2
3
4
5
6
7
8
9
10
Time (min)
0
10
15
20
25
30
40
45
55
65
Q
(m3/s)
0.0000
0.0097
0.0153
0.0200
0.0344
0.0227
0.0149
0.0061
0.0020
0.0003
Zn
1.34
0.21
0.2
0.2
0.21
0.16
0.26
0.21
0.26
0.25
∆t(min) Avg Q(m3/s)
0
10
5
5
5
5
10
5
10
10
0
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Avg Vol
(m3)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
Σ
Avg Vol (L)
0
2910
3750
5295
8160
8565
11280
3150
2430
690
46230
Avg
Conc(mg/L)
0
0.775
0.205
0.2
0.205
0.185
0.21
0.235
0.235
0.255
Σ
Mass(mg)
0
2255.25
768.75
1059
1672.8
1584.525
2368.8
740.25
571.05
175.95
11196.38
EMC
0.24
183
Oil & Grease
Sample Time (min)
1
0
2
10
3
15
4
20
5
25
6
30
7
40
8
45
9
55
10
65
Q
(m3/s)
0.0000
0.0097
0.0153
0.0200
0.0344
0.0227
0.0149
0.0061
0.0020
0.0003
O&G ∆t(min)
Q
(m3/s)
0.0000
0.0097
0.0153
0.0200
0.0344
0.0227
0.0149
0.0061
0.0020
0.0003
NH3-N ∆t(min)
0
0
0
0
0
0
0
0
0
0
0
10
5
5
5
5
10
5
10
10
Avg
Q(m3/s)
0
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Avg Vol
(m3)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
Σ
Avg Vol
(L)
0
2910
3750
5295
8160
8565
11280
3150
2430
690
46230
Avg
Conc(mg/L)
0
0
0
0
0
0
0
0
0
0
Σ
Mass(mg)
0
0
0
0
0
0
0
0
0
0
0
EMC
0.00
NH3-N
Sample Time (min)
1
0
2
10
3
15
4
20
5
25
6
30
7
40
8
45
9
55
10
65
0.39
0.25
0.17
0.06
0.04
0.03
0.03
0.2
0.19
0.07
0
10
5
5
5
5
10
5
10
10
Avg
Q(m3/s)
0
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Avg Vol
(m3)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
Σ
Avg Vol (L)
0
2910
3750
5295
8160
8565
11280
3150
2430
690
46230
Avg
Conc(mg/L)
0
0.32
0.21
0.115
0.05
0.035
0.03
0.115
0.195
0.13
Σ
Mass(mg)
0
931.2
787.5
608.925
408
299.775
338.4
362.25
473.85
89.7
4299.6
EMC
0.09
184
NO3-N
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
Q (m3/s)
NO3-N
∆t(min)
Avg Q(m3/s)
Avg Vol (m3)
0.0000
0.0097
0.0153
0.0200
0.0344
0.0227
0.0149
0.0061
0.0020
0.0003
0.22
0.04
0.08
0.11
0.1
0.13
0.11
0.13
0.13
0.09
0
10
5
5
5
5
10
5
10
10
0
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
Σ
Avg Vol
(L)
0
2910
3750
5295
8160
8565
11280
3150
2430
690
46230
Avg Vol
(m3)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
Σ
Avg Vol
(L)
0
2910
3750
5295
8160
8565
11280
3150
2430
690
46230
NO2-N
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
Q (m3/s) NO2-N
0.0000
0.0097
0.0153
0.0200
0.0344
0.0227
0.0149
0.0061
0.0020
0.0003
0.008
0.001
0.003
0.004
0.002
0.003
0.003
0.002
0.001
0
∆t
(min)
0
10
5
5
5
5
10
5
10
10
Avg
Q(m3/s)
0
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Avg Conc(mg/L)
Mass(mg)
0
0.13
0.06
0.095
0.105
0.115
0.12
0.12
0.13
0.11
Σ
0
378.3
225
503.025
856.8
984.975
1353.6
378
315.9
75.9
5071.5
Avg
Conc(mg/L)
0
0.0045
0.002
0.0035
0.003
0.0025
0.003
0.0025
0.0015
0.0005
Σ
Mass(mg
)
0
13.095
7.5
18.5325
24.48
21.4125
33.84
7.875
3.645
0.345
130.725
EMC
0.110
EMC
0.003
185
PO4
Sample
1
2
3
4
5
6
7
8
9
10
Time
(min)
0
10
15
20
25
30
40
45
55
65
Q (m3/s)
PO4
0.0000
0.0097
0.0153
0.0200
0.0344
0.0227
0.0149
0.0061
0.0020
0.0003
0.58
0.22
0
0.22
0.09
1.58
0.11
0.33
1.72
0.02
∆t
(min)
0
10
5
5
5
5
10
5
10
10
Avg
Q(m3/s)
0
0.00485
0.0125
0.01765
0.0272
0.02855
0.0188
0.0105
0.00405
0.00115
Avg Vol
(m3)
0
2.91
3.75
5.295
8.16
8.565
11.28
3.15
2.43
0.69
Σ
Avg Vol
(L)
0
2910
3750
5295
8160
8565
11280
3150
2430
690
46230
Avg
Conc(mg/L)
0
0.4
0.11
0.11
0.155
0.835
0.845
0.22
1.025
0.87
Σ
Mass
(mg)
0
1164
412.5
582.45
1264.8
7151.775
9531.6
693
2490.75
600.3
23891.18
EMC
0.52
186
APPENDIX I
DESIGN CRITERIA CALCULATION
1050
1700
1700
1500
B
Outflow
(a)
Inflow
500
2100
(b)
The sizing of (a) Rubbish Trap (b) Section B
1. Determination of required removal efficiency:
From Table 4.5, for condition Drainage system Upgrading, we get the reduction
in Annual Average Pollutant Load from existing condition is 20% (for sediment)
Therefore, the trap will be sized to trap 20% of the sediment ≥0.04mm dia.
2. Determination of catchment area, % urban area and soil type in the
catchment:
Catchment Area, Ac = 0.16 ha
% Urban Area, U
= 80%
3. Trial trap area ratio, R:
Try R=2.25E-5
Soil Type
= Silty Sand
187
4. Calculation of required trap area by trial and error:
From Design Chart 34.A1, with R=2.25E-5, U=80%, Curve A, we get the
P0.04=20% (ACCEPTABLE!)
Therefore, the required minimum trap size is:
At = R × Ac
=2.25E-5 × (0.16×10^4) m2
=0.036 m2
5. Trap Area, At
Trap area of rubbish trap = (1.70×1.05) + (1/2×1.5×1.05) + (1.5×1.7)
= 5.1225 m2
5.1225 m2 > Required minimum trap size (0.036m2)
Therefore, the size of the rubbish trap is OK!
6. Sediment Load, M
Event
M(kg)
4/8/08
0.005
7/8/08
0.011
12/8/08
7.794
4/9/08
0.180
Take the maximum M = 7.794kg =
7.794×10^-3
7. Average Annual Pollutant Retention, P0.01
From Design Chart 34.A1, with R=2.25E-5, U=80%, Curve B, we get the
P0.01=14%
From Design Chart 34.A2, with R=2.25E-5, (Silty Sand), we get F2=3.3
From Equation 34.4:
P0.01* = P0.01 × F2
= 14% × 3.3
= 46.2%
188
8. Required minimum sediment trap depth
From Equation 34.5:
Dt = 0.0065×P0.01×M/At
= 0.0065×46.2×7.794×10^-3 tonne/5.1225m2
= 4.57×10^-4 m
9. Peak flow, Q0.25
From Rational Method, the Q0.25 = 0.213m3/s
10. Determination of trash rack height, Hr
From Equation 34.7:
Hr = 1.22 (Q0.25/Lr)2/3
= 1.22 (0.213/2.1)2/3
= 0.206 m
= 26.5 cm
Hr of rubbish trap (50cm) > 26.5cm
Therefore, the trash rack height of the rubbish trap is OK!
11. Determination of flow velocity, V0.25
Determination of the nominal flow velocity V0.25 in the water quality design
storm is using Equation 34.8. Increase the dimensions of the sediment trap pool
or increase the track rack height if the flow velocity V is greater than 1.0 m/s, to
minimize the re-entrainment of deposited sediment.
From Equation 34.8:
V0.25 = Q0.25/(Dw+Hr)Wt
= (0.213m3/s) / (4.57×10^-4 m + 0.5m) 1.7m
= 0.25 m/s
0.25 m/s < 1.0 m/s, so no increment of Hr is needed
12. SUMMARY
From the design calculation, it can be concluded that the rubbish trap size (Figure
27(a) and 27(b) is acceptable.