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الرحمن الله بسمالرحيم
الله صدقالعظيم
سورة 85ية اآلإسراءال
STUDYING CONSTRUCTED WETLANDS
PERFORMANCE IN TREATING WASTEWATER
STUDYING CONSTRUCTED WETLANDS
PERFORMANCE IN TREATING WASTEWATER
By Abdallah Abdelazim Mahmoud M. Sc. of Sanitary Engineering Environmental Engineering Department Faculty of Engineering Zagazig University
Dr.Dr. Diaa Eldin A. ElqussyDiaa Eldin A. Elqussy Prof.&Prof.& Ex-Deputy Chairman Ex-Deputy Chairman National Water Research National Water Research
CenterCenter
Dr.Dr. Ahmed Awad MohamedAhmed Awad Mohamed
Prof.Prof. & & Head of Agronomy DepartmentHead of Agronomy Department
Faculty of Agriculture Suez Canal Faculty of Agriculture Suez Canal UniversityUniversity
Dr.Dr. Hazem Ibrahim M. Hazem Ibrahim M. SaleSale
Prof. of Sanitary & Prof. of Sanitary & Environmental EngineeringEngineeringFaculty ofFaculty of EngEng. . MenoufiaMenoufia UniversityUniversity
Dr.Dr. Mahmoud Abd El-Shafy Mahmoud Abd El-Shafy
IbrahimIbrahim Prof. of Sanitary Prof. of Sanitary & & Environmental
EngineeringEngineering
Faculty ofFaculty of EngEng. . MenoufiaMenoufia UniversityUniversity
Supervised by
ContentsContentsThis thesis is organized in six chapters &
Appendices including the followings:
Chapter (1): Introduction
Chapter (2): Literature Review
Chapter (3): Experimental work
Chapter (4): Results and discussions
Chapter (5): Theoretical Interpretation. Chapter (6): Conclusion and Conclusion and
Recommendations.Recommendations.
INTRODUCTIOINTRODUCTIONN
Statement of Statement of ProblemProblem The drains in Egypt are currently
suffering from increased pollution loads by discharge of untreated wastewater.
Bilbeas drain and El-Qalyoubia Drain is two considered of the most polluted
agriculture drains in Egypt.
The largest drains in The largest drains in EgyptEgypt
El-Qalyoubia Drain
3.97 million m3/d
Length=73.15Km
Bilbeas Drain
3.14 million m3/d
Length=66.0Km
Bahr El-Baqar Drain
Lake Manzalah
A. Discharge of Domestic Wastewater into Drain
Sources of the organic Sources of the organic pollution in drainspollution in drains
B. B. Discharge of Industrial Wastewater into Discharge of Industrial Wastewater into the Drainthe Drain
C. Discharge of Solid Wastes into C. Discharge of Solid Wastes into the Drainthe Drain
D. Discharge of the D. Discharge of the Agricultural RunoffAgricultural Runoff
Objectives Objectives of the Study the Study
1-1-Investigating the effect of using Investigating the effect of using constructed wetlands in enhancing constructed wetlands in enhancing
drain water quality this was done for drain water quality this was done for both Bilbeas and Bahr El Baqar both Bilbeas and Bahr El Baqar
Drains.Drains.
2-2-Investigating the possibility of using a Investigating the possibility of using a combined wetland system providing combined wetland system providing both Surface Flow and Sub-Surface both Surface Flow and Sub-Surface
Flow that benefits from both systemsFlow that benefits from both systems
3-3-Investigating the effects of different Investigating the effects of different inter-related parameters on the obtained inter-related parameters on the obtained
results.results.
Wetlands Use in Wastewater Wetlands Use in Wastewater TreatmentTreatment
** Constructed wetlands are currently Constructed wetlands are currently widely used for wastewater treatment widely used for wastewater treatment
providing secondary or tertiary providing secondary or tertiary (polishing) level of treatment depending (polishing) level of treatment depending
on the design of work.on the design of work.
•Constructed wetlands can further be Constructed wetlands can further be divided into two types namely Surface divided into two types namely Surface
Flow (SF) System; and Sub-Surface Flow (SF) System; and Sub-Surface Flow (SSF) SystemFlow (SSF) System..
Types of Wetland SystemsTypes of Wetland SystemsTypes of Wetland SystemsTypes of Wetland Systems
Surface Flow
Emergent Macrophyte Based
Free floating Macrophyte Based
Submerged Macrophyte Based
Wetland
Surface Flow Sub-Surface Flow
Sub-Surface Flow
Horizontal Flow Vertical Flow
Surface Flow (SF) Surface Flow (SF) SystemSystem
SF system typically consists of SF system typically consists of parallel basins or channels with a parallel basins or channels with a
relatively impermeable bottom soil relatively impermeable bottom soil or subsurface barrier, emergent or subsurface barrier, emergent
vegetation, and shallow water vegetation, and shallow water depth of 0.1 to 0.6m.depth of 0.1 to 0.6m.
Sub-Surface Flow (SSF) Sub-Surface Flow (SSF) systemsystem
SSF consist of channels or trenches with SSF consist of channels or trenches with relatively impermeable bottoms filled with relatively impermeable bottoms filled with
sand or rock media to support emergent sand or rock media to support emergent vegetation. These systems are sometimes vegetation. These systems are sometimes
called (root zone).called (root zone).
The system is known for its better The system is known for its better oxygenation capacity through introducing oxygenation capacity through introducing oxygen to wastewater from the root zone oxygen to wastewater from the root zone thus the higher efficiency of removal for thus the higher efficiency of removal for
biodegradable waste.biodegradable waste.
Design ConsiderationsDesign Considerations of of WetlandsWetlands
The required land area.The required land area. Water Containment.Water Containment.
Water transport.Water transport. The vegetation typesThe vegetation types
Determination of system size Determination of system size Reduction of specific water quality Reduction of specific water quality
parameters such as BODparameters such as BOD55, TSS, , TSS, nutrients, trace inorganics and nutrients, trace inorganics and
organics, pathogens, etc.organics, pathogens, etc. The principal variables, typically The principal variables, typically
including loading rate and inlet including loading rate and inlet concentration.concentration.
Operational conditions (temperature Operational conditions (temperature and pH value).and pH value).
EXPERIMENTAEXPERIMENTALL
WORKSWORKS
Locations of Experiments
Location of Lake Manzala and Bilbeas Drain CW
North
DrainCanalPumping stationLocations of Experiments
Lake Manzala Constructed Lake Manzala Constructed Wetland ElementsWetland Elements
Lake Manzala CW Lake Manzala CW Components Components Sedimentation basins: Two
250 m length, 90 m width and 1.5 m water depth, 25000 m3
sedimentation basins. Ten beds planted with Phragmites Australis were
constructed and run at different flow rates to assess
the effect of these beds on enhancing water quality. Each bed is 50 m width and
250 m length with an average depth of 0.40 m as to allow
proper growth of the plants, each bed is divided into five
cell (50m x 50m). Flow rates corresponding to low and high
hydraulic loadings were 0.04 and 0.36 m3/m2/d (m/d)
respectively.
1 2 3 4 5 6 7 8 9 10
C
D
E
G
D
G
D
E
G
D
G
B
A
H
Bahr El Baqar Drain
Screw Pump
B
Sdeimentation Basin
Sdeimentation Basin
Surface flow Wetland
High Flow
Surface flow Wetland
Low Flow
Collecting Channel
Distribution Channel
Col
lect
ing
Cha
nnel
F F F F
1, 2, 3, ...... Cell No.A, B, C, ...... Smple location
Note:
SimplifiedSimplified Layout of the Main Layout of the Main Wetland and Sampling LocationWetland and Sampling Location
SF System at Lake SF System at Lake ManzalaManzala
B- Combined SF/SSF System at Bilbeas
Four streams pilotAt Bilbeas drain; at El-Sahafa bridge (KP. 35.0)
Description of Experiment
C1
C2
C3
F1
F2
F3
Bilbeas drain
Typha plantWater
hyacinths
G1
G2
G3
9.012.0
15.018.5
5.0
2.0 40.0 1.0
pump
pumps
Pilot (1)
Pilot (2)
screen
Cairo - Ismailia Road
22.5
Flow direction
Drain water inlet
(Embankment)
Standard Cells
The used channel
(Drain Bed)(A)
B1
B2
B3
D1
D2
D3
E1
E2
E3
Sex channels
used in other studies
Two main area (or cells) in series were prepared as follows: Cell (1) (Surface Flow/Sub-Surface Flow) 15 meter long by 5 meters wide at a depth consisting of 0.35 m gravel at (1- 4 cm ) diameter & 0.35 m water, planted with Typha
Latofolia (cattail) with a density of one plant /m2, the free flow of wastewater at a level of 35 cm over the planted gravel bed. Cell (2) Surface Flow (Floating Aquatic Plant) 7.5 meter long by 5 meters wide and A depth of 0.7 meter water. Water – hyacinths was
introduced to this cell, density of one kilogram /m2 (covering approximately 66.7% of the total area of this cell).
The Pilot Components
The Pilot Plant Components
Typha plant Waterhyacinths
7.515.0
+0.35
+0.70
Polluted DrainWaterInfluent .FWS Section
.
SSF SectionInterface Layerbetween (FWS & SSF) CW
Combined FWS/ SSF CW
Effluent pipeTreated
Operating conditions of the wetlandsOperating conditions of the wetlands
ITEMS SF/SSF SF (FAP)
Length (total) 15 m 7.5 m
Width 5.0 m 5.0 m
Depth 0.35 m gravel and 0.35 m water 0.7 m water
Plant Typha latofolia (cattail) Water hyacinths
Density 1 / m2 1 Kg / m2
HRT 3 days 2 days
HLR 0.167 m3/ m2/d 0.333 m3/ m2/d
Operating conditions of the wetlandsOperating conditions of the wetlands Operating conditions of the wetlandsOperating conditions of the wetlands
Analyses were conducted from Physical and chemical parameters including: ºC, pH, Ec TSS, TDS, DO and BOD5, COD, NH4
- N, NO3 - N, PO43-, Cu, Zn, Pb, and Cd and
Fecal Coli form Light Interseption & Leaf Area IndexLight Interseption & Leaf Area Index Sodium Adsorption RatioSodium Adsorption Ratio Statistical AnalysesStatistical Analyses (Lake Manzala and Bilbeas Drain).
In addition Redox potential (Eh) was In addition Redox potential (Eh) was measured, and Hydraulic Conductivity (K) measured, and Hydraulic Conductivity (K) was estimated for the SSF section of was estimated for the SSF section of Bilbeas CW.Bilbeas CW.
��Analyses��Analyses
Drains Water Characteristics
Bilbeas and El-Qalyoubia drains join together to form Baher El Baqar drain. Bahr El Baqar drain is an example for high polluted drain in Egypt.
Main physico-chemical characteristics of raw drains water
Parameter PH CODtot BODtot TSS NH4-N DO
Unit mg/L mg/L mg/L mg/L mg/L
Bilbeas Drain
7.1 -7.8
95-136 33-115 70 -180 2.0-5.3 0.41-1.04
Bahr El-Baqar Drain
7.3 - 7.3 - 7.67.6
72-122 28-100 50 -130 1.8 - 4.5 1.0 -2.10
A- Lake Manzala CW Results Biochemical Oxygen Demand (BOD5)
Average BOD5 removal efficiency was 53% LFR and 47% of HFR . The maximum influent concentration of BOD5 is 65 mg/l and the minimum effluent is 24 mg/l
0
10
20
30
40
50
60
0 50 100 150 200 250
Bed Length (m)
BO
D 5 (m
g/l)
H. F. R. L. F. R. BOD Limit
Variation of BOD5 along the bed length
Results:Results:Results:Results:chemical Oxygen Demand (COD)
Average COD removal efficiency was 52% of LFR and 46% of HFR.
The maximum influent concentration of COD is 140 mg/l and the minimum effluent is 62 mg/l
0
40
80
120
160
0 50 100 150 200 250
Bed Length (m)
CO
D (m
g/l)
H. F. R. L. F. R. COD Limit
Variation of COD along the bed length
The calculation ratio was between 0.4 to 0.6 during winter and highly fluctuation
between 0.2 – 0.8 during summer.
BOD:COD ratioBOD:COD ratioBOD:COD ratioBOD:COD ratio Results:Results:Results:Results:
0
0.2
0.4
0.6
0.8
1
Months
BO
D :
CO
D
BOD:COD
BOD:COD ratio for wastewater during the study period
Results:Results:Results:Results:Total Suspended Solids (TSS)The TSS values of the influent ranged from 90 to 130 mg/l
throughout the study. In summer the mean TSS values of the influent was 110 mg/l, while some increase was observed
in autumn. the lowest at the end of the treatment beds Raped flow rate with about 82.0 % removal, while the removal efficiency of TSS at the end of the slow flow rate of approx 89.0%.
0
25
50
75
100
125
0 50 100 150 200 250
Bed Length (m)
TS
S (m
g/l)
H. F. R. L. F. R. TSS Limit
Variation of TSS Concentration along the bed length
Results:Results:Results:Results:
The mean of inlet concentration of DO in wastewater entering the Beds ranged from 0.80 to
1.30 mg/l. The concentration increased through Beds length and were the highest at the effluent side reaching about 8.0 mg/l for the Beds (LFR)
Dissolved Oxygen (DO)
Mean seasonal variation of DO values in inlet and outlet water
0
2
4
6
8
10
Inlet Outlet
DO
mg
/ l
Summer Autumn
Winter Spring
B- Bilbeas Drain CW Results Biochemical Oxygen Demand (BOD5)
The mean of inlet concentration of BOD5 in wastewater entering the cells was about 110 mg/l. The BOD5 were the
lowest at the end of the treatment cell 1 planted with Tupha latofolia with about 50% removal, the BOD5 at the end of the cell 2 with planted is Water hyacinths approx
18% BOD5 removal may be attribute to the voids between the gravel particles in the beds and accumulation of the sludge according to attached growth
Influence of HRT on the Biochemical Oxygen Demand
0
20
40
60
80
100
120
0 1 2 3 4 5HRT day
Co
nc. B
OD 5
mg
\l
BOD5 measure BOD5 Limit
Cell 1
Cell 2
Results:Results:Results:Results:
The maximum treatment removal efficiency of cell 1 is 44.7 %, and cell 2 is 20.90 % while the minimum treatment removal efficiency of cell 1 is 40.50 %, cell 2 is 20.0 %.
chemical Oxygen Demand (COD)
Influence of HRT on the Chemical Oxygen Demand
0
40
80
120
160
0 1 2 3 4 5HRT day
Co
nc
. C
OD
mg
\l
COD measure CODLimit
Cell 1 Cell 2
Results:Results:Results:Results:Dissolved Oxygen (DO)
The mean of inlet concentration of DO in wastewater entering the Beds ranged from 0.32 to
0.75 mg/l. The concentration increased through Cell 1 about 5.10 mg/l. The concentration of DO
were lower at the end of the cell 2 planted is Water hyacinths cell reaching about 4.84mg/l.
Influence of HRT on the Dissolved Oxygen
0
1
2
3
4
5
6
0 1 2 3 4 5HRT day
Co
nc
. D
O m
g\l
DO measure DOLimit
Cell 1
Cell 2
Results:Results:Results:Results:Total Suspended Solids (TSS)
Average TSS removal efficiency was 60% of cell 1 and 22% of cell 2. The particulate matter
increased in the first reed bed followed by a decrease with distance along the bed.
Influence of HRT on the TSS
0
40
80
120
160
200
0 1 2 3 4 5HRT day
Co
nc
. T
SS
mg
\l
TSS measure TSS Limit
Cell 1
Cell 2
Results:Results:Results:Results:
TDS of influent wastewater ranged from 1100 to 1400mg/l. The maximum treatment removal
efficiency of cell 1 is 15.60 %, cell 2 is 13..95 % while the minimum treatment removal efficiency
of cell 1 is 10.80 %, cell 2 is 2.30 %.
Total Dissolved Solids (TDS)
Influence of HRT on the Total Dissolved Solids
0
500
1000
1500
2000
2500
0 1 2 3 4 5
HRT day
Co
nc
. TD
S m
g\l
TDS measure TDS Limit
Cell 1Cell 2
Results:Results:Results:Results:Phosphorus (PO43-)
The influent range for Phosphorus PO43- was 1.90
– 3.50 mg/l. Average Phosphorus (PO43-) removal
efficiency was 23% of cell 1 and 15% of cell 2. Wetlands remove phosphorus through soil
sorption, precipitation or plant uptake.
Influence of HRT on the PO43-
0
0.5
1
1.5
2
2.5
3
0 1 2 3 4 5HRT day
Co
nc.
PO
4 m
g/l
PO4 measure PO4 Limit
Cell 1 Cell 2
Results:Results:Results:Results:Trace Metals (Cadmium)
In wastewater, cadmium typically occurs as Cd (II) and is most soluble at low pH in water with low
hardness. Solute compounds with carbonate, sulfate, chloride, and hydroxides. Cadmium may be
removed from solution by formation of cadmium sulfide.
Influence of HRT on the Cadmium (Cd)
0
0.005
0.01
0.015
0.02
0.025
0 1 2 3 4 5HRT day
Co
nc.
Cd
mg
/l
Cd measure Cd limit
Cell 1Cell 2
Redox Potential for Sedimentation Layer
The value of Eh is a mean of two measurements in the consecutive two
layers (1.5 and 2.5cm) of the sediment. In general, all Eh values are negative overall
the study period which extended for 12 months. The initial value of Eh was 80 mV
at May and was decreased to 25 mV at July, then it dcreased to -40 at August.
After this time the redox potential values were decreased with proceed time. The
lowest value of Eh -135 mV was noticed at October where purification cell was
drained and the sediment was flashed out.
Redox Potential (Eh) at depth 1.5 & 2.5 cm
Due to the well Known fact that oxidation reactions are dominant at Eh values between -100
to +100 mV, the above findings indicate that reduction reactions in the sediment beginning
after 4 months under conditions of this study. This means that continuous of purification without
removal of the sediment will lead to release of heavy metals especially manganese, iron and
copper and consequently decreased quality of water
-150
-100
-50
0
50
100
Month
Re
do
x P
ote
nti
al E
h(m
V)
Eh mVflash out
befor after
-150
-100
-50
0
50
100
Month
Re
do
x P
ote
nti
al E
h (
mV
)
Eh mVflash out
befor after
Redox potential (Eh) and change of removal efficiency
-150
-100
-50
0
50
100
Month
Re
do
x P
ote
nti
al E
h (
mV
)
Eh mVflash out
befor after
55
60
65
70
75
Month
% R
emo
val B
OD
5
Effici.BOD5flash out
afterbefor
50
55
60
65
70
Month
% R
emo
val C
OD
% Remo.flash out
70
75
80
85
90
Month
% R
emo
val
TS
S
Effici.TSSflash out
40
50
60
70
80
Month% R
emo
val N
H4
-N
% Removal
مضلع. (%(Removal
flash out
Effect of Leaf Area &Light Interception
Water hyacinth light interception was 70.33% through leaves in addition to other interceptions
as roots. Light penetration was 29.67% (as the leaf area index LAI=3.27) because water hyacinth
leaves are wide, flat and increasing in numbers rapidly leading to high negative effect on light
penetration and decrease the treatment efficacy. Typha latofolia (cattail.) light interception was
16.48% through leaves in addition to other interceptions as roots. So, light penetration was
higher 83.16% (as leaf area index LAI = 0.41) because cattail leaves are narrow, in vertical
position and increasing in numbers slowly. There for the negative effect of light is low leaving to a
high treatment efficacy.
Cells W(m)
L(m)
Plant type Density ofPlants
Average area of leaf / plant-cm2
Leave Area Index
Light Interception%
Cell 1 5 15 Cattial 1 / m2 2750.7 0.41 16.84
Cell 2 5 7.5 hyacinths 1 Kg / m2 12735.06 3.27 70.33
Effect of Clogging on the Treatment Process
During this part of the study a hydraulic conductivity test, was done the clean media, in
1.0m length in flow direction and total depth of channel 0.35m and width 0.4m. At flow rate 3.94m3 /day, the water depth upstream and
downstream was measured and tabulated by using the following equation to calculated the
value of K. Figure 3.6 shown the hydraulic conductivity test (Khalifa et al. 2003).
h1h2
Lb
Q
Cross-section a-a a
a
Figure 3.6: Hydraulic Conductivity test in open channe
Q = { k b (h12 – h2
2 )} / 2L …………………(3.3)
Effect of Clogging on the Treatment Process
0
5
10
15
20
25
30
35
Month
Hy
dra
ulic
Co
nd
icti
vit
y (
Kc
)
m/h
r
Kc (m/hr)
The relation between of average hydraulic conductivity with time.
Maximum media clogged after four month the average hydraulic conductivity that values decreased from 29.67 m/hr to 14.51 m/hr.
Total solids concentration in influent polluted water effect in average clogging. Generally, media
clogging can be expected with the increasing of organic load in polluted water.
0
0.5
1
1.5
2
2.5
3
Month
Q m
3 /da
y
Q m3/day
Hydraulic Conductivity (Kc) through the through the gravel mediagravel media From value of hydraulic conductivity
calculations the maximum percentage of flow passing through the gravel media is about 20% of total applied flow rate. Figure show the amounts of flow rate through gravel media in cell 1 during this studies.
The Correlation between Studied Parameters Strong negatively correlation between Strong negatively correlation between temperature and DO. However, there are temperature and DO. However, there are
weak negative correlations between weak negative correlations between temperature and BODtemperature and BOD55, COD, and TSS. On , COD, and TSS. On
the other hand, temperature is weakly the other hand, temperature is weakly positively correlated with pH, and some positively correlated with pH, and some
heavy metals (e.g. Cu, and Zn).heavy metals (e.g. Cu, and Zn).
Such results may be attributing to Such results may be attributing to the negative and positive effects of the negative and positive effects of temperature on: (1) multiplications temperature on: (1) multiplications and growth rate of micro and and growth rate of micro and macro flora, (2) decomposition rate macro flora, (2) decomposition rate of organic compounds, and (3) of organic compounds, and (3) activities of chemical ion speciesactivities of chemical ion species
Results:Results:Results:Results:
The pH of untreated wastewater has a weak The pH of untreated wastewater has a weak negative correlation with concentration of DO, negative correlation with concentration of DO, BODBOD55, COD, TSS, EC, NH, COD, TSS, EC, NH44-N, and some heavy -N, and some heavy metals (e.g. Fe, Cu, and Zn), while it has a weak metals (e.g. Fe, Cu, and Zn), while it has a weak positive correlation with NOpositive correlation with NO33-N-N
Almost these findings are logic and Almost these findings are logic and expected due to one or more of: (1) expected due to one or more of: (1) releasing of organic compounds, especially releasing of organic compounds, especially organic acids, as a result of vigorous organic acids, as a result of vigorous microbial activities, (2) increasing both microbial activities, (2) increasing both TSS and EC depress OH- ions concentration TSS and EC depress OH- ions concentration and thus decreasing pH, and (3) presence and thus decreasing pH, and (3) presence of heavy metals in readily soluble forms of heavy metals in readily soluble forms (reduced valence) occurred at lowering pH. (reduced valence) occurred at lowering pH.
Correlation Coefficients
Results:Results:Results:Results:Correlation Coefficients Negative and significant correlations were Negative and significant correlations were
found between DO and each of BODfound between DO and each of BOD55, COD, TSS, , COD, TSS, EC, NHEC, NH44-N, PO-N, PO44
3-3--P and heavy metals. However, -P and heavy metals. However, the correlation between DO and NOthe correlation between DO and NO33-N is strong -N is strong positive. It well known that nitrification is a positive. It well known that nitrification is a biochemical process in which NHbiochemical process in which NH44-N is oxidized -N is oxidized to NOto NO33-N by nitrifying bacteria and thus DO is a -N by nitrifying bacteria and thus DO is a limiting factor for NOlimiting factor for NO33-N concentration.-N concentration.
Biological and Chemical Oxygen Demands Biological and Chemical Oxygen Demands (BOD(BOD55 &COD) has strong positive correlation &COD) has strong positive correlation with concentration of TSS, EC, NHwith concentration of TSS, EC, NH44-N, PO-N, PO44
3-3--P, -P, Cu, and Fe, while it has a moderate positive Cu, and Fe, while it has a moderate positive correlation with Zn. On the other hand, the correlation with Zn. On the other hand, the correlation between both BOD5 &COD with correlation between both BOD5 &COD with NONO33-N is strong negative.-N is strong negative.
THEORETICAL INTERPRETATION
The first order removal model is widely used in constructed wetland design:
Ce / Co = exp ( -KT. HRT ) ……… (2.6 )
KT = K20 * 1.06 (T – 20) ----------- (2.7 )KT = K20 * 1.06 (T – 20) ----------- (2.7 )
0
0.2
0.4
0.6
0.8
0 50 100 150 200 250 300
Bed Length (m)
Ce / C
o Ra
tio
Theoretical
Field Results
Relationship between ammonia concentration and Bed length in Lake Manzala CW.
0
0.2
0.4
0.6
0.8
5 10 15 20 25Bed Length (m)
Ce /
Co R
atio
Theoretical
Field Results
Cell 1 Cell 2
Relationship between ammonia concentration and Bed length in Bilbeas CW.
0
0.2
0.4
0.6
0.8
0 50 100 150 200 250 300
Bed Length (m)
Ce
/Co
Rat
io
Theoretical
Field Results
Relationship between BOD5 concentration
and Bed length in Lake Manzala CW.
0
0.2
0.4
0.6
0.8
1
5 10 15 20 25
Bed Length (m)
Ce/
Co
Rat
io
Theoretical
Field Results
Cell 1 Cell 2
Relationship between BOD5 concentration
and Bed length in Bilbeas CW.
0
0.2
0.4
0.6
0.8
0 50 100 150 200 250 300
Bed Length (m)
Ce
/Co
Rat
io
Theoretical
Field Results
Relationship between TSS concentration and Bed length in Lake Manzala CW.
0
0.2
0.4
0.6
5 10 15 20 25Bed Length (m)
Ce
/Co
Rat
io
Theoretical
Field Results
Cell 1 Cell 2
Relationship between TSS concentration and Bed length in Bilbeas CW.
….
Conclusions:Conclusions:Conclusions:Conclusions:
The results of the experimental investigations conducted on
rapid flow rate and slow flow rate applying various hydraulic
loadings showed the followings:
A- Lake Manzala CW
Wetland treatment cell could remove about 57.0% of the BOD5, while the design range was 50 – 70%. As for TSS the wetland treatment cell could remove about 79.5% of the load while the design range was 74 – 85%. DO concentration in the treated effluent reached 6.8
mg/l and 5.6 mg/l The mean influent NH4 -N was 6.53 mg/l while the
middle of 3.83 mg/l followed by the same value up to the wetland outlet.
Conclusions:Conclusions:Conclusions:Conclusions:
The concentration of Nitrate-N levels was highest at the end of the treatment beds reaching approx 13.10 mg/l and 11.60 mg/l for
the treatment beds Phosphorus removal along the beds was clearly Phosphorus removal along the beds was clearly denoted as the influent concentration of 3.0 denoted as the influent concentration of 3.0 mg/l. POmg/l. PO44
3-3- were the lowest at the end of the were the lowest at the end of the
treatment beds with about 52.0%.treatment beds with about 52.0%. Results of biological contamination removal of Results of biological contamination removal of FC. The FC was reduced sharply from 28000 FC. The FC was reduced sharply from 28000 CFU/100 ml at inflow water samples to 555 CFU/100 ml at inflow water samples to 555 CFU/100 ml at the outlet.CFU/100 ml at the outlet.
The mean removal efficiency for Trace Metals The mean removal efficiency for Trace Metals was about 53% for the Iron, 38 % Cupper, 48 % was about 53% for the Iron, 38 % Cupper, 48 %
Zinc, and 52 %Lead.Zinc, and 52 %Lead.
Lake Manzala CW
B- Bilbeas Drain CW Conclusions:Conclusions:Conclusions:Conclusions:
The removal of BODThe removal of BOD55 was about 50% in cell 1, was about 50% in cell 1,
27 % in cell 227 % in cell 2 Wetland treatment cell could remove about Wetland treatment cell could remove about 47.0% of the COD in cell 1, while remove about 47.0% of the COD in cell 1, while remove about
23 % in cell 2.23 % in cell 2. DO concentration in the treated effluent reached DO concentration in the treated effluent reached 5.6 mg/l for cell 1 planted with Tupha latofolia, 5.6 mg/l for cell 1 planted with Tupha latofolia, reduced to 4.5 mg/l for cell 2 planted with Water reduced to 4.5 mg/l for cell 2 planted with Water
hyacinth.hyacinth. Removal efficiencies of TSS reached 73.5% and Removal efficiencies of TSS reached 73.5% and 23.5 % for cell 1 and cell 2 consecutively 23.5 % for cell 1 and cell 2 consecutively
The mean removal efficiency for Trace Metals was The mean removal efficiency for Trace Metals was about 42 % for the Iron, 51 % Cupper, 38.5 % about 42 % for the Iron, 51 % Cupper, 38.5 %
Zinc, and 46.0 %Lead.Zinc, and 46.0 %Lead. The mean removal efficiency for NHThe mean removal efficiency for NH44-N was -N was
about47 % for the cell 1, and 18 % for the cell 2. about47 % for the cell 1, and 18 % for the cell 2.
Bilbeas Drain CW Conclusions:Conclusions:Conclusions:Conclusions:
The value of Eh is a mean of two The value of Eh is a mean of two measurements conducted in the measurements conducted in the consecutive two layers (1.5 and consecutive two layers (1.5 and 2.5cm) of the sediment. These 2.5cm) of the sediment. These results may be attributed to one or results may be attributed to one or more of the reasons: more of the reasons:
The conversion from aerobic to The conversion from aerobic to anaerobic conditions with anaerobic conditions with development the sediment development the sediment
Vigorous microbial activity with timeVigorous microbial activity with time The changes in temperature with The changes in temperature with
different monthsdifferent months The changes in the dissolved oxygen The changes in the dissolved oxygen
content in the watercontent in the water
Recommendation:Recommendation:Recommendation:Recommendation:
The study did not address the removal of pathogens which is another important parameter
for treated wastewater Studying the pollutant concentrations in soil and Studying the pollutant concentrations in soil and plant parts in additions to water might explain plant parts in additions to water might explain more clearly the mechanisms of treating and more clearly the mechanisms of treating and
removal of different pollutant types.removal of different pollutant types.
Identifying the more efficient plant type for pollutant treatment in SF/SSF constructed wetland. Comparative studies are required to either select the best plant in pollutant removal.
Studying the environmental impact assessment on the surrounding area in order to foresee the consequence of introducing pollution to
constructed wetlands.
High evaporation and vegetation evapo-transpiration rates in arid and semi-arid climatic regions have a direct impact on pollutant removal.
