Sewer sediment halcrow apr08

Halcrow Seminar Series

Sharjah, April 2008

SEWER SEDIMENTS - CHARACTERISTICS AND CONTROL IN ARID AREAS

Mamdouh NOUHProfessor of Civil (Water Resources& Environmental) Engineering

OBJECTIVES

Discuss the sources and characteristics of sewer sediments in arid and semiarid climates.

Recommend a method to predict the transportation and deposition of sediments under the effect of flash floods in arid and semiarid climates.

Discuss the feasible control methods that can prevent (or reduce) sewer-sediment accumulations in arid and semiarid climates.

MAIN PROBLEMS OF DESIGN (developing arid and semiarid catchments)

Scarcity of reliable data High variability of climatic factors (rainfall, temperature,

humidity,…, etc.) Existence of heavy duststorms Traditional method of design oversizes sewers’

diameters to convey many times the anticipated peak flowrates and to accommodate accumulated deposits (This leads to failure of maintaining setteable solids in suspension).

MAIN PROBLEMS OF OPERATIONS (developing arid and semiarid catchments)

Maintenance and management schemes (including cultural and socioeconomic schemes)

Highly polluted first-flush phenomenon of overflow event.

Blockage of sewer by sediment accumulation.

FLOODED URBAN AREA IN SHARJAH

FLOODED URBAN AREA IN SHARJAH

LANDSLIDES IN SOUTHWEST OF SAUDI ARABIA

DAMAGES OF ROADS IN ABHA (SAUDI ARABIA) AND IN MUSCAT (OMAN)

LANDSLIDES AND DAMAGE OF PAVEMENTS IN SAUDI ARABIA

EFFECT OF URBANIZATION ON

SHAPE OF

HYDROGRAPH

INPUT – OUTPUT

RELATIONSHIPS

IN

HYDROLOGIC PROCESSES

MODELS IN ANALYSES OF WATER SYSTEMS

0.01 0.1 1 10 100

TIME (min)

0

20

40

60

80

100M

EA

N S

ED

IME

NT

CO

NC

EN

TR

AT

ION

S (g

pl)

C (With)

C (Without)

Sediment Concentrations With and Without Duststorms

0.01 0.1 1 10 100

TIME (min)

1

1.5

2

2.5

3

CO

NC

EN

TR

AT

ION

WIT

H /

CO

NC

EN

TR

AT

ION

WIT

HO

UT NO2/NO3 (Large) NO2/NO3 (Small)

NH4 (Large) NH4 (Small)

TP (Large) TP (Small)

Variation of Nutrients Concentrations With and Without Duststorms

0 0.2 0.4 0.6 0.8 1

COEFFICIENT OF VARIATION OF DUSTSTORM

0

10

20

30

40

50

60

70

80

90

100

ME

AN

CO

NC

EN

TR

AT

ION

OF

SUSP

EN

DE

D S

ED

IME

NT

"C

", g

pl

TSP (ug/cu.m) = 800 - 1000

TSP (ug/cu.m) = 1800 - 2000

TSP (ug/cu.m) = 3000 - 3200

0 0.5 1 1.5 2 2.5-0.5

COEFFICIENT OF KURTOSIS OF DUSTSTORM

0

10

20

30

40

50

60

70

80

90

100

ME

AN

CO

NC

EN

TR

AT

ION

OF

SUSP

EN

DE

D S

ED

IME

NT

"C

", g

pl

TSP (ug/cu.m) = 800-1000

TSP (ug/cu.m) = 1800 - 2000

TSP (ug/cu.m) = 3000 - 3200

0 500 1000 1500 2000 2500 3000

CONCENTRATION OF TSP, ug/cu.m

30

40

50

60

70

80

90

100

CO

NC

EN

TR

AT

ION

OF

SUSP

EN

DE

D S

ED

IME

NT

"C

", g

pl

C = 33.0 + 0.0147*TSPR-sq = 96.5%

0 1000 2000 3000 4000

TSP CONCENTRATION, ug/cu.m

70

80

90

100

110

120

CO

NC

EN

TR

AT

ION

OF

SUSP

EN

DE

D S

ED

IME

NT

"C

", g

pl

C = 73.2 + 0.00848*TSPR-sq = 59.2%

Summary of performed regression analyses on prediction of Cd

RegressionParticle size diameter

“d”, mm

Correlation

coefficientInter-

correlation coefficient

1 2

Value (95% confidence

level)


level)


level)

Original regression All regression data 0.812 0.242 12.83 (16.48,

9.18)

0.56(0.68, 0.44)

0.06(0.051,0.039

)

1st particle size diameter class regression

d > 0.20 0.96 0.102 5.68(6.79, 4.57)

0.68(0.89, 0.49)

0.05 (0.059, 0.041)

2nd particle size diameter class regression

0.20 d > 0.06 0.96 0.273 4.67(5.92, 3.42)

0.65(0.78, 0.52)

0.06 (0.068, 0.052)

3rd particle size diameter class regression

0.06 d > 0.02 0.94 0.280 3.95(5.00, 2.90)

0.59(0.75, 0.43)

0.07(0.079, 0.061)

4th particle size diameter class regression

0.02 d > 0.002 0.95 0.286 2.85 (3.98, 1.72)

0.51 (0.62, 0.40)

0.07(0.079,0.061

)

5th particle size diameter class regression

0.002 d 0.93 0.286 2.58 (3.41, 1.75)

0.48 (0.60, 0.36)

0.08(0.096, 0.064)

ddddd SqC 21

where C is the concentration of suspended sediments (gram per liter); q is the average stormwater runoff depth over

catchment (mm); S is the concentration of duststorm over the catchment (g/ m 3 ); , and are regression parameters

0

50

100

150

200

250

0 50 100 150 200 250Predicted concentrations (gram per liter)

Mea

sure

d c

on

cen

trat

ion

s (g

ram

per

lit

er)

Measured and predicted suspended sediment concentrations

-60

-40

-20

0

20

40

60

0 50 100 150 200 250

Measured concentrations (gram per liter)

Err

or p

redi

ctio

n (g

ram

per

lite

r)

Error of prediction against measured suspended sediment concentration

0

50

100

150

200

250

0 50 100 150 200 250

Predicted concentrations (gram per liter)

Mea

sure

d co

ncen

trat

ions

(gr

am p

er li

ter)

Measured and predicted suspended sediment concentrations using division diameter size

0

50

100

150

200

250

-40 -30 -20 -10 0 10 20 30 40 50

Error in prediction (gram per liter)

Mea

sure

d co

ncen

trat

ions

(gra

m p

er li

ter)

Error of prediction against measured suspended sediment concentration using division diameter size

Regression analyses for heavy metals concentrations.

Metal Particle diameter “d”, mm

a b Coefficient of determination “R2”

Sum of squares for error

Mean square for error

Calculated F statistics Critical F-statistics

“Fc”

Copper “Cu” d > 0.20 0.032 0.046 0.8921 4.20765 0.242 17.387 4.08

0.20 d > 0.06 0.042 0.057 0.8744 4.60672 0.305 15.104

0.06 d > 0.02 0.776 0.093 0.8921 4.50954 0.315 14.316

0.02 d > 0.002 0.916 0.153 0.8216 3.70962 0.333 11.140

0.002 d 1. 034 0.196 0.7649 3.23248 0.356 9.080

Lead “Pb” d > 0.20 0.007 0.051 0.8844 0.02861 0.002 14.306

0.20 d > 0.06 0.009 0.063 0.8869 0.09272 0.007 13.245

0.06 d > 0.02 0.005 0.096 0.8761 0.07513 0.009 8.348

0.02 d > 0.002 0.006 0.162 0.8196 0.06351 0.009 7.057

0.002 d 0.011 0.123 0.7929 0.06949 0.013 5.345

Nickel “Ni” d > 0.20 0.056 0.009 0.8521 0.33768 0.016 21.105

0.20 d > 0.06 0.062 0.031 0.8249 0.35435 0.019 18.650

0.06 d > 0.02 0.061 0.053 0.7481 0.30378 0.027 11.251

0.02 d > 0.002 0.072 0.096 0.8096 0.22719 0.022 10.327

0.002 d 0.083 0.104 0.7761 0.37713 0.039 9.670

Zinc “Zn” d > 0.20 0.033 0.106 0.8241 0.44357 0.019 23.346

0.20 d > 0.06 0.029 0.098 0.8329 0.30628 0.026 11.780

0.06 d > 0.02 0.045 0.102 0.8084 0.30690 0.031 9.900

0.02 d > 0.002 0.061 0.120 0.7896 0.40032 0.048 8.340

0.002 d 0.097 0.155 0.8225 0.33825 0.055 6.150

Iron “Fe” d > 0.20 0.089 0.117 0.8384 1.12902 0.093 12.140

0.20 d > 0.06 0.097 0.103 0.7561 1.20175 0.115 10.450

0.06 d > 0.02 0.107 0.125 0.7861 1.73152 0.172 10.067

0.02 d > 0.002 0.126 0.216 0.7269 1.98288 0.216 9.180

0.002 d 0.133 0.238 0.7981 1.65858 0.231 7.180

dbddddCaP

1

1.5

2

2.5

1 1.5 2 2.5

Measured Cu concentrations (mg per liter)

Cu

conc

entr

atio

ns (

mg

per

liter

) pr

edic

ted

by E

q. 4

1

1.5

2

2.5

1 1.5 2 2.5


Cu

conc

entr

atio

ns (m

g pe

r lit

er)

pred

icte

d by

Eqs

1 &

4

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

1 1.5 2 2.5


Err

or o

f pr

edic

tion

usin

g E

q. 4

(mg

per

liter

)

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

1 1.5 2 2.5


Err

or o

f pr

edic

tion

usin

g E

qs 1

& 4

(m

g pe

r lit

er)

Measured and predicted concentrations (top) and error of prediction (bottom) of copper “Cu”

2

4

6

8

10

2 4 6 8 10

Measured Pb concentrations (0.001xmg per liter)

Pb c

once

ntra

tions

(0.

001x

mg

per

liter

) pr

edic

ted

by E

q. 4

2

4

6

8

10

2 4 6 8 10Measured Pb concentrations (0.001xmg per liter)

Pb c

once

ntra

tions

(0.

001x

mg

per

liter

) pr

edic

ted

by E

qs 1

& 4

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

2 4 6 8 10


Err

or o

f pr

edic

tion

(0.

001x

mg

per

liter

) by

usi

ng E

q. 4

-1.5

-1

-0.5

0

0.5

1

1.5

2

2 4 6 8 10


Err

or o

f pr

edic

tion

(0.0

01xm

g pe

r lit

er)

by u

sing

Eqs

1 &

4

Measured and predicted concentrations (top) and error of prediction (bottom) of lead “Pb”

4

6

8

10

4 6 8 10

Measured "Ni" concentrations (0.01xmg per liter)

"Ni"

con

cent

ratio

ns (

0.01

xmg

per

liter

) pr

edic

ted

by E

q. 4

4

6

8

10

4 6 8 10


"Ni"

con

cent

ratio

ns (

0.01

xmg

per

liter

) pr

edic

ted

by E

qs 1

& 4

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

4 6 8 10

Measured Ni concentrations (0.01xmg per liter)

Err

or o

f pr

edic

tion

(0

.01

xmg

per

liter

) by

usi

ng E

q. 4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

4 5 6 7 8 9 10 11


Erro

r of p

redi

ction

(0

.01

xmg

per l

iter

) by

usin

g Eq

s 1

& 4

Measured and predicted concentrations (top) and error of prediction (bottom) of Nickel “Ni”

4

8

12

16

4 8 12 16

Measured "Zn" concentrations (0.01xmg per liter)

"Zn

" co

ncen

trat

ions

(0.

01xm

g pe

r lit

er)

pred

icte

d by

Eq

. 4

4

8

12

16

4 8 12 16Measured "Zn" concentrations (0.01xmg per liter)

"Zn"

con

cent

rati

ons

(0.0

1xm

g pe

r lit

er)

pred

icte

d by

Eqs

1 &

4

0

0.5

1

1.5

2

2.5

4 8 12 16Measured Zn concentrations (0.01xmg per liter)

Err

or o

f pr

edic

tion

(0.

01xm

g pe

r lit

er)

by u

sing

Eq.

4

-4

-3

-2

-1

0

1

2

3

4 8 12 16

Measured "Zn" concentrations (0.01xmg per liter)

Err

or o

f pr

edic

tion

(0.0

1xm

g pe

r lit

er)

by u

sing

E

qs 1

& 4

Measured and predicted concentrations (top) and error of prediction (bottom) of Zinc “Zn”

2

3

4

5

2 3 4 5

Measured "Fe" concentrations (0.1xmg per liter)

"Fe"

con

cent

ratio

ns (

0.1x

mg

per

liter

) pr

edic

ted

by E

q. 4

2

3

4

5

2 3 4 5


"Fe"

con

cent

ratio

ns (

0.1x

mg

per

liter

) pr

edic

ted

by E

qs 1

& 4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

2 3 4 5


Err

or o

f pr

edic

tion

(0.

1xm

g pe

r lit

er)

by u

sing

Eq.

4

-0.4

-0.2

0

0.2

0.4

0.6

0.8

2 3 4 5


Err

or o

f pr

edic

tion

(0.0

1xm

g pe

r lit

er)

by u

sing

E

qs 1

& 4

Measured and predicted concentrations (top) and error of prediction (bottom) of Iron “Fe”

Transport of Sewer Sediments

• Bedload TransportComposed of relatively heavy particles; move near the sewer bottom;

travel by rolling, sliding, or saltating along the pipe invert (or deposited bed). Its rate is a function of shear velocity, particle size, density of

water, hydraulic radius of flow, and energy slope.

• Suspended-Load TransportFine lighter particles that have at one time been deposited and have

subsequently been swept from the bed load into the overlaying flow. Its travel is in suspension and is primary influenced by turbulent fluctuations

in the flow, which in turn are influenced by bed shear. Its rate is computed by integrating relationships identifying the concentration of suspended

sediments at various depths of sewer.

• Washload TransportParticles inter the pipe at its upstream junction and remained in suspension

under certain flow conditions but may settle when the flow conditions change. Critical conditions are identified in literature.

Bedload Transport:

qsb = f (shear velocity, median particle diameter)

Qsb(d) = 0.035 (Nouh, et al. (2004)

Suspended Load Transport:

Qss(d) = 0.685 (Nouh, et. al., 2004)

Total Solid Deposition:

TSD (d) = 0.006904 L-0.47 …….(R2 = 0.87)

…………………………(Nouh, 2005)

ddC

672.0

ddC

098272.0

ddC

3572.0

Traditional Method of DesignPreliminary investigation

To evaluate the feasibility of a project and to justify bond issues, assessments against property, or other methods of fund raising.

Preliminary designs are based on estimated flows, approximate ground contours, the location of the streets or sewer easement, and the location or

locations to which the sewage is to be taken.

Design principles

Detailed maps (or aerial photography) are used.

Flows are assumed STEADY and SEDIMENT free.

Flow velocities are high enough to keep solids in suspension (at least 0.60 m/s; normally 0.75 – 2.50 m/s).

A vertical profile is prepared for each sewer line at a horizontal scale of 1:500 to 1:1000 and a vertical scale about 10 times greater.

Self-Cleansing & the Limit of Deposition Method of Design

Sewer sediments may be defined as any settable materials found in water sewage, which is able to form bed deposits in the sewerage system.

The design is based on specifying a minimum “self-cleansing” flow velocity that should be achieved at a particular depth of flow (or with a particular

frequency of occurence).

• EntrainmentWhen the hydrodynamic lift and drag forces on the bed particles exceed the restoring forces of sediment

submerged weight, interlocking, and cohesion (if present) entrainment occurs, resulting in movement of the particles.

• TransportEntrained sediments travel in suspension or as a bed load. Finer lighter material tends to travel in suspension,

whereas heavier material travels as bedload.

• DepositionIf the flow velocity and/or turbulence level decreases transport of suspended sediment and/or bed load will cease,

and deposition of particles occurs.

Practically:A single value of minimum velocity, unrelated to the characteristics of the

sediment and other hydraulic behavior of the sewers, does not properly represent the ability of sewer flows to transport sediment.

Recommended Method of Design in Arid and Semiarid Climates

• Consideration of the large amounts of transported sediments• Consideration of shortage of data• Consideration of the high variability of rainfall, temperature…etc

(characteristics of arid climates)• Consideration of the local management and operation

constraints

Variation of Rainfall with Height

Flood Estimate by Extension of Short Record (3-10 yrs):

1. Extract maximum flows at the short term station

2. Extract maximum flows at nearby long term station

3. Normalize data (take the logarithms)

4. Derive regression equations for the short term

station using long term station “s”5. The regression equations are used to extend

the short term record.

Relative Information is defined as the variance

of the record estimate divided by the variance of the

estimate based on the extended records.

When this is greater than unity it is worth extending

the series.

sss

s

sss

ss

I

II I

I

I

I

II

OO

OO O

OO

O

210 ELEVAREAQav

Growth Curves for M5 of Various Rainfall Durations

Growth Factors

Percentage of Seasonal to Annual Rainfall Maxima for Different Return Periods

Areal Reduction Factors (%)

Prediction of Runoff:

Qp = 0.129(A)0.025 (S)0.87 (h)0.54 (TMP) 4.38 ………..[R 2 =0.82]

C v = 310.7 (A)0.025(1+TSP/1000) (S)1.87 (T)4.38 (h)0.54 (TMP)2.78 ……..[R 2 = 0.89]

The volumetric sediment concentration “Cv” (discharge rate of sediment/discharge rate of water) as a function of the average of total suspended particulate matter concentrations “TSP” (g/m 3 ) over catchment, area of catchment “A” (ha), slope of land “S” (cm/km), average depth of rainstorm over catchment “h” (mm), dry period preceding the rainstorm “T” (days), and the coefficient of kurtosis of rainstorm (measure of temporal variation of rainstorm over catchment) “TMP”.

Equ.Regression variable R2

Cv A h S TMP TSP T 0.89

A h S 0.56

A h S TSP 0.61

A h S TSP T 0.71

A h S TMP TSP 0.76

Q A h S TMP 0.82

A h S 0.69

A h TMP 0.74

Routing flows through sewers(Development in Muskingum Flow routing method)

Q 0 = a Q i ……………………………………. (1)

Where 0 ‹ a ‹ 1 = coefficient, determined by using a regression equation developed in this study as

a = 0.0136 D 0.46 n -0.72 S 0 -0.028 Qp

-0.18 C v 0.54 . (2)

where Q p is the peak inflow discharge, D is the sewer diameter, and n is Manning coefficient.

The routing of flow is as follow:1. Select ∆t and ∆x2. Calculate “a” using Eq. 23. Calculate Q0 with Eq. 1 and inflow discharge4. Calculate the initial depth and angle of the flow cross sections

2

0

0

sin2

2sin225

3

Q

LA

K

sin)sin2

2sin25(16

2sin2(3

2

1

20

LS

DX ]}1)

sin2

2sin25(

3

1{1[ 2

02

2 F

KXt

XKtC

)1(5.0

5.01

KXt

XKtC

)1(5.0

5.02

KXt

KXtC

)1(5.0

)1(5.03

ni

nni

ni QCQCQCQ 1312

11

11

Calculate K and X as

Calculate C1 , C2 , and C3 by

Route flows by

Ratios of Computed to Measured Parameters of Ratios of Computed to Measured Parameters of Hydrographs and PollugraphsHydrographs and Pollugraphs

(average of 38 events)(average of 38 events)

Peak Average Duration

Ratio “q”

(hydrograph) 0.82 0.92 0.96

Ratio “C”

(pollugraph) 0.74 0.86 0.79

Sewer-Sediment Control

Solids-source management Inline and in-sewer control Treatment facilities

Effect of sewer slope on solids removalEffect of sewer slope on solids removal(circular cross section of s = 0.0008 m/m)(circular cross section of s = 0.0008 m/m)

0

10

20

30

40

50

60

70

20 30 50 100

0.001

0.0012

0.0015

Sewer Diameter in Centimeter

Per

cen

tag

e o

f S

oli

d R

emo

vals

Effect of sewer shape of cross section Effect of sewer shape of cross section on solids removalon solids removal

(slope = 0.0008 m/m)(slope = 0.0008 m/m)

0

10

20

30

40

50

60

70

Circular Square Rectangular Trapezoidal

0.001

0.0012

0.0015

Per

cen

tag

e o

f S

oli

d R

emo

vals

Shape of Cross Section

Effect of circular sewer cleansing on solids removal

(slope = 0.0008 m/m)

0

10

20

30

40

50

60

70

80

90

100

Circular Square Rectangular Trapezoidal

Once YearlyTwice YearlyAfter Each Rainstorm

Per

cen

tag

e o

f S

oli

d R

emo

vals

Shape of Cross Section

A hypothetical Sewer Network

Sewer network design with and without consideration of transported sediment to sewer

Sewernumber

With sediment washoff

Without sediment washoff

Diam. (mm)

Bed slope a coeff.

Diam. (mm)

Bed slope

1 2,000 0.005 0.11 2,000 0.006

2 2,000 0.005 0.13 3,000 0.006

3 2,000 0.008 0.19 3,000 0.01

4 4,000 0.008 0.21 5,000 0.01

5 5,000 0.007 0.23 7,000 0.02

6 5,000 0.005 0.32 9,000 0.03

7 6,500 0.005 0.39 10,000 0.03

8 6,500 0.004 0.43 10,000 0.02

9 8,000 0.004 0.44 12,000 0.015

10 8,000 0.003 0.49 12,000 0.015

11 9,000 0.003 0.41 14,000 0.01

12 10,000 0.0025 0.43 14,000 0.01

13 12,000 0.0025 0.39 15,000 0.0025

14 14,000 0.002 0.29 15,000 0.002

Effect of sewer flushing on removal of deposits.

Shape of cross section

% of slope increase

Frequency of flushing % deposits removals

Circular 5 Once/yr 13

Twice/yr 31

7 Once/yr 28

Twice/yr 55

10 Once/yr 42

Twice/yr 73

15 Once/yr 76

Twice/yr 89

Square 5 Once/yr 7

Twice/yr 29

7 Once/yr 12

Twice/yr 50

10 Once/yr 31

Twice/yr 58

15 Once/yr 56

Twice/yr 69

Rectangular 5 Once/yr 9

Twice/yr 32

7 Once/yr 39

Twice/yr 55

10 Once/yr 70

Twice/yr 73

15 Once/yr 70

Twice/yr 75

CONCLUSIONSCONCLUSIONS

Suspended sediment concentrations in the stormwater runoff of arid residential catchments are significantly affected by the characteristics of both stormwater runoff and duststorms over the catchments. Thus, stormwaters runoff and duststorms are recommended to be considered for proper prediction of the sediments in the stormwater runoff.

The concentrations of heavy metals in the stormwater runoff in arid catchments vary with variations in the concentrations of transported

suspended sediments. Thus, the developed regression equations relating these types of concentrations are recommended to be used by scientists

and engineers.

The concentrations of transported suspended sediment and those of heavy metals in the runoff of the investigated arid catchments vary with the

variations in the particle size of transported sediment. Thus, identification of the transported suspended sediment particle size range is of importance

for accurate prediction of the concentrations in the runoff.

CONCLUSIONSCONCLUSIONS The developed regression equations for the prediction of both concentrations of

transported suspended sediment and those of heavy metals in the stormwater runoff of the investigated catchments with considerations of particle sizes of transported suspended sediment are more accurate than those developed without considerations of the sediment sizes. Thus, the equations which consider the particle sizes of transported suspended sediment are recommended, if possible, to be used for the prediction of the concentrations of suspended sediment and heavy metals in the stormwater runoff of the investigated arid residential catchments

Transported sediments in arid and semiarid areas are influenced by rainstorm and duststorm as well as by catchment characteristics.

Suspended, bedload as well as wash load transport loads from arid and semiarid catchments may be predicted by a simple regression type relationships.

Increase of sewer slope increases the removal of deposited solids

The removal volume of deposited solids by increasing the sewer slope varies with the shape of sewer cross section.

Sewer flushing is an effective way to increase the removal volume of deposited solids

An optimization methodology could be applied for the sewer slope, sewer shape of cross section, and interval of sewer flushing in order to identify the conditions of feasible maximum removal of deposited solids.

The removal of deposited solids from the sewer system should be integrated with a management scheme to reduce solids from source, and with treatment of facilities

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