Receiving water impacts from wastewater systems

Faculty of Environmental Sciences Department of Hydro Science

Institute for Urban Water Management

Karlsruher Flussgebietstage KIT, 20 June 2013

Peter Krebs , Jin Zhang, Nora Schindler and Jens Tränckner

The system

Particles loading

Flush from sewers

Extreme events analysis

Predictor analysis

The system

Particles loading

Flush from sewers

Extreme events analysis

Predictor analysis

Water, compounds

Flux analysis on urban catchment scale

Atmosphere

Traffic Industry Hospital Households

Urban surface Communal wastewater

Infiltration device

Industrial WWTP

Communal WWTP

Combined sewer system

Urban systems

Unsaturated zone

Sorption, transformation

Groundwater aquifer

Sorption, transformation

Soil and groundwater

Water

Sediment

Sedimentation, resuspension

transformation

Surface water

Water, compounds

System of today’s presentation

Atmosphere

Traffic Households

Urban surface Communal wastewater

Communal WWTP

Combined sewer system

Urban systems

Water

Sediment

Sedimentation, resuspension

transformation

Surface water

0

10

20

30

40

50

60

70

0 91 182 273 364

Days

Lo

ad

(k

g/d

)

WWTP, Diffuse sources

Combined sewer overflow events

Continuous and acute loading

The system

Particles loading

Flush from sewers

Extreme events analysis

Predictor analysis

a) b)TSS TOM

a) b)

Surface accumulation of particles and organic matter

Zhang et al. (2013a)

Site Road class Pavement

quality

ADWPa Average daily

trafficb

Heavy

traffic “land use”

Days Vehicle/day %

Walpurgisstr. W Main road Good 1, 8 12600 3 - 4 Commercial

centre

Albertplatz

(Glacisstr.) A

Secondary

road Average 1, 8 800 2

Residential

area

Bannewitz B Federal

highway Average 1, 8 15900 5

Petrol and bus

station

Südhöhe S Main road Good 1, 8 6800 3 Bus station

Industrial area

(Hermann-Mende-Str.) I

Secondary

road Average 1, 8 1100 14 Industrial area

Rural area

(Nöthnitz) R

Secondary

road Good 1, 8 50 < 1

Residential

area

a ADWP: Antecedent dry weather period b The traffic loads were determined in one flow direction (Straßen- und Tiefbauamt Dresden).

a b

Sampling sites

Zhang et al. (2013b)

0,00

1,00

2,00

3,00

4,00

5,00

W1 A1 B1 S1 I1 R1 W8 A8 B8 S8 I8 R8

Su

rface l

oad

of

Zn

(m

g/m

2)

Sampling site

1000-400 µm 400-100 µm 100-63 µm 63-0.45 µm Total

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

W1_

#1

W1_

#2

W1_

#3

W1_

#4

W8_

#1

W8_

#2

W8_

#3

W8_

#4

B1

_#1

B1

_#2

B1

_#3

B1

_#4

B8

_#1

B8

_#2

B8

_#3

B8

_#4

R1

_#1

R1

_#2

R1

_#3

R1

_#4

R8

_#1

R8

_#2

R8

_#3

R8

_#4

Zn concentrations in in sequentially extracted fractions (mg/g)

Exchangeable Reducible Oxidizable Residual

Zn (zinc)

Tire ruber (potential source)

•1.2 % of ZnO for car tires (min 0.4 %,

max 2.9 %);

•2.1 % (min 1.2 %, max 4.3 %) for truck

tires

Brake pad (potential source)

Stepa Fraction Reagent

1 Exchangeable CH3COOH

2 Reduciable NH2OH·HCl

3 Oxidisable H2O2, NH4OAc

4 Residual H2O2, HNO3

a Three-step sequential extraction and total digestion protocols

Heavy metal surface loading

Zhang et al. (2013c)

Particle loading pattern fingerprinting

River bed sediments

Suspended sediments in river

Stormwater particulate matter PM

PCA on particle-associated compounds concentrations

after angular transformation

Separation in PCA explained by

particulate N,

Cu and ZN David et al. (2013)

The system

Particles loading

Flush from sewers

Extreme events analysis

Predictor analysis

First flush of particles

0,0

2,0

4,0

6,0

8,0

10,0

12,0

10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00

Q/Q

d ,

TS

S /

TS

S 0

0

2

4

6

8

10

12

14

16

TS

S -L

oad

/ (

Q d

· TS

S 0

)

TSS-Conc.

Inflow rate

TSS-Load

1 u

Wave propagation and flow

cub

Agv

Combined sewer with stormwater inflow: The wave front is propagating faster than flow

(with dilution)

u

Dynamic transport of dissolved compounds

Wash-out of dissolved compounds

0

2

3

4

10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00

N

H4+

-Lo

ad

/ (

Q d

· C 0

)

0

2

3

4

5

6

7

8

Q / Q

d

NH4+-Conc.

NH4+-Load

Inflow rate

1

1 C /

C0

The system

Particles loading

Flush from sewers

Extreme events analysis

Predictor analysis

• 2000 ha urban catchment (part of real catchment and sewer system)

• scaled WWTP

• virtual receiving water with moderate self-purification capacity

• COD (O2) and TKN (NH4+-N)

• Rain input: 10 years data with 5 minutes resolution

CATCHMENT

Horton infiltration

nonlinear reservoir

expon. acc/erosion

WWTP

ASM1

3 layer secondary

clarifier

SEWER SYSTEM

Saint-Venant

CSTR (quality)

RECEIVING WATER Saint-Venant

RWQM1

SIMBA®

SIMBA-Sewer®

SWMM

Generating data: 10 years long-term simulation with 5 min time steps

Model set-up

0

1

2

3

4

5

6

7

8

11.6.01 12:00 11.6.01 16:48 11.6.01 21:36

Date and time

Co

ncen

trati

on

in

mg

/l

0

20

40

60

80

100

120

140

160

180

200

Rain

in

ten

sit

y in

mm

/hr

O2

NH4-N

Rain

0

1

2

3

4

5

6

7

8

27.7.03 9:36 27.7.03 19:12 28.7.03 4:48 28.7.03 14:24

Date and time

Co

ncen

trati

on

in

mg

/l

0

20

40

60

80

100

120

140

160

180

200

Rain

in

ten

sit

y in

mm

/hr

O2

NH4-N

Rain

Different events induce critical NH3 and Oxygen concentrations

Analysis of peak impacts

0

20

40

60

80

100

120

4 5 6 7 8

SNH Konz in mg/l

Re

ge

nh

öh

e in

1/1

0 m

mScatter plots: rain vs. concentrations

NH4+ Concentration (mg/l)

ma

x. R

ain

in

ten

sity (0

.1m

m/5

min

)

From Schindler et al. (2010)

0

20

40

60

80

100

120

0 0,5 1 1,5 2

O2 concentration (mg/l)

Rai

n in

ten

sity

(0

.1 m

m/

5 m

in)

0.1 0.2 0.5 1.0 2.0 5.0 10.0 20.0

56

78

9

Return Level Plot

Return Period (Years)

Re

turn

Le

ve

l

Extreme rain events ≠ extreme concentrations in river

Extreme value statistics: analysis of NH4+ and rain

NH4+ – concentration (mg/l)

0.2 0.5 1.0 2.0 5.0 10.0 20.0

20

40

60

80

10

0

Return Level Plot

Return Period (Years)

Re

turn

Le

ve

l

Rain (0.1 mm / 5 min)

From Schindler et al. (2010)

The system

Particles loading

Flush from sewers

Extreme events analysis

Predictor analysis

Model setup

• Software: EPA-SWMM 5.0

• Sewer:

Sewage as daily pattern

Detailed spatial analysis of land use types with different

accumulation characteristics

• Lockwitzbach:

Hydrologic model for rainfall-runoff

Hydrodynamic flow for river bed

Combined simulation in one model (2005 – 2011)

Statistical analysis of results

Tränckner et al. (2013)

Potentially influencing parameters

“Predictors”

• Rain event:

Rain height

Rain duration

Mean Rain intensity

• Preceding period

Days with dry weather

Climate water balance since start of simulation

Climate water balance of the last 30 days

GVF Wetting index = ETA/KWB 30d (-1…+1)

Correlation: emitted TS load – predictors

-60 -40 -20 0

GVF

0 20 40 60 800 50 100-600 -400 -200 0-100 0 1000 50 1000 50 1000 200 400

-60

-40

-20

0

R-Dauer

0

20

40

60

80

TW-Tage

0

50

100

KWBges

-600

-400

-200

0

KWB30d

-100

0

100

rN

0

50

100

hN

0

50

100

0

200

400

Fracht TSS

x<1

1<=x<50

50<=x<102

102<=x<306

306<=x

0.674

0.448

-0.153

-0.133

0.043

0.285

-0.001

Tränckner et al. (2013)

Regression trees

Critical events: • TSS-load:

intense + long rain events (Less intense events + dry period before)

• Critical NH4+ concentration:

Short intense events Less intense events + preceding dry period (plus temperature for NH3) Clock time

• Partition of data into homogeneous subsets

• Binary splitting of predictor variables with respect to response variable

Tränckner et al. (2013)

Distinguish between continuous and acute pollution

Polycyclic aromatic hydrocarbons (PAHs) and heavy metals from surface particles and sewer sediments

COD and nutrients from sewage

Integrated modelling and statistical analysis to identify impacts

Complex pattern of relevant predictors is to be identified

Missing link between chemical parameters and ecological status

Conclusions

… do not only model!

Many thanks To the funders BMBF, DFG, HIGRADE To scientists Telse David, Jana Seidel, Thomas Käseberg, Thomas Krause

To you

0 50 100 150 200 250 300 350

-50

00

00

10

00

00

02

50

00

00

Flow 2002

time in days

flo

w in

m³/

d

400 500 600 700

-50

00

00

10

00

00

02

50

00

00

Flow 2003

time in days

flo

w in

m³/

dFlo

w

Flo

w

Flow in a wet and a dry year

0 50 100 150 200 250 300 350

05

10

20

30

Ammonia 2002

time in days

co

nce

ntr

atio

n in

mg

/l

400 500 600 700

05

10

20

30

Ammonia 2003

time in days

co

nce

ntr

atio

n in

mg

/l

Ammonia peaks in a wet and a dry year

Under construction...

WWTP

Catch- ment

Sewer system

River

Groundwater

Projections of

Climate change

Demografic change

Diffuse sources

(agriculture and forest management)

Lipidsenker: Predominantly people above 60

Sexual hormone and Gynäkologikum: Young and older people

Anti-biotic, anti-infective: Homogeneous application to all age classes

Pharmaceuticals = f(Demographie)

> 60 20 – 60 < 20

Worst case: „predicted“ emissions/base flow

0.00

1.00

2.00

3.00

4.00

5.00

W1 A1 B1 S1 I1 R1 W8 A8 B8 S8 I8 R8

Surf

ace

load

of

Zn (m

g/m

2 )

Sampling site

1000-400 µm 400-100 µm 100-63 µm 63-0.45 µm Total

0.00

0.50

1.00

1.50

2.00

2.50

W1 A1 B1 S1 I1 R1 W8 A8 B8 S8 I8 R8

Surf

ace

load

of

Cu (m

g/m

2 )

sampling site

1000-400 µm 400-100 µm 100-63 µm 63-0.45 µm Total

0.00

1.00

2.00

3.00

W1 A1 B1 S1 I1 R1 W8 A8 B8 S8 I8 R8

Surf

ace

load

of

Cd (µ

g/m

2 )

Sampling site

1000-400 µm 400-100 µm 100-63 µm 63-0.45 µm Total

(a)

(b)

(c)

0%10%20%30%40%50%60%70%80%90%

100%

W1_

#1

W1_

#2

W1_

#3

W1_

#4

W8_

#1

W8_

#2

W8_

#3

W8_

#4

B1_#

1

B1_#

2

B1_#

3

B1_#

4

B8_#

1

B8_#

2

B8_#

3

B8_#

4

R1_#

1

R1_#

2

R1_#

3

R1_#

4

R8_#

1

R8_#

2

R8_#

3

R8_#

4

Zn concentrations in in sequentially extracted fractions (mg/g)

Exchangeable Reducible Oxidizable Residual

0%10%20%30%40%50%60%70%80%90%

100%

W1_

#1

W1_

#2

W1_

#3

W1_

#4

W8_

#1

W8_

#2

W8_

#3

W8_

#4

B1_#

1

B1_#

2

B1_#

3

B1_#

4

B8_#

1

B8_#

2

B8_#

3

B8_#

4

R1_#

1

R1_#

2

R1_#

3

R1_#

4

R8_#

1

R8_#

2

R8_#

3

R8_#

4

Cu concentrations in sequentially extracted fractions (mg/g)

Exchangeable Reducible Oxidizable Residual

0%10%20%30%40%50%60%70%80%90%

100%

W1_

#1

W1_

#2

W1_

#3

W1_

#4

W8_

#1

W8_

#2

W8_

#3

W8_

#4

B1_#

1

B1_#

2

B1_#

3

B1_#

4

B8_#

1

B8_#

2

B8_#

3

B8_#

4

R1_#

1

R1_#

2

R1_#

3

R1_#

4

R8_#

1

R8_#

2

R8_#

3

R8_#

4

Cd concenrations in sequentially extracted fractions (µg/g)

Exchangeable Reducible Oxidizable Residual

(a)

(b)

(c)

Heavy metals distribution

Polycyclic aromatic hydrocarbons (PAHs) source apportionment by

PCA-MLR (Principal component analysis with multiple linear regression)

Size fraction 1000 – 400 µm 400 – 100 µm 100 – 63 µm 63 – 0.45 µm

PAHs component 1 2 1 2 3 1 2 3 1 2

NAP -0.687 0.618 0.382 -0.252 0.883 0.081 -0.405 0.886 0.959 -0.010

ACE -0.793 -0.115 -0.178 -0.118 0.972 -0.510 0.274 0.785 0.974 0.221

FLU -0.838 -0.206 -0.255 0.673 0.674 0.737 0.426 0.307 0.064 0.989

PHE 0.685 0.707 0.907 0.004 0.417 0.381 0.144 0.907 0.964 0.236

ANT 0.225 0.967 0.928 0.362 -0.081 0.947 0.233 0.071 0.966 0.190

FLUH 0.976 0.134 0.939 0.334 -0.064 0.945 0.278 -0.051 0.967 0.187

PYR 0.946 0.176 0.993 0.099 -0.008 0.969 -0.101 0.069 0.965 0.131

BaA 0.982 0.107 0.533 0.835 -0.133 0.526 0.829 -0.066 0.988 0.125

CHY 0.985 -0.007 0.888 0.449 -0.085 0.925 0.372 0.052 0.981 0.178

BbF 0.996 -0.015 0.671 0.735 -0.089 0.918 0.358 0.021 0.991 0.100

BkF 0.988 0.032 0.500 0.857 -0.124 0.671 0.719 -0.075 0.986 0.079

BaP 0.480 0.800 0.486 0.839 -0.140 0.789 0.198 -0.107 0.974 -0.031

DBA 0.059 0.636 0.160 0.982 -0.091 0.387 0.846 -0.362 0.958 0.003

BgP 0.931 0.224 0.821 0.551 -0.104 0.950 -0.056 0.015 0.997 -0.037

IDP -0.196 0.943 0.135 0.990 0.001 -0.129 0.900 0.268 0.953 -0.111

% of Variancea 63.73 23.81 64.55 19.32 15.30 59.74 17.35 16.12 89.34 7.41

Cumulative %b 63.73 87.53 64.55 83.87 99.17 59.74 77.08 93.21 89.34 96.75

Sourcec Vehicular

emission

Wood

combus

tion

Coal

combustion

Crankcase

oil/

vehicular

Coal

tar

Tire

debris

Crankc

ase oil

Coal

tar Multiple

Air

deposition

Contribution %d 63.3 36.7 65.4 25.5 9.1 67.3 15.0 17.6 87.9 12.1

a The percentage of the total variability (initial eigenvalues) explained by each principal component. b The amount of variance accounted for by each component. c Potential source assignments by means of principal component analysis (PCA). d The contribution of each identified source by means of multiple linear regression analysis (MLR).

PCA-MLR receptor model (rotated component matrix)

Polycyclic aromatic hydrocarbons (PAHs)

The following 16 U.S. EPA criteria PAH concentrations were

quantified: Naphthalene (NAP), Acenaphthylene (ACY),

Acenaphthene (ACE), Fluorene (FLU), Phenanthrene (PHE),

Anthracene (ANT), Fluoranthene (FLUH), Pyrene (PYR),

Benz(a)anthracene (BaA), Chrysene (CHY),

Benzo(b)fluoranthene (BbF), Benzo(k)fluoranthene (BkF),

Benzo(a)pyrene (BaP), Indeno(1,2,3-cd)pyrene (IDP),

Dibenzo(a,h)anthracene (DBA), Benzo(g,h,i)perylene (BgP). The

concentration of PAHs in size-fractionated road-deposited

sediments along with the potential risk assessments of exposure

to PAHs using Benzo(a)pyrene-equivalent (BaPE)

carcinogenicity index and toxic equivalency factor (TEF) which is

initially adopted by U.S. EPA, then modified by Nisbet and LaGoy

(1992),

Separate analysis of Summer and Winter

pH Temperatur

(°C)

(NH4++NH3)-N

(mg/l)

NH3-N

(mg/l)

NH3-N-limit (Lammersen 1997)

(mg/l)

All events

May to October

November to April

8

8

7

20

20

5

7.1

7.7

6.1

0.27

0.31

0.008

0.24

0.24

0.08

Summer Winter

Case study: Großzschachwitz, Lockwitzbach

Lockwitzbach (LfULG, 2008, itwh, 2011)

Kanalnetz

AE 84 km²

MNQ 0,022 m³/s

MQ 0,344 m³/s

HQ1 5,81 m³/s

HQ1p,nat 5,94 m³/s

inhabitants 4000

A [ha] 144 ha

Aroof [ha] 24 ha

Astreet [ha] 13 ha

sewer

length

MW 8,9 km

SW 2,0 km

RW 2,6 km