Influence of different exchangeable cations (Li , Na , Ca ...
Faculty of Environmental Sciences Department of...
Transcript of Faculty of Environmental Sciences Department of...
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