WP2. HYDROL - Surface and groundwater hydrology. Associated processes at different scales.
Presentation about: work done and work to do in the next future
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TASKS
Three major tasks: i) To analyze the impact of the interaction processes in water interfaces (water and sediments accumulated in dams, river beds, hyporreic zone, infiltration ponds,…) on water quality in the study basins ii) To characterize the effects of artificial recharge operations on water quality iii) To determine the likelihood of chemical compounds to reach the water bodies in concentrations exceeding a given threshold.
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D2.1. Characterization of processes taking place at the different interfaces within water bodies, with emphasis on reactive transport development (UPC) (month 18).
Training activity: Managed artificial recharge for sustainable water management under varying climate conditions: quantitative and qualitative aspects. Organized by UPC in collaboration with UPM and IDAEA-CSIC.
• So, first processes; then applications to the sites
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10
7.5
5
2.5
0
0
100
DOC
NO
3
NO
2
Alk
1 time [d]
10
DCF a)
100 b)
50
0
0
LDet
100
SMX
50
SMX
1
Time [d]
10
DCF
100 c)
0
0 0.1
1
Time [d]
10 100
Figure 1: results for
“Experiment 1” (individual pollutant at initial concentration of 1microg/L ).
a) chemical evolution with time in the biotic NO3reducing experiment; b) evolution with time of the average normalized concentration (with respect to the initial value C
0
) of diclofenac (DCF) and sulfamethoxazole (SMX) in the biotic test. “LDet” stays for Limit of Determination; c) idem in the abiotic test.
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80
60
40
20 a)
0
125
0
100
75
50
25
0
125 0
100
75
50
25
0
0 b) c)
DOC
NO
3
1
1
SMX
APP
1
Time [d]
Alk time [d]
10
DCF
10
10
APP
NO
2
100
100
DCF
100
Figure 2: results for
“Experiment 2” (individual pollutant at initial concentration of 1mg/L ).
a) chemical evolution with time in the biotic NO3reducing experiment; b) evolution with time of the average normalized concentration (with respect to the initial value C
0
) of
Acetaminophen (APP), DCF and SMX in the biotic test. “ c) idem in the abiotic test.
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1.0
0.8
0.6
0.4
0.2
0.0
1
1.2
1.0
0.8
0.6
0.4
0.2
0.0
1.2
1 average - Biotic 1ug/L average - Biotic 1ug/L time [d]
10
DCF
NO2-DCF
Nitrite a) c)
100
5
4
0
SMX
4-NO2-SMX
Nitrite
3
2
1
100
0
5
4
3
2
1 time [d]
10
1000
800
600
400
200
0
1200
1
1000
800
600
400
200
0
1 average - Biotic 1mg/L
DCF
NO2-DCF
Nitrite average - Biotic 1mg/L time [d] 10
SMX
4-NO2-SMX
Nitrite time [d] 10 b) 10
8
6
4
2 d)
100
0
10
8
6
4
2
100
0
Figure 3: Evolution of DCF, Nitro-DCF (NO2-DCF), and nitrite in the biotic series of “Experiment 1” (plot “a)”) and “Experiment 2” (plot “b”).
Evolution of SMX, 4-Nitro-SMX (4-NO2-SMX), and nitrite in the biotic series of “Experiment 1” (plot “c)”) and “Experiment 2” (plot “d”).
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• Based on column experiments
• Artificial recharge facility
• Organic matter layer: 60 cm of compost + natural soil (40 % – 60%)
• Plus some iron hydroxide
• The test has just started…
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 Soil wetting and feeding
 Biofilm development
 Biofilm
Dessication /scrubbing
 Soil rewetting
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Sensor and experimental set up
Tank to couple hydrology and biology
Coarse and sandy soil collected from the pound in 3 locations
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Abiotic measurments
 Soil moisture, EC and temperature  Water flow
 Water suction
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Biotic measurments
 Microlysimeter, collection of liquid samples
 Dissolved oxygen, conductivity, pH/ORP nitrate, chloride and temperature
 Eventually planar octopodes to measure oxygen
Imaging surface
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INFILTRATION /FEEDING
P
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BIOFILM FORMATION
P
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BIOFILM CLOGGING
P
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DESSICATION/SCRUB
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REWETTING
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• Very similar to CSI
• With little (to no) information, reconstruct as best as possible the undersampled formation
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• Tool development, to be started soon
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Realization 1 Realización 2 Realización 3
Original figure. Selection of 10 random samples
Realización 50
Realización 100
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Realización 1 Realización 2 Realización 3
Realización 50
Realización 100
Classsical Kernel Regression Orden 2
CKR2 (Iteración 0)
Figura original
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Realización 1 Realización 2 Realización 3
Realización 50
Realización 100
Steering Kernel Regression Orden 2
SKR2 (Iteración 1)
Figura original
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Realización 1 Realización 2 Realización 3
Realización 50
Realización 100
Steering Kernel Regression Orden 2
SKR2 (Iteración 2)
Figura original
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Concentric formations
0,30
0,25
0,20
0,15
0,10
0,05
0,00
Iter 0 Iter 1 Iter 2
NNC
Kernel
Optimizado
Iter 3
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ARTIFICIAL RECHARGE ACTIVITIES
Infiltrómetro de “Doble
Anillo”
En zanjas En superficie
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Sitio de estudio en Sant Vicenç dels Horts:
Ensayos puntuales para la medición del capacidad de infiltración de la superficie de la balsa
II. Interpretación
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Sitio de estudio en Sant Vicenç dels Horts:
Ensayos puntuales para la medición del capacidad de infiltración de la superficie de la balsa
III. Resultados
Punto
S1
S2
S3
S4
S5
S6
Infiltración
(m/día)
Enero 09
0.2
2.6
2.9
3.3
12.9
12.6
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Sitio de estudio en Sant Vicenç dels Horts:
Mapa de variabilidad espacial de los parámetros físicos y hidráulicos en la superficie de la balsa de infiltración (SIP)
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Sitio de estudio en Sant Vicenç dels Horts:
Resultados de un ensayo de inundación
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Sitio de estudio en Sant Vicenç dels Horts:
Estado de la balsa antes del ensayo de infiltración
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Sitio de estudio en Sant Vicenç dels Horts:
Estado de la balsa durante el ensayo
Colmatación por error humano
(«human failure»)
Error de cálculo, diseño, aleatoriedad de estabilidad de las estructuras, eventos extremos, vandalismo, …
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Sitio de estudio en Sant Vicenç dels Horts:
Estado de la balsa después del ensayo de infiltración
Colmatación por efectos naturales
Crecimiento de algae, trapping de coloides, sedimentación de material fino en suspencion, precipitacíon de minerales , …
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LOCAL INFILTRATION VARIATIONS
Punto
S1
S2
S3
S4
S5
S6
S7
S8
S9
Infiltración (m/día)
Junio 09
0.18
2.1
2.5
1.1
1.2
6.3
0.17
3.04
0.75
Diferencia con el valor anterior (antes del ensayo)
- 6 %
- 20 %
- 14 %
- 66 %
- 91 %
- 50 %
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EFFECTIVE PARAMETERS
Model:
I = I
_0 exp (- λ
t) + (I_R-I_0) e
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Sitio de estudio en Sant Vicenç dels Horts:
Oscilaciones de la temperatura y su relación con el gradiente hidráulico
0,4
0,3
0,2
0,1
0
-0,1
-0,2
-0,3
-0,4
-0,5
8-3 10-3 12-3 14-3 16-3 18-3 20-3
Fecha de medición
22-3 24-3 26-3 dh/dl (m/dia)
T ºC
28-3
20
22
30-3
24
10
12
14
6
8
16
18
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1) Identifying contaminant source releases & environmentally sensitive targets.
2) Data acquisition used to infer modeling parameters!
Site characaterization.
3) Final task: Estimate human health risk toward decision making! Should a site be remediated or not? Is the exposed population at risk?
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CC
11
CC
12

SF
OR
CC ij
AND

CC nm
System Failure
Critical
Concentrations
Sources-Receptors
CS i
AND

PR j OR
PW ijp
AND
FAT ijp
AND
 Pathways-Processes
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
CS i
PR j
AND
WELL
1


WELL k
OR
AND
BP ijk
FAT ijk

AND
WELL nw

BP ijk
FAT ijk OR
OBS k
SA ijk
Sources-
Receptors
Observation wells
Pathways-Processes
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Computation of probabilities for a monitoring system of two wells:
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Evolution of Risk with time T: The most sensitive failure mode is the occurrence of simultaneous small sampling frequency
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NAPLs: Non-Aqueous Phase Liquids
Fluids capable to stay in the subsurface in a different (non-aqueous) phase thanks to its low solubility
LNAPLs (gasoline and other Hydrocarbons)
 density below water density
DNAPLs (Chlorinated solvents)
 density higher than water
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C
RISK AFTER REMEDIATION
END-POINT
Time
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PROBLEM STATEMENT
EVALUATE THE RISK IS DIFFICULT DUE TO:
MANY PATHS, PROCESSES, RECEPTORS, SOURCES,
SAMPLING, OBSERVATION
Vapor flux
Dissolved plume
PATH 4
PATH 3
PATH 2
PATH 1
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Failure due to Sampling Frequency
P[NA

FS | NAPL]
   t freq
]
C
OBS RECEPTO
R
 time
SOURCE ZONE c in
OBS c m
RECEPTOR
DNA
PL time
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C
Failure due to Bypassing
P[NA

BP | NAPL]
RECEPTO
R
OBS
SOURCE ZONE c in
OBS time c m
RECEPTOR
DNA
PL time
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Fate and transport
•
We need a transport model or a set of transport models to generate a large number of replicates of the system based on some uncertain parameters c m

F ( x , t ;

)
 

 architectu re
,
 bio
,
 heterogene ity

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CONTAMINATE
D SITE
( x , y
0 0
)
L
Model Parameters  c in f
  
N
, M , F
0 bio
 time v
 velocity
RECEPTOR
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Mass Depletion with Time c (t) in c
0 in

1 F bio




M(t)
M
0




Mass depletion exponent
M(t)

 
  
N t M
1

0
M exp
0
 
N t

   
 
 
1
1

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Review of literature
Canadian Forces Base Borden Site, Ontario
Hill Air Force Base
Dover National Test Site
NASA Lunch Complex 34
Air Force Plant 4
Prior Knowledge
Sages Dry Cleaners
Tucson International Airport
Paducah Gaseous Diffusion Plant
Camp Lageune
Former Recycling Facility
Savannah River Site
Pinellas Site
0.92
0.62
5.80
0.31
0.61
0.15
1.64
1.19
0.35
0.72
1.03
2.36
1.29
0.64
1.00
0.24
0.63
0.80
1.74
Beta Remediation Method
0.32
natural gradient water flush in situ chemical oxidation natural gradient water flush surfactant enhanced aquifer remediation cosolvent surfactant enhanced aquifer remediation cyclodextrin flushing
Ethanol flush n-Propanol flush surfactant enhanced aquifer remediation in situ chemical oxidation emulsified zero-valent iron
Six Phase heating cosolvent pump-and-treat
Six Phase heating surfactant enhanced aquifer remediation in situ chemical oxidation in situ chemical oxidation rotary steam stripping c (t) in
0 c in
 


M(t)
M
0




Contaminant
TCM, TCE, PCE
PCE
TCE, 1,1-DCE
TCE, PCBs, VOCs
PCE
PCE,TCE,cis-DCE

f
Integration of data in real time
Measurements are incorporated into PRA using Bayes f
 c m
)

 f c m
 f
 f c m
 f
 
PRIOR
KNOWLEDGE f
 c m
) POSTERIOR
KNOWLEDGE


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Algorithm
•
Choose prior knowledge f
•
Update pdf with Bayes f
•
Generate many replicates of the system based on f
 c m
) f
 c m
)
•
Compute probability of failure
P [ SF ]

P [ NA

P [ NAPL ]
P [ FO ]
 j
P [ FO ]
 j
FS | NAPL ]

P[NA
P[NA
|

FS |
P [ NA
NAPL, FO
 j
]
NAPL,

BP
FO j
]
| NAPL ]

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Example of application
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Observations
OBS
RECEPTOR
SAMPLING
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Prior realizations Posterior realizations
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TO BE DECIDED
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