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WP 2. HYDROL
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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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.
TASKS
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The boundary conditions…
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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Fate of micropollutants: batch experiments (UPC + IDAEA)
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0
2.5
5
7.5
10
0 1 10 100time [d]
C [
mM
]
0
50
100
0 1 10 100Time [d]
C/C
o [
%]
0
50
100
0 1 10 100Time [d]
C/C
o [
%]
NO3 NO2 Alk
DOC
DCF
SMX
SMX
DCF
a)
b)
c)
0.1
LDet
Figure 1: results for “Experiment 1” (individual pollutant at initial concentration of 1microg/L ).
a) chemical evolution with time in the biotic NO3-reducing experiment;
b) evolution with time of the average normalized concentration (with respect to the initial value C0) 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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0
20
40
60
80
0 1 10 100time [d]
C [
mM
]
0
25
50
75
100
125
0 1 10 100Time [d]
C/C
o [
%]
0
25
50
75
100
125
0 1 10 100Time [d]
C/C
o [
%]
NO3
Alk
DCF
SMX
SMX
DCF
DOC
APP
APP
NO2
a)
b)
c)
Figure 2: results for “Experiment 2” (individual pollutant at initial concentration of 1mg/L ).
a) chemical evolution with time in the biotic NO3-reducing experiment;
b) evolution with time of the average normalized concentration (with respect to the initial value C0) of Acetaminophen (APP), DCF and SMX in the biotic test. “
c) idem in the abiotic test.
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0.0
0.2
0.4
0.6
0.8
1.0
1.2
1 10 100time [d]
DC
F, N
O2
-DC
F
[mic
rog
/L]
0
1
2
3
4
5
NO
2 [
mm
ol/L
]
DCF
NO2-DCF
Nitrite
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1 10 100time [d]
SM
X, 4
-NO
2-S
MX
[mic
rog
/L]
0
1
2
3
4
5
NO
2 [
mm
ol/L
]
SMX
4-NO2-SMX
Nitrite
0
200
400
600
800
1000
1 10 100time [d]
DC
F, N
O2
-DC
F
[mic
rog
/L]
0
2
4
6
8
10
NO
2 [
mm
ol/L
]
DCFNO2-DCFNitrite
0
200
400
600
800
1000
1200
1 10 100time [d]
SM
X, 4
-NO
2-S
MX
[mic
rog
/L]
0
2
4
6
8
10
NO
2 [m
mo
l/L
]
SMX
4-NO2-SMX
Nitrite
a)
c)
b)
d)
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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Fate of micropollutants: real site (UPC + IDAEA)
• 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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Exchange processes: coupling cation exchange with sorption
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Biofilm transient impact upon recharge/ clogging (UPC + ICRA)
Soil wetting and feeding
Biofilm Dessication /scrubbing
Biofilm development
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 suction
Water flow
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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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Processes: facies delineation/reconstruction
• Very similar to CSI
• With little (to no) information, reconstruct as best as possible the undersampled formation
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Modelling efforts on reactive transport (UPC+ UPM)
• Tool development, to be started soon
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Original figure. Selection of 10 random samples
Realization 1 Realización 2 Realización 3
Realización 50 Realización 100
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Classsical Kernel Regression Orden 2
CKR2 (Iteración 0)
Figura original
Realización 1 Realización 2 Realización 3
Realización 50 Realización 100
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SKR2 (Iteración 1)
Figura original
Realización 1 Realización 2 Realización 3
Realización 50 Realización 100
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SKR2 (Iteración 2)
Figura original
Realización 1 Realización 2 Realización 3
Realización 50 Realización 100
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Concentric formations
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ARTIFICIAL RECHARGE ACTIVITIES
En zanjas En superficie
Infiltrómetro de “Doble Anillo”
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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 Infiltración (m/día)
Enero 09
S1 0.2
S2 2.6
S3 2.9
S4 3.3
S5 12.9
S6 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 naturalesCrecimiento de algae, trapping de coloides, sedimentación de material fino en suspencion, precipitacíon de minerales , …
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LOCAL INFILTRATION VARIATIONS
Punto Infiltración (m/día) Junio 09
Diferencia con el valor anterior (antes
del ensayo)
S1 0.18 - 6 %
S2 2.1 - 20 %
S3 2.5 - 14 %
S4 1.1 - 66 %
S5 1.2 - 91 %
S66.3 - 50 %
S7 0.17
S8 3.04
S9 0.75
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EFFECTIVE PARAMETERS
Model:
I = I_0 exp (- λe t) + (I_R-I_0)
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Sitio de estudio en Sant Vicenç dels Horts:
Oscilaciones de la temperatura y su relación con el gradiente hidráulico
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Risk Assessment: Overview and Challenges
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Illustration of the Process
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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OR
AND
System Failure
Critical Concentrations
Sources-Receptors
Pathways-Processes
CC11 CC12 CCij CCnm
CSi PRj
SF
OR
AND
PWijp FATijp
AND
AND
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CSi PRj AND
OR
SAijk
AND
OBSk
BPijk
WELL1 WELLk WELLnw
BPijk FATijk
AND
FATijk OR
Sources-Receptors
Pathways-Processes
Observation wells
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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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APPLICATIONS?so far NAPLs?
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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Failure of Remediation
Time
END-POINT
C
RISK AFTER REMEDIATION
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Vapor flux
Dissolved plume
PROBLEM STATEMENT
EVALUATE THE RISK IS DIFFICULT DUE TO:
MANY PATHS, PROCESSES, RECEPTORS, SOURCES,
SAMPLING, OBSERVATION
PATH 1
PATH 2
PATH 3
PATH 4
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Failure due to Sampling Frequency
SOURCE ZONE
DNAPL
inc
time
RECEPTOR
mcOBS
C
time
OBS RECEPTOR
freqP[NA FS | NAPL] P[ t ]
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Failure due to Bypassing
SOURCE ZONE
DNAPL
inc
time
RECEPTOR
mcOBS
C
time
OBS
RECEPTOR
P[NA BP | NAPL]
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Fate and transport
);,( tFcm x
• 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
ityheterogenebiorearchitectu ,,
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Model Parameters
RECEPTOR
inc
time
0 0( , )x y
L
CONTAMINATED SITE
OBSERVATION
S
N 0 biof , ,M ,F
v velocity
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Mass Depletion with Time
inbio0
in 0
c (t) M(t)1 F
c M
1/ 11N 0
0 N
1 t M 1M(t)
M exp t 1
Mass depletion exponent
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Review of literature
Beta Remediation Method Contaminant
Canadian Forces Base Borden Site, Ontario
0.32 natural gradient water flush TCM, TCE, PCE
in situ chemical oxidation
0.24 natural gradient water flush
0.63 surfactant enhanced aquifer remediation
Hill Air Force Base
0.80 cosolvent
1.74 surfactant enhanced aquifer remediation
0.35 cyclodextrin flushing
Dover National Test Site
0.72 Ethanol flush
1.03 n-Propanol flush
2.36 surfactant enhanced aquifer remediation
NASA Lunch Complex 34
1.29 in situ chemical oxidation
0.64 emulsified zero-valent iron
Air Force Plant 4
1.00 Six Phase heating
0.92
Sages Dry Cleaners 0.62 cosolvent PCE
Tucson International Airport 5.80 pump-and-treat TCE, 1,1-DCE
Paducah Gaseous Diffusion Plant 0.31 Six Phase heating TCE, PCBs, VOCs
Camp Lageune 0.61 surfactant enhanced aquifer remediation PCE
Former Recycling Facility 0.15 in situ chemical oxidation PCE,TCE,cis-DCE
Savannah River Site 1.64 in situ chemical oxidation
Pinellas Site 1.19 rotary steam stripping TCE, methylene chloride, DCE, VC
in0in 0
c (t) M(t)
c M
Prior Knowledge
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Integration of data in real time
( | ) ( )( | )
( | ) ( )m
m
m
f c ff c
f c f d
Measurements are incorporated into PRA using Bayes
PRIOR KNOWLEDGE
POSTERIOR KNOWLEDGE
( )f ( | )mf c
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Algorithm
• Choose prior knowledge
• Update pdf with Bayes
• Generate many replicates of the system based on
• Compute probability of failure
j
j
FOP
FOP
NAPLBPNAPNAPLFSNAPNAPLP
SFP
]FONAPL,|FSP[NA][
]FONAPL,|P[NA][
]|[]|[][
][
j
j
( )f
( | )mf c( )f
( | )mf c
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Example of application
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SAMPLING
RECEPTOR
OBS
Observations
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Prior realizations Posterior realizations
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Evolution of Risk with time
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MORE Applications
TO BE DECIDED