WP 2. HYDROL

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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

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0 1 10 100time [d]

C [

mM

]

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0 1 10 100Time [d]

C/C

o [

%]

0

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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

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4

5

NO

2 [

mm

ol/L

]

DCF

NO2-DCF

Nitrite

0.0

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1 10 100time [d]

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X, 4

-NO

2-S

MX

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rog

/L]

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2 [

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ol/L

]

SMX

4-NO2-SMX

Nitrite

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F, N

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ol/L

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DCFNO2-DCFNitrite

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X, 4

-NO

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MX

[mic

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/L]

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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


Logo grupoSteering Kernel Regression Orden 2

SKR2 (Iteración 1)

Figura original



Logo grupoSteering Kernel Regression Orden 2

SKR2 (Iteración 2)

Figura original



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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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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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Mapa de variabilidad espacial de los parámetros físicos y hidráulicos en la superficie de la balsa de infiltración (SIP)

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Resultados de un ensayo de inundación

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Estado de la balsa antes del ensayo de infiltración

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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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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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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


0.35 cyclodextrin flushing

Dover National Test Site

0.72 Ethanol flush

1.03 n-Propanol flush


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

WP 2. HYDROL

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