1. No category

4/22/2015
Nonpoint Source Pollution Assessment:
Framework, Vulnerability Analysis, and Modeling
Thomas Harter
Outline
• Driver: regulatory framework
• Understanding nonpoint source transport dynamics (v. point sources)
• Vulnerability assessment
•
•
•
Dubrovsky et al., USGS, 2010
Dubrovsky et al., USGS, 2010
Nitrate: Impacted regions within the Central Valley
Scoring methods
Statistical methods
Modeling
All Water Systems
red dots: wells above MCL for nitrate
CVSALTS, Tasks 7 and 8 – Salt and Nitrate Analysis for the Central Valley Floor
Final Report, December 2013
Figure 7-14
Estimated locations of the area’s roughly 400 regulated community public and state-documented state small water
systems and of 74,000 unregulated self-supplied water systems. Source: Honeycutt et al. 2012; CDPH PICME 2010.
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4/22/2015
Regulating Water Pollution Sources
Regulating Water Pollution Sources
Point Sources of Pollution
Point Sources of Pollution
1970s ‐ now
Clean Water Act: NPDES Permits
Surface Water Quality
1970s ‐ now
Clean Water Act: NPDES Permits
Ground Water Quality
Surface Water Quality
Ground Water Quality
2000s ‐ now
Clean Water Act: TMDL
Nonpoint Sources of Pollution
Nonpoint Sources of Pollution
RCRA Groundwater Monitoring
Regulating Water Pollution Sources
Point Sources of Pollution
•
Affected parties:
o
TSDFs (transport, storage, and disposal facilities)
o
MSWFs (municipal solid waste landfills)
•
1970s ‐ now
Clean Water Act: NPDES Permits
Surface Water Quality
1980s ‐ now
Superfund, TSCA, RCRA, FIFRA
Ground Water Quality
2000s ‐ now
Clean Water Act: TMDL
Nonpoint Sources of Pollution
Regulatory Approaches to
Groundwater Protection and Monitoring
Modified from: EOS, Transactions, AGU 2001
•
•
Detection monitoring
o
1 or more monitoring wells upgradient
o
3 or more monitoring wells downgradient
o
Objective: SSI (statistically significant increase)?
Compliance monitoring / Assessment monitoring
o
•
Permitted facilities vs. Interim facilities (existed prior to RCRA rules)
Objective: groundwater protection standards exceeded?
Corrective Action
o
Treatment
o
Cleanup
o
Cease and desist
o
….
http://www.epa.gov/osw/hazard/tsd/td/ldu/financial/gdwater.htm
http://www.epa.gov/solidwaste/nonhaz/municipal/landfill/financial/gdwmswl.htm
Regulatory Approaches to
Groundwater Monitoring
from: Parker, Beth L., Cherry, John A. & Swanson,
Benjamin J., 2006. A Multilevel System for HighResolution Monitoring in Rotasonic Boreholes.
Ground Water Monitoring & Remediation 26 (4),
57-73.
doi: 10.1111/j.1745-6592.2006.00107
from: http://www.ems-i.com
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4/22/2015
Regulating Water Pollution Sources
Monitoring w/ Monitoring Wells
Point Sources of Pollution
1970s ‐ now
Clean Water Act: NPDES Permits
1980s ‐ now
Superfund, TSCA, RCRA, FIFRA
Surface Water Quality
Ground Water Quality
2000s ‐ now
Clean Water Act: TMDL
???
Nonpoint Sources of Pollution
Farm Contaminant Sources: Regional Scale
•
Modified from: EOS, Transactions, AGU 2001
Farm Contaminant Sources: Farm Scale
•
Source of N (2007) in CA:
o
o
o
o
o
Dairy Farm Contaminant Sources: Management Units
Sources of N:
o
Fertilizer use (varies with farm / farming practices) 740,000 tons
Animal Manure 240,000 tons
Septic leach fields 27,000 tons
Irrigation water source & mgmt.
Treated municipal effluent 31,000 tons
o
o
o
o
o
o
Feedlot
Lagoon
Storage areas
Manured fields
Fertilized fields
Various crops
Septic system
Farm Sources: Field Scale
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4/22/2015
Why is Nonpoint Source Pollution Different from Point Source Pollution of Groundwater?
•
Scale
o
•
CHALLENGE 1: there is ALWAYS recharge in agriculture, EVERYWHERE
Non-recharging source, incidental release
Millions of acres vs. 1‐10 acres
Intensity
o
Within ~1 order magnitude above MCL vs. many orders of magnitude r=0
Target:
UST
q
above MCL
•
o
•
Recharge vs. non‐leaky
r

Frequency
o
•
Recharging source: planned/frequent release
Hydrologic Function
Target:
Agriculture/
Land Application
Ongoing/seasonally repeated vs. incidental
Heterogeneity & Adjacency
q
Source Area of a Monitoring Well
Source Area of a Monitoring Well
in a Recharge Area
r

d
Slope of
the water
table: i
q
s
Monitored source length, s = d * q/r
Horizontal flow: q = K * i
(Darcy’s law)
Vertical flow: r (recharge)
Aquifer hydraulic conductivity: K
source area
Source Area of a Monitoring Well
in a Recharge Area
Horizontal flow: q = K * i
(Darcy’s law)
Monitoring Design for Varying Water Table Depth
gw flow
Vertical flow: r (recharge)
(a) water level high
source area
Monitored source length, s = d * q/r
•
•
•
•
Recharge rate, r: 1 ft/yr = 0.003 ft/d
Horizontal gradient, i: 0.3% = 0.003
Length of screen below water table, d: 20 ft
K (ft/d) ‐
1
10
20
50
100
500
q (ft/d) ‐
0.003
0.03
0.06
0.15
0.3
1.5
(b) water level intermediate
s (ft)
source area
20
200
400
1,000
2,000
10,000
(c) water level deep
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4/22/2015
CHALLENGE: There is NO floating product – redefine “First Encountered Groundwater”
MW Well Design: Varying Water Table in Heterogeneous Aquifer
monitored source area
monitored source area (a few feet long)
regional
gw flow
regional
gw flow
(a) Screen (length ~ 20’) located at water table, but not intersecting sand layer
(a) Screen (length ~ 20’) located at water table, but not intersecting sand layer
monitored source area (several hundred feet long)
monitored source area
sand
sand
sand
sand
regional
gw flow
regional
gw flow
loamy clay
sand
(b) Screen (length ~ 20’) located in sand layer
regional
gw flow
regional
gw flow
loamy clay
sand
(b) Screen (length ~ 20’) located in sand layer
CHALLENGE: There is NO PLUME to chase – nitrate, nitrate everywhere, all the time!......
• Regional variations in landuse / hydrogeology
• Farm‐to‐farm differences in management
• 1 Farm = Many Sources (Management Units)
• Within‐source/field variability (soil, irrigation)
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4/22/2015
Farm Groundwater Monitoring:
Tile Drain Monitoring
V18
V25
V4
Average N of monitoring wells on dairies withAverage
tile drains
N of two tile drains
V24 100
100
V21
V2
V5
V20
V14
Source Area of a Barn / Irrigation Well
90
Mean
Mean
V8
90
V17
V7
V16
80
80 V10
V1
V15
total N [mg/l]
V6
W23
W19
W18
W17
W22
Wells
V22
70V19
W9
60W3 W15
50
W14
W11
total N [mg/l]
V13
W8
W10
W1
W4 W2
W16 40
70
V9
W31
60
Drains
W29
W5
50
W28
W30
W6
40
W12
30
20
20
W27
Nov-1995
Feb-1996
May-1996
Jul-1996
Sep-1996
Jan-1997
Apr-1997
Jul-1997
Oct-1997
Jan-1998
Feb-1998
Nov-1995
May-1998
Feb-1996
Jun-1998
May-1996
Aug-1998
Jul-1996
Nov-1998
Sep-1996
Feb-1999
Jan-1997
Apr-1999
Apr-1997
Jun-1999
Jul-1997
Sep-1999
Oct-1997
Dec-1999
Jan-1998
Apr-2000
Feb-1998
Jul-2000
May-1998
Oct-2000
Jun-1998
Jan-2001
Aug-1998
Apr-2001
Nov-1998
Jul-2001
Feb-1999
Oct-2001
Apr-1999
Jan-2002
Jun-1999
Apr-2002
Sep-1999
Jul-2002
Dec-1999
Sep-2002
Apr-2000
Jan-2003
Jul-2000
Apr-2003
Oct-2000
Jan-2001
Apr-2001
Jul-2001
Oct-2001
Jan-2002
Apr-2002
Jul-2002
Sep-2002
Jan-2003
Apr-2003
W7
30
sampling date
sampling date
Domestic Well Monitoring
Production Well Monitoring
source area
source area
recharge
domestic well
regional gradient
effective gw flow direction
Source Area of a Barn / Irrigation Well
recharge
barn well /
irrigation well
regional gradient
effective gw flow direction
Source Area of a Barn / Irrigation Well
Crosssection
2 miles
x
200 ft
•
•
•
Planview
Water flow is horizontal & vertical
Horizontal travel distances are generally MUCH longer than travel vertical distances
Different depths of the well screen capture different water!
•
•
•
Water flow is horizontal & vertical
Horizontal travel distances are generally MUCH longer than travel vertical distances
Different depths of the well screen capture different water!
2 miles
x
2 miles
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4/22/2015
Source Area of a Barn / Irrigation Well
Source Area of a Barn / Irrigation Well
•
•
•
Water flow is horizontal & vertical
Horizontal travel distances are generally MUCH longer than travel vertical distances
Different depths of the well screen capture different water!
Deeper Groundwater
= Older Groundwater
Future Impacts Likely Increase:
Delay of Impact due to GW Age
Groundwater Age [Years]
at 30 m (top), 100 m (bottom)
Age at 100 ft
(30 m) depth
Age at 300 ft
(100 m) depth
Recharge Rate (color map) &
Reverse Particle Paths from 100m wellpoints
Harter et al., 2009
Measured Groundwater Age in Multilevel Groundwater Wells
Regulating Water Pollution Sources
Tritium/Helium‐3 Groundwater Age (2‐20 yrs)
The Dairy
 DTW: 80-120 ft bgs
 Wells: multi-level
Point Sources of Pollution
1970s ‐ now
Clean Water Act: NPDES Permits
6
6B: <2 yr
Surface Water Quality
5
4
4: 4-20 yr
N
Observations
 Young groundwater present
 Age increases with depth in
both multi-level wells & across
the site
 No significant saturated-zone
denitrification in monitor wells
7
2B: 5 yr
1B: 2 yr
3 3B: 20 yr
1980s ‐ now
Superfund, TSCA, RCRA, FIFRA
Ground Water Quality
2000s ‐ now
Clean Water Act: TMDL
2
1
one-half mile
Courtesy, Brad Esser & Jean Moran, LLNL, 2009
Nonpoint Sources of Pollution
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4/22/2015
Focus: Enforcement Monitoring
Example of Working with a Regulation: Speed Limit
Responsible Party:
Driver
Feedback:
Speedometer
Management Tool:
Brakes
Focus: Enforcement Monitoring
Focus: Enforcement Monitoring
Applying Point Source Approach to Nonpoint Source:
Responsible Party:
Landowner
Enforcement:
Radar Controls
Alternative Monitoring Approach to Nonpoint Source:
Responsible Party:
Landowner
Feedback:
missing
Management Tool:
$$$ “agronomic”
Enforcement:
Monitoring Wells
Regulating Water Pollution Sources
Enforcement:
Annual Nitrogen Budget
+
Management Practice Assessment
+
Management Tool:
Regional Trend Monitoring
Water and Nutrient Management
Feedback:
Nutrient/Water Monitoring
& Assessment
Source Area of a Barn / Irrigation Well
Point Sources of Pollution
1970s ‐ now
Clean Water Act: NPDES Permits
Surface Water Quality
2000s ‐ now
Clean Water Act: TMDL
1980s ‐ now
Superfund, TSCA, RCRA, FIFRA
Ground Water Quality
1980s – now
CA pesticide contamination
prevention act 2010s ‐ future
CA Porter‐Cologne:
Dairy Order
ILRP/Ag Orders
CV‐SALTS
Nonpoint Sources of Pollution
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4/22/2015
Assessment: Field Trials & Modeling Transport and Fate of Nitrate and Salts
=> improved management practices
Average shallow groundwater nitrate-N [mg/l]
lbs N per acre
1401200
Alternative Monitoring Approach to Nonpoint Source:
Groundwater Assessment / Vulnerability Analysis => prioritize planning/enforcement
Measured
106 mg/l NO3-N
fertilizer N (commercial)
120
Modeled
org-N (manure)
1000
Responsible Party:
Landowner
NH4-N (manure)
100
N surplus
Plant N uptake
800
80
43 mg/l
60600
40
Focus: Enforcement Monitoring
Enforcement:
Annual Nitrogen Budget
+
Management Practice Assessment
+
Management Tool:
Regional Trend Monitoring
Water and Nutrient Management
Feedback:
Nutrient/Water Monitoring
& Assessment
10 mg/l
400
20
200
5/1/00
1999
2000
10/31/00
5/2/99
1998
11/1/99
5/2/98
1997
10/31/98
5/1/97
1996
10/31/97
5/1/96
1995
10/30/96
5/1/95
1994
10/31/95
5/1/94
1993
10/31/94
5/1/93
0
10/30/93
0
Crop YearDate
Sampling
VanderSchans et al., Ground Water, 2009
for further publications: http://groundwater.ucdavis.edu/gw_201.htm
Vulnerability Analysis: Overview
Vulnerability Analysis: Some Modeling Examples
Another Vulnerability Scheme: Nitrate Hazard Index
Nitrate Contamination Study Area Based on:
Soil
Crop
Irrigation
Dzurella, Pettygrove et al.,
Journal Soil Water Conservvation, 2015
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4/22/2015
Explaining the Mass Balance Approach to Estimate N Leaching to Groundwater
Explaining the Mass Balance Approach to Estimate N Leaching to Groundwater
After setting “storage change in N” to zero and rearranging the mass balance equation, we obtain the following formula to estimate N leaching to groundwater:
Mass balance requires that:
Synthetic fertilizer N
+
Wastewater effluent N
+
Biosolids N
+
Dairy manure N
+
Atmospheric deposition N
+
Irrigation water N
+
=
Atmospheric losses N
+
Harvested N
+
Surface runoff N
+
Leaching N to groundwater
+
Storage Change in N in root zone
=
-
Scale:
50 m
+
Atmospheric losses N
+
Harvested N
+
Surface runoff N
50 m (1 acre) scale
● 0.9
+
+
+
Leaching N
to
groundwater
Synthetic fertilizer N
+
Wastewater effluent N
+
Biosolids N
+
Dairy manure N
+
Atmospheric deposition N
+
Irrigation water N
+
=
study area scale
tons N/yr
440,000
Cropland Area
Cropland Area
(without Alfalfa)
Total Nitrogen Inputs:
Previous Slide: spatial resolution ‐
50 m x 50 m (~1 acre)
420,000 tons N/yr
Below: spatial resolution ‐ study area total
Irrigation water
Atmosphere
4M ac
Synthetic
Fertilizer
330,000
3M ac
220,000
2M ac
110,000
1M ac
Scale:
Study Area
Atmosphere
Runoff
Leaching to Groundwater
Biosolids
Effluent
Poultry, Swine 1940 1950
1960 1970 1980 1990
2000 2010
Dairy Manure
Harvest
Total Nitrogen Outputs:
420,000 tons N/yr18
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4/22/2015
Role of thick unsaturated soils/sediments in nonpoint source transport & travel time
Alluvial Fan Stratigraphy
Modeling Approach
Results (Velocity)
• Model 2 “Heterogeneous ‐
SF”
– Given the information for the spatial correlation structure of the scaling factors, a value of scaling factor at each grid is generated
Homogeneous
Hetero - SF
Hetero - VG
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4/22/2015
Nitrate in a 16 m thick alluvial unsaturated zone
Fresno, California
Results (Conc.)
Homogeneous
Hetero - VG
Hetero - SF
Vadose Zone Residence Time of Nitrate
Results (High subplot in kg N)
Homogeneous
Hetero - SF
Hetero - VG
Input N
2400
2400
2400
Root Uptake
835
838
827
Leaching to GW
956
839
916
Stored in RZ
93
95
89
Stored in Deep VZ
515
565
543
•
•
•
Depth to the water table
+
Cropland water budgets (deep percolation)
+
Soil type
d
Measured N = 87 Kg/ha
Predicted N (MB) = 478 kg/ha
Vadose Zone Travel Time
Vadose Zone Travel Time
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4/22/2015
Nonpoint Source Groundwater Modeling:
Key Challenge (why MODPATH is not enough)
High Resolution Flow Field
spatially distributed sinks: wells
spatio‐temporally distributed sources:
loading to water table
adaptive mesh grid refinement
Kourakos et al., Water Resour. Res, 2012
Kourakos and Harter, Env. Simulation, 2014
Kourakos and Harter, Comp. Geosciences, 2014
Adaptive Mesh Refinement
Finite Element Grid
Kourakos et al., Water Resour. Res, 2012
Kourakos and Harter, Env. Simulation, 2014
Kourakos and Harter, Comp. Geosciences, 2014
Water Table Distribution
Kourakos et al., Water Resour. Res, 2012
Kourakos and Harter, Env. Simulation, 2014
Kourakos and Harter, Comp. Geosciences, 2014
Kourakos and Harter, 2012, 2014, 2014
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4/22/2015
Streamlines: Coarse vs. Fine Discretization
FINE
“NPS Assessment Tool”
COARSE
Kourakos et al., WRR, 2012
“mSim” NPS Assessment Tool
Unit Response Functions: Examples
Adaptive Mesh Refinement (DEAL.II)
TRILINOS
Matlab code available at:
http://groundwater.ucdavis.edu/mSim
(to be updated soon with adaptive mesh refinement code)
Kourakos et al., Water Resour. Res, 2012
Kourakos and Harter, Env. Simulation, 2014
Kourakos and Harter, Comp. Geosciences, 2014
Source Loading + Transfer Function to Wells =
Simulated Nitrate Concentration History
Validation
spatially distributed sinks: wells
spatio‐temporally distributed sources:
loading to water table
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4/22/2015
Deep Percolation of Salinity
Predictions Using
Groundwater Nitrate Loading
Exceedance Probability,
Nitrate above 45 mg/L (MCL)
Eastern Tulare Lake Basin
A’San Joanquin river
Alluvial Fan Boundary
A
N
Hydrogeologic cross section of study area [Belitz and Phillips,1995]
Location of study area.
Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006
Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006
3
1
5
2
1—upper fan
2—inter-fan
3—distal fan II
4—distal fan I
5—Sierran Sand
4
Multi-unit division of the study area.
Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006
Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006
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4/22/2015
Upper fan realization.
1.5
cumulative distribution curve
1.5
1
1
0.5
0.5
0
averaged breakthrough
curve of wells in upfan
mean+1.96stdev
-0.5
0
mean-1.96stdev
-0.5
-1
0
500
1000
1500
averaged breakthrough curve of wells in
interfan
mean+1.96stdev
2000
mean-1.96stdev
-1
time
0
1.5
1.5
1
1
500
1000
1500
2000
Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006
arrival time of first 10% mass
Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006
500
400
upfan
300
inter-fan
distal fan I
200
distal fan II
Sierra Sand
100
0
0
50
100
150
200
250
depth of top screen
0.5
averaged breakthrough curve of
wells in distal fan I
mean+1.96stdev
0
mean-1.96stdev
mean-1.96stdev
-0.5
-0.5
0
500
1000
1500
2000
0
500
1000
1500
2000
1.5
1
0.5
averaged breakthrough curve of
wells in Sierra Sand
mean+1.96stdev
0
arrival time of first 10% mass
0.5
averaged breakthrough curve of
wells in distal fan I
mean+1.96stdev
0
500
400
upfan
300
inter-fan
distal fan I
200
distal fan II
Sierra Sand
100
0
0
mean-1.96stdev
30
60
90
120
length of well screen
-0.5
0
500
1000
1500
2000
2500
Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006
Breakthrough curves of salinity: probability of arrival
Microbial nonpoint source pollution
Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006
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4/22/2015
Fallow Field During Pre‐Irrigation
with Manure
Downgradient monitoring well
Groundwater
flow direction
Upgradient monitoring well
Fallow Field During Pre‐Irrigation
with Manure
Conceptual Model
Transport Equation
Downgradient monitoring well
Dispersion
Advection
(Flow)
Groundwater
flow direction
Filtration
Upgradient monitoring well
Modeling Source Loading
Modeling Source Loading
HETEROGENOUS:
POISSON
(High Intensity)
HOMOGENEOUS
HETEROGENOUS:
POISSON
(Intermediate
Intensity)
HETEROGENOUS:
GAUSSIAN
HETEROGENOUS:
POISSON
(Low Intensity)
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4/22/2015
Principle of Poisson Loading
• Defined by 4 simple Poisson distributed parameters
• High, intermediate and low density considered (10 realizations each).
• Poisson loading consistent with zones of preferential infiltration, or multiple distributed point sources
Heterogeneous Aquifer Model
Parameter
Description
Λc
Expected
number of
Clusters
Expected
number of
pulses per
cluster
Expected
radius of
pulse from
cluster centre
(m)
Radius of a
pulse (m)
Mean spatial
coverage (%)
γn
ρr
φr
Poisson Pulse Density
High
InterLow
mediate
50
25
10
10
5
2
3
3
3
1
1
1
53
22
4
Facies‐Specific Parameters
Facies
Volumetric Proportion
(%)
1 of 10 random realizations
Heterogeneous Aquifer Model
Gravel
Coarse Sand
Medium Sand
Sandy Loam
Clay
21
17
31
26
5
Mean Length (m)
2.56
1.7
Background
2.47
1.69
Hydraulic Conductivity
(m/day)
100
50
10
1
0.001
Collector diameter (mm)
10
1.0
0.3
0.03
3.3 x 10-4
Enterococcus
Escherichia Coli
1.01
5.98
0.85
3.70
1.80
5.89
14.01
25.44
716.4
1.36 x 104
Salmonella
Campylobacter
4.38
3.99
2.75
2.66
4.44
4.58
20.38
24.93
1.23 x 104
1.46 x 104
One of 14,040 hypothetical monitoring well locations within this modeling domain (excluding areas near the simulation domain boundary).
Collector Efficiency ηc*
Collision Efficiency αc
4.5 x
10-5
1x
10-4
1x
10-3
5x
10-3
0.5
Filtration Coefficient λ*
(m-1)
Enterococcus 4.75 x 10-
8.93 x 10-2
6.31
2453
1.02 x 1010
0.39
20.63
4452
2.13 x 1010
0.29
15.56
3566
1.93 x 1010
0.28
16.05
4363
2.29 x 1010
3
Escherichia Coli 2.83 x 102
Salmonella 2.07 x 102
Campylobacter 1.88 x 10-
1 of 10 random realizations
2
Homogeneous Aquifer
Probability of Prevalence in Time
Heterogeneous
Aquifer
Loading:
Homogeneous
High loading rate
Heterogeneous Aquifer
Medium loading
rate
Low loading rate
Homogeneous Aquifer
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4/22/2015
Probability of Prevalence in Time
Probability of Prevalence in Time
Loading:
Gaussian
Loading:
Poisson
- high intensity
Heterogeneous Aquifer
Heterogeneous Aquifer
Homogeneous Aquifer
Homogeneous Aquifer
Probability of Prevalence in Time
Generation of additional transport
“modes” owing to downgradient
transport and secondary loading for
a spatially discontinuous source. For
repeated loading, observed
concentration at any point is a
function of the aquifer transport
properties and loading density
Loading:
Poisson
- high intensity
Heterogeneous Aquifer
Homogeneous Aquifer
Probability of Prevalence in Time
Homogeneous Aquifer
Probability of Prevalence in Time
Loading:
Poisson
- intermediate
intensity
Loading:
Poisson
- low intensity
Heterogeneous Aquifer
Heterogeneous Aquifer
Homogeneous Aquifer
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4/22/2015
Conclusions
•
fine grained alluvial aquifers: strong attenuation of
fecal microorganism, disease outbreaks events
most likely occur from loading immediately
adjacent to wells / faulty well construction
•
High permable/low attenuation strata: large
transport distances of fecal microorganisms
•
Poisson pulse source term - consistent with
sporadic observations of fecal microorganisms in
groundwater, even adjacent to persistent sources of
contamination
•
Further research: spatial variation in fecal sources;
remobilization of microbes at field/farm scale =>
better risk model parametrization
•
Additional processes: vadose zone,
non-ideal
behavior
http://www.whymap.org/
20