Inferring the source of pathogens in contaminated streams during flood events

1
Inferring the source of pathogens in contaminated
streams(during flood events)

• Graham McBride

2
Current conceptual diagram
Campylobacter Ecology Reservoirs, Amplifiers and
Transmission Routes

carcass preparation
slaughter house
X
-
contamin
food processing
food industry
Veg/Fruit/Cereal
Food
food distribution
Safety
animal contact
retail
Agriculture
Animal Conservation
food preparation
home/cater/service
Primary Producer
consumption
consumption
Human
Animal
drinking
-
water
Systems
excreta
excreta
excreta
food
recreation
drinking
sewage treatment
sewage treatment
aquatic environments
drinking
-
water
drinking
-
water
Reservoirs Amplifiers
Environmental
treatment
treatment
Transmission Routes
Health
Direction of Transmission
drinking
-
water
3
Current conceptual diagram
Campylobacter Ecology Reservoirs, Amplifiers and
Transmission Routes

carcass preparation
slaughter house
X
-
contamin
food processing
food industry
Veg/Fruit/Cereal
Food
food distribution
Safety
animal contact
retail
Agriculture
Animal Conservation
food preparation
home/cater/service
Primary Producer
consumption
consumption
Human
Animal
drinking
-
water
Systems
excreta
excreta
excreta
food
recreation
drinking
sewage treatment
sewage treatment
aquatic environments
drinking
-
water
drinking
-
water
Reservoirs Amplifiers
Environmental
treatment
treatment
Transmission Routes
Health
Direction of Transmission
drinking
-
water
4
Complex ecological cycle

• How do these pathogens enter the (human) food
  chain?
• Strong signals from (limited) environmental
  monitoring of Campylobacter often found in
  rivers, especially during floods
• Campylobacter inactivated (by sunlight) much
  faster than a faecal indicator (E. coli)
• Campylobacter peak coincides with flood peak
  faecal indicator (E. coli) precedes it

5
Field data
6
Field data
7
Field data
8
Kinematic wave model

• Wave travels faster than the water
• Hypotheses (to explain differential timing)
• E. coli predominantly mined from sediments as
  the wave arrives
• Campylobacter predominantly derived from land
  runoff and so arrives with the water
• Field data support stream sediments
• depauperate in Campylobacter
• rich in E. coli (108 per m2)

9
Kinematic wave model
10
Kinematic wave modelctd.

• Uses mass conservation equation replaces
  momentum equation by a simple form A aQb,
  where
• A channel cross-section area (m2)
• Q rate of flow of water (m3 s-1)
• a, b parameters
• f(friction, slope)
• b 3/5 (Manning)
• Inflow constant during runoff period

11
Kinetics

• x distance (m)
• t time (s)
• q lateral inflow per unit channel length (m3
  s-1 m-1)
• S sediment bacteria per unit channel length (
  m-1)
• C aquatic bacteria per unit water volume
  (100 mL)-1
• Cl bacteria in lateral inflow (100 mL)-1
• k inactivation coefficient (day-1)
• es entrainment coefficient (s-1)
• U stream velocity (m s-1)
• Ub baseflow velocity (m s-1)

12
(No Transcript)
13
Explicit Numerical Solution
14
Parameter set

• As in Chapra (1997, Surface Water-Quality
  Modeling)
• And

15
Results (no inactivation, constant Cl)
16
Results (inactivation kC 50 kE 10)
17
Conclusions

• Field data and model ? differential delivery
  mechanisms
• BMPs for faecal indicator may not coincide with
  those for (at least some) zoonotic pathogens
• Value of field dataexemplary commitment of
  colleagues
• In the model, need to
• minimise numerical dispersion
• include alternative entrainment assumptions
• apply to NZ stream settings (non-prismatic
  channels)
• Do a night-time survey?
• How to include in general campylobacteriosis risk
  model?
• Vital to include FST findings