EU-5th Framework program: Energy, environment and sustainable development; project...

© Kiwa 2006 1

EU-5th Framework program: Energy, environment and sustainable development; project EVK1-CT-2002-00123: MICRORISK

Microbiological Risk Assessment

A scientific basis for managing drinking water safety from source to tap

www.microrisk.comMICRORISK

Know yoursource water

quality

Target yourtreatment

Safedrinking water

Know yourcatchment

Protect your distribution

© Kiwa 2006 2


Microbiologically safe drinking waterCouncil directive 98/83/EC

Article 1. Objective is to protect human health from the adverse effects of any contamination by ensuring that it is wholesome and clean.

Article 4. Water is wholesome and clean if it is free from any micro-organisms and parasites and from any substances which, in numbers or concentrations, constitute a potential danger to human health

Operationally translated (Article 4) into meeting the requirements in Annex I.

Microbiological safety: E.coli 0/100ml (Clostridium perfringens 0/100ml) enterococci 0/100ml

© Kiwa 2006 3


E.coli as quality standardMilestone 1: culture media for bacteria

1885 - 1908 Professor Hygiene Berlin

1879: first proof of bacteria (anthrax) as agents of disease

1881: solid culture media for isolation of bacteria

1883: isolation of causative agent of cholera

1905: Nobelprice Physiology and Medicine

Robert Koch (1843-1910)

© Kiwa 2006 4


E.coli as quality standardMilestone 2: indicator-bacteria

1898 -1929Professor HygieneRU Utrecht

1904: Method for detection of fecal contamination of water with test at 46°C(Eijkman- test)

1929: Nobelprice Physiology and Medicine (discovery of role of vitamins)

Christiaan Eijkman (1858-1930)

© Kiwa 2006 5


The indicator concept

Presence of bacteria that are abundant in human and animal feces (E.coli, enterococci) in water indicates health risk (pathogens may be present)

Absence of indicator bacteria are indication of safety

© Kiwa 2006 6


Connections to water supply and incidence of typhoid

0

10

20

30

40

50

60

70

1900 1910 1920 1930 1940 1950 1960 1970

30

40

50

60

70

80

90

100

0

10

20

30

40

50

60

70

1900 1910 1920 1930 1940 1950 1960 1970

30

40

50

60

70

80

90

100% connection to mains supply% connection to mains supply

Cases of typhoid per 100000Cases of typhoid per 100000

© Kiwa 2006 7


E. coli has served us well, but….

Too late Outbreaks in absence of E. coli “New” pathogens more resistant Demonstrate due diligence: not retrospective but

proactive Consumer health & safety moving towards risk

approach (food safety) Scientific development of QMRA

WHO: Water Safety Plans IWA: Bonn charter

© Kiwa 2006 8






quality


Safedrinking water

Know yourcatchment


© Kiwa 2006 9


Objective

Provide scientific basis for QMRA

Develop a harmonised, evaluated and validated framework for risk assessment, providing adequate information for risk management

© Kiwa 2006 10


Consortium Basic science - applied science - end users

Kiwa Water Research

Ondeo Services

Centre for Water and Wastewater Technology, Australia

Water Technology Centre

Technical University Delft

University of East Anglia

Anjou Recherche

Vivendi Water PartnershipInsitute for Hygiene, Univ. Bonn

Water Research Centre NSF

Institute for Infectious Disease Control

© Kiwa 2006 11


Microrisk results

Scientific data on pathogen occurrence, efficiency of treatment processes, distribution system integrity, consumption of drinking water

QMRA using these data, (un)certainty assessment

Comparison between QMRA and epidemiological data on GI-illness/waterborne outbreaks

Protocol for QMRA, esp. exposure assessment

© Kiwa 2006 13


Waterborne outbreaks in Europe 1990-2004

Pathogen Isolated in Cases

Bacterial Protozoal Viral Water Supply

Country No.

Outbreaks Campylobacter Shigella Cryptosporidium Giardia Norovirus

Viral (undete-rmined)

Mixed Pathogen

Gastroenteritis Ground- water

Surface Water

Mixed Not

Reported

Finland 12 4 - - - 6 1 - 1 10 2 - -

France 7 - - 2 - - - 3 2 5 - - 2

Germany 2 - - - 1 1 - - - 1 - - 1

Greece 3 - 2 - 1 - - - - 2 1 - -

Italy 1 - - - - - - - 1 - - - 1

Netherlands 1 - - - - - - - 1 1 - - -

Rep. Ireland 2 - - 1 - - - - 1 1 1 - -

Spain 6 1 1 1 - - - - 3 1 - 1 4

Sweden 7 3 - - - 1 - 1 2 3 3 - 1

UK (England) 29 - - 28 - - - - 1 5 14 4 6

UK (N.Ireland) 3 - - 3 - - - - - - 3 - -

UK (Scotland) 6 - - 5 - - - 1 - - 6 - -

UK (Wales) 1 1 - - - - - - - - - - 1

UK (unspecified) 6 - - 6 - - - - - 3 2 1 -

No. Outbreaks 86 9 3 46 2 8 1 5 12 32 32 6 16

Cases 72546 16222 531 7772 232 11408 2500 2511 31370* 43571 23047* 906 5022

© Kiwa 2006 14


Fault tree analysis

Non-Micro.

Monitor. Failure

Response Failure Detection Failure

Compr./ Report Failure

Correct-ion

Failure

+ AND gate: Produces output if all inputs co-exist Inclusive OR gate: Produces output if one or more inputs exist

Top/Intermediate event: Represents undesirable event. Located within and at the tree top, antecedent events feed to this event

Transfer gate: Transfer in & out of inputs due to space limitations.

KEY

Filtration Failure

Treatment Failure

Source Water Contamination

Pre-Distribution Contamination

Monitoring

Micro. Monitor. Failure

Pathway

Temporary Disinfect-ion Failure

Chronic Filtration Failure

Source Pathway

+

Distribution System Contamination

Waterborne Outbreak

Commun-ication Failure

Drinking Water Contamination Detection & Response to Contamination

Temporary Filtration Failure

Disinfection Failure

Chronic Disinfect-ion Failure

Source

or

or

or

or or

or

or or

+

+

+

or

Base event: An initiating event which is located at the bottom of the tree.

© Kiwa 2006 15


Factors causing the outbreaks

Source failure Rainfall 44% livestock activity 41% Sewage discharge 10%

Treatment Chronic filtration failure 38%

Distribution Backflow/cross connection 15%

Detection Failure to recognize problem 18%

© Kiwa 2006 16


CTS Locations

1

2

3

4 5

7

6

8

9

10

© Kiwa 2006 17


Source water

© Kiwa 2006 18


WP2 Source water quality

Objective: design monitoring strategy for pathogens in raw water

Peak events are risk events, so include peak events in the strategy

First know your catchment, then design monitoring strategy.

Catchment survey (sources, contamination level, indicators for peak events)

Design and test monitoring strategy Several catchments, several pathogens

© Kiwa 2006 19


12 “Catchment to Tap Systems”

Distribution CTS Source water Treatment

Cl2 Pop. size 1 River Pre-O3 (Cl2 in summer) - Coa - Sed - RF - O3 - GAC - super/deCl2 Yes 224,000 2 Reservoir AR - RF - O3 - GAC – SSF No 440,000 3 River Pre -O3 - Coa - Sed - RF – O3 - GAC - Cl2 (3 systems) Yes 34,000 4 River Coa - Sed - O3 – GAC - Cl2 Yes 18,000 5 River with controlled intake Coa - Sed - O3 or Cl2 – GAC - Cl2 (2 systems) Yes 6 Reservoir Reservoir - Cl2 (summer) - Coa - Sed - GAC - Cl2 (2 systems) Yes

571,600

7 River Bank filtration – SSF - O3 – GAC Yes 120,000 8 Reservoirs Coa - Sed - RF - GAC - Cl2 Yes 50,000 9 Reservoir ( + river) RSF - O3 - GAC - SSF No 440,000

10 Mountain reservoirs Pre-Cl2 - Coa - Sed – RF – O3 - Cl2 Yes 47,600 11 Protected reservoirs DF - RF - Cl2/ClO2 Yes 300,000 12 Protected groundwater RF - Cl2 Yes 24,300

Coa =coagulation; Sed = sedimentation; RF is rapid filtration; O3 is ozonation; Cl2 is chlorination; ClO2 I schlorine dioxide; GAC is granular activated carbon filtration; AR is artificial recharge; SSF is slow sand filtration.

© Kiwa 2006 20


Pathogens of interest

Parasites Cryptosporidium parvum Giardia intestinalis

Bacteria Campylobacter Escherichia Coli O157:H7

Virus Enterovirus Norovirus

© Kiwa 2006 21


Catchment Survey

Objectives : Identification of pathogen sources and occurrence of peak events

Methodology: Location Description of water abstraction Description of the catchment

- Hydrogeology & Hydrology- Climate- Land use

Location & description of potential sources of faecal contamination

Human Origin Animal Origin

© Kiwa 2006 22


Example for catchment description : CTS 3

Physical characteristics

0

40

80

120

Jan March May July Sept Nov

Pre

cip

ita

tio

n

0

5

10

15

20

Te

mp

era

ture

Average precipitation (mm)

Average temperature (°C)

45 m

910 m

Impervious

Pervious

Riv

er 1

Riv

er

2

Forests & meadows

Crops

Riv

er 1

Riv

er

2

20 Km

N

© Kiwa 2006 23



Human activities

0

10

20

30

40

50

Nuclear power plant

Industry Agriculture Drinking water

Mm

3/a

n

Surface water Ground water

Water uses

Pathogen sources : animal breeding & WWTP

Bovine

Caprine

Poultry

Porcine

Ovine

WWTP 30 000 PE

WWTP 25 000 PE

WWTP 285 000 PE

WWTP 160 000 PE

Drinking water treatment plant

N

20 Km

© Kiwa 2006 24



Occurrence of peak events Daily flow and turbidity 2002-2004

0

200

400

600

800

July-02 Jan-03 July-03 Jan-04

m3 /

s

0

50

100

150

200

NT

UFlowTurbidity r²=0,7

Peak relatively rare event (9 peaks per year # 25%)

Criteria : Qthreshold =147 m3/s & Tthreshold =12 NTU

© Kiwa 2006 25


Raw water monitoring: what do we sample?

0

200

400

600

800

1 000

j f m a m j j a s o n d

Flow

m3 /s

Daily flow

Monthly flow

Annual flow (1918-2004)

Sample

CTS 3France

CTS 7Germany

Peak contamination

monitoring

© Kiwa 2006 26


High bird loads

© Kiwa 2006 27


Campylobacter in off-stream storage reservoir water (1994 & 2001)

0.1

1

10

100

1000

J F M A M J J A S O N D

Month

Nu

mb

er

(MP

N/l)

2001

1994

© Kiwa 2006 28


Bird counts on reservoir

Totaal aantal dominante vogels op de Petrusplaat 1994

0

500

1000

1500

2000

2500

3000

3500

09-02-94

23-02-94

09-03-94

23-03-94

06-04-94

20-04-94

04-05-94

18-05-94

01-06-94

15-06-94

29-06-94

13-07-94

27-07-94

10-08-94

24-08-94

07-09-94

21-09-94

05-10-94

19-10-94

02-11-94

16-11-94

30-11-94

14-12-94

28-12-94

11-01-95

25-01-95

Kuifeend Smient Wilde eend Meerkoet Fuut Krakeend Wintertaling Slobeend

© Kiwa 2006 29


Pathogens in source water summary

Baseline contamination Rain event contamination

Faecal indicators

E. coli 102-104 MPN/L 103-104 MPN/L and up to 50,000 MPN/L

Clostridia 3000 n/L and up to 17,500 n/L 5,000-6,000 n/L

Enterococci 102-103 n/L > 103 n/L

Total Coliforms 103-105 MPN/L 30,000-130,000 MPN/L

Pathogens

Cryptosporidium 0.05-0.5 n/L and up to 4.6 n/L Concentrations not clearly higher

Giardia 0.01-1 n/L and over 40 n/L in one case Concentrations not clearly higher

Campylobacter 0-100 MPN/L but up to 15,000 in one case Concentrations not clearly higher

E. coli 0157:H7 10-100 CFU/L and up to >1,000 CFU/L Concentrations not clearly higher

Enterovirus Rarely detected 300 n/L in one CTS

Norovirus Not detected (one CTS tested) 150 n/L in one CTS

© Kiwa 2006 30


Conclusion

Quantitative Microbiological Risk Assessment (QMRA)

Know your catchment (survey it) in order to:Identify pathogen sources

Occurrence and frequency of peak events

Monitor your catchment in order to assess:Chronic contamination

Magnitude of peak events

© Kiwa 2006 31


WP3 Treatment efficiency

Objective: design strategy to describe and control treatment efficiency (QMRA and Water Safety Plan)

Describe treatment efficiency (QMRA) Collect and review state-of-the-art Full scale indicator bacteria Full scale porcess parameters Pilot plant research

© Kiwa 2006 32


Treatment framework

Literature reviewMean Elimination Capacity

MEC

Reduction creditsBased on unit processes

Estimated pathogen elimination

4.2.2 4.2.2 4.2.3 4.2.4 4.2.5 4.2.64.2.7

© Kiwa 2006 33


Tier 1: Log credits

MIN DEC MAX DECMEC

Efficacy of treatment process in literature(7 studies for one pathogen and one process)

No site infoNot disinfection

© Kiwa 2006 34


Tier 1: Log credits Quantify UNCERTAINTY

MIN DEC MAX DECMEC

Apply triangular PDF

No site infoNot disinfection

© Kiwa 2006 35


Tier 2: Refine based on turb or design

MIN DEC MAX DECMEC

POOR GOOD

0

0.5

1

1.5

2

2.5

3

Jan 2001 Jan 2002 Jan 2003 Jan 2004 Jan 2005

Tu

rbid

ity

NT

U

Raw waterFiltrate

0.01

0.1

1

10

100

Jun 1999 Jun 2000 Jun 2001 Jun 2002 Jun 2003

Tu

rbid

ity

(NT

U)

Source

Treated

© Kiwa 2006 36


Treatment framework


MEC



DesignCharacteristics

Specific MEC

4.2.2 4.2.2 4.2.3 4.2.4 4.2.5 4.2.64.2.7

© Kiwa 2006 37


Model from pilots and literature

Inactivation of E-coli

0

1

2

3

4

5

6

7

0 0.5 1 1.5 2 2.5 3

Ct (mg*l/min)

DE

Escherichia coli ( Finch, G.R. 1988 ) Escherichia coli ( Restaino, Lawrence 1995 )

Escherichia coli ( Zhou, H. 1994 ) HOM model 20°C

HOM model 5°C

© Kiwa 2006 38


Treatment models

0

5

10

15

20

25

28-10-1995 11-3-1997 24-7-1998 6-12-1999 19-4-2001 1-9-2002

T0

0.5

1

1.5

2

2.5

28-10-1995 11-3-1997 24-7-1998 6-12-1999 19-4-2001 1-9-2002

Ct

0

2

4

6

8

28-10-1995 11-3-1997 24-7-1998 6-12-1999 19-4-2001 1-9-2002

DE

0

20

40

60

80

28-10-1995 11-3-1997 24-7-1998 6-12-1999 19-4-2001 1-9-2002

Co

li44

in

0.00000001

0.000001

0.0001

0.01

1

28-10-1995 11-3-1997 24-7-1998 6-12-1999 19-4-2001 1-9-2002

Co

li44

ou

t

Temperature

Ct

DE (log removal)

Coli untreated

Coli treated

&

=

*

=

© Kiwa 2006 39


Treatment framework


MEC

Full-scale measurementsVariation of process

Conditions at full scale


(On-line) Residuals

Model Disinfection +Identify events



Specific MEC

(On-line) Surrogate

Refine MEC+Identify events

4.2.2 4.2.2 4.2.3 4.2.4 4.2.5 4.2.64.2.7

© Kiwa 2006 40


Ruwwaterverdeelbak DynaSandfiltersBufferreservoir

Ozondesinfektie

3 / 4

Ozondesinfektie

1 / 2

W EDSF

SnelfiltersLoenderveen

W 1500

Ontharding

Koolfilterstraat 1 Koolfilterstraat 2 Koolfilterstraat 3

LZF straat 2 LZF straat 1

Reinwaterreservoir 2 Reinwaterreservoir 1

POMPW 7100

W 7010 W 7009

W 6823 W 6821

W 67K83W 67K82W 67K81

W 6440

W 6013 W 6012

W 6313 W 6312

W 6007

W 6005

OntrekkingW aterleidingplas

W 1110

© Kiwa 2006 41


Clostridium spore removal over time

0,1

1,0

10,0

100,0

0 200 400 600 800 1000 1200-> days

SS

RC

per

10

0ml

Influent

Effluent

Days

100

10

1

0.11200200 600400 10008000

SSRC per 100 mlInfluent Effluent

Days

100

10

1

0.11200200 600400 10008000 1200200 600400 10008000

SSRC per 100 mlInfluent EffluentInfluent Effluent

CoagulationCoagulation

OzoneOzone

© Kiwa 2006 42


Lab is not practice: ozone example

Ozonation CT 2 mg.min/l Expected E. coli inactivation: >>5 logs Full scale E. coli data:

- E. coli present in 4% of samples after ozone- Removal 2.7 log

0.01

0.1

1

10

100

1000

Dec-02 Mar-03 Jun-03 Oct-03 Jan-04 Apr-04 Aug-04 Nov-04

E. coli

concentr

atio

n (

CF

P/l)

Ozone in

Ozone out

© Kiwa 2006 43


Bad days: example of chlorination: inactivation modelled on CT, hydraulics and temperature

0

0.5

1

1.5

2

2.5

3

3.5

27 Apr 07 May 17 May 27 May 06 Jun 16 Jun

Log

ina

ctiv

atio

n

© Kiwa 2006 44


Pilot studies: Cryptosporidium removal by pilot slow sand filter

0.001

0.01

0.1

1

10

100

1000

10000

0 50 100 150 200 250Looptijd (dagen)

Co

nce

ntr

ati

e (

n/l

)

Influent

Effluent

Effluent < analysegrens

Geen meting mogelijk door neerslag

Dag 28 - 35 zonder dosering

© Kiwa 2006 45


Treatment processes log removal

Coagulation-Sedimentation-Filtration-GAC filtration-Chlorination- 4.3

Coagulation-Sedimentation-Filtration-GAC filtration-Ozone-Chlorination- 4.3



Impoundment-Coagulation-Sedimentation-Dissolved Air Flotation-Filtration-GAC filtration-Chlorination-

3.2

Impoundment-Coagulation-Sedimentation-Filtration-GAC filtration-Chlorination- 3.2

Coagulation-Sedimentation-GAC filtration-Ozone-Chlorination- 3.1


UK Crypto data: Treatment

© Kiwa 2006 46


Treatment framework


MEC

Full-scale measurementsVariation of process

Conditions at full scale


(On-line) Residuals

Model Disinfection +Identify events


Full-scale samplesPathogenic and indicator

organisms before andafter treatment

Stochastic model

IndicatorsTranslate toPathogens

PathogensDirect

Analysis

CombinedIndicators and

Pathogens


Specific MEC

(On-line) Surrogate

Refine MEC+Identify events

4.2.2 4.2.2 4.2.3 4.2.4 4.2.5 4.2.64.2.7

Available dataSite specific

© Kiwa 2006 47


Total treatment Monte Carlo

© Kiwa 2006 49


Distribution

Faecal contamination incidents Netherlands 1994 – 2003 7 water companies 11 million people supplied 50 incidents reported 5 - 50,000 people affected per incident

Probability of being affected by incident:

1.7 x 10-3 pppy

© Kiwa 2006 50


Distribution incidents: duration

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-10 0 10 20 30 40 50 60

Duration (days)

CD

F p

erc

en

tile

s (%

of

inc

ide

nts

wit

h d

ura

tio

n <

= x

)

From detection (day 0) to end of incidentFrom cause to detection (day 0) of incident

From detection (day 0) to protective measuresPeak of E. coli concentration

© Kiwa 2006 51


Distribution incidents: magnitude

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.01 0.1 1 10 100 1000

E. coli concentration (CFP per 100 ml)

CD

F p

erc

en

tile

s (

% o

f in

cid

en

ts w

ith

[E

. co

li]

<=

x)

Mean

90-percentile

Maximum

First sample

© Kiwa 2006 52


Translation to pathogens

E. coli data in distribution network No pathogen data

E. coli AND pathogen data in sewage Assume: contamination source is sewage Use pathogen to E. coli ratio’s to translate E. coli to

pathogens

Sensitivity analysis: Pathogen/E.coli ratío from different matrices

© Kiwa 2006 53


Microrisk WP5: consumption

How much cold tap water do we consume?

What information is available? How reliable/useful is the available

information? How is tap water consumption

distributed? Can we come to a useful estimate for

QMRA?

literature review

statistical analysis of data

© Kiwa 2006 54


Literature review

Review available studies on tap water consumption > 25 studies found

Evaluate design of the studies Way of recording consumption Factors that might influence tap water consumption

USA Nl Ger Fr S UK Aus Can Italy0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

con

sum

ed c

old

ta

p w

ate

r (l

/da

y)

© Kiwa 2006 55


Study design

(24h-) Dietary recall Retrospective Interview/questionnaire Measures recent consumption

Dietary record/diary Prospective Measures current consumption

Food frequency questionnaire Retrospective Consumption in general

Combination of questionnaire & diary

© Kiwa 2006 56


Literature review: results

Tap water consumption average consumer Cold tap water: 0.2 - 1.55 L/day Total tap water: 0.71 - 2.58 L/day

Comparibility influenced by: Population participating Year of survey Season of data collection Method of data collection Method to assess volume tap water consumed Types of water included

No conclusions regarding effects of age, season, gender on tap water consumption

Physical activity, income and perceived health status were reported to increase consumption

© Kiwa 2006 57


Australia

Final: case-control study sporadic cryptosporidiosis Melbourne & Adelaide (Q) (N = 950, N = 644)

Melbourne best performance Poisson distr. Adelaide: again Poisson best, but poor similarity of shape Nr of non-consumers higher than nr consuming 1 glass Diary study in pilot most valuable

0 2 4 6 8 10 12 0%

5%

10%

15%

20%

25%Melbourne study

consumption (#glasses/ day)

rela

tive

fre

quen

cy o

ccur

ence

s

Empir ical data

Poisson

Exponential

Gamma

Log-normal

0 2 4 6 8 10 12 0%

5%

10%

15%

20%

25%

Adelaide study

consumption (#glasses/ day)re

lati

ve f

requ

ency

occ

uren

ces

Empir ical data

Poisson

Exponential

Gamma

Log-normal

© Kiwa 2006 58


Recommendations for QMRA

Use country specific data and distributions (when available) Use mean as conservative approach, instead of median (skewed

distribution) Beware for inter-country AND intra-country differences (Adelaide

vs Melbourne) Several datasets available? Use data generated with best study

design Otherwise: use highest consumption estimates as conservative

approach (data Melbourne)

© Kiwa 2006 60


WP6 Risk assessment

Objective: protocol for risk assessment (integration of WP2-5 and dose-response data) and protocol for uncertainty analysis

Select most appropriate mathematical techniques for integration of information into risk assessment

Include uncertainty analysis into protocol

Case studies: evaluate protocol: determine risk and uncertainty level

© Kiwa 2006 62


QMRA design

Stage/Barrier Functions and Coefficients Describing Stages and Barriers Input Source Water Dry weather concentration Campylobacter.L-1 described by a lognormal PDF with coefficients:

µ (log10) = 1.46; sigma (log10)= 1.85 Wet weather Campylobacter.L-1 concentration described by a Gamma PDF distribution with coefficients: alpha = 1.98; beta = 24.7 Proportion of time in dry weather based on flow analysis: Spring = 0.16; Summer = 0.017; Autumn =0.065; Winter = 0.329

Barrier A (Reservoir) Point estimates for seasonal decimal (log10) elimination capacity (DEC’s) were: Spring = 2.69; Summer = 2.46; Autumn =2.16; Winter = 2.37 These reductions were averaged to generate the reduction figure used.

Barrier B (Coagulation + Flocculation + DAF)

DEC described by a normal PDF: µ = 2.38; sigma = 0.38 Decimal (i.e. log10) reduction statistics are: 5th percentile = 1.75; Mode = 2.38; 95th percentile = 3.00

Barrier C (Rapid Sand Filter)

DEC described by a normal PDF: µ = 1.12; sigma = 0.40 Decimal (i.e. log10) reduction statistics are: 5th percentile = 0.46; Mode = 1.12; 95th percentile = 1.78

Barrier D (Chlorination + Short term storage)

Complex DEC with the following statistics: 5th percentile = 3.49; Mode = 3.88; 95th percentile = 6.53 Coefficients and inputs used in reduction calculation were: A = 6.31E9; E = 48699; R = 8.314; T = 273 + Temperature in oC where oC was obtained by re-sampling of a table of percentiles of water temperature entering the water treatment plant ; [Cl2] mg.L-1 = lognormal PDF with (µ = 3.86, sigma = 0.44, correction factor = -2.13); Fraction of storage volume = 0.1*beta function; Flow (ML.d-1) = resample of percentile lookup table of flows into the plant.

Consumption PDF of litres consumed per day per person described by Poisson distribution with the following coefficient: Gamma = 2.86 Consumption PDF has the following statistics: 5th percentile = 0 L; Mode = 0.75 L; 95th percentile = 1.5 L

Dose-response Variation on beta Poisson where dose is always 0 or 1: Prob. of infection (P) =e(-(alpha/(alpha + beta))*Dose) Where beta = 0.011 and alpha =0.024 The Maximum likelihood curve is: P=1-e(-Dose)

© Kiwa 2006 65


Sensitivity analysis

]inf[

]inf[10 ExpectedP

WorstCasePLogitivityFactorSens

5.0% 95.0%

Expected Value

Worst Case

© Kiwa 2006 66


Factor Sensitivity Results - Adelaide

Parameter Expected Value

Worst Case Factor Sensitivity

Source Water Concentration (oocyst.L-1)

0.2 5 1.40

Flow Rate (m3.s-1) 0.15 1 0.82

EMC (oocyst.L-1) 2.25 50 1.32

Event Volume (ML) 213 500 0.02

Reservoir Dilution Factor 9 6 0.18

Treatment Removal 4.5 2 2.50

Consumption 1 4 0.60

Event/Nominal 4.26

© Kiwa 2006 67


Effect of chlorination failure time

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+00

0.001 0.01 0.1 1 10 100 1000

Duration of Event (days)

Pro

b.

infe

ctio

n/p

ers

on

/y

Average

Median

95th percentile

99th percentile

(0)

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