Rafael Bastos (1), Paula Bevilacqua (2), Richard Gelting (2), Demétrius Viana (1), João Pimenta...

Rafael Bastos (1), Paula Bevilacqua (2), Richard Gelting (2), Demétrius Viana (1) , João Pimenta (1)

(1) University of Viçosa, Brazil

(2) US Center for Disease Control and Prevention

Seasonal fluctuations in source Seasonal fluctuations in source water quality and health related water quality and health related

risks. risks. A QMRA approach applied to Water A QMRA approach applied to Water

Safety Plans.Safety Plans.

Water Safety Conference 2010

Water Safety Plans

Water Safety ConferenceNovember 2-4 2010, Kuching, Malaysia

Davison et al. (2006)

Introduction

Frequency/likelihood consequence/severity

matrix


Deere et al. (2006)

WSPWSP

Hazards / hazardous events identification

/ prioritization

Risk characterization

Control measures

Qualitative / semi-quantitative approach

subjective judgement

High risk


WSP QMRAWSP QMRA

Quantitative Microbial Risk Assessment (QMRA)

Exposure model + Dose-response model

Risk estimates

Objective / quantitative input for risk assessment and management in WSP

(Smeets et al., 2010; Medema & Ashbolt 2006)


Quantitative Microbial Risk Assessment

Hazardous events Seasonal fluctuations in source water

quality (rainfall)Water treatment performance

Risk estimatesLong and shorter-

terms Annual, seasonal,

daily

Objectives

“provide opportunities for improved risk management, with an incentive to reduce the occurrence and impact of event-driven peaks” (Signor & Ashbot , 2009).

10-4 pppy

10-6 pppd

Methods


≈ 650 m; 20º 45' 14" S; 42º 52' 53" W

≈ 70,000 inhabitants (90% urban)

10º C (winter) - 30º C (summer)

rainy season (November – March); dry season (April - October)

Viçosa – Minas Gerais

(Southeast Brazil)

UFV (1926)


DW supply system

WSP

WTP 1 (100 L/s)

WTP UFV(50 L/s)

WTP 2(100 L/s)

São Bartolomeu Stream

Turvo River

Rainy season

70% SB + 30% TR

Dry season

70% TR + 30 SB

150 km

Viçosa DW water supply systemViçosa DW water supply system

Lagoa 1

Lagoa 2

WTP UFV(50 L/s)

Storage reservoir

WTP 1 (100 L/s)

8 km

Storage reservoir

UFV DW water supply systemUFV DW water supply system

Rainy season ≈ 200L/s

Dry season ≈ 100 L/s

SB Catchment

(≈ 2000 ha)

Conventional treatment

UFV WTPUFV WTP

coagulation (aluminum sulphate), hydraulic

rapid mixture and flocculation, conventional

sedimentation, rapid sand filtration, and

disinfection with chlorine. Water Safety ConferenceNovember 2-4 2010, Kuching, Malaysia

QMRA model


d = dose

C =Cryptosporidium concentration in source water (oocysts /L)

r = recovery fraction of the oocysts enumeration method (%)

R = oocysts removal efficiency (log) (filtration)

V = volume of water consumed per day (L/d)

Exposure model

d = C x (1/r) x R x V

QMRA model


Dose – response model (exponential) (Haas et al. , 1999)pd = 1 - exp (-θd) (daily)

pΣ = 1- (1- pd)n [seasonal: prain and pdry; and annual)

risk of infection (pd) in an individual following

ingestion of a single pathogen dose d, i.e. per exposure event (daily risk)

total probability of infection over n exposures to the single pathogen dose d

Methods – Results


Cryptosporidium concentration in source water (oocysts /L)

PDF : β distribution

Monitoring (five previous studies, 2002-2008)

r = recovery fraction of the oocysts enumeration method (%)

30-60% (uniform distribution)

Methods – Results


R = oocysts removal efficiency (log) (filtration)

log10 removal Cryptosporidium oocysts = 0.9631 log10

removal turbidity + 1.009(Nieminsky & Ongerth, 1990)

turbidity removal 0.29 to 3.79 logRdry = 0.29 to 2.72 log - Rrain = 0.5 to 3.8 log

Oocysts removal 1.38 to 4.76 logRdry = 1.38 to 3.72 log - Rrain = 1.58 to 4.76 log

Triangular distribution ≈ pilot experiment s

Methods – Results


θ = 0.042 ± 25% - variation in susceptibility (as most existing dose-response models derive from oral challenge data from healthy adult volunteers)

Uniform distributionV = volume of water consumed per day (L/d)

Poisson (λ=0.87 L/day) (Australian)

Methods – Results

Stochastic modelling –

Monte Carlo Simulation

50,000 iterations

Variability and Uncertainty

Methods – Results

0,0

00

00

,28

33

0,5

66

50

,84

98

1,1

33

01

,41

63

1,6

99

51

,98

28

2,2

66

0Valores em Milésimos

0,0%

14,3%

28,6%

42,9%

57,1%

71,4%

85,7%

100,0%

highly skewed risk probability distributions

typical of long-term variability in which the overall mean value is highly sensitive to the rarely occurring but relatively ‘extreme’ higher risk periods

Results – risk estimates (pooled data)

0,0

00

00

,28

33

0,5

66

50

,84

98

1,1

33

01

,41

63

1,6

99

51

,98

28

2,2

66

0

Valores em Milésimos

0,0%

14,3%

28,6%

42,9%

57,1%

71,4%

85,7%

100,0%

0,0

00

00

0,0

70

38

0,1

40

75

0,2

11

13

0,2

81

50

0,3

51

88

0,4

22

25

0,4

92

63

0,5

63

00

0,0%

14,3%

28,6%

42,9%

57,1%

71,4%

85,7%

100,0%

5.6x10-4 1.1x10-3 1.6x10-3 2.2x10-30

0 7x10-2 2.1x10-1 3.5x10-1 5.6x10-14.2x10-1

Pdaily

Pannual

50% = 2 x 10-6 (Signor & Ashbolt, 2009)

95% = 2.2 x 10-3

50% = 6.9 x 10-4

(EPA)

95% = 5.6 x 10-1

Results – risk estimates (dry season)

Pdaily

50% = 4.6 x 10-6

95% = 2 x 10-3

50% = 8.3 x 10-4

(EPA)

95% = 3.1 x 10-1

0,0

00

00

,33

45

0,6

69

01

,00

35

1,3

38

01

,67

25

2,0

07

0


0

2000

4000

6000

8000

10000

12000

14000

0,0%

14,3%

28,6%

42,9%

57,1%

71,4%

85,7%

100,0%

Pdaily

0,0

00

00

0,0

61

24

0,1

22

48

0,1

83

72

0,2

44

96

0,3

06

20

0

5

10

15

20

25

30

35

40

0,0%

12,5%

25,0%

37,5%

50,0%

62,5%

75,0%

87,5%

100,0%

6x10-2 1.2x10-1 1.8x10-1 2.5x10-1 3.1x10-10

3x10-4 6.7x10-4 1x10-3 1.4x10-3 1.7x10-3 2x10-30

Pseason

0,0

00

0,0

47

0,0

94

0,1

41

0,1

88

0,2

35

0,2

82

0,3

29

0,3

76

0

5

10

15

20

25

30

0,0%

16,7%

33,3%

50,0%

66,7%

83,3%

100,0%

0,0

00

0,5

24

1,0

48

1,5

72

2,0

96

2,6

20


0,0%

16,7%

33,3%

50,0%

66,7%

83,3%

100,0%

Results – risk estimates (rainy season)

Pdaily

50% = 1.9 x 10-5

95% = 2.6 x 10-3

50% = 3.4 x 10-3

95% = 3.8 x 10-1

Pseason

5.2x10-4 1.1x10-3 1.6x10-3 2.1x10-3 2.6x10-30

9.4x10-2 1.9x10-1 2.8x10-1 3.8x10-10

Results – sensitivity analysis

Variable

Spearman rank correlation coefficient (rs)

Dry seasonRain

seasonPolled data

Occurrence of Cryptosporidium in source water – C (oocysts/L)

+0.30 +0.20 +0.23

Recovery of the oocysts enumeration method – r (%)

-0.02 -0.01 -0.02

Cryptosporidium oocysts removal in the WTP – R (log)

-0.16 -0.27 -0.20

Drinking-water consumption – V (L/day) +0.84 +0.85 +0.84Dose response parameter - θ +0.03 +0.02 +0.02

Sensitivity of probability of infection to variation in input random variables


need of data collection on drinking-water consumption in Brazil the importance of reliable data on oocysts occurrence/removal and properly specifying statistical distributions for these variables.

Results – sensitivity analysis

VariableDaily/annual risks

(polled data)

Daily/seasonal risks

Rainy season Dry season

Occurrence of Cryptosporidium in source water

Highest + 1.8 + 0.9 + 2.2

Lowest- 0.8

-0.9 -1.0

Cryptosporidium oocysts removal in the WTP

Highest - 1.7 - 1.5 -1.1

Lowest + 1.7 + 1.7 + 1.3

Sensitivity analysis : log10- values of the decreased or increased median risk compared to when the total distribution is used


Results – Scenario analysis

Variable

Annual risks Seasonal risks

>10-1 <10-2 <10-3 <10-4

Dry season Rain season

>10-1 <10-2 <10-3 <10-4 >10-1 <10-2 <10-3 <10-4

C +1.28 83.3%

+0.6874.6%

R -0.84 17.9%

-0.63 26.6%

-0.81 18.5%

V +1.07 94.2%

-1.07 41.8%

-1.07 41.8%

+1.07 94.2%

-1.07 41.8%

Scenario analysis results: combinations of inputs which lead to risk of infection targets

Figures within parenthesis: percentile of the subset median of the input variable in the

complete distribution; figures outside parenthesis difference between the subset and the

overall medians divided by the standard deviation of the original simulation; the higher

this number, the more significant is the input variable in achieving the output target value.


Conclusions

Seasonal fluctuations in source water quality

(rainfall) and treatment performance ►

Hazardous events ► WSP (Signor et al., 2005;

Signor & Ashbolt, 2009; Smeets et al., 2010).

Seasonal risk fluctuations seems to be

attenuated over the annualized estimates.

Case for shorter-term risk estimates (seasonal,

daily) ►acceptable targets (Signor & Ashbolt,

2009).

Conclusions

QMRA ► objective quantitative input ► WSP

(Smeets et al., 2010).

QMRA models :

pathogens in source water : reliable data,

PDF (variability & uncertainty)

pathogens removal : indicators (turbidity) ???

Critical limits

Thank you !!!!!!!!Thank you !!!!!!!!

[email protected]

Water Safety Conference 2010

Rafael Bastos (1), Paula Bevilacqua (2), Richard Gelting (2), Demétrius Viana (1), João Pimenta...

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