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Rafael Bastos (1), Paula Bevilacqua (2), Richard Gelting (2), Demétrius Viana (1), João Pimenta (1) (1) University of Viçosa, Brazil (2) US Center for.

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Presentation on theme: "Rafael Bastos (1), Paula Bevilacqua (2), Richard Gelting (2), Demétrius Viana (1), João Pimenta (1) (1) University of Viçosa, Brazil (2) US Center for."— Presentation transcript:

1 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 water quality and health related risks. A QMRA approach applied to Water Safety Plans. Water Safety Conference 2010

2 Water Safety Plans Water Safety Conference November 2-4 2010, Kuching, Malaysia Davison et al. (2006) Introduction

3 Frequency/likelihood consequence/severity matrix Water Safety Conference November 2-4 2010, Kuching, Malaysia Deere et al. (2006) WSP WSP Hazards / hazardous events identification / prioritization Risk characterization Control measures Qualitative / semi-quantitative approach subjective judgement High risk

4 Water Safety Conference November 2-4 2010, Kuching, Malaysia WSP QMRA WSP 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)

5 Water Safety Conference November 2-4 2010, Kuching, Malaysia Quantitative Microbial Risk Assessment Hazardous events Seasonal fluctuations in source water quality (rainfall) Water treatment performance Risk estimates Long 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

6 Methods Water Safety Conference November 2-4 2010, Kuching, Malaysia ≈ 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)

7 UFV (1926) Water Safety Conference November 2-4 2010, Kuching, Malaysia DW supply system WSP

8 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 system

9 Lagoa 1 Lagoa 2 WTP UFV (50 L/s) Storage reservoir WTP 1 (100 L/s) 8 km Storage reservoir UFV DW water supply system

10 Rainy season ≈ 200L/s Dry season ≈ 100 L/s SB Catchment ( ≈ 2000 ha)

11 Conventional treatment UFV WTP coagulation (aluminum sulphate), hydraulic rapid mixture and flocculation, conventional sedimentation, rapid sand filtration, and disinfection with chlorine. Water Safety Conference November 2-4 2010, Kuching, Malaysia

12 QMRA model Water Safety Conference November 2-4 2010, Kuching, Malaysia 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

13 QMRA model Water Safety Conference November 2-4 2010, Kuching, Malaysia Dose – response model (exponential) (Haas et al., 1999) p d = 1 - exp (-θd) (daily) p Σ = 1- (1- p d ) n [seasonal: p rain and pdry; and annual) risk of infection (p d ) 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

14 Methods – Results Water Safety Conference November 2-4 2010, Kuching, Malaysia 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)

15 Methods – Results Water Safety Conference November 2-4 2010, Kuching, Malaysia R = oocysts removal efficiency (log) (filtration) log 10 removal Cryptosporidium oocysts = 0.9631 log 10 removal turbidity + 1.009 (Nieminsky & Ongerth, 1990) turbidity removal 0.29 to 3.79 log Rdry = 0.29 to 2.72 log - Rrain = 0.5 to 3.8 log Oocysts removal 1.38 to 4.76 log Rdry = 1.38 to 3.72 log - Rrain = 1.58 to 4.76 log Triangular distribution ≈ pilot experiment s

16 Methods – Results Water Safety Conference November 2-4 2010, Kuching, Malaysia θ = 0.042 ± 25% - variation in susceptibility (as most existing dose-response models derive from oral challenge data from healthy adult volunteers) Uniform distribution V = volume of water consumed per day (L/d) Poisson (λ=0.87 L/day) (Australian)

17 Methods – Results Stochastic modelling – Monte Carlo Simulation 50,000 iterations Variability and Uncertainty

18 Methods – Results  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

19 Results – risk estimates (pooled data) 5.6x10 -4 1.1x10 -3 1.6x10 -3 2.2x10 -3 0 07x10 -2 2.1x10 -1 3.5x10 -1 5.6x10 -1 4.2x10 -1 P daily P annual 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

20 Results – risk estimates (dry season) P daily 50% = 4.6 x 10 -6 95% = 2 x 10 -3 50% = 8.3 x 10 -4 (EPA) 95% = 3.1 x 10 -1 P daily 6x10 -2 1.2x10 -1 1.8x10 -1 2.5x10 -1 3.1x10 -1 0 3x10 -4 6.7x10 -4 1x10 -3 1.4x10 -3 1.7x10 -3 2x10 -3 0 P season

21 Results – risk estimates (rainy season) P daily 50% = 1.9 x 10 -5 95% = 2.6 x 10 -3 50% = 3.4 x 10 -3 95% = 3.8 x 10 -1 P season 5.2x10 -4 1.1x10 -3 1.6x10 -3 2.1x10 -3 2.6x10 -3 0 9.4x10 -2 1.9x10 -1 2.8x10 -1 3.8x10 -1 0

22 Results – sensitivity analysis Variable Spearman rank correlation coefficient (r s ) Dry season Rain season Polled 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+0.84 Dose response parameter - θ +0.03+0.02 Sensitivity of probability of infection to variation in input random variables Water Safety Conference November 2-4 2010, Kuching, Malaysia  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.

23 Results – sensitivity analysis Variable Daily/annual risks (polled data) Daily/seasonal risks Rainy seasonDry season Occurrence of Cryptosporidium in source water Highest + 1.8 + 0.9+ 2.2 Lowest - 0.8 -0.9 Cryptosporidium oocysts removal in the WTP Highest - 1.7 - 1.5-1.1 Lowest + 1.7 + 1.3 Sensitivity analysis : log 10 - values of the decreased or increased median risk compared to when the total distribution is used Water Safety Conference November 2-4 2010, Kuching, Malaysia

24 Results – Scenario analysis Variable Annual risksSeasonal risks >10 -1 <10 -2 <10 -3 <10 -4 Dry seasonRain season >10 -1 <10 -2 <10 -3 <10 -4 >10 -1 <10 -2 <10 -3 <10 -4 C +1.28 83.3% +0.68 74.6% 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. Water Safety Conference November 2-4 2010, Kuching, Malaysia

25 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).

26 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

27 Thank you !!!!!!!! rkxb@ufv.br Water Safety Conference 2010

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