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Seasonal fluctuations in source water quality and health related risks.

Water Safety Conference 2010. Seasonal fluctuations in source water quality and health related risks. A QMRA approach applied to Water Safety Plans. Rafael Bastos (1) , Paula Bevilacqua (2) , Richard Gelting (2) , Demétrius Viana (1) , João Pimenta (1)

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Seasonal fluctuations in source water quality and health related risks.

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

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

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

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

  5. Objectives 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 10-4 pppy 10-6 pppd “provide opportunities for improved risk management, with an incentive to reduce the occurrence and impact of event-driven peaks” (Signor & Ashbot , 2009). Water Safety Conference November 2-4 2010, Kuching, Malaysia

  6. Methods • Viçosa – Minas Gerais • (Southeast Brazil) • ≈ 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) Water Safety Conference November 2-4 2010, Kuching, Malaysia

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

  8. Viçosa DW water supply system 150 km Dry season 70% TR + 30 SB WTP 1 (100 L/s) WTP 2 (100 L/s) WTP UFV (50 L/s) São Bartolomeu Stream Rainy season 70% SB + 30% TR Turvo River

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

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

  11. UFV WTP Conventional treatment 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 • Exposure model d = C x (1/r) x R x V • 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) Water Safety Conference November 2-4 2010, Kuching, Malaysia

  13. QMRA model • Dose – response model (exponential) (Haas et al. , 1999) pd = 1 - exp (-θd) (daily) • risk of infection (pd) in an individual following ingestion of a single pathogen dose d, i.e. per exposure event (daily risk) pΣ = 1- (1- pd)n [seasonal: prain and pdry; and annual) • total probability of infection over n exposures to the single pathogen dose d Water Safety Conference November 2-4 2010, Kuching, Malaysia

  14. Methods – Results • Cryptosporidium concentration in source water (oocysts /L) • Monitoring (five previous studies, 2002-2008) • PDF : β distribution • r = recovery fraction of the oocysts enumeration method (%) • 30-60% (uniform distribution) Water Safety Conference November 2-4 2010, Kuching, Malaysia

  15. 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 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 Water Safety Conference November 2-4 2010, Kuching, Malaysia

  16. 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 distribution • V = volume of water consumed per day (L/d) • Poisson (λ=0.87 L/day) (Australian) Water Safety Conference November 2-4 2010, Kuching, Malaysia

  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. 0 7x10-2 2.1x10-1 3.5x10-1 5.6x10-1 4.2x10-1 5.6x10-4 1.1x10-3 0 1.6x10-3 2.2x10-3 Results – risk estimates (pooled data) 50% = 2 x 10-6 (Signor & Ashbolt, 2009) 95% = 2.2 x 10-3 Pdaily 50% = 6.9 x 10-4 (EPA) 95% = 5.6 x 10-1 Pannual

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

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

  22. Results – sensitivity analysis 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. Water Safety Conference November 2-4 2010, Kuching, Malaysia

  23. Results – sensitivity analysis Sensitivity analysis : log10- 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 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. Water Safety Conference 2010 Thank you !!!!!!!! rkxb@ufv.br

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