Where next to reduce uncertainties in QMRA of drinking water systems?

Disclaimer: This presentation does not necessarily reflect official U.S. EPA policy

Assessing Pathogen Fate, Transport and Risk in Natural & Engineered Water Treatment

Banff Conference Centre, Alberta, September 25th, 2012

Nicholas J. Ashbolt (Ashbolt.Nick@epa.gov) U.S. EPA/ORD/Cincinnati

Key Points • Assumptions drive estimated risks

– So standardized approach needed • Source of pathogens & indicators critical

– Fecal and environmental pathogens • However, the more important role of QMRA is to

better understand a system – Hence, iterative approach as required: reduce

uncertainties & identify target control levels 2

2012 US Government Microbial Risk Assessment

Guidelines • Focus on pathogens in food and water • Prepared by the Interagency

Microbiological Risk Assessment Guideline Workgroup – Co-led by USDA & U.S. EPA

Released July, 2012 3

QMRA role in WSP

PUBLIC HEALTH STATUS

HEALTH TARGETS

Tolerable risk goal

Risk management

(WSP)

Risk

Ch

arac

teris

ation

Risk characterisation Integration into risk estimate for each pathogen under baseline and event conditions. Uncertainty analysis.

Prob

lem

Form

ulatio

n

Hazard identification Catchment to tap system description, selection of index pathogens and identification of hazardous events

Effe

ct As

sess

men

t

Dose response Selection of appropriate models for index pathogens and population exposed

Expo

sure

As

sess

men

t

Source water Pathogen concentration

Treatment Pathogen removal/inactivation

Distribution Pathogen ingress and inactivation

Consumption Volume of water consumed

Medema et al. 2006 4

Source: EPA 600-1-84-004

Total Coliform Rule (1989, 2012) E. coli

< 1 cfu/100 mL

LT2 Enhanced Surface Water Treatment Rule (2006) Cryptosporidium bins: e.g.

< 1 oocyt/10,000 L; 3-log 4-log viruses by treatment

Microbial criteria: Drinking waters

5

Quantitative Microbial Risk Assessment (QMRA) model

Problem formulation & Hazard identification Describe physical system, selection of reference pathogens & identification of hazardous events

STEP 1 SETTING

6

8 reference pathogens accounts for >97% of non-foodborne GI illness from known pathogens in US

Soller et al. (2010) Wat Res 44:4736-4747

‘Pathogens’ & system surrogates Pathogens

Viruses Bacteria Parasitic protozoa (Norovirus) (V. cholerae, Campy) (Crypto & Giardia)

Example Ref pathogens:

7

Surrogates for different system barriers

Phages (total virus-size counts)

Enterococci Clostridium perfringens spores or microspheres

Epi-indicator data available

8

GM > 35 cfu GM > 33 cfu enterococci cfu/100 mL

NEEAR studies* (Wade et al., 2008, 2010)

Log10 Enterococci density / 100mL0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

HC

GI ra

te / 1

000 s

wim

mers

-5

0

5

10

15

20

25

8 HCGI / 1000

NEEAR marine data

NEEAR fresh data

33-35 cfu/100mL

Bacterial criteria: Recreational waters

*National Epidemiological & Environmental Assessment Recreational Water (NEEAR)

Bather GI risks

9

Poorer relationship with E. coli: time to move on for freshwater/drinking water indicator?

Importance of minor human impact • Key uncertainty here

is qPCR precision? • <20% Ent. from

humans maybe sig, but qPCR MST probably not that precise

• CFU vs qPCR enterococci differ

Schoen & Ashbolt (2010) Environ. Sci. Tech. 44:2286-91 % of 35 ent /100 mL from gulls

Prob

. GI I

llnes

s

10

Risk from sewage

Risk from gulls

5-95%ile

10

GullCattle Pigs

Poultry

Raw Sewage

Secon. Efflu

ent

Prob

abilt

y of

Illn

ess

10-6

10-5

10-4

10-3

10-2

10-1

100

Fecal source matters

Wastewater Raw Treated Gull Poultry

Cattle Pig

Prob

abili

ty o

f illn

ess

For 35 enterococci/100mL

Benchmark Risk (~ 3%)

11 Soller et al. (2010) Wat Res 44: 4674-4691

Public health costs from water • CDC estimate waterborne disease costs > $970 m/y

– Addressing giardiasis, cryptosporidiosis, Legionnaires’ disease, otitis externa, and non-tuberculous mycobacterial infections, causing over 40 000 hospitalizations year-1 vs $780 m/y total GI pathogens

Collier et al. (2012) Epi Inf

Hazard identification & characterization Describe physical system, selection of reference pathogens and identification of hazardous events

STEP 1 SETTING

Disease $ / hospitalization Total cost Cryptosporidiosis $16 797 $45 770 572 Giardiasis $9 607 $34 401 449 Legionnaires’ disease $33 366 $433 752 020 NTM infection/Pulmonary $25 985 / $25 409 $425 788 469/ $194 597 422

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DW-QMRA

Hazard identification & characterization Describe physical system, selection of reference pathogens and identification of hazardous events

STEP 1 SETTING

STEP 2 EXPOSURE

For each reference pathogen:

Source water Pathogen density (PDF)

Treatment Pathogen removal

Ingress/Growth Fecal ingress &

growth of opportunistic path’s

In-premise plumbing versus DS Consumption

Volume water inhaled/ingested

(Pingress) Distribution

Pathogen loss (biofilm/death)

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Pathogen recovery estimates • Most important to include recoveries for waters

with ~2-10% pathogen recovery; – As 10-100 fold underestimations of path densities

• Recoveries generally not necessary in every sample if able to collect ~20 recovery samples to describe uncertainty

• Recoveries (as for all likely variables impacting results: infectivity, D-R etc.) need uncertainties to be included in reporting QMRA estimates

14 Petterson et al. 2007 J Wat Health 5(S1):51-65

Pathogens scaled over orders of magnitude in environmental waters

• Theoretically pathogen counts in drinking and source waters shown to have discrete Weibull (DW) or related discrete growth distribution (DGD) – DW & DGD offer theoretical basis for extrapolation to

important high count events, unlikely in typical pathogen datasets

– 500–1,000 random samples required for reliable assessment of mean ±10%, though 50–100 samples estimate within one log

Englehardt et al. (2012) J Wat Health 10:197–208 15

Contaminant intrusion due to low pressure & physical gaps in DWDS

Outside source of contamination

Pathway

Failure to maintain adequate pressure

Or during a mains repair

CONTAMINANT INTRUSION

Besner et al. (2011) Water Research 45:961-979

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Distribution system biofilms • Yet to account for biofilm-sequestered fecal pathogens (intruders), let alone opportunistic pathogens (residents, e.g. Legionella, MAC, Pseudomonas aeruginosa, amoebae)

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• Current model shows that: – For WW intrusion, Pinf Giardia > Pinf from Crypto – Starting time of intrusion greatly influences the level of

risk to which population is exposed • Increasing Cl2 residual will not necessarily reduce the

exposure, alternative strategies may be needed (i.e. booster chlorination for Giardia cysts)

– A chloramine residual does not provide significant protection against oo/cyst exposure

Intrusion model DWDS risks*

*Besner et al. (2011) Water Res 45:961-979 18

DWDS Norovirus risk* • Maintaining a chloramine residual did not

appear to significantly reduce viral risk • Effectiveness of ensuring separation distances

from sewer mains to reduce risk may be system-specific

• Leak detection/repair and cross-connection control should be prioritized in areas vulnerable to negative pressure transients

*Yang et al. (2011) J Water Health 9:291–305 19

WSP

So what next for DWDS pathogen risks?

• Intrusion risks seem real (epi & QMRA) and current chlorine residual not very effective

• While engineering vulnerabilities known, they need to be addressed as GTR & TCR-related monitoring inadequate for short-term intrusion management – Need for inexpensive ‘on-line’ intrusion monitoring

• So what analytes / target values? 20

low press. GI RR 1.6 Nygård et al (2007) Int J Epi 36:873-880

Rationale for indicator qPCR vs pathogen detection – a numbers game (~ 100-fold)

• Target pathogen density (rec water 0.03 GI risk swim-1) – e.g. for one of the most numerous known pathogens: 9 Norovrius genomes L-1 of rec water 0.03 GI risk Changing Norovirus morbidity based on infection from best estimate

0.6 to 0.1 increases target density to 80 Norovrius genomes L-1 (half to a tenth if recovery accounted for)

• Bacteroides HF183 target for same level of contamination from sewage to cause the benchmark (0.03 GI) illness: – 8600 Bacteroides HF183 genome copies L-1

Ashbolt et al. (2010) Wat Res 44:4692-4703 21

Biofilm colonization and

detachment

Inhalation

Deposition 1-1,000 CFU in lung for potential illness

QMRA for critical Legionella densities

Schoen & Ashbolt (2011) Water Res 45: 5826-5836

Critical # in DW 106 – 108 CFU L-1

based on QMRA model Needs hosts to reach that

22

Aerosolization Critical # 35 – 3,500 CFU m-3 based on QMRA model

23

Legionella risk parameter sensitivity Deposited dose

Partitioning coef.

Inhalation rate Fraction microbes aerosolized Fraction respirable aerosols

Schoen & Ashbolt (2011) Water Res 45(18): 5826-5836 23 Log Legionella density in shower water (‘cfu’ L-1)

In-situ biofilm inactivation: Legionella • Using a whole genome Legionella microarray*

• RT-qPCR assays (developed with Faucher**) • We have seen a high proportion of genes involved

in metabolism, transcription, translation, replication-repair and tRNA expressed

• Using RT-qPCR to understand biofilm & intra-amoeba stress for Legionella during drinking water disinfection & metagenomics (16 & 18 S rDNA)

24 *Hovel-Miner et al., 2009 J. Bact. 91:2461-2473

**Lu et al. (submitted)

U.S. Environmental Protection Agency 25

Single-hit D-R model: too simplistic for a range of sequelae

25 Need to understand the interactions with the human microbiome

and novel genogroup human change tests

Further research gaps Zoonotic pathogens & indicators ? • Known pathogens (Sal, Campy, EcO157 etc.):

– Source attributions? (human>cattle>pig/poultry) • Emerging pathogens & what surrogates?

– HEV, T. gondii, P. aeruginosa, amoeba-resisting bacteria…

• Antibiotic-resistant bacteria/genes from animal feeding operations & environment

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Conclusions: what next • General treatment understood, short-term treat.

variation not – need on-line assessment &/or much larger datasets to include distribution ‘tails’ – Target levels based on QMRA to aid WSP

• Need qPCR targets correlated to actual fecal & environmental pathogens for F&T modeling – Linking pathogen densities/behavior with surrogates – Emerging roles for omics-ID/chemical markers – Emergence of ARG and antibiotic-resistant pathogens

• Dose-response models for various sequelae 27

Acknowledgements Helen Buse, Jingrang Lu, Ian Struewing,

Jorge Santo Domingo, Randy Revetta, Jacquie Thomas, Michael Storey, Eunice Chern & Dawn King

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QMRA within risk management Explore system risks (QMRA)

Prioritize system risks

(harmonize)

Identify control surrogates & control levels

Research knowledge gaps

Reassess system

• Pathogens in sources • Surrogate fate & transport

QMRA (inputs)

• Vulnerability assessment & better SOP

validated for mains repairs & on/off of pumps to reduce pressure waves

• ‘On-line’ detection systems

Management

• Novel pathogens in sources • Molecular & physical surrogates • Source impacts/role native microbes &

as ‘new’ surrogates Research

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