Current Stormwater Harvesting Guidelines Are Inadequate for

Oct 16, 2017 - Campylobacter is a pathogen frequently detected in urban stormwater worldwide. It is one of the leading causes of enteric disease in ma...
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Current stormwater harvesting guidelines are inadequate for mitigating risk from Campylobacter during non-potable reuse activities Heather Murphy, Ze Meng, Rebekah Henry, Ana Deletic, and David Mccarthy Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b03089 • Publication Date (Web): 16 Oct 2017 Downloaded from http://pubs.acs.org on October 17, 2017

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Current stormwater harvesting guidelines are inadequate for mitigating risk from

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Campylobacter during non-potable reuse activities

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Heather M. Murphy1, Ze Meng2, Rebekah Henry2, Ana Deletic2, David T. McCarthy2,*

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1

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PA, USA, 19122

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2

9

Engineering, Monash Infrastructure Institute, Monash University, Clayton, VIC, Australia,

Division of Environmental Health, College of Public Health, Temple University, Philadelphia,

Environmental and Public Health Microbiology Laboratory (EPHM Lab), Department of Civil

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3800

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*Corresponding author- [email protected]

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Abstract (150-200 words)

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Campylobacter is a pathogen frequently detected in urban stormwater worldwide. It is one

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of the leading causes of enteric disease in many developed countries and is the leading

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cause of enteric disease in Australia. Prior to harvesting stormwater, adequate treatment is

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necessary to mitigate risks derived from such harmful pathogens. The goal of this research

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was to estimate the health risks associated with the exposure to Campylobacter when

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harvesting urban stormwater for toilet flushing and irrigation activities, and the role

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treatment options play in limiting risks. Campylobacter data collected from several urban

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stormwater systems in Victoria, Australia, were the inputs of a Quantitative Microbial Risk

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Assessment model. The model included seven treatment scenarios, spanning wetlands,

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biofilters and more traditional treatment trains including those recommended by the

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Australian Guidelines for Water Recycling. According to our modelling and acceptable risk

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thresholds, only two treatment scenarios could supply water of sufficient quality for toilet

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flushing and irrigation end-uses: (1) using stormwater biofilters coupled with UV-treatment

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and (2) a more conventional coagulation, filtration, UV and chlorination treatment plant.

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Importantly, our modelling results suggest that current guidelines in place for stormwater

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reuse are not adequate for protecting against exposure to Campylobacter. However, more

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research is required to better define whether the Campylobacter detectable in stormwater

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are pathogenic to humans.

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Abstract art

34 Source: David McCarthy (author) (2017).

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Key words: green stormwater infrastructure, Water Sensitive Urban Design (WSUD),

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stormwater management, stormwater harvesting, pathogen, bacteria

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1. Introduction

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Campylobacter is the leading cause of enteric disease in Australia and, in the State of

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Victoria, the rate of Campylobacter illness for 2016 was 139 cases/ 100,000 people1. The

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number of cases of illness has been increasing annually; between 2014 and 2016, the annual

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number of reported cases has increased by 14% (from 7236/ year to 8243/ year).2 The most

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common Campylobacter outbreaks globally have been linked to water exposure and the

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consumption of poultry.3 Campylobacter is a pathogen found frequently in untreated water

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supplies, including stormwater run-off. 4 Therefore, it is important to ensure adequate

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treatment of these supplies before consumption or re-use resulting in human exposure.

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Stormwater harvesting is an important component of integrated water management in

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urban settings, and critical for delivery of a water sensitive city. 5 A water sensitive city is

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one that is resilient, liveable, productive and sustainable- a city that collects and recycles

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water for different re-use activities.5 Stormwater is most commonly harvested for non-

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potable uses such as municipal irrigation, private garden irrigation and toilet flushing.6

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However, examples already exist where stormwater has been harvested for indirect potable

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use.7 Pressure to harvest urban stormwater for higher end-uses (such as direct potable

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reuse) will likely increase as a result of climate change and population growth. Although

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stormwater is not yet being widely used for reuse activities in the US and in Europe,

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recurrent droughts and pressures on water resources mean that stormwater harvesting will

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become an important water management strategy in the future.

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The Australian Guidelines for Water Recycling recommend a risk-based approach to mitigate

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the health risks posed by urban stormwater.8 A key component in such an approach is to

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characterize the pathogen levels in untreated urban stormwater. However, when the

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Australian Guidelines for Water Recycling were produced, very little data on pathogen

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occurrence and concentrations in stormwater supplies were available, including for

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Campylobacter. For example, data collected to develop the Australian guidelines included

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59 sampling points from stormwater systems, of which only two were positive for

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Campylobacter spp. The maximum concentration of Campylobacter spp. observed in those

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samples was 15 most probable number (MPN)/ L. More recent datasets suggest much

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higher Campylobacter spp. concentrations in urban stormwater, with Henry et al.4 showing

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a geometric mean Campylobacter concentration of 65 MPN/ L for one of their urban

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stormwater sites and Lampard et al.9 demonstrating even higher concentrations. New risk

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assessments for stormwater harvesting are therefore required in response to these recent

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datasets.

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The National Health and Medical Research Council (NHMRC) (2009) suggest a number of

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different treatment processes to mitigate the risks posed by pathogens when harvesting

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stormwater. However, these guidelines focus on traditional drinking and wastewater

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treatment technologies (such as filtration, chlorination, ozonation and UV disinfection).

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Importantly, the contributions that could be made from typical stormwater treatment

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infrastructure (such as wetlands, biofilters, swales, etc.) was ignored, even though these

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systems have been proven to tolerate the high pollutant and flow variability seen in urban

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stormwater. Recent research clearly demonstrates the potential of green-infrastructure in

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pathogen removal,10–12 and hence warrants further investigation into whether these can be

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used as part of a stormwater harvesting treatment train.

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The objective of this research was to apply a quantitative microbial risk assessment (QMRA)

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approach to estimate the health risks associated with the harvesting of urban stormwater

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for toilet flushing and irrigation activities. Campylobacter data collected from an urban

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stormwater catchment in Melbourne, Australia were used as inputs to the QMRA. Various

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treatment scenarios were explored, including both those which are recommended in the

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guidelines and those which are more typically used in urban stormwater management

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(biofilters and wetlands). The results were compared to the existing Australian Stormwater

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Harvesting Guidelines which currently underestimate Campylobacter concentrations in

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stormwater. The results underscore the need for more data on the concentration of

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pathogens in stormwater supplies to inform treatment recommendations that will protect

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public health during water reuse activities.

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2. Materials and Methods

101 1022.1. Model Framework 103 104

A QMRA methodology was applied to estimate the health risks associated with the use of

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stormwater (under various treatment regimes) for non-potable applications. Campylobacter

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was selected as the reference pathogen for the risk assessment. Annual probability of

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infection and Disability Adjusted Life Years (DALY) were chosen as the end points for the risk

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model. The desired health targets used in the study were a probability of infection of less

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than 1 x 10-4 infections per year and less than 10-6 DALYs per person per year. Risk estimates

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were generated using Monte Carlo simulation (10,000 iterations using @Risk software

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(Palisade Corp., USA). Sensitivity analyses were performed by simultaneously varying model

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input values and examining the resulting variability in the mean probability of infection.

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A schematic of the risk framework is presented in Figure 1.

115 1162.2. Model Inputs

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2.2.1. Campylobacter data

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Microbial and treatment performance data were collected over an 18-month period from

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March 2014 to September 2015 from the Troups Creek Wetland, located in a south-eastern

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suburb of Melbourne, Australia (Figure 2). This surface-flow wetland is located at the end of

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the Troups Creek West Branch and receives water upstream from a mixed-land use

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urbanised catchment. The catchment is comprised mainly of medium and low-density

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residential developments, peri-urban areas and a small amount of commercial shopping

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districts. After a thorough sanitary survey, the catchment is not known to have any sewer

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overflow devices, but there is a small percentage (≈10%) of the dwellings that use on-site

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wastewater treatment systems (there is no hard evidence to suggest that these are not

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currently performing adequately). The wetland outflow is treated by flocculation, direct

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filtration followed by UV and chlorination before being supplied to a community of roughly

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50 households to be used for non-potable uses such as toilet flushing and irrigation.

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Specific details of the study site and the sampling regime are described elsewhere (Meng et

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al., submitted)13. In total, 49 samples were collected from the inlet and 49 were collected

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from the outlet of the wetland (Table S1), during both wet weather and dry weather

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periods, using a combination of routine sampling (i.e. grab sampling) and automatic

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samplers (EMCs). Campylobacter spp. concentrations were enumerated using an 11 tube

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MPN and followed the methods of Henry et al.4. This enumeration technique showed

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enhanced recovery of Campylobacter spp. from water samples as compared to the

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Australian Standard AS 4276.19:20014.

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This study site is serving as a case-study for more in depth analysis to understand the

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potential health impacts associated with stormwater reuse activities in terms of exposure to

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Campylobacter contamination.

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Exposure from a single event (Ee) Ee = C * 1/R* I * 10-LR * V where: C= concentration of pathogen (organisms/ litre) (Table 1) R= mean recovery of Campylobacter using the MPN method (Table 1) I= fraction of pathogens that are infectious (Table 1) LR= log reduction value for the treatment system used (Table 3) V= daily volume of water ingested (mL) via the specified exposure route ( e.g. irrigation, toilet flushing, etc. Table 2) Probability of infection from a single event ( Pinf,e) Pinf,e = 1-1F1 (α, α+β, – Ee) (exact beta-Poisson dose response model) where: α, β = dose-response functions 1F1 = a Kummer confluent hypergeometric function Annual probability of infection ( Pinf,year) Pinf,year = ( 1- ( 1-Pinf,e)days ) where: Pinf,e = probability of infection from a given event days= number of days per year that event is likely to occur (Table 2)

DALY DALY/ year = Pinf, year * Pill/inf * DALY/ case * sfr where: Pinf,year = probability of infection per year Pill/inf= probability that infection leads to symptomatic illness ( Section 2.2.5) DALY/ case= DALY/ case of Campylobacter illness accounting for severity and duration of the illness sfr= Susceptibility fraction ( Section 2.2.5) 146

Figure 1. Quantitative Microbial Risk Assessment framework and secondary burden

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equations used to estimate the probability of infection and disability-adjusted life-years

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(DALYs) from exposure to Campylobacter during stormwater re-use activities.

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149 Wetland Inlet

N

Wetland Outlet

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Figure 2. Inlet and outlet sampling locations for Troups Creek wetland (Map Data: Google,

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2017).

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Campylobacter spp. inlet and outlet concentrations of the wetland (Table S1) were fit to a

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log-normal distribution and are presented in Table 1. The log-normal distribution was the

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best fit for the data per the Kolmogorov–Smirnov statistic. Mean Campylobacter spp.

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concentrations found in the inlet and outlet samples were similar at 808 MPN/ L and 901

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MPN/ L, respectively. There was no observed decrease in concentrations of Campylobacter

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through the wetland, although there was an observed reduction in concentrations of E. coli

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(Meng et al.,13 submitted, Table S1). Campylobacter concentrations increased slightly

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through the wetland on average, potentially as a result of re-suspension from sediment and

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input from waterfowl species (Meng et al.13, submitted, Table S1). This further underlines

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the necessity of collecting pathogen data in addition to indicator organisms when

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understanding the survival and treatment capability of these water reuse systems.

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The QMRA model also accounted for the recovery of Campylobacter spp. based on the

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culturing methods employed in the study, as well as the estimated proportion of

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Campylobacter in the stormwater that is expected to be human infectious. Mean recovery

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values (R) were inputted as point estimates and a uniform distribution was applied to the

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proportion of Campylobacter spp. that was likely to be human infectious (Table 1).

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Table 1. Inlet and outlet concentrations and fitted distributions of Campylobacter in the

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Troups Creek wetland, including method recovery and proportion of human infectious

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Campylobacter applied in the risk models

174 Proportion Mean (SD)

Concentration

Recovery

N

Human MPN/L

Model Inputs

a

(R)

Infectiousb Log-normalc Inlet

Outlet

49

49

808 (929)

Uniformd 68%

(1891,16243)

(0.57, 1)

Log-normalc (1371,

Uniformd

901 (974)

68% 5464)

(0.57, 1)

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a

Mean recovery of Campylobacter during the sampling period;

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b

We assumed that most of the Campylobacter at the study site would be from waterfowl and applied a range

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of proportion of human infectious Campylobacter from these sources from Soller et al. 2010.

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c

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d

Lognormal distribution (mean, standard deviation) Uniform distribution (min, max)

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2.2.2. Risk exposure pathways

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Table 2 outlines the various exposure pathways investigated during the risk assessment. The

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pathways selected are consistent with those presented in the Australian Guidelines for

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Water Recycling8, except for swimming. Swimming was added on as an exposure route for

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children, as during the study period, three children were observed swimming in the Troups

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Creek Wetland. The volumes of water ingested for each exposure scenario were taken

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directly from the Australian Guidelines for Water Recycling8 so that we could make direct

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comparisons with the guidelines in our analyses. However, the values used in the present

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model as also in the range of what is currently being applied by other researchers in similar

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and recent risk assessment studies.15,15–20

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Table 2. Risk exposure scenarios, corresponding volumes of water ingested and annual

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frequency of exposure used in the QMRA model. Exposure

Frequency Volume (ml/ (times/

Reference

a

event)

person/year)a Residential Garden Irrigation 0.1

90

NHMRC (2009)

1

90

NHMRC (2009) 8

100

1

NHMRC (2009) 8

1

50

NHMRC (2009) 8

(ingestion of aerosols) Residential Garden Irrigation (routine ingestion-hand to mouth exposure) Garden Irrigation (accidental drinking) Municipal Irrigation (routine ingestion from hand to

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mouth from participants in sporting activities) Toilet flushing Swimming (child)b

0.01

1100

NHMRC (2009)8

37 (+/- 9.5)

1

Dufour et al. 200621

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a

b

Point estimate values; Pert distribution ( 5%, mean, 95% confidence intervals )

2.2.3. Stormwater treatment scenarios

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Seven stormwater treatment scenarios were tested in the QMRA model (Table 3) assuming

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a certain log reduction of Campylobacter. Typical stormwater treatment schemes applied in

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Melbourne were examined including the use of wetlands and/or biofilters and disinfection

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by ultraviolet (UV) light.

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Scenario 1 applied the direct use of untreated urban stormwater (base-case), while Scenario

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2 was based on the treatment through the wetland. Scenarios 3 and 4 used biofilter

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treatment and biofilter followed by UV, respectively. The log removal values for

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Campylobacter through the biofilter were taken from field experiments from two biofilters.

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The average of the 5%, means and 95% confidence levels for log removals achieved through

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the biofilters were used as inputs into a PERT distribution.

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Scenarios 5 and 6 apply the recommended treatment guidelines for the reuse of stormwater

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for municipal irrigation (Scenario 5) and dual reticulation (indoor and outdoor use) (Scenario

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6) as per the Australian Water Reuse Guidelines.8 The recommended log removal values for

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Campylobacter for these scenarios were 1.3 log and 2.4 log, respectively. The Australian

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Water Reuse Guidelines also suggest that, for catchments that contain on-site wastewater

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treatment systems and agricultural practices, additional investigations and assessments of

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these log removal values should be conducted if these additional sources of pollution are

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known to be inadequately managed. At the time of writing this paper, there is insufficient

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evidence to suggest that these are managed inadequately at the Troups Creek site.

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Furthermore, the authors (see Henry et al.4 and Lampard et al.9) have found similarly high

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Campylobacter spp. levels in other urban stormwater catchments around Melbourne that

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do not have on-site wastewater management systems or agricultural practices. As such, in

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this paper, we apply the 1.3 and 2.4 log reductions, carefully noting this commentary.

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Scenario 7 examined the current treatment train used to treat Troups Creek’s effluent prior

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to harvesting. In this system, the wetland’s effluent is further treated by direct filtration

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(coagulation/ filtration through sand and granular activated carbon), UV disinfection and

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chlorination before use within the homes of the surrounding community. Using ranges

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published in Murphy et al.22 for log removal of Campylobacter by direct filtration and

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chlorination, and the log removal for UV disinfection specified in the Australian Guidelines

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for Water Recycling8, we applied a log removal range of 5.37 to 13.36.

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Table 3. Stormwater treatment scenarios, log removal values, and distributions applied in

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the QMRA model

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237 Scenario

Treatment

Log Removal

Distribution

1

No treatment (storage)

0

Point

2

Wetland treatment

Reference

Used wetland outlet concentrationsa Combined data

3

0.13,0.85,1.92b

Biofilter

Pert

from two field Biofiltersa

4

Biofilter + UV

3.13-5.92c

Uniform

NHMRC (2009

1.3

Point

NHMRC (2009)

2.4

Point

NHMRC (2009)

Recommended reduction 5 for municipal irrigation Recommended reduction 6

for dual reticulation (indoor & outdoor use) Current treatment system

Murphy et al. at Troups Ck (Coagulation, 5.37-13.36d

7

Uniform

2016; NHMRC

Direct filtration, UV, (2009) chlorination) 238

a

see Table 1 for distribution details

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b

Average of the 5%, mean, 95% of two biofilter experiments conducted at two different sites

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c

Low and high Biofilter log removals ( 0.13, 1.92) + range of UV disinfection (3.0-4.0 log) for Campylobacter

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published in the Australian reuse guidelines = (3.13, 5.92)

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d

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2.37- 9.36 (Murphy et al. 22 Supplemental Contents) + Range of UV disinfection applied in the Australian Reuse

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Guidelines (3.0-4.0 log)= 5.37- 13.36

Low and high log removal values for coagulation, direct filtration and chlorine disinfection for Campylobacter

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2.2.4. Dose Response Model

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The exact beta-Poisson dose response model as described by Schmidt et al. 23 was used to

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estimate the probability of infection of Campylobacter per exposure event. We used the

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exact beta-Poisson dose response model used as it abides by the theoretical maximum

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dose-response relationship. The maximum likelihood parameters for α and β described by

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Schmidt et al. 23 were used (α = 0.1453, β = 8.007). 23

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To explore the possible variability in dose-response relationships due to the uncertainties

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inherent in the Campylobacter human feeding trial data24, some scenarios were re-run using

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dose-response models with different parameter sets. Two other parameter sets were trialed

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from Schmidt et al.23: α = 0.253, β = 528; α = 0.140, β = 1.09. These were carefully selected

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to represent the lower bound and the upper bound (respectively) of the possible dose-

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response relationships when calibrating the exact beta-Poisson model to the dataset of

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Black et al. 24 using maximum likelihoods. For further comparisons, the exact beta-Poisson

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model presented by Teunis et al.25 was also trialed.

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2.2.5. Calculation of DALY/ year

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To estimate the number of DALYs per person per year associated with stormwater exposure

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in Melbourne, Australia, we assumed that for every infection, the probability that an

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infected individual presents with symptomatic illness was between 0.1- 0.6.26 We applied a

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uniform distribution between these two values. To be consistent with the Australian

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Guidelines, we applied a DALY/ case for Campylobacter of 0.0046 and a susceptibility

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fraction of 100%.8

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3. Results and Discussion

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This study represents one of very few studies that have examined the risk of exposure to

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Campylobacter in stormwater during water re-use activities.20 Other studies have looked at

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the exposure to Campylobacter from rainwater during re-use or from recreational exposure

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stormwater.19,27–29 The data utilized in the models developed in our study is the largest

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known cultured Campylobacter spp. dataset available for stormwater globally. The dataset

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was generated from a field scale stormwater system used to feed a residential community

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water re-use supply in Australia (Meng et al. 13submitted) and therefore makes an excellent

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case study for examining the Australian Guidelines for Water Recycling.

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Table 4 presents the probability of infection following a single event, the annual probability

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of infection and DALYs lost per person per year from exposure to stormwater containing

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Campylobacter spp. under the seven different scenarios.

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Table 4. Probability of infection per exposure event, annual probability of infection and DALYs lost per person per year due to annual exposure (Bolded numbers represent all scenarios where risk is acceptable per the prescribed thresholds of P inf, year=1.0x10-4 and DALY/ person/year= 1.0 x10-6) Scenario

Treatment

1

No treatment

2

Wetland treatment

3

Biofilter

Exposure

P inf, event

P inf,year (Acceptable risk -4 target= 1.0x 10 ) Mean 95%

DALY/ person/ year (Acceptable risk -6 target= 1. 0x 10 ) Mean 95%

Garden Irrigation (aerosols) Garden Irrigation (routine ingestion) Garden Irrigation (accidental drinking) Municipal Irrigation (routine ingestion) Toilet flushing Swimming (child) Garden Irrigation (aerosols) Garden Irrigation (routine ingestion) Garden Irrigation (accidental drinking) Municipal Irrigation (routine ingestion) Toilet flushing Swimming (child) Garden Irrigation (aerosols) Garden Irrigation (routine ingestion) Garden Irrigation (accidental drinking) Municipal Irrigation (routine ingestion) Toilet flushing

3.1 x 10-3 1.9 x 10-2 2.2 x 10-1 -2 1.9 x10 -4 3.7 x 10 -1 1.5 x 10 2.6 x 10 -3 1.9 x 10 -2 -1 2.4 x 10 1.9 x 10 -2 2.8 x 10-4 -1 1.66 x 10 -4 5.6 x 10 4.4 x 10-3 -2 9.4 x 10 -3 4.4 x 10 5.9 x 10-5

1.4 x 10-1 4.4 x 10 -1 2.2 x 10 -1 -1 3.4 x 10 -1 1.6 x 10 -1 1.5 x 10 -1 1.4 x 10 2.4 x 10-1 -1 2.4 x 10 3.9 x 10-1 1.7 x 10-1 -1 1.7 x 10 3.6 x 10-2 1.7 x 10-1 -2 9.4 x 10 -1 1.2 x 10 4.2 x 10 -2

7.0 x 10 -1 1.0 4.9 x 10 -1 -1 9.9 x 10 -1 7.8 x 10 -1 4.1 x 10 -1 6.1 x 10 1.0 -1 4.7 x 10 9.8 x 10-1 7.0 x 10-1 -1 3.9 x 10 1.7 x 10-1 8.4 x 10-1 -1 3.7 x 10 -1 6.4 x 10 2.1 x 10-1

2.2 x 10 -1 7.0 x 10-4 3.5 x 10-4 -4 5.4 x 10 -4 2.5 x 10 -4 2.4 x 10 2.3 x 10-4 3.9 x 10-4 -4 3.9 x 10 6.2 x 10-4 2.7 x 10-4 -4 2.7 x 10 5.7 x 10-5 2.7 x 10-4 -4 1.5 x 10 -4 1.9 x 10 6.8 x 10-5

1.1 x 10-3 2.2 x 10-3 9.5 x 10 -4 -3 1.9 x 10 -3 1.2 x 10 -4 7.6 x 10 9.9 x 10-4 2.2 x 10-3 -4 9.3 x 10 1.9 x 10-3 1.1 x 10-3 -4 7.4 x 10 2.8 x 10-4 1.3 x 10-3 -4 5.9 x 10 -4 9.9 x 10 3.3 x 10-4

5.1 x 10-7 -6 5.0 x 10 -4 4.2 x 10 -6 5.0 x 10 -8 5.1 x 10

4.5 x 10-5 -4 4.4 x 10 -4 4.2 x 10 -4 2.5 x 10 -5 5.5 x 10

1.1 x 10-4 -3 1.1 x 10 -3 1.2 x 10 -4 6.2 x 10 -4 1.4 x 10

7.0 x 10-8 -7 6.7 x 10 -7 6.6 x 10 -7 3.8 x 10 -8 8.5 x 10

1.8 x 10-7 -6 1.8 x 10 -6 1.9 x 10 -7 9.8 x 10 -7 2.2 x 10

1.7 x 10 -3

5.7 x 10 -2

2.9 x 10-1

9.2 x 10-5

4.6 x 10-4

4

Biofilter + UV

Garden Irrigation (aerosols) Garden Irrigation (routine ingestion) Garden Irrigation (accidental drinking) Municipal Irrigation (routine ingestion) Toilet flushing

5

1.3 log removal (reduction suggested for municipal irrigation)

Municipal Irrigation (routine ingestion)

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7

2.4 log removal (reduction suggested for indoor and outdoor use) Coagulation, Direct filtration, UV, chlorination (current system at Troups Creek)

Garden Irrigation (aerosols) Garden Irrigation (routine ingestion) Garden Irrigation (accidental drinking) Municipal Irrigation (routine ingestion) Toilet flushing Garden Irrigation (aerosols) Garden Irrigation (routine ingestion) Garden Irrigation (accidental drinking) Municipal Irrigation (routine ingestion) Toilet flushing

1.5 x 10-5 -4 1.5 x 10 -3 9.7 x 10 -4 1.5 x 10 -6 1.5 x 10 1.1 x 10-9 1.1 x 10-8 1.1 x 10-6 -8 1.1 x 10 -10 1.1 x 10

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1.3 x 10-3 -2 1.2 x 10 -3 9.7 x 10 -3 6.8 x 10 -3 1.6 x 10 1.0 x 10-7 1.0 x 10-6 1.1 x 10-6 -7 5.6 x 10 -7 1.2 x 10

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4.8 x 10-3 -2 4.7 x 10 -2 4.5 x 10 -2 2.6 x 10 -3 5.9 x 10 1.4 x 10-7 1.4 x 10-6 1.6 x 10-6 -7 7.9 x 10 -7 1.7 x 10

2.2 x 10-6 -5 1.9 x 10 -5 1.6 x 10 -5 1.1 x 10 -6 2.7 x 10 -10 1.3 x 10 1.3 x 10-9 1.4 x 10-9 -10 7.2 x 10 -10 1.6 x 10

7.6 x 10-6 -5 7.3 x 10 -5 7.0 x 10 -5 4.1 x 10 -6 9.3 x 10 -10 2.2 x 10 2.2 x 10-9 2.5 x 10-9 -9 1.2 x 10 -10 2.7 x 10

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3.1.

No treatment and wetland treatment scenarios (1 & 2)

289

In Scenarios 1 and 2, the risk of infection associated with the use of untreated stormwater

290

and stormwater treated through a constructed wetland was assessed for residential garden

291

irrigation exposures (aerosol water droplets, hand to mouth exposure and accidental

292

drinking), municipal irrigation (hand to mouth), toilet flushing and swimming (Table 4). All of

293

the exposures studied in the model resulted in a mean annual probability of infection and

294

DALY per person per year well above the recommended health thresholds (mean Pinf,year =

295

1.4 x 10-1 to 4.4 x 10-1). An incident of accidental drinking (100mL) of either the untreated or

296

treated stormwater risks a 21-24% probability of becoming infected. Although, the

297

swimming exposure scenario infection should be uncommon, following one swimming

298

event, the mean probability of infection was 15% or 17% for the untreated and treated

299

stormwater, respectively (Table 4).

300 301

The risks of infection for an accidental drinking incident or swimming event are comparable

302

to those reported by deMan et al. 30 for exposure to Campylobacter spp. during recreation

303

in floodwaters from storm sewers in the Netherlands (~20% risk of infection for children). In

304

this study, the average volume of water consumed by a child was 1.7mL, with

305

Campylobacter spp. concentrations ranging from 0- 3697 MPN/L. In the present study, the

306

assumed consumption volumes were much higher (100mL) and concentrations of

307

pathogens were similar (6-2852 MPN/L), however, risks were nearly the same. The cause of

308

this difference is that the present study used a less-conservative dose response model than

309

the one reported by deMan et al. 30, thereby confirming the need to conduct sensitivity

310

testing of dose-response models when conducting QMRAs.

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Non-potable uses such as residential and municipal irrigation exposures and toilet flushing

313

resulted in high annual probabilities of infection and DALYs. It is common in Australia that

314

stormwater is used directly or treated through a wetland before municipal irrigation. In a

315

review of 16 stormwater recycling systems in Australia, Hatt et al.31 reported that the end

316

use of the water from all 16 systems was for irrigation purposes. Of the 16 systems, 7 used a

317

wetland as treatment, mean health risks to users from these 7 systems could be high

318

depending on the microbial quality of the stormwater leaving the wetland.

319 320

3.2.

Biofiltration treatment scenarios (3 & 4)

321

Scenarios 3 and 4 modelled the health risks from exposure to stormwater treated by

322

biofiltration (Scenario 3) and biofiltration followed by UV treatment (Scenario 4). The results

323

showed that biofiltration on its own is inadequate in protecting users from Campylobacter

324

infections on an annual basis with an annual risk of infection ranging from 9.41 x10-2 to 1.18

325

x10-1 which exceeds the acceptable health risk threshold by 2-3 orders of magnitude. On a

326

single event basis, biofiltration is even inadequate at protecting users from Campylobacter

327

infection with the exception of a toilet flushing event (mean Pinf,year = 5.86 x10-5). If

328

biofiltration is coupled with UV treatment (Scenario 4), risks drop by 3 orders of magnitude

329

and the annual probability of infection goes below the acceptable threshold for garden

330

irrigation (exposure to aerosols) and toilet flushing (mean Pinf,year = 4.53 x10-5 to 5.53 x10-5).

331

However, this level of treatment is still inadequate for protecting populations from an

332

accidental drinking exposure event, or routine ingestion exposure from garden irrigation or

333

municipal irrigation (mean Pinf,year = 2.46 x10-4 to 4.35 x10-4). In the review of Australian

334

stormwater recycling systems by Hatt et al. 31 , 9 of the 16 systems studied used infiltration

335

(biofiltration) systems for treatment before irrigation. Two of the nine systems provided

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water for toilet flushing and/or other outdoor uses without any additional treatment.

337

Another two of the nine systems used advanced treatment coupled with disinfection before

338

providing water for toilet flushing and/or other outdoor uses. The findings of the present

339

study suggest that biofiltration on its own before irrigation, toilet flushing or other outdoor

340

uses could be inadequate treatment for the protection of end users in 9/ 16 systems

341

identified by Hatt et al. 31

342 343

3.3.

Recommended treatment scenarios (5 & 6)

344

Scenarios 5 and 6 were based on the recommendations provided by the Australian

345

Guidelines for Water Recycling8; 1.3 log removal is required for municipal irrigation end-uses

346

and 2.4 log removal is required for indoor and outdoor uses at the household level). In both

347

scenarios, the Australian guidelines were not protective in terms of reducing the annual risk

348

of infection to below the acceptable health threshold. In all exposure routes (irrigation to

349

toilet flushing), the risks were 1-2 orders of magnitude higher than the 1x10-4 annual

350

probability of infection threshold. These findings demonstrate that the current Australian

351

guidelines for treatment of stormwater may be inadequate to reduce health risks from

352

exposure to stormwater. Currently, the Australian guidelines are based on the assumption

353

that a concentration of 15 MPN/ L Campylobacter is a worst case scenario concentration

354

that can be expected in urban stormwater. 8 The mean Campylobacter concentrations in the

355

present study site was 808 MPN/ L, which over 15 times the concentration prescribed in the

356

guidelines. The concentrations of Campylobacter in stormwater around other drains in

357

Melbourne were also well in excess of that used by the NHRMC4,8,9 (Meng et al. submitted).

358

Globally, Campylobacter concentrations are also elevated in urban stormwater. For

359

instance, de Man et al.30 and Sales-Ortells & Medema32 reported even higher concentrations

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of Campylobacter spp. in stormwater in the Netherlands ranging from 0 to 3697 MPN/L, and

361

35 to 1001 genomic copies/ L, respectively. These datasets demonstrate that the

362

contamination of stormwater is site specific, and consequently pathogen concentrations

363

should not be generalized.33 In addition, several researchers have highlighted that levels of

364

pathogen contamination in watersheds will increase over time, therefore it is imperative

365

that guidelines are set using a specific target concentration, that these values be updated as

366

data becomes available.

367 368

3.4.

Current treatment scenario (7)

369

The Australian Guidelines for Water Recycling guidelines8, specify that a log reduction of 2.4

370

is required for the end-uses implemented at the Troups Creek harvesting site (i.e. indoor

371

toilet flushing and household irrigation). The results of Scenario 6 clearly demonstrate that if

372

the treatment system at this site was built according to these guidelines, the mean annual

373

probability of infection would have exceeded the acceptable health threshold. Thankfully,

374

with its 5-13 log reduction treatment train, the ‘as-built’ system at this site significantly

375

exceeded the requirements of these guidelines. While there was a stigma that this system

376

was significantly overdesigned, the data presented here demonstrates that the guidelines

377

may have been insufficient to protect public health. According to the results presented

378

herein, the actual treatment train used in Troups Creek Harvesting system is adequate for

379

mitigating health risks for its specified end-use (i.e. for garden irrigation and toilet flushing,

380

mean Pinf, year= 1.0 x 10-7 to 1.0 x 10-6).

381 382

3.5.

Influence of the dose response model

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When assessing health risks using QMRA, the selection of the dose response model is an

384

important consideration as it can significantly affect the projected risks of infection.34 In this

385

research, the hypergeometric dose response parameters presented by Schmidt et al.23 were

386

selected over the parameters developed by Teunis et al. 25. The Teunis et al. 25 model

387

includes outbreak data from children exposed to Campylobacter, in addition to data from

388

healthy adults in a human feeding study. Children require a lower dose of Campylobacter to

389

become infected, therefore using the Teunis et al. 25 model increases the probability of

390

infection and subsequent projected health risks. Table 5 demonstrates the influence that

391

the dose response models can have on the risk outputs. At high concentrations of

392

Campylobacter (Scenario 1), the risk of infection increases by 2-3 fold when one applies the

393

Teunis et al. 25 model compared to the model developed by Schmidt et al. 23 At low doses of

394

pathogen (Scenario 7), the Teunis et al. 23 model increases health risks by 10-100 fold

395

depending on the exposure route. This is evident when we compare the results from our

396

study with those reported by deMan et al.30. deMan et al. .30 used the Campylobacter spp.

397

dose response model prepared by Teunis et al. 25 and produced higher risk estimates (2-6

398

times higher) than the model selected in the present study (Table 5).

399

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Table 5. Influence of the dose response model on the annual probability of infection per year due to annual exposure Scenarios 1,4 and 7 (Bolded numbers represent all scenarios where risk is acceptable per the prescribed threshold of 1.0 x 10-4 ) Scenario

1

4

7

Treatment

Exposure

Garden Irrigation (aerosols) Garden Irrigation (routine ingestion) Garden Irrigation No treatment (accidental drinking) Municipal Irrigation (routine ingestion) Toilet flushing Swimming (child) Garden Irrigation (aerosols) Garden Irrigation (routine ingestion) Garden Irrigation Biofilter + UV (accidental drinking) Municipal Irrigation (routine ingestion) Toilet flushing Garden Irrigation (aerosols) Garden Irrigation (routine Current treatment ingestion) at Troups Creek (Coagulation, Direct Garden Irrigation filtration, UV, (accidental drinking) chlorination) Municipal Irrigation (routine ingestion) Toilet flushing

Schmidt et al. 2013 (upper bound)1 α = 0.14, Beta=1.09 Mean 95%

Schmidt et al. 2013 (Mean)1 α = 0.1453, β = 8.007 Mean 95% -1

-1

Schmidt et al. 2013 (lower bound)1 α = 0.253, Beta=528 Mean 95%

1.4 x 10

7.0 x 10

3.7 x 10

-1

1.0

8.2 x 10

4.4 x 10-1

1.0

7.4 x 10-1

1.0

5.6 x 10-2

2.2 x 10-1

4.9 x 10-1

3.8 x 10-1

6.2 x 10-1

3.4 x 10-1

9.9 x 10-1

6.5 x 10-1

1.0

-1

-1

7.8 x 10 -1 4.1 x 10

-1

4.0 x 10 -1 3.0 x 10

1.0 -1 5.7 x 10

-5

1.1 x 10

-4

1.1 x 10

-6

2.9 x 10

-6

4.4 x 10

-4

1.1 x 10

-3

1.1 x 10

-5

2.9 x 10

-5

4.2 x 10-4

1.2 x 10-3

1.3 x 10-5

1.6 x 10 -1 1.5 x 10 4.5 x 10

-6

1.1 x 10-6 5.6 x 10

-7

1.2 x 10-7

9.4 x 10-1

1.0

4.3 x 10-2

2.0 x 10-1

6.9 x 10-1

7.4 x 10-1

3.5 x 10-2

1.7 x 10-1

8.9 x 10-1

1.0

-3

-2

3.9 x 10 -1 1.0 x 10

-1

6.9 x 10 -1 6.4 x 10

1.0 -1 7.3 x 10

2.6 x 10

-4

7.6 x 10

-4

1.4 x 10

-3

4.5 x 10

2.4 x 10

-3

7.6 x 10

-3

1.1 x 10

-2

4.4 x 10

3.3 x 10-5

2.2 x 10-4

8.3 x 10-3

-3

-2

1.1 x 10-2

4.9 x 10-2

1.6 x 10

-5

1.4 x 10

-3

4.2 x 10

-3

6.7 x 10

-3

2.5 x 10

-4

1.4 x 10

-6

3.6 x 10

-6

3.1 x 10

-4

9.3 x 10

-4

1.7 x 10

-3

5.5 x 10

1.4 x 10

1.0 x 10

2.8 x 10-1

6.7 x 10

-6

-5

1.0 x 10-7

1.0

9.9 x 10 -2 2.1 x 10

-2

6.4 x 10

6.2 x 10

5.5 x 10

-1

3.2 x 10

-4

-4

2.5 x 10

-3

Teunis et al. 2005 (Mean)2 α = 0.024, Beta=0.011 Mean 95%

1.4 x 10-7 1.4 x 10

-6

1.6 x 10-6 7.9 x 10

-7

1.7 x 10-7

6.3 x 10-7 6.2 x 10

-6

6.9 x 10-6 3.5 x 10

-6

7.6 x 10-7

9.6 x 10-7 9.6 x 10

-6

1.1 x 10-5 5.4 x 10

-6

1.2 x 10-6

ACS Paragon Plus Environment

2.3 x 10-9 2.3 x 10

-8

2.5 x 10-8 1.3 x 10

-8

2.8 x 10-9

3.8 x 10-9 3.8 x 10

-8

4.2 x 10-8 2.1 x 10

-9

4.5 x 10-9

-2 -3

2.6 x 10-6 2.6 x 10

-5

2.8 x 10-5 1.4 x 10

-5

3.1 x 10-6

5.4 x 10-6 5.4 x 10

-5

6.0 x 10-5 3.0 x 10

-5

6.6 x 10-6

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402 0 Teunis et al., 2005

log(probability of infection)

-1 -2

Schmidt et al., 2013

-3 -4 -5 -6 -7 -5

-3

-2

-7 -7 -7 -8 -8 -8 -8 -8 -9 -9 -9

-1 0 1 log(dose) [cfu/event]

2

3

4

5

Irrigation (acc. drinking) Swimming Irrigation (hand-mouth) Irrigation (aerosols)

Exposure types

403

-4

Toilet

-5

-4

-3

-2

-1 0 1 log(dose) [cfu/event]

2

3

4

404 405

Figure 3. Top - comparison of the Campylobacter dose response curves from Teunis et al.

406

(2005) (grey line) and Schmidt et al. (2013) (black line). Bottom – range of Campylobacter

407

doses for each exposure type (5th to 95th percentile).

5

408 409

The selection of the parameters within a dose response model itself can significantly

410

influence the range of predicted risk. In Table 5, the impact of using the upper and lower

411

bound dose-response relationships from Schmidt et al.16 on the predicted risk of infection

412

were investigated. The predicted annual risks of infection varied by 10 to almost 100-fold

413

from when the mean alpha and beta values were used. Scenario 7 clearly shows the impact

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414

of these selected dose-response parameter sets; the 95th percentile annual probabilities of

415

infection for accidental ingestion from garden irrigation was 1.1x10-5 when using the upper

416

bound dose-response model whereas it decreased to just 4.2x10-8 when using the lower

417

bound. Regardless of the model used, the current treatment train installed at the Troups

418

Creek Harvesting site is adequate for public health protection.

419 420

Van Abel et al.34 compare the risks predicted by various norovirus dose-response models

421

and emphasized that the dose response model selected needed to be appropriate for the

422

environmental media, exposure scenario and dose of pathogen. Similar to the

423

Campylobacter dose response models explored herein, the probabilities of infection for

424

norovirus can vary by 1-3 orders of magnitude depending on the dose response model

425

selected and the dose of pathogen. 34

426 427

3.6.

Sensitivity of the model to other inputs

428

As described in the previous section, our QMRA models are sensitive to the selection of the

429

dose response model and the parameters used to describe that dose response model.

430

Figure 3 also shows the sensitivity of the model to the ingestion dose of Campylobacter spp.

431

(which is a function of the exposure volume and Campylobacter spp. concentrations). For

432

example, depending on the exposure type, varying the concentration of Campylobacter

433

between the 5th and 95th percentiles resulted a 3 order of magnitude difference in the

434

resulting probabilities of infection per event. We further explored the sensitivity of the

435

other inputs into our QMRA models. This sensitivity analyses revealed that for the log

436

removal applied for the treatment scenario selected, the range of probabilities of infection

437

varied by the same order of magnitude. For instance, if the treatment performance varied

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by between 1-3 log, there was a 0-2 order of magnitude range in the probability of infection.

439

The models were less sensitive to the proportion of Campylobacter that is infectious

440

assuming the true proportion of infectious Campylobacter falls between 0.53-1.

441 442

3.7.

Implications for stormwater harvesting

443

The results from this study suggest that current guidelines in place for stormwater reuse in

444

Australia, and similar guidelines around the world, may not be adequate for protecting from

445

exposure to Campylobacter. The findings also question whether these guidelines have

446

misrepresented the risks derived from other pathogen-types (such as protozoa and viruses),

447

especially with advances in the viral and protozoan enumeration techniques that can

448

significantly improve recovery rates.35,36 Combined with future studies to explore other

449

pathogen-types, our study suggests that the increase in stormwater reuse seen across the

450

globe will contribute to burden of illness unless appropriate treatment is in place.

451 452

There is very limited data on the occurrence of culturable Campylobacter spp. in stormwater

453

globally. The results in the present study are representative of the concentrations of

454

Campylobacter spp. found in stormwater in the Netherlands.30,32 In a surfer health study

455

from California, USA, Campylobacter coli, Campylobacter lari and Campylobacter jejuni

456

concentrations in stormwater discharges ranged from 3 to 116.2 gc/ 100mL.27 Crudely

457

converting these to concentrations to culturable units using a published relationship in Corsi

458

et al.37, the concentrations in the surfer study ranged from 2.5 to 107 CFU/ L for pathogenic

459

strains of Campylobacter. Although these concentrations are lower than those observed in

460

the present study, this range of concentration exceeds the maximum concentration of 15

461

MPN for Campylobacter spp. defined in the current Australian guidelines meaning the

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treatment recommendations prescribed in those guidelines would be inadequate for the

463

stormwater in this part of USA.

464 465

Establishing guidelines for stormwater reuse is challenging given that pathogen occurrence

466

is highly variable and site specific. When using QMRA methodologies to support decision-

467

making and guideline setting, it is critical that the models account for the uncertainty in

468

pathogen concentrations and plan for worst-case scenario pathogen levels. Other important

469

factors to consider in model development is the proportion of pathogens found in the

470

environment that are human infectious as well as the selection of the dose response model

471

that is representative of the population at risk and environmental conditions. When using

472

QMRA, others have suggested that when multiple dose response models exist, best practice

473

would be to try multiple models for the pathogen of concern to account for the uncertainty

474

and provide a range of predicted probabilities of infections.

475

Supporting Information

476

Supporting Information Available: “SI.docx” listing the raw Campylobacter spp.

477

concentrations used as input into the risk assessment. This information is available free of

478

charge via the Internet at http://pubs.acs.org.

479

Acknowledgments

480

The authors would like to acknowledge the Smart Water Fund and South East Water for

481

funding the collection of the datasets, the Victoria Fellowship scheme for funding travel and

482

Richard Williamson, Christelle Schang, Gayani Chandrasena and Peter Kolotelo for collection

483

and analysis of data from Troups Creek wetland.

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References

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(1) Australian Government Department of Health. National Notifiable Diseases Surveillance System http://www9.health.gov.au/cda/source/cda-index.cfm (accessed May 26, 2017). (2) Victoria Health and Human Services. Notified cases for Victorian by sex, age group and year of notification https://www2.health.vic.gov.au/public-health/infectiousdiseases/infectious-diseases-surveillance/search-infectious-diseases-data/victoriaage-sex-distribution (accessed May 26, 2017). (3) Kaakoush, N. O.; Castaño-Rodríguez, N.; Mitchell, H. M.; Man, S. M. Global Epidemiology of Campylobacter Infection. Clin. Microbiol. Rev. 2015, 28 (3), 687–720 DOI: 10.1128/CMR.00006-15. (4) Henry, R.; Schang, C.; Chandrasena, G. I.; Deletic, A.; Edmunds, M.; Jovanovic, D.; Kolotelo, P.; Schmidt, J.; Williamson, R.; McCarthy, D. Environmental Monitoring of Waterborne Campylobacter: Evaluation of the Australian Standard and a Hybrid Extraction-Free MPN-PCR Method. Front. Microbiol. 2015, 6 DOI: 10.3389/fmicb.2015.00074. (5) Wong, T. H.; Allen, R.; Brown, R.; Deletić, A.; Gangadharan, L.; Gernjak, W.; Jakob, C.; Johnstone, P.; Reeder, M.; Tapper, N.; others. blueprint2013–stormwater Management in a Water Sensitive City. Melb. Aust. Coop. Res. Cent. Water Sensitive Cities ISBN 2013. (6) State of Victoria- Department of Health. Review of the Public Health Regulatory Framework for Alternative Water Supplies in Victoria- Supporting the Safe Use of Sewage, Greywater and Stormwater- Stakeholder Discussion Paper; p 47, Figure 8. (7) Boyd, J. Potable Stormwater Harvesting, The Orange Experience; , Institute of Public Works Engineering Australasia( IPWEA), 2011. (8) NHMRC. Australian Guidelines for Water Recycling (Phase 2): Stormwater Harvesting and Reuse. Canberra: Natural Resource Management Ministerial Council, Environment Protection and Heritage Council, National Health and Medical Research Council. 2009. (9) Lampard, J.; Chapman, H.; Stratton, H.; Roiko, A.; McCarthy, D.; others. Pathogenic Bacteria in Urban Stormwater Drains from Inner-City Precincts. In WSUD 2012: Water sensitive urban design; Building the water sensiitve community; 7th international conference on water sensitive urban design; Engineers Australia, 2012; p 993. (10) Chandrasena, G.; Shirdashtzadeh, M.; Li, Y.; Deletic, A.; Hathaway, J.; McCarthy, D. Retention and Survival of E. Coli in Stormwater Biofilters: Role of Vegetation, Rhizosphere Microorganisms and Antimicrobial Filter Media. Ecol. Eng. 2017, 102, 166–177. (11) Li, Y. L.; Deletic, A.; McCarthy, D. T. Removal of E. Coli from Urban Stormwater Using Antimicrobial-Modified Filter Media. J. Hazard. Mater. 2014, 271, 73–81. (12) Hathaway, J.; Hunt, W.; Graves, A.; Bass, K.; Caldwell, A. Exploring Fecal Indicator Bacteria in a Constructed Stormwater Wetland. Water Sci. Technol. 2011, 63 (11), 2707–2712. (13) Meng, Z.; Chandrasena, G.; Henry, R.; Deletic, A.; Kolotelo, P.; McCarthy, D. (accepted with revisions) Stormwater Constructed Wetlands: A Source or a Sink of Campylobacter Spp. Water Res. 2017.

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