Current Stormwater Harvesting Guidelines Are Inadequate for

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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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Environmental Science & Technology

1

Current stormwater harvesting guidelines are inadequate for mitigating risk from

2

Campylobacter during non-potable reuse activities

3 4

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

5 6

1

7

PA, USA, 19122

8

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

10

3800

11

*Corresponding author- [email protected]

12 13

Abstract (150-200 words)

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

15

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

17

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

19

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

20

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

21

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

22

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

23

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

ACS Paragon Plus Environment


25

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

26

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

27

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

29

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

30

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

31

are pathogenic to humans.

32 33

Abstract art

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

35 36

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

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stormwater management, stormwater harvesting, pathogen, bacteria

38 39

1. Introduction

40 41

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.

49 50

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.

61 62

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

74

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

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datasets.

76 77

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).

81

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.

87 88

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

98

public health during water reuse activities.

99 100

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

106

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

107

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

108

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

109

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

111

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

112

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

113 114

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

115 1162.2. Model Inputs



117 118

2.2.1. Campylobacter data

119 120

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

126

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

127

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

129

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

130

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

131

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

132 133

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

134

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

135

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

137

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

138

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

139

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

140

Australian Standard AS 4276.19:20014.


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141 142

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

144

Campylobacter contamination.

145



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

148

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


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149 Wetland Inlet

N

Wetland Outlet

150 151

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

152

2017).

153

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

154

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

155

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

159

(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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165

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

166

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

168

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

169

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

170 171

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

172

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)

175

a

Mean recovery of Campylobacter during the sampling period;

176

b

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

177

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

178

c

179

d

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

180 181

2.2.2. Risk exposure pathways

182


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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

189

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

192

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

193

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

194 195

a

b

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

2.2.3. Stormwater treatment scenarios

196 197

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

200

by ultraviolet (UV) light.

201 202

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

204

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.

208 209

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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213

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

220

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

221

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

222 223

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.

230 231 232 233 234 235

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

239

b

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

240

c

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

241

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

242

d

243

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

244

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

245


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246


2.2.4. Dose Response Model

247

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

248

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

249

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

250

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

251

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

252 253

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

254

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

255

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

257

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

258

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

259

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

260

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

261 262

2.2.5. Calculation of DALY/ year

263

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

264

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

265

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

266

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

267

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

268

fraction of 100%.8

269



270 271

3. Results and Discussion

272

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

273

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

274

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

275

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

276

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

277

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

278

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

279

case study for examining the Australian Guidelines for Water Recycling.

280 281

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

282

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

283

Campylobacter spp. under the seven different scenarios.

284


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285 286 287


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)



6

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


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

Page 19 of 31

288


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.

311



312

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


Page 20 of 31

Page 21 of 31


336

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



360

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


Page 22 of 31

Page 23 of 31


383

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



400 401

Page 24 of 31

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


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

Page 25 of 31


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



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


Page 26 of 31

Page 27 of 31


438

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



462

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.


Page 28 of 31

Page 29 of 31


484

References

485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528

(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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(14) Soller, J. A.; Schoen, M. E.; Bartrand, T.; Ravenscroft, J. E.; Ashbolt, N. J. Estimated Human Health Risks from Exposure to Recreational Waters Impacted by Human and Non-Human Sources of Faecal Contamination. Water Res. 2010, 44 (16), 4674–4691. (15) Schoen, M. E.; Xue, X.; Hawkins, T. R.; Ashbolt, N. J. Comparative Human Health Risk Analysis of Coastal Community Water and Waste Service Options. Environ. Sci. Technol. 2014, 48 (16), 9728–9736. (16) O’Toole, J.; Keywood, M.; Sinclair, M.; Leder, K. Risk in the Mist? Deriving Data to Quantify Microbial Health Risks Associated with Aerosol Generation by WaterEfficient Devices during Typical Domestic Water-Using Activities. Water Sci. Technol. 2009, 60 (11), 2913–2920. (17) Maimon, A.; Tal, A.; Friedler, E.; Gross, A. Safe on-Site Reuse of Greywater for Irrigation-a Critical Review of Current Guidelines. Environ. Sci. Technol. 2010, 44 (9), 3213–3220. (18) Hamilton, K. A.; Ahmed, W.; Toze, S.; Haas, C. N. Human Health Risks for Legionella and Mycobacterium Avium Complex (MAC) from Potable and Non-Potable Uses of Roof-Harvested Rainwater. Water Res. 2017, 119, 288–303. (19) Fewtrell, L.; Kay, D. Quantitative Microbial Risk Assessment with Respect to Campylobacter Spp. in Toilets Flushed with Harvested Rainwater. Water Environ. J. 2007, 21 (4), 275–280. (20) Ayuso-Gabella, N.; Page, D.; Masciopinto, C.; Aharoni, A.; Salgot, M.; Wintgens, T. Quantifying the Effect of Managed Aquifer Recharge on the Microbiological Human Health Risks of Irrigating Crops with Recycled Water. Agric. Water Manag. 2011, 99 (1), 93–102. (21) Dufour, A. P.; Evans, O.; Behymer, T. D.; Cantu, R. Water Ingestion during Swimming Activities in a Pool: A Pilot Study. J. Water Health 2006, 4 (4), 425–430. (22) Murphy, H. M.; Thomas, M. K.; Schmidt, P. J.; Medeiros, D. T.; McFADYEN, S.; Pintar, K. D. M. Estimating the Burden of Acute Gastrointestinal Illness due to Giardia, Cryptosporidium, Campylobacter, E. Coli O157 and Norovirus Associated with Private Wells and Small Water Systems in Canada. Epidemiol. Infect. 2015, 1–16 DOI: 10.1017/S0950268815002071. (23) Schmidt, P. J.; Pintar, K. D.; Fazil, A. M.; Topp, E. Harnessing the Theoretical Foundations of the Exponential and Beta-Poisson Dose-Response Models to Quantify Parameter Uncertainty Using Markov Chain Monte Carlo. Risk Anal. 2013, 33 (9), 1677–1693. (24) Black, R. E.; Levine, M. M.; Clements, M. L.; Hughes, T. P.; Blaser, M. J. Experimental Campylobacter Jejuni Infection in Humans. J. Infect. Dis. 1988, 157 (3), 472–479. (25) Teunis, P.; Van den Brandhof, W.; Nauta, M.; Wagenaar, J.; Van den Kerkhof, H.; Van Pelt, W. A Reconsideration of the Campylobacter Dose–response Relation. Epidemiol. Infect. 2005, 133 (4), 583–592. (26) US EPA. Quantitative Microbial Risk Assessment to Estimate Illness in Freshwater Impacted by Agricultural Animal Sources of Fecal Prevalence. 2010. (27) Schiff, K.; Griffith, J.; Steele, J.; Arnold, B.; Ercumen, A.; Benjamin-Chung, J.; Colford, J. M.; Soller, J.; Wilson, R.; McGee, C. The Surfer Health Study- A Three-Year Study Examining Illness Rates Associated with Surfing During Wet Weather. SCCWRP Tech. Rep. 943 2016.


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Current Stormwater Harvesting Guidelines Are Inadequate for

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