Science of the Total Environment 487 (2014) 20–25

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Parameters affecting greywater quality and its safety for reuse

Adi Maimon a, Eran Friedler b, Amit Gross a,⁎a Albert Katz International School for Desert Studies, Department of Environmental Hydrology and Microbiology, Zuckerberg Institute for Water Research,The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 84990, Israelb Faculty of Civil & Environmental Engineering, Technion — Israel Inst. of Technology, Technion, Haifa 3200, Israel

H I G H L I G H T S

• Adequately-treated greywater is safe for reuse.• Disinfection is highly recommended for safe greywater reuse.• Barriers that reduce exposure (gloves, drip irrigation) would reduce overall risk.• The type of treatment has a significant effect on the treated greywater quality.

⁎ Corresponding author. Tel.: +972 8 6596896.E-mail address: amgross@bgu.ac.il (A. Gross).

http://dx.doi.org/10.1016/j.scitotenv.2014.03.1330048-9697/© 2014 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 20 February 2014Received in revised form 27 March 2014Accepted 27 March 2014Available online xxxx

Editor: D. Barcelo

Keywords:GreywaterQMRARisk assessmentMicrobial qualityOnsite reuseGarden irrigation

Reusing greywater (GW) for on-site irrigation is becoming a common practice worldwide. Alongside its benefits,GWreusemight posehealth and environmental risks. The current study assesses the risks associatedwith on-siteGW reuse and themain factors affecting them. GW from 34 households in Israel was analyzed for physicochem-ical parameters, Escherichia coli (as an indicator for rotavirus), Pseudomonas aeruginosa and Staphylococcusaureus. Each participating household filled out a questionnaire about their GW sources, treatment and usages.Quantitative microbial risk assessment (QMRA) was performed based on the measured microbial quality, andon exposure scenarios derived from the questionnaires and literature data. The type of treatment was found tohave a significant effect on the quality of the treatedGW. The average E. coli counts inGW(which exclude kitcheneffluent) treated by professionally-designed system resulted in acceptable risk under all exposure scenarioswhile the risk from inadequately-treated GW was above the accepted level as set by the WHO. In conclusion,safe GW reuse requires a suitable and well-designed treatment system. A risk-assessment approach should beused to adjust the current regulations/guidelines and to assess the performance of GW treatment and reusesystems.

© 2014 Elsevier B.V. All rights reserved.

1. Introduction

Reuse of greywater (GW) for on-site irrigation is becoming a com-mon practice worldwide, particularly in areas that face water scarcity.Reusing GW is expected to bring a significant reduction in domesticwater demand and maximize the exploitation efficiency of this scarceor restricted resource on site (Ghisi and Ferreira, 2007). However,alongside its benefits, GW reuse might pose some risks, which can begenerally divided into two, sometimes overlapping, categories: publichealth risks and environmental risks.

Environmental risks associated with on-site GW reuse are mainlythose that might negatively alter soil characteristics, adversely affectplant growth, or contaminate surface-/ground-water (Eriksson et al.,2002; Negahban-Azar et al., 2013). However, not much quantitative

information is available on direct environmental risks and only slightlymore information is available regarding public health risks (e.g. Barkeret al., 2013a; Maimon et al., 2010; O'Toole et al., 2012; Ottoson andStenstrom, 2003; WHO, 2006). The latter is the focus of this study.

Pathogenic organisms are considered to be themain hazard to publichealth fromGWreuse (Dixon et al., 1999; Chen et al., 2013).Macro- andmicro-“chemical” pollutants, as well as air pollution, may jeopardizepublic health to some extent; however, these seem to be minor forGW reuse and these pollutants are discussed in the literature only as en-vironmental hazards (Eriksson et al., 2002, 2009; Stevens et al., 2011).

Pathogens that are potentially found in GW originate from threemain sources: fecal contamination, peripheral pathogens (e.g. skin andmucous tissue), and those stemming from food handling (Maimonet al., 2010). Traditionally, the focus has been on oral–fecal-route path-ogens, as most existing GW regulations are based on regulations for do-mestic wastewater effluent quality. Although GW does not containfeces, the counts of fecal pathogens (although much lower than in

21A. Maimon et al. / Science of the Total Environment 487 (2014) 20–25

blackwater) are not negligible (e.g. Ottoson and Stenstrom, 2003;Winward et al., 2008), and any risk assessment should incorporatethem into its calculations. Moreover, the risks from opportunistic andother pathogens, such as those which are considered problematic inbathing water, should also be investigated (Winward et al., 2008).

The risk assessment in this study was performed by employing thequantitative microbial risk assessment (QMRA) framework (Haaset al., 1999). QMRA is a methodology for the prediction of pathogenicrisk based on mathematical models and experimental data. It is com-prised of four steps: 1) hazard identification — identifying the mostrelevant pathogens, their prevalence and characteristics; 2) exposureassessment — identifying the exposed populations, the possible expo-sure routes and scenarios and extent of exposure; 3) dose–responsemodel — defining mathematical relationships between exposure to apathogen and the probability of infection; and 4) risk characterization —

processing the data from the first three stages to obtain the probabilityof infection. This method has been found to produce probabilitiessimilar to those found in parallel epidemiological studies (Mara et al.,2007) and is gaining popularity in decision-making processes (Barkeret al., 2013b; NRMMCand EPHC, 2006;WHO, 2006). However, knowingonly the probability of infection is not enough to make decisions, asthe outcomes of different diseases may vary widely, from a marginaleffect such as skin irritation to the extreme event of death. The DALY(disability-adjusted life years) index provides a solution to this problemby normalizing all outcomes and enabling their comparison (Homedes,1996). The WHO (2008) suggests 10−6 DALY person−1·year−1 as themaximum tolerable risk for waterborne diseases.

The objective of this study was to assess the effect of factors such astype of GW source being reused (e.g. kitchen water), type of treatment,system design, and the presence of young children and pets in thehousehold on GW quality and consequently, on its associated healthrisks. Three representative pathogens were chosen as indicators forthe health risk assessment: rotavirus as a gastroenteritic pathogen,and Pseudomonas aeruginosa and Staphylococcus aureus as peripheralopportunistic pathogens. Rotavirus was estimated based on a ratiofound in the literature between rotaviruses and Escherichia coli(Maimon et al., 2010; Ottoson and Stenstrom, 2003).

2. Materials and methods

GW samples were taken from 34 households in Israel (variousgeographical locations) representing several treatment schemes(CW— constructedwetland; RCW— recirculating constructedwetland;PT — primary treatment, and RAW — no significant treatment). In fivecases, GW quality was analyzed twice, before and after treatment, andtherefore n = 39 (greater than the number of households, 34). Noneof the GW samples (raw or treated) were disinfected. Each GW samplewas analyzed for physicochemical and microbial properties: 5-daybiochemical oxygen demand (BOD) was analyzed using standard BODbottles; total suspended solids (TSS) using the gravimetric methodwith GF–AE filters; electrical conductivity (EC) and pH using corre-sponding meters. All measurements and analyses were conductedaccording to standard methods (APHA, 2005). E. coli was measuredby membrane filtration method (APHA, 2005) and grown on HicromeE. coli selective agar (Hi-labs) in 44 °C for 24 h. P. aeruginosa andS. aureus were measured using membrane filtration (APHA, 2005) onPseudomonas Isolation Agar and Baird Parker Agar, respectively.Rotavirus was estimated according to the rotavirus-to-E. coli ratio of1.7 × 10−5, as further discussed in Section 3.

Each participating household was asked to answer a detailed ques-tionnaire regarding the GW sources being reused on the property, thetreatment system, uses of the GW, maintenance, cases of diarrhea inthe household, and the presence of young children and pets. Based onthe data obtained from the questionnaires and the water quality analy-ses, the parameters thatmost affect GWqualitywere elucidated. Conse-quently, parameters potentially affecting GW quality were statistically

compared by ANOVA and principal component analysis (PCA) forthe following groups: a) method of treatment; b) treatment designand installation (by the owner or by a “professional” person/firm);c) inclusion/exclusion of kitchen wastewater stream; d) presence/ab-sence of young children (b6 years of age); and e) presence/absence ofpets (dogs and cats) in the house.

PCA is a statistical method that aims at reducing the dimensionalityof a dataset containing large number of variables, and at the same timekeeping the variation in the dataset. This is done by converting the datainto a new set of variables which are the principal components (PCs).The PCs are uncorrelated and ordered in a way that the first PC explainsthe highest variation in the data. PCA analysis can serve as a simple vi-sual way to detect the existence of clusters (Jolliffe, 2002).

Other data from the questionnaires plus literature data were used todevelop exposure scenarios for the QMRA (Haas et al., 1999; Maimonet al., 2010) which was performed for the abovementioned pathogens.The assessed risks were: gastroenteritis from rotavirus (Gerba et al.,1996; Maimon et al., 2010), skin infection from S. aureus (Rose andHaas, 1999) and intestinal colonization of P. aeruginosa (Mena andGerba, 2009). The dose–response models for each pathogen were re-trieved from their corresponding references.

3. Results and discussion

Initially, assessment of the effects of factors such as treatment typeand system design on the quality of GW being reused was conductedby two statistical approaches. The first included comparison of eachparameter and the second included a multivariate PCA analysis. PCAwas performed on the complete dataset and five PCs were computed.The first two PCs explained 62% of the variance between samples(Supporting information, Table SI1) thus further analysis was con-ducted only on these two PCs. None of the variables had correlationwith the others (Supporting information, Table SI2). The weight ofeach variable on a given PC is presented by the vectors in Fig. 1 and indetails in Table SI3. Lastly, assessment of the associated health riskswas evaluated.

3.1. Effects of various factors on GW quality characteristics

Type of treatment had a significant (P b 0.05) effect on GW quality(Table 1, Fig. 1A). As GW treatment is primarily conducted by simplefiltration/sedimentation and biological treatment, the factors most af-fected were organic matter, suspended solids and E. coli (Table 1). Forexample, the average BOD in the RCW, CW, PT and RAW treatmentswas 11, 28, 73, and 124mg L−1 respectively. Other chemical parameterssuch as pH and EC were less affected, with the exception of horizontalflow CW where evapotranspiration seemed to have increased the EC(salinity) in the treated GW by about 0.5 mS cm−1 as compared withthe other treatments.

As expected, the more intensive treatment (RCW) exhibited bestperformance as demonstrated by its better GW quality, followed byCW, PT and raw GW. This can be also visualized by the RCW clusterwhich was virtually separated from the raw and primarily treated GWand greater overlap with the CW treated GW (Fig. 1A). In spite of this,full separation between clusters is rarely seen due to the high variationin GW quality within a specific source, and over time which can maskdifferences (Eriksson et al., 2009).

Many GW reuse standards prohibit the use of kitchen effluent dueto its lower quality. This study reconfirms and demonstrates thatwhen kitchen effluent is included, GW has higher concentrations ofBOD and E. coli (79.1 mg L−1 and 1.6 × 105 CFU 100 mL−1 comparedto 29.5 mg L−1 and 2.3 × 103 CFU 100mL−1 respectively). The increasein E. coli was not expected as kitchen effluent does not typically havesignificant fecal contamination (Friedler, 2004). These results may beexplained by E. coli regrowth due to high availability of organic matterand other nutrients (Table 1), as suggested by Zapater et al. (2011). It

Fig. 1. Principal component analysis (PCA) for greywater (GW). (A) Four treatment types: constructedwetland (CW), recirculating constructed wetland (RCW), raw GW (RAW) and pri-mary treatment (PT); (B) owner-designed systems that include kitchen effluent versus professionally-designed systems that exclude kitchen effluent.

22 A. Maimon et al. / Science of the Total Environment 487 (2014) 20–25

is therefore recommended that kitchen effluent should be excludedfrom on-site GW reuse. It should be noted that 80% of the systemsthat excluded kitchen effluent were designed by professionals and it islikely that the higher water quality can be partially attributed to theassumingly better design and installation as compared with ‘in-house’design and installation. Unfortunately, these factors could not be sepa-rated due to limited sample size. However, by grouping these twoparameters, designer skills and GW source, in the PCA analysis, twoclusters could be visually depicted: one grouping all professionally de-signed treatment of GW without kitchen, and the other is grouping allself-designed treatment with kitchen (Fig. 1B). It is interesting to notethat even in the best scenario of kitchen-excluded GW treated by a pro-fessionally designed system, the average E. coli count was higher than102 CFU 100 mL−1, breaching many standards for unlimited reuse.

Table 1Greywater (GW) quality as a function of treatment type, treatment design and installation, an

Factor pH EC BOD[−] [mS cm−1] [mg L

Treatment type RCWn = 10

Average 7.7 1.1a 10.9Median 7.8 1.1 11.3STD 0.3 0.3 11.5

CWn = 14

Average 7.4 1.7b 27.6Median 7.4 1.6 9.8STD 0.5 0.4 38.6

PTn = 5

Average 7.5 1.2ab 72.9Median 7.4 1.2 68.3STD 0.3 0.3 44.9

RAWn = 10

Average 7.5 1.2a 123.9Median 7.6 1.1 117.3STD 0.5 0.3 75.7

Ownern = 19

Average 7.3 1.4 69.6Design & installation Median 7.4 1.2 53.0

STD 0.5 0.5 73.4Professionaln = 15

Average 7.7 1.4 11.8Median 7.8 1.2 6.8STD 0.3 0.4 17.2

Kitchen effluent Includedn = 18

Average 7.3 1.5 79.1Median 7.3 1.2 61.4STD 0.4 0.5 77.8

Excludedn = 21

Average 7.7 1.3 29.5Median 7.8 1.2 12.5STD 0.3 0.4 37.4

n, number of samples; RCW, recirculating constructed wetland; CW, constructed wetland; PT,a,b Statistically significant differences (P b 0.05, ANOVA) between treatments: RCW/CW/PT/RA⁎ n = 5.

Unlike E. coli counts, concentrations of P. aeruginosa and S. aureuswere not affected by any treatment with concentrations ranging fromb1 × 10° to 3.9 × 103 and b1 × 10° to 2.4 × 104 CFU 100 mL−1 respec-tively (Table 1). These findings suggest that risk assessment would benecessary to determine themicrobial risk and neededmeans formitiga-tion for safe GW reuse. The presence of children under 6 years of age orpets in the house did not show any significant effect on any of the qual-ity parameters measured (data not shown).

3.2. Health risk assessment

To demonstrate the importance of GW source, system design andtreatment type on the GW quality and consequently on the associatedhealth risks, a QMRAwas employed as a tool to establish the conditions

d inclusion/exclusion of kitchen effluent.

TSS E. coli P. aeruginosa S. aureus−1] [mg L−1] [CFU 100 mL−1] [CFU 100 mL−1] [CFU 100 mL−1]

a 23.6ab 4.6 · 102a 1.5 · 102a 1.0 · 102a

23.6 8.7 · 101 3.0 · 101 b1.013.4 1.0 · 103 2.0 · 102 2.6 · 102

ab 20.7a 9.8 · 104b 5.4 · 102a 1.9 · 103a

14.4 3.8 · 103 1.7 · 102 b1.023.1 2.3 · 105 9.2 · 102 6.4 · 103

bc 66.8b 2.8 · 104b 6.9 · 102a 2.0 · 101a

67.2 1.5 · 104 1.2 · 102 b1.044.9 3.2 · 104 1.0 · 103 4.5 · 101

c 75.9b 1.4 · 105b 7.9 · 102a⁎ 5.0 · 103a⁎

53.1 2.0 · 104 b1.0 b1.074.2 3.4 · 105 1.7 · 103 1.0 · 103

b 41.0a 1.4 · 105b 4.9 · 102a 7.2 · 101a

27.0 2.1 · 104 1.0 · 101 b1.032.0 3.1 · 105 9.5 · 102 2.6 · 102

a 23.3a 5.1 · 103a 3.9 · 102a 2.2 · 103a

17.0 2.0 · 102 1.5 · 102 b1.023.4 1.6 · 104 6.2 · 102 6.9 · 103

b 47.0a 1.6 · 105b 3.2 · 102a 7.9 · 101a

35.7 4.5 · 104 1.0 · 101 b1.036.4 3.1 · 105 8.0 · 102 2.7 · 102

a 36.8a 2.3 · 103a 5.9 · 102a 2.0 · 103a

24.5 2.4 · 102 1.8 · 102 b1.057.8 6.4 · 103 8.2 · 102 6.6 · 103

primary treatment; RAW, raw GW.W; owner/professional design and installation; with/without kitchen effluents.

Exposure volume (mL)0.01 0.1 1 50 100

Pro

babi

lity

of in

fect

ion

Pi(d

)

1e-6

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

8.0·104

E. coli/100ml

1.35·102

E. coli/100ml

Fig. 2. Cumulative probability for rotavirus infection as a function of exposure volumes fortwo E. coli concentrations in the greywater. Open triangles represent the average concentra-tion found in the survey and circles represent the average concentrationof the best treatment(professionally designed treatment of greywater excluding kitchen effluent). Exposure vol-umes between 0.01 and 1 mL relate to repeated exposures (47 days year−1); 50 and100 mL represent a single exposure event. The horizontal line represents the WHO's maxi-mum tolerable risk for rotavirus infection.

23A. Maimon et al. / Science of the Total Environment 487 (2014) 20–25

for safe GW reuse in terms of public health. Rotavirus was estimated byusing a ratio between rotavirus and E. coli as suggested byWHO (2006)and the Australian guidelines for water reuse (NRMMC and EPHC,2006). Some researchers have challenged this method and suggestedthe use of epidemiological data when a direct estimation of rotavi-ruses is not available (O'Toole et al., 2013). Interestingly, estimatedrotavirus-to-E. coli ratios from two independent studies using epide-miological data (Ottoson and Stenstrom, 2003; Barker et al., 2013a)were compared to the ratios suggested by WHO (2006) and NRMMCand EPHC (2006) and were found similar. Thus the median ratio of1.7 × 10−5 from the four references was used; this is also the ratiofound in Ottoson and Stenstrom (2003).

In this study, two reference-concentrations of E. coli were used assurrogates for rotavirus. These were used for the QMRA of rotavirus in-fection estimates. Onewas the average E. coli count (per 100 mL) foundin the full survey (average of all observations). The second was the av-erage E. coli count in GW treated by professionally-designed systemswhich excluded kitchen effluent (the best possible quality in thestudy). The number of annual exposures of GW due to maintenance ofthe system and contact with the irrigated garden was assessed accord-ing to the questionnaire filled by the participating households, whilethe volume of exposure by each activity was evaluated using literaturedata (Table 2).

According to the presented estimation, garden use showed thehighest risk among the three exposure scenarios. This is mainly due tothe long exposure time for that activity in comparison to the othertwo activities (garden work and system maintenance). The exposurevolume was highly variable and depended on the type of activitypreformed in the garden, irrigation method, etc. Little research hasbeen done to estimate exposure volumes from system maintenanceand different garden uses. This information is needed for a more robustexposure estimation and risk assessment.

The probability of infection by rotaviruses in all scenarios wascompared with the tolerable risk for rotavirus infection, defined bythe WHO as 1.4 × 10−3 infections person−1·year−1 (WHO, 2008).The average E. coli concentration in kitchen-excluded GW treated by aprofessionally designed systemwas 1.35 × 102 CFU 100mL−1, resultingin acceptable risk for all exposure scenarios, even without disinfec-tion (Fig. 2). When calculated from the average of all samples, E. coliwas 8 × 104 CFU 100 mL−1, resulting in unacceptable risk for all expo-sure scenarios (Fig. 2). Interestingly, according to the questionnaire, 1person out of the 133 residents of the participating households (1 /133 = 7.5 × 10−3) reported an episode of gastroenteritis duringthe year of the survey. This infection rate is of the same order of magni-tude as the tolerable risk accepted by the WHO (2008) for rotavirusinfection.

Of the possible health risks associatedwith S. aureus, the risk for skininfection seems to be themost relevant for exposure scenarios involvingGW irrigation. The probability of skin infection was modeled usingexposure time as suggested by the survey questionnaire and a range

Table 2Annual probability of rotavirus infection in three exposure scenarios and two E. coli concentrat

E. coli 100 mL−1 Exposurescenario

Garden work System

h a days mL day−1 b Pi(A)(d) h a

8.0 · 104 Average 68 9 0.1 7.2 · 10−3 6.6Min 2 0.25 0.1 2.0 · 10−4 0Max 365 46 0.1 3.6 · 10−2 23

1.4 · 102 Average 68 8.5 0.1 9.6 · 10−5 6.6Min 2 0.25 0.1 2.8 · 10−6 0Max 365 45.6 0.1 5.2 · 10−4 23

Pi(A)(d), annual probability of infection for the specific exposure scenario.a Questionnaire estimation.b Mara et al. (2007).c NRMMC and EPHC (2006).

of exposure area estimates. Since no effect of the user-dependent factors(treatment system, design, etc.) on S. aureus counts in the GW wasfound, these were not considered here. S. aureus was found in only29% of the samples and was always lower than 105 CFU 100 mL−1.The QMRA showed that for the average S. aureus found in the survey(1.2 × 103 CFU 100 mL−1), even an extreme scenario with exposureover an extended area (40 cm2) for a long time (24 h) would resultin low probability (10−3) of infection (Fig. 3A). S. aureus of up to105 CFU 100 mL−1 are reported in the literature for GW (Friedleret al., 2011) and therefore, a second scenario with this higher S. aureuswas assessed. Results of the QMRA analysis suggested an increasedrisk thatmight be significant (Fig. 3B). Unfortunately, amaximum toler-able risk for skin infection is not available in the literature, making theinterpretation of infection probability hard to evaluate. These findingsshow that the risk of S. aureus skin infection is generally low. However,they also emphasize the importance of usingmeans to reduce exposure,in addition to disinfection (that was not employed in the surveyedhouseholds), in risk-management schemes. Exposure reduction mightbe achieved by technical means such as subsurface or drip irrigation,as well as behavioral means such as wearing gloves and washinghands. Moreover, the results stress the need to develop GW-specificregulations that address its unique risk factors.

These results strengthen the recommendation of treatingGWbeforeusing it (Al-Jayyousi, 2003; Eriksson et al., 2002). However, as suggestedby Gross et al. (2008), not all available treatment systems are appropri-ate for treating GW. One of the basic characteristics of GW is that its

ions.

maintenance Garden use

days mL day−1c Pi(A)(d) h a days mL day−1 c Pi(A)(d)

7 1 5.4 · 10−2 256 32 1 2.2 · 10−1

0 1 0 52 6.5 1 5.0 · 10−2

23 1 1.7 · 10−1 832 104 1 5.6 · 10−1

7 1 9.9 · 10−5 256 32 1 4.5 · 10−4

0 1 0 52 6.5 1 9.2 · 10−5

23 1 3.3 · 10−4 832 104 1 1.5 · 10−3

Exposed skin area (cm2)

5 10 15 20 25 30 35 40

Pi = 10-1

Pi = 8x10-2

Pi = 6x10-2

Pi = 4x10-2

Pi = 2x10-2

Exposed skin area (cm2)

5 10 15 20 25 30 35 40

Exp

osur

e tim

e (h

)

5

10

15

20Pi = 1.4x10-3

Pi = 1.2x10-3

Pi = 10-3

Pi = 8.0x10-4

Pi = 6.0x10-4

Pi = 4.0x10-4

Pi = 2.0x10-4

Fig. 3. Cumulative probability for S. aureus skin infection based on increasing exposure time and exposed body area forwater containing (A) the average concentration found in the survey(1.2 × 103 CFU 100 mL−1), (B) 1 × 105 CFU 100 mL−1 (maximum concentration found in the literature data). The contour lines represent different infection probabilities (Pi).

24 A. Maimon et al. / Science of the Total Environment 487 (2014) 20–25

quality and quantity vary significantly with time and from household tohousehold. Therefore, there is a need for treatment systemswhich havebeen proven capable of treating a wide range of water qualities andquantities.

P. aeruginosa was found in 72% of the samples, with average andmaximum counts of 4 × 102 and 4 × 103 CFU 100 mL−1, respectively.The tested risk scenario was intestinal colonization from ingestion of100 mL of GWwith different P. aeruginosa concentrations. The calculat-ed risk lay in the range of 10−6 person−1·year−1 for the maximumconcentration found in the literature (104 CFU 100 mL−1) (Friedleret al., 2011). This suggests a low risk of P. aeruginosa intestinal coloniza-tion from GW reuse.

4. Conclusions

The current study assessed the effects of various factors on GWqual-ity and thus on the health risks associated with its reuse. Three factorswere found to have an effect on water quality: the type of treatment,the skills of the system designer and whether kitchen effluent wasincluded/excluded from theGW. The average E. coli count in GW treatedby a professionally-designed system that exclude kitchen effluent re-sulted in acceptable risk under all exposure scenarios, evenwith no dis-infection. However, when treatment was not sufficient, the calculatedrisk exceeded acceptable risk (as outlined by the WHO (2008)). There-fore, as a preventive measure, both suitable treatment and disinfectionare recommended for on-site GW reuse. The probabilities of S. aureusskin infection further emphasize the need for disinfection and the useof exposure barriers such as drip irrigation,wearing gloves andwashinghands. The probabilities of P. aeruginosa borne gastroenteritis werefound to be low. The use of risk-assessment tools (as presented in thisstudy) will advance the establishment of a code of practice for safeGW use. Moreover, a quantitative framework to weigh environmentalrisks is also required to make the risk-assessment approach morecomprehensive.

Acknowledgments

This research was partially funded by the Tzuk Maccabi Fund andIsrael Water Authority.

Appendix A. Supplementary data

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.scitotenv.2014.03.133.

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