Human exposure assessment to antibiotic-resistant ...

Science of the Total Environment 616–617 (2018) 1356–1364

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Human exposure assessment to antibiotic-resistant Escherichia coli through drinking water E. O'Flaherty a,⁎, C.M. Borrego b,c, J.L. Balcázar b, E. Cummins a a b c

University College Dublin, School of Biosystems and Food Engineering, Belfield, Dublin 4, Ireland Catalan Institute for Water Research (ICRA), Scientific and Technological Park of the University of Girona, Girona, Spain Group of Molecular Microbial Ecology, Institute of Aquatic Ecology, University of Girona, Girona, Spain

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Exposure to antibiotic-resistant E. coli was between 3.44 × 10−7 and 2.95 × 10−1 cfu/da. • UV treatment gave the largest reduction of antibiotic-resistant E. coli. • Model can be used to set possible monitoring criteria in pre-treated water.

a r t i c l e

i n f o

Article history: Received 16 September 2017 Received in revised form 17 October 2017 Accepted 17 October 2017 Available online 8 November 2017 Editor: D. Barcelo Keywords: Antibiotic resistance Drinking water Human exposure assessment Escherichia coli

a b s t r a c t Antibiotic-resistant bacteria (ARB) are a potential threat to human health through drinking water with strong evidence of ARB presence in post treated tap water around the world. This study examines potential human exposure to antibiotic-resistant (AR) Escherichia coli (E. coli) through drinking water, the effect of different drinking water treatments on AR E. coli and the concentration of AR E. coli required in the source water for the EU Drinking Water Directive (DWD) (Council Directive 98/83/EC, 0 CFU/100 ml of E. coli in drinking water) to be exceeded. A number of scenarios were evaluated to examine different water treatment combinations and to reflect site specific conditions at a study site in Europe. A literature search was carried out to collate data on the effect of environmental conditions on AR E. coli, the effect of different water treatments on AR E. coli and typical human consumption levels of tap water. A human exposure assessment model was developed with probability distributions used to characterise uncertainty and variability in the input data. Overall results show the mean adult human exposure to AR E. coli from tap water consumption ranged between 3.44 × 10–7 and 2.95 × 10–1cfu/day for the scenarios tested and varied depending on the water treatments used. The level of AR E. coli required in the source water pre-treatment to exceed the DWD varied between 1 and 5 log cfu/ml, depending on the water treatments used. This can be used to set possible monitoring criteria in pre-treated water for potential ARB exposure in drinking water. © 2017 Elsevier B.V. All rights reserved.

1. Introduction Obtaining clean and safe drinking water is a global challenge that requires continuous research. One of the objectives of the UN Sustainable Development Goals (UN 2015) is to reduce the number of deaths and ⁎ Corresponding author. E-mail address: eithne.o-fl[email protected] (E. O'Flaherty).

http://dx.doi.org/10.1016/j.scitotenv.2017.10.180 0048-9697/© 2017 Elsevier B.V. All rights reserved.

illnesses from contaminated water and to ensure safe drinking water to all. Antibiotic-resistant bacteria (ARB) are a growing public health problem around the world and when consumed can lead to serious illness. For example when AR (antibiotic-resistant) Escherichia coli (E. coli) are ingested through food or water they can colonize the gastrointestinal tract (Salvadori et al. 2004). Furthermore, AR E. coli can transfer antibiotic resistant genes (ARGs) to other E. coli strains and bacterial species when they are present in the gastrointestinal tract (Salvadori

E. O'Flaherty et al. / Science of the Total Environment 616–617 (2018) 1356–1364

et al. 2004). AR E. coli are a frequent cause of both community and hospital acquired bacteraemia and are the most common cause of urinary tract infections (Salvadori et al. 2004). Armstrong et al. (1981) was one of the first studies to report the presence of ARB in drinking water. In recent times there is strong evidence showing the presence of ARB in drinking water around the world (Xi et al. 2009; Vaz-Moreira et al. 2011; Bai et al. 2015; Khan et al. 2016; Xu et al. 2016). However, ARB are not only a problem confined to low income and poor sanitation water supply areas, but their presence has also been reported in higher income areas (Fernando et al. 2016; Madec et al. 2016). Drinking water treatments could act as a protection mechanism against ARB through drinking water. The multiple barrier principle is recommended at drinking water treatment plants (DWTP) in case a single treatment fails in the multiple processes and thus preventing a complete breakdown of the treatment (WHO 2004). Russell (2016) suggests that safe drinking water can only be accomplished by using a combination of treatments that are suitable to the existing conditions of an area. Traditionally water treatment processes include using a clean source of water followed by coagulation, flocculation, sedimentation (Coag/Flocc/ Sed), filtration and disinfection (LeChevallier and Au 2002). However, the presence of ARB in source water requires drinking water treatments to efficiently remove them and thus reducing the potential human exposure (Bai et al. 2015; Bergeron et al. 2015). There are various disinfection treatments that can be used in a DWTP, such as ultra violet light (UV), ozone and the most commonly used disinfection treatment, chlorine (Van der Hoek et al. 2012). It is important that the appropriate water treatments and combinations of water treatments are used to ensure ARB are eliminated from drinking water. The human health consequence from ARB through drinking water is currently unknown (Salvadori et al. 2004; Manaia 2017). There is an urgent need for human exposure models examining ARB to help gain a better understanding of this problem and to find methods of preventing human exposure (Manaia 2017). This is the first study to the author's knowledge that creates a model examining the potential human exposure to AR E. coli through drinking water. This study also examines the effect of different drinking water treatment combinations on AR E. coli, the concentration of AR E. coli required in the source water for the DWD (Council Directive 98/83/EC, 0 CFU/100 ml of E. coli in drinking water) to be exceeded and the potential concentration of AR E. coli humans are exposed to through drinking water. 2. Material and methods 2.1. ARB and water sample collection E. coli was chosen for this study because it is used for the routine monitoring of water supplies, the DWD (Council Directive 98/83/EC) use it as an indicator for fecal contamination of water, it is commonly used to study AR patterns and it can disseminate AR through horizontal gene transfer (Williams and Braun-Howland 2003; Ibekwe et al. 2011; Maal-Bared et al. 2013; Pereira et al. 2013). Particularly, subpopulations of E. coli resistant to amoxicillin (Amox), ciprofloxacin (Cipro) and cefotaxime (Cefo) were investigated in this study in water samples collected at the river Ter in Sant Gregori (Girona, 41°58′16″N 2°44′59″E) approximately 400 m upstream from the Montfullà DWTP that supplies drinking water to the city of Girona and suburbs. Triplicate water samples were collected in summer (July 2016) and winter (December 2016) approximately 0.2 to 0.3 m below the surface using 1litre sterile glass bottles that were immediately stored at 4 °C in a portable icebox until arrival to the laboratory (less than 2 h after collection). Enumeration of AR E. coli was made based on the membrane filtration method (Environment Agency 2002). AR E. coli were enumerated on Membrane Lactose Glucuronide Agar (MLGA; Oxoid, Hampshire, UK) supplemented with amoxicillin (32 mg/l, Amox, Sigma-Aldrich, St. Louis, MO, USA), ciprofloxacin (4 mg/l, Cipro, Sigma-Aldrich), and cefotaxime (4 mg/l, Cefo, Sigma-Aldrich). These concentrations were

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selected according to the susceptibility breakpoint values defined by the Clinical and Laboratory Standards Institute (CLSI 2014). All procedures were done in triplicate. A best fit distribution was used to characterise the initial levels of AR E. coli found at the study site (Fig. 2; Table 1). Site specific environmental conditions were collated for use in a number of scenarios in the model, they include the water temperature, water depth and drinking water treatments used at the site (scenario 1) (Tables 1 and 2). 2.2. Model structure and development An exposure assessment model was developed to examine the human exposure to AR E. coli through drinking tap water for a number of water treatment scenarios. A framework of the main steps and data inputs required to create the exposure assessment model is shown in Fig. 1. The approach adopted follows standard protocol for exposure assessment and in its use of scientific literature and monitoring data to model stages along the pathway. This approach is well established and has been used in assessing exposure to other pathogens and through different pathways (Harris et al. 2014; Hamouda et al. 2016). Data on the levels of AR E. coli at the study site were collected (Table 1) and this was the first step of the exposure assessment model. Data were then collected on the effect of environmental factors on AR E. coli since bacteria remain in the river water before being collected by the DWTP. The effect of each drinking water treatment (Coag/Flocc/Sed, carbon filtration, sand filtration, chlorination, UV and ozone) were modelled through the collection of existing data through scientific literature on each process. Data on the amount of tap water consumed by potential human consumers were collected from existing scientific literature. The human exposure to AR E. coli through tap water was then estimated. The model was validated through collecting data from scientific literature on the effect of each step on AR E. coli. Probability distributions were used to account for uncertainty and variability in the input data. Data on sensitive E. coli was used as a substitute if there was no input data available on AR E. coli. 2.2.1. Effect of environmental factors and time in river The water samples at the study site were taken upstream (~400 m) of a DWTP and therefore this model assumes bacteria were exposed to environmental factors before entering the DWTP. To take this into account, Mancini's equation (Eq. 1) was used to estimate the effect of environmental factors on AR E. coli in the river before the water enters the DWTP. This equation estimates the decay rate (k) of E. coli as an effect of water temperature (T), surface solar radiation (IA), light extinction coefficient (et), salinity (% seawater) and depth of water (H) (Mancini 1978). Local sampling data and data from the scientific literature were used to represent the local conditions found at the study site (Table 1). Data on the local water temperature were collected at the site, July water temperatures ranged from 18.3 to 18.5 °C and in December 10.6 to 11.2 °C. Solar radiation values ranged from 22 to 25 ly/h in July and 5 to 7 ly/h in December (Calbó et al., 2016). Light extinction coefficient values ranged from 0.88 to 0.70 m –1 in July and 0.91 to 0.93 m–1 in December (Armengol et al., 2003). The salinity of the river in that area was reported to be between 0.34 and 0.35% (Alcaraz and GarcíaBerthou, 2007). The depth of the river at the site location was between 0.6 and 0.8 m. Probability distributions were used to represent uncertainty and variability in the data inputs (Table 3). k ¼ ½0:8 þ 0:006 ð%seawaterÞ 1:07ðT−20Þ þ IA

1−e−et H etH

ð1Þ

The amount of time the AR E. coli was assumed to be in river was between 1 and 10 min due to the relatively short distance from the sample point to the DWTP (~400 m). In order to calculate the amount of bacteria remaining after exposure to environmental conditions and at the DWTP abstraction point (ECenv), Eq. 2 (Chan et al. 2015) was used to

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Table 1 Model inputs (J = July/D = December). Symbol

Description

ECamox-J

Concentration of Amox-resistant E. coli (J)

ECamox-D ECcipro-J ECcipro-D

Concentration of Amox-resistant E. coli (D) Concentration of Cipro-resistant E. coli (J) Concentration of Cipro-resistant E. coli (D)

Symbol

Description

T

Water temperature

IA

Surface solar radiation

et H % seawater

Light extinction coefficient Depth of water Salinity

k t ECenv

Decay rate in river Time in river Level at DWTP abstraction point

Symbol

Description

CFS SSF RSF CF C UV Oz ECtap

Coag/Flocc/Sed Slow sand filtration Rapid sand filtration Carbon Filtration Chlorination UV Ozone AR E. coli after water treatment

Symbol

Description

TWC

Tap water consumption

100 ml

100 ml consumption

Symbol

Description

HETWC HE100ml

Model/equation

Human exposure through tap water consumption Human exposure through 100 ml of water

Model/equation

Model/equation

Model/equation

°C ly/h m−1 M % day−1 Day cfu/ml

Decimal reduction Decimal reduction Decimal reduction Decimal reduction Decimal reduction Log reduction Decimal reduction cfu/ml Units

4. Water Consumption Log normal (mean 1423, SD 771) 100 Model/equation

ml/day Ml Units

5. Human exposure Eq. 3 Eq. 4

2.2.2. Drinking water treatments Data were collected on the effect of different drinking water treatment processes on the level of AR E. coli in the water as it passes through a DWTP. Data were collected from scientific literature on the effect of

cfu/ml cfu/ml cfu/ml

Units

3. Water treatments Triangular (min 0.89, most likely 0.99, max 0.99) Triangular (min 0.10, most likely 1, max 1) Uniform (min 0.18, max 0.97) Triangular (min 0.49, most likely 0.99, max 0.99) Uniform (min 0.95, max 0.99) Uniform (min 5.7, max 6.2) Triangular (min 0.84, most likely 0.99, max 0.99) Table 2

ð2Þ

cfu/ml

Units

2. Environmental factors J- Triangular (min 18.3, most likely 18.3, max18.5); D- Triangular (min 10.6, most likely 10.7, max 11.2) J- Normal (mean 23, SD 1); D- Normal (mean 6, SD 0.7) J- Uniform (min 0.7, max 0.88); D-Uniform (min 0.91, max 0.93) Uniform (min 0.6, max 0.8) Uniform (min 0.34, max 0.35) Eq. 1 Uniform (min 0.001, max 0.007) Eq. 2

estimate this value. Where EC is the level of AR E. coli (cfu/ml) in the river (ECamox-J; ECamox-D;ECcipro-J;ECcipro-D; Table 1), t is the time in the river (days) and k is the decay rate (day−1). EC env ¼ EC e−kt

Units

1. Initial ARB levels in source water Uniform (min 0.11, max 0.99) Triangular (min 0, most likely 0, max 0.35) Triangular (min 0.02, most likely 0.02, max 0.12) Triangular (min 0, most likely 0, max 0.05)

cfu/day cfu/day

each water treatment (Coag/Flocc/Sed, sand filtration, carbon filtration, chlorination, ozone and UV) (Table 1). 2.2.2.1. Primary treatments. Coag/Flocc/Sed is a commonly used primary water treatment for drinking water. A coagulant is a chemical (typically aluminium sulfate or ferric sulfate) that is added to contaminated water to cause particles to stick together while the water is mixed to form flocs (flocculation). The flocs are then allowed to settle (sedimentation) and are then usually removed through filtration (Pritchard et al. 2010). Data were unavailable on the effect of Coag/Flocc/Sed on AR E. coli, however, data on sensitive E. coli were used to substitute the lack of data in this area.

Table 2 Equations for each drinking water treatment scenario. Scenarios

Water treatment scenarios

Equations for each water treatment scenario

1 2 3 4

Coag/Flocc/Sed; rapid sand filtration; carbon filtration and chlorination Carbon Filtration; rapid sand filtration; ozone and chlorination Coag/Flocc/Sed; slow sand filtration and uv Chlorination; Coag/Flocc/Sed; rapid sand filtration; ozone; carbon filtration and chlorination Coag/Flocc/Sed; rapid sand filtration and chlorination

ECtap = ECenv ×(1 − CFS) × (1 − RSF) × (1 −CF) × (1 − C) ECtap = ECenv ×(1 − CF) × (1 − RSF) × (1 − Oz) × (1 − C) ECtap = ECenv ×(1 − CFS) × (1 − SSF) × (1 − UV) ECtap = ECenv ×(1 − C) × (1 − CFS)× (1 − RSF) × (1 −Oz) × (1 − CF) × (1 − C)

5

ECtap = ECenv ×(1 − CFS) × (1 − RSF) × (1 −C)


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Fig. 1. Framework including the main steps involved and data input required for the exposure assessment model.

Pritchard et al. (2010) found a reduction of E. coli of between 89.9 and 99.8% using aluminium sulfate as a coagulant. Marois-Fiset et al.(2013) found a 2 log reduction of E. coli was caused by the combined effect of Coag/Flocc/Sed. Barany (2003) found a log reduction of

between 2 and 5 logs after coagulation treatment with aluminium sulfate. A triangular probability density distribution was used to represent the uncertainty and variability in the data reported in the scientific literature (Table 1).

Table 3 Mean exposure level to Amox-resistant E. coli through standard 100 ml tap water consumption (cfu/100 ml) and measured adult tap water consumption per day (cfu/day).

2.2.2.2. Secondary treatments. Sand filtration is a simple method that can be used to remove harmful bacteria from drinking water (Langenbach et al. 2009). There are mixed reports on the effect of sand filtration on AR E. coli. Grabow et al. (1976) suggests that sand filtration could decrease ARB as the stony surfaces could damage sex pili and prevent conjugation. However, in contrast the prolonged hydraulic retention time in the filters may encourage horizontal gene transfer and levels of ARB may increase. Lüddeke et al. (2015) found that using slow sand filtration to treat sewage reduced the percentage of AR E. coli by 17.7%. The same result was

Scenario

Exposure level July cfu/100 ml (cfu/day)

Exposure level December cfu/100 ml (cfu/day)

1 2 3 4 5

3.50 × 10−3 (4.95 × 10−2) 1.83 × 10−4 (2.61 × 10−3) 7.67 × 10−7 (1.07 × 10−5) 6.09 × 10−6 (8.71 × 10−5) 2.09 × 10−2 (2.95 × 10−1)

3.45 × 10−4 (4.89 × 10−3) 1.83 × 10−5 (2.61 × 10−4) 7.44 × 10−8 (1.04 × 10−7) 6.11 × 10−7 (8.77 × 10−6) 2.05 × 10−3 (2.90 × 10−2)

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found by El-Zanfaly (2015) who examined the percentage of AR fecal bacteria was reduced by 17.7% after rapid sand filtration. There was a lack of data on the effect of sand filtration on AR E. coli and therefore data on sensitive E. coli was also used to estimate the effect of this treatment. Langenbach et al. (2009) found that there was between a 1.9 and 2.6 log removal of E. coli from slow sand filtration. Bauer et al. (2011) examined a mean log removal of 4.11 E. coli from slow sand filtration. Li et al.(2012) found there was a 0.6 to 1.5 log removal of fecal bacteria from rapid sand filtration. Bagundol et al. (2013) showed a percentage reduction of between 69 and 100% of E. coli from slow sand filtration. Best fit probability distributions were used to represent the data, which were divided into slow (triangular distribution; Fig. 2) and rapid sand filtration data (uniform distribution) (Table 1). Carbon/charcoal filtration is mostly used to remove micropollutants and organic matter (Pal et al. 2006; Hijnen et al. 2010). It has been assumed in previous quantitative risk assessment models that the effect of carbon/charcoal filtration on microorganism is negligible (Hijnen et al. 2010). However, it has also been suggested the microbial elimination capacity is similar to rapid sand filtration due to the

grain size and filtration rates (Hijnen et al. 2010). In comparison, the research also suggests that bacteria may survive and breed on the carbon/ charcoal filter and this may lead to an increase of bacterial contamination (Pal et al. 2006; Bai et al. 2015). There is a lack of data on the effect of carbon/charcoal filtration on bacteria and especially on AR E. coli (Schijven and de Roda Husman 2006). Data on sensitive E. coli were also used to gain a better understanding on the effect of this filtration method on the bacteria. Lüddeke et al. (2015) reported that carbon filtration caused a 3.1 mean log reduction AR E. coli. Pal et al. (2006) reported that levels of E. coli (sensitive) were reduced by between 1.88 and 2.51 log after carbon/charcoal filtration. Hijnen et al.(2010) examined log reduction values of between 0.4 and 1.1 log of E. coli. A triangular distribution was used to represent the variability and uncertainty within the reported results (Table 1). 2.2.2.3. Disinfection. In Europe a disinfection treatment is used in 88% of DWTP, where 62% of the time chlorine is used, 12% use UV and ozone use is low at 2% (Van der Hoek et al. 2012). WHO (2011) advises a minimum residual free

Fig. 2. The best fit distributions representing initial levels in source water of Cipro- resistant E. coli July (A, ECcipro-J), Cipro- resistant E. coli July (B, ECcipro-D), Amox-resistant E. coli in December (C, ECamox-D), decimal reduction by slow sand filtration treatment (D, SSF).


chlorine of 0.5 mg/l after 30 min contact time at pH 7 or a contact time of 15 mg.min/l. Pang et al. (2016) examined the effect of chlorination on ampicillin resistant E. coli and results showed some resistance to the treatment. Log reduction values varied from between 0.25 and 3.1 log with higher chlorine levels giving higher reduction values (Pang et al. 2016). In contrast, Huang et al.(2013) found that tetracycline-resistant E. coli was more susceptible to chlorination in comparison to sensitive E. coli, with a 5 log reduction after chlorination. Miranda et al. (2016) reported a 6 log reduction of AR E. coli from chlorination. Due to the varying results reported on the effect of chlorination on AR E. coli, a uniform distribution was used to take into account uncertainty and variability in the scientific literature (Table 1). UV is another disinfection treatment that is routinely used in DWTP. In comparison to chlorination, UV treatment doesn't leave any byproducts or chemical residues behind and it is effective at killing harmful bacteria (McKinney and Pruden 2012). In drinking water the recommended dose of UV is 40 mJ/cm2 to achieve a 6 log reduction of bacteria (Austrian Standards Institute 2001). Using a UV dose of 40 mJ/cm2 to treat ampicillin-resistant E. coli resulted in a 5.7 log reduction of the bacteria (Pang et al. 2016). Huang et al. (2013) found that at a dose of 40 mJ/cm2 a 6.2 log reduction was achieved for tetracycline-resistant E. coli. Using the recommended dose of 40 mJ/cm2 should achieve a high log-reduction of AR E. coli. A uniform distribution was then used to represent the variability and uncertainty regarding the influence of UV on AR E. coli (Table 1). Ozone is a disinfection process that is less commonly used in DWTP in comparison to chlorination and UV. Currently the mode of action and the microbial cell structures targeted by ozone are not fully understood (Heß 2015). Heß (2015) examined the treatment of ampicillin-resistant E. coli with ozone, and the results showed varying levels of reduction from between 0.8 and 3.9 log. AR E. coli were not less sensitive to ozone in comparison to sensitive E. coli (Heß 2015). Lüddeke et al. (2015) examined a mean log reduction of 3.5 log of AR E. coli from ozone treatment. A triangular distribution was then used to account for uncertainty and variability in the reported work (Table 1). 2.2.3. Drinking water scenarios Five different DWTP scenarios were created to examine the effect of different combinations of water treatments on the level of AR E. coli. Table 2 represents five different DWTP scenarios, scenario 1 represents a DWTP located near the study site. The four other scenarios were selected to represent typical drinking water treatment combinations and operating conditions used around Europe. Decimal reduction was used to quantify the reduction of AR E. coli after each water treatment (Table 2). The level after each treatment (ECtap) was calculated by multiplying the level of AR E. coli (cfu/ml) after environmental factors and at the DWTP abstraction point (ECenv) by the decimal reduction of each treatment (Tables 1 and 2). 2.2.4. Human consumption/exposure Cold tap water consumption was considered in this model to estimate the human exposure to ARB. Mons et al. (2007) recommends (if data are available) using country specific data on human consumption patterns when creating a quantitative risk assessment model. Two litres per day have been assumed and used in other risk assessment models to represent the water consumption levels (Esteban et al. 2014). However, for this model water consumption data of adults from the site area were collected to represent more accurately the amount of tap water consumed per day. Research shows varying levels of water consumption. Ferreira-Pêgo et al. (2014) assessed the mean total water consumed (included natural mineral water, tap water, filtered tap water, sparkling mineral water, flavoured water and fountain water) was 1011 ml/day for adults, women consumed 1050 ml/day and men consumed 973 ml/day. Nissensohn et al. (2016) observed the mean amount of water (including tap and bottled) consumed by women was 582 ml/day and for men it was 598 ml/day. Elmadfa and Meyer

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(2015) found that 566 ml/day of drinking water was consumed. de Francisco and Martínez Castelao (2010) examined a mean value of 1423 ml/day and a standard deviation of 771 ml/day of tap water was consumed by adults. A normal distribution was used with de Francisco and Martínez Castelao (2010) data as this study provides the most accurate data on the amount of tap water consumed in comparison with the previously mentioned studies where the total, bottled and unknown types of water were included. de Francisco and Martínez Castelao (2010) also found that between 55 and 68% of adults surveyed consumed tap water. Measured tap water consumption by adults (TWC) and consumption of a standard 100 ml of tap water (100 ml) were examined in this model to investigate the possible ARB exposure. The human exposure (HE) for each consumption level was estimated by multiplying the consumption level by the AR E. coli level after the DWTP (ECtap) (Eqs. 3 and 4). HETWC ¼ TWC EC tap

ð3Þ

HE100ml ¼ 100 ml EC tap

ð4Þ

2.2.5. Model run and software This model was created in Microsoft Excel 2013 with the @Risk 7.5 add on (Palisade Corporation, Newfield, NY). Monte Carlo Simulation was performed on the model output data to characterise uncertainty and variability in the model input data, the model was run for 10,000 iterations. A sensitivity analysis was performed using the Spearman's rank order correlation to examine how model predictions are dependent on variability and uncertainty in the model input parameters. The Goal seek function in @Risk was used to calculate the initial level of AR E. coli required at the DWTP abstraction point in order for the final tap water to exceed the DWD of 0 CFU/100 ml E. coli (i.e. level required at abstraction point for 1 CFU/100 ml of AR E. coli in tap water). In the Goal seek function the “goal cell” was set to HE100ml, the statistic value was set to the 95th percentile, the “goal value” was set to 1 CFU/100 ml and the “by changing” value was set to ECenv. 3. Results and discussion 3.1. Human exposure and dose response The model generated a number of output distributions providing a range of informative results about human exposure to AR E. coli through drinking water. The model was run with the initial levels of AR E. coli sampled at a study site in July and December (Table 1). The DWD (Council Directive 98/83/EC) use E. coli as a fecal contamination indicator in drinking water and require 0 CFU/100 ml should be present in final drinking water (Madec et al. 2016). Table 3 and table 4 show the human exposure results for each of the drinking water treatment scenarios and for two consumption levels. The results for the exposure following consumption of a standard 100 ml of tap water show they do not exceed the DWD. Therefore this shows the initial AR E. coli levels found at the sampling point in July and December will not exceed the DWD using any of the 5 different treatment scenarios. The reason why the exposure levels are higher in July is due to the higher initial levels of AR E. Table 4 Mean exposure level to Cipro-resistant E. coli through standard 100 ml tap water consumption (cfu/100 ml) and measured adult tap water consumption per day (cfu/day). Scenario

Exposure level July cfu/100 ml (cfu/day)

Exposure level (December) cfu/100 ml (cfu/day)

1 2 3 4 5

7.34 × 10−4 (1.04 × 10−2) 3.89 × 10−5 (5.52 × 10−4) 1.32 × 10−7 (2.28 × 10−6) 1.30 × 10−6 (1.86 × 10−5) 4.45 × 10−3 (6.30 × 10−2)

1.12 × 10−4 (1.61 × 10−3) 5.89 × 10−5 (8.60 × 10−5) 2.47 × 10−8 (3.44 × 10−7) 1.96 × 10−7 (2.89 × 10−6) 6.72 × 10−4 (9.59 × 10−3)

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coli sampled in July (Table 1). The results also show that scenario 3 treatment combination (Coag/Flocc/Sed; sand filtration and UV) provided the largest reduction of AR E. coli and therefore the lowest human exposure levels to AR E. coli (Table 3/Table 4). Scenario 3 was the only scenario that had a UV system included and again highlights the potential of adding a UV system to a DWTP. The infection dose of ARB is unknown, this type of data is urgently needed to create quantitative risk assessment models examining ARB (Xi et al. 2009; Manaia 2017). There is a crucial need for dose response models on the health outcomes due to consumption of ARB and the possible long term impact they may have on individuals (Ashbolt et al. 2013; Manaia 2017). The maximum acceptable annual infection risk for pathogenic fecal bacteria in drinking water (Dutch Drinking Water Decree) is 10−4 (Hijnen et al. 2010). However, using the already published dose response models (e.g. Haas et al. 2000) on antibiotic sensitive bacteria does not give an accurate representation of the human health consequence from ARB as it is unknown if the infection dose of antibiotic sensitive and antibiotic resistant bacteria are the same (Ashbolt et al. 2013; Manaia 2017). However, if an AR infection is contracted in comparison to an infection that is sensitive to antibiotics, the AR infection is a more serious infection due to the difficulty in treating it and in particular for people who are immunocompromised. 3.2. Back calculation This calculation estimates the level of AR E. coli required at the DWTP abstraction point in order for 1 CFU/100 ml of AR E. coli to be present in the tap water. The results in Table 6 show the level of AR E. coli required in the river for each of the water treatment scenarios. The results highlight that scenario 3 requires the largest amount of AR E. coli (5 log cfu/ml) to be present at the DWTP abstraction point in comparison to scenario 5 that requires the least amount (1 log cfu/ml). Article 7 of the Water Framework Directive (2000/60/EC) states that it is necessary to protect source water that are abstracted for drinking water, so that the final drinking water is in accordance with the DWD. The data generated from this model could help develop legislations and in defining maximum permissible levels of AR E. coli in source water. It is also important to consider that even if average values of ARB are low in a source water, bacterial cells can still be found in aggregates (Manaia 2017). When bacterial cells are in aggregates this can exceed acceptable levels and could pose a higher human health risk (Manaia 2017). This is important to consider when choosing water treatments for a DWTP, it would be advisable to use a combination of treatments that gives the

lowest reduction of ARB in case one of the water treatments in the process failed to work. 3.3. Sensitivity analysis A sensitivity analysis was performed to investigate the effect of the model input data on the model output data. Fig. 3 shows the sensitivity analysis for human exposure to Amox-resistant E. coli through tap water consumption for the July sampling campaign using scenario 1 (treatments used at study site). The results show the largest reductions of AR E. coli were caused by the water treatments, Coag/Flocc/Sed (−0.48) and carbon filtration (−0.48) which had the largest influence on the data output. The positive correlation coefficient value (0.27) for the initial levels of AR E. coli sampled at the study site highlight the significance and importance of continuously monitoring source water. If the levels in the source water exceed the results shown from the back calculation (Table 5) this could lead to human exposure to ARB in the tap water. The amount of water consumed also had a significant effect on the output data (correlation coefficient, 0.23). The environmental factors did not have a significant effect on the level of AR. E. coli and this is a result of the short time period the bacteria were exposed to these factors (results not shown, correlation coefficient values were between −0.1 and 0.1 for environmental factors). The sensitivity analysis highlights areas that can reduce human exposure to AR E. coli through drinking water. ARB enter water environments through animal and human sources (Baquero et al. 2008; Harris et al. 2013), it is vital that source water supplies are protected from potential contamination routes (e.g. agriculture and wastewater). Monitoring source water for total and AR E. coli could help estimate the percentage of E. coli that are AR. This type of information could be collected over time and may help identify sources and areas that contribute to increasing levels of ARB in a particular area. Drinking water treatments are the last protection barrier against ARB in drinking water and having the correct water treatment combination for a specific source water site is vital. 3.4. Water treatments and distribution systems As mentioned previously (section 2.2.2) there are mixed reports on the effect of water treatments on ARB (Hassani et al. 1999; Templeton et al. 2009; Huang et al. 2013; Pang et al. 2016). However, the uncertainty and variability in the reported work were characterised using probability distributions in this model. Adding a UV system to a DWTP plant could help ensure ARB are eliminated effectively from the water. UV

Fig. 3. Sensitivity analysis (Correlation Coefficients - Spearman Rank) for human exposure to Amox-resistant E. coli through tap water consumption.

E. O'Flaherty et al. / Science of the Total Environment 616–617 (2018) 1356–1364 Table 5 Level of AR E. coli (log cfu/ml) required at the abstraction point of the DWTP in order for 1 cfu/100 ml of AR E. coli to be present in final tap water at the 95% level. Scenario

AR E. coli at DWTP abstraction point (log cfu/ml)

1 2 3 4 5

2 3 5 4 1

treatment damages the DNA of bacterial cells, this stops the cells from replicating and results in the harmful bacterial cells dying (Zimmer and Slawson 2002). Conversely, there is evidence to suggest that bacteria can reactivate after UV treatment when exposed to visible light (Zimmer and Slawson 2002; Guo et al. 2012). Therefore, it is key to store clean water in closed tanks after UV treatment to prevent this possibility. When the water leaving a DWTP is considered ARB free there could also be an effect of the water distribution system on contaminating the water as it travels from the DWTP to the consumers tap. This was not included in the model due to the lack of data available in this area. However, there is evidence to suggest that distribution systems may contribute to contaminating or increasing levels of bacteria in the final tap water (Williams and Braun-Howland 2003; Xi et al. 2009; Bai et al. 2015). It is important to consider that water distributions systems may act as a breeding ground for certain ARB and could aid in the spread of antibiotic resistance to harmful bacteria (Xi et al. 2009). Biofilms present in distribution systems provide optimal environments for ARGs transfer that could also lead to multidrug-resistant bacteria (Bai et al. 2015). This could be an area of concern as the water may leave the DWTP clean and could then become contaminated by the time it reaches the tap water through the distribution systems. Further investigation into this area could show if distribution systems have an influence on the level of ARB in final tap water. More quantitative risk assessment models examining the risk from ARB are required to understand the risk they pose to human health through different water pathways (i.e drinking water, recreational water, irrigation water) (Harris et al. 2014; O'Flaherty and Cummins 2017). A strategic priority in tackling the presence, and human exposure to, ARB through the environment is by means of using risk assessment models (Berendonk et al. 2015). There are some knowledge gaps and with more research in the areas highlighted in this paper, this could significantly improve the quality of future risk assessment models. The data generated from this model could help bridge the gap between research and the policy regulation makers. It is important that ARB are considered when designing DWTP and including ARB in drinking water standards may help to evaluate if DWTP are performing efficiently (El-Zanfaly 2015). Defining acceptable levels of ARB in source water for DWTP is a step towards preventing human exposure to these harmful bacteria. AR is a challenge that needs to be tackled today and this exposure assessment model is helping to gain important knowledge and understanding of this problem and highlights the research areas that need further work. 4. Conclusion A model examining the human exposure to AR E. coli through drinking water was created. Overall human exposure to AR E. coli through tap water were low, with mean levels ranging from 3.44 × 10− 7 to 2.95×10−1 cfu/day depending on the water treatments used. Scenario 3 (Coag/Flocc/Sed; slow sand filtration and UV) gave the largest reduction and the lowest human exposure to AR E. coli, this was mainly caused by the UV system included in this water treatment scenario. The amount of AR E. coli required in the source water in order for the DWD (Council Directive 98/83/EC, 0 CFU/100 ml of E. coli in drinking water) to be exceeded ranges from between 1 and 5 log cfu/ml depending on the water treatment combination used. Drinking water

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treatments can act as a protection mechanism and can reduce the risk of human exposure to ARB if the appropriate combinations are used. This model has developed science in this area and has identified data gaps for future work. Areas of research that could be improved for future models include, ARB dose response models, data on the effect of distribution systems, more research on the effect of water treatments on ARB and including sampling along the drinking water treatment process for validation. This model provides informative data on human exposure to ARB through drinking water and can help in defining maximum permissible ARB levels in source water supplies.

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