Viral Surrogates in Potable Reuse Applications: Evaluation of a Membrane Bioreactor and Full Advanced Treatment

Papp, Katerina; Moser, Duane; Gerrity, Daniel

doi:10.1061/(ASCE)EE.1943-7870.0001617

Open access

Technical Papers

Nov 21, 2019

Viral Surrogates in Potable Reuse Applications: Evaluation of a Membrane Bioreactor and Full Advanced Treatment

Authors: Katerina Papp https://orcid.org/0000-0001-8126-4822 [email protected], Duane Moser [email protected], and Daniel Gerrity https://orcid.org/0000-0001-8019-9723 [email protected]Author Affiliations

Publication: Journal of Environmental Engineering

Volume 146, Issue 2

https://doi.org/10.1061/(ASCE)EE.1943-7870.0001617

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Abstract

This study employed quantitative polymerase chain reaction (qPCR) to evaluate the occurrence and removal of five microbial surrogates at two water reuse facilities. The surrogates were (1) the 16S rRNA gene; (2) the AllBac assay for Bacteroides; (3) the Bacteroides bacteriophage

ϕ B 124 - 14

; (4) the Bacteroides bacteriophage ϕcrAssphage; and (5) the pepper mild mottle virus (PMMoV). Log removal values (LRVs) were quantified for a membrane bioreactor (MBR) and across a full advanced treatment (FAT) train. PMMoV,

ϕ B 124 - 14

, and

ϕ crAssphage

were detected in the MBR feed at concentrations of approximately

10^{3} gene copies (gc) / mL

,

10^{5} gc / mL

, and

10^{6} gc / mL

, respectively. Only PMMoV was above the limit of quantification (LoQ) in the MBR filtrate (

25 \pm 8 gc / mL

), resulting in a wide range of viral LRVs:

1.4 \pm 0.5

for PMMoV,

> 3.9 \pm 0.3

for

ϕ B 124 - 14

, and

> 6.2 \pm 0.3

for

ϕ crAssphage

. All molecular targets were above the LoQ in the biologically treated FAT feed, but only the bacterial 16S rRNA gene was

> LoQ

after ozonation and biological activated carbon (BAC) and in the reverse osmosis (RO) concentrate. The gene was

< LoQ

in the RO permeate and after the UV advanced oxidation process (AOP).

Introduction

Fresh water supplies are diminishing as a result of human population growth and the impacts of climate change, greater urbanization, and increased water demands. These effects are sometimes exacerbated by competition across the municipal, industrial, agricultural, and environmental sectors (Kummu et al. 2010; Oki and Kanae 2006; Vörösmarty et al. 2000). Water reuse offers a viable strategy to alleviate this pressure, either by reducing environmental withdrawals, augmenting fresh water supplies, or directly supplying finished drinking water. This is accomplished with conventional or advanced treatment trains that can be optimized based on local wastewater characteristics to achieve a water quality consistent with the intended use (i.e., fit-for-purpose) and regulatory framework (Gerrity et al. 2013). Benchmark examples include the full advanced treatment (FAT) train employed by the Orange County Water District in California and the ozone-biofiltration treatment train employed by Gwinnett County, Georgia (Fig. 1). Although these treatment trains are able to remove a wide range of chemical and microbial contaminants (Pecson et al. 2015), there are potential risks that must be assessed and mitigated (Dominguez-Chicas and Scrimshaw 2010). For example, intermittent failures may increase microbial and/or chemical loads to downstream barriers (Pecson et al. 2018), potentially leading to compound failures (i.e., domino effects) and increased public health risks (Amoueyan et al. 2017, 2019).

Fig. 1. Indirect potable reuse treatment trains currently operating at (a and b) full-scale; and (c) demonstration-scale. Treatment train (c) also represents the treatment train at the potable reuse study site. Treatment trains (d) and (e) represent hypothetical alternatives employing membrane bioreactors instead of independent secondary/tertiary treatment and low-pressure membrane filtration systems. Treatment train (e) is shown with a UV advanced oxidation process because it is considered a more robust alternative for microbial inactivation and $N$ -nitrosodimethylamine (NDMA) photolysis in potable reuse applications.

Regulatory frameworks currently rely on log removal values (LRVs) to estimate pathogen attenuation during treatment. Specifically, each qualifying unit process within a treatment train (e.g., ozonation, filtration, etc.) is awarded an LRV for each target pathogen (Sano et al. 2016; SWRCB 2016; Amarasiri et al. 2017). The sum of the LRVs across the whole treatment train is then calculated for each pathogen and compared against regulatory requirements or public health benchmarks (e.g.,

10^{- 4}

annual risk of infection). These water reuse regulations are administered on a state-by-state basis in the United States (USEPA 2012), but there is currently little standardization across states or between countries (EPHC 2008; USEPA 2012; Paranychianakis et al. 2015; WHO 2017). For example, LRVs of 12/10/10 for viruses, Cryptosporidium, and Giardia are required for potable reuse in California, while North Carolina is considering LRVs of 6/5/4 for E. coli, MS2 bacteriophage, and Clostridium perfringens (USEPA 2012). These frameworks use raw sewage as the starting point for the LRV calculation. In contrast, Texas requires LRVs of 8/5.5/6 for viruses, Cryptosporidium, and Giardia, with conventional wastewater treatment plant effluent as the starting point for the LRV calculation (TWDB 2015). The LRV awarded for a particular unit process may even vary by facility within the same jurisdiction (SWRCB 2016).

One of the principal treatment processes affecting water quality and operational performance in water reuse applications is secondary biological treatment. This has traditionally been accomplished with activated sludge basins and secondary clarifiers, with low-pressure membranes often applied downstream of secondary treatment (Gerrity et al. 2013). Concurrent with the expansion of water reuse applications, the use of membrane bioreactors (MBRs) integrating biological treatment with semipermeable membranes has rapidly increased in popularity due to their reduced structural footprint and potential for improved water quality (Zhang and Farahbakhsh 2007; De Luca et al. 2013; Purnell et al. 2016). MBRs have been shown to remove microorganisms more effectively than conventional secondary treatment (De Luca et al. 2013; Francy et al. 2012; Hmaied et al. 2015; Ottoson et al. 2006), making them well suited for water reuse applications where human exposure is anticipated (Hai et al. 2014).

Despite their efficacy, MBRs are rarely awarded pathogen LRVs, while independent microfiltration (MF) or ultrafiltration (UF) membranes are generally awarded 0/4/4 LRVs for viruses, Cryptosporidium, and Giardia (SWRCB 2016). The resulting LRV deficiencies are problematic for potable reuse systems seeking to substitute an MBR for independent secondary and tertiary wastewater treatment processes (Fig. 1). In MBRs, larger microorganisms such as protozoa and bacteria should be retained by size exclusion, considering that nominal membrane pore sizes are often

\sim 0.1 μm

(i.e., MF) or smaller (i.e., UF). Based on size alone, many bacteriophages and human viruses should pass through these membranes. However, studies have shown that MBRs can achieve high virus removal (Chaudhry et al. 2015; Kuo et al. 2010; Simmons et al. 2011), either due to inactivation during biological treatment (Bertucci et al. 1977; Ward 1982; Kim and Unno 1996) or physical removal facilitated by particle attachment (Van den Akker et al. 2014; Miura et al. 2015), cake or fouling layer development (Ueda and Horan 2000; Farahbakhsh and Smith 2004; Marti et al. 2011), or direct membrane interactions (Chaudhry et al. 2015; Lv et al. 2006; Wu et al. 2010). Cake layer disruption following membrane backwashing or cleaning may promote viral passage, but the degree of passage will depend on the pathogen, membrane pore size, and operational parameters (Hirani et al. 2014; Lv et al. 2006; Van den Akker et al. 2014; Erdal and Vorheis 2015).

From a design perspective, discrepancies between observed versus regulatory LRVs can result in overly conservative and potentially unsustainable treatment train designs (Schimmoller et al. 2015). Therefore, the water reuse industry is now considering virus LRVs for membrane-based processes, particularly for MBRs, and to identify rapid methods for validating process integrity and performance. This is often hindered by low ambient virus concentrations in membrane feeds, which necessitates spiking of surrogate viruses (e.g., MS2 bacteriophage) during challenge tests. However, alternative viral surrogates may occur in sufficient quantities under ambient conditions to facilitate characterization of membrane performance. These surrogates include plant viruses found in certain foods [e.g., pepper mild mottle virus (PMMoV)] and bacteriophages associated with nonpathogenic human gut microbiota (e.g.,

ϕ crAssphage

and

ϕ B 124 - 14

). PMMoV is a rod-shaped (

18 \times 312 nm

; Wetter et al. 1984) RNA virus with a low isoelectric point (3.2–4.9; Haramoto et al. 2013; Shirasaki et al. 2017). It is also one of the most abundant viruses in human feces (up to

10^{9}

virions per gram of dry feces; Zhang et al. 2006), presumably due to consumption of pepper-based foods. Consequently, PMMoV is abundant in wastewater (Rosario et al. 2009) and in drinking water sources (Haramoto et al. 2013). The Bacteroides-specific bacteriophages are spherical, double-stranded DNA viruses (Jofre et al. 2014; Stachler et al. 2017) with diameters of approximately 50 nm for

ϕ B 124 - 14

(Ogilvie et al. 2012) and 75 nm for

ϕ crAssphage

(Shkoporov et al. 2018), with isoelectric points potentially similar to that of MS2 (

p I = 3.9

; Rhodes et al. 2016). These bacteriophages have been found at high concentrations in wastewater: up to

10^{6} gc / mL

for

ϕ crAssphage

and up to

10^{2}

plaque forming units (PFU)/mL for

ϕ B 124 - 14

(Jofre et al. 2014; Stachler and Bibby 2014). Because PMMoV,

ϕ B 124 - 14

, and

ϕ crAssphage

are abundant in feces—and ultimately raw sewage—they can serve as indicators of fecal contamination and as valuable surrogates for treatment process performance in water reuse applications.

In this study, we used quantitative polymerase chain reaction (qPCR) to monitor these endogenous viral surrogates and their bacterial analogues at a full-scale MBR facility and at a demonstration-scale FAT facility. The objectives of this study were to (1) quantify the occurrence of the five molecular targets in wastewater matrices; (2) characterize their removal across common unit processes in water reuse applications; and (3) determine their suitability for awarding virus LRVs (e.g., across an MBR).

Methods

Sample Collection

Samples were collected from two different water reuse study sites: (1) a full-scale MBR facility that produces an average of

15,000 m^{3} / d

of recycled water for nonagricultural irrigation in southern Nevada; and (2) a demonstration-scale potable reuse system that produces an average of

4,000 m^{3} / d

of water in southern California. The MBR facility treats primarily domestic wastewater for nonpotable reuse with coarse screening, grit chambers, fine screening, an MBR, and final disinfection with sodium hypochlorite (Fig. S1). Following a recent virus monitoring study (Erdal and Vorheis 2015), the facility received a waiver to maintain its existing UV disinfection system in standby mode. The MBR is operated at a solids retention time (SRT) of 8–10 days with full nitrification and partial denitrification. Solids separation is achieved with a UF membrane with a nominal pore size of 0.04 μm. Additional operational and performance details (e.g., water quality data) related to the MBR are summarized in Table S1 and Bacaro et al. (2019). For the current work, duplicate 10-liter samples of MBR feed (raw wastewater after screening and grit removal) and MBR filtrate (prior to chlorine disinfection) were collected in precleaned Nalgene carboys in April (2 feed and 2 filtrate samples) and June (2 feed and 2 filtrate samples) of 2018, as summarized in Table S2. The samples were immediately transported to the laboratory for concentration with hollow-fiber ultrafiltration (HFUF). HFUF concentrates were stored at 4°C overnight until further processing.

The demonstration-scale potable reuse system in southern California receives tertiary effluent from an adjacent full-scale treatment facility. The full-scale facility receives up to

114,000 m^{3} / d

of primarily domestic wastewater that is treated with activated sludge with biological nutrient removal, secondary clarification, anthracite media filtration, and chlorination/dechlorination. The demonstration-scale facility receives the tertiary effluent prior to disinfection and further treats it with ozone (

O_{3} / TOC = 0.93

and

CT = 1.5 mg - \min / L

on sample collection day), biological activated carbon (BAC) with an empty bed contact time (EBCT) of 15 min, ultrafiltration, reverse osmosis (RO), and a UV advanced oxidation process (UV AOP) (Fig. S2). The RO-AOP combination qualifies as full advanced treatment in California, which allows for indirect potable reuse (IPR) via groundwater or surface water augmentation. Additional operational and performance details related to this demonstration-scale facility are summarized in Table S1 and Tackaert et al. (2019). Duplicate 10-liter samples of the FAT feed and the effluent from each treatment process (including RO permeate and RO concentrate) were collected during a single sample event in July of 2018, as summarized in Table S3. The samples were concentrated onsite to

< 1 L

with HFUF and immediately stored on ice for transport to the laboratory.

Sample Concentration

Each 10-liter sample was concentrated using a HFUF setup adapted from Hill et al. (2005) (Fig. 2 and Table 1). Briefly, MBR feed samples were prefiltered with 100-μm particle retention filter paper (Whatman grade 0965) to prevent clogging during HFUF, removing

\sim 2 g

of wet solids per sample. The solids were stored at

- 20 ° C

and later processed for molecular analyses, and the filtrates were returned to Nalgene carboys for HFUF. Immediately prior to HFUF, each REXEED-25S ultrafilter (29 kDa) was blocked by recirculating 250 mL of 0.01% sodium polyphosphate (NaPP) for 3 min using a Masterflex L/S Easy Load II peristaltic pump (Model 77200-62) and sterile silicone tubing (L/S 24). Samples were also amended with 0.01% NaPP for particle dispersion (Hill et al. 2005). Ultrafiltration was performed by recirculation at

1,500 mL / \min

, yielding an average of

240 \pm 23 mL

of retentate per 10-L sample. Retentates were immediately stored at 4°C. Residual particulates in the REXEED filter and tubing were eluted by recirculating 300 mL of an elution solution (0.01% Tween 80, 0.01% NaPP, 0.001% Antifoam Y30) for 5 min, yielding an average volume of

427 \pm 58 mL

of eluate. Retentates and eluates were then combined to yield an average volume of

666 \pm 69 mL

of concentrate per 10-L sample (Table 1). HFUF concentrates (20–50 mL) were processed with Centricon Plus-70 Centrifugal Filter Devices (30 kDa) in triplicate. The resulting concentrates (

\leq 1 mL

each) were stored at

- 20 ° C

prior to nucleic acid extraction. This sample concentration procedure resulted in an equivalent sample volume of

\sim 10 mL

for each 10-L sample (Table 1).

Fig. 2. Schematic of (a) the HFUF setup; and (b) sample concentration and analysis, including HFUF, Centricon filter concentration, and nucleic acid extraction. This procedure resulted in equivalent sample volumes of 3–15 mL from the original 10-L samples.

Table 1. Summary of average sample volumes of experimental and analytical replicates at each sample processing stage

Sample	HFUF concentrate volume (mL)	Centricon filter (CF) sample volume (mL)	CF concentrate volume (mL)	CF concentrate extracted (mL)	DNA extract volume (μL)	qPCR assay (μL)	Equivalent sample volume (mL)
MBR feed	$672 \pm 35$	$23 \pm 2$	$1 \pm 0$	0.35	50	1	$2.66 \pm 1.04$
MBR filtrate	$712 \pm 43$	$43 \pm 7$	$0.8 \pm 0.2$	0.35	50	1	$5.33 \pm 1.45$
FAT feed	$625 \pm 5$	$50 \pm 0$	$0.70 \pm 0.08$	0.35	50	1	$7.72 \pm 0.94$
$O_{3}$	$668 \pm 57$	$50 \pm 0$	$0.58 \pm 0.14$	0.35	50	1	$9.47 \pm 1.60$
BAC	$635 \pm 5$	$50 \pm 0$	$0.50 \pm 0.02$	0.35	50	1	$11.42 \pm 0.47$
UF	$587 \pm 80$	$50 \pm 0$	$0.42 \pm 0.07$	0.35	50	1	$14.31 \pm 2.83$
RO	$795 \pm 11$	$50 \pm 0$	$0.55 \pm 0.08$	0.35	50	1	$8.20 \pm 1.23$
Brine	$600 \pm 55$	$50 \pm 0$	$0.98 \pm 0.20$	0.35	50	1	$6.15 \pm 1.20$
UV AOP	$645 \pm 14$	$50 \pm 0$	$0.55 \pm 0.06$	0.35	50	1	$9.92 \pm 0.90$

Note: Data represent averages $\pm 1$ standard deviation. The starting volume for all samples was 10 L.

Nucleic Acid Extraction and Analysis

DNA and RNA were extracted from 350-μL Centricon concentrates or from 0.2 g of solids obtained during the prefiltration step for the MBR feed using the PureLink™ Viral RNA/DNA Mini Kit. DNA was quantified with a Qubit 3.0 Fluorometer and a dsDNA HS Assay kit.

Complementary DNA (cDNA) was synthetized from neat or 10-fold diluted DNA/RNA extracts using an iScript™ Select cDNA Synthesis Kit. Reactions were carried out in a gradient cycler in 20-μL volumes containing 5 μL of template, 8 μL of water, 4 μL of 5X iScript select reaction mix (1X final concentration), 2 μL of 50 μM random primer (5 μM final concentration), and 1 μL of iScript reverse transcriptase. Reactions were incubated at 25°C for 5 min, 42°C for 30 min, and 85°C for 5 min and then held at 4°C.

Nucleic acids were analyzed for the presence of five qPCR targets (Table 2). The 16S rRNA gene assay (V1/V2 region) quantified all bacteria, the AllBac assay quantified bacteria belonging to the genus Bacteroides, the B124-14 and crAssphage assays quantified Bacteroides-specific bacteriophages (

ϕ B 124 - 14

and

ϕ crAssphage

), and the PMMoV assay quantified the pepper mild mottle virus. These targets are emerging viral surrogates (or their bacterial analogues) relevant for water reuse applications. Human pathogens and other common bacteriophages were omitted from this study because they were not detected (e.g., enterovirus, hepatitis A, rotavirus, and poliovirus) or detected at relatively low concentrations (e.g., adenovirus, norovirus GI and GII, MS2, and somatic coliphage) in a previous virus monitoring study at the same MBR facility (Erdal and Vorheis 2015).

Table 2. Summary of the qPCR assays used in this study

Assay	Target	Primer name	Sequence (5′ → 3′)	Annealing Temp. (°C)	Reference
16S rRNA gene	16S rRNA gene of	28F	GAGTTTGATCNTGGCTCAG	60	Gerrity et al. (2018)
16S rRNA gene	bacteria (V1/V2)	388R	TGCTGCCTCCCGTAGGAGT	60	Gerrity et al. (2018)
AllBac	Bacteroides	AllBac296F	GAGAGGAAGGTCCCCCAC	57	Reischer et al. (2013)
AllBac	genus	AllBac412R	CGCTACTTGGCTGGTTCAG	57	Reischer et al. (2013)
B124-14	Bacteroides	B124-14_F	TTTTGCAGCAACACGCCTAC	55	Current study
B124-14	ϕB124-14	B124-14_R	GGTGCGGGACTTACCTTTGA	55	Current study
crAssphage	Bacteroides	CP_064_F	TGTATAGATGCTGCTGCAACTGTACTC	60	Stachler et al. (2018)
crAssphage	ϕcrAssphage	CP_064_R	CGTTGTTTTCATCTTTATCTTGTCCAT	60	Stachler et al. (2018)
PMMoV	Pepper Mild Mottle	PMMoV_F	GTGGCAGCAAAGGTAATGGT	55	Hamza et al. (2011)
PMMoV	Virus (PMMoV)	PMMoV_R	ATTTGCTTCGGTAGGCCTCT	55	Hamza et al. (2011)

For the qPCR assays, primers and standards were purchased from Integrated DNA Technologies (Coralville, Iowa). Standards were resuspended in 1X TE (

10 mM Tris / 0.1 mM EDTA

) buffer to

10 ng / μL

and quantified with Qubit 3.0 and the dsDNA HS Assay kit. A

10^{8} gc / μL

stock was made for each standard by diluting the purchased stock in an appropriate volume of 1X TE. Volumes were calculated using a DNA Copy Number and Dilution Calculator (ThermoFisher Scientific, Waltham, Massachusetts). Tenfold serial dilutions were made from each

10^{8} gc / μL

stock to generate standard curves ranging from

10^{8}

to

10^{0} gc / μL

(example standard curves are shown in Fig. S3). Triplicate 10-μL qPCR reactions contained 1 μL of neat or 10-fold diluted template, 3.6 μL of nuclease-free water, 5.0 μL of 2X iTaq Universal SYBR Green Supermix (1X final concentration), and 0.2 μL of each 20-μM primer (0.4 μM final concentration). Assays were run on a CFX96 Touch™ Real-Time PCR Detection System set to 95°C for 2 min for initial denaturation and 39 cycles of denaturation at 95°C for 5 s, annealing at the assay-specific temperature (Table 2) for 30 s, and extension at 72°C for 30 s. The protocol concluded with a melt curve from 65°C to 90°C in increments of 0.5°C for 5 s and a final plate read. Assay efficiencies were

> 98 %

for all targets.

Quality Assurance/Quality Control

For each assay, the limit of quantification (LoQ) was determined by analyzing a set of known test samples (1,000 to

1 gc / μL

of template) and controls [no template control (NTC) and a blank] using a t-test. First, the method detection limit (MDL) for each assay was determined by examining dilution linearity, consistent amplification, and melt curve characteristics. Dilutions that failed to meet these requirements (i.e.,

< MDL

) were omitted from the standard curves (Fig. S3). The test samples (six replicates) were prepared at the MDL concentration using IDT standards and analyzed by qPCR. The LoQs (

gc / μL

of template) were calculated using two different approaches: (1) a one-sided t-test with 99% confidence [Eq. (1)], as recommended by the US Environmental Protection Agency; and (2) a one-sided t-test with 95% confidence [Eq. (2)]. The more conservative USEPA approach was used for the 16S rRNA gene assay, while the alternative approach was used for the remaining molecular targets. The justification for use of the higher LoQ for the 16S rRNA gene was to mitigate the effects of false amplification, which can be common for 16S rRNA gene assays due to inherent DNA contamination of master mixes (Czurda et al. 2015; Mühl et al. 2010; Rand and Houck 1990; Rueckert and Morgan 2007; Salter et al. 2014; Spangler et al. 2009; Tondeur et al. 2004). The assay-specific LoQs are summarized in Table S4

L o Q = t s

(1)

where

t

= student’s t-test statistic (

α = 0.01

and

n - 1

degrees of freedom); and

s

= standard deviation

L Q = t s / \sqrt{N}

(2)

where

t

= student’s t-test statistic (

α = 0.05

and

n - 1

degrees of freedom);

s

= standard deviation; and

N

= number of replicates.

Data Analysis

All qPCR data and sample amplification curves were generated and visually inspected with CFX96 software. For each assay, raw Cq values were compiled into a single document and converted to

gc / μL

of template DNA using a combined regression equation for each assay across all qPCR runs. Data were corrected for equivalent sample volumes (Table 1) to determine the corresponding concentrations in the original samples. LRVs were calculated using Eq. (3), with LoQs substituted for censored data

LRV = \log_{10} (C_{initial, i} / C_{final, i})

(3)

where

C_{initial}

= concentration in the feed to treatment process

i

; and

C_{final}

= concentration in the effluent from treatment process

i

(or LoQ).

Gene copy data were log transformed and analyzed with a three-way ANOVA (for the MBR facility) or a two-way ANOVA (for the FAT facility) using SPSS statistical software. Normality assumption was assessed with the Shapiro-Wilk test, and assumption of homogeneity of variances was assessed with Levene’s test. Statistical significance was set at

α = 0.05

for all tests. LRV data were analyzed with a two-way mixed and a two-way ANOVA in SPSS 25 using nontransformed data.

Results and Discussion

Membrane Bioreactor Feed

This study builds upon a separate virus monitoring study previously conducted at the same MBR study site by a different research team (Erdal and Vorheis 2015); the corresponding data are summarized in Table S5. Their objective was to demonstrate the MBR’s efficacy in attenuating human pathogenic viruses and surrogate coliphages. Across five sample sets, the geometric mean concentrations for the MBR feed were

\sim 10^{2} gc / mL

for adenovirus and norovirus GI (detected in 3 of 5 samples),

\sim 10^{1} gc / mL

for norovirus GII (detected in 3 of 5 samples), and

\sim 10^{1} PFU / mL

for MS2 and somatic coliphage. Coliphages were often detected in MBR filtrate during high flux conditions or immediately after membrane cleaning, but human viruses were generally not detected in MBR filtrate under any operational condition. Considering all data (with LoQs substituted for nondetects), the corresponding LRVs were

4.9 \pm 1.6

for human pathogens and

3.0 \pm 0.6

for coliphages (

p > 0.50

within groups and

p = 0.045

between groups). Building upon these preliminary data, the current study aimed to determine whether the emerging surrogate viruses might allow for LRV quantification across a greater range (i.e., potentially greater abundance in the MBR feed) and with more precision (i.e., detection in both the MBR feed and filtrate).

In this study, concentrations of all molecular targets in the prefiltered solids from the MBR feed constituted a negligible fraction of the total (i.e.,

< 0.1 %

). In contrast, the liquid fraction of the MBR feed contained high concentrations of the molecular targets, as illustrated in Fig. 3. The 16S rRNA gene averaged

(1.16 \pm 0.25) \times 10^{7} gc / mL

in April 2018 and significantly increased to

(4.14 \pm 2.15) \times 10^{7} gc / mL

in June 2018 (

p < 0.005

). Members of the genus Bacteroides comprised a relatively large portion (85% and 30%) of the bacterial community in the MBR feed, with average concentrations of

(9.81 \pm 2.61) \times 10^{6} gc / mL

in April and

(1.25 \pm 0.69) \times 10^{7} gc / mL

in June (

p = 0.511

). Bacteroides (and more generally the phylum Bacteroidetes) dominate the western human gut microbiota (Ley et al. 2006), hence their relative prominence in sewage should not be surprising. For comparison, Gerrity and Neyestani (2019) reported a lower, yet still significant, relative abundance of 7% for Bacteroides in a similar primary effluent; this value is roughly consistent with other surveys of human-derived bacteria in sewage (Fisher et al. 2015; Newton et al. 2015). The difference in relative abundance between these studies might be attributable to the slight difference in wastewater matrix or differences between semiquantitative methods (i.e., qPCR versus 16S rRNA gene sequencing). Nevertheless, these data demonstrate that genetic markers for Bacteroides, including their associated bacteriophages (Ogilvie et al. 2012; Stachler and Bibby 2014), are useful indicators of fecal contamination in environmental samples (Ahmed et al. 2008).

Fig. 3. Concentrations of microbial targets ( $gc / mL$ ) in the MBR feeds (black) and filtrates (gray) collected in April (solid) or June (striped). The data indicate gene copies per mL of original sample. The < symbols indicate that the corresponding concentrations were below the limits of quantification designated by the column. Columns represent averages ( $\pm 1$ standard deviation) of experimental and analytical replicates after correcting for sample-specific equivalent sample volumes.

Bacteroides-specific

ϕ B 124 - 14

was abundant in all MBR feed samples but at a significantly higher concentration in April

(2.46 \pm 1.11) \times 10^{5} gc / mL

than in June

(5.64 \pm 1.74) \times 10^{4} gc / mL

(

p = 0.003

). There was a slight correlation between the abundance of the bacteriophage and its host in April (

R^{2} = 0.60

) but to a lesser extent in June (

R^{2} = 0.36

, Fig. S4). The relatively poor correlations might be explained by the inherently different characteristics of the bacterium and its phage, including size (Marti et al. 2011) and isoelectric point (Xagoraraki et al. 2014), which may have affected removal during preliminary treatment steps (i.e., screening or grit removal) or recovery efficiencies during sample concentration. For example, Enterococcus faecalis recovery was higher (

93 % \pm 16 %

) than MS2 bacteriophage (

57 % \pm 7.7 %

) for low turbidity water in Smith and Hill (2009).

The other Bacteroides phage,

ϕ crAssphage

, was also highly abundant in the MBR feed, with average concentrations of

(1.47 \pm 1.87) \times 10^{6} gc / mL

in April and

(7.31 \pm 2.40) \times 10^{5} gc / mL

in June, but there was no significant difference between the two sample events (

p = 0.290

). This phage has been detected at relatively high numbers in other raw wastewater samples:

2.9 \times 10^{3} gc / ng

of total DNA (Stachler et al. 2017),

7.9 \times 10^{6} gc / mL

(García-Aljaro et al. 2017), and

7.1 \times 10^{6} gc / mL

(Ahmed et al. 2018). Stachler et al. (2018) also reported a

ϕ crAssphage

concentration of

8.7 \times 10^{3} gc / mL

in an urban stream impacted by combined sewer overflows. This virus was cultured only recently (Shkoporov et al. 2018), so its original identification (Dutilh et al. 2014) and subsequent monitoring efforts have generally relied upon molecular analyses.

In contrast with the bacteriophages but comparable to the pathogens in Erdal and Vorheis (2015), the plant virus, PMMoV, was detected at relatively low concentrations in the MBR feed, with an average of

(1.96 \pm 0.37) \times 10^{3} gc / mL

in April and

(3.10 \pm 2.16) \times 10^{2} gc / mL

in June (

p = 0.001

). According to the literature, PMMoV concentrations vary considerably between studies and by water matrix:

10^{3}

to

10^{7} gc / mL

in raw wastewater (Symonds et al. 2018),

< 10^{- 2}

to

10^{6} gc / mL

in treated wastewater (Symonds et al. 2018),

10^{1}

to

10^{3} gc / mL

in surface water (Hamza et al. 2011; Kato et al. 2018), and

\sim 10^{- 1} gc / mL

in finished drinking water (Kato et al. 2018). PMMoV concentrations in this study appear low for raw wastewater, but the specific reason(s) for this discrepancy cannot be determined at this time.

Membrane Bioreactor Filtrate

MBR filtrate concentrations for the five molecular targets are shown in Fig. 3. The 16S rRNA gene averaged

(1.68 \pm 0.34) \times 10^{4} gc / mL

in April and

(8.80 \pm 2.07) \times 10^{3} gc / mL

in June (

p = 0.003

). The Bacteroides-specific marker also amplified in all MBR filtrate samples but was below the LoQ. There are several possible explanations for detection of bacteria in MBR filtrate, which is unexpected given the small membrane pore size (0.04 μm). Bacterial detection could indicate colonization (e.g., biofilm growth) downstream of the MBR (DeCarolis and Adham 2007), which is more likely for environmental versus host-specific bacteria. This was supported by the abundance of the bacterial 16S rRNA gene in the MBR filtrate and the lack of Bacteroides, which thrive in the gastrointestinal tracts of their hosts and have low potential for growth in the environment (Ahmed et al. 2008; Layton et al. 2006). For this reason, Bacteroides (or other fecal indicators such as total coliform and E. coli) represent a more suitable surrogate than the more general 16S rRNA gene when evaluating membrane performance and integrity, particularly for an MBR that has significant upstream biological activity. Alternatively, the presence of extracellular DNA (eDNA), which can persist for a long time in the environment (Nielsen et al. 2007; Dlott et al. 2015) and is abundant in activated sludge systems (Dominiak et al. 2011), might also explain the detection of 16S rRNA genes and Bacteroides (

albeit < LoQ

) in the MBR filtrate.

Both Bacteroides bacteriophages were

< LoQ

in all MBR filtrate samples (Fig. 3). These data show that both phages were removed to the extent measurable by this method, consistent with other phage studies (Zanetti et al. 2010) and specifically for ϕB124-14 (Purnell et al. 2016); no other studies have evaluated ϕcrAssphage removal by MBR. In contrast, PMMoV was detected at quantifiable levels, with average concentrations of

(2.58 \pm 0.44) \times 10^{1} gc / mL

in April and

(2.37 \pm 1.05) \times 10^{1} gc / mL

in June (

p = 0.072

). PMMoV has been detected at high concentrations in other MBR filtrates (Jumat et al. 2017), thereby indicating its potential to persist through activated sludge systems and pass through MF or UF membranes.

Membrane Bioreactor Log Removal Values

Table 3 summarizes the observed LRVs for the MBR. The LRVs for the 16S rRNA gene were

3.25 \pm 0.19

across both sampling events, but these values are not indicative of MBR performance given the potential for bacterial growth downstream of the MBR. The Bacteroides data are more consistent with expectations of a UF membrane, with LRVs of at least five for all samples (limited by the LoQ).

ϕ crAssphage

(

LRV > 6.15 \pm 0.28

across both sampling events) exhibited greater removal than

ϕ B 124 - 14

(

LRV > 3.87 \pm 0.30

across both sampling events), but this was an artifact of

ϕ B 124 - 14

’s lower abundance in the MBR feed coupled with its higher LoQ. Interestingly, PMMoV exhibited considerably lower LRVs, with significant differences between April (

1.88 \pm 0.14

) and June (

0.96 \pm 0.26

) (

p = 0.023

).

Table 3. Average log removal values across the MBR and FAT train based on experimental and analytical replicates

Microbial target	MBR		FAT train
Microbial target	April	June	$O_{3}$	BAC	UF	RO-UV AOP
Bacteria	$2.84 \pm 0.09$	$3.65 \pm 0.28$	$1.62 \pm 0.50$	$- 0.49 \pm 0.45$	$1.36 \pm 0.22$	$> 1.00 \pm 0.10$
Bacteroides	$> 5.36 \pm 0.10$	$> 5.61 \pm 0.24$	$> 1.91 \pm 0.23$	$N / A$	$N / A$	$N / A$
ϕB124-14	$> 4.09 \pm 0.25$	$> 3.65 \pm 0.15$	$> 0.14 \pm 0.06$	$N / A$	$N / A$	$N / A$
ϕcrAssphage	$> 6.14 \pm 0.38$	$> 6.16 \pm 0.19$	$> 2.95 \pm 0.25$	$N / A$	$N / A$	$N / A$
PMMoV	$1.88 \pm 0.14$	$0.96 \pm 0.26$	$> 2.91 \pm 0.37$	$N / A$	$N / A$	$N / A$

Note: Data represent averages $\pm 1$ standard deviation. Calculation of some LRVs was not possible due to analytical limitations; these LRVs were calculated based on the corresponding limits of quantification and are denoted by > symbols. N/A = not applicable due to analytical limitations for the preceding treatment step.

These LRV data are consistent with MBR literature. For example, Purnell et al. (2016) reported LRVs of

> 6

for enterococci and coliform bacteria and

\sim 4.0

for

ϕ B 124 - 14

, while Hamza et al. (2011) reported lower LRVs for PMMoV ranging from 1.7 to 3.7. Because PMMoV,

ϕ B 124 - 14

, and

ϕ crAssphage

are likely negatively charged in environmental systems, the observed differences in removal are most likely due to their differing morphologies (rod-shaped PMMoV versus spherical bacteriophages). To the best of our knowledge, no other data are available for

ϕ crAssphage

removal by MBRs, presumably due to its relatively recent discovery (Stachler and Bibby 2014) and even more recent establishment as an indicator of fecal contamination (Ahmed et al. 2018). In comparison with Erdal and Vorheis (2015), the

ϕ B 124 - 14

and

ϕ crAssphage

data from this study are consistent with the LRVs for human pathogenic viruses (i.e., adenovirus and norovirus), while PMMoV data are closer to MS2 and somatic coliphage LRVs immediately following membrane cleaning (Table S5).

It is also important to differentiate LRVs calculated from MBR feed and MBR filtrate (this current study) versus MBR mixed liquor and MBR filtrate (Simmons et al. 2011). For the latter case, mixing of the MBR feed with concentrated return activated sludge (RAS) may result in higher concentrations of target microorganisms and higher LRVs. Simmons et al. (2011) reported average LRVs of 5.5, 5.1, and 3.9 for adenovirus, enterovirus, and norovirus GII, respectively, using MBR mixed liquor as the baseline, but the average LRVs decreased to approximately 3.1, 3.6, and 4.7 when using the MBR feed as the baseline. The lower LRVs observed in Simmons et al. (2011) suggest that

ϕ B 124 - 14

might be a more suitable surrogate than

ϕ crAssphage

for virus removal by MBR, although

ϕ crAssphage

was more consistent with pathogen removal observed in Erdal and Vorheis (2015). Further, because of its unique morphology and exceptionally low LRVs, PMMoV may be an overly conservative surrogate when evaluating the performance of an MBR, or low pressure membranes generally.

Full Advanced Treatment Train

The FAT feed was a tertiary effluent (activated sludge, secondary clarification, and anthracite media filtration) from an adjacent full-scale wastewater treatment plant. The corresponding raw sewage and primary effluent were inaccessible at the time of sample collection so a direct comparison with the aforementioned MBR feed was not possible. The FAT feed is consequently more representative of the MBR filtrate, albeit without the effects of membrane filtration, which explains why its concentrations were generally lower than those of the MBR feed but higher than those of the MBR filtrate (Fig. 4):

16 S rRNA gene = (2.79 \pm 1.40) \times 10^{5} gc / mL

;

B a c t e r o i d e s = (1.76 \pm 1.02) \times 10^{3} gc / mL

;

ϕ B 124 - 14 = (3.10 \pm 0.73) \times 10^{1} gc / mL

;

ϕ crAssphage = (3.38 \pm 2.66) \times 10^{2} gc / mL

; and

PMMoV = (8.69 \pm 7.69) \times 10^{2} gc / mL

. Interestingly, the PMMoV concentration in the FAT feed was comparable to that of the MBR feed (

p = 0.143

), thereby suggesting minimal removal during standard secondary/tertiary wastewater treatment and/or higher raw sewage concentrations at the southern California site.

Fig. 4. Concentrations of microbial targets ( $gc / mL$ ) across the full advanced treatment train. The data indicate gene copies per mL of original sample. The < symbols indicate that the corresponding concentrations were below the limits of quantification designated by the columns. Columns represent averages ( $\pm 1$ standard deviation) of experimental and analytical replicates after correcting for sample-specific equivalent sample volumes. All assays were < LoQ for the RO and UV AOP samples.

Low concentrations of the remaining surrogates limited our capability to calculate and demonstrate LRVs across the FAT train (Table 3). With the exception of the 16S rRNA gene, all of the molecular targets were <LoQ following ozonation. The corresponding LRVs for ozonation were

1.62 \pm 0.50

for the 16S rRNA gene,

> 1.91 \pm 0.23

for Bacteroides,

> 0.14 \pm 0.06

for

ϕ B 124 - 14

,

> 2.95 \pm 0.25

for

ϕ crAssphage

, and

> 2.91 \pm 0.37

for PMMoV. For comparison, an

O_{3} / TOC

ratio of

\sim 1.0

is expected to achieve LRVs of at least 2, 5, and 6 for spore-forming bacteria, vegetative bacteria, and viruses, respectively (Gamage et al. 2013). There was a 0.5-log increase in 16S rRNA gene concentration following BAC, as might be expected of a biological process with potential for biomass sloughing (Howell and Atkinson 1976).

The UF process achieved an LRV of only

1.36 \pm 0.22

for the 16S rRNA gene, but again, this is not indicative of membrane performance/integrity because of the confounding factors associated with the 16S rRNA gene. In fact, the 16S rRNA gene was detected at similar levels across another FAT train in southern California (Stamps et al. 2018). Relative to UF filtrate, there was a three-fold increase in the concentration of the 16S rRNA gene in the RO concentrate, with all other targets

< LoQ

. Considering that RO processes are known to concentrate a variety of wastewater-derived contaminants (Alturki et al. 2010), the fact that no other molecular targets were detected in RO brine is either a testament to the upstream UF or the additional robustness provided by the

O_{3} - BAC

processes that are not commonly included in FAT. Finally, all molecular targets were

< LoQ

in the RO permeate and UV AOP effluent.

Conclusions

Use of MBRs is increasing in water reuse applications, but broad implementation for potable reuse requires a regulatory framework for awarding LRVs. Such a framework requires extensive datasets, rapid methods, and suitable surrogates to document process performance and integrity with sufficient frequency. In this study, Bacteroides–associated

ϕ B 124 - 14

,

ϕ crAssphage

, and plant-associated PMMoV were highly abundant in the MBR feed, which allowed for demonstrating

LRVs > 6 \times logs

for

ϕ crAssphage

. However, data in the literature suggest that the more conservative LRVs observed for

ϕ B 124 - 14

(overall average

LRV > 3.9

) may be more consistent with human pathogen removal by MBR. PMMoV proved to be a valuable indicator of human fecal contamination due to its persistence through wastewater treatment, but it may be an overly conservative surrogate for treatment performance (overall average

LRV = 1.4

). Finally, FAT was effective in achieving the LoQs for all molecular targets, but surrogate abundance was insufficient to demonstrate LRVs beyond the initial ozone treatment process, at least with the methods used in this study. Although data provided here report a case study, this information expands our knowledge by increasing the existing body of scientific literature devoted to various aspects of water treatment and reuse. Yet, a more comprehensive study evaluating viral surrogates in multiple MBR and FAT facilities is warranted to characterize the broad applicability of the data presented here.

Supplemental Data

Figs. S1–S4 and Tables S1–S5 are available online in the ASCE library (www.ascelibrary.org).

Supplemental Materials

File (supplemental_data_ee.1943-7870.0001617_pappa.pdf)

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2.05 MB

Data Availability Statement

All data generated or used during this study are available from the corresponding author by request.

Acknowledgments

This research was supported by funding from the University of Nevada Las Vegas/Desert Research Institute (

UNLV / DRI

) Joint Postdoctoral Fellowship Program and a grant from the National Institute of General Medical Sciences (P20GM103440). The authors would also like to acknowledge Mitch Stoker, Willie Frehner, and Sal Huerta from the Southern Nevada Water Authority; Dr. Aleksey Pisarenko and Elise Chen from Trussell Technologies; Zhiyin Qin, Mayra Sarria, and Fernanda Bacaro from UNLV; and the two study sites for access to their facilities.

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Information & Authors

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Journal of Environmental Engineering

Volume 146 • Issue 2 • February 2020

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This work is made available under the terms of the Creative Commons Attribution 4.0 International license, http://creativecommons.org/licenses/by/4.0/.

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Received: Feb 22, 2019

Accepted: May 7, 2019

Published online: Nov 21, 2019

Published in print: Feb 1, 2020

Discussion open until: Apr 21, 2020

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Katerina Papp https://orcid.org/0000-0001-8126-4822 [email protected]

Postdoctoral Researcher, Dept. of Civil and Environmental Engineering and Construction, Univ. of Nevada Las Vegas, 4505 S. Maryland Pkwy., P.O. Box 454015, Las Vegas, NV 89154; Postdoctoral Researcher, Division of Hydrologic Sciences, Desert Research Institute, 755 E. Flamingo Rd., Las Vegas, NV 89119; Postdoctoral Researcher, Applied Research and Development Center, Southern Nevada Water Authority, P.O. Box 99954, Las Vegas, NV 89193. ORCID: https://orcid.org/0000-0001-8126-4822. Email: [email protected]; [email protected]; [email protected]

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Duane Moser [email protected]

Associate Professor, Division of Hydrologic Sciences, Desert Research Institute, 755 E. Flamingo Rd., Las Vegas, NV 89119. Email: [email protected]

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Daniel Gerrity https://orcid.org/0000-0001-8019-9723 [email protected]

Associate Professor, Dept. of Civil and Environmental Engineering and Construction, Univ. of Nevada Las Vegas, 4505 S. Maryland Pkwy., P.O. Box 454015, Las Vegas, NV 89154; Principal Research Laboratory Scientist, Applied Research and Development Center, Southern Nevada Water Authority, P.O. Box 99954, Las Vegas, NV 89193 (corresponding author). ORCID: https://orcid.org/0000-0001-8019-9723. Email: [email protected]; [email protected]

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Abstract

Introduction

Methods

Sample Collection

Sample Concentration

Nucleic Acid Extraction and Analysis

Quality Assurance/Quality Control

Data Analysis

Results and Discussion

Membrane Bioreactor Feed

Membrane Bioreactor Filtrate

Membrane Bioreactor Log Removal Values

Full Advanced Treatment Train

Conclusions

Supplemental Data

Supplemental Materials

Data Availability Statement

Acknowledgments

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