City of Santa Barbara

Public Works Department

Memorandum

DATE: February 17, 2022

TO: Water Commission

VIA: Joshua Haggmark, Water Resources Manager

FROM: Arturo Keller, Water Commissioner

SUBJECT: What Is Direct Potable Reuse And What Are Some Key Considerations

For Its Implementation In California

The State Water Resource Control Board has been actively working on developing

regulations that would support state wide Direct Potable Reuse (DPR). Is California ready

for DPR? In this presentation, we will explore the technological, economic, and

environmental considerations associated with DPR.

ATTACHMENT: Direct Potable Reuse: Are We Ready? A Review of Technological,

Economic, and Environmental Considerations

6-b

Direct Potable Reuse: Are We Ready? A Review of Technological,

Economic, and Environmental Considerations

Arturo A. Keller,* Yiming Su, and David Jassby

Cite This: https://doi.org/10.1021/acsestengg.1c00258

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Supporting Information

ABSTRACT: Meeting future water demand will require serious consideration of direct potable reuse

(DPR) for many water agencies. There has been tremendous progress in the technologies needed to

address the concerns that conventional and novel water contaminants pose. Yet, to date, only a few

relatively small DPR operations have been installed. As we get closer to the point where regulations are

ﬁnalized and serious investments are planned, there is a need to ask: Are we ready? In this Review, we

explore the technological, economic, and environmental considerations associated with DPR. In

particular, we focus on the status of technologies for addressing the most challenging water pollutants,

the cost estimates for DPR, and the energy demand and associated implications of DPR. We ﬁnd that,

although the technologies are nearly ready for DPR, the most critical issue will be real-time monitoring

of a number of molecules that pose distinct challenges to advanced treatment trains. In addition, there

is a need to consider emergency planning, both in terms of emergency buﬀer reservoir(s) and planning

for situations in which the treated water does not meet speciﬁcations. Since any advanced treatment

train will result in a signiﬁcant increase in embedded energy, it will be particularly important to plan for

renewable energy to minimize environmental impacts.

KEYWORDS: Emerging pollutants, water quality, reuse, monitoring, sensors

■

INTRODUCTION

Rising temperatures, increasing variability in precipitation

patterns, more extended droughts, growing populations, and

limited alternatives for new traditional water sources in semiarid

regions with frequent water scarcity episodes, such as the

southwestern US and around the world, are major drivers for a

serious consideration of direct potable reuse (DPR).

The

concept of “closing the loop”

in terms of the urban water cycle,

as part of the “Fourth Water Revolution”, considers potable

water reuse as one of its key pillars.

The employment of high-

quality treated wastewater for planned indirect potable reuse

(IPR), by employing an environmental buﬀer as in the case of

groundwater recharge or reservoir water augmentation, is

already a current practice in many regions (e.g., NEWater

Singapore,

4,5

Orange County Water District Groundwate r

Replenishment System (California),

Upper Occoquan Service

Authority (Virginia),

Montebello Forebay Groundwater

Recharge Project (Los Angeles, California), Western Corridor

Recycled Water Scheme (South East Queensland, Australia)

Summaries of several pilot or operational DPR and IPR systems

are provided by Guo et al.

However, the direct introduction of

high-quality treated eﬄuent to a public water system or for raw

water augmentation immediately upstream of a water treatment

plant, as planned in DPR, requires additional considera-

tions.

10−12

Given the high cost of treating water for potable reuse, the ﬁrst

step must be to implement a community-wide water

conservation plan. There are many success stories, throughout

California, the US, and around the world,

13−20

that implement

appliances and ﬁxtures with higher water (and energy) use

eﬃciency, convert landscapes to vegetation that requires almost

no watering or t o permeabl e hardscapes, eliminate leaks

throughout the entire water system, and provide incentives to

consumers.

21,22

The employment of an environmental buﬀer, essentially

storing the treated water in a large compartment, either an

aquifer or a reservoir, provides a dilution of constituents that

may be present in the treated water as well as time for natural

attenuation and detection of any unexpected changes in water

quality.

23−26

In California, Title 22 requires that water for IPR

be treated with reverse osmosis (RO) and an advanced oxidation

process (AOP) plus a minimum of two months of subsurface

travel time or reservoir retention time with speciﬁc consid-

erations for dilution ratios.

In fact, a longer residence time,

greater than 6 months, is desirable; less than two months

requires additional considerations. The goal is to ensure that the

concentrations of inorganic and organic chemicals are below

their respective maximum concentration levels (MCLs), water

quality objectives (WQOs), or notiﬁcation levels (NLs), that

Special Issue: Technology Baselines and Innovation

Priorities for Water Treatment and Supply

Received: July 8, 2021

Revised: September 16, 2021

Accepted: September 17, 2021

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levels of enteric viruses have a 12-log reduction, and that levels of

Giardia cysts and Cryptos poridium oocysts have a 10-log

reduction.

However, many communities do not have a nearby

reservoir or aquifer capacity for the 2+ months of residence time

or dilution ratios, or the hydrogeochemistry may be unsuitable

for IPR. In addition, the elimination of the environmental buﬀer

may be more cost-eﬃcient than IPR.

Many communities that extract their raw water from rivers,

such as the Ohio River, the Colorado River, and many others

around the world, already conduct unplanned IPR.

29,30

Upstream communities discharge their treated wastewater into

the river, in many cases with only secondary treatment, which is

diluted and then extracted downstream by other communities,

with much less than the recommended two months of residence

time. Technologies, regulations, and management approaches

that are relevant for DPR are thus valid for many other locations.

The elimination of the environmental buﬀer raises concerns

with regards to illegal or accidental discharges of chemicals into

the wastewater sewer collection stream or a stormwater

conveyance that connects to the wastewater system that may

not be regularly monitored.

31,32

Concerns about water security

also increase when there is a short time to react to such events.

The current COVID-19 pandemic has been a wake-up call to

also address unexpected, novel pathogens that may be present in

wastewater.

33−42

Thus, even for DPR, a small emergency buﬀer

should be built into the design to handle any ﬂows that are

suspected of not meeting drinking water quality goals and a

means to divert ﬂow temporarily to a receiving water body until

goals are met.

24,43,44

Closing the loop raises concerns with regards to the

accumulation of common constituents (e.g., chloride, nitrate,

borate) throughout the urban water system, even in the case of

IPR, where the constituents can increase in the aquifer or

reservoir. Current treatment levels for most wastewater

treatment plants (WWTPs) do not entirely remove chemicals

of emerging concern (CECs) such as pharmaceuticals and

personal care products (PPCPs), per- and polyﬂuoroalkyl

substances (PFASs) , disinfection byproducts (DBPs), and

nanomaterials and their residual ions as well as many industrial

chemicalsthatarenotregulated or monitored (e.g.,

acetone).

6,45−50

Conventional WWTPs are not required to

remove these contaminants, and although many can be partially

removed from the eﬄuent, residuals can remain. The potential

eﬀects of chronic exposure to low-level residuals of these

contaminants have not been fully characterized.

51−56

Thus,

there is a need for advanced treatment and real-time monitoring

of low (ng/L) levels of these currently unregulated contami-

nants. There is particular concern with low molecular mass

(<200 Da) neutral chemicals that may not be removed entirely

by RO and/or AOPs,

such as those presented in Table 1.

To address these concerns, advanced online monitoring of

chemicals and rapid oﬀ-line analytical capabilities will be

necessary.

57−63

Online monitoring of unregulated CECs cannot

rely on currently available total organic carbon (TOC) sensors,

even if they can detect organic molecules at 0.5 mg/L, or even

0.1 mg/L, since that is still orders of magnitude greater than the

ng/L levels at which the CECs may be present. Although

nontargeted and semitargeted analysis can be employed, these

methods are capital intensive and have high labor costs,

requiring novel tools for real-time monitoring. In the long term,

online monitoring will have to be reliable, convenient, and

aﬀordable for WWTPs. Several sampling points will be needed

along the treatment trains and distribution systems (Figure 1).

In addition, raw wastewater contains a high level of pathogens,

including enteric bacteria, viruses, and protozoa. Raw waste-

water may contain virus concentrations of 107 to 109 gene

copies per liter for qPCR-based analyses, virus concentrations of

3 to 1300 per liter for culture-based analyses, and protozoa

concentrations of 6 to 17 000 per liter.

104

Their mass loading

signiﬁcantly decreases through the conventional WWTP

process, and most can be removed by advanced technologies

such as RO and AOPs; however, there is a need to be vigilant and

implement online monitoring to avoid the risk of exposing the

population to these pathogens.

105,106

Sewersheds that include a signiﬁcant number of industrial and

commercial activity will need additional administrative meas-

ures, such as identiﬁcation of all possible sources, inventories of

chemicals in use, regular updating of inventories, enforcement of

Table 1. Partial List of Chemicals of Potential Concern after

Advanced Treatment

chemical potential eﬀects reference

Inorganics

arsenic (arsenite) cancer and other diseases 65

boron developmental eﬀects, toxic for plants 66

bromate carcinogen, developmental

neurotoxicity, negative eﬀects on

crop plants

67, 68

chlorate carcinogen 69−71

Organics

1,4-dioxane liver and kidney damage 72

2,4,6-trichlorophenol carcinogen 71

2,4,6-trichloroanisole organoleptic threshold 73, 74

2,4-dichloroanisole organoleptic threshold 73, 74

2-methyl-isobomeol organoleptic threshold 73, 74

acetaldehyde carcinogen 71

acetone and other VOCs taste and odor 75

atenolol developmental toxicity 76, 77

benzoquinones DNA and protein damage 78

bisphenol A endocrine disruption 79

bromoacetonitrile carcinogen 71

bromoform carcinogens 80, 81

carbamazepine toxic to pregnant women and fetuses 82−84

chloroacetonitrile carcinogen 71, 80, 81

chloroform carcinogens 80, 81

dichloromethane carcinogen 71

enedials damage to hepatic proteins 85, 86

estrogen endocrine disruption 87

ﬁpronil liver toxicity 88, 89

formaldehyde carcinogen 71

geosmin organoleptic threshold 73, 74

glyoxal oxidative stress 90, 91

halogenated disinfection

byproducts

carcinogens, mutagens 92

imidacloprid reproductive toxicity 88,

nitrosamines carcinogens 71, 92,

94, 95

N-

nitrosodimethylamine

(NDMA)

hepatotoxic and carcinogen 96

oxoenals damage to hepatic proteins 85, 86

perﬂuoroalkyl

substances (PFASs)

thyroid disorders, cancer 97, 98

tri(2-chloroethyl)

phosphate (TCEP)

carcinogen 76,

99−101

tris(1-chloro-2-propyl)

phosphate (TCPP)

DNA-damage potential 102

triclosan endocrine disruption 103

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ACS EST Engg. XXXX, XXX, XXX−XXX

pretreatment (i.e., treatment at the industrial or commercial

source), and education of all employees as to the potential

eﬀects of any discharge into the sewer that could result in

unexpected chemicals passing through the treatment systems

and into the water distribution system.

Other important considerations for DPR include (1) very low

total dissolved solids (TDS) after RO, which requires proper

dosing of dissolved solids to avoid corrosion problems that can

result in distribution system failures (e.g., leaking old

distribution pipes) and possible leaching of Pb and Cu;

107−109

(2) disposal of the RO brine, which can be a challenge for

communities far from the ocean and with sensitive nearby

habitats;

110,111

(3) higher embedded energy by incorporating

RO, AOP, chlorination, and any additional processes after the

conventional WWTP and the corresponding impacts on

greenhouse gases and other issues;

(4) additional pumping

costs to transport water upgradient to the high points of the

watersheds;

112

(5) increased capital and operating costs; (6)

additional complexity and training within the treatment plants;

(7) redundant capital investment in key processes (e.g., RO, NF,

activated carbon) to ensure these treatment steps are 100%

operational; (8) the need for close cooperation and coordina-

tion between the WWTP(s) and the agencies in charge of raw

water treatment and distribution to maintain uniform ﬂows,

manage emergency reservoir(s), and implement action plans in

case the treated eﬄuent does not meet water quality objectives.

In this Review, we consider the: (1) technologies available for

advanced tertiary treatment applicable for DPR and their ability

to address the contaminants indicated in Table 1; (2) tools for a

cost-eﬀectiveness comparison; (3) energy and other consid-

erations for advanced tertiary treatment trains; (4) the critical

role for sensors that are being proposed to meet the challenges of

real-time monitoring at multiple locations for chemicals of

potential concern as well as for pathogens; (5) ﬁnal

recommendations on our readiness to deploy DPR.

■

TREATMENT TECHNOLOGIES: EFFECTIVENESS

AND CHALLENGES

Low-pressure microﬁltration (MF) and ultraﬁltration (UF)

membranes remove a signiﬁcant fraction of particulates and

large (>200 Da) organic molecules remaining after conventional

(primary and secondary) wastewater treatment (Figure 2).

Figure 1. Process diagram for direct and indirect potable reuse with potential sampling locations.

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ACS EST Engg. XXXX, XXX, XXX−XXX

Nanoﬁltration (NF) membranes can remove many additional

small molecules but do not remove low molecular mass

monovalent ions and cannot alone meet TDS requirements

for DPR. Reverse osmosis (RO) membranes remove most of the

remaining organic molecules (down to 150−200 Da) and

divalent ions as well as a fraction of the monovalent ions,

meeting TDS requirements. More than 90% of semivolatiles and

the majority of PPCPs and PFAS are well rejected by NF and

RO.

113−115

However, a signiﬁcant fraction of small molecules,

such as chloroform, bromoform, bromodichloromethane, and

dibromochloromethane, are poorly rejected.

116

Short-chain

PFAS, TCEP, and other phosphoric acid esters can also pass

to the permeate at detectable concentrations.

76,117

A major

challenge for membrane technologies, in particular for RO and

NF membranes, is the irreversible adsorption of natural organic

matter and the formation of bioﬁlms, which obstruct the passage

of water, known as fouling.

118−120

Particulate deposition can

also result in fouling.

121

Deposits of inorganic ions (i.e., scaling)

can also aﬀect membrane performance over time, increasing the

pressure needed to maintain water ﬂux.

121

Considerable eﬀort

has been spent on reducing fouling and scaling via membrane

design, chemical additions, and pretreatment.

122,123

Bioﬁlm

formation (biofouling) can be limited using disinfectants (e.g.,

chloramine), scaling by lowering pH and with antiscalants, and

NOM and particle deposition via UF and MF pretreat-

ment.

123,124

Signiﬁcant advances have been made in membrane design and

performance using simulation tools from the molecular level to

system wide optimization. Molecular dynamics simulation of

membranes can serve to evaluate diﬀerent membrane materials

in terms of treatment performance, energy requirements, and

fouling. In these studies, there is generally a focus on inorganic

ion (e.g., Na

,Cl

−

) transport through diﬀerent membrane

materials. Molecular dynamics can serve to guide membrane

material development (e.g., use of functionalized carbon

nanotubes,

125

graphene,

126

and boron nitride nanotubes

127,128

for higher ion rejection and water ﬂow rates), selectivity based

on membrane characteristics (e.g., pore size, membrane

structure and thickness, surface modiﬁcations), water and ion

dynamics, and fouling.

120,129

Membrane performance simu-

lations at larger scales with speciﬁc input water conditions (e.g.,

salinity, operating pressure and temperature, ﬂow rate) can be

used to select among the many types of membranes available for

water reuse.

130

Simulations are also used to optimize system

design, consider single- vs two-step conﬁgurations, and

incorporate energy recovery devices and pretreatment as a

means to reduce energy requirements as well as other costs.

131

Process optimization can also be achieved using adaptive control

strategies for backwash frequency, disinfectant addition, RO

ﬂux, water recovery (i.e., fraction of feedwater present in treated

water), pH, and antiscalants dose.

132

In addition to lowering

energy and chemical use, optimization can also increase

membrane lifetime and important operating costs.

132

AOPs can generally be divided into O

-based, UV-based,

plasma-based, and electrochemical approaches and can remove

many of the remaining organic molecules that pass through the

RO membranes, albeit with diﬀerent eﬀectiveness. For AOPs,

there have been a number of studies implementing artiﬁcial

neural networks (ANN) for determining removal eﬃciencies,

operational control, and optimization

133

as well as for predicting

performance.

134

In terms of removal eﬃciencies, the approach

to date is to consider speciﬁc target chemicals (e.g., textile

dyes,

135−138

pharmaceuticals,

139

pesticides, MTBE

140

) gener-

ally using a speciﬁc AOP,

141

but there is a need for a more

comprehensive approach. Overall, there is a major research gap

in the development of tools for predicting cost and performance

of AOPs in part due to the wide range of chemicals considered,

processes, and operational conditions.

A low-energy alternative to AOPs is granular activated carbon

(GAC), which can also be biologically enhanced (BAC) to

increase removal eﬃciency. For BAC, models have been

developed to predict the adsorption and biodegradation

performance as a function of operational conditions with good

accuracy.

142,143

Modeling was more accurate for poorly to

moderately adsorbing trace organics, indicating that the model is

more accurate for biodegradation if the kinetics are known.

143

few studies have modeled the combination of ozone and BAC to

predict the removal eﬃciency of trace organics in water reuse.

144

However, there is also a need for performance and optimization

models as well as to more accurately predict costs and energy

requirements based on operating conditions.

The focus of this section is on those molecules that pose

signiﬁcant challenges for DPR, such as small monovalent ions

and organics.

Small Inorganic Molecules. The majority of heavy metals

(e.g., copper, chromium, nickel, etc.) are found in an ionic form

at neutral pH, are well-rejected by RO membranes, and are not

anticipated to be an issue in most streams.

146

However, As(III)

and boron, which are found as uncharged oxides/hydroxides at

neutral pH, are not well-rejected by RO membranes and could

pose a danger (although boron is a problem primarily for plants

and not mammals).

147−150

Arsenic is naturally present in many minerals, and although

typically dissolved concentrations are low, it can be found at up

to 2000 μg/L in some groundwater sources. While many water

supply systems in the US and around the world remove a

signiﬁcant fraction of the arsenic load from their raw water, it

could be introduced into the system by domestic or industrial

users using local wells; careful monitoring is needed to avoid

buildup of As concentrations in a DPR system. Even at low

levels, arsenic can lead to a number of cancers (skin, lungs,

bladder, liver, kidney), and the eﬀects may not be observed for

years until they are irreversible.

151

Although a fraction of the

particulate and dissolved arsenic can be removed by coagulation

or adsorption, it may not be suﬃcient. MF/UF have pore sizes

Figure 2. Size and molecular mass of pollutants and pathogens removed

by diﬀerent techniques. DBPs = disinfection byproducts, EDCs =

endocrine disrupting chemicals, PPCPs = pharmaceuticals and personal

care products, and PFASs = per- and polyﬂuoroalkyl substances.

Membrane cutoﬀ ranges from ref 145.

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ACS EST Engg. XXXX, XXX, XXX−XXX

that are too large to reject the ionic form.

152

RO can reject 60−

98% As(V) and/or 65−80% As(III) in the inﬂuent; thus, in

some cases, two or more steps are needed to reach WQOs.

147

the source water is anoxic, preoxidation of arsenite to arsenate is

recommended, since at typical pH arsenite is present as a neutral

form, while arsenate is anionic, increasing rejection. However, if

pH adjustment is needed to remove arsenate, it may be more

cost-eﬀective to have two-step RO ﬁltration.

147,148

Sea water (SWRO) and brackish water (BWRO) polyamide

membranes reject boron by 80−93% and 30−80%, respectively.

Consequently, a single-pass RO process is usually unable to

remove boron down to WQOs. Boron removal can be improved

by (a) pre- and post-treatment techniques;

153

(b) double-pass

RO;

153

149,150

(d) electroactive

membranes.

154

Removal of boron using a second pass or

alternative method increases water cost by approximately 10−

20%.

153

While Br

−

by itself does not pose a concern, the potential

formation of bromate (BrO

−

)andotherbrominated

disinfection byproducts (Br-DBPs) during ozone-based dis-

infection processes can be a problem if local surface or

groundwater contains high l evels of Br

−

(e.g., Australia).

While Br

−

removal eﬃciency of the RO process usually ranges

from 93% to 99.3% when total dissolved salt (TDS)

concentrations are low, it could decline to 74% when TDSs

increased to 400 ppm.

155

In terms of Br-DBPs, their removal

eﬃciencies in the RO process are relatively low, ranging from 0

(i.e., bromomethane) to 80% (i.e., 1,2-dibromoethane).

156

Moreover, while AOP processes can degrade Br-DBPs, the

risk of forming bromate is quite high. To lower the levels of Br-

DBPs, it is important to remove Br

−

before the AOP process.

Double-pass RO is an option,

157

and new approaches such as

−

selective ion-exchange resin and membrane capacitive

deionization are being explored.

157,158

Low Molecular Mass Volatile, Semivolatile, and

Nonvolatile Organic Compounds. The (secondary) bio-

logical treatment in a WWTP can eﬃciently remove many

volatile organic compounds (VOCs) or semi-VOCs (e.g., 96.7 ±

1.6% of 2-butanone, 91.7 ± 5.9% of acetone), but the

degradation of halogenated VOCs declines signiﬁcantly and

may be even nil (e.g., chloroform, cis-1,3-dichloropropene, and

1,2-dichloroethane).

159

Some may be lost to the atmosphere,

and a reaeration step may help lower their concentrations. VOCs

are poorly rejected by RO due to their small molecular mass and

neutral charge.

156

For instance, the rejection ratios of

acetonitrile, acrylonitrile, ch loroethane, 1,1-dich loroethene,

and trichloroethene are 23 ± 10.6%, 18 ± 8.1%, 15 ± 3.6%,

17 ± 9%, and 46 ± 2.9%, respectively.

156

Moreover, membrane

fouling caused by many VOCs signiﬁcantly decreases water ﬂux.

For instance, fouling caused by hydroquinone, 4-nitrophenol,

and 4-chlorophenol reduced permeate water ﬂux by 2.7%, 4.8%,

and 8.6%, respectively,

160

indicating that high loads of these

pollutants may deteriorate membrane performance.

GAC and other porous materials (e.g., zeolites) can remove

most organic compounds to a large extent, particularly if it is

biologically active.

161−163

VOCs can be removed by inter-

ception, hydrophobic interactions, electrostatic interaction,

multiple hydrogen bo nding, and various types of π ·· ·Cl

interactions.

161,162

The massive number of adsorption sites on

the huge surface areas of these porous materials are responsible

for the large removal capacity and nanoadsorbents are in

development for speciﬁc classes of pollutants.

164,165

Bioﬁlms can

form on the GAC or other adsorbents to further increase the

removal capacity of many halogenated VOCs (e.g., methylene

chloride, chlorobenze ne, carbon tetrachloride, tetrachloro-

ethylene, 95% o-chlorophenol, trichloroethene, cis -1,2-dichloro-

ethylene, trans-1,2-DCE, and vinyl chloride).

166,167

Removal

and destruction of volatile and semivolatile organic compounds

can be enhanced by combining it with ozone, since activated

carbon can serve to produce hydroxyl radicals from ozone.

168

This can also remove many of the precursors to disinfection

byproducts. To increase the eﬀectiveness, combinations of

AOPs with GAC, such as O

+ GAC + UV/H

, can be used to

enhance the destruction of the organics.

169

The most cost-

eﬀective conﬁguration for many organics was O

+ GAC +

169

Photoreactors, in some cases using nano-TiO

and UV,

are being considered as alternatives for the degradation of

challenging organics.

170

Many PPCPs can be removed by these

processes, either GAC alone or i n combination with

AOPs.

171−174

PFASs can also be removed to a certain extent

with GAC, although short-chain PFASs exhibited desorption,

and branched PFASs and those with carboxylic acids exhibited

lower adsorption than PFASs that are linear or contain sulfonic

acids.

175

Electrochemical systems can also function as advanced

oxidation/reduction processes for VOC degradation. Over

80% of chloroform, benzene, toluene, and trichloroethene in

solution can be oxidized within 2 h on Ir/Pd doped titanium

electrodes.

176

In addition, halogenated VOCs can be eﬃciently

electrochemically reduced to halogen-free products on diﬀerent

types of cathodes at relatively low potentials from −0.3 to −1.4

V (versus standard hydrogen electrode).

177,178

Thus, heteroge-

neous AOP processes, including the electrochemical system, are

increasingly widely adopted for organic pollutant removal.

179,180

In these processes, the reduction/oxidation rate is positively

correlated to the surface areas of the catalysts; thus, the

application of nanoscale materials becomes attractive.

180

The

introduction of nanomaterials not only concentrates the trace

pollutants on electrodes but also delivers electrons onto

adsorbed pollutants, which can signiﬁcantly increase pollutant

removal performance and energy eﬃcacy as well.

181,182

In the

future, the heterogeneous AOP process for VOC degradation

may play an important role in DPR.

Two m olecules pose partic ular challenges, namely, N-

nitrosodimethylamine (NDMA) and 1,4-dioxane. NDMA

(74.08 Da), both a former industrial chemical and a

chloramination byproduct, is detected in drinking water and

wastewater treatment plants, typically at levels below 100 ng/

183−185

but in industrial areas, concentrations up to 8230 ng/L

have been observed.

186

The USEPA screening level is 0.42 ng/L.

The removal eﬃciency of NDMA and other nitrosamines in

conventional WWTP biological processes ranges from 0% to

96% from plant to plant and, even the same plant, can experience

a wide range of removal,

183,184

making it particularly challenging

for DPR. The minimization of nitrite yield in the biological

process and/or the addition of sand ﬁltration after secondary

sedimentation can reduce NDMA levels in secondary treatment

eﬄuent,

183,187

but it still remains as a challenge to fully eliminate

it through biological treatment. Due to the existence of NDMA

precursors in secondary eﬄuent, it is very likely that NDMA and

other nitrosamines would be generated again during disinfection

if they are not fully removed before this step.

Treatment options, before feeding the secondary treatment

eﬄuent into an RO system, include ﬁltration (i.e., sand ﬁltration,

GAC adsorption, and nanoﬁltration) and coagulation/ﬂoccu-

lation, which all exhibit limited to moderate removal capacity for

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NDMA (16−44% with an NDMA concentration ranging

between 14 and 370 ng/L) and NDMA precursors.

188

Removal

eﬃciency of these processes varied widely depending on raw

water qualities and the speci ﬁc biological treatment process. In

fact, studies report that coagulant dosing and bioﬁltration can

contribute to the formation of NDMA precursors.

188

Nano-

ﬁltration (NF) can remove up to 90% of NDMA precursors,

189

but there could be signiﬁcant leaching of NDMA precursors

(∼180−450 ng/L in permeate) after membrane fouling.

189

Due to its small molecular radius (0.248 nm), high

hydrophilicity, and neutral charge at pH 6−8, NDMA is not

eﬀectively removed by RO membranes.

190,191

To enhance

NDMA removal, strategies such as (a) heat treatment of three

types of RO membranes (HYDRA, ESPAB, and ESPA2)

improved NDMA rejection from 74%, 62%, and 53% to 88%,

79%, and 62%, respectively;

192

(b) plugging the RO membrane

with dodecylamine increased rejection from 42% to 81%;

193

(c)

modiﬁcation of RO with graphene oxide nanosheets enhanced

NDMA rejection from 76.5% to 82.7%.

194

However, while these

strategies increased NDMA rejection, the trade-oﬀ was that the

membranes exhibited lower permeability.

194

These strategies

are still in the research stage, and with current RO membranes,

there is no guarant ee that NDMA would be complet ely

removed; for instance, it was reported that the concentration

of NDMA in RO permeate ranged from 8.8 to 31 ng/L.

195

Moreover, the rejection of NDMA precursors declined with

increasing membrane age and after membrane cleaning; the

NDMA rejection ratio decreased 6−9% during the ﬁrst 4 h

before increasing back to the precleaning rejection ratio.

196,197

In terms of AOPs, H

, ozone, and ClO

alone decrease the

concentrations of NDMA and NDMA precursors to some

degree, but the potential formation of NDMA in subsequent

chloramination and its partial rejection of RO highlight the need

for a combined AOP.

198,199

Since the removal eﬃciencies of

NDMA using UV + H

,UV+O

, and UV + monochloramine

are all above 95% and that using UV/free chlorine is 81− 95%,

UV-based technologies are favored.

195

However, excessive UV

radiation and oxidant dosage can increase NDMA formation

potential.

1,4-Dioxane (88.11 Da) is a stabilizer added to chlorinated

organic solvents, which is found as a contaminant in some urban

groundwater basins.

It is also poorly rejected by NF and RO

due to its high polarity and small size.

200,201

Traditional AOPs

such as O

or UV + H

are not suﬃciently eﬀective to remove

it from the RO permeate.

202,203

Thus, research is ongoing to

achieve higher removal eﬃciencies for 1,4-dioxane, such as

reductive electrochemical activation of hydrogen peroxide.

203

Figure 3. Estimated (A) total capital costs; (B) annual operating expenses; (C) capital costs per 1000 m

of capacity; (D) operating annual expenses

per 1000 m

. From Guo et al.: RO = reverse osmosis, UF/MF = ultraﬁltration/microﬁltration, MBR = membrane bioreactor, AS = activated sludge,

COAG = coagulation, H

(1) = peroxone, and GAC = granular activated carbon.

From Plumlee et al.: NF = nanoﬁltration, UV + H

ultraviolet + hydrogen peroxide, O

= ozone, BAC = biologically activated carbon, and H

(2) = peroxone.

218

All costs adjusted for inﬂation to

2021.

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BAC and O

−BAC may also be cost-eﬀective approaches for

1,4-dioxane.

204,205

Pathogens. Pathogens, including viruses, have a particle size

large than 10 nm,

206

and thus, RO should be able to achieve

100% removal based on size exclusion theory.

207−209

Forward

osmosis followed by RO treatment was reported to achieve 6.7-

log removal of spiked MS2 bacteriophage in graywater and

sewage, 5.4-log removal of native Escherichia coli (E. coli)in

graywater, and 7.9-log removal of native E. coli in sewage.

210

However, regrowth of the native bacteria in RO permeate was

still observed, indicating that additional disinfection is

needed.

210

In addition, monitoring virus removal after RO still

presents challenges.

211,212

Thus, UV, O

, and/or chlorination

treatment is highly recommended after RO. For instance, dosing

sodium hypochlorite can completely eliminate SARS-CoV-2 in

septic tank wastewater.

213

■

UNIT PROCESSES AND TREATMENT TRAIN COST

ESTIMATES

There are many combinations of advanced tertiary treatment

trains that can be considered for a given location for DPR,

29,214

on the basis of local water sources and conditions, existing

WWTP infrastructure and space, availability of brine disposal to

the ocean vs inland, energy costs, funding mechanisms, etc.

These factors as well as the selected treatment train, scale (i.e.,

ﬂow rate), and local regulatory requirements can substantially

aﬀect the ﬁnal installed capital and operating costs. Nevertheless,

it is useful to consider the cost of the various treatment units to

plan for DPR to make an informed decision about the various

water supply options. A particular challenge in obtaining cost

information is that most publications are based on small-scale

lab studi es or a relatively limited number of large-scale

operations with very diﬀerent conditions.

214−216

Guo et al.

conducted a very detailed study of the capital (CAPEX) and

operating (OPEX) expenses of many of the unit operations (e.g.,

RO, UF + MF, membrane bioreactor (MBR), activated sludge

(AS), coagulation (COAG), peroxone (H

), and

granular activated carbon (GAC)) and generated scaling

equations.

9,217

Figure 3 presents the inﬂation-adjusted CAPEX

(Figure 3A) and OPEX (Figure 3B) costs (from 2012 to 2021)

based on equations for plant capacities ranging from 100 to

500 000 m

/d. Chlorination is expected to be a very small

fraction of the total cost of the DPR train.

217

To complement the

information, the cost equations developed by Plumlee et al. were

also included in Figure 3, which were also adjusted for inﬂation

(from 2011 to 2021).

218

These authors consider that the

CAPEX and OPEX for RO are exactly the same as for NF (thus,

they are not presented in Figure 3). In terms of overall capital

costs, the capital investment per unit operation for a 100 000

/d (∼26.4 million gallons per day) plant ranges from around

$10M to $162M, excluding coagulation and peroxone treat-

ment,

111

as estimated using the information from Guo et al.

Most IPR and DPR plants built to date have a capacity of around

100 000 m

/d.

219

While in general the cost functions from Guo

et al.

and Plumlee et al.

218

are comparable in their estimates for

a given technology, there is a major discrepancy in the estimates

for peroxone (H

), CAPEX, and OPEX. Both equations

used to estimate this are presented in Figure 3. The data used for

Figure 3 is provided in Tables S1 and S2.

On the basis of these equations, at 100 000 m

/d, CAPEX is

ranked as follows: COAG ≪ O

(Plumlee et al.

218

)

=UV+H

< GAC < BAC < RO < UF/MF < AS < NF < MBR

(Guo et al.

); OPEX is ranked as O

(Plumlee et al.

218

)<UV+H

< BAC < AS < COAG < GAC <

UF/MF = MBR < RO < NF < H

(Guo et al.

). These

cost estimates have an uncertainty of −30/+50%, and local

conditions may result in signiﬁcant diﬀerences. Thus, there

could be substantial overlap in the range of estimates for the

various technologies, which aﬀect the ranking. Naturally, the

assumptions embedded in the cost estimate equations are

important and may diﬀer for each WWTP. For example, BAC as

estimated here assumes a 10 min

218

empty bed contact time

(EBCT), but other EBCTs may increase or decrease both

CAPEX and OPEX.

218

is considered pre-RO, while H

is intended to be post-RO. O

dosage is considered at 6 mg/L

in the inﬂuent, but since some is consumed by the organic

matter, it is estimated at an eﬀective dose of 3 mg/L with a 5 min

hydraulic contact time.

218

The increase of the eﬀective dose of

ozone from 1.5 to 9 mg/L can result in an increase of 8% in

CAPEX for an ∼100 000 m

/d plant and 22% increase in

OPEX.

218

Low pressure (e.g., MF/UF) and high pressure (e.g.,

RO, NF) membranes and labor costs can be combined, reducing

overall OPEX.

218

The cost of electricity can be quite signiﬁcant

as a fraction of OPEX and will diﬀer substantially for each region.

The eﬀect of scale is clearer when CAPEX and OPEX are

normalized by plant capacity (m

/d) and annual ﬂow (m

respectively (Figure 3C,D). CAPEX decreases substantially with

increasing scale for most technologies, except for UV + H

which remains near $115 per m

/d, and for peroxone as

calculated from the equation in Guo et al.,

in which CAPEX ﬁrst

decreases slowly until the plant capacity increases above

∼10 000 m

/d, and then unit costs begin to increase again

with increasing plant capacity. This behavior diﬀers considerably

from the prediction using the equation in Plumlee et al.

218

for

peroxone, which has a continuous decrease in CAPEX with

scale. In terms of OPEX, most technologies exhibit a gradual but

consistent decrease with scale, except GAC and UF/MF, for

which OPEX decreases very rapidly with increasing plant

capacity. However, BAC, a very similar technology to GAC,

appears to reach a ﬂat cost of around $0.045 per m

above a plant

capacity of 10 000 m

/d.

The CAPEX and OPEX of NF are expected to be similar to

those of RO.

218,220

NF can operate at slightly lower pressure

than RO, which reduces OPEX.

The retentate from NF and

RO contains a signiﬁcant fraction of the CECs that were not fully

removed in the conventional wastewater treatment process and

may require additional treatment before being disposed of, for

example, using AOPs, such as ozone.

220

Since NF and RO have a

recovery ratio of 50−85%,

220

the waste stream to treat and

dispose can be signiﬁcant. If ozone is used for this treatment,

with or without UV, the transfer eﬃciency of ozone from the gas

phase where it is generated to the liquid phase needs to be high,

around 70−90%.

221,222

Although some NF can perform very

well compared to RO in the removal of most CECs, they may

not meet the California requirement of TOC < 0.5 mg/L and

also have a poor rejection of nitrate and other monovalent

ions.

For example, a study to remove a combination of PFAS

using NF from concentrate estimated OPEX of around $0.25−

0.50/m

at a ﬂow rate of 11.5 m

/d, depending on the treatment

goal, which is similar to the estimate using the equation in

Plumlee et al.

218

(Figure 3D).

115

Some NF can lower most CEC

concentrations below 100 ng/L and, if bromide is not a concern,

NF can be more economical due to the lower pressure

requirements and fouling potential, which can result in 50%

fewer cleanings and higher overall utilization compared to RO.

The cost of fouling is signiﬁcant and can represent up to 24 ± 3%

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of OPEX for RO and 11 ± 1% of OPEX for NF.

223

Fouling

results in additional energy, earlier membrane replacement, and

minor additional cleaning costs.

223

Fouling also results in

additional downtime, which can be an important loss of use if

cleaning-in-place events are frequent.

223

Four advanced tertiary treatment trains were considered

(Figure 4), although there are many other possible combina-

tions. The least expensive in terms of CAPEX and OPEX is MF−

−BAC as proposed by Snyder et al.,

224

which can produce

high eﬄuent quality largely eliminating most CECs, although it

may not achieve TDS treatment goals and thus may be more

suitable for IPR.

224

CAPEX and OPEX are essentially the same

for MF−RO−UV + H

and MF−O

−RO,

218

and these two

treatment trains have the potential to achieve very high eﬄuent

quality and meet the strict California requirements. The most

expensive treatment train would be MF−RO−H

218

which also has the lowest economies of scale. The CAPEX

estimated here corresponds well (within −30/+50%) to those in

operational or pilot-scale plants in the US.

219

In all cases, the

added cost of these advanced tertiary treatment trains will have

an important impact on the overall cost of water for ratepayers.

However, CAPEX for RO and NF continues to decrease with

increasing installed capacity,

225,226

which has not been taken

into consideration in these estimates. On the other hand, we are

approaching the thermodynamic limit of separation

227,228

with

costs for membranes reaching an asymptote.

229

■

ENERGY AND OTHER ENVIRONMENTAL

CONSIDERATIONS

Energy considerations are paramount to potable reuse, given the

signiﬁcant increase in the energy required per m

of treated

water. For the unit operations being considered for potable

reuse, the largest requirement would be from the high-pressure

membranes (Figure 5) followed by the energy requirements for

UV + H

, which are very similar to those of BAC and MF/UF.

As with the cost estimates, these values have an uncertainty of

−30/+50%, and local conditions may result in signiﬁcant

diﬀerences. The range of values also depends on the level of

technology (e.g., older vs newer membranes, ozone generating

system, UV lamps). Thus, treatment trains that consider RO (or

NF) would have 80−90% higher energy requirements than

alternatives such as MF−O

−BAC. These estimates based on

modeling equations can be compared to a recent study that

found the electricity intensity in 70 operating, planned, or pilot

fully advanced treatment systems to range from 0.9 to 2.2 kWh/

with operational systems reporting from 1.1 to 1.4 kWh/

219

To compare the energy required to degrade CECs, it is useful

to consider the concept of electrical energy per order (EE/O, in

kWh/m

), which is the electrical energy needed to degrade a

particular chemical by 1 order of magnitude in 1 m

of water.

230

A recent review of the EEOs of 13 AOPs for a large number of

molecules concluded that O

alone had in general the lowest

median EEO followed by H

< electron beam < UV +

chlorine < UV + persulfate < UV + O

<UV+H

< photo-

Fenton < plasma.

231

The median EEO values for the ﬁrst seven

EEOs in the ranking are <1 kWh/m

. For photo-Fenton and

plasma, the median EEO values are 3−5 kWh/m

. There is a

signiﬁcant diﬀerence in the EEO for the various chemicals,

depending on the molecular structure and physicochemical

properties, in some cases ranging over 5 orders of magnitude

(e.g., from 10

−3

to 10

kWh/m

for O

231

EEO is also

dependent on the dose of H

, UV lamp type and arrangement,

and other operating conditions such as the eﬃciency with which

Figure 4. Integrated advanced tertiary treatment train (A) capital costs; (B) annual operating expenses. The treatment train costs are calculated with

the equations from Guo et al.

and Plumlee et al.

218

All costs adjusted for inﬂation to 2021.

Figure 5. Energy/water ratio for diﬀerent unit processes as well as three

possible treatment trains for DPR or IPR. Energy data from Plumlee et

al.

218

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can be transferred from the gas phase to water.

231−238

EEOs

are also reported only for the AOP process itself and does not

take into account energy requirements for purchased chemicals

or other life-cycle stages.

231

While it is useful to consider EEO, it

is important to note that the removal eﬃciency generally

decreases as follows: plasma < UV-based AOPs < O

-based

AOPs.

239

To select the technology for a given DPR system, one

will need to consider the most recalcitrant contaminants in

wastewater that are not removed by the previous or subsequent

processes in the treatment train.

The advanced tertiary treatment train not only will increase

the energy footprint of the WWTP and the embedded energy

but also, depending on the composition of the electrical grid,

may result in increased emissions of greenhouse gases,

particulate matter of all sizes, mercury, and other air pollutants

as well as its on water demand.

240,241

On the other hand, as the

electrical grid relies more and more on renewable energy, in

general with lower environmental impacts, the increased

energy/water ratio may not result in a much higher environ-

mental footprint. It should also be noted that the transport of

water and wastewater to the corresponding treatment plants

requires a considerable amount of energy, in some cases more

than the energy of conventional wastewater treatment.

242

Domestic water heating also represents a much higher energy

intensity (35−70 kWh/m

, depending on inlet and outlet

temperatures as well as fuel source)

243

than those in these

treatment trains, highlighting the need to minimize unnecessary

water heating; proper accounting of the water volumes should be

taken into consideration in these assessments. Since domestic

water heating is controlled by consumers, it is important to

educate them on the potential energy savings that can be

achieved at the consumer level. In addition, there is an

opportunity to use DPR systems as a means to store renewable

energy by using it only when it is cheap and abundant on the grid

(i.e., during peak solar hours during the day in California or at

peak wind hours at night in Texas) and then store the treated

water in the emergency buﬀer reservoir.

244

Creative thinking can

be used to solve these two challenges (i.e., storing renewable

energy and implementing DPR).

■

CRITICAL ROLE OF SENSING IN DPR

IPR and conventional water treatment systems already monitor

a suite of physical, chemical, and biological contaminants on

both continuous and intermittent schedules.

245

However, since

the DPR process provides little buﬀer capaci ty (if any),

contaminants that do pass through the treatment train can

rapidly spread throughout the distribution system, exposing

consumers to enhanced risk.

245,246

Therefore, the development

and use of real-time sensing capabilities are critical for the safe

use of DPR, as these capabilities can be used to inform operators

and consumers, in real time, of danger to their drinking water

supply.

247

In addition, this information can be used as input to

automated systems used to control the treatment and

distribution of reclaimed water, which can alert operators,

intensify treatment, shut the process down, and divert

contaminated water from the distribution system. However,

while there is a clear need for real-time sensing and monitoring

capabilities, few real-time sensing platforms are currently used,

particularly for the detection of patho gens, trace organic

pollutants (e.g., PPCPs), and trace metals/metalloids, all of

which are present in wastewater at elevated concentrations and

present risks during DPR.

248

The sensing of DPR treatment train performance can be

separated into two categories: (1) sensing of the performance of

individual steps in the treatment train (e.g., the sensing of RO

rejection); (2) sensing of contaminants of concern in the ﬁnal

treated water before it is introduced to the distribution system.

In many ways, the evaluation of individual treatment steps is

easier, as there are easily measurable water quality properties

that correlate with the overall performance of each step. When

one considers a common treatment train used in potable reuse

(e.g., MF/UF−RO−AOP), the performance of each one of

these steps can be readily inferred by using oﬀ-the-shelf sensing

platforms. For instance, optical turbidity meters and UV-

transmittance measurements that deliver readings in real time

are used to monitor the performance of MF and UF

membranes;

249,250

an increase in permeate turbidity can indicate

damage to the membrane, which would require the membrane

to be pinned (if in a hollow-ﬁber module) or replaced.

251

For

RO membranes, which are designed to reject all charged species

(including monovalent ions), monitoring permeate conductivity

is a simple way to evaluate RO performance, in real time,

although the sensitivity of this method is rather low;

252,253

since

solution conductivity is highly sensitive to ion concentrations,

damage to the RO membrane will manifest in a measurable

increase in conductivity, informing operators that the RO

process needs attention.

253

Fluorescence excitation−emission

measurements (which can measure the presence of small organic

molecules) are another method to evaluate the integrity of an

RO membrane in near real time.

254

In terms of the performance

of AOPs, there are numerous examples of hydroxyl radical

sensors as well as sensors that evaluate the concentrations of

hydrogen peroxide (used as a hydroxyl radical source in the UV/

and O

systems). Direct measurement of hydroxyl

radicals (the main reactive component in AOPs) and the

measure of H

disappearance (H

is consumed during

hydroxyl radical g eneration) give a measure of process

performance.

255−257

However, in all the cases mentioned

above, while these measurements can fairly accurately determine

the overall integrity of the diﬀerent treatment steps, these

measurements say nothing about speciﬁc contaminants of

concern. Also, these measurements are sensitive to feedwater

quality. For example, increased turbidity and conductivity of the

feed stream will result in elevated readings in the membrane

permeate streams. Therefore, these measurements must be

conducted in both the feed and permeate streams to properly

evaluate process performance. Another example is the ionic

composition of the feedwater; if there is a change in the

composition (e.g., increased Na

or Cl

−

concentrations), this

can lead to a drop in the observed RO rejection and could lead to

an erroneous conclusion that the treatment step is failing.

Various approaches have been explored for the monitoring of

speciﬁc contaminants of concern in treated wastewater. These

approaches can be further divided into two categories: (1) the

sensing of speciﬁc contaminants; (2) the measurement of bulk

water quality metrics (i.e., surrogate metrics) that are correlated

to the presence of trace contaminants.

29,258

While the ﬁrst

approach oﬀers a more accurate view of what is in the water, the

large number of potential contaminants makes the utilization of

this approach diﬃcult, as it requires numerous sensors that need

constant updating to keep up with the evolving ﬁeld of potential

contaminants.

Because the second approach relies on

correlations between the presence of easily measurable species

and trace contaminants, this approach is thought to be more

cost-eﬀective (and feasible), albeit while sacriﬁcing speciﬁcity.

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However, these surrogate measurements are somewhat “water

speciﬁc” and require “tuning” to a local water quality proﬁle.

259

A large amount of eﬀort has gone into the development of

sensors for various pathogens, including viruses, bacteria, and

protozoa; a (non)comprehensive list of such sensors can be

found in a recent review.

260

These sensors operate using

diﬀerent methods but can be grouped into (i) sensors that

operate by monitoring changes to surfaces (e.g., electrical

properties) when a target pathogen attaches to speciﬁc binding

sites, which allows for detection in real time; (ii) sensors that

detect speciﬁc pathogens by amplifying DNA; (iii) optical

sensors (e.g., cytometry, ﬂuorescence); (iv) colorimetric

sensors.

260

While many of these sensors are highly accurate

and sensitive, many require some time for the detection process

to take place (e.g., to amplify DNA), making real-time detection

impossible. For sensors that oﬀer results in near real time, these

sensors can become fouled by other aquatic species, such as

dissolved organic matter that can form nonspeciﬁc (e.g.,

hydrophobic) interactions with the surface, which can block

binding sites and reduce sensitivity.

Sensors for the detection of speciﬁc PPCPs (e.g.,

pharmaceuticals, endocrine disruptors, etc.) have also been

explored. Approaches toward their detection include the

fabrication of electrodes with speciﬁc binding sites to these

compounds

261,262

as well as spectroscopic analysis (e.g., using

ﬂuorescence spectroscopy, Raman spectroscopy, and infrared

spectroscopy) of water.

263,264

While these sensors often exhibit

high sensitivity and speciﬁcity in pure samples, electrochemical

sensors are prone to fouling, and spectroscopic signals can be

complicated to deconvolute in a mixed sample environment.

That being said, the highly treated water resulting from the

multiple treatment step used for DPR has very low

concentrations of other contaminants, making these detection

methods highly attractive. In terms of metal/metalloid sensing,

many reports have described online sensors for the detection of

arsenic and other heavy metals.

265−268

Many arsenic electro-

chemical sensors have been demonstrated, using such methods

as anodic stripping voltammetry and molecular imprinting of

electrodes.

267

To the best of our knowledge, no online sensors

have been developed for boron. There is likely an interesting

opportunity to combine machine learning methods with

monitoring tools for the rapid and ﬂexible detection of a range

of contaminants.

269−272

Speciﬁcally, the ability to train a

computer to deconvolute and decipher the complex spectro-

scopic signals resulting from the analysis of aqueous streams may

prove transformative, as the algorithm can be trained to identify

target contaminants as they emerge. For instance, software using

a convolutional neural network was able to recognize nitrates,

some pharmaceuticals, microplastics, and their additives after

being trained on a large set of spectra collected from a deep-UV

Raman/ﬂuorescence spectroscope.

271

A multivariate model

utilizing machine learning algorithms based on a back-

propagation neural network was successfully developed and

trained to accurately read various spectra collected from soil by

laser-induced breakdown spectroscopy for the detection of trace

element analysis.

273

UV−vis spectroscopic data was used to

train a fitness-support vector machine classiﬁer, which was then

able to serve as an early warning system for water contamination

events.

274

While they require signiﬁcant amounts of high-quality

data for training, machine learning approaches can oﬀer high

reliability for detecting and monitoring contaminants in

diﬀerent water matrixes. For instance, after being trained with

a data set of 12 560 UV spectra, a system using machine learning

algorithms successfully detected target contaminants in 107

times out of a total of 109 measurements and did not generate

any false positive signals.

274

The installation of sensor systems will likely increase the

capital costs of water treatment systems. However, a system with

a series of in-line sensors has the potential of not only

minimizing operational failures but also informing data-driven

system control algorithms (using artiﬁcial intelligence ap-

proaches) that optimize water treatment processes and reduce

operational costs.

275

For example, it has been demonstrated that

a data-driven approach could reduce pump energy consumption

in a wastewater treatment plant by 18.5%;

276

a model based on

artiﬁcial neural networks cut coagulant dosage by 10%,

277

and an

in-line control system built on a genetic algorithm utilizing a

fuzzy wavelet neural network algorithm provided robust and

eﬀective dissolved oxygen control and reduced the demand for

aeration.

278

In addition, it is anticipated that the beneﬁts of in-

line sensor systems coupled to artiﬁcial-intelligence optimiza-

tion algorithms will increase the adoption of such sensors, which

will potentially reduce the costs of these sensors, bringing down

the associated capital costs.

■

RECOMMENDATIONS

A number of treatment trains (e.g., MF−RO−AOP, MF−O

−

BAC) have been successfully tested (e.g., in pilot tests and IPR

systems) and can produce the high-quality eﬄuent needed for

DPR. Costs and energy requirements continue to decrease for

RO and NF, although thermodynamic limits are being reached,

and a reduction in cost and energy is a technological

challenge.

145

The most signiﬁcant challenges are in online,

real-time sensing of a number of small molecules that may be

present in the inﬂuent; breakthroughs in this area will be needed

for full deployment of DPR to ensure high reliability and

consumer safety. Of particular concern would be high episodic

loads from accidental or unreported discharges to the sewer

system that could move quickly through the treatment system

and pass to the distribution system at concentrations that could

pose a concern as well as the rapidly evolving ﬁeld of trace

contaminants that ﬁnd their way through the treatment system

and can be harmful to consumers when chronically present in

the water.

Establish Online and Real-Time Water Quality Mon-

itoring Systems. There have been important advances in water

quality monitoring, but there is a critical need to quantitatively

monitor a number of molecules and conditions, which can serve

as indicators of unit process or treatment train performance.

Monitoring has to have a high d egree of reliability and

redundancy to avoid failures as well as provide warning in real

time to operators and consumers. Since there is little buﬀering

capacity, rapid measurements are critical to safety. There may be

an attractive opportunity to utilize machine learning approaches

to analyze spectroscopic data (which can be rapidly collected

and analyzed) and inform operators of potential problems.

Redundancy in Treatment Processes to Ensure a High

Level of Removal. If DPR is to get main-stream acceptance

and become a regular water source, redundant treatment

equipment will be needed to handle maintenance as well as load

surges and other emergencies. This will increase the overall

CAPEX. In any case, this will likely be the most expensive water

source for most municipalities, even if CAPEX and OPEX costs,

particularly those of RO/NF membranes, continue to decrease

as more capacity is installed.

ACS ES&T Engineering pubs.acs.org/estengg Review

https://doi.org/10.1021/acsestengg.1c00258

ACS EST Engg. XXXX, XXX, XXX−XXX

Emergency Buﬀer and Plans for Dealing with Oﬀ-Spec

Water. If the monitoring system detects a deviation from

speciﬁcations or there is a detected equipment failure or under-

performance, there will be a need for an emergency buﬀer

reservoir to store the oﬀ-spec water and a clear process for

dealing with it for either reprocessing or discharge into an

environmental receiving water body without causing undue

environmental damage.

Consideration of Renewable Energy. Most DPR treat-

ment trains will have a substantial impact on the embedded

energy in water. This may become a barrier for adoption, unless

there is a plan to employ renewable energy that will result in

lower emissions of carbon and many pollutants associated with

conventional fuels. Building renewable energy at the same pace

as DPR is implemented must be part of the plan as well as

developing creative ideas for storing renewable energy from the

grid by ﬁlling the emergency reservoir with treated water from

DPR. There is also a need to educate the consumer about

opportunities for energy reduction in domestic water use, which

can be as important as water treatment in the overall balance.

■

ASSOCIATED CONTENT

sı

Supporting Information

The Supporting Information is available free of charge at

https://pubs.acs.org/doi/10.1021/acsestengg.1c00258.

Two tables with capital and operating expenses for

diﬀerent unit processes and treatment trains (PDF)

One spreadsheet with equations to calculate capital and

operating expenses (XLSX)

■

AUTHOR INFORMATION

Corresponding Author

Arturo A. Keller − Bren School of Environmental Science and

Management, University of California Santa Barbara, Santa

Barbara, California 93106, United States;

orcid.org/

0000-0002-7638-662X; Email: [email protected]

Authors

Yiming Su − Department of Civil and Environmental

Engineering, University of California Los Angeles, Los Angeles,

California 93106, United States;

orcid.org/0000-0001-

6035-7384

David Jassby − Department of Civil and Environmental

Engineering, University of California Los Angeles, Los Angeles,

California 93106, United States;

orcid.org/0000-0002-

2133-2536

Complete contact information is available at:

https://pubs.acs.org/10.1021/acsestengg.1c00258

Notes

The authors declare no competing ﬁnancial interest.

■

REFERENCES

(1) Haddeland, I.; Heinke, J.; Biemans, H.; Eisner, S.; Flörke, M.;

Hanasaki, N.; Konzmann, M.; Ludwig, F.; Masaki, Y.; Schewe, J.;

Stacke, T.; Tessler, Z. D.; Wada, Y.; Wisser, D. Global Water Resources

Affected by Human Interventions and Climate Change. Proc. Natl.

Acad. Sci. U. S. A. 2014, 111 (9), 3251−3256.

(2) Papa, M.; Foladori, P.; Guglielmi, L.; Bertanza, G. How Far Are

We from Closing the Loop of Sewage Resource Recovery? A Real

Picture of Municipal Wastewater Treatment Plants in Italy. J. Environ.

Manage. 2017, 198,9−15.

(3) Sedlak, D. Water 4.0, The Past, Present, and Future of the World’s

Most Vital Resource; Yale University Press, 2015.

(4) Seah, H.; Tan, T. P.; Chong, M. L.; Leong, J. NEWater-Multi

Safety Barrier Approach for Indirect Potable Use. Water Sci. Technol.:

Water Supply 2008, 8 (5), 573−582.

(5) Lee, H.; Tan, T. P. Singapore’s Experience with Reclaimed Water:

NEWater. Int. J. Water Resour. Dev. 2016, 32 (4), 611−621.

(6) Marron, E. L.; Mitch, W. A.; von Gunten, U.; Sedlak, D. L. A Tale

of Two Treatments: The Multiple Barrier Approach to Removing

Chemical Contaminants during Potable Water Reuse. Acc. Chem. Res.

2019, 52 (3), 615−622.

(7) Onyango, L.; Leslie, G.; Wood, J. G. Global Potable Reuse Case

Study 2: Upper Occoquan Service Authority; Brisbane, Australia, 2014.

(8) Apostolidis, N.; Hertle, C.; Young, R. Water Recycling in Australia.

Water (Basel, Switz.) 2011, 3 (3), 869−881.

(9) Guo, T.; Englehardt, J.; Wu, T. Review of Cost versus Scale: Water

and Wastewater Treatment and Reuse Processes. Water Sci. Technol.

2014, 69 (2), 223−234.

(10) USEPA. Guidelines for Water Reuse; EPA/600/R-12/618;

USEPA, 2012.

(11) Schimmoller, L. J.; Kealy, M. J.; Foster, S. K. Triple Bottom Line

Costs for Multiple Potable Reuse Treatment Schemes. Environ. Sci.

Water Res. Technol. 2015, 1 (5), 644−658.

(12) Wester, J.; Broad, K. Direct Potable Water Recycling in Texas:

Case Studies and Policy Implications. J. Environ. Policy Plan. 2021, 23

(1), 66−

83.

(13) Sahin, O.; Bertone, E.; Beal, C. D. A Systems Approach for

Assessing Water Conservation Potential through Demand-Based Water

Tariffs. J. Cleaner Prod. 2017, 148, 773−784.

(14) Gonzales, P.; Ajami, N. Social and Structural Patterns of

Drought-Related Water Conservation and Rebound. Water Resour. Res.

2017, 53 (12), 10619−10634.

(15) Corral-Verdugo, V.; Carrus, G.; Bonnes, M.; Moser, G.; Sinha, J.

B. P. Environmental Beliefs and Endorsement of Sustainable Develop-

ment Principles in Water Conservation: Toward a New Human

Interdependence Paradigm Scale. Environ. Behav. 2008, 40 (5), 703−

725.

(16) Clark, W. A.; Finley, J. C. Determinants of Water Conservation

Intention in Blagoevgrad, Bulgaria. Soc. Nat. Resour. 2007, 20 (7), 613−

627.

(17) Aprile, M. C.; Fiorillo, D. Water Conservation Behavior and

Environmental Concerns: Evidence from a Representative Sample of

Italian Individuals. J. Cleaner Prod. 2017, 159, 119−129.

(18) Tang, X.; Jin, Y.; Feng, C.; McLellan, B. C. Optimizing the

Energy and Water Conservation Synergy in China: 2007−2012. J.

Cleaner Prod. 2018, 175,8−17.

(19) Goette, L.; Leong, C.; Qian, N. Motivating Household Water

Conservation: A Field Experiment in Singapore. PLoS One 2019, 14

(3), No. e0211891.

(20) Inman, D.; Jeffrey, P. A Review of Residential Water

Conservation Tool Performance and Influences on Implementation

Effectiveness. Urban Water J. 2006, 3, 127−143 September.

(21) Cooley, H.; Phurisamban, R. The Cost of Alternative Water Supply

and Eﬃciency Options in California; Paciﬁc Institute: Oakland, CA,

2016.

(22) Diringer, S.; Shimabuku, M. Stacked Incentives: Co-Funding Water

Customer Incentive Programs; Paciﬁc Institute: Oakland, CA, 2021.

(23) Sen, S.; Lau, A.; Olney, B.; Ding, L.; Semper, J. P.; Russell, P.

Evaluation of Surface Water Augmentation at Lake Jennings. In Water

Environment Federation Technical Exhibition and Conference 2017,

WEFTEC 2017; Water Environment Federation, 2017; Vol. 3,pp

2119−

2132; DOI: 10.2175/193864717822153715.

(24) Gerrity, D.; Pecson, B.; Trussell, R. S.; Trussell, R. R. Potable

Reuse Treatment Trains throughout the World. Aqua 2013, 62 (6),

321−338.

(25) Pecson, B. M.; Trussell, R. S.; Triolo, S. C.; Trussell, R. R.

Examining Reservoirs in Potable Reuse, Part 1: Groundwater Recharge

and Surface Water Augmentation. J. - Am. Water Works Assoc. 2018, 110

(8), 34−40.

ACS ES&T Engineering pubs.acs.org/estengg Review

https://doi.org/10.1021/acsestengg.1c00258

ACS EST Engg. XXXX, XXX, XXX−XXX

(26) Haak, L.; Sundaram, V.; Pagilla, K. Sustainability Assessment for

Indirect Potable Reuse: A Case Study from Reno, Nevada. Water

Environ. Res. 2018, 90 (8), 748−760.

(27) California State Water Resources Control Board. California

Drinking Water-Related Laws; https://www.waterboards.ca.gov/

drinking_water/certlic/drinkingwater/Lawbook.html (accessed June

15, 2021).

(28) Lahnsteiner, J.; van Rensburg, P.; Esterhuizen, J. Direct Potable

Reuse - A Feasible Water Management Option. J. Water Reuse Desalin.

2018, 8 (1), 14−28.

(29) Leverenz, H. L.; Tchobanoglous, G.; Asano, T. Direct Potable

Reuse: A Future Imperative. J. Water Reuse Desalin. 2011, 1 (1), 2−10.

(30)Drewes,J.E.;Hu

bner, U.; Zhiteneva, V.; Karakurt, S.

Characterization of Unplanned Water Reuse in the EU; Garching,

Germany, 2017.

(31) Voulvoulis, N. Water Reuse from a Circular Econ omy

Perspective and Potential Risks from an Unregulated Approach. Curr.

Opin. Environ. Sci. Health 2018, 2,32−45.

(32) Binz, C.; Razavian, N. B.; Kiparsky, M. Of Dreamliners and

Drinking Water: Developing Risk Regulation and a Safety Culture for

Direct Potable Reuse. Water Resour. Manag. 2018, 32 (2), 511−525.

(33) Ahmed, W.; Angel, N.; Edson, J.; Bibby, K.; Bivins, A.; O’Brien, J.

W.; Choi, P. M.; Kitajima, M.; Simpson, S. L.; Li, J.; Tscharke, B.;

Verhagen, R.; Smith, W. J. M.; Zaugg, J.; Dierens, L.; Hugenholtz, P.;

Thomas, K. V.; Mueller, J. F. First Confirmed Detection of SARS-CoV-

2 in Untreated Wastewater in Australia: A Proof of Concept for the

Wastewater Surveillance of COVID-19 in the Community. Sci. Total

Environ. 2020, 728, 138764.

(34) Baldovin, T.; Amoruso, I.; Fonzo, M.; Buja, A.; Baldo, V.;

Cocchio, S.; Bertoncel lo, C . SARS-CoV-2 RNA De tection and

Persistence in Wastewater Samples: An Experimental Network for

COVID-19 Environmental Surveillance in Padua, Veneto Region (NE

Italy). Sci. Total Environ. 2021, 760, 143329.

(35) Kumar, M.; Patel, A. K.; Shah, A. V.; Raval, J.; Rajpara, N.; Joshi,

M.; Joshi, C. G. First Proof of the Capability of Wastewater Surveillance

for COVID-19 in India through Detection of Genetic Material of

SARS-CoV-2. Sci. Total Environ. 2020, 746, 141326.

(36) Hata, A.; Hara-Yamamura, H.; Meuchi, Y.; Imai, S.; Honda, R.

Detection of SARS-CoV-2 in Wastewater in Japan during a COVID-19

Outbreak. Sci. Total Environ. 2021, 758, 143578.

(37)Venugopal,A.;Ganesan,H.;SudalaimuthuRaja,S.S.;

Govindasamy, V.; Arunachalam, M.; Narayanasamy, A.; Sivaprakash,

P.; Rahman, P. K. S. M.; Gopalakrishnan, A. V.; Siama, Z.; Vellingiri, B.

Novel Wastewater Surveillance Strategy for Early Detection of

Coronavirus Disease 2019 Hotspots. Curr. Opin. Environ. Sci. Health

2020, 17,8−13.

(38) McCall, C.; Wu, H.; O’Brien, E.; Xagoraraki, I. Assessment of

Enteric Viruses during a Hepatitis Outbreak in Detroit MI Using

Wastewater Surveillance and Metagenomic Analysis. J. Appl. Microbiol.

2021, 131 , 1539.

(39) Hou, C.; Hua, Z.; Xu, P.; Xu, H.; Wang, Y.; Liao, J.; Di, B.

Estimating the Prevalence of Hepatitis B by Wastewater-Based

Epidemiology in 19 Cities in China. Sci. Total Environ. 2020 , 740 ,

139696.

(40) Bibby, K.; Aquino De Carvalho, N.; Wigginton, K. Research

Needs for Wastewater Handling in Virus Outbreak Response. Environ.

Sci. Technol. 2017, 2534−2535.

(41) Bibby, K.; Fischer, R. J.; Casson, L. W.; Stachler, E.; Haas, C. N.;

Munster, V. J. Persistence of Ebola Virus in Sterilized Wastewater.

Environ. Sci. Technol. Lett. 2015, 2 (9), 245−249.

(42) Abu Ali, H.; Yaniv, K.; Bar-Zeev, E.; Chaudhury, S.; Shagan, M.;

Lakkakula, S.; Ronen, Z.; Kushmaro, A.; Nir, O. Tracking SARS-CoV-2

RNA through the Wastewater Treatment Process. ACS ES&T Water

2021, 1 (5), 1161−1167.

(43) Pecson, B. M.; Trussell, R. S.; Pisarenko, A. N.; Trussell, R. R.

Achieving Reliability in Potable Reuse: The Four Rs. J. - Am. Water

Works Assoc. 2015, 107 (3), 48−58.

(44) Stanford, B. D.; Becker, W. C.; Debroux, J. F.; Ishii, S. K. L.;

Khan, S. J.; Khunjar, W. O. Planning for Direct Potable Reuse:

Operational Aspects of an Integrated Drinking Water System. J. - Am.

Water Works Assoc. 2016, 108 (4), 48−55.

(45) Heo, J.; Kim, S.; Her, N.; Park, C. M.; Yu, M.; Yoon, Y. Removal

of Contaminants of Emerging Concern by FO, RO, and UF Membranes

in Water and Wastewater. In Contaminants of Emerging Concern in

Water and Wastewater; Elsevier Inc., 2020; DOI: 10.1016/B978-0-12-

813561-7.00005-5.

(46) Park, M.; Snyder, S. A. Attenuation of Contaminants of Emerging

Concerns by Nano Fi Ltration Membrane: Rejection Mechanism and

Application in Water Reuse. In Contaminants of Emerging Concern in

Water and Wastewater; Elsevier Inc., 2020; DOI: 10.1016/B978-0-12-

813561-7.00006-7.

(47) Teodosiu, C.; Gilca, A. F.; Barjoveanu, G.; Fiore, S. Emerging

Pollutants Removal through Advanced Drinking Water Treatment: A

Review on Processes and Environmental Performances Assessment.

Cleaner Prod. 2018, 197, 1210−1221.

(48) Khan, J. A.; Sayed, M.; Khan, S.; Shah, N. S. Advanced Oxidation

Processes for the Treatment of Contaminants of Emerging Concern. In

Contaminants of Emerging Concern in Water and Wastewater; Elsevier

Inc., 2020; DOI: 10.1016/B978-0-12-813561-7.00009-2.

(49) Cartagena, P.; El Kaddouri, M.; Cases, V.; Trapote, A.; Prats, D.

Reduction of Emerging Micropollutants, Organic Matter, Nutrients

and Salinity from Real Wastewater by Combined MBR-NF/RO

Treatment. Sep. Purif. Technol. 2013, 110, 132−143.

(50)Snyder,S.A.;Westerhoff,P.;Yoon,Y.;Sedlak,D.L.

Pharmaceuticals, Personal Care Products, and Endocrine Disruptors

in Water: Implications for the Water Industry. Environ. Eng. Sci. 2003,

20, 449−469.

(51) Vandenberg, L. N.; Colborn, T.; Hayes, T. B.; Heindel, J. J.;

Jacobs, D. R.; Lee, D. H.; Shioda, T.; Soto, A. M.; vom Saal, F. S.;

Welshons, W. V.; Zoeller, R. T.; Myers, J. P. Hormones and Endocrine-

Disrupting Chemicals: Low-Dose Effects and Nonmonotonic Dose

Responses. Endocr. Rev. 2012, 33, 378−455.

(52) Leung, H. W.; Jin, L.; Wei, S.; Tsui, M. M. P.; Zhou, B.; Jiao, L.;

Cheung,P.C.;Chun,Y.K.;Murphy,M.B.;Lam,P.K.S.

Pharmaceuticals in Tap Water: Human Health Risk Assessment and

Proposed Monitoring Framework in China. Environ. Health Perspect.

2013, 121 (7), 839−846.

(53) Xu, Z.; Liu, J.; Wu, X.; Huang, B.; Pan, X. Nonmonotonic

Responses to Low Doses of Xenoestrogens: A Review. Environ. Res.

2017, 155, 199−207.

(54) Lin, T.; Yu, S.; Chen, W. Occurrence, Removal and Risk

Assessment of Pharmaceutical and Personal Care Products (PPCPs) in

an Advanced Drinking Water Treatment Plant (ADWTP) around

Taihu Lake in China. Chemosphere 2016, 152,1−9.

(55) Jenkins, S.; Wang, J.; Eltoum, I.; Desmond, R.; Lamartiniere, C.

A. Chronic Oral Exposure to Bisphenol a Results in a Nonmonotonic

Dose Response in Mammary Carcinogenesis and Metastasis in Mmtv-

Erbb2Mice. Environ. Health Perspect. 2011, 119 (11), 1604−1609.

(56) Riva, F.; Castiglioni, S.; Fattore, E.; Manenti, A.; Davoli, E.;

Zuccato, E. Monitoring Emerging Contaminants in the Drinking Water

of Milan and Assessment of the Human Risk. Int. J. Hyg. Environ. Health

2018, 221 (3), 451−457.

(57) Storey, M. V.; van der Gaag, B.; Burns, B. P. Advances in On-Line

Drinking Water Quality Monitoring and Early Warning Systems. Water

Res. 2011, 45 (2), 741−747.

(58) Soller, J. A.; Eftim, S. E.; Warren, I.; Nappier, S. P. Evaluation of

Microbiological Risks Associated with Direct Potable Reuse. Microb.

Risk Anal. 2017, 5,3−14.

(59) DeCory, T. R.; Durst, R. A.; Zimmerman, S. J.; Garringer, L. A.;

Paluca, G.; DeCory, H. H.; Montagna, R. A. Development of an

Immunomagnetic B ead-I mmunoliposome Fluorescence Assay for

Rapid Detection of Escherichia Coli O157:H7 in Aqueous Samples

and Comparison of the Assay with a Standard Microbiological Method.

Appl. Environ. Microbiol. 2005, 71 (4), 1856−1864.

(60) Connelly, J. T.; Baeumner, A. J. Biosensors for the Detection of

Waterborne Pathogens. Anal. Bioanal. Chem. 2012, 402, 117−127.

(61) Vandegrift, J.; Hooper, J.; da Silva, A.; Bell, K.; Snyder, S.; Rock,

C. M. Overview of Monitoring Techniques for Evaluating Water

ACS ES&T Engineering pubs.acs.org/estengg Review

https://doi.org/10.1021/acsestengg.1c00258

ACS EST Engg. XXXX, XXX, XXX−XXX

Quality at Potable Reuse Treatment Facilities. J. - Am. Water Works

Assoc. 2019 , 111 (7), 12−23.

(62) Anumol, T.; Snyder, S. A. Rapid Analysis of Trace Organic

Compounds in Water by Automated Online Solid-Phase Extraction

Coupled to Liquid Chromatography-Tandem Mass Spectrometry.

Talanta 2015, 132,77−86.

(63) Fujioka, T. Removal of N-Nitrosodimethylamine for Potable

Reuse: Reverse Osmosis Treatment and Monitoring Technologies. In

Water and Wastewater Treatment Technologies; Springer Nature, 2019;

pp 167−185; DOI: 10.1007/978-981-13-3259-3_9.

(64) Maruya, K. A.; Wong, C. S. Evaluating Analytical Methods for

Detecting Unknown Chemicals in Recycled Water; https://www.

waterrf.org/research/projects/evaluating-analytical-methods-

detecting-unknown-chemica ls-recycled- water (accessed June 18,

2021).

(65) Ahmad, A.; van der Wens, P.; Baken, K.; de Waal, L.;

Bhattacharya, P.; Stuyfzand, P. Arsenic Reduction to <1 μg/L in

Dutch Drinking Water. Environ. Int. 2020, 134, 105253.

(66) Igra, A. M.; Harari, F.; Lu, Y.; Casimiro, E.; Vahter, M. Boron

Exposure through Drinking Water during Pregnancy and Birth Size.

Environ. Int. 2016, 95,54−60.

(67) DeAngelo, A. B.; George, M. H.; Kilburn, S. R.; Moore, T. M.;

Wolf, D. C. Carcinogenicity of Potassium Bromate Administered in the

Drinking Water to Male B6C3F1Mice and F344/N Rats. Toxicol.

Pathol. 1998, 26 (5), 587−594.

(68) Crofton, K. M. Bromate: Concern for Developmental Neuro-

toxicity? Toxicology 2006, 221 (2−3), 212−216.

(69) Ali, S. N.; Ansari, F. A.; Arif, H.; Mahmood, R. Sodium Chlorate

Induces DNA Damage an d DNA-Pro tein Cross-Linking in Rat

Intestine: A Dose Dependent Study. Chemosphere 2017, 177, 311−316.

(70) Aranda-Rodriguez, R.; Lemieux, F.; Jin, Z.; Hnatiw, J.; Tugulea,

A. M. (Yet More) Challenges for Water Treatment Plants: Potential

Contribution of Hypochlorite Solutions to Bromate, Chlorate, Chlorite

and Perchlorate in Drinking Water. Aqua 2017, 66 (8), 621−631.

(71) Hebert, A.; Forestier, D.; Lenes, D.; Benanou, D.; Jacob, S.; Arfi,

C.; Lambolez, L.; Levi, Y. Innovative Method for Prioritizing Emerging

Disinfection By-Products (DBPs) in Drinking Water on the Basis of

Their Potential Impact on Public Health.

Water Res. 2010, 44 (10),

3147−3165.

(72) McElroy, A. C.; Hyman, M. R.; Knappe, D. R. U. 1,4-Dioxane in

Drinking Water: Emerging for 40 Years and Still Unregulated. Curr.

Opin. Environ. Sci. Health 2019, 7, 117−125.

(73) Young, W. F.; Horth, H.; Crane, R.; Ogden, T.; Arnott, M. Taste

and Odour Threshold Concentrations of Potential Potable Water

Contaminants. Water Res. 1996, 30 (2), 331−340.

(74) Suffet, I. H.; Khiari, D.; Bruchet, A. The Drinking Water Taste

and Odor Wheel for the Millennium: Beyond Geosmin and 2-

Methylisoborneol. Water Sci. Technol. 1999, 40,1−13.

(75) Linge, K. L.; Blair, P.; Busetti, F.; Rodriguez, C.; Heitz, A.

Chemicals in Reverse Osmosis-Treated Wastewater: Occurrence,

Health Risk, and Contribution to Residual Dissolved Organic Carbon.

Aqua 2012 , 61 (8), 494−505.

(76) Bellona, C.; Heil, D.; Yu, C.; Fu, P.; Drewes, J. E. The Pros and

Cons of Using Nanofiltration in Lieu of Reverse Osmosis for Indirect

Potable Reuse Applications. Sep. Purif. Technol. 2012, 85,69−76.

(77) Tabacova, S.; Kimmel, C. A.; Wall, K.; Hansen, D. Atenolol

Developmental Toxicity: Animal-to-Human Comparisons. Birth

Defects Res., Part A 2003, 67 (3), 181−192.

(78) Zhao, Y.; Anichina, J.; Lu, X.; Bull, R. J.; Krasner, S. W.; Hrudey,

S. E.; Li, X. F. Occurrence and Formation of Chloro- and Bromo-

Benzoquinones during Drinking Water Disinfection. Water Res. 2012,

46 (14), 4351−4360.

(79) Santhi, V. A.; Sakai, N.; Ahmad, E. D.; Mustafa, A. M. Occurrence

of Bisphenol A in Surface Water, Drinking Water and Plasma from

Malaysia with Exposure Assessment from Consumption of Drinking

Water. Sci. Total Environ. 2012, 427−428, 332−338.

(80) Chu, I.; Villeneuve, D. C.; Secours, V. E.; Becking, G. C.; Valli, V.

E. Toxicity of Trihalomethanes: I the Acute and Subacute Toxicity of

Chloroform, Bromodichloromethane, Chlorodibromomethane and

Bromoform in Rats. J. Environ. Sci. Health, Part B 1982, 17 (3), 205−

224.

(81) Khallef, M.; Liman, R.; Konuk, M.; Ciierci, I

. H.; Benouareth, D.;

Tabet, M.; Abda, A. Genotoxicity of Drinking Water Disinfection By-

Products (Bromoform and Chloroform) by Using Both Allium

Anaphase-Telophase and Comet Tests. Cytotechnology 2015, 67 (2),

207−213.

(82) Mcdowell, D. C.; Huber, M. M.; Wagner, M.; Von Gunten, U.;

Ternes, T. A. Ozonation of Carbamazepine in Drinking Water:

Identification and Kinetic Study of Major Oxidation Products. Environ.

Sci. Technol. 2005, 39 (20), 8014−8022.

(83) Kumar, A.; Xagoraraki, I. Human Health Risk Assessment of

Pharmaceuticals in Water: An Uncertainty Analysis for Meprobamate,

Carbamazepine, and Phenytoin. Regul. Toxicol. Pharmacol. 2010, 57

(2−3), 146−156.

(84) Houeto, P.; Carton, A.; Guerbet, M.; Mauclaire, A. C.; Gatignol,

C.; Lechat, P.; Masset, D. Assessment of the Health Risks Related to the

Presence of Drug Residues in Water for Human Consumption:

Application to Carbamazepine. Regul. Toxicol. Pharmacol. 2012, 62 (1),

41−48.

(85) Moro, S.; Chipman, J. K.; Antczak, P.; Turan, N.; Dekant, W.;

Falciani, F.; Mally, A. Identification and Pathway Mapping of Furan

Target Proteins Reveal Mitochondrial Energy Production and Redox

Regulation as Critical Targets of Furan Toxicity. Toxicol. Sci. 2012, 126

(2), 336−352.

(86) Prasse, C.; Ford, B.; Nomura, D. K.; Sedlak, D. L. Unexpected

Transformation of Dissolved Phenols to Toxic Dicarbonyls by

Hydroxyl Radicals and UV Light. Proc. Natl. Acad. Sci. U. S. A. 2018,

115 (10), 2311−2316.

(87) Conley, J. M.; Evans, N.; Mash, H.; Rosenblum, L.; Schenck, K.;

Glassmeyer, S.; Furlong, E. T.; Kolpin, D. W.; Wilson, V. S. Comparison

of in Vitro Estrogenic Activity and Estrogen Concentrations in Source

and Treated Waters from 25 U.S. Drinking Water Treatment Plants. Sci.

Total Environ. 2017, 579, 1610−1617.

(88) Sadaria, A. M.; Labban, C. W.; Steele, J. C.; Maurer, M. M.;

Halden, R. U. Retrospective Nationwide Occurrence of Fipronil and Its

Degradates in U.S. Wastewater and Sewage Sludge from 2001 - 2016.

Water Res. 2019, 155, 465−473.

(89) Kartheek, R. M.; David, M. Assessment of Fipronil Toxicity on

Wistar Rats: A Hepatotoxic Perspective. Toxicol. Reports 2018, 5, 448−

456.

(90) Wert, E. C.; Rosario-Ortiz, F. L.; Drury, D. D.; Snyder, S. A.

Formation of Oxidation Byproducts from Ozonation of Wastewater.

Water Res. 2007, 41 (7), 1481−1490.

(91) Papageorgiou, A.; Voutsa, D.; Papadakis, N. Occurrence and Fate

of Ozonation By-Products at a Full-Scale Drinking Water Treatment

Plant. Sci. Total Environ. 2014, 481 (1), 392

−400.

(92) Richardson, S. D.; Plewa, M. J.; Wagner, E. D.; Schoeny, R.;

DeMarini, D. M. Occurrence, Genotoxicity, and Carcinogenicity of

Regulated and Emerging Disinfection by-Products in Drinking Water:

A Review and Roadmap for Research. Mutat. Res., Rev. Mutat. Res. 2007,

636, 178−242.

(93) Bal, R.; Tu

rk, G.; Tuzcu, M.; Yilmaz, O.; Kuloglu, T.; Gundogdu,

R.; Gu

r, S.; Agca, A.; Ulas, M.; Çambay, Z.; Tuzcu, Z.; Gencoglu, H.;

Guvenc, M.; Ozsahin, A. D.; Kocaman, N.; Aslan, A.; Etem, E.

Assessment of Imidacloprid Toxicity on Reproductive Organ System of

Adult Male Rats. J. Environ. Sci. Health, Part B 2012, 47 (5), 434−444.

(94) Hrudey, S. E.; Bull, R. J.; Cotruvo, J. A.; Paoli, G.; Wilson, M.

Drinking Water as a Proportion of Total Human Exposure to Volatile

N-Nitrosamines. Risk Anal. 2013, 33 (12), 2179−2208.

(95) Bei, E.; Shu, Y.; Li, S.; Liao, X.; Wang, J.; Zhang, X.; Chen, C.;

Krasner, S. Occurrence of Nitrosamines and Their Precursors in

Drinking Water Systems around Mainland China. Water Res. 2016, 98,

168−175.

(96) Farré, M. J.; Radjenovic, J.; Gernjak, W. Asses sment of

Degradation Byproducts and NDMA Formation Potential during Uv

and UV/H2o2 Treatment of Doxylamine in the Presence of

Monochloramine. Environ. Sci. Technol. 2012, 46 (23), 12904−12912.

ACS ES&T Engineering pubs.acs.org/estengg Review

https://doi.org/10.1021/acsestengg.1c00258

ACS EST Engg. XXXX, XXX, XXX−XXX

(97) Crone, B. C.; Speth, T. F.; Wahman, D. G.; Smith, S. J.;

Abulikemu, G.; Kleiner, E. J.; Pressman, J. G. Occurrence of Per- and

Polyfluoroalkyl Substances (PFAS) in Source Water and Their

Treatment in Drinking Water. Crit. Rev. Environ. Sci. Technol. 2019,

49 (24), 2359−2396.

(98) Domingo, J. L.; Nadal, M. Human Exposure to Per- and

Polyfluoroalkyl Substances (PFAS) through Drinking Water: A Review

of the Recent Scientific Literature. Environ. Res. 2019, 177, 108648.

(99) Maddela, N. R.; Venkateswarlu, K.; Megharaj, M. Tris(2-

Chloroethyl) Phosphate, a Pervasive Flame Retardant: Critical

Perspective on Its Emissions into the Environment and Human

Toxicity. Environ. Sci.: Processes Impacts 2020, 22, 1809−1827.

(100) Rodil, R.; Quintana, J. B.; Concha-Gran

a, E.; López-Mahía, P.;

Muniategui-Lorenzo, S.; Prada-Rodríguez, D. Emerging Pollutants in

Sewage, Surface and Drinking Water in Galicia (NW Spain).

Chemosphere 2012, 86 (10), 1040−1049.

(101) Ding, J.; Shen, X.; Liu, W.; Covaci, A.; Yang, F. Occurrence and

Risk Assessment of Organophosphate Esters in Drinking Water from

Eastern China. Sci. Total Environ. 2015, 538, 959−965.

(102) Bukowski, K.; Wysokinski, D.; Mokra, K.; Wozniak, K. DNA

Damage and M ethy lati on In duced by Organophosphate Flame

Retardants: Tris(2-Chloro ethyl) Phosphate and Tris(1-Chloro-2-

Propyl) Phosphate in Human Peripheral Blood Mononuclear Cells.

Hum. Exp. Toxicol. 2019, 38 (6), 724−733.

(103) Olaniyan, L. W. B.; Mkwetshana, N.; Okoh, A. I. Triclosan in

Water, Implications for Human and Environmental Health. Spring-

erPlus 2016, 5, 1639.

(104) Salveson, A.; Dickenson, E.; Soller, J.; Angelotti, B.; Parker, A.

Pathogen Risk Evaluation of Treatment and Monitoring System Perform-

ance for Potable Reuse; Denver, CO, 2018.

(105) Fujioka, T.; Makabe, R.; Mori, N.; Snyder, S. A.; Leddy, M.

Assessment of Online Bacterial Particle Counts for Monitoring the

Performance of Reverse Osmosis Membrane Process in Potable Reuse.

Sci. Total Environ. 2019, 667, 540−544.

(106) Amoueyan, E.; Ahmad, S.; Eisenberg, J. N. S.; Pecson, B.;

Gerrity, D. Quantifying Pathogen Risks Associated with Potable Reuse:

A Risk Assessment Case Study for Cryptosporidium. Water Res. 2017,

119, 252−266.

(107) Shrestha, J.; Li, J. Influence of Permeate from Domestic Reverse

Osmosis Filters on Lead Pipes Corrosion and Plastic Pipes Leaching. J.

Water Process Eng. 2017

, 18, 126−133.

(108) Xu, X.; Liu, S.; Liu, Y.; Smith, K.; Wang, X.; Li, J.; Ma, Z.; Wang,

Z.; Cui, Y. Water Quality Induced Corrosion of Stainless Steel Valves

during Long-Term Service in a Reverse Osmosis System. J. Environ. Sci.

(Beijing, China) 2020, 89, 218−226.

(109) Negm, A.; Fouda, A. e.; Shalof, R.; Mohamed, S.; Youssif, t.

Cooling System Pipeline Corrosion Behavior after Reusing of Reverse

Osmosis Reject Plant Water as Feed Water Source and Using a New

Isatine Derivatives as Corrosion Inhibitors. Egypt. J. Chem. 2019, 62

(2), 257−280.

(110) Cipollina, A.; Micale, G.; Rizzuti, L. A Brine Evaporative

Cooler/Concentrator for Autonomous Thermal Desalination Units.

Desalin. Water Treat. 2011, 31 (1−3), 269−278.

(111) Lee, S.; Choi, J.; Park, Y. G.; Shon, H.; Ahn, C. H.; Kim, S. H.

Hybrid Desalination Processes for Beneficial Use of Reverse Osmosis

Brine: Current Status and Future Prospects. Desalination 2019, 454,

104−111.

(112) Fournier, E. D.; Keller, A. A.; Geyer, R.; Frew, J. Investigating

the Energy-Water Usage Efficiency of the Reuse of Treated Municipal

Wastewater for Artificial Groundwater Recharge. Environ. Sci. Technol.

2016, 50 (4), 2044−2053.

(113) Wang, Y.; Wang, X.; Li, M.; Dong, J.; Sun, C.; Chen, G. Removal

of Pharmace utica l and Personal Care Products ( PPC Ps) from

Municipal Waste Water with Integrated Membrane Systems, MBR-

RO/NF. Int. J. Environ. Res. Public Health 2018, 15 (2), 269.

(114) Wang, Y.; Huang, H. Carbon Nanotube Composite Membranes

for Microﬁltration of Pharmaceuticals and Personal Care Products. In

Advanced Nanomaterials for Membrane Synthesis and its Applications;

Elsevier Inc., 2019; DOI: 10.1016/B978-0-12-814503-6.00008-2.

(115) Franke, V.; Ullberg, M.; McCleaf, P.; Wålinder, M.; Köhler, S. J.;

Ahrens, L. The Price of Really Clean Water: Combining Nanofiltration

with Granular Activated Carbon and Anion Exchange Resins for the

Removal of Per- And Polyfluoralkyl Substances (PFASs) in Drinking

Water Production. ACS ES&T Water 2021, 1 (4), 782−795.

(116) Yangali-Quintanilla, V.; Maeng, S. K.; Fujioka, T.; Kennedy, M.;

Li, Z.; Amy, G. Nanofiltration vs. Reverse Osmosis for the Removal of

Emerging Organic Contaminants in Water Reuse. Desalin. Water Treat.

2011, 34 (1−3), 50−56.

(117) Mastropietro, T. F.; Bruno, R.; Pardo, E.; Armentano, D.

Reverse Osmosis and Nanofiltration Membranes for Highly Efficient

PFASs Removal: Overview, Challenges and Future Perspectives. Dalt.

Trans. 2021, 50 (16), 5398−5410.

(118) Zheng, L.; Yu, D.; Wang, G.; Yue, Z.; Zhang, C.; Wang, Y.;

Zhang, J.; Wang, J.; Liang, G.; Wei, Y. Characteristics and Formation

Mechanism of Membrane Fouling in a Full-Scale RO Wastewater

Reclamation Process: Membrane Autopsy and Fouling Character-

ization. J. Membr. Sci. 2018, 563, 843−856.

(119) Jiang, S.; Li, Y.; Ladewig, B. P. A Review of Reverse Osmosis

Membrane Fouling and Control Strategies. Sci. Total Environ. 2017,

595, 567−583.

(120) Ridgway, H. F.; Orbell, J.; Gray, S. Molecular Simulations of

Polyamide Membrane Materials Used in Desalination and Water Reuse

Applications: Recent Developments and Future Prospects. J. Membr.

Sci. 2017, 524, 436−448.

(121) Tang, C. Y.; Chong, T. H.; Fane, A. G. Colloidal Interactions

and Fouling of NF and RO Membranes: A Review. Adv. Colloid Interface

Sci. 2011, 164 (1−2), 126−143.

(122) Park, J.; Jeong, K.; Baek, S.; Park, S.; Ligaray, M.; Chong, T. H.;

Cho, K. H. Modeling of NF/RO Membrane Fouling and Flux Decline

Using Real-Time Observations. J. Membr. Sci. 2019, 576,66−77.

(123) Zhao, D.; Yu, S. A Review of Recent Advance in Fouling

Mitigation of NF/RO Membranes in Water Treatment: Pretreatment,

Membrane Modification, and Chemical Cleaning. Desalin. Water Treat.

2015, 55 (4), 870−891.

(124) Tian, M.; Xu, H. J.; Yao, L.; Wang, R. A Biomimetic

Antimicrobial Surface for Membrane Fouling Control in Reverse

Osmosis for Seawater Desalination. Desalination 2021, 503, 114954.

(125) Vatanpour, V.; Safarpour, M.; Khataee, A.; Zarrabi, H.;

Yekavalangi, M. E.; Kavian, M. A Thin Film Nanocomposite Reverse

Osmosis Membrane Containing Amine-Functionalized Carbon Nano-

tubes. Sep. Purif. Technol. 2017, 184, 135−143.

(126) Cohen-Tanugi, D.; Grossman, J. C. Nanoporous Graphene as a

Reverse Osmos is Membrane: Recent Insights from Theory and

Simulation. Desalination 2015, 366

,59−70.

(127) Jafarzadeh, R.; Azamat, J.; Erfan-Niya, H.; Hosseini, M.

Molecular Insights into Effective Water Desalination through Function-

alized Nanoporous Boron Nitride Nanosheet Membranes. Appl. Surf.

Sci. 2019, 471, 921−928.

(128) Zhang, L.; Jia, L.; Zhang, J.; Li, J.; Liang, L.; Kong, Z.; Shen, J.

W.; Wang, X.; Zhang, W.; Wang, H. Understanding the Effect of

Chemical Modification on Water Desalination in Boron Nitride

Nanotubes via Molecular Dynamics Simulation. Desalination 2019,

464,84−93.

(129) Ebro, H.; Kim, Y. M.; Kim, J. H. Molecular Dynamics

Simulations in Membrane -Based Water Treatment Processes: A

Systematic Overview. J. Membr. Sci. 2013, 438, 112−125.

(130) Alsarayreh, A. A.; Al-Obaidi, M. A.; Farag, S. K.; Patel, R.;

Mujtaba, I. M. Performance Evaluation of a Medium-Scale Industrial

Reverse Osmosis Brackish Water Desalination Plant with Different

Brands of Membranes. A Simulation Study. Desalination 2021, 503,

114927.

(131) Okamoto, Y.; Lienhard, J. H. How RO Membrane Permeability

and Other Performance Factors Affect Process Cost and Energy Use: A

Review. Desalination 2019 , 470, 114064.

(132) Galizia, A.; Mamo, J.; Blandin, G.; Verdaguer, M.; Comas, J.;

Rodríguez-Roda, I.; Monclu

s, H. Advanced Control System for Reverse

Osmosis Optimization in Water Reuse Systems. Desalination 2021, 518,

115284.

ACS ES&T Engineering pubs.acs.org/estengg Review

https://doi.org/10.1021/acsestengg.1c00258

ACS EST Engg. XXXX, XXX, XXX−XXX

(133) Yu, R.-F.; Chen, H.-W.; Cheng, W.-P.; Hsieh, P.-H. Dosage

Control of the Fenton Process for Color Removal of Textile

Wastewater Applying ORP Monitoring and Artificial Neural Networks.

J. Environ. Eng. 2009, 135 (5), 325−332.

(134) Giwa, A.; Yusuf, A.; Balogun, H. A.; Sambudi, N. S.; Bilad, M. R.;

Adeyemi, I.; Chakraborty, S.; Curcio, S. Recent Advances in Advanced

Oxidation Processes for Removal of Contaminants from Water: A

Comprehensive Review. Process Saf. Environ. Prot. 2021, 146, 220−256.

(135) Aleboyeh, A.; Kasiri, M. B.; Olya, M. E.; Aleboyeh, H.

Prediction of Azo Dye Decolorization by UV/H2O2 Using Artificial

Neural Networks. Dyes Pigm. 2008, 77 (2), 288−294.

(136) do Nascimento, G. E.; Napolea

o, D. C.; da Rocha Santana, R.

M.; Charamba, L. V. C.; de Oliveira, J. G. C.; de Moura, M. C.; Coelho,

L. C. B. B.; Duarte, M. M. M. B. Degradation of Textile Dyes Remazol

Yellow Gold and Reactive Turquoise: Optimization, Toxicity and

Modeling by Artificial Neural Networks. Water Sci. Technol. 2018, 2017

(3), 812−823.

(137) Zarei, M.; Niaei, A.; Salari, D.; Khataee, A. R. Removal of Four

Dyes from Aqueous Medium by the Peroxi-Coagulation Method Using

Carbon Nanotube-PTFE Cathode and Neural Network Modeling. J.

Electroanal. Chem. 2010, 639 (1−2), 167−174.

(138) Zarei, M.; Khataee, A. R.; Ordikhani-Seyedlar, R.; Fathinia, M.

Photoelectro-Fenton Combined with Photocatalytic Process for

Degradation of an Azo Dye Using Supported TiO2 Nanoparticles

and Carbon Nanotube Cathode: Neural Network Modeling. Electro-

chim. Acta 2010, 55 (24), 7259−7265.

(139) Jing, L.; Chen, B.; Wen, D.; Zheng, J.; Zhang, B. Pilot-Scale

Treatment of Atrazine Production Wastewater by UV/O3/Ultrasound:

Factor Effects and System Optimization. J. Environ. Manage. 2017, 203,

182−190.

(140) Vaferi, B.; Bahmani, M.; Keshavarz, P.; Mowla, D. Experimental

and Theoretical Analysis of the UV/H2O2 Advanced Oxidation

Processes Treating Aromatic Hydrocar bons and MTBE from

Contaminated Synthetic Wastewaters. J. Environ. Chem. Eng. 2014, 2

(3), 1252−1260.

(141) Amani-Ghadim, A. R.; Dorraji, M. S. S. Modeling of

Photocatalyatic Process on Synthesized ZnO Nanoparticles: Kinetic

Model Development and Artificial Neural Networks. Appl. Catal., B

2015, 163 , 539−546.

(142) Liang, C. H.; Chiang, P. C.; Chang, E. E. Modeling the

Behaviors of Adsorption and Biodegradation in Biological Activated

Carbon Filters. Water Res. 2007, 41 (15), 3241−3250.

(143) Zhiteneva, V.; Ziemendorf, E

.; Sperlich, A.; Drewes, J. E.;

bner, U. Differentiating between Adsorption and Biodegradation

Mechanisms While Removing Trace Organic Chemicals (TOrCs) in

Biological Activated Carbon (BAC) Filters. Sci. Total Environ. 2020,

743, 140567.

(144) Gerrity, D.; Owens-Bennett, E.; Venezia, T.; Stanford, B. D.;

Plumlee, M. H.; Debroux, J.; Trussell, R. S. Applicability of Ozone and

Biological Activated Carbon for Potable Reuse. Ozone: Sci. Eng. 2014,

36 (2), 123−137.

(145) Tang, C. Y.; Yang, Z.; Guo, H.; Wen, J. J.; Nghiem, L. D.;

Cornelissen, E. Potable Water Reuse through Advanced Membrane

Technology. Environ. Sci. Technol. 2018, 52 (18), 10215−10223.

(146) Ozaki, H.; Sharma, K.; Saktaywin, W. Performance of an Ultra-

Low-Pressure Reverse Osmosis Membrane (ULPROM) for Separating

Heavy Metal: Effects of Interference Parameters. Desalination 2002,

144 (1−3), 287−294.

(147) Víctor-Ortega, M. D.; Ratnaweera, H. C. Double Filtration as an

Effective System for Removal of Arsenate and Arsenite from Drinking

Water through Reverse Osmosis. Process Saf. Environ. Prot. 2017, 111,

399−408.

(148) Abejón, A.; Garea, A.; Irabien, A. Arsenic Removal from

Drinking Water by Reverse Osmosis: Minimization of Costs and

Energy Consumption. Sep. Purif. Technol. 2015, 144,46−53.

(149) Bernstein, R.; Belfer, S.; Freger, V. Toward Improved Boron

Removal in RO by Membrane Modification: Feasibility and Challenges.

Environ. Sci. Technol. 2011, 45 (8), 3613−3620.

(150) Jung, B.; Kim, C. Y.; Jiao, S.; Rao, U.; Dudchenko, A. V.; Tester,

J.; Jassby, D. Enhancing Boron Rejection on Electrically Conducting

Reverse Osmosis Membranes through Local Electrochemical PH

Modification. Desalination 2020, 476, 114212.

(151) Mel’nik, L. A.; Babak, Y. V.; Goncharuk, V. V. Problems of

Removing Arsenic Compounds from Natural Water in the Pressure

Driven Treatment Process. J. Water Chem. Technol. 2012, 34 (3), 162−

167.

(152) Uddin, T.; Mozumder, S. I.; Figoli, A.; Islam, A.; Drioli, E.

Arsenic Removal by Conventional and Membrane Technology: An

Overview. Indian J. Chem. Technol. 2007, 14, 441.

(153) Kabay, N.; Gu

ler, E.; Bryjak, M. Boron in Seawater and

Methods for Its Separation - A Review. Desalination 2010, 261, 212

−

217.

(154) Zhu, X.; Jassby, D. Electroactive Membranes for Wate r

Treatment: Enhanced Treatment Functionalities, Energy Consider-

ations, and Future Challenges. Acc. Chem. Res. 2019, 52 (5), 1177−

1186.

(155) Dorji, P.; Choi, J.; Kim, D. I.; Phuntsho, S.; Hong, S.; Shon, H.

K. Membrane Capacitive Deionisation as an Alternative to the 2nd Pass

for Seawater Rev erse Osmosis Desalinatio n Plant f or Brom ide

Removal. Desalination 2018, 433, 113−119.

(156) Breitner, L. N.; Howe, K. J.; Minakata, D. Effect of Functional

Chemistry on the Rejecti on of Low-Molecular Weight Neutral

Organics through Reverse Osmosis Membranes for Potable Reuse.

Environ. Sci. Technol. 2019, 53 (19), 11401−11409.

(157) Dorji, P.; Inhyuk, D.; Hong, S.; Phuntsho, S.; Kyong, H. Pilot-

Scale Membrane Capacitive Deionisation for e Ff Ective Bromide

Removal and High Water Recovery in Seawater Desalination.

Desalination 2020, 479, 114309.

(158) Soyluoglu, M.; Ersan, M. S.; Ateia, M.; Karanfil, T. Removal of

Bromide from Natural Waters: Bromide-Selective vs. Conventional Ion

Exchange Resins. Chemosphere 2020, 238, 124583.

(159) Govind, R.; Lai, L.; Dobbs, R. Integrated Model for Predicting

the Fate of Organics in Wastewater Treatment Plants. Environ. Prog.

1991, 10 (1), 13−23.

(160) Arsuaga, J. M.; Sotto, A.; López-Mun

oz, M. J.; Braeken, L.

Influence of Type and Position of Functional Groups of Phenolic

Compounds on NF/RO Performance. J. Membr. Sci. 2011, 372 (1−2),

380−386.

(161) Cheng, H.; Reinhard, M. Sorption of Trichloroethylene in

Hydrophobic Micropores of Dealuminated Y Zeolites and Natural

Minerals. Environ. Sci. Technol. 2006, 40 (24), 7694−7701.

(162) Abdelbassit, M. S.; Popoola, S. A.; Saleh, T. A.; Abdallah, H. H.;

Al-Saadi, A. A.; Alhooshani, K. R. DFT and Kinetic Evaluation of

Chloromethane Removal Using Cost-Effective Activated Carbon.

Arabian J. Sci. Eng. 2020, 45 (6), 4705−4716.

(163) Valdés, H.; Riquelme, A. L.; Solar, V. A.; Azzolina-Jury, F.;

Thibault-Starzyk, F. Removal of C hlorinated Volatile Organic

Compounds onto Natural and Cu-Modified Zeolite: The Role of

Chemical Surface Characteristics in the Adsorption Mechanism. Sep.

Purif. Technol. 2021, 258, 118080.

(164) Ece, M. Ş.; Kutluay, S.; Şahin, O

.; Horoz, S. Development of

Novel Fe3O4/AC@SiO2@1,4-DAAQ Magnetic Nanoparticles with

Outstanding VOC Removal Capacity: Characterization, Optimization,

Reusability, Kinetics, and Equilibrium Studies. Ind. Eng. Chem. Res.

2020

, 59 (48), 21106−21123.

(165) Adeleye, A. S.; Conway, J. R.; Garner, K.; Huang, Y.; Su, Y.;

Keller, A. A. Engineered Nanomaterials for Water Treatment and

Remediation: Costs, Benefits, and Applicability. Chem. Eng. J. 2016,

286, 640−662.

(166) Narayanan, B.; Suidan, M. T.; Gelderloos, A. B.; Brenner, R. C.

Anaerobic Treatment of Volatile and Semivolatile Organic Compounds

in Municipal Wastewater. Water Environ. Res. 1995, 67, 46.

(167) Wu, W.; Shi, J.; Hickey, R. F. Long-Term Performance of Co-

Metabolic Degradation of Trichloroethylene in a Fluidized Bed Reactor

Fed with Benzene, Toluene and Xylene. J. Chem. Technol. Biotechnol.

2008, 83, 513− 523.

ACS ES&T Engineering pubs.acs.org/estengg Review

https://doi.org/10.1021/acsestengg.1c00258

ACS EST Engg. XXXX, XXX, XXX−XXX

(168) Jans, U.; Hoigné, J. Activated Carbon and Carbon Black

Catalyzed Transformation of Aqueous Ozone into OH-Radicals.

Ozone: Sci. Eng. 1998, 20 (1), 67−90.

(169) Sánchez-Polo, M.; Salhi, E.; Rivera-Utrilla, J.; Von Gunten, U.

Combination of Ozone with Activated Carbon as an Alternative to

Conventional Advanced Oxidation Processes. Ozone: Sci. Eng. 2006, 28

(4), 237−245.

(170) Marghade, D.; Vaidya, A.; Yenkie, M. K. N.; Pokale, W. K.

Feasibility Study of UV Assisted Different Heterogenous Photo-

Catalytic Methods for Degradation of 2-Nitrophenol in Aqueous Phase.

Mater. Today Proc. 2020, 29, 1166−1170.

(171) Gerrity, D.; Snyder, S. Review of Ozone for Water Reuse

Applications: Toxicity, Regulations, and Trace Organic Contaminant

Oxidation. Ozone: Sci. Eng. 2011, 33 (4), 253−266.

(172) Salimi, M.; Esrafili, A.; Gholami, M.; Jonidi Jafari, A.; Rezaei

Kalantary, R.; Farzadkia, M.; Kermani, M.; Sobhi, H. R. Contaminants

of Emerging Concern: A Review of New Approach in AOP

Technologies. Environ. Monit. Assess. 2017, 189 (8), 1−22.

(173) Oneby, M. A.; Bromley, C. O.; Borchardt, J. H.; Harrison, D. S.

Ozone Treatment of Secondary Effluent at U.S. Municipal Wastewater

Treatment Plants. Ozone: Sci. Eng. 2010, 32 (1), 43−55.

(174) Rodriguez, E.; Campinas, M.; Acero, J. L.; Rosa, M. J.

Investigating PPCP Removal from Wastewater by Powdered Activated

Carbon/Ultrafiltration. Water, Air, Soil Pollut. 2016, 227 (6), 177.

(175) McCleaf, P.; Englund, S.; O

stlund, A.; Lindegren, K.; Wiberg,

K.; Ahrens, L. Removal Efficiency of Multiple Poly- and Perfluoroalkyl

Substances (PFASs) in Drinking Water Using Granular Activated

Carbon (GAC) and Anion Exchange (AE) Column Tests. Water Res.

2017, 120 ,77−87.

(176) Zhang, Y.; Ozcer, P.; Ghoshal, S. A Comprehensive Assessment

of the Degradation of C1 and C2 Chlorinated Hydrocarbons by

Sulfidated Nanoscale Zerovalent Iron. Water Res. 2021, 201 (March),

117328.

(177) Liu, Z.; Betterton, E. A.; Arnold, R. G. Electrolytic Reduction of

Low Molecular Weight Chlorinated Aliphatic Compounds: Structural

and Thermodynamic Effects on Process Kinetics. Environ. Sci. Technol.

2000, 34 (5), 804−811.

(178) Fernández-Verdejo, D.; Sulonen, L. K.; Pérez-Trujillo, M.;

Marco-Urrea, E.; Blánquez, P. Electrochemical Dehalogenation of to

Non-Toxic Products Using a Carbon Fiber Brush Electrode. J. Chem.

Technol. Biotechnol. 2021, 96

(2), 335−340.

(179) Iervolino, G.; Zammit, I.; Vaiano, V.; Rizzo, L. Limitations and

Prospects for Wastewater Treatment by UV and Visible-Light-Active

Heterogeneous Photocatalysis: A Critical Review. Top. Curr. Chem.

2020, 378 (1), 225−264.

(180) Hodges, B. C.; Cates, E. L.; Kim, J. H. Challenges and Prospects

of Advanced Oxidation Water Treatment Processes Using Catalytic

Nanomaterials. Nat. Nanotechnol. 2018, 13 (8), 642−650.

(181) Su, Y.; Rao, U.; Khor, C. M.; Jensen, M. G.; Teesch, L. M.;

Wong, B. M.; Cwiertny, D. M.; Jassby, D. Potential-Driven Electron

Transfer Lowers the Dissociation Energy of the C - F Bond and

Facilitates Reductive Defluorination of Perfluorooctane Sulfonate

(PFOS). ACS Appl. Mater. Interfaces 2019, 11 (37), 33913−33922.

(182) Urtiaga, A. M.; Pérez, G.; Ibán

ez, R.; Ortiz, I. Removal of

Pharmaceuticals from a WWTP Secondary Effluent by Ultrafiltration/

Reverse Osmosis Followed by Electrochemical Oxidation of the RO

Concentrate. Desalination 2013, 331,26−34.

(183) Krauss, M.; Longree, P.; Dorusch, F.; Ort, C.; Hollender, J.

Occurrence and Removal of N -Nitrosamines in Wastewater Treatment

Plants. Water Res. 2009, 43, 4381−4391.

(184) Yoon, S.; Nakada, N.; Tanaka, H. Occurrence and Fate of N

-Nitrosamines and Their Formation Potential in Three Wastewater

Treatment Plants in Japan Suchul Yoon, Norihide Nakada and Hiroaki

Tanaka. Water Sci. Technol. 2013, 68 (10), 2118−2127.

(185) Asami, M.; Oya, M.; Kosaka, K. A Nationwide Survey of NDMA

in Raw and Drinking Water in Japan. Sci. Total Environ. 2009, 407 (11),

3540−3545.

(186) Yoon, S.; Nakada, N.; Tanaka, H. Occurrence and Removal of

NDMA and NDMA Formation Potential in Wastewater Treatment

Plants. J. Hazard. Mater. 2011, 190 (1−3), 897−902.

(187) Padhye, L.; Tezel, U.; Mitch, W. A. Occurrence and Fate of

Nitrosamines and Their Precursors in Municipal Sludge and Anaerobic

Digestion Systems. Environ. Sci. Technol. 2009, 43 (9), 3087−3093.

(188) Krasner, S. W.; Westerhoff, P.; Mitch, W. A.; Hanigan, D.;

McCurry, D. L.; Von Gunten, U. Behavior of NDMA Precursors at 21

Full-Scale Water Treatment Facilities. Environ. Sci. Water Res. Technol.

2018, 4 (12), 1966

−1978.

(189) Ersan, M. S.; Ladner, D. A.; Karanfil, T. N-Nitrosodimethyl-

amine (NDMA) Precursors Leach from Nanofiltration Membranes.

Environ. Sci. Technol. Lett. 2015, 2 (3), 66−69.

(190) Fujioka, T.; Khan, S. J.; McDonald, J. A.; Roux, A.; Poussade, Y.;

Drewes, J. E.; Nghiem, L. D. N-Nitrosamine Rejection by Nano-

filtration and Reverse Osmosis Membranes: The Importance of

Membrane Characteristics. Desalination 2013, 316,67−75.

(191) Mitch, W. A.; Sharp, J. O.; Trussell, R. R.; Valentine, R. L.;

Alvarez-Cohen, L.; Sedlak, D. L. N-Nitrosodimethylamine (NDMA) as

a Drinking Water Contaminant: A Review. Environ. Eng. Sci. 2003,

389−404.

(192) Mitch, W. A.; Sharp, J. O.; Trussell, R. R.; Valentine, R. L.;

Alvarez-Cohen, L.; Sedlak, D. L. N -Nitrosodimethylamine (NDMA) as

a Drinking Water Contaminant: A Review. Environ. Eng. Sci. 2003, 20

(5), 389−404.

(193) Fujioka, T.; Osako, M.; Tanabe, S.; Kodamatani, H.; Shintani,

T. Plugging Nonporous Polyamide Membranes for Enhance d

Rejection of Small Contaminants during Advanced Wastewater

Treatment. Sep. Purif. Technol. 2020, 253, 117490.

(194) Croll, H.; Soroush, A.; Pillsbury, M. E.; Romero-Vargas

Castrillón, S. Graphene Oxide Surface Modification of Polyamide

Reverse Osmosis Membranes for Improved N-Nitrosodimethylamine

(NDMA) Removal. Sep. Purif. Technol. 2019, 210, 973−980.

(195) Roback, S. L.; Ishida, K. P.; Chuang, Y.-H.; Zhang, Z.; Mitch, W.

A.; Plumlee, M. H. Pilot UV-AOP Comparison of UV/Hydrogen

Peroxide, UV/Free Chl orine, and UV/Monochloramine for the

Removal of N -Nitrosodimethylamine (NDMA) and NDMA

Precursors. ACS ES&T Water 2021, 1 (2), 396−406.

(196) Fujioka, T.; Ishida, K. P.; Shintani, T.; Kodamatani, H. High

Rejection Reverse Osmosis Membrane for Removal of N - Nitros-

amines and Their Precursors. Water Res. 2018, 131,45−51.

(197) Roback, S. L.; Ishida, K. P.; Plumlee, M. H. Chemosphere In Fl

Uence of Reverse Osmosis Membrane Age on Rejection of NDMA

Precursors and Formation of NDMA in Fi Nished Water after Full

Advanced Treatment for Potable Reuse. Chemosphere 2019, 233, 120−

131.

(198) Pisarenko, A. N.; Stanford, B. D.; Yan, D.; Gerrity, D.; Snyder, S.

A. Effects of Ozone and Ozone/Peroxide on Trace Organic

Contaminants and NDMA in Drinking Water and Water Reuse

Applications. Water Res. 2012, 46 (2), 316−326.

(199) Kommineni, S.; Ela, W. P.; Arnold, R. G.; Huling, S. G.; Hester,

B. J.; Betterton, E. A. NDMA Treatment by Sequential GAC

Adsorption and Fenton-Driven Destruction. Environ. Eng. Sci. 2003,

20 (4), 361−373.

(200) Fujioka, T.; Kodamatani, H.; Takeuchi, H.; Tanaka, H.;

Nghiem, L. D. Online Monitoring of: N -Nitrosodimethylamine for the

Removal Assurance of 1,4-Dioxane and Other Trace Organic

Compounds by Reverse Osmosis. Environ. Sci. Water Res. Technol.

2018, 4 (12), 2021−2028.

(201) Schoonenberg Kegel, F.; Rietman, B. M.; Verliefde, A. R. D.

Reverse Osmosis Followed by Activated Carbon Filtration for Efficient

Removal of Organic Micropollutants from River Bank Filtrate. Water

Sci. Technol. 2010, 61 (10), 2603−2610.

(202) Andaluri, G.; Suri, R. Removal of 1,4-Dioxane and Volatile

Organic Compounds from Groundwater Using Ozone-Based Ad-

vanced Oxidation Process. Ozone: Sci. Eng. 2017, 39 (6), 423−434.

(203) Weng, C.; Chuang, Y. H.; Davey, B.; Mitch, W. A. Reductive

Electrochemical Activation of Hydrogen Peroxide as an Advanced

ACS ES&T Engineering pubs.acs.org/estengg Review

https://doi.org/10.1021/acsestengg.1c00258

ACS EST Engg. XXXX, XXX, XXX−XXX

Oxidation Process for Treatment of Reverse Osmosis Permeate during

Potable Reuse. Environ. Sci. Technol. 2020, 54 (19), 12593−12601.

(204) Vatankhah, H.; Szczuka, A.; Mitch, W. A.; Almaraz, N.;

Brannum, J.; Bellona, C. Evaluation of Enhanced Ozone-Biologically

Active Filtration Treatment for the Removal of 1,4-Dioxane and

Disinfection Byproduct Precursors from Wastewater Effluent. Environ.

Sci. Technol. 2019, 53 (5), 2720−2730.

(205) Myers, M. A.; Johnson, N. W.; Marin, E. Z.; Pornwongthong, P.;

Liu, Y.; Gedalanga, P. B.; Mahendra, S. Abiotic and Bioaugmented

Granular Activated Carbon for the Treatment of 1,4-Dioxane-

Contaminated Water. Environ. Pollut. 2018, 240, 916−924.

(206) Duan, D. From the Smallest Virus to the Biggest Gene:

Marching Towards Gene Therapy for Duchenne Muscular Dystrophy.

Discovery Med. 2006, 6 (33), 103−108.

(207) Bennett, A. Drinking Water: Pathogen Removal from Water -

Technologies and Techniques. Filtr. Sep. 2008, 45 (10), 14−16.

(208) Sirivithayapakorn, S.; Keller, A. Transport of Colloids in

Saturated Porous Media: A Pore-Scale Observation of the Size

Exclusion Effect and Colloid Acceleration. Water Resour. Res. 2003,

39,1.

(209) Adham, S. S.; Trussell, R. S.; Gagliardo, P. F.; Trussell, R. R.

Rejection of MS-2 Virus by RO Membranes. J. - Am. Water Works Assoc.

1998, 90 (9), 130−135.

(210) Szczuka, A.; Chuang , Y.-H.; Chen, F. C.; Zhang, Z.;

Desormeaux, E.; Flynn, M.; Parodi, J.; Mitch, W. A. Removal of

Pathogens and Chemicals of Emerging Concern by Pilot-Scale FO-RO

Hybrid Units Treating RO Concentrate, Graywater, and Sewage for

Centralized and Decentralized Potable Reuse. ACS ES&T Water 2021,

1 (1), 89−100.

(211) Antony, A.; Blackbeard, J.; Leslie, G. Removal Efficiency and

Integrity Monitoring Techniques for Virus Removal by Membrane

Processes. Crit. Rev. Environ. Sci. Technol. 2012, 42 (9), 891− 933.

(212) Pype, M. L.; Lawrence, M. G.; Keller, J.; Gernjak, W. Reverse

Osmosis Integrity Monitoring in Water Reuse: The Challenge to Verify

Virus Removal - A Review. Water Res. 2016, 98, 384−395.

(213) Zhang, D.; Ling, H.; Huang, X.; Li, J.; Li, W.; Yi, C.; Zhang, T.;

Jiang, Y.; He, Y.; Deng, S.; Zhang, X.; Wang, X.; Liu, Y.; Li, G.; Qu, J.

Potential Spreading Risks and Disinfection Challenges of Medical

Wastewater by the Presence of Severe Acute Respiratory Syndrome

Coronavirus 2 (SARS-CoV-2) Viral RNA in Septic Tanks of Fangcang

Hospital. Sci. Total Environ. 2020, 741, 140445.

(214) Diaz-Elsayed, N.; Rezaei, N.; Guo, T.; Mohebbi, S.; Zhang, Q.

Wastewater-Based Resource Recovery Technologies across Scale: A

Review. Resources, Conservation and Recycling

2019, 145 ,94−112.

(215) Hernández-Chover, V.; Bellver-Domingo, A

.; Hernández-

Sancho, F. Efficiency of Waste water Treat ment F aciliti es: The

Influence of Scale Economies. J. Environ. Manage. 2018, 228,77−84.

(216) Worthington, A. C.; Higgs, H. Economies of Scale and Scope in

Australian Urban Water Utilities. Util. Policy 2014, 31,52−62.

(217) Guo, T.; Englehardt, J. D. Principles for Scaling of Distributed

Direct Potable Water Reuse Systems: A Modeling Study. Water Res.

2015, 75, 146.

(218) Plumlee, M. H.; Stanford, B. D.; Debroux, J. F.; Hopkins, D. C.;

Snyder, S. A. Costs of Advanced Treatment in Water Reclamation.

Ozone: Sci. Eng. 2014, 36 (5), 485−495.

(219) Sim, A.; Mauter, M. S. Cost and Energy Intensity of U.S. Potable

Water Reuse Systems. Environ. Sci. Water Res. Technol. 2021, 7 (4),

748−761.

(220) Mendret, J.; Azais, A.; Favier, T.; B rosillon, S. Urban

Wastewater Reuse Using a Coupling between Nanofiltration and

Ozonation: Techno-Economic Assessment. Chem. Eng. Res. Des. 2019,

145,19−28.

(221) Margot, J.; Kienle, C.; Magnet, A.; Weil, M.; Rossi, L.; de

Alencastro, L. F.; Abegglen, C.; Thonney, D.; Che

vre, N.; Schärer, M.;

Barry, D. A. Treatment of Micropollutants in Municipal Wastewater:

Ozone or Powdered Activated Carbon? Sci. Total Environ. 2013, 461−

462, 480−498.

(222) Mitani, M. M. M.; Keller, A. A. A.; Sandall, O. C. C.; Rinker, R.

G. G. Mass Transfer of Ozone Using a Microporous Diffuser Reactor

System. Ozone: Sci. Eng. 2005, 27 (1), 45−51.

(223) Jafari, M.; Vanoppen, M.; van Agtmaal, J. M. C.; Cornelissen, E.

R.; Vrouwenvelder, J. S.; Verliefde, A.; van Loosdrecht, M. C. M.;

Picioreanu, C. Cost of Fouling in Full-Scale Reverse Osmosis and

Nanofiltration Installations in the Netherlands. Desalination 2021, 500,

114865.

(224) Snyder, S. A.; von Gunten, U.; Amy, G.; Debroux, J. F. Use of

Ozone in Water Reclamation for Contaminant Oxidation; Report No.

WRF-08-05; Alexandria, VA, 2014.

(225) Loutatidou, S.; Chalermthai, B.; Marpu, P. R.; Arafat, H. A.

Capital Cost Estimation of RO Plants: GCC Countries versus Southern

Europe. Desalination 2014

, 347, 103−111.

(226) Caldera, U.; Breyer, C. Learning Curve for Seawater Reverse

Osmosis Desalination Plants: Capital Cost Trend of the Past, Present,

and Future. Water Resour. Res. 2017, 53 (12), 10523−10538.

(227) Lanjewar, S.; Mukherjee, A.; Khandewal, P.; Ghosh, A. K.;

Mullick, A.; Moulik, S.; Roy, A. Thermodynamics of Synthesis and

Separation Performance of Interfacially Polymerized “Loose” Reverse

Osmosis Membrane: Benchmarking for Greywater Treatment. Chem.

Eng. J. 2021, 417, 127929.

(228) Elimelech, M.; Phillip, W. A. The Future of Seawater

Desalination: Energy, Technology, and the Environment. Science

(Washington, DC, U. S.) 2011, 333 (6043), 712−717.

(229) Mayor, B. Unraveling the Historical Economies of Scale and

Learning Effects for Desalination Technologies. Water Resour. Res.

2020, 56 (2), No. e2019WR025841.

(230) Bolton, J. R.; Bircher, K. G.; Tumas, W.; Tolman, C. A. Figures-

of-Merit for the Technical Development and Application of Advanced

Oxidation Technologies for Both Electric- and Solar-Driven Systems.

Pure Appl. Chem. 2001, 73 (4), 627−637.

(231) Miklos, D. B.; Remy, C.; Jekel, M.; Linden, K. G.; Drewes, J. E.;

bner, U. Evaluation of Advanced Oxidation Processes for Water and

Wastewater Treatment - A Critical Review. Water Res. 2018, 139, 118−

131.

(232) Farkas, J.; Náfrádi, M.; Hlogyik, T.; Cora Pravda, B.; Schrantz,

K.; Hernádi, K.; Alapi, T. Comparison of Advanced Oxidation

Processes in the Decomposition of Diuron and Monuron-Efficiency,

Intermediates, Electrical Energy per Order and the Effect of Various

Matrices. Environ. Sci. Water Res. Technol. 2018, 4 (9), 1345−1360.

(233) Sanches, S.; Barreto Crespo, M. T.; Pereira, V. J. Drinking Water

Treatment of Priority Pesticides Using Low Pressure UV Photolysis

and Advanced Oxidation Processes. Water Res. 2010, 44 (6), 1809−

1818.

(234) Shu, Z.; Bolton, J. R.; Belosevic, M.; Gamal El Din, M.

Photodegradation of Emerging Micropollutants Using the Medium-

Pressure UV/H2O2 Advanced Oxidation Process. Water Res. 2013, 47

(8), 2881−2889.

(235) Ikehata, K.; El-Din, M. G.; Snyder, S. A. Ozonation and

Advanced Oxidation Treatment of Emerging Organic Pollutants in

Water and Wastewater. Ozone: Sci. Eng. 2008, 30,21−26.

(236) Benitez, F. J.; Acero, J. L.; Real, F. J.; Roldan, G.; Casas, F.

Comparison of Different Chemical Oxidation Treatments for the

Removal of Selected Pharmaceuticals in Water Matrices. Chem. Eng. J.

2011, 168 (3), 1149−1156.

(237) Huber, M. M.; Göbel, A.; Joss, A.; Hermann, N.; Löffler, D.;

McArdell, C. S.; Ried, A.; Siegrist, H.; Ternes, T. A.; Von Gunten, U.

Oxidation of Pharmaceuticals during Ozonation of Municipal Waste-

water Effluents: A Pilot Study. Environ. Sci. Technol. 2005, 39 (11),

4290−4299.

(238) Swaim, P.; Royce, A.; Smith, T.; Maloney, T.; Ehlen, D.; Carter,

B. Effectiveness of UV Advanced Oxidation for Destruction of Micro-

Pollutants. Ozone: Sci. Eng. 2008, 30,34−42.

(239) Wardenier, N.; Liu, Z.; Nikiforov, A.; Van Hulle, S. W. H.; Leys,

C. Micropollutant Elimination by O3, UV and Plasma-Based AOPs: An

Evaluation of Treatment and Energy Costs. Chemosphere 2019, 234,

715−724.

ACS ES&T Engineering pubs.acs.org/estengg Review

https://doi.org/10.1021/acsestengg.1c00258

ACS EST Engg. XXXX, XXX, XXX−XXX

(240) Gingerich, D. B.; Mauter, M. S. Air Emissions Damages from

Municipal Drinking Water Treatment under Current and Proposed

Regulatory Standards. Environ. Sci. Technol. 2017, 51 (18), 10299−

10306.

(241) Keller, A. A.; Tellinghuisen, S.; Lee, C.; Larson, D.; Dennen, B.;

Lee, J. Projection of California’s Future Freshwater Requirements for

Power Generation. Energy Environ. 2010, 21 (2), 1−20.

(242) King, C. W.; Holman, A. S.; Webber, M. E. Thirst for Energy.

Nature Geoscience 2008, 1, 283−286.

(243) Plappally, A. K.; Lienhard, V. J. H. Energy Requirements for

Water Production, Treatment, End Use, Reclamation, and Disposal.

Renewable Sustainable Energy Rev. 2012, 16, 4818−4848.

(244) Liu, Y.; Mauter, M. S. Assessing the Demand Response Capacity

of U.S. Drinking Water Treatment Plants. Appl. Energy 2020, 267,

114899.

(245) Olivieri, A. W.; Crook, J.; Anderson, M. A.; Bull, R. J.; Drewes, J.

E.; Haas, C. N.; Jakubowski, W.; McCarty, P. L.; Nelson, K. L.; Rose, J.

B.; Sedlak, D. L.; Wade, T. J. Expert Panel Final Report: Evaluation of the

Feasibility of Developing Uniform Water Recycling Criteria for Direct

Potable Reuse; National Water Research Institute: Fountain Valley, CA,

2016.

(246) Etchepare, R.; van der Hoek, J. P. Health Risk Assessment of

Organic Micropollutants in Greywater for Potable Reuse. Water Res.

2015, 72, 186−198.

(247) Dewettinck, T.; Van Houtte, E.; Geenens, D.; Van Hege, K.;

Verstraete, W. HACCP (Hazard Analysis and Critical Control Points)

to Guarantee Safe Water Reuse and Drinking Water Production - A

Case Study. Water Sci. Technol. 2001, 43,31−38.

(248) Roccaro, P. Treatment Processes for Municipal Wastewater

Reclamation: The Challenges of Emerging Contaminants and Direct

Potable Reuse. Curr. Opin. Environ. Sci. Health 2018, 2,46−54.

(249) Jolis, D.; Hirano, R.; Pitt, P. Tertiary Treatment Using

Microfiltration and UV Disinfection for Water Reclamation. Water

Environ. Res. 1999, 71 (2), 224−231.

(250) Gabelich, C. J.; Yun, T. I.; Coffey, B. M.; Suffet, I. H. Pilot-Scale

Testing of Reverse Osmosis Using Conventional Treatment and

Microfiltration. Desalination 2003, 154

(3), 207−223.

(251) O’Connell, P. M.; Winters, M. Making a Case for Membrane

Replacement. Opflow 2019, 45 (2), 18−22.

(252) Adham, S.; Gagliardo, P.; Smith, D.; Ross, D.; Gramith, K.;

Trussell, R. Monitoring the Integrity of Reverse Osmosis Membranes.

Desalination 1998, 119 (1−3), 143−150.

(253) Kumar, M.; Adham, S.; DeCarolis, J. Reverse Osmosis Integrity

Monitoring. Desalination 2007, 214 (1−3), 138−149.

(254) Pype, M. L.; Patureau, D.; Wery, N.; Poussade, Y.; Gernjak, W.

Monitoring Reverse Osmosis Performance: Conductivi ty ver sus

Fluorescence Excitation-Emission Matrix (EEM). J. Membr. Sci. 2013,

428, 205−211.

(255) Naughton, D. P.; Grootveld, M.; Blake, D. R.; Guestrin, H. R.;

Narayanaswamy, R. An Optical Hydroxyl Radical Sensor. Biosens.

Bioelectron. 1993, 8 (6), 325−329.

(256) Anderson, T. N.; Lucht, R. P.; Meyer, T. R.; Roy, S.; Gord, J. R.

Diode-Laser-Based Ultraviolet-A bsorption Sensor for High-Speed

Detection of the Hydroxyl Radical. Opt. Lett. 2005, 30 (11), 1321.

(257) Modrzejewska, B.; Guwy, A. J.; Dinsdale, R.; Hawkes, D. L.

Measurement of Hydrogen Peroxide in an Advanced Oxidation Process

Using an Automated Biosensor. Water Res. 2007, 41 (1), 260−268.

(258) Song, Z. M.; Xu, Y. L.; Liang, J. K.; Peng, L.; Zhang, X. Y.; Du,

Y.; Lu, Y.; Li, X. Z.; Wu, Q. Y.; Guan, Y. T. Surrogates for On-Line

Monitoring of the Attenuation of Trace Organic Contaminants during

Advanced Oxidation Processes for Water Reuse. Water Res. 2021, 190,

116733.

(259) Drewes, J. E.; Anderson, P.; Denslow, N.; Jakubowski, W.;

Olivieri, A.; Schlenk, D.; Snyder, S. Monitoring Strategies for Constituents

of Emerging Concern (CECs) in Recycled Water; Southern California

Coastal Water Research Project: Costa Mesa, CA, 2018.

(260) Li, J.; Zhu, Y.; Wu, X.; Hoffmann, M. R. Rapid Detection

Methods for Bacterial Pathogens in Ambient Waters at the Point of

Sample Collection: A Brief Review. Clinical Infectious Diseases 2020, 71,

S84−S90.

(261) Shamsipur, M.; Jalali, F.; Ershad, S. Preparation of a Diclofenac

Potentiometric Sensor and Its Application to Pharmaceutical Analysis

and to Drug Recovery from Biological Fluids. Journal of Pharmaceutical

and Biomedical Analysis 2005, 37, 943−947.

(262) Nagaraj, V. J.; Jacobs, M.; Vattipalli, K. M.; Annam, V. P.;

Prasad, S. Nanochannel-Based Electrochemical Sensor for the

Detection of Pharmaceutical Contaminants in Water. Environ. Sci.

Process. Impacts 2014, 16 (1), 135−140.

(263) Wasswa, J.; Mladenov, N.; Pearce, W. Assessing the Potential of

Fluorescence Spectroscopy to Monitor Contaminants in Source Waters

and Water Reuse Systems. Environ. Sci. Water Res. Technol. 2019, 5 (2),

370−382.

(264) Zulkifli, S. N.; Rahim, H. A.; Lau, W. J. Detection of

Contaminants in Water Supply: A Review on State-of-the-Art

Monitoring Technologies and Their Applications. Sens. Actuators, B

2018, 255, 2657−2689.

(265) Mulvihill, M.; Tao, A.; Benjauthrit, K.; Arnold, J.; Yang, P.

Surface-Enhanced Raman Spectroscopy for Trace Arsenic Detection in

Contaminated Water. Angew. Chem., Int. Ed. 2008, 47 (34), 6456−

6460.

(266) Garciá-Miranda Ferrari, A.; Carrington, P.; Rowley-Neale, S. J.;

Banks, C. E. Recent Advances in Portable Heavy Metal Electrochemical

Sensing Platforms. Environ. Sci.: Water Res. 2020, 6, 2676−2690.

(267) Sedki, M.; Zhao, G.; Ma, S.; Jassby, D.; Mulchandani, A. Linker-

Free Magnetite-Decorated Gold Nanoparticles (Fe3 O4-Au): Syn-

thesis, Characterization, and Application for Electrochemical Detection

of Arsenic (III). Sensors 2021, 21 (3), 883.

(268) Buledi, J. A.; Amin, S.; Haider, S. I.; Bhanger, M. I.; Solangi, A.

R. A Review on Detection of Heavy Metals from Aqueous Media Using

Nanomaterial-Based Sensors. Environ. Sci. Pollut. Res. 2020,1−9.

(269) Iyer, S.; Thakur, S.; Dixit, M.; Katkam, R.; Agrawal, A.; Kazi, F.

Blockchain and Anomaly Detection Based Monitoring System for

Enforcing Wastewater Reuse. In 2019 10th International Conference on

Computing, Communication and Networking Technologies, ICCCNT

2019; Institute of Electrical and Electronics Engineers Inc., 2019;

DOI: 10.1109/ICCCNT45670.2019.8944586.

(270) Gilmore, A. M.; Chen, L. Improving Aromatic Water-

Contaminant Detection with Machine-Learning Classification and

Regression for Simultaneous Absorbance-Transmission Excitation

Emission Matrix (A-TEEM) Spectroscopy; SPIE-Intl Soc. Optical Eng.

2020, 11233, 55.

(271) Post, C.; Bru

lisauer, S.; Waldschläger, K.; Hug, W.; Gru

neis, L.;

Heyden, N.; Schmor, S.; Förderer, A.; Reid, R.; Reid, M.; Bhartia, R.;

Nguyen, Q.; Schu

ttrumpf, H.; Amann, F. Application of Laser-Induced,

Deep Uv Raman Spectroscopy and Artificial Intelligence in Real-Time

Environmental Monitoring

Solutions and First Results. Sensors 2021,

21 (11), 3911.

(272) Kukula, K.; Farmer, D.; Duran, J.; Majid, N.; Chatterley, C.;

Jessing,J.;Li,Y.RapidDetectionofBacteriaUsingRaman

Spectroscopy and Deep Learning. In 2021 IEEE 11th Annual Computing

and Communication Workshop and Conference, CCWC 2021; Institute of

Electrical and Electronics Engineers Inc., 2021; pp 796−799;

DOI: 10.1109/CCWC51732.2021.9375955.

(273) Sun, C.; Tian, Y.; Gao, L.; Niu, Y.; Zhang, T.; Li, H.; Zhang, Y.;

Yue, Z.; Delepine-Gilon, N.; Yu, J. Machine Learning Allows

Calibration Models to Predict Trace Element Concentration in Soils

with Generalized LIBS Spectra. Sci. Rep. 2019, 9 (1), 1−18.

(274) Asheri Arnon, T.; Ezra, S.; Fishbain, B. Water Characterization

and Early Contamination Detection in Highly Varying Stochastic

Background Water, Based on Machine Learning Methodology for

Processing Real-Time UV-Spectrophotometry. Water Res. 2019, 155,

333−342.

(275) Hall, J.; Zaffiro, A. D.; Marx, R. B.; Kefauver, P. C.; Radha

Krishnan, E.; Haught, R. C.; Herrmann, J. G. On-Line Water Quality

Parameters as Indicators of Distribution System Contamination. J. -

Am. Water Works Assoc. 2007, 99 (1), 66−77.

ACS ES&T Engineering pubs.acs.org/estengg Review

https://doi.org/10.1021/acsestengg.1c00258

ACS EST Engg. XXXX, XXX, XXX−XXX

(276) Torregrossa, D.; Hansen, J.; Hernández-Sancho, F.;

Cornelissen, A.; Schutz, G.; Leopold, U. A Data-Driven Methodology

to Support Pump Performance Analysis and Energy Efficiency

Optimization in Waste Water Treatment Plants. Appl. Energy 2017,

208, 1430−1440.

(277) Deveughe

le, S.; Do-Quang, Z. Neural Networks: An Efficient

Approach to Predict on-Line the Optimal Coagulant Dose. Water Sci.

Technol.: Water Supply 2004, 4 (5−6), 87−94.

(278) Ruan, J.; Zhang, C.; Li, Y.; Li, P.; Yang, Z.; Chen, X.; Huang, M.;

Zhang, T. Improving the Efficiency of Dissolved Oxygen Control Using

an On-Line Control System Based on a Genetic Algorithm Evolving

FWNN Software Sensor. J. Environ. Manage. 2017, 187, 550−559.

ACS ES&T Engineering pubs.acs.org/estengg Review

https://doi.org/10.1021/acsestengg.1c00258

ACS EST Engg. XXXX, XXX, XXX−XXX

What is it and what are considerations for

its implementation in California

By Arturo A. Keller, Ph.D.

Professor, Bren School UCSB

Start

• Establish on-line and real time water quality monitoring systems

• Years away from commercial systems that have very high reliability

• Redundancy in treatment processes to ensure high level of removal

• Will require higher level of investment

• Emergency storage and plans for dealing with off-spec water

• Large enough storage (weeks) near treatment plant

• Where would off-spec water be discharged?

• Use renewable energy

• Consider creative times to treat (peak solar production)

• Assess implementation of DPR very, very carefully

• Conserve, conserve, conserve….!