Selection and evaluation of water pretreatment technologies for managed aquifer recharge (MAR) with reclaimed water

Chemosphere xxx (xxxx) xxx

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Selection and evaluation of water pretreatment technologies for managed aquifer recharge (MAR) with reclaimed water Jie Yuan*, Michele I. Van Dyke, Peter M. Huck NSERC Chair in Water Treatment, Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada

h i g h l i g h t s
Multi-criteria approach to select water pretreatment for managed aquifer recharge.
Treatment evaluation based on the categories of critical contaminants.
Treatment options were identified based on treatment efficiency targets and credits.
The treatment trains were evaluated in terms of treatability, cost, and sustainability.
Approach was successfully applied to a case study for indirect potable reuse.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 June 2019 Received in revised form 14 September 2019 Accepted 16 September 2019 Available online xxx

Managed aquifer recharge with reclaimed water is a promising strategy for indirect potable reuse. However, residual contaminants in the treated wastewater effluent could potentially have adverse effects on human health. Hence, adequate water pretreatment is required. A multi-criteria approach was used to select and evaluate suitable water pretreatment technologies that can remove these critical contaminants in wastewater effluent for MAR identified in a previous study (Yuan et al., 2017). The treatment efficiency targets were calculated based on the concentrations and the suggested limits of critical contaminants. Treatment efficiency credits were then assigned to each treatment option for the removal of critical contaminants based on literature data. Treatment units that resulted in the highest efficiency credit scores were selected and combined into treatment train options, which were evaluated in terms of treatability, cost, and sustainability. This paper proposes an approach for the selection and evaluation of water treatment options, which will be helpful to guide the future implementation of MAR projects with reclaimed water. © 2019 Elsevier Ltd. All rights reserved.

Handling Editor: Tsair-Fuh Keywords: Managed aquifer recharge Reclaimed water Critical contaminant Water treatment Selection Evaluation

1. Introduction With the increase in water demands and a lack of fresh water resources, reclaimed water has drawn more attention as an alternative potable water source. To supplement water supplies, highly treated wastewater can be recharged into aquifers via surface spreading or direct injection for indirect potable reuse. Although managed aquifer recharge (MAR) can provide a natural system to remove microbial and chemical contaminants from wastewater (Schmidt et al., 2007; Maeng et al., 2011), some pathogens and trace

* Corresponding author. Department of Civil & Mineral Engineering, University of Toronto, Toronto, ON, M5S 1A4, Canada. E-mail address: [email protected] (J. Yuan).

chemicals may persist in the reclaimed water and cause adverse impacts on groundwater quality and human health (Asano and Cotruvo, 2004). Hence, adequate water pretreatment is needed for MAR with reclaimed water. To remove a wide range of chemical and microbial contaminants, diverse technologies, ranging from conventional to advanced wastewater or drinking water treatments, have been applied in this field (Gerrity et al., 2013). Conventional wastewater treatment, especially tertiary treatment, can effectively remove microbial pathogens, large particles, most dissolved organic matter, and some nutrients and inorganic compounds (Gerrity et al., 2013; U.S.EPA, 2017). To achieve a higher level of water quality, treatment modifications including an increased solid retention time (SRT) and the addition of microbial and chemical substances can be made to further remove nitrogen,

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phosphorus, and trace organics. Drinking water treatment also plays an important role in potable reuse. Coagulation and flocculation are often used in water reclamation to remove trace organics, and can be quite effective under optimized conditions (HuertaFontela et al., 2008, 2011; Suarez et al., 2009). Filtration including granular media filtration (e.g., Adin, 2003), biofiltration (e.g., Sun et al., 2018), and membrane filtration (e.g., Krzeminski et al., 2017) have also shown good performance in removing prevalent contaminants in wastewater effluent, including pathogens, suspended particulates, nutrients, and trace organics. In particular, high-pressure membrane filtration, including nanofiltration (NF) and reverse osmosis (RO), has a demonstrated ability to reclaim wastewater (e.g., Alturki et al., 2010; Fujioka et al., 2013). Granular activated carbon (GAC) and powdered activated carbon (PAC) can adsorb soluble organic and inorganic contaminants that are not effectively removed by conventional wastewater treatment (Shon and Vigneswaran, 2006; Kazner et al., 2008; Hatt et al., 2013). In addition, advanced oxidation processes (AOPs) are widely applied in water reclamation due to their high level of treatment performance, including the removal of trace organics that have a high chemical stability (Andreozzi et al., 1999; Rosal et al., 2008). Due to the wide variety of treatment technologies that are available, it is important to select and evaluate appropriate water treatment alternatives for the removal of specific contaminants in wastewater. Several studies have employed a multi-criteria analysis to select feasible water treatment processes. For example, Lienert et al. (2011) evaluated different alternatives that could remove pharmaceuticals from hospital wastewater using a multiple-criteria decision analysis. Sudhakaran et al. (2013) developed a decision support system to recommend the best water treatment process for organic micropollutant removal. Castillo et al. (2017) used an environmental decision support system (EDSS) to select the most feasible wastewater treatment technologies for the food, drink, and milk industry. Ren (2018) applied a multi-attribute decision analysis (MADA) approach to rank ballast water treatment technologies. However, few studies have discussed an approach that could be used in selecting the most suitable water pretreatment technologies for MAR with reclaimed water. Additionally, although other publications (e.g., Kazner et al., 2012; National Research Council, 2012) included abundant information regarding treatment technologies for MAR, none have proposed a general approach for treatment selection and evaluation, and the information presented in those publications is typically based on case studies and not generally applicable. Moreover, a review of the literature shows very little information on assigning a removal efficiency credit of treatments for contaminants, which makes it impossible to accurately evaluate and select appropriate treatment technologies. In the present study, an approach was developed to select and evaluate water pretreatment technologies to remove critical contaminants in wastewater effluent for MAR and ensure that MAR with reclaimed water can provide a final water quality suitable for potable use. Critical contaminants that are grouped by predominant contaminants, potential additional contaminants, and potential emerging contaminants in wastewater effluent for MAR applications were identified by Yuan et al. (2017). Based on this list of the critical contaminants, and using regulatory documents, literature data, and wastewater treatment plant (WWTP) effluent monitoring data, the present paper assigns the treatment efficiency credit of treatment units for each contaminant, and proposes potential treatment alternatives for a case study in Canada. The selected water treatment units were combined to form alternative treatment trains, which were evaluated and ranked in terms of treatability, cost, and sustainability. The approach applied in this study can support decision making in the initial feasibility assessment of water pretreatment for future MAR projects.

2. Approach A multi-criteria approach for selecting and evaluating water pretreatment technologies for MAR with reclaimed water was developed using four steps that are outlined as follows. This approach was applied to a case study site in Ontario, Canada. The municipal WWTP used in this study receives wastewater mainly from households and little or no discharge from industry. The WWTP system includes activated sludge biological treatment with tertiary filtration and ultraviolet light (UV) disinfection. 2.1. Proposed treatment goals The first step is to determine the treatment goals, which make it possible to select the technologies for water treatment. According to Yuan et al. (2017), twenty-two critical contaminants for MAR with reclaimed water were identified that would require further treatment to meet potable water standards and are shown on Table 1. The list of the critical contaminants was divided into three groups: predominant contaminant, potential additional contaminant, and potential emerging contaminant. Predominant contaminants are the regulated contaminants that are routinely monitored in a wastewater effluent. Potential additional contaminants are not routinely monitored in WWTP effluents, but they are regulated and have a high probability to exist in wastewater effluents. Potential emerging contaminants are not currently regulated but may be important for MAR. The critical contaminants were selected based on their occurrence in municipal wastewater effluents and the possibility to cause adverse human health effects. The identified representative contaminants were used to guide the selection of the treatment technologies. The study by Yuan et al. (2017) was done using the same case study WWTP that was used in the current research. To ensure that the treatment option can achieve the required treatment goal, the removal efficiency should exceed the target treatment efficiency, which can be expressed as follows:

Target treatment efficiency ¼

Max  Limit Max

(1)

where Max is the maximum concentration of the critical contaminant, and Limit is the water quality limit for the critical contaminant. For each critical contaminant, the target treatment efficiency was calculated based on the upper value of the typical concentration range in treated wastewater effluent and the suggested water quality targets from Yuan et al. (2017) as summarized in Table 1. For the regulated contaminants, the treatment targets were established based on the water quality limits as established by regulatory or guideline values (Yuan et al., 2017). For some emerging contaminants (i.e., ibuprofen, carbamazepine, erythromycin, and sulfamethoxazole), there are no regulated or suggested water quality limits. In this case, the maximum concentrations of these compounds detected in untreated Ontario drinking water sources (Kormos, 2007) were set as the treatment goals. In addition, the target treatment efficiency of the microbial contaminants (E.coli, Giardia, and Cryptosporidium) was expressed as log reduction values. 2.2. Treatment efficiency credit The second step is to propose treatment units that can remove the critical contaminants below the water quality limits as identified in the first step (section 2.1), and then to assign a treatment efficiency credit to each unit.

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Table 1 Target treatment efficiency for critical contaminants. a

b

Category

Contaminant

Range in wastewater effluents based on case study (mg/L except noted)

Predominant contaminant (regulated)

E.coli Nitrite (as nitrogen) Nitrate (as nitrogen) Total Ammonia (as nitrogen) Total Phosphorus Aluminum Manganese

0e280 CFU/100 mL 0e2.03 9.4e27.9 <0.02e4.51

None detectable 1 10 0.02

3 log reduction 51% 64% 99%

0.03e0.59 0.068e0.724

0.01 0.1 0.05

98% 86% 52%

250 6 Log Reduction 6 Log Reduction

35% 6 Log reduction 6 Log reduction 10%

Potential additional contaminant (regulated)

Potential emerging contaminant (unregulated)

a b c

Chloride Giardia Cryptosporidium Atrazine Diuron Di(2-ethyexyl)phthalate (DEHP) Nitrilotriacetic acid (NTA) Perfluorooctanesulfonic acid (PFOS) Perfluorooctanoic acid (PFOA) Ibuprofen

8:4  103  0:105 247e388 20 - 1100 N/L 1e160 N/L

Treatment goal except noted)

(mg/L

Target treatment efficiency c

4  105  5:6  103 2  105 - 0.153 8:3  108 - 0.182

5  103 0.15

5  103 - 0.41 1:8  106 - 4:6  104

0.4 2  104

2% 57%

5:8  105 - 1:1  103

4  104

62%

3:9  105

99%

6:1  104

87%

1  104

86%

1:45  104

95%

2:5  105

98%

7  104

91%

5

106 -

8:2 

103

Carbamazepine

5  106 - 4:6  103

Diclofenac

1  106 - 6:9  104

Erythromycin

2:7  106 - 2:8  103

Sulfamethoxazole

3  106 - 1:2  103

Nonylphenol

1  104 - 7:8  103

4  103

2% 98%

Values are based on the concentration range in wastewater effluents from Tables 2 and 3 in Yuan et al. (2017). The suggested water quality limits are based on the regulation/guidelines or maximum concentrations detected in untreated Ontario drinking water sources. The calculation is based on eq. (1), and is rounded to the nearest whole percentage point.

To select the best available treatment technologies, the treatment efficiency was determined based on a literature survey, and results are included in Table A1 of the Supplementary Material. Since treatment efficiency can vary depending on the type of source water, influent concentration, and operating conditions, at least two studies were used as a reference for most treatment units; however, in some cases, only one study was available. The study results sometimes showed different values for the removal of the critical contaminants, so the criteria described below were used to assign a treatment efficiency credit for each type of treatment technology with respect to a specific critical contaminant. When several studies investigated the same treatment technology, were operated under similar conditions, and achieved similar removal efficiencies (i.e., difference of less than 10% for chemicals, or 1 log for microorganisms), the treatment credit assigned was based on the median removal value. In some cases, quite different removal efficiencies (i.e., difference greater than 10% for chemicals or 1 log for microorganisms) were recorded in studies. If the selected studies used different types of water for the experiments, which were conducted under similar operating conditions, the treatment credit was assigned based on the study that used a water source similar to a wastewater matrix. For example, Delgado et al. (2012) reported 52% removal of nonylphenol by PAC
ndez-Leal et al. (2011) reported a 94% in surface water, but Herna removal in ultrapure water. Since surface water is closer to a wastewater matrix than ultrapure water, the treatment efficiency was assigned using the results of Delgado et al. (2012). The source water selection criterion was based on the following sequence: wastewater > treated grey water > surface water > groundwater > drinking water > model solution (i.e., ultrapure water). In some cases, large differences in removal efficiency between studies were possibly due to differences in operating conditions. In

this circumstance, the operating conditions in each study were analyzed, and data were only used if treatment was conducted using conditions similar to those recommended by the guidelines (e.g., Ontario Design Guidelines for Drinking Water Systems, and U.S. EPA water reuse guidelines). For example, Shu et al. (2013) showed that 2470 mJ/cm2 UV and 25 mg/L H2O2 could remove 90% of ibuprofen while Kruithof and Martijn (2011)used 540 mJ/ cm2 UV and 6 mg/L H2O2 and achieved 78% removal of ibuprofen. Since the typical UV dose applied in the AOP systems in water reuse application is around 500 mJ/cm2 (Monge, 2011), the Shu et al. (2013) study using a very high UV dose was excluded, and the credit for UV/H2O2 to remove ibuprofen was assigned as 78%. If no studies were conducted under typical conditions, the treatment credit was instead assigned based on other available technical documents for that particular treatment technology. For example, Bischoff et al. (2013) achieved 4.3 log reduction of E. coli by using 15 mJ/cm2 UV, and Nasser et al. (2006) achieved a 5 log removal using 20 mJ/cm2 UV. The UV dose in the selected studies is lower than the commonly applied UV dose of 40 mJ/cm2 for water supply systems in Canada (Health Canada, 2012b). Based on the dose-response relationship between UV dose and E. coli inactivation shown in the National Primary Drinking Water Regulations: Long Term 2 Enhanced Surface Water Treatment (U.S.EPA, 2006), approximately 6 log reduction of E. coli can be achieved when a 40 mJ/cm2 of UV dose is applied. This approach was also applied to estimate the removal of Giardia and Cryptosporidium by UV/chemical disinfection, whose performance is largely influenced by UV/disinfectant dose. In certain cases, when study results in Table A1 showed inconsistencies, the data were thoroughly analyzed to provide reasonable removal data for a given treatment option. For example, Jacangelo et al. (1995) found that  5.2 log reduction of Giardia was achieved by UF, but the actual removal was likely higher since the

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study was limited by the Giardia concentration in the influent water. Moreover, a 6 log reduction of Cryptosporidium, which has a smaller size than Giardia, by the larger pore size microfiltration (MF) membrane, was recorded in both Lovins III et al. (2002) and States et al. (2000), so a 6 log removal credit could be assigned for both MF and UF for the removal of Cryptosporidium and Giardia. 2.3. Treatment selection The third step in the multi-criteria analysis is to select the treatment units that can best achieve removal of specific critical contaminant groups, and combine them into alternative treatment trains that can effectively remove all of the identified critical contaminants. Each treatment unit whose assigned credit was larger than the target treatment efficiency was identified. However, some treatment units were only capable of achieving necessary removals of the critical contaminants at the 90th percentile of the concentration range but not at the maximum value, but the combination of several treatment units in a treatment train may be able to meet the treatment goals. Due to this reason, treatment units that could meet the target treatment efficiency within 10% (chemicals) or 1 log (microorganisms) were also selected as candidates. 2.4. Evaluation of treatment trains Certain treatment units may only be effective for specific contaminants but cannot simultaneously remove all the identified critical contaminants (Table 1). Hence, different identified treatment units will need to be combined to form treatment trains. Once identified, alternative treatment trains were evaluated and ranked based on the criteria described as follows. 2.4.1. Treatability To quantitatively evaluate the performance of a treatment train, the overall removal efficiency for a specific target contaminant is a useful tool, which was calculated as follows:

Rij ¼ 1 

Yn k¼1



1  rikj



(2)

where Rij is the treatment efficiency of jth contaminant by ith treatment train, rikj is the treatment efficiency of the jth contaminant by the kth treatment unit in ith treatment train, and n is the number of treatment units in the treatment train. For some critical contaminants, there is no data available on their removal by a specific treatment unit, and in these cases the removal efficiency was assigned as 0%. However, the selection of a treatment train must be assessed based on the removal efficiencies of all critical contaminants, which results in a highly dimensional dataset. To simplify this process, principal component analysis (PCA) was performed using MATLAB (R2018b, MathWorks) to reduce the dataset dimensions by generating statistically significant principal components. To perform PCA, the efficiency data of each treatment train option was mathematically transformed to remove the sources of unwanted variations (eq. (3)).

Rij  R,j R*ij ¼ qffiffiffiffiffi Sij

(3)

where R*ij is the transformed treatment efficiency of the jth contaminant by the ith treatment train, R,j is the average treatment efficiency of the jth contaminant based on all treatment trains, Sij is the variance of the treatment efficiency of jth contaminant based on all treatment trains.

2.4.2. Cost Treatment cost includes both capital and operating expenses. A wide range of factors, including life expectancy of the plant, plant throughput, chemicals and electricity, civil and mechanical works, and local costs for labor, should be considered. Hence, it is quite difficult to evaluate the treatment cost over a broad range of situations (WHO, 2008). For this reason, treatment cost was evaluated using a qualitative ranking system adapted from WHO (2008), which is based on the technical complexity of infrastructure and operation (Table 2). The comparative ranking scheme can provide an approximate cost comparison between alternative water treatment trains. It should be noted that the WHO gave MF, UF, NF, and RO a rank of 6. However, the costs of these four types of membrane filtration are not the same. For example, MF, which has the largest pore size, would have lower infrastructure and operational costs than finer membranes (e.g., RO) since MF materials are cheaper and €fer its energy costs are lower due to the lower feed pressure. Scha et al. (2001) estimated the cleaning and energy cost of these membranes, and found that the high-pressure membrane (e.g., NF) has higher cost than the low-pressure membrane (e.g., UF and MF), which would not be unexpected. To distinguish their cost differences, in this research, MF was assigned a score of 6.0, while the scores for UF, NF, and RO were assigned as 6.25, 6.5, and 6.75, respectively. Based on Table 2, treatment units in the treatment train option can be assigned a cost score (i.e., rank), and the overall cost of one treatment train is the sum of the scores were each treatment unit. 2.4.3. Sustainability The environmental impact is an important factor that should be considered for treatment selection. Energy-intensive processes that have larger emissions of greenhouse gases and/or wastes are less favorable. In this study, treatment was evaluated qualitatively in terms of energy requirements and residual generation. The qualitative values in Table 3 were obtained based on a consensus study report by a professional judgement committee (National Research Council, 2012). The advanced processes (i.e., NF, RO, ozonation, and AOPs) have much larger energy consumption than conventional treatment (i.e., chlorine/chloramine disinfection, chemical reduction, and oxidation (excluding ozone), ion exchange, GAC/PAC adsorption, and conventional drinking water treatment). In terms of residual generation, high-pressure membrane filtration, electrodialysis, and AOPs release most wastes. Each treatment train can be assigned a score based on the summation of the qualitative value of each treatment unit in Table 3. 2.4.4. Multi-criteria analysis of treatment trains 2.4.4.1. Standardization. The measurement scales for each criterion are different and therefore cannot be compared directly. Hence, the data were standardized to a form of dimensionless score ranging from 0 to 1. In this study, two linear standardization methods, goal standardization and maximum standardization, were used for quantitative criteria data and qualitative criteria data, respectively. For the quantitative criteria (i.e., removal efficiency), the value was transformed using eq. (4) where a highest value and a lowest value are specified. In terms of the qualitative criteria (i.e., cost, energy requirements, and residual generation), the value was standardized using eq. (5), which results in a negative correlation between score and effect (e.g., a higher cost score results in a lower score value).

Score ¼

criteria value  minimum value maximum value  minimum value

(4)

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Table 2 Ranking of complexity and cost of water treatment processes(Adapted from WHO, 2008). Ranking

a

Treatment processes Simple chlorination, filtration (rapid sand, slow sand) Pre-chlorination plus filtration, aeration Chemical coagulation, process optimization for control of disinfection by-products (DBPs) Granular/powdered activated carbon (GAC/PAC) treatment, ion exchange Ozonation Advanced oxidation processes, UV disinfection, membrane treatment (MF: 6.0; UF: 6.25; NF: 6.5; RO: 6.75)

1 2 3 4 5 6

b

MF: microfiltration; UF: ultrafiltration; NF: nanofiltration; RO: reverse osmosis; UV: ultraviolet light. a Higher ranking means most complexity and higher costs. b Membranes assigned individual credit.

Table 3 Environmental impact of water treatment processes (adapted from a consensus study report (National Research Council, 2012)). Treatment process

Energy requirements

Ultraviolet light (UV) disinfection Chlorine/chloramine/chlorine dioxide disinfection Microfiltration/ultrafiltration Nanofiltration/reverse osmosis Electrodialysis Chemical precipitation Oxidation (excluding ozone) Ozonation Ion exchange Granular/powered activated carbon (GAC/PAC) adsorption Advanced oxidation processes (AOPs) Conventional drinking water treatment (coagulation/flocculation/sedimentation/filtration)

2 1 2 3 3 1 1 3 1c 1 3 1

a

Residual generation

b

1 1 1 3 3 2 1 1 2c 2 1 1

a

Higher value represents more energy consumption. Higher value represents more generation of residuals. Ion exchange is not included in the original report (National Research Council, 2012), but its environmental impact is considered to be equal to that of GAC/PAC adsorption in this study. b c

Score ¼ 1 

criteria score maximum score

(5)

where criteria value is the value of the quantitative criteria for a treatment train, minimum value is the minimum value of the quantitative criteria for a treatment train, maximum value is the maximum value of the quantitative criteria for a treatment train, criteria score is the value of the qualitative criteria for a treatment train, and maximum score is the maximum value of the qualitative criteria for a treatment train.

2.4.5. Sensitivity analysis Sensitivity analysis was conducted to investigate if changes in the weights affect the final ranking. First, the weight for the prioritized criterion was increased by 30% (i.e., weight assigned for treatment was increased from 50% to 80%; weight for the other criteria was set at 10% each). Then, the weight for the prioritized criterion was decreased by 30% (i.e., weight for treatability was set at 20%; weight for the other criteria was set at 40% each). 3. Results and discussion 3.1. Technologies for the treatment of critical contaminants

2.4.4.2. Assigning weight. The weight of each criterion could significantly affect the final ranking, and it is based on a number of factors. In this study, treatability is the most important factor, and therefore it was prioritized over other criteria. In this way, 50% weight was assigned to treatability while 25% weight was assigned to both cost and sustainability. For the sustainability criterion, half of weight (12.5%) was assigned for energy requirements and half for residual generation.

2.4.4.3. Ranking. To rank the alternative treatment trains, the overall score of each option was calculated by summing the score of each criterion multiplied by its corresponding weight (eq. (6)).

VðiÞ ¼

n X

Wj ,Vj ðiÞ

(6)

j¼1

where VðiÞ is the total score of ith alternative treatment train, Wj is the weight of jth criterion, and Vj ðiÞ is the score of ith alternative treatment train on jth criterion.

The proposed water treatment technology that could be used to remove each identified critical contaminant in wastewater effluent was studied. Table 4 shows the assigned credits for the removal of the critical contaminants by the specific proposed treatment. The treatment units that could effectively remove critical contaminants are identified in bold text in Table 4. 3.1.1. Removal/inactivation of microorganisms E. coli is a commonly used indicator of bacterial pathogens, and shows that water contains recent faecal contamination or was not adequately treated. In order to achieve a target treatment efficiency of 3 log removal for E. coli, several technologies are suitable including inactivation/disinfection (e.g., UV, ozone, chlorine, chorine dioxide, and chloramine), or removal by filtration (e,g., UF, NF, and RO). The best removal (i.e., 6 log reduction) is by UV disinfection (U.S.EPA, 2006), followed by removal (i.e., 4 log reduction) by membrane filtration or chemical disinfection (Li et al., 2008; Health Canada, 2012a; Krzeminski et al., 2017; Schwermer et al., 2018). Giardia and Cryptosporidum are typically used to

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Table 4 Treatment efficiency credit for the removal of critical contaminants (values in bold are those can meet the target treatment efficiency based on the selection criteria). Critical contaminants

UV

Microorganisms

6 log 1  4 log 2  6 log 1 6 log 1 6 log 6 log -

E.coli Giardia Cryptosporidium

Nutrients

Inorganics

CD

MF

Nitrate/Nitrite Ammonia Phosphorus

-

-

-

Aluminum Manganese

-

-

-

Chloride Regulated organics Atrazine Diuron DEHP NTA Emerging organics PFOS PFOA Ibuprofen

UF

NF

4 log 3  4 log 3   5 6 log 5 6 log 5 5   6 log 5 6 log 5 70% 8 21% 14 95% 19 -

-

-

-

40% -

-

-

-

-

91% 92%

42

85% 81% 87% 88%

24 29

38 43 49 56

100%

62

64

-

-

-

-

99% 98% 96%

Carbamazepine Diclofenac Erythromycin Sulfamethoxazole Nonylphenol -

-

-

37% -

90% 77 96% 82 67% 42 95% 93 92% 100

88

64 70

RO

ED

CP

OD

OZ

 4 log 3  6 log 5  6 log 5 95% 9 93% 15  95% 19 92% 25 95% 30

-

-

-

4.5 log 4 -

-

-

-

3 log

90% 96% 97% 99%

39 44 50 57

100%

62

65

99% 98% 69 > 99% 71 98% 77 97% 83 75% 89 98% 94 97% 101

-

10

-

81% 62% 90% 16 17% 20 95%

11

26

27

77% 97% 96% -

31

40

95% 99%

71%

6

3.3 log

21 -

32

IX

7

-

33

83%

34

-

GAC

PAC

AD

AOPs

CDWT BNR

-

-

-

-

-

-

-

-

-

3 log

1

1

-

-

-

3 log

22 -

-

-

-

-

87% 96% 89%

-

95%

-

-

20% 55  56% 61 -

e

67% 97% 94% 93% 99%

28

17

35

86%

36

-

17% 45 24% 51 30% 58

-

99% 46 88% 52 75% 59

95% 96% -

-

-

75%

63

-

-

-

-

-

82%

-

-

-

96% 78 99% 84 73% 89 93% 95 83% 102

72

e

12

-

-

-

66

47 53

 83% 91% 41 94% 98% e 88% e

67

-

66% 93% 47% 66 88% 91% 73 91%

-

84% 79 90% 80 89% 85 90% 80 74% 90 96% 91 82% 96 62% 97 94% 103 52% 104 -

67 74

-

37

 75% 90%

13 18 23

34

48 54 60

-

63

75

7% 0% 0%

68 68 76

-

95% 81 0% 76 99% 86 7% 87 98% 92 83% 89 97% 98 27% 99 100% 105 67% 106 -

AD: adsorption (the adsorbent is not GAC or PAC); AOPs: advanced oxidation processes; BNR: biological nutrient removal process; CD: chemical disinfection; CDWT: conventional drinking water treatments (coagulation/flocculation/sedimentation/filtration); CP: chemical precipitation; DEHP: di(2-ethyexyl)phthalate; ED: electrodialysis; GAC: granular activated carbon adsorption; IX: ion exchange; MF: microfiltration; NF: nanofiltration; NTA: nitrilotriacetic acid; OD: oxidation (excluding ozone); OZ: ozonation; PAC: powdered activated carbon adsorption; PFOA: perfluorooctanoic acid; PFOS: perfluorooctane sulfonic acid; RO: reverse osmosis; UF: ultrafiltration; UV: ultraviolet light disinfection. When there are only two values, the median value is the average of two values. 1 U.S.EPA, 2006, 2 Health Canada, 2012a, 3 median value (Li et al., 2008; Krzeminski et al., 2017; Schwermer et al., 2018), 4 median value (Mezzanotte et al., 2007; Miranda et al., 2014; Singh et al., 2015), 5 median value (States et al., 2000; Lovins III et al., 2002), 6 median value (Ran and Li, 2013; Passos et al., 2014) 7 median value (Rennecker et al., 1999; Craik et al., 2003), 8 Parlar et al., 2018, 9 median value (Cevaal et al., 1995; Goncharuk et al., 2013; Parlar et al., 2018), 10 median value (Chebi and Hamano, 1995; Hell et al., 1998), 11 Luk and Au-Yeung, 2002, 12 median value (Richard, 1989; Clifford and Liu, 1993), 13 median value (Matĕj u et al., 1992; Urbain et al., 1996), 14 median value (Kurama et al., 2002; Krzeminski et al., 2017), 15 median value (Koyuncu et al., 2001; Kurama et al., 2002; Krzeminski et al., 2017), 16 Song et al., 2012, 17  median value (Thornton et al., 2007; Vassileva and Voikova, 2009; Siljeg et al., 2010), 18 median value (Stembal et al., 2005; Lytle et al., 2007), 19 median value (Acero et al., 2010; dos Santos et al., 2014), 20 Ward et al., 2018; 21 median value (Lin and Carlson, 1975; Shannon and Verghese, 1976), 22 median value (Zhu et al., 2017; Martin et al., 2018), 23 median value (Sun et al., 2015; Díez-Montero et al., 2016), 24 Ates and Uzal, 2018; 25 median value (Srinivasan et al., 1999; Ates and Uzal, 2018), 26 Dalla Costa et al., 2002 27 median value (Licsko and Szakal, 1988; Nilson 1992), 28 median value (Petrie et al., 1984; Venkataramani et al., 1988), 29 median value (Patil et al., 2017; Juholin et al., 2018), 30 median value (Palma et al., 2016; Ambiado et al., 2017), 31 median value (Melnyk and Goncharuk, 2009; Mel’nik, 2011), 32 median value (Oncel et al., 2013; Tolonen et al., 2014), 33 Zhu et al., 2009, 34 El Araby et al., 2009, 35 median value (White and Asfar-Siddique, 1997; Kononova et al., 2015), 36 Qomi et al., 2014, 37 Funes et al., 2014, 38 median value (Hedayatipour et al., 2017; Juholin et al., 2018), 39 median value (Jevtitch and Bhattacharyya, 1986; Pei et al., 2018),

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J. Yuan et al. / Chemosphere xxx (xxxx) xxx 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106

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median value (Becheleni et al., 2015, Mahmoud et al., 2015), median value (Abu-Arabi et al., 2013; Iakovleva et al., 2015), Yoon et al., 2006, median value (Devitt et al., 1998; Shen et al., 2014; Karimi et al., 2016), median value (Chian et al., 1975; Heo et al., 2013; Albergamo et al., 2019), median value (Ormad et al., 2010; Yang et al., 2017a), average (Selm and Wang, 1994; Snyder et al., 2007), median value (Snyder et al., 2007; Zou et al., 2014), median value (Antoniou and Andersen, 2014; James et al., 2014), median value (Hofman et al., 1993; Benitez et al., 2009), median value (Mehta et al., 2015; Albergamo et al., 2019), median value (Ormad et al., 2010; Solís et al., 2016), median value (Baup et al., 2002; Di Bernardo Dantas et al., 2011), median value (Ayele et al., 1998; Baup et al., 2002),
rez et al., 2006; Djebbar et al., 2008; Oturan et al., 2011), median value (Pe El-Dib and Aly, 1977, median value (Shen et al., 2014; Zolfaghari et al., 2018), Fang et al., 2018, Anandan et al., 2013, median value (Adhoum and Monser, 2004; Asakura et al., 2004), median value (Chen et al., 2007; Chen, 2010; Esmaeli et al., 2011), Theepharaksapan et al., 2011; Drewes et al., 2003, Games and Staubach, 1980; median value (Lipp et al., 2010; Appleman et al., 2013), median value (Tang et al., 2006; Lipp et al., 2010), median value (Eschauzier et al., 2012; Flores et al., 2013), median value (Hansen et al., 2010; Yu et al., 2014), Xiao et al., 2013, median value (Thompson et al., 2011; Appleman et al., 2014), median value (Botton et al., 2012; Wei et al., 2018), median value (Alturki et al., 2010; Boleda et al., 2011), median value (Huber et al., 2003; Yao et al., 2018), median value (Mestre et al., 2011; Butkovskyi et al., 2017), median value (Jung et al., 2013; Noutsopoulos et al., 2014), median value (Zwiener and Frimmel, 2000; Huber et al., 2003; Kruithof and Martijn, 2013) Kim et al., 2007, median value (Beier et al., 2010; Gur-Reznik et al., 2011), median value (Lei and Snyder, 2007; Bourgin et al., 2018), median value (Ternes et al., 2002; Yang et al., 2011), median value (Altmann et al., 2014; Ruhl et al., 2014), € der, 2007; Rosario-Ortiz et al., 2010; Lester et al., 2014), median value (Ternes et al., 2002; Gebhardt and Schro median value (Beier et al., 2010; Xu et al., 2019), median value (Beier et al., 2010; Sahar et al., 2011), median value (Kim et al., 2009a; Yao et al., 2018), median value (Ternes et al., 2002; Yang et al., 2011), median value (Kim et al., 2009a, b), median value (Simazaki et al., 2008; Rigobello et al., 2013), median value (Yoon et al., 2006; Boleda et al., 2011), Boleda et al., 2011, Yang et al., 2011, median value (Serrano et al., 2011; Liu et al., 2013), median value (Kim et al., 2009a, b; Derrouiche et al., 2013),
pez-Mun
oz et al., 2012), median value (Dolar et al., 2012; Lo median value (Simon et al., 2009; Dolar et al., 2012), median value (Nakada et al., 2007; Gao et al., 2014), median value (Altmann et al., 2016; Ma et al., 2018), median value (Altmann et al., 2016; Lompe et al., 2018),
n et al., 2012; Alharbi et al., 2017; Yang et al., 2017b), median value (Beltra Nakada et al., 2007, median value (Gallenkemper et al., 2003; Abtahi et al., 2019), median value (Al-Rifai et al., 2011; Garcia et al., 2013), median value (Zhang et al., 2008; Hern
andez-Leal et al., 2011),
ndez-Leal et al., 2011), median value (Choi et al., 2005; Herna Delgado et al., 2012, Median value (Derrouiche et al., 2013; Karci et al., 2013a, b), Nam et al., 2014.

measure the removal of protozoan pathogens. They are more resistant to chemical disinfection, especially chlorination, but target treatment efficiency (6 log removal) can be achieved by UV disinfection and membrane filtration. Removal by ozone and conventional treatment (i.e., coagulation/flocculation/sedimentation/ filtration) is only 3 log (Rennecker et al., 1999; U.S.EPA, 2006; Craik et al., 2013; Ran and Li, 2013; Passos et al., 2014), and is therefore

lower than the required target for MAR. We did not evaluate technologies for virus removal because there is far less information in the literature. With the exception of inactivation of certain viruses by UV, protozoan pathogens are the most difficult type of pathogen to inactivate/disinfect, and therefore determine the required design (Hijnen et al., 2006).

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3.1.2. Removal of nutrients Nitrate/nitrite may cause adverse health effects (e.g., methaemoglobinemia or thyroid effects) when exceeding the water quality limits. Nitrite is not stable and can react with oxygen in water to form nitrate, so its concentration is typically below 0.3 mg/ L (0.1 mg NO2eN/L) (U.S.EPA, 2002) and treatment technologies are rarely studied (Department of National Health and Welfare, 1993). In addition, many technologies are expected to be effective for the removal of both nitrate and nitrite. Hence, only the treatment options for the removal of nitrate were studied. NF, RO, eletrodialysis, chemical reduction, ion exchange, and a biological nutrient removal process (BNR) were found to effectively reduce nitrate concentrations to below the water quality limits. Over 80% removal can be achieved by RO (Cevaal et al., 1995; Goncharuk et al., 2013; Parlar et al., 2018), electrodialysis (Chebi and Hamano, 1995; Hell et al., 1998), and BNR (Matĕj u et al., 1992; Urbain et al., 1996), but around 70% nitrate reduction can be achieved by NF (Parlar et al., 2018), chemical reduction, and ion exchange. An elevated level of ammonia may lead to nitrification, resulting in a potential increase in nitrate/nitrite. To achieve 99% ammonia removal, only four treatments (i.e., RO, electrodialysis, ion exchange, BNR) are suitable. An excessive amount of phosphorus present in water that is recharged into the aquifer could adversely affect aquatic ecosystems. For phosphorus treatment, effective technologies include NF, RO, chemical reduction, ion exchange, and BNR. 3.1.3. Removal of inorganics Due to the potential health and environmental impacts and negative aesthetic effects, inorganics such as metals and salts should be treated for potable reuse. For aluminum removal, NF, RO, electrodialysis, chemical precipitation and ion exchange are suitable methods. Over 90% removal can be achieved by membrane filtration (Srinivasan et al., 1999; Ates and Uzal, 2018), chemical precipitation (Licsko and Szakal, 1988; Nilson, 1992), and ion exchange (Petrie et al., 1984; Venkataramani et al., 1988) while 77% removal can be achieved with electrodialysis (Dalla Costa et al., 2002). A wide range of technologies including high-pressure membrane filtration, electrodialysis, chemical precipitation, oxidation, ion exchange, and adsorption are effective for reducing manganese concentration below the limits. Chemical precipitation (Oncel et al., 2013; Tolonen et al., 2014), ion exchange (White and Asfar-Siddique, 1997; Kononova et al., 2015), electrodialysis (Melnyk and Goncharuk, 2009; Mel'nik, 2011), and NF/RO (Palma et al., 2016; Ambiado et al., 2017; Patil et al., 2017; Juholin et al., 2018) can achieve over 95% manganese removal, but adsorption and oxidation have relatively lower removal efficiencies (i.e., around 70e80%) (El Araby et al., 2009; Qomi et al., 2014). Excessive concentrations of chloride can make water unpleasant to drink and can have toxic effects on aquatic organisms. To achieve the treatment target for chloride (i.e., 35% reduction), NF, RO, electrodialysis, and adsorption are feasible technologies. 3.1.4. Removal of organics Trace organics include known or potential carcinogens and endocrine disrupting compounds (EDCs), so the removal of these contaminants has drawn more attention in water reuse. For the regulated organics, the herbicides/pesticides group contains the largest number of contaminants. As representatives of this group, both atrazine and diuron can be effectively removed by highpressure membrane filtration (Chian et al., 1975; Hofman et al., 1993; Devitt et al., 1998; Benitez et al., 2009; Heo et al., 2013; Shen et al., 2014; Mehta et al., 2015; Karimi et al., 2016; Albergamo et al., 2019), GAC/PAC adsorption (Selm and Wang, 1994; Ayele et al., 1998; Baup et al., 2002; Snyder et al., 2007; Di Bernardo
rez et al., 2006; Dantas et al., 2011; Zou et al., 2014), and AOPs (Pe

Djebbar et al., 2008; Oturan et al., 2011; Antoniou and Andersen, 2014; James et al., 2014), with over 80% removals. Although these two compounds are ozone refractory compounds (Ormad et al., 2010), ozonation is still considered as a feasible treatment alternative for this case study due to the low target treatment efficiencies required (i.e., 10% for atrazine, 2% for diuron). Additionally, UF and conventional coagulation/flocculation/sedimentation/ filtration are suitable options to remove atrazine and diuron, respectively (although the removals for diuron are not high, they are above the required level). Di(2-ethyexyl)phthalate (DEHP) is a regulated and extensively used plasticizer, which has adverse effects in the environment at trace levels (Staples et al., 1997). To prevent DEHP contamination during MAR, effective pretreatment technologies include NF (Shen et al., 2014; Zolfaghari et al., 2018), RO (Fang et al., 2018), and AOPs (Chen et al., 2007; Chen, 2010; Esmaeli et al., 2011), which can achieve over 85% removal efficiencies. Since it is a refractory organic compound with low water solubility, the degradation rate of DEHP by ozonation is very slow (Anandan et al., 2013). Although GAC adsorption would remove 75% of DEHP (Adhoum and Monser, 2004; Asakura et al., 2004), it still cannot meet the treatment target (i.e., 98%). Moreover, conventional water treatment processes such as coagulation/flocculation/ sedimentation/filtration have limited capability to remove DEHP from water. Nitriloacetic acid (NTA) is a typical chelating agent, which has the potential to cause human cancers (WHO, 1999). High-pressure membrane filtration, ozonation, and AOPs could greatly reduce its concentration below the water quality limit. Emerging trace organics are contaminants that are not regulated but have potential adverse effects on human health. There are four major categories: perfluorochemicals, pharmaceuticals, antibiotics, and personal care products (Yuan et al., 2017). For perfluorochemicals, perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) are unlikely to be removed by conventional drinking water treatment (i.e., coagulation/flocculation/sedimentation/filtration) due to their high hydrophilicity. In addition, the abundance of the most electronegative element (i.e., fluorine) and functional groups with high electron density in their molecules makes these two compounds resistant to oxidation processes such as ozonation and AOPs. The feasible technologies that can remove over 80% of PFOS and PFOA are NF (Lipp et al., 2010; Appleman et al., 2013), RO (Tang et al., 2006; Lipp et al., 2010; Thompson et al., 2011; Appleman et al., 2014), and PAC adsorption (Hansen et al., 2010; Yu et al., 2014)). Only partial removal (i.e. 40e60%) of both compounds can be achieved by GAC adsorption (Eschauzier et al., 2012; Flores et al., 2013). However, due to the lower target treatment efficiency of PFOS (i.e., 57%), GAC adsorption is considered as a feasible option for its removal. Ibuprofen, carbamazepine, and diclofenac are three representative contaminants of the pharmaceuticals group and can be greatly removed by high-pressure membrane filtration (Alturki et al., 2010; Beier et al., 2010; Boleda et al., 2011; Gur-Reznik et al., 2011; Sahar et al., 2011; Botton et al., 2012; Wei et al., 2018; Xu et al., 2019), ozonation (Huber et al., 2003; Lei and Snyder, 2007; Kim et al., 2009a; Bourgin et al., 2018; Yao et al., 2018), GAC/PAC adsorption (Ternes et al., 2002; Mestre et al., 2011; Yang et al., 2011; Jung et al., 2013; Altmann et al., 2014; Noutsopoulos et al., 2014; Ruhl et al., 2014; Butkovskyi et al., 2017, and AOPs (Zwiener and Frimmel, 2000; Ternes et al., 2002; Huber et al., 2003; Gebhardt €der, 2007; Kim et al., 2009a, b; Rosario-Ortiz et al., and Schro 2010; Kruithof and Martijn, 2013; Lester et al., 2014), with over 80% removal efficiencies. However, 99% target treatment efficiency for ibuprofen makes it impossible to select ozonation as a suitable treatment. Furthermore, these three compounds are quite resistant to conventional drinking water treatment. The representatives of the antibiotics group, erythromycin and sulfamethoxazole, can

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both be effectively removed by AOPs. In addition to AOPs, PAC adsorption is a feasible option for the removal of erythromycin. Although coagulation and flocculation can remove over 80% of erythromycin (Boleda et al., 2011), they are not suitable treatments since the treatment efficiency credit is lower than the target treatment efficiency. However, unlike erythromycin, sulfamethoxazole adsorption by PAC is only around 60% (Altmann et al., 2016; Lompe et al., 2018), which is much lower than the target treatment efficiency (i.e., 98%). Similar to other trace organics, sulfamethoxazole is unlikely to be removed by conventional drinking water treatment (i.e., coagulation/flocculation/sedimentation/filtration). High-pressure membrane filtration and ozonation are considered as the effective treatments for sulfamethoxazole. Nonylphenol is a priority contaminant of the personal care products group, which is persistent in the environment (Janex-habibi et al., 2009). Various techniques, including NF, RO, ozonation, GAC adsorption, and AOPs, can effectively reduce its concentration below the limits. PAC adsorption and conventional drinking water treatment (i.e., coagulation/flocculation/sedimentation/filtration) can only achieve a moderate removal efficiency (i.e., 50e60%) (Delgado et al., 2012; Nam et al., 2014), and therefore are not considered as feasible treatments.

3.2. Potential treatment trains A treatment train is a combination of treatment units, which is designed to treat water to the desired effluent quality. From a system perspective, treatment units that can remove a broader range of the critical contaminants are favored since they can reduce the redundancy of similar treatment units in one treatment train and increase the robustness of treatment systems. Table 5 shows the identified three potential treatment train options. Based on Table 4, RO or NF can effectively remove the greatest number of identified critical contaminants, and therefore were selected as the core unit in the treatment train. RO is a frequently used technology in water reuse due to its high removal efficiency for inorganics, organics, and even pathogens (Pype et al., 2016). Treatment train option 1 uses MF preceding RO as the RO

9

pretreatment. Moreover, as RO could not achieve the target treatment efficiency for erythromycin, AOPs were included as an additional barrier in this train. A UV-based AOP was selected since it can serve as both an oxidation and microbial inactivation. In the California groundwater replenishment regulation, the California Department of Public Health defined a term “full advanced treatment” (FAT), which encompasses MF, RO, and AOPs (California Department of Public Health, 2014). In addition, numerous indirect potable reuse projects (e.g., the Orange County Groundwater Replenishment System (GWRS)) use this treatment model (Yuan et al., 2016). The second treatment train option (Table 5) included UV instead of a UV-based AOP. This treatment train (MF, RO, and UV) has been used in water reuse in Singapore and known as the “Singapore model” (U.S.EPA, 2017). Additionally, to effectively reduce the erythromycin concentration, PAC is dosed at the start of the treatment train to ensure sufficient contact time. For the third option, NF alone was found to be not effective in removing ammonia and erythromycin, so additional treatment units such as ion exchange or electrodialysis should be included in the treatment train. Additionally, the biological operation of ion exchange filters was reported to be a robust, affordable pretreatment approach to minimize membrane fouling (Schulz et al., 2017). As a result, treatment train option 3 includes ozonation, biological ion exchange (BIEX) filtration, NF, and ozonation (Table 5). In contrast to RO, ozone-biologically active filtration (BAF) is a robust treatment alternative, which can not only effectively remove at least some trace contaminants without producing a brine stream but also reduce membrane fouling for the subsequent NF (Huck and
ski, 2008). The post-ozonation can provide additional disinSozan fection before groundwater recharge. Table 5 shows the removal efficiency of the three treatment train options. It can be seen that all the options meet the treatment performance requirements, with over 90% removals for most contaminants. Except for ammonia and phosphorus, the target treatment efficiency of the critical contaminants can be met by all three alternative treatment trains. The existence of microorganisms in the treatment train may be able to further reduce the ammonia and

Table 5 Removal efficiency of each critical contaminant by alternative treatment trains. Critical contaminants

Option 1 MF/RO/ UV-based AOP

Option 2 PAC/ MF /RO/UV

Option 3 Ozone/BIEX /NF/Ozone

Target treatment efficiency

E.coli Giardia Cryptosporidium Nitrate/Nitrite Ammonia Phosphorus Aluminum Manganese Chloride Atrazine Diuron DEHP NTA PFOS PFOA Ibuprofen Carbamazepine Diclofenac Erythromycin Sulfamethoxazole Nonylphenol

10 log reduction 18 log reduction 18 log reduction 95% 93% 95% 92% 99% 90% 99% 99% 99% 100% 99% 98% 99% 99% 99% 99% 99% 100%

10 log reduction 18 log reduction 18 log reduction 95% 93% 95% 92% 95% 90% 99% 99% 99% 100% 99% 99% 99% 99% 99% 99% 99% 98%

13 log reduction 12 log reduction 12.6 log reduction 90% 97% 99% 99% 99% 85% 86% 92% 94% 100% 99% 98% 99% 99% 99% 97% 99% 99%

3 log reduction 6 log reduction 6 log reduction 51%/64% 99% 98% 86% 52% 35% 10% 2% 98% 2% 57% 62% 99% 87% 86% 95% 98% 91%

BIEX: biological ion exchange; DEHP: Di(2-ethyexyl)phthalate; MF: microfiltration; NF: nanofiltration; NTA: Nitrilotriacetic acid; PAC: powdered activated carbon; PFOA: Perfluorooctanoic acid; PFOS: Perfluorooctanesulfonic acid; RO: reverse osmosis; UV: ultraviolet light.

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phosphorus level, but the system should be optimized to ensure that the biological removals are complete. 3.3. Assessment of potential treatment trains 3.3.1. Treatment efficiency PCA results PCA was performed on the transformed treatment efficiencies of the critical contaminants by alternative treatment trains. Eigenvectors and eigenvalues can determine the direction and magnitude of principal components, respectively. Each coefficient in an eigenvector can represent the contributions of each variable to the PC. In this study, the PCA identified only two principal components. As shown in Table 6, the positive eigenvector values mean the positive weights, but the negative values indicate the negative

Table 6 Eigenvectors and eigenvalues of treatment efficiency PCA. Critical contaminants

PC1 eigenvector

PC2 eigenvector

E.coli Giardia Cryptosporidium Nitrate/Nitrite Ammonia Phosphorus Aluminum Manganese Chloride Atrazine Diuron Di(2-ethyexyl)phthalate (DEHP) Nitrilotriacetic acid (NTA) Perfluorooctanesulfonic acid (PFOS) Perfluorooctanoic acid (PFOA) Ibuprofen Carbamazepine Diclofenac Erythromycin Sulfamethoxazole Eigenvalues % of total variance

0.5610 0.7934 0.0724 0.0648 0.0648 0.0857 0.0325 0.0724 0.1168 0.0857 0.0724 0 0 0.0163 0 0 0 0.0458 0 0.0001 5.50 99.4%

0.0169 0.0239 0.0022 0.0020 0.0020 0.0026 0.7391 0.0022 0.0035 0.0026 0.0022 0 0 0.3696 0 0 0 0.0014 0 0.5623 0.03 0.5%

contributions of each variable to the PC and were not included in the further analysis. The treatment efficiencies of Giardia, Cryptosporidium, manganese, chloride, atrazine, diuron, PFOS, and diclofenac made substantial contributions to PC1, in which the removal of Giardia accounts for the greatest portion (even larger than all the others put together). The proportion of the total variance captured by PC1 was 99.4%, which is much greater than that captured by PC2 (Table 6). Hence, it is reasonable to use the PC1 score to represent the treatment ability of each treatment train. The PC1 score was calculated by summing the removal efficiency of each contaminant multiplied by the corresponding eigenvector coefficient. 3.3.2. Ranking of treatment trains Table 7 shows the rankings of treatments based on weights in three scenarios. In scenario 1, 50% weight was assigned to treatability while 25% weight was assigned to both cost and sustainability. In scenario 2, the weight for the prioritized criterion was increased by 30%. In scenario 3, the weight for the prioritized criterion was decreased by 30%. It is interesting to see that option 1 has the highest overall score of all three scenarios. When the treatability weight was increased, the score difference between option 1 and 2 decreased, but there is an increased difference between option 1 and 3. As the treatability weight was decreased, the score difference between option 1 and 2 increased, while the difference between option 1 and 3 decreased. This may be because both options 1 and 2 use RO, which has the highest treatability but the highest cost and most severe environmental impacts. Based on the ranking, treatment train option1 (MF/RO/UV-based AOP) is the recommended pretreatment for MAR with reclaimed water based on the case study data used in this assessment. 4. Conclusions Water pretreatment for MAR with reclaimed water is important for the removal of residual contaminants in wastewater effluents. Different types of water treatment technologies, including conventional wastewater treatment, as well as conventional and advanced drinking water treatments, can be useful to remove

Table 7 Score and ranking of alternative treatment trains. Treatment options

Treatability

Scenario 1 (50% treatability, 25% cost, 25% sustainability) Option 1: 1 MF/RO/ UV-based AOP Option 2: 1 PAC/ MF/ RO/ UV 0 Option 3 Ozone/ BIEX/ NF/ Ozone Scenario 2 (80% treatability, 10% cost, 10% sustainability) Option 1: 1 MF/RO/ UV-based AOP Option 2: 1 PAC/ MF/ RO/ UV 0 Option 3 Ozone/ BIEX/ NF/ Ozone Scenario 3 (20% treatability, 40% cost, 40% sustainability) Option 1: 1 MF/RO/ UV-based AOP Option 2: 1 PAC/ MF/ RO/ UV 0 Option 3 Ozone/ BIEX/ NF/ Ozone

Cost

Sustainability

Overall score

Ranking

0.18

0.13

0.58

1

0

0

0.50

2

0.32

0.13

0.11

3

0.18

0.13

0.83

1

0

0

0.80

2

0.32

0.13

0.05

3

0.18

0.13

0.32

1

0

0

0.20

2

0.32

0.13

0.18

3

AOPs: advanced oxidation processes; BIEX: biological ion exchange; MF: microfiltration; NF: nanofiltration; PAC: powdered activated carbon; RO: reverse osm32osis; UV: ultraviolet light.

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critical contaminants. Based on the types of the critical contaminants, different treatment alternatives can be proposed and evaluated based on the literature data in previous studies. The most feasible treatment train for the pretreatment of MAR with reclaimed water can be recommended. According to the results, the following conclusions can be drawn:
Different types of treatment alternatives can be proposed according to the categories of the critical contaminants for MAR with reclaimed water.
The removal of the critical contaminants by the proposed treatment alternatives can be studied and evaluated based on data available in published studies. Based on these data, a treatment efficiency credit can be assigned to each treatment option. Where different treatment efficiencies have been reported, judgement is used in assigning the credit.
Alternative treatment trains can be proposed through a combination of different treatment units.
Based on data from a case study, the most feasible treatment train for the pretreatment of MAR with reclaimed water for this situation was determined to be MF/RO/UV-based AOP. Acknowledgements Funding for this project was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) in the form of an Industrial Research Chair in Water Treatment at the University of Waterloo (https://uwaterloo.ca/nserc-chair-water-treatment/). At the time this research was conducted, Chair partners were: Associated Engineering Group Ltd., the cities of Barrie, Brantford, Guelph, Hamilton and Ottawa, Conestoga-Rovers & Associates Limited, EPCOR Water Services, GE Water & Process Technologies Canada, Lake Huron and Elgin Area Water Supply Systems, the Ontario Clean Water Agency (OCWA), the Regions of Durham, Halton, Niagara and Waterloo, RAL Engineering Ltd., Toronto Water, and the Walkerton Clean Water Centre. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.chemosphere.2019.124886. References Abtahi, S.M., Marbelia, L., Gebreyohannes, A.Y., Ahmadiannamini, P., JoannisCassan, C., Albasi, C., de Vos, W.M., Vankelecom, I.F.J., 2019. Micropollutant rejection of annealed polyelectrolyte multiplayer bases nanofiltration membranes for treatment of conventionally-treated municipal wastewater. Separ. Purif. Technol. 209, 470e481. Abu-Arabi, M.K., Emeish, S., Hudaib, B.I., 2013. Chloride removal from Eshidiya phosphate mining wastewater. Desalin. Water Treat. 51 (7e9), 1634e1640. Acero, J.L., Benitez, F.J., Leal, A.I., Real, F.J., Teva, F., 2010. Membrane filtration technologies applied to municipal secondary effluents for potential reuse. J. Hazard Mater. 177 (1e3), 390e398. Adhoum, N., Monser, L., 2004. Removal of phthalate on modified activated carbon: application to the treatment of industrial wastewater. Separ. Purif. Technol. 38 (3), 233e239. Adin, A., 2003. Slow granular filtration for water reuse. Water Sci. Technol. Water Supply 3 (4), 123e130. €fer, A.I., 2011. Removal of pharmaceuticals and Al-Rifai, J.H., Khabbaz, H., Scha endocrine disrupting compounds in a water recycling process using reverse osmosis systems. Separ. Purif. Technol. 77 (1), 60e67. Albergamo, V., Blankert, B., Cornelissen, E.R., Hofs, B., Knibbe, W.-J., van der Meer, W., de Voogt, P., 2019. Removal of polar organic micropollutants by pilotscale reverse osmosis drinking water treatment. Water Res. 148, 535e545. Alharbi, S.K., Kang, J., Nghiem, L.D., van de Merwe, J., Leusch, F.D., Price, W.E., 2017. Photolysis and UV/H2O2 of diclofenac, sulfamethoxazole, carbamazepine, and trimethoprim: identification of their major degradation products by ESI-LC-MS and assessment of the toxicity of reaction mixtures. Process Saf. Environ. 112 (Part B), 222e234. Altmann, J., Ruhl, A.S., Zietzschmann, F., Jekel, M., 2014. Direct comparison of

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