Inactivation kinetics of antibiotic resistant Escherichia coli in secondary wastewater effluents by peracetic and performic acids

Journal Pre-proof Inactivation kinetics of antibiotic resistant Escherichia coli in secondary wastewater effluents by peracetic and performic acids Neus Campo, Cecilia De Flora, Roberta Maffettone, Kyriakos Manoli, Siva Sarathy, Domenico Santoro, Rafael Gonzalez-Olmos, Maria Auset PII:

S0043-1354(19)31001-2

DOI:

https://doi.org/10.1016/j.watres.2019.115227

Reference:

WR 115227

To appear in:

Water Research

Received Date: 18 August 2019 Revised Date:

22 October 2019

Accepted Date: 23 October 2019

Please cite this article as: Campo, N., De Flora, C., Maffettone, R., Manoli, K., Sarathy, S., Santoro, D., Gonzalez-Olmos, R., Auset, M., Inactivation kinetics of antibiotic resistant Escherichia coli in secondary wastewater effluents by peracetic and performic acids, Water Research (2019), doi: https:// doi.org/10.1016/j.watres.2019.115227. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

1

Inactivation Kinetics of Antibiotic Resistant Escherichia coli in

2

Secondary Wastewater Effluents by Peracetic and Performic Acids

3 4

Neus Campo*1, Cecilia De Flora*1, Roberta Maffettone2,3, Kyriakos Manoli2,3, Siva Sarathy2,3,

5

Domenico Santoro2,3, Rafael Gonzalez-Olmos1, Maria Auset§1

6 7

1

8

Barcelona, Spain

9

2

Trojan Technologies, London, ON N5V4T7, Canada

10

3

Department of Chem. and Biochem. Eng., Western University, London, ON N6A5B9, Canada

11

*Both authors contributed equally to this manuscript

12

§

Department of Bioengineering, IQS-School of Engineering, Ramon Llull University, 08017

Corresponding author

13 14

ABSTRACT

15

While disinfection processes have been central for public health protection, new concerns

16

have been raised with respect to their ability to control the spread of antibiotic resistance in the

17

environment. In this study, we report the inactivation kinetics by peracetic and performic acids of a

18

typical indicator, Escherichia coli and its corresponding antibiotic-resistant subpopulation, in

19

secondary settled wastewater effluent. Performic acid always showed greater inactivation efficiency

20

than peracetic acid, whether or not the indicator was Ampicillin-resistant. Observed inactivation

21

data, fitted with an exposure-based inactivation model, predicted very well the inactivation profile

22

of both total and ampicillin resistant Escherichia coli. Notably, the antibiotic resistance percentage

23

decreased significantly in treated wastewater compared to untreated wastewater thus making the

24

peracid-based disinfection processes beneficial in controlling antibiotic resistance in secondary 1

25

settled wastewater. Moreover, the minimum inhibitory concentration values remained unchanged.

26

Finally, antibiotic-resistant-specific inactivation kinetics were used to predict the disinfection

27

efficiency in continuous-flow reactors under ideal and non-ideal hydraulics thus providing useful

28

information for future design and operation of disinfection process in antibiotic-resistance

29

controlling mode.

30 31

KEYWORDS: antibiotic resistant bacteria; inactivation kinetics, Escherichia coli, peracetic acid;

32

performic acid; secondary effluent

33

34

1. Introduction

35

The excessive use of antibiotics in human, animal and plant therapies has considerably

36

reduced their effectiveness and caused the spread of antibiotic-resistant bacteria (ARBs) in the

37

environment (He et al., 2019) by facilitating the transfer of resistance genes between non-

38

pathogenic ARBs to pathogenic bacteria (Manaia et al., 2010; Marti and Balcázar, 2013, Yoon et

39

al., 2017). As a result, the World Health Organization (WHO) has been paying increased attention

40

to this issue by establishing antibiotic resistance as a critical global public health issue of this

41

century (Sharma et al., 2016).

42

Some environmental compartments offer ideal conditions for the spread of ARBs in the

43

environment (Rizzo et al., 2013; Roca et al., 2015; Manaia et al., 2016; McConnell et al., 2018).

44

Among those, wastewater has been reported to be an important vehicle of dissemination through

45

which resistance genes are introduced into natural bacterial ecosystems. For instance, hospital

46

wastewater, which represents a concentrated stream of antibiotic-resistant organisms (Baquero

47

2008), has been documented as hotspot for the release of antibiotics and ARBs into the environment

48

(Ferro et al., 2016; Luprano et al., 2016, Makowska et al., 2016).

2

49

Irrespective of the process used, wastewater disinfection, i.e. the final treatment step in

50

wastewater treatment, is a unit process designed to inactivate pathogenic microorganisms sensitive

51

to biocides rather controlling antibiotic resistant organisms. Consequently, inappropriate amounts of

52

disinfectants could inadvertently be selected for the ARBs, as reported in Rizzo et al. (2013).

53

ARBs have shown to present good tolerance to conventional chlorine disinfection (Liu et al.,

54

2018). Several studies indicated that chlorination of wastewater does not eliminate the risk of

55

antibiotic resistance release and proliferation in receiving bodies (Yuan et al., 2015). Templeton et

56

al. (2009) demonstrated a major resistance to chlorine treatment for trimethoprim-resistant

57

Escherichia coli. Furthermore, the inactivation caused by chlorine-based disinfecting agents such as

58

sodium hypochlorite could induce the release of antibiotic resistance genes from damaged cells

59

(Turolla et al., 2018, Liu et al 2018) which will be later acquired by other bacteria becoming ARBs.

60

A potential relationship between amount of chlorine and ARBs formation was suggested (Di Cesare

61

et al. 2016). Additional studies have demonstrated an increase in the ratio of ARBs over the total

62

population of the same strain after chlorination treatment. Specifically, Murray et al. (1984) and

63

Staley et al. (1988) reported an increased percentage of tetracycline-resistant bacteria after

64

chlorination.

65

Peracetic acid (PAA, CH3CO3H) is an emerging chemical disinfectant with good

66

antimicrobial properties against a wide range of microorganisms (e.g., coliform bacteria) (Santoro

67

et al 2007, Turolla et al., 2017) and negligible formation of disinfection by-products (Dell’Erba

68

2007; Nurizzo et al 2005; Crebelli et al 2005). During disinfection, PAA acts mainly by oxidizing

69

sulfhydryl and sulfur bonds in proteins and enzymes (Biswal et al., 2014). PAA inactivation

70

mechanism mainly involves the disruption of the chemiosmotic function of the microbial cell,

71

caused by rupture or dislocation of the cell walls altering lipoprotein cytoplasmic membrane and

72

transport systems. Secondary inactivation mechanisms could also contribute to the overall

3

73

inactivation, via the formation of hydroxyl radicals (·OH), Lubello et al. (2002), and peroxide

74

radicals (oxygen – oxygen bond), Kitis, (2004).

75

Similarly, to PAA, performic acid (PFA, CH2O3), i.e. the organic peroxide of formic acid, is

76

a highly effective disinfectant recently tested for wastewater treatment (Gehr et al., 2009). PFA is an

77

unstable chemical that needs to be generated on site as a quaternary equilibrium mixture of PFA,

78

formic acid (FA), hydrogen peroxide (H2O2) and water. Generally, PFA provides a faster and more

79

effective disinfection compared to PAA for municipal wastewater (Karpova et al., 2013, Ragazzo et

80

al., 2013) and combined sewer overflow (Chhetri et al., 2015). To date, PFA has not been assessed

81

against the inactivation of ARBs.

82

Bacteria could develop resistance to ampicillin via the production of b-lactamases that break

83

the b-lactam ring of the antibiotic and the production of modified PBPs that are not recognized by

84

ampicillin (Godfrey 1981). Ampicillin (AMP) is a widely used chemical against Gram-positive and

85

Gram-negative bacteria, belonging to the class of b-lactams antibiotics. It acts on peptidoglycan,

86

which prevents the formation of cross-linkages of the cell wall by transpeptidase enzymes called

87

penicillin binding proteins (PBPs). PBPs remove a terminal D-alanine residue from the

88

pentapeptide, releasing energy that is used for cross-linking in peptidoglycan. Ampicillin inhibits

89

PBPs by a competitive mechanism, because it resembles the D-ala-D-ala dimer (Malouin et Bryan,

90

1986).

91

Irrespective of the biocide considered, available inactivation kinetics, which are central in

92

the design of disinfection processes, are only limited to the total bacterial population (which is

93

constituted by both ARBs and non-ARBs). While many antibiotic resistant bacteria have been

94

reported, E. coli is considered the most representative organism to study the behavior of such

95

resistant organisms (Guo et al., 2013).

96

In this work, two categories of bacteria were used, herein defined as total E. coli (a

97

population that represents the entire group of E. coli, both sensitive and resistant to the selected 4

98

antibiotic) and the “AR sub-population” indicating the sub-population of E. coli resistant to

99

ampicillin (AmpR E. coli). As such, we aimed at establishing the inactivation kinetics for both total

100

E. coli and AmpR E. coli against PAA and PFA, with the objective of understanding if such kinetics

101

differs from each other and how much are impacted by water quality. The microbial inactivation

102

parameters, estimated through modeling of batch experimental data, were used to simulate the

103

performance of ARB inactivation in continuous-flow system under realistic wastewater treatment

104

conditions, including ideal and non-ideal reactor hydraulics. Such simulations could be used to

105

guide the development and the adoption of emerging disinfectants such as PAA and PFA aiming to

106

the control of pathogens as well as the release in the environment of antibiotic resistant bacteria.

107 108

2. Materials and methods

109

2.1. Wastewater characteristics

110

Secondary effluent was collected from a municipal wastewater treatment plant (WWTP) in

111

Spain (373,000 equivalents habitant).

112

characteristics of this effluent.

113

Table 1 reports the chemical and microbiological

Table 1. Main water quality characteristics of the secondary effluent (average of 3 measurements). Parameter

Value

pH

7.5

Chemical Oxygen Demand, COD (mg/L)

44.3

Nitrates, NO3-–N (mg N/L)

8.5

Nitrites, NO2-–N (mg N/L)

0.7

Ammonia, NH4+–N (mg N/L)

2.2

Total Solids, TS (mg/L)

820

Total Suspended Solids, TSS (mg/L)

20

Total Dissolved Solids, TDS (mg/L)

800 5

Total E. coli (CFU/100 mL)

5.9x104

E. coli AMP 16 mg/L resistant (CFU/100 mL)

2.1x104

114 115

2.2. Experimental procedures

116

2.2.1 Culture media preparation

117

Chromocult coliform agar (CCA) (PanReac AppliChem) was used for E. coli enumeration.

118

AmpR E. coli quantification was performed with CCA supplemented with 16 mg/L of ampicillin

119

sodium salt (Sigma-Aldrich). This concentration was chosen based on the minimum inhibitory

120

concentration (MIC) of the antibiotic from EUCAST (2018).

121 122

2.2.2 Inoculum and sample preparation

123

A volume of 0.1 mL of wastewater effluent was placed on CCA and CCA with AMP to

124

select total and AmpR E. coli, respectively, and incubated overnight at 37 °C. Grown E. coli and

125

AmpR E. coli colonies were picked up, re-cultivated in MacConkey Broth (MCB) (PanReac

126

AppliChem) and MCB with 16 mg/L of AMP and incubated for 24h at 37°C in stirring at 250 rpm.

127

Subsequently, the suspension was centrifuged at 4500 rpm for 10 minutes and diluted with 0.9%

128

NaCl solution to achieve 1.2x106 CFU/100 mL (Abs600nm=0.12) both for E. coli and AmpR E. coli.

129

Both the saline solution (NaCl 0.9%) and the secondary effluent were inoculated with ARBs

130

suspension to augment the ARBs initial concentration, which allowed for a more precise

131

characterization of their inactivation kinetics.

132 133

2.2.3 Disinfection tests

134

The inactivation profiles of total and AmpR E. coli were evaluated both in secondary effluent

135

(pH 7.5±0.1) and, as a comparison, in NaCl 0.9% solution (0.9% saline solution) (pH 6.2±0.1) with

136

no bioavailable carbon and free of suspended solids. In this medium, bacteria remained stable

137

because of their totally compatible osmotic cell pressure. NaCl 0.9 % was selected among the 6

138

various saline solutions as it was successfully been used in previous studies (Keser et al., 2018;

139

López-Briz et al., 2018), including those with a disinfection focus (Oguma et al., 2013; Ghaseminan

140

et al., 2017).

141

For the PAA disinfection tests, a solution of 15% PAA was used (PanReac AppliChem).

142

PFA solution was prepared immediately before the disinfection tests by mixing 85 wt% formic acid

143

(PRS Panreac) and 30 wt% H2O2 (PanReac AppliChem) in the presence of 96 wt% H2SO4

144

(PanReac AppliChem) as catalyst, according to Gehr et al. (2009). PFA concentration in the final

145

solution was between 9-12 wt%. Two initial concentrations (1-2 mg/L for PAA and 0.5-1 mg/L for

146

PFA in 0.9% saline solution; 3-4 mg/L for PAA and 1-2 mg/L for PFA in wastewater) were used.

147

Residual disinfectant measurements and microbial analysis were performed on samples

148

withdrawn at defined contact times and transferred to sterile vials, pre-loaded with 0.1 M Na2S2O3

149

(PanReac AppliChem) to quench disinfectant residues. Residual disinfectants were measured by the

150

colorimetric DPD (N,N-diethyl-p-phenylenediamine) method using a commercial Spectroquant kit

151

(Merck, Darmstadt, Germany) allowing a measure in a range of 0.01 to 8.00 mg/L PAA, and 0.01 to

152

6.50 mg/L PFA.

153

In order to evaluate the PAA and PFA disinfection efficacy on AmpR E. coli, antibiotic

154

resistance was also investigated by quantifying the MIC before and after the treatment, to

155

understand if the resistance remained constant or underwent changes during the disinfection

156

process.

157 158

2.3. Analytical methods

159

2.3.1 Bacterial enumeration

160

Total and AmpR E. coli microbial analyses were performed in triplicate, according to the

161

standard membrane filtration method (9222G) (APHA et al., 2017). Each sample was filtered

162

through 0.45 µm cellulose nitrate filters (Sartorius Stedim), placed onto CCA and CCA

163

supplemented with 16 mg/L of AMP and incubated at 37 °C for 18-24 h. 7

164 165

2.3.2 AmpR E. coli assay

166

E-test with AMP antibiotic strips (OXOID M.I.C. Evaluator) was performed to measure

167

MIC values. The strips were marked by a growing antibiotic concentration that corresponds to the

168

MICs. In the ampicillin strips, the MIC range was 0.015–256 µg/mL. A calibration line with

169

McFarland turbidity standards was constructed for the test. The standard 0.5 McFarland was

170

prepared with 0.5 mL of 0.048 M BaCl2 1.175% wt /vol BaCl2 .2H2O (Panreac) and 99.5 mL of

171

0.18 M H2SO4 1% vol/vol (Panreac AppliChem) continuously mixing. By reading the

172

spectrophotometer, the 0.5 MacFarland corresponded to the absorbance at 625 nm of 0.10.

173

AmpR E. coli colonies grown on CCA supplemented with 16 mg/L of AMP were placed in

174

0.9% saline solution until the measured turbidity reached the value of 0.5 McFarland, which

175

corresponded to a concentration of 1.5×108 CFU/mL. The solutions were spread homogeneously

176

with sterile tampon in Muller-Hinton agar plates (Merck KGaA) and AMP strips were placed on the

177

surface of the media. The plates were incubated for 24 h at 37 °C. The procedure was repeated in

178

duplicate. AmpR E. coli growth became visible near the inhibition halo formed around the strips.

179

The point at which the inhibition eclipse began to intersect the strips corresponded to the value of

180

the MIC.

181 182

2.4. Models

183

2.4.1. Chemical disinfectant decay model

184

When a chemical disinfectant (i.e. PAA or PFA) is added to wastewater, it initially

185

undergoes an instantaneous demand (D) followed by a first-order decay (k) (Haas et Joffe, 1994;

186

Haas et Karra, 1984) described by Eq. 1:

187

C(C , D, k, t) = (C − D) ∙ e

(1)

188

The concentration of disinfectant, C (mg/L), at time, t (min), is a function of the initial

189

concentration of disinfectant, C0 (mg/L), the disinfectant initial demand, D (mg/L), and the first8

190

order rate constant, k (min-1). The integral estimate of the time-dependent residual disinfectant

191

concentration (ICT dose in mg·min/L), defined as the area under the disinfectant demand/decay

192

curve, is given by Eq. 2 (Santoro et al., 2015):

193

ICT(C , D, k, t) =

(

)

∙ 1−e

(2)

194

Eq. 2 is obtained by integrating Eq. 1, and it can be applied to calculate the ICT dose in

195

batch or ideal plug flow reactor (PFR). For non-ideal continuous-flow reactor, where the residence

196

time distribution (RTD) in the reactor can be described by a number (n) of continuous stirred tank

197

reactors (n-CSTRs) in series, the ICT dose is given by Eq. 3 (Manoli et al., 2019):

198

ICT

(n, V, Q) =

(

" #

'

)∙ ! · %& % " #

∙)! ∙ %& *

'

']

(3)

199

Eq. 3 is obtained by integrating the RTD for n-CSTRs times the ICT for ideal reactor (Eq.

200

2). Since C0, D, and k are determined using Eq. 2, the ICT dose for n-CSTRs is a function of the

201

continuous-flow (hydraulic) parameters, i.e. the number of CSTRs in series, n, the volume of the

202

reactor, V (L), and the water flow, Q (L/min).

203 204

2.4.2. Microbial inactivation kinetic model

205

It has been shown that the removal of bacteria includes an initial fast inactivation of

206

dispersed microbes followed by a slower inactivation of particles-associated microbes also known

207

as tailing effect (Santoro et al., 2015). This biphasic behavior of microbial inactivation can be

208

described by the sum of 2 exponential terms (double exponential model) each corresponding to

209

dispersed and particles-associated phases. Recently, a term (m) was added to the double exponential

210

model to account for shoulder effects observed at low ICT doses for the inactivation of bacteria by

211

disinfectants (Manoli et al., 2019). The ICT-based double exponential model is given by Eq. 4:

212

N(N , β, k - , k . , m, ICT) = N (1 − β)e

01

2

+ N (β)e

9

41

(4)

213

where N0 and N are the initial and final concentrations of culturable microbes, respectively, in

214

colony-forming units (CFU)/100 mL. Apart from N0, N is a function of the fraction of particles-

215

associated microbes, β, the ICT dose dependent inactivation rate constants for dispersed, kd ((L mg-1

216

min-1)m), and particles-associated microbes, kp (L mg-1 min-1), the shoulder-effect parameter, m, and

217

the ICT dose.

218

For ideal PFR or batch reactors, a time-based double exponential model (Eq. 5) can be

219

obtained by substituting ICT with Eq. 2, in the ICT-based double exponential model (Eq. 4):

220

N(N , β, k - , k . , m, t) = N (1 − β)e

221

0∙

(5 67) ∙ 8

9 :68; <

2

+ N (β)e

4∙

(5 67) ∙ 8

9 :68; <

(5)

In the case of a continuous-flow non-ideal reactor, the ICT in Eq. 4 should be substituted by

222

the ICTn-CSTRs (Eq. 3). This results in a time-based double exponential reactor model (Eq. 6):

223

N N , β, k . , k - , m, t, n, V, Q =

224

N (1 − β)e

?(5 67)∙ !" ·8%@'%' 6'' ]C # > B 0∙ ' > B " 8∙)! ∙8%@'* # = A

2

+ N (β)e

?(5 67)∙ !" ·8%@'%' 6'' ]C # B 4 ∙> ' > B " 8∙)! ∙8%@'* # = A

(6)

225

Eq. 6 can be used to predict the concentration of microbes at the outlet (N) of a continuous-

226

flow, non-ideal reactor, as a function of the concentration of microbes at the inlet of the reactor

227

(N0), the microbial inactivation kinetic parameters (i.e. β, kd, kp, m), and the continuous-flow

228

(hydraulic) parameters (i.e. n, V, and Q).

229 230

2.5. Antibiotic resistant percentage

231

The antibiotic resistant percentage (AR %) was evaluated for the initial indigenous bacteria

232

and the bacteria that survived disinfection by PAA and PFA. The AR % was estimated according to

233

the Eq. 7 (Novo et Manaia, 2010):

234 235

NOPQRS TSRRU (VOWX PYWOQOZWO[)

% FGHIHJKLMG = NOPQRS TSRRU (VOWXZ\W PYWOQOZWO[) ] 100

236 10

(7)

237

3. Results and discussion

238

3.1. Decomposition of PAA and PFA

239

Experimental and model predicted peracid decay in secondary effluent and in 0.9% saline

240

solution are shown in Figure 1. Both PAA and PFA followed first-order decay kinetics (k) with

241

initial demand (D). In secondary effluent wastewater, PAA suffers a decomposition of 21% in 32

242

minutes, at both initial concentrations of 3 and 4 mg/L. In the case of PFA, a reduction of 29% and

243

35% in 15 minutes, at initial concentrations of 1 and 2 mg/L, respectively, was observed. The

244

determined k values were higher for PFA than PAA (Table 2). The faster decomposition of PFA

245

compared with PAA was also observed in combined sewer overflow disinfection experiments

246

(Chhetri et al., 2014).

247

In 0.9% saline solution, peracids degradation is lower than in wastewater. For instance, PAA

248

decreased 9% in both concentrations, and PFA decreased 10% at 0.5 mg/L and 20% at 1 mg/L. The

249

higher decomposition in wastewater was expected due to side reactions of disinfectants with water

250

contaminants. The factors affecting the decomposition rate of peracids in wastewater are its

251

temperature, pH, the amount of organic material, the presence of suspended solids or transition

252

metal ions, salinity and water hardness (Luukkonen and Pehkonen, 2016, Sarathy et al., 2016).

253

11

254 255

Figure 1. Decomposition of PAA and PFA in secondary effluent wastewater (a-b) and in 0.9 % saline

256

solution (c-d).

257

PAA and PFA decompositions were modeled using Eq. 1. Determined D and k are given in

258

Table 2. The model could predict well the decomposition of PAA and PFA in 0.9% saline solution

259

and wastewater (Figure 1).

260 261

Table 2. Initial demand (D) and first-order rate constant (k) for the decomposition of PAA and PFA. PAA [PAA]

D

k

(mg/L)

(mg/L)

(min-1)

0.9% saline

1

0.045

0.002

solution

2

0.169

0

12

Secondary

3

0.024

0.006

effluent

4

0.294

0.006

[PFA]

D

k

(mg/L)

(mg/L)

(min-1)

0.9% saline

0.5

0.014

0.007

solution

1

0.000

0.014

Secondary

1

0.163

0.020

effluent

2

0.077

0.023

PFA

262 263 264

3.2. Inactivation of total and AmpR E. coli by PAA and PFA

265

The inactivation kinetics of total and AmpR E. coli by PAA and PFA in secondary effluent

266

and in 0.9% saline solution are shown in Figures 2 and 3, respectively. The ICT were calculated

267

using Eq. 2. In secondary effluent, the initial concentration was 6.0 and 5.6 log unit of total and

268

AmpR E. coli respectively. A complete inactivation by PAA was observed at ICT values of ~80 mg

269

min/L and ~55 mg min/L, for total and AmpR E. coli respectively, indicating faster inactivation of

270

AmpR E. coli than total E. coli. Shoulder effects were seen in both cases (Figure 2a). For example,

271

AmpR E. coli removal of half log was observed with PAA ICT=15.8 mg min/L, whereas almost one

272

log inactivation was obtained at a PAA ICT of ~30 mg min/L. Results are in agreement with

273

previous studies in wastewater, although the concentrations of disinfectant used were higher than

274

ours (PAA=2-8 mg/L; time=27-60 min; log reduction=2-4.4) (Dell’Erba et al., 2004; Koivunen and

275

Heinonen-Tanski, 2005a; Caretti et Lubello, 2003).

276

In 0.9% saline solution, shoulder effects were seen at PAA ICT dose up to 8 mg min/L,

277

being bacteria much more sensitive to inactivation than in wastewater, where shoulder effects were

278

observed up to ICT of 20 mg min/L (Figure 2). Similar inactivation curve of total and AmpR E. coli 13

279

by PAA was observed in 0.9% saline solution. At PAA ICT of 29.0 mg min/L, 5 log removal was

280

achieved.

281 282

Figure 2. Inactivation of total E. coli and AmpR E. coli by PAA in secondary effluent wastewater (a) and

283

0.9% saline solution (b).

284 285

In the case of PFA, a 4-log removal of total E. coli was observed at ICT of 15 mg min/L, in

286

secondary effluent (Figure 3a). For AmpR E. coli, 1 and 2 log reductions were achieved at ICT of

287

4.7 and 6.8 mg min/L respectively, and abatement values below 100 CFU/100 mL were observed at

288

PFA ICT of 10 mg min/L. The faster inactivation of AmpR E. coli compared to total E. coli, by PFA

289

in wastewater, is consistent with their inactivation by PAA (Figures 2a and 3a). In 0.9% saline 14

290

solution, the inactivation of AmpR E. coli was similar to the total E. coli, which is consistent with

291

what was observed in the case of PAA (Figures 2b and 3b). A 4-log removal was observed at PFA

292

ICT of 4.3 mg min/L. All PFA results showed that most of the bacteria removal took place at ICT

293

of <10 mg min/L. This agrees with other studies where the majority of microorganisms were

294

efficiently inactivated at a concentration of PFA of 2 ppm in 5 minutes, corresponding to an ICT of

295

<10 mg min/L (Karpova et al., 2013).

296

Clearly, PFA requires lower ICT compared to PAA for the same bacteria log reduction, in

297

both wastewater and 0.9% saline solution (Figures 2 and 3). A previous study comparing the

298

disinfecting power of PAA and PFA reported that at the same concentration (1.5 mg/L) and contact

299

time (60 min), 1.6 and 3.5 log reductions of total E. coli were achieved respectively (Luukkonen et

300

al., 2015). Similar observation of faster PFA inactivation compared to PAA was reported by other

301

research groups for total E. coli and Clostridium tyrobutyricum (Mora, 2018; Ragazzo et al., 2013).

302

The aforementioned studies focused on the total E. coli. To the best of our knowledge, this is the

303

first time that PAA and PFA are compared in terms of inactivating AmpR E. coli.

304

15

305 306

Figure 3. Inactivation of total E. coli and AmpR E. coli by PFA in secondary effluent wastewater (a) and

307

0.9% saline solution (b).

308 309

Inactivation curves were characterized by three phases. Firstly, a lag phase at the beginning

310

of the process which represents the shoulder trend, indicating an initial resistance to PAA diffusion

311

and bacteria inactivation, likely due to the action of bacteria antioxidant enzymes that form the first

312

line of defense against free radicals. Secondly, an exponential decay with maximum inactivation

313

rate where the disinfectant penetrates into microbial pathogens, generating disruption of cell

314

membranes and blockage of enzymatic and transport systems in the microorganisms. Finally, an

315

asymptotic deceleration phase, called tailing, where the inactivation rate decreased (Mezzanotte et

316

al., 2007). 16

317

Shoulder effects were observed in all cases (Figures 2 and 3). However, such effect was less

318

pronounced for 0.9% saline solution compared to secondary effluent. Some studies have concluded

319

that disinfection is dependent on the water quality due to the presence of organic matter in different

320

proportions. The 0.9% saline solution and secondary effluent contain different amounts of organic

321

matter, solids and initial concentration of bacteria and, for that reason, in secondary effluent, a

322

higher concentration of disinfectant is required to obtain the same disinfection efficiency as in 0.9%

323

saline solution (Koivunen et Heinonen-Tanski, 2005b; Karpova et al., 2013). Likewise, the larger

324

shoulder effect in secondary effluent was consistent with the inactivation mechanism of the

325

chemical disinfectant that had to diffuse firstly through particles and secondly through the cell

326

membrane.

327

During tailing phase, inactivation decreased leaving the bacteria that survived to disinfection

328

at even higher ICT. This occurrence could depend on suspended solids and microbial clumping that

329

protect microbes (Koivunen et Heinonen-Tanski, 2005b). According to Kacem et al. 2014, the tail

330

could be related to the development of bacterial resistance during the disinfection treatment with a

331

phenomenon of inhibition produced by the competitive action of the organic products released into

332

the environment. Since the 0.9% saline solution is free of suspended solids, it is hypothesized that

333

the tailing trend is caused only by an aggregating bacterial tendency (Bohrerova and Linden, 2006;

334

Mir et al., 1997). The bacteria located on the outside of the aggregate were more sensitive to

335

disinfectant's attack and consequently they were inactivated faster, whereas the bacteria in the

336

center of the aggregate have a higher survival rate and therefore these bacteria can be found and

337

quantified during the tailing phase (Domínguez Henao et al., 2018).

338

In wastewater kinetic curves, total E. coli and AmpR E. coli showed a starting tailing at PAA

339

ICT of 80 and 60 mg min/L respectively, while in 0.9% saline solution a final tailing phase was

340

observed at PAA ICT slightly higher than 20 mg min/L. With PFA, the tailing phase began in an

341

ICT range between 10-15 mg min/L in wastewater compared with ICT values of ~7 mg min/L in

17

342

0.9% saline solution. In general, lower ICT was required in 0.9% saline solution compared to

343

secondary effluent to achieve the same inactivation of both total and AmpR E. coli.

344

As mentioned, the disinfection is dependent on the water quality (i.e., pH, bacterial

345

aggregation and suspended solids), that characterize the two aqueous matrices. The pH value of

346

secondary effluent was about 7.5. The initial pH of 0.9% saline solution was 6.2; adding PAA at 1

347

and 2 ppm, reduced the pH at the values of 5.3 and 4.8 respectively, while the pH decreased to 4.9

348

and 4.5 respectively with 0.5 and 1 mg/L of PFA. Lower pH of 0.9% saline solution probably

349

caused the enhancement in terms of inactivation. The presence of suspended solids in wastewater

350

may also play a role in the slower inactivation in wastewater than 0.9% saline solution in the

351

absence of suspended solids. Domínguez Henao et al. (2018) reported negative impact of suspended

352

solids on disinfection of total E. coli by PAA because of their protective role on bacteria.

353

In all cases, the double-exponential model (Eq. 4) in the ICT domain, fitted very well the

354

data (Figures 2 and 3). The determined microbial inactivation kinetic parameters are given in Table

355

3. The values of β and m agreed with previous results of Sarathy et al. (2016): β=0.0011 and

356

m=2.27. Table 3. Kinetic parameters for PAA and PFA.

357

PAA kd

kp

([L/(mg min)]m)

(L/(mg min))

β

m

0.9% saline

Total E. coli

0.884·10-5

0.126·10-5

0.000

3.464

solution

AmpR E. coli

0.850·10-5

0.600·10-2

0.000

2.222

Secondary

Total E. coli

0.148·10-4

0.485·10-3

0.000

2.305

effluent

AmpR E. coli

0.200·10-3

0.127·10-3

0.000

2.831

kd

kp

m

PFA β

18

([L/(mg min)]m)

(L/(mg min))

0.9% saline

Total E. coli

0.367·10-4

9.06·10-1

0.545

1.607

solution

AmpR E. coli

0.170·10-4

15.44·10-1

0.050

1.080

Secondary

Total E. coli

0.934·10-3

2.7·10-2

0.117

2.284

effluent

AmpR E. coli

0.533·10-4

2.9·10-2

0.030

2.615

358 359 360

3.3. Inactivation of indigenous AmpR E. coli by PFA

361

Experiments with real concentration of indigenous AmpR E. coli were performed to

362

investigate their inactivation by PFA and to rule out a possible effect of the inoculum. Results of

363

inactivation of indigenous total and AmpR E. coli are presented in Figure 4. Initial indigenous total

364

and AmpR E. coli concentrations were 4.8 and 4.4 log unit respectively. The kinetic curves were

365

represented as a function of time (Figures 4a and 4c) and as a function of ICT (Figures 4b and 4d).

366

Indigenous bacteria followed the same trend as the inoculated population.

367

368 19

369

Figure 4. Inactivation of indigenous total E. coli and AmpR E. coli by PFA in secondary effluent wastewater.

370 371

Two different curves were observed when the concentration of total or AmpR E. coli was

372

plotted as a function of contact time, for two different concentrations of PFA (Figures 4a and 4c).

373

When the x-axis was corrected for ICT dose (Eq. 2), a single curve could fit the data at both initial

374

concentrations of PFA (i.e. ICT dose response) (Figures 4b and 4d). The same kinetic parameters

375

were used for time-curves (Eq. 5) and ICT-based curves (Eq. 4) (Table 4).

376

Compared to PFA tests with inoculated E. coli, the β and kd values were greater, while kp

377

and m were lower. This indicates that in real wastewater there was a higher particle-associated

378

fraction of total and AmpR E. coli (β) and lower shoulder effects (m). It could be hypothesized that

379

the smaller m value was associated with less bacterial aggregation, considering that in the

380

inoculated wastewater effluent the order of magnitude of bacteria was 106 CFU/100 mL, compared

381

to only 104 CFU/100 mL of indigenous bacteria. In any case, through this experimental test it could

382

be affirmed that indigenous bacteria had the same behavior as those inoculated in wastewater. Both

383

indigenous and inoculated bacteria can be fitted to the ICT model and this was consistent for both

384

total and AmpR E. coli. The ICT-based kinetic model could predict the inactivation of E. coli as a

385

function of time as well as a function of ICT.

386

To guarantee an adequate performance of the disinfection process it is important to check

387

the quality of the effluent and the dose of disinfectant to be used according to the contact time. The

388

use of the model can be a fundamental control strategy that responds to variations during

389

disinfection in real-time (Manoli et al., 2019). ICT model can have practical implications in

390

designing a disinfection system. For example, the ICT dose response can be used to select an ICT to

391

achieve desired disinfection performance, i.e. final concentration of microbes.

392 393

R Table 4. Kinetic parameters for inactivation of indigenous total and Amp E. coli in wastewater by PFA.

20

kd

kp

([L/(mg min)]m)

(L/(mg min))

Β

m

Total E. coli

0.001

0.562

0.090

1.119

AmpR E. coli

0.002·10-1

0.382

0.001

1.353

394 395 396

3.4. Resistance assay of E. coli

397

In secondary effluent, the AR% of indigenous E. coli was calculated as a ratio of number of

398

bacteria grown while exposed to 16 mg/L of ampicillin to bacteria grown without antibiotic. The

399

AR% was 38.6%, which agrees with previous studies reporting AR% from 34-47% (Luczkiewicz et

400

al., 2010; Huang et al., 2012). After 16 min of contact time, the AR% of indigenous bacteria

401

decreased from 38.6% to 30.4% and 25% for 1 and 2 mg/L of PFA respectively.

402

The inoculum increased the number of total E. coli and AmpR E. coli from 4.4-4.8 to up to

403

5.6-6.0 log unit for the non-inoculated and the inoculated secondary effluent, respectively. Due to

404

the increased amount of AmpR E. coli, an increase of percentage of residual resistance was observed

405

for the augmented wastewater samples with values between 40% and 88 %. In the saline solution,

406

the inoculation led to AR% of ~ 85 % in the all tested samples.

407

Although a variation of initial AR% was achieved in the inoculated samples, a reduction in

408

%AR between 68. 3 % and 93.3 % for PAA, and greater than 85.4 % for PFA was observed in the

409

points corresponding to the tailing phase (Figure 5). For example, in wastewater, the AR%

410

decreased from 40% to 2.7%, at 3 mg/L of PAA and contact time of 16 min. Treatment with PFA

411

resulted in a reduction of AR% from 93.9% to 3.5% at 1 mg/L of PFA. Reduction of AR% was also

412

observed in 0.9% saline solution for both PAA and PFA. Figure 5 compares the %AR before the

413

disinfection treatment, the %AR reached observed at the tailing phase, and the respective reduction

414

in antibiotic resistance. 21

415

Among the major determinants of b-lactam antibiotic resistance there are the b-lactamase

416

enzymes that can be transferred by resistant bacteria occurring in wastewater to indigenous aquatic

417

bacteria through plasmids or other mobile genetic elements. Generally, in WWTP, the high

418

concentration of bacteria, nutrients and in particular suspended solids are all elements that favor

419

horizontal gene transfer (Guardabassi et Dalsgaardand, 2002) and consequently could induce an

420

increase in ARB concentrations. Chlorine-based disinfectants can increase the percentage of ARBs.

421

Al-Jassim et al. (2015) compared the degree of resistance to various antibiotics including ampicillin

422

among the isolated bacteria from wastewaters treated with chlorine reporting that the AR%

423

increased by 28% after chlorination. Ferreira da Silva et al. (2007) showed that resistance to

424

antibiotics amoxicillin, ciprofloxacin, tetracycline and cephalothin in Escherichia spp. isolates

425

increased by 5-10% in treated wastewater compared to untreated wastewater. These studies suggest

426

that chlorination promoted the enrichment and selection of ARBs.

427

The reduction of AR% by PAA and PFA observed herein is important since this is a major

428

challenge for WWTP using chlorine-based disinfectants. In wastewater, among the AmpR E. coli

429

that survived the treatments, the MIC was analyzed to determine if the AMP concentration to which

430

E. coli was resistant underwent variations. The results showed that the resistance remained constant

431

at MIC values >256 µg/mL of AMP in all the following PFA-ICT values 0.2, 0.4, 7.1, 7.6 mg

432

min/L and PAA-ICT of 0.5, 0.9, 29, 31.1 mg min/L. Results suggest that in the presence of PAA

433

and PFA, the resistant bacteria became weaker. Further investigations are needed to understand how

434

these disinfectants act on β-lactamases and on other ampicillin resistance genes.

435 436

22

437 438

Figure 5. Antibiotic resistance percentage (AR%) before and after treatment with PAA and PFA, and

439

relative reduction in resistance for each test.

440 441

3.5. Prediction of continuous-flow disinfection performance

442

The chemical and microbial inactivation kinetic parameters determined herein in batch lab-

443

scale experiments in wastewater (Tables 2 and 3) were used to simulate the reduction of total and

444

AmpR E. coli under realistic continuous-flow conditions. The same concentration, after the initial

445

demand, was used for both PAA and PFA, i.e. C0-D = 2 mg/L. Considering that the disinfection of

446

wastewater usually takes place in a continuous-flow reactor, the hydraulics of the reactor should be

447

considered when trying to predict the disinfection performance. The ICT dose for a continuous-flow

448

reactor is a function of its hydraulic behavior (Eq. 3). Two cases were studied, i.e. ideal PFR (n=10)

449

and a poor-hydraulics reactor (n=1).

450

Eq. 6 was applied to predict the concentration of total and AmpR E. coli at the outlet (N) of a

451

continuous-flow reactor as a function of contact time, i.e. hydraulic residence time (HRT) which is

452

the volume of the reactor (V) over the water flow (Q). Results are presented in Figure 6. In all

453

cases, the difference between ideal and non-ideal hydraulics was more obvious at high HRTs. For 23

454

example, the greatest difference between ideal (Nn=10) and non-ideal (Nn=1) hydraulics for PAA

455

inactivation of total E. coli was determined at HRT of 45 min, where Nn=1 was around 5 times

456

higher than Nn=10 (Figure 6a). Such difference in concentration of total E. coli at the outlet of the

457

reactor, due to different hydraulics, may have practical implications in meeting the regulations for

458

disinfection of secondary effluent wastewater. In the case of PAA inactivation of AmpR E. coli,

459

lower HRTs were required for the same log removal compared to total E. coli, which agrees with

460

results obtained in batch experiments (Figures 2a, 2b, 6a, and 6b). For AmpR E. coli, the difference

461

in N between ideal and non-ideal reactor was not that important, i.e., the biggest difference was

462

observed at HRT of 25 min where the Nn=1 was around 2.5 times higher than Nn=10 (Figure 6b).

463

Shorter HRTs were required by PFA than PAA (Figure 6). This was expected due to the

464

faster microbial inactivation kinetics of PFA compared to PAA (Figures 2, 3 and 6 and Table 3).

465

For example, an HRT of 45 min and 9 min was needed by PAA and PFA respectively, to achieve a

466

concentration of total E. coli at the outlet of the reactor of around 100 CFU/100 mL. In the case of

467

AmpR E. coli, the required HRT was lower, i.e. 5 min and 32 min for PFA and PAA respectively.

468

The fast kinetics of PFA resulted in overall less effect of hydraulics than the effect seen for PAA,

469

for both total and AmpR E. coli (Figure 6). The concentration of total and AmpR E. coli at the outlet

470

of the reactor under poor hydraulics (Nn=1) was at maximum around 2 times higher than ideal

471

hydraulics (Nn=10), in the case of PFA (Figures 6c and 6d).

472

Results obtained herein through the modeling of a continuous-flow reactor under realistic

473

conditions may have practical implications in designing PAA and PFA disinfection processes. The

474

prediction of the concentration of total and AmpR E. coli at the outlet of the reactor, as a function of

475

HRT under ideal PFR and poor-hydraulics conditions, is important in directing operators on the

476

optimization of the process of chemical disinfection. This may result in environmental and

477

economic benefits.

478 24

479 480

Figure 6. Model-predicted continuous flow disinfection efficiency for enriched secondary effluent

481

wastewater (dashed line represents the E. coli ratio of non-ideal (N(n=1)) to ideal (N(n=10))

482

hydraulic conditions).

483 484

4. Conclusions Based on the results and discussions illustrated above, the following conclusions could be

485 486 487

made: •

The assay of ampicillin resistance revealed that sensitive and resistant E. coli were

488

affected by PAA and PFA in a similar manner. The AR% was decreased by both

489

PAA and PFA, indicating a great potential of the proposed chemical disinfectants in

490

controlling antibiotic resistance in wastewater. MIC analysis showed that resistant E.

491

coli maintained the same level of AMP resistance (>256 µg/mL) during treatment

25

492

with PAA or PFA in wastewater, making these disinfectants attractive alternatives to

493

chlorine.

494

•

In all conditions tested, PFA was more effective than PAA. In secondary effluent

495

wastewater, complete log removal was achieved with PFA ICT of ~15 mg min/L

496

compared to PAA ICT of >60 mg min/L. The double exponential microbial

497

inactivation kinetic model predicted well the inactivation of total and AmpR E. coli

498

by PAA and PFA.

499

•

The continuous disinfection flow model, extended in this study to antibiotic-resistant

500

E. coli, is a powerful tool which can be used to derive useful information for sizing

501

PAA and PFA disinfection system under realistic hydraulic conditions, with the

502

intent of controlling not only total bacteria but also those resistant to antibiotics.

503 504

Acknowledgements

505

This research was supported by the REGIREU project funded by FEDER-RIS 3 (grant #

506

COMRDI16-1-0062). Maria Auset also thanks the University Ramon Llull for additional funding

507

through the Research Fellowship 2018-URL-Proj-051.

508 509 510 511

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Highlights •

Disinfection kinetics by peracetic and performic acids have been studied in wastewater effluent

•

Antibiotic-resistant E. coli were less resistant to inactivation than total E. coli

•

Performic acid showed greater inactivation efficiency than peracetic acid.

•

Antibiotic resistance decreased after disinfection treatment.

•

Simulation of bacteria inactivation through modeling of batch experimental data

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: