Dissertation Entitled Investigation on Disinfection by Products (Dbps)

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Dissertation

Entitled

Investigation on disinfection by products (DBPs) degradation in water distribution

systems

Mohsen Behbahani

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the

Doctor of Philosophy Degree in Engineering

______Dr Youngwoo Seo , Committee Chair

______Dr Defne Apul , Committee Member

______Dr Cyndee Gruden , Committee Member

______Dr Dong-Shik Kim , Committee Member

______Dr Ashok Kumar , Committee Member

______Dr. Amanda Bryant-Friedrich , Dean College of Graduate Studies

The University of Toledo

May 2018

This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author

An Abstract of

Investigation on disinfection by products (DBPs) degradation in water distribution systems

Mohsen Behbahani

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Doctor of Philosophy Degree in Engineering

The University of Toledo

May 2018

Disinfection by-products (DBPs) are ubiquitous by-products of disinfection process in water systems. It has been implicated that DBPs play a major role in deterioration of water quality and the increase of public health risks. Since the discovery of DBPs in 1974, there has been a lot of research works conducted to understand the formation and fate of

DBPs in water systems. However, most of the previous studies focused on the formation of DBPs and our current understanding of DBP degradation is still limited especially in water distribution systems.

The objective of this study is to investigate both abiotic and biological degradation of DBPs in water distribution systems. In the first objective, response surface methodology

(RSM) was applied to investigate the degradation of major haloacetic acids (HAAs) in aqueous solutions using iron powder. The individual and combined effects of initial pH, iron dosage, and reaction time were considered as three major controlling factors. For all

HAAs, the decrease of initial pH value and the increase of iron dosage improve degradation efficiency. The increase of reaction time was found to be influential on all HAA

I degradation (except DCAA and TCAA). However, its effect was not as significant as that of the initial pH and iron dosage. Brominated HAAs showed higher degradation rates than chlorinated ones in similar experimental conditions. According to the ANOVA (analysis of variance) test outcomes, all the developed regression models could predict HAA degradation with high R 2 values which confirms the applicability of polynomial regression models for HAA removal estimation.

The objective of second study was to evaluate the influence of water distribution system conditions (pH, total organic carbon, residual chlorine, and phosphate) on haloacetic acids (HAAs) biodegradation. A series of batch microcosm tests were conducted to determine biodegradation kinetics and collected biomass was used for real time quantitative reverse transcription polymerase chain reaction analyses to monitor how these drinking water distribution system conditions affect the relative expression of bacterial dehalogenase genes. It was observed that tested water distribution system conditions affected HAA biodegradation with different removal efficiencies (0-100%). HAA biodegradation was improved in tested samples with TOC (3 mg/L) and pH 8.5 compared to those of TOC (0 mg/L) and pH 7, respectively. However, slight improvement was observed with the increased PO 4 concentration (3.5 mg/L), and the presence of residual chlorine even at low concentration prohibited biodegradation of HAAs. The observed trend in the relative expression of dehII genes was compatible with the HAA biodegradation trend. Overall relative expression ratio of dehII genes was lower at pH 7, phosphate (0.5 mg/L), and TOC (0 mg/L) in comparison with pH 8.5, phosphate (3.5 mg/L), and TOC (3 mg/L) in the same experimental conditions.

The objective of third chapter was to investigate the biodegradation of emerging

II nitrogenous DBPs (N-DBPs). Considering the prevalence of dichloroacetonitrile (DCAN) and trichloronitromethane (TCNM) formation in water systems, these two DBPs were selected as target compounds in studying N-DBP degradation. DCAN biodegradation was observed at pH 6 and 7.5. However, the degradation efficiency was not statistically different at these pH values (P-values > 0.05). In contrast to pH 6 and 7.5, the hydrolysis

(abiotic control) and biodegradation curves almost overlapped at pH 9. This observation indicates no potential biodegradation of DCAN at pH 9. The production of DCAA at different concentrations as the end-product of DCAN degradation via both hydrolysis and biodegradation may implicate that the mechanism of DCAN biodegradation is similar to

DCAN hydrolysis. TCNM was also found to be biodegradable at all tested pH values and the order of biodegradation was pH 6 > pH 7.5 > pH 9. The results of statistical analysis also showed significant differences in TCNM biodegradation (P-value <0.05) between pH

6 and 9, and pH 7.5 and 9. The TCNM biodegradation pathway includes the formation of considerable amounts of DCNM (as TCNM degradation by-product) which demonstrates that reductive dehalogenation is the major degradation mechanism.

III

This work is dedicated to all of my family members especially my wife, without whom none of my success would be possible. I am also grateful of my parents who supported and encouraged me all this time and for many comforts in their life they sacrificed for me.

Acknowledgement

I would like to express my sincere acknowledgement to all the people who helped make this dissertation possible.

First of all, I wish to thank my PhD adviser, Dr Youngwoo Seo for all his support, encouragement, and guidance in the last few years.

I also appreciate my committee members – Dr Defne Apul, Dr Cyndee Gruden, Dr Dong-

Shik Kim, and Dr Ashok Kumar for their very constructive and helpful insights, comments, and suggestions.

Special thanks to Ms Tamara Phares and Dr Boren Lin for providing guidance and instructions for molecular biology and gene expression analysis.

I would like to thanks my colleagues and friends Dr One Choi, Dr Sang-hoon Lee, Farhad

Batmanghelich, Lei Li, Lijia Liu, Youchul Jeon, Joe Calvilo, Zahra Nabati and all the other persons who helped me to finish this work.

Finally, I acknowledge the National Science Foundation for providing financial support

(CBET: 1236433)

Contents

Abstract:………………………………………………………………………………….. I

Acknowledgement……………………………………………………………………….V

Contents ………………………………………………………………………………...VI

List of Tables …………………………………………………………………………...IX

List of Figures …………………………………………………………………………...X

List of Abbreviations ………………………………………………………………...XIII

1. Overview……………………………………………………………………………….1

2. Literature Review ……………………………………………………………………..4

2.1. Aged water distribution systems……………………………………………..4 2.1.1. Corrosion…………………………………………………………….. 5 2.1.2. Biofilm in drinking water distribution systems…………………….6 2.2. Disinfection by-products (DBPs)……………………………………………..9 2.2.1. Types of DBPs……………………………………………………….10 2.2.1.1. Carbonaceous DBPs………………………………………..10 2.2.1.1.1. HAA Speciation and Toxicity……………………..10 2.2.1.1.2. THM Speciation and Toxicity……………………..12 2.2.1.2. Nitrogenous DBPs…………………………………………..13 2.2.2. DBP Regulations…………………………………………………….18 2.2.3. DBP stability and degradation in water distribution systems…….19 2.2.3.1. Abiotic degradation of DBPs………………………………20 2.2.3.2. Biological degradation of DBPs……………………………26 2.2.3.2.1. Biological degradation of HAAs…………………..26 2.2.3.2.2. Biological degradation of THMs……………….…31 2.2.3.2.3. Biological degradation of N-DBPs……………...…33

3. Research Objectives …………………………………………………………………37

4. Investigation of haloacetic acid (HAA) degradation by iron powder: Application of response surface methodology……………………………………………………….…41

4.1. Introduction……………………………………………………………….…41 4.2. Materials and Methods……………………………………………………...44 4.2.1. Materials………………………………………………………….…44 4.2.2. Batch Experimental Procedure………………………………….…44 VI

4.2.3. Analytical Methods……………………………………………….…45 4.2.4. Experimental design and data analysis………………………….…45 4.3. Results and Discussions……………………………………………………...47 4.3.1. Characterization of Iron Powder………………………………..…47 4.3.2. Development of regression model equations………………………50 4.3.3. Regression model validation……………………………………..…54 4.3.4. 3D surface plots for evaluating effects of experimental factors on HAA degradation…………………………………………………...58 4.3.5. Kinetics of HAA degradation by iron powder………………….…64 4.3.6. Evaluation of developed HAA removal models using data from Literature……………………………………………………………66 4.4. Conclusions………………………………………………………………..…67

5. Understanding the impact of water distribution system conditions on the biodegradation of haloacetic acids and expression of bacterial dehalogenase genes……………………………………………………………………………………..69

5.1. Introduction……………………………………………………………….…69 5.2. Materials and Methods……………………………………………………...71 5.2.1. Chemicals……………………………………………………………71 5.2.2. Bacterial Enrichment and Isolation………………………………..72 5.2.3. Batch biodegradation tests……………………………………….…73 5.2.4. Analytical methods……………………………………………….…74 5.2.5. Bacterial genomic DNA extraction, RNA isolation and reverse Transcription…………………………………………………...…...75 5.2.5.1. Genomic DNA extraction………………………………..…75 5.2.5.2. RNA isolation…………………………………………….…75 5.2.5.3. cDNA synthesis……………………………………………..76 5.2.6. Dehalogenase gene expression using RT-qPCR…………………...76 5.3. Results and Discussions…………………………………………………...…78 5.3.1. Biodegradation of HAA 5 under selected DWDS conditions………78 5.3.1.1. Effect of pH…………………………………………………78 5.3.1.2. Effect of TOC…………………………………………….…81 5.3.1.3. Effect of Phosphate…………………………………………86 5.3.1.4. Effect of residual chlorine……………………………….…89 5.3.1.5. HAA biodegradation kinetics……………………………...91 5.3.2. Monitoring bacterial dehalogenase gene expressions under different DWDS conditions…………………………………………94 5.3.2.1. Detection of dehalogenase genes and specificity of deh primers for gene expression analysis…………………..…94 5.3.2.2. Relative expression of dehII genes using q-PCR…………97 5.4. Conclusions…………………………………………………………………101

6. Biodegradation of dichloroacetonitrile and chloropicrin by multi-species bacteria from a water distribution system……………………………………………………..103

VII

6.1. Introduction………………………………………………………………...103 6.2. Materials and methods…………………………………………………..…105 6.2.1. Chemicals………………………………………………………..…105 6.2.2. Bacterial enrichment and isolation…………………………….…106 6.2.3. Batch biodegradation tests………………………………………...107 6.2.4. Analytical methods……………………………………………...…107 6.3. Results and Discussions…………………………………………………….108 6.3.1. Biodegradation of DCAN………………………………………….108 6.3.2. Biodegradation of TCNM…………………………………………116 6.4. Conclusions…………………………………………………………………122

7. Conclusion and Future recommendations…………………………………………124 7.1. Conclusion………………………………………………………………..…124 7.2. Future Recommendations……………………………………………….…126

References……………………………………………………………………………...128

VIII

List of Tables

2.1: Major disinfection by-products (C-DBPs & N-DBPs)…………………………….... 17

2.2: USEPA stage 1 and 2 DBP rule (a) regulated contaminants (b) regulated disinfectants……………………………………………………………………………... 19

4.1: Experimental range and levels of the test factors………………………………….… 46

4.2: RSM design and its observed and predicted removals (%) (a) Chlorinated HAA (b)

Brominate HAA…………………………………………………………………………. 50

4.3: Estimated regression coefficients for HAA removals (%) in coded units (a)

Chlorinated HAA (b) Brominated HAA…………………………………………………. 52

4.4: Analysis of variance (ANOVA) for HAA removal efficiencies (%)……………..…55

5.1: deh and reference gene primers for q-PCR………………………………………….. 77

5.2: HAA biodegradation kinetics for HAA 5 under DWDS conditions………………… 93

5.3: Relative dehII gene expression calculation for different DWDS conditions (pH, PO 4, and TOC) in comparison with control samples using the delta delta method……………. 99

6.1: DCAN degradation at different pH values…………………………………………. 113

6.2: Abiotic and biological degradation kinetics for DCAN and TCNM at different water pH………………………………………………………………………………………. 114

6.3: TCNM degradation at different pH values………………………………………… 120

List of Figures

2.1: Biofilm formation and growth over time in water distribution system ……………… 7

2.2: Proposed abiotic degradation pathway for TBAA…………………………………. 21

2.3: Proposed mechanisms for the degradation of DCAN……………………………… 25

2.4: Schematic potential biodegradation pathway for MCAA………………………….. 27

2.5: Proposed mechanisms for enzymatic dehalogenation of HAAs (a) dehI genes (b) dehII genes…………………………………………………………………………………….. 28

2.6: Potential THM Co-metabolic Degradation Pathway under Aerobic Conditions…... 32

2.7: Proposed degradation pathway for TCNM………………………………………… 34

2.8: Proposed reaction scheme for DCAN biodegradation ...... 36

4.1: SEM/EDS and XRD images for iron powder characterization analysis (a) SEM image of virgin iron particles before reaction, (b) SEM image of iron particles after reaction, (c)

EDS spectrum of iron particles, (d) XRD analysis of iron powder before and after reaction……………………………………………………………………………..……. 48

4.2: The actual and predicted response plots of HAA removal efficiency (%) (a)

Chlorinated HAAs (b) Brominated HAAs…………………………….………………… 51

4.3: Residuals versus fitted plots for HAAs removal efficiencies (a) TCAA, (b) DCAA, (c)

MCAA, (d) TBAA, (e) DBAA, (f) MBAA………………………………………..…….. 57

4.4: Three dimensional surface plots of HAA removal efficiency (%) as function of initial pH and iron dosage at the reaction time of 3.5 hrs (a) TCAA, (b) DCAA, (c) MCAA, (d)

TBAA, (e) DBAA, and (f) MBAA………………………………………………………. 59

4.5: Three dimensional surface plots of HAAs removal efficiency (%) as function of initial

X pH and reaction time at iron dosage of of 1.1 g/l (a) TCAA, (b) DCAA, (c) MCAA, (d)

TBAA, (e) DBAA, and (f) MBAA………………………………………………………. 62

4.6: Three dimensional surface plots of HAAs removal efficiency (%) as function of iron dosage and reaction time at initial pH of 5 (a) TCAA, (b) DCAA, (c) MCAA, (d) TBAA,

(e) DBAA, and (f) MBAA………………………………………………………..……… 63

4.7: HAA degradation kinetic at selected conditions (initial pH of 3, iron dosage of 1.1 g/l and initial HAA concentration of 300 µg/l) (a) HAA degradation over time (b) Pseudo-first order kinetic plots for degradation of HAAs…………………………………………….. 66

5.1: HAA removal efficiency under the effect of water pH (a) MCAA (b) chlorinated

HAAs (c) brominated HAAs (TOC = 0 mg/L, PO 4 = 0.5 mg/L, residual chlorine = 0 mg/L)

Values not followed by a common letter are statistically different for each HAA (P <

0.05)……………………………………………………………………………………... 80

5.2: HAA removal efficiency under the effect of TOC (a) MCAA (b) chlorinated HAAs (c) brominated HAAs (pH= 8.5, PO 4 = 0.5 mg/L, residual chlorine = 0 mg/L) Values not followed by a common letter are statistically different for each HAA (P < 0.05)….……. 83

5.3: HPC results under different water distribution system condition: (a) TOC (b) PO 4 (c)

Residual chlorine………………………………………………………………………… 85

5.4: HAA removal efficiency under the effect of PO 4 (a) MCAA (b) chlorinated HAAs (c) brominated HAAs (pH= 8.5, TOC = 0 mg/L, residual chlorine = 0 mg/L) Values not followed by a common letter are statistically different for each HAA (P < 0.05)………... 88

5.5: HAA removal efficiency under the effect of residual chlorine (a) chlorinated HAAs

(b) brominated HAAs (pH = 8.5, TOC = 0 mg/L, PO 4 =0.5 mg/L)………….…………..90

5.6: PCR amplification products obtained from mixed and isolated bacteria species. Lanes

1, 7 DNA ladder; Lanes 2,5,10 DNA free negative controls ( dehI and dehII primers); Lanes

3, 4 ( dehI ForI , dehI RevI ); Lanes 6, 8 ( dehI ForI , dehI RevII ); Lane 9 (mixed template, dehII For , dehII rev ); Lane 11 (isolated template, dehII For , dehII rev )……………………………….… 95

5.7: PCR results for checking specificity of primers: amplification products for mixed and isolated bacteria species “Lane 1 DNA free negative controls (dehII gene primers); Lane

11 DNA free negative controls (reference gene primers); Lanes 6,12 DNA ladder; Lanes

2,3 ( dehII gene primers-mixed bacteria); Lanes 7,8 ( dehII gene primers-isolated bacteria);

Lanes 4,5 (reference gene primers-mixed bacteria); Lanes 9,10 (reference gene primers- isolated bacteria)………………………………………………………………………… 96

5.8: melt curve analysis using real time PCR…………………………………………… 97

5.9: Relative expression of dehII genes under different DWDS conditions (a) Control: pH=

8.5, PO 4 = 0.5 mg/L, TOC= 0 mg/L, (b) pH impact: pH= 7, PO 4= 0.5 mg/L, TOC= 0 mg/L,

(c) PO 4 impact: PO 4= 3.5mg/L, pH= 8.5, TOC= 0 mg/L, (d) TOC impact: TOC= 3 mg/L, pH= 8.5, PO 4 = 0.5 mg/L. Values not followed by a common letter are statistically different from control (P < 0.05)………………………………………………….…………..….. 100

6.1: DCAN removal efficiency under the effect of pH (a) pH = 6 (b) pH = 7.5 (c) pH = 9

(PO 4 = 0.5 mg/L, TOC = 0 mg/L, residual chlorine = 0 mg/L)…………………………. 110

6.2: DCAN degradation and DCAA formation (pH = 6, initial DCAN concentration ≈ 2000

μg/L)…………………………………………………………………………………… 116

6.3: TCNM removal efficiency under the effect of pH (a) pH = 6 (b) pH = 7.5 (c) pH = 9

(PO 4 = 0.5 mg/L, TOC = 0 mg/L, residual chlorine = 0 mg/L)………………………… 119

6.4: TCNM degradation and DCNM formation (pH = 6, initial TCNM concentration ≈

2000 μg/L)……………………………………………………………………………… 122

XII

List of Abbreviations

ANOVA………. Analysis of variance

ASCE…………. American Society of Civil Engineering

AWWA………. American Water Works Association

BBD…………... Box Behnken Design

BCAA………… Bromochloroacetic acid

BCAcAm……... Bromochloroacetamide

BCAN………… Bromochloroacetonitrile

BCNM….…...... Bromochloronitromethane

BDCAA………. Bromodichloroacetic acid

BDCAN………. Bromodichloroacetonitrile

BDCAcAm….... Bromodichloroacetamide

BDCM………... Bromodichloromethane

BDCNM……… Bromodichloronitromethane

C-DBP………... Carbonaceous DBP

CFU………...… Colony forming unit

CH……………. Chloral hydrate

CNBr…………. Cyanogen bromide

CNCl…………. Cyanogen chloride

DBAA………… Dibromoacetic acid

DBAcAm……... Dibromoacetamide

DBAN………… Dibromoacetonitrile

XIII

DBCAA………. Dibromochloroacetic acid

DBCAcAm…… Dibromochloroacetamide

DBCAN………. Dibromochloroacetonitrile

DBCM………... Dibromochloromethane

DBCNM……… Dibromochloronitromethane

DBP…………... Disinfection by-product

DBPF………… Disinfection by-product formation

DBNM……….. Dibromonitromethane

DCAA………... Dichloroacetic acid

DCAcAm…….. Dichloroacetamides

DCAN………… Dichloroacetonitrile

DCNM………... Dichloronitromethane

1,1-DCP………. 1,1-Dichloropropanone

DDBPR……….. Disinfectants/Disinfection Byproducts Rule

DGGE………… Denaturing gradient gel electrophoresis

DON…………... Dissolved organic nitrogen

DWDS………... Drinking water distribution system

ECD………..…. Electron capture detectors

EPS………..….. Extracellular polymetric substances

GC………….… Gas chromatograph

HAA……….…. Haloacetic acid

HAcAms……... Haloacetamide

HAD……….…. Haloacid dehalogenase

XIV

HAN…………. Haloacetonitrile

HK…………… Haloketones

HNM………… Halonitromethane

HOCl………… Hypochlorous acid

HPC………….. Heterotrophic plate count

MBAA……….. Monobromoacetic acid

MBAcAm……. Monobromoacetamide

MBAN……….. Monobromoacetonitrile

MBNM………. Monobromonitromethane

MCAA………. Monochloroacetic acid

MCAcAm…… Monochloroacetamides

MCAN………. Monochloroacetonitrile

MCL………… Maximum contaminant level

MCNM……… Monochloronitromethane

2-MCPA…….. 2-Monochloropropionic acid

MIC…………. Microbial induced corrosion

MRTL………. Maximum residence time location

MTBE………. Methyl tert-butyl ether

N-DBP……… Nitrogenous DBP

NCl 3………… Trichloramine

NDMA……… N-nitrosodimethylamine

NH 2Cl………. Monochloramine

NHCl 2………. Dichloroamine

NM…………. Nitromethane

NOM……….. Natural organic matter

NZVI……….. Nano zero valent iron

OCl -………… Hypochlorite ion

PCR………… Polymerase chain reaction q-PCR……… Quantitative polymerase chain reaction

RAA………… Running annual average

RSM………… Response surface methodology

SEM………… Scanning electron microscope

SRB…………. Sulfate reducing bacteria

TBAA………. Tribromoacetic acid

TBAcAm…… Tribromoacetamide

TBAN………. Tribromoacetonitrile

TCAA………. Trichloroacetic acid

TCAcAm…… Trichloroacetamides

TCAN………. Trichloroacetonitrile

TBM………… Bromoform

TBNM………. Tribromonitromethane

TCM………… Chloroform

TCNM………. Trichloronitromethane

1,1,1-TCP…… 1,1,1-Trichloropropanone

THM………… Trihalomethane

TOC…………. Total organic carbon

XVI

TOX…………. Total organic halogen tRFLP……….. Terminal restriction fragment length polymorphism

USEPA………. United States Environmental Protection Agency

XRD…………. X-ray diffraction

XVII

Chapter 1.

Overview

Water is the most valuable chemical on earth, however, only 2.6% of its total volume (1.4×10 9 km 3) is available as freshwater. Additionally, a very small percentage of the freshwater found in wetlands, lakes, rivers, etc. is accessible as a potential source of drinking water. The availability of drinking water has been the most critical factor for survival throughout the development of all life but the natural supply of freshwater becomes limited as the population increases [1]. By the end of 1990s, it was estimated that

1.1 billion people around the world lacked access to an adequate supply of drinking water and more than 2.4 billion lacked access to adequate sanitation systems [2]. As a result, they are forced to drink contaminated water despite the risk of consuming pathogenic microorganisms that transmit water-borne diseases such as diarrhea, ascaris, cholera, etc.

[3]. One of the most valuable public health advances in the last century is water disinfection. This has dramatically reduced water-borne diseases by microbial inactivation and an increasing number of people worldwide receive quality drinking water every day from their public water systems [4]. In contrast to this advantage, however, chemical disinfection has raised another public health issue which is the formation of disinfection by-products (DBPs) [5]. Besides being effective for killing harmful microorganisms, chemical disinfectants (chlorine, chlorine dioxide, chloramine and ozone) can oxidize natural organic matters (NOMs) and inorganic compounds (bromide and iodide) that are naturally present in most water sources to form DBPs [6]. Research concerning DBPs began in 1974 when the formation of trihalomethanes (THMs) was linked to reactions

1 between chlorine and NOM in Dutch drinking water [7]. Two years later the US

Environmental Protection Agency (USEPA) published the results of a national survey that showed chloroform and other THMs are ubiquitous in chlorinated drinking water. In addition, the National Cancer Institute published results showing that chloroform is carcinogenic [4]. Based on these observations the USEPA issued a regulation in 1979 to control total THMs at an annual average of 100 µg/L in drinking water [6]. DBP surveys in the 1980s and 1990s provided data for assessing a new maximum contaminant level

(MCL) for THMs as well as developing regulations for other DBPs [8]. It was found that halogenated compounds cumulatively accounted for 30% of the total organic halogen

(TOX) in drinking water samples. Moreover, on a weight basis, THMs were the largest class of DBPs detected and the second largest fraction was haloacetic acids (HAAs) [8, 9].

In general over the last 40 years since THMs identification, more than 600 DBPs have been discovered. However, only a small number has been assessed quantitatively. The DBPs that have been quantified in drinking water are generally present at ng/L (ppt) to µg/L (ppb)

[4]. Currently significant research efforts have been directed toward increasing our understanding of DBP formation, degradation, and health effects. In the case of DBP degradation, both abiotic and biological degradation techniques have been successfully used for the removal of HAAs [10-13]. Despite valuable information obtained from these studies, the influence of major water distribution system conditions (e.g. pH) is not as well studied. In addition, a majority of the previously conducted biodegradation research has been focused on HAAs and there is a knowledge gap regarding the biodegradability of emerging N-DBPs, such as haloacetonitriles (HANs) and halonitromethanes (HNMs). In this study, we will begin with a brief review of the problems related to aged water

2 distribution systems. Then we will go through the introduction of DBPs, types of DBPs,

DBP regulations, and stability and degradation of DBPs in water distribution systems

(chapter 2). The scope and objective of our experiments regarding both abiotic and biological degradation techniques will be explained in the chapter 3. The results of abiotic degradation of HAAs using iron powder will be discussed in chapter 4. In Chapters 5 and

6 the biodegradation of HAAs and N-DBPs will be discussed, respectively. Finally in chapter 7, a brief explanation regarding the conclusion of this study and recommendations for future works will be presented.

Chapter 2

Literature Review

2.1. Aged water distribution systems

Water distribution pipes have been used for hundreds of years to transport potable water to consumers. The majority of distribution system pipes are composed of iron materials: cast iron (38%), ductile iron (22%), and steel (5%) [14]. The American Society of Civil Engineers (ASCE) gave America’s drinking water distribution system (DWDS) a grade of ‘D’ in 2013, designated as “poor”. The main reasons mentioned are frequent water main breaks and aged pipelines. It has been reported that several regions have more than

100 year old pipes and over 240,000 water main breaks happen per year in the US [15]. In a survey conducted at the end of the 1990s, the American Water Works Association

(AWWA) estimated that it will cost US water utilities $325 billion over the next 20 years to upgrade water distribution systems. This AWWA value was based on the USEPA estimates of $77.2 billion for service and replacement of transmission and distribution system lines over the next 20 years [16]. More than a decade later in the newest USEPA’s drinking water infrastructure needs survey, $325 billion was modified to $384 billion for all 50 states, Puerto Rico, the District of Columbia, tribes, and US Territories [17]. While there is a dire need to undertake these capital investments, an understanding of the issues involved in drinking water transport is required to successfully prioritize investment. Two issues of importance in iron pipes are corrosion and biofilm formation.

2.1.1. Corrosion

The thermodynamically stable form of iron in contact with atmospheric oxygen is ferric iron (Fe (III)) and exposure to aqueous conditions results in the corrosion of iron.

Iron corrosion in water distribution systems is a process that consists of a series of electrochemical reactions occurring at the metal surface in contact with water and its constituents [18]. In these reactions, the metal (Fe) is converted into ferrous solids (e.g.

Fe(OH) 2), which then is converted to ferric solids (e.g. Fe(OH) 3) after reaction with oxygen.

There is wide variation in the composition of iron oxides typically found in water distribution systems [19]. Some of the common iron corrosion products found in distribution system are goethite ( α-FeOOH), magnetite (Fe 3O4), lepidocrocite ( γ-FeOOH), and in some cases green rust. Other well-known iron oxide species found in distribution system are maghemite ( γ-Fe 2O3), hematite (Fe 2O3), and siderite (FeCO 3) [10]. These corrosion products not only restrict the flow of water, but also adversely affect water quality in DWDS through different ways. First, corrosion products are a source of iron, which can result in red water when released into water. Second, corrosion products are excellent breeding ground for excessive biofilm growth. To control biofilm growth, higher levels of residual disinfectant would be required which can increase the rate of metal corrosion. [20, 21]. Third, besides accelerating corrosion rates, high demands for disinfectants like chlorine and chloramine significantly increase disinfection by-product formation (DBPF) due to the reaction of residual disinfectant with natural organic matter

(NOM) and biofilm in distribution systems [22]. On the other hand, few studies have reported NOM removal by zero valent iron and iron corrosion products which reduces DBP formation potential [23, 24]. There are also studies available regarding the reduction of

5 different chlorinated and brominated DBPs by iron corrosion products [25, 26], carbonate green rust [27], goethite and magnetite [28], and zero valent iron Fe(0) [29-31]. Therefore,

Fe(0) and iron corrosion products in the cast iron pipes may play significant roles on the formation and fate of DBPs in DWDSs.

2.1.2. Biofilm in drinking water distribution systems

Biofilms in an aquatic environment are defined as a complex mixture of microbes, organic and inorganic material held together in a polymeric matrix attached to a substratum such as pipes, tubercules or sediments deposits [32, 33]. A prerequisite of biofilm formation in DWDSs is the establishment of a conditioning film that are formed through the adsorption of proteins, lipids carbohydrates, nucleic acids onto pipe surfaces. [34].

Under suitable conditions biofilm develops as a result of successful attachment and subsequent growth/colonization of microorganisms on the pipes internal surface. Bacteria subsequently develop into a consortium of species within the polysaccharide matrix which consists of organic polymers that are produced by biofilm micro-organisms and is referred to as extracellular polymeric substances (EPS) [33]. EPS production is instrumental towards biofilm stability as it protects the embedded bacteria [35]. Biofilm growth and establishment on pipe internal surfaces may reach a plateau within months or more depending on water distribution system conditions such as residual disinfectant types and concentrations, microorganisms resistance to disinfectants, water temperature, and pipe material [36]. Biofilms are thin in water distribution systems, reaching maximum thickness of few hundred micrometers [33]. Total number of culturable heterotrophic plate count

(HPC) bacteria in established biofilm can vary between 10 1 – 10 6 colony forming units

(CFU) per cm -2. Figure 1 shows a schematic diagram of biofilm formation and growth trend as the function of time in a water distribution system.

Figure 2.1 Biofilm formation and growth over time in water distribution system (adopted

from [37])

The presence of biofilm has some notable effects on finished water quality in

DWDS such as (I) microbial induced corrosion (MIC), (II) taste, odor, color problems, and

(III) disinfectant consumption [38]. A chemical gradient that develops between the pipe surface beneath a microbial colony and the bulk fluid is one of the causes for MIC. This gradient can be created primarily by three bacterial groups including iron oxidizing bacteria, sulfur oxidizing bacteria, and sulfur reducing bacteria [39]. Iron-oxidizing

7 bacteria, such as Gallionella , oxidize soluble reduced iron (Fe 2+ ) at the corroded pipe/water interface causing Fe 3+ precipitation. Sulfur-oxidizing microbial activity, such as

Thiobacillus , could generate sulfate and hydrogen ions, lowering water pH in the surrounding environment that could promote pipe pitting. Sulfur-reducing microbes under anaerobic conditions could generate hydrogen sulfide gas that accelerates corrosion rates

[38, 40]. Some microbial reactions may cause esthetic concerns, such as water discoloration, taste and odor issues. Actinomycetes and sulfate reducing bacteria are types of microbes often associated with esthetic issues in drinking water. Actinomycetes produce compounds bearing unpleasant odor such as geosmin. Sulfate-reducing bacteria (SRB) found within the structure of iron corrosion scales were associated with taste complaints and the visual coloration of finished water (black water) [38]. Residual disinfectant in finished water is considered as the key parameter that influences bacterial regrowth in drinking water distribution systems. However, biofilm EPS can react with chemical disinfectants thereby decreasing residual disinfectant concentration in DWDS. [41].

Therefore, maintenance of residual chlorine and/or chloramines in distant areas of DWDSs is often problematic. Moreover, chlorine/chloramine contact with bacterial EPS may be another source for carbonaceous and nitrogenous DBPs beside the reaction of residual disinfectants with NOM [35]. On the other hand, some water utilities and research groups have observed decreases in DBP concentrations (specifically HAAs) with increasing residence time and low residual disinfectant concentrations [42, 43]. The observed loss of

HAA is usually attributed to aerobic microbial degradation [44]. It is widely accepted that most of the microbial biomass in DWDS is located within biofilms (~ 95% bacterial counts in DWDS are located in pipe surfaces, while only 5% are found in the water phase and

8 detected by routine sampling schemes) [45]. So, it can be concluded that biofilm plays a critical role in both the formation and biodegradation of DBPs in DWDSs.

2.2. Disinfection by products (DBPs)

Disinfection by products (DBPs) are a group of chemicals that are formed during the reaction between NOM in raw water and disinfectants used in water treatment like chlorine and chloramine [46]. Chlorine is by far the most widely used disinfectant in the

US. Chlorine is popular due to its lower cost in comparison with other disinfectants as well as its high oxidizing potential, which provides a minimum level of chlorine residual throughout the distribution system and protects against microbial recontamination [47].

Chlorine is added to water either as a gaseous form or hypochlorite salt (sodium or calcium hypochlorite) form. All forms of chlorine are hydrolyzed to form hypochlorous acid which further dissociates into hypochlorite ion (OCl -) and hydrogen ions (H +) depending on pH and temperature. Both hypochlorous acid (HOCl) and hypochlorite ion (OCl -) can react with organic compounds as free chlorine and produce DBPs [10, 48]. In addition to free chlorine, chloramine is another alternative/secondary disinfectant which reacts with NOM to form DBPs but at a much slower rate. Chloramines are formed by combining appropriate quantities of chlorine and ammonia. Monochloramine (NH 2Cl), dichloramine (NHCl 2), and trichloramine (NCl 3) are three species of chloramines. The dominant chloramine species is a function of the chlorine-to-ammonia ratio and pH. Under typical drinking water conditions, monochloramine is the dominant chloramine species [49]. Water utilities are increasingly switching from chlorination to alternative disinfectants, particularly chloramine, in order to minimize the formation of regulated DBPs [50]. For example, with

9 chloramine and increasing pH values, the formation of trihalomethanes (THMs) decreases which is the opposite of the trend observed for free chlorine [10]. However, it has been reported that the application of chloramine raises concerns over the formation of unregulated DBPs such as different kinds of nitrogenous DBPs that are suspicious to have higher levels of toxicity [9].

2.2.1 Types of DBPs

2.2.1.1 Carbonaceous DBPs

DBPs have become one of the major driving forces in drinking water regulations, research, and water utility operations since their discovery in the early 1970’s. Since then more than 600 DBPs have been identified in literature [4, 6, 8, 51]. DBP research has focused primarily on by-products resulting from chlorination of NOM constituents such as humic and fulvic acids. Since humic and fulvic acids are nitrogen poor chemicals, chlorination DBP research was on halogenated carbonaceous DBPs (C-DBPs) rather than nitrogenous DBPs (N-DBPs) [9, 52].

2.2.1.1.1 HAA Speciation and Toxicity

HAAs are non-volatile carboxylic acids in which a halogen atom takes the place of a hydrogen atom in acetic acid. HAAs are ubiquitous contaminants that present in the environment due both to natural processes (e.g. photodegradation of some herbicides) and to human activities (e.g. pesticide industry, pulp bleaching, precursor and intermediate products of synthesis of various chemicals) [49, 53]. In drinking water, HAAs represent the second most prominent class of halogenated DBPs, after THMs. HAAs can be formed by the reaction of organic materials such as humic or fulvic acid with different disinfectants

(e.g. chlorine, chloramine, and chlorine dioxide); however, they are generally formed at highest levels with chlorination. HAAs can also be produced through the hydrolysis of other DBPs such as haloacetonitriles (HANs) [6, 54, 55]. HAAs are present at ng/L to μg/L concentrations in surface waters and μg/L concentrations in drinking WDS [13]. For example, the mean concentration of HAAs in UK drinking water was found to be 35-95

μg/L, and the maximum observed concentration of HAAs was 244 μg/L. It was also found that total HAA levels were significantly correlated with temperature, pH, and chlorine concentration [56]. In another study at Quebec City in Canada, it was observed that seasonal and geographical variations of HAAs were particularly important. The average

HAA concentrations in spring were about four times higher than in winter. HAA concentrations began to decrease in the extremities of the distribution system, and this decrease was significantly higher in warm water than in cold water. These observations showed the importance of residence time and increased microbial activities in the fate of

HAAs in WDSs [57]. HAAs are toxic chemicals known to have carcinogenic, mutagenic, and adverse reproductive outcomes [6, 58-61]. The toxicological characteristics of HAAs varies depending on the extent of halogen substitution, and the presence of iodine, bromine or chlorine. Overall it has been reported that monohaloacetic acids are more toxic than di and trihaloacetic acids [49, 62]. Additionally, numerous studies conducted on the cytotoxicity (alteration in cell integrity with or without DNA damage) and genotoxicity

(alteration in cell integrity with a destructive effect on cell genetic material such as DNA and RNA) of haloacetic acids showed that iodoacetic acids are more toxic in comparison with bromo and chloroacetic acids. The order of monohaloacetic acid toxicity is IAA >

BrAA >> ClAA [63-67].

2.2.1.1.2 THM Speciation and Toxicity

THMs are chemical compounds in which three of the four hydrogen atoms of methane (CH 4) are replaced by a halogen atom. Many trihalomethanes find uses in industry as solvents or refrigerants. For example, chloroform is a very common solvent used in organic chemistry. It is a significantly less polar solvent than water, well-suited to dissolving many organic compounds [68]. In 1974, chloroform and other volatile THMs were the first group of DBPs identified in chlorinated drinking water [7]. THMs consist of four distinct (THM 4) but related compounds: chloroform (TCM), bromodichloromethane

(BDCM), dibromochloromethane (DBCM), and bromoform (TBM) [69]. The total THMs

(TTHMs) represent the sum of the concentrations of these four compounds which have been regulated in the US since 1979 [6]. THMs can be produced from numerous organic components present in water, including some ketones (relatively slow THM precursors), aromatic compounds with specific groups (e.g., resorcinol, which rapidly forms THMs), and humic and fulvic substances [49]. By concentration, THMs represent the most prevalent group of DBPs in typical chlorinated drinking water. For example, in one study at Quebec City WDS (Quebec, CA), it was observed that THM concentration increased and stabilized whereas HAAs begin to increase, and then decrease (mainly in the extremities of the distribution system) [57]. In another study the temporal variability of

THMs and HAAs in Massachusetts public drinking water systems was monitored for several years. The measured annual average for THM 4:HAA 5 ratio varied by year (range:

1.5 - 2.3) and quarter (Quarter 1 range: 1.5 - 1.6; Quarter 2 range: 1.6 - 2.3; Quarter 3 range:

2.1 - 2.4; Quarter 4 range: 1.8 - 2.3) [70]. Type of disinfectant, chlorine versus chloramine, also has a deep influence on concentration of THMs. For example, the formation of THMs

12 from chlorination and chloramination of water from Jinlan Reservoir in China was investigated in one study. It was observed that the concentrations of THMs in chloraminated water are 8% of the concentrations in chlorinated (6.3 - 16.8 μg/L) water

[71]. Soon after the discovery of THMs in 1974, the National Cancer Institute published results showing that THMs were carcinogenic in laboratory animals [54]. In addition, the first reports appeared in the late 1970s showing that organic extracts of drinking water were mutagenic in the Salmonella mutagenicity assay [6]. Subsequently, epidemiological studies based on routine THM monitoring data have been carried out for several cancers and several non-cancer health endpoints, such as birth outcomes [72]. Consistent positive associations have been found only for bladder cancer as meta- and pooled analyses have demonstrated exposure–response relationships between average residential THM concentrations and bladder cancer [5, 73]. In terms of non-cancer outcomes, small positive associations have been reported for still births, gestational age, birth defects, spontaneous abortion, and congenital anomalies [58, 74].

2.2.1.2 Nitrogenous DBPs

With growing water demand and water resource shortages, many water utilities have been forced to exploit source water influenced by wastewater effluents and/or algal blooms, both of which are known to be key sources of dissolved organic nitrogen (DON) and N-DBP precursors. By introducing reactive nitrogen into water supplies, there is a potential to enhance N-DBP formation [75, 76]. Moreover, to reduce the formation of

THMs and HAAs, some utilities are utilizing alternative disinfectants (such as chloramine) rather than chlorine. Unfortunately, some of these alternative disinfectants reduce THM

13 and HAA formation yet increase N-DBP formation. Haloacetonitriles (HANs), halonitromethanes (HNMs), haloacetamides (HAcAms), and N-nitrosodimethylamine

(NDMA) are among the most important N-DBPs [77]. DON reacts with chlorine or chloramine to form N-DBPs. Numerous formation pathways, based largely on model compound studies, have been suggested for individual N-DBP species. HANs can be produced from the chlorination of free amino acids, heterocyclic nitrogen in nucleic acids, proteinaceous materials, and combined amino acids bound to humic structures [76]. Many organic nitrogen compounds and humic acids have been shown to be HNM precursors in drinking water. Chloropicrin is thought to be the major chemical of concern among other

HNMs. Chloropicrin formation in WDSs has mostly been associated with ozonation followed by chlorination [78]. It has been reported that ozonation converts methylamine to nitromethane at ~100% yield. Subsequent chlorination converts nitromethane to chloropicrin at ~50% yield [79]. Nitrophenols also act as precursors, with 3-nitrophenol having the highest conversion yield of 53%, compared with 0.91–5.7% for 2-nitrophenol.

Moreover, in the presence of nitrite, resorcinol, a non-nitrogenous compound, showed 0.41% conversion to chloropicrin, illustrating how certain compounds can be nitrated under water treatment conditions [77]. The HAcAms are a group of DBPs known to be produced mostly from hydrolysis of the HANs and can themselves degrade to the corresponding HAAs [80].

Furthermore, laboratory studies using isotopically-labelled monochloramine and model precursors showed that HAcAm formation pathways exist which are separate from HAN hydrolysis and that HAcAm formation was promoted by chloramination. It has also been demonstrated that monochloramine reacts with chloroacetaldehyde to form N,2- dichloroacetamide [81, 82]. NDMA is among the most widely detected nitrosamines in

14 drinking water. Formation of NDMA has been attributed to the use of quaternary amine- based coagulants and anion exchange resins, and wastewater-impaired source water.

Specific NDMA precursors in wastewater-impacted source water may include tertiary amine-containing pharmaceuticals or other quaternary amine-containing constituents of personal care products [83]. NDMA formation is theoretically governed by a rate-limiting step involving the oxidation of DON and is generally enhanced when monochloramine is used for disinfection as compared to free chlorine [76]. Of the three major N-DBP groups

(HANs, HNMs, HAcAms) captured by existing analytical methodologies in the 2000-2002

US survey, HANs occurred at the highest concentrations with median and maximum levels of 3 and 14 μg/L, respectively. The median and maximum recorded concentrations, respectively, were 1.4 and 7.4 μg/L for the sum of HAcAms and 1 and 10 μg/L for the sum of HNMs [46]. Regarding NDMA, a wide range of concentrations were detected at different locations around the world. In 11 US water treatment works (WTWs), NDMA was quantified at 3.3 ng/L on a 75% basis in chloraminated finished water and was not recorded in chlorinated samples [77]. The average NDMA concentration in Chinese finished and tap water was reported at 11 and 13 ng/L, respectively [84]. In a nationwide survey of NDMA in Japanese raw and finished water samples from drinking WTPs,

NDMA was detected at concentrations up to 2.6 ng/L in the summer and up to 4.3 ng/L in winter [85]. The highest concentration of NDMA (180 ng/L) was observed in chloraminated drinking water from Canada [54]. Although N-DBPs often occur at much lower concentrations than C-DBPs (HAAs and THMs), their importance regarding the overall health issues of disinfected water is considerable [76]. For example, in vitro mammalian cell assays have demonstrated that N-DBPs such as HANs have higher levels

15 of cytotoxicity and genotoxicity than HAAs and THMs [86]. Similarly the toxicity study of BANs using metabolomics combined with histopathology and oxidative stress analysis indicated that BAN exposure induced liver and kidney injury in mice [87]. HAcAms were also observed to be an order of magnitude more genotoxic and 2 orders of magnitude more cytotoxic than the corresponding HAAs [88]. The toxicity and mutagenicity of HNMs was investigated by few research groups. HNMs found to be one of the most cytotoxic and genotoxic classes among the N-DBPs. For example, DBNM was reported to be 82.6 times more cytotoxic and 67.2 times more genotoxic than its analogous HAAs [89]. In the case of mutagenicity, all nine HNMs showed induced DNA damage in CHO cells and

Salmonella [90]. Furthermore, a low drinking water NDMA concentration of 0.7 ng/L is associated with 10 -6 lifetime cancer risk, and NDMA has a cancer potential approximately

600 times greater than THM 4 [52]. Therefore, although N-DBPs often occur at lower concentrations than C-DBPs, their importance regarding the overall toxicity of disinfected water is considerable. Table 1 shows all major C-DBPs and N-DBPs with their relative groups and abbreviations.

Table 2.1 : Major disinfection by-products (C-DBPs & N-DBPs)

Carbonaceous DBPs Nitrogenous DBPs Species Abbreviations Species Abbreviations HAAs HNMs Monochloroacetic acids MCAA Monochloronitromethane MCNM Dichloroacetic acids DCAA Dichloronitromethane DCNM Trichloroacetic acids TCAA Trichloronitromethane TCNM Monobromoacetic acids MBAA Monobromonitromethane MBNM Dibromoacetic acids DBAA Dibromonitromethane DBNM Tribromoacetic acids TBAA Tribromonitromethane TBNM Bromochloroacetic acids BCAA Bromochloronitromethane BCNM Bromodichloroacetic acids BDCAA Dibromochloronitromethane DBCNM Dibromochloroacetic acids DBCAA Bromodichloronitromethane BDCNM HANs THMs Monochloroacetonitrile MCAN Chloroform TCM Dichloroacetonitrile DCAN Bromodichlorometane BDCM Trichloroacetonitrile TCAN Dibromochloromethane DBCM Monobromoacetonitrile MBAN Bromoform TBM Dibromoacetonitrile DBAN Tribromoacetonitrile TBAN Oxyhalids Bromochloroacetonitrile BCAN Bromate Dibromochloroacetonitrile DBCAN Chlorite Bromodichloroacetonitrile BDCAN HAcAms Haloketones Monochloroacetamides MCAcAm 1,1-Dichloropropanone 1,1-DCP Dichloroacetamides DCAcAm 1,1,1-Trichloropropanone 1,1,1-TCP Trichloroacetamides TCAcAm Monobromoacetamide MBAcAm Aldehydes Dibromoacetamide DBAcAm Trichloroacetaldehyde CH Tribromoacetamide TBAcAm Bromochloroacetamide BCAcAm Dibromochloroacetamide DBCAcAm Bromodichloroacetamide BDCAcAm Nitrosamines N-nitrosodimethylamine NDMA Cyanogen halides Cyanogen chloride CNCl Cyanogen bromide CNBr

2.2.2 DBP Regulations

Due to the potential adverse effects of chlorination by-products on human health, in 1998, U.S EPA issued the Stage 1 Disinfectants/Disinfection Byproducts Rule (DDBPR) which lowered the maximum contaminant level (MCL) for total THMs from 100 µg/l to

80 µg/l. This rule also established a MCL for the sum of five haloacetic acids (HAA 5) of

60 µg/l, bromate 10 µg/l, and chlorite 1 mg/l. MCL compliance is calculated using the running annual average (RAA) of all samples from all monitoring locations across the system [91]. During the development of the Stage 2 DBP Rule, three issues were raised over the RAA. First, some portions of the distribution system could exceed the MCL, as long as the entire system met the MCL on an RAA basis. Second, some portions of the system could have relatively high short-term exposures to DBPs, which might increase the risk of adverse health effects. Third, the sampling requirement for the maximum residence time location (MRTL) was based on the presumption that the maximum DBP concentrations will be found at the MRTL, which is not always the case [4, 10]. In the negotiated Stage 2 DBP Rule compliance with an MCL will be changed to a locational

RAA, in which each sample site will need to meet the MCL on an annual average basis. In addition, new sample sites will need to be utilized that have maximum THM and maximum

HAA occurrence, which may not be at the MRTL. The Stage 2 requirements, therefore, were designed to consider the spatial variability in DBP concentrations and the potential for formation and degradation of DBPs in distribution systems [10, 92]. Table 2 (a, b) summarizes the regulatory requirements of stage 1 and stage 2 DBP rule.

Table 2.2: USEPA stage 1 and 2 DBP rule (a) regulated contaminants (b) regulated

disinfectants [93]

Stage 1 DBPR Stage 2 DBPR Regulated Contaminants MCL (mg/l) MCLG (mg/l) MCL (mg/l) MCLG (mg/l) TTHM 0.08 Unchanged Chloroform - 0.07 Bromodichloromethane 0 Unchanged Dibromochloromethane 0.06 Unchanged Bromoform 0 Unchanged HHA5 0.06 Unchanged Monochloroacetic acid - 0.07 Dichloroacetic acid 0 Unchanged Trichloroacetic acids 0.3 0.2 Monobromoacetic acids - - Dibromoacetic acids - - Bromate 0.01 0 Unchanged Unchanged Chlorite 1 0.8 Unchanged Unchanged

(a)

Stage 1 DBPR Stage 2 DBPR MRDL MRDLG MRDL MRDLG Regulated disinfectants (mg/l) (mg/l) (mg/l) (mg/l)

Chlorine 4 as Cl 2 4 Unchanged Unchanged

Chloramines 4 as Cl 2 4 Unchanged Unchanged Chlorine dioxides 0.8 0.8 Unchanged Unchanged

(b)

2.2.3 DBP stability and degradation in Water Distribution Systems

In comparison with the formation of DBPs, which has been evaluated extensively, there are fewer studies available regarding the fate and stability of DBPs in DWDS. Several factors affect DBP concentrations in a distribution system. These factors include pH, water temperature, total organic carbon (TOC) concentrations, chlorine residuals, bromide 19 concentration, and microbial activity [10]. The concentration of DBPs within the distribution may also vary seasonally and spatially. Seasonal variations are because of changes in source water quality, such as NOM concentrations, chlorine demand, pH, and temperature. Spatial variations are due to long reaction time of chlorine and NOM, as well as degradation/formation of DBPs and intermediate byproducts in the distribution system

[57]. In the distribution system free chlorine in the treated water could react with NOM and produce more DBPs. Therefore, DBP concentrations may be higher in the distribution system than in the plant effluent. This is true for THMs as higher THMs concentration are often found in the distribution systems than in the plant effluents, especially at maximum residence time locations [94]. However, HAA concentration does not follow similar pattern as HAAs are easily biodegradable at the locations with high bioactivity. These locations are often associated with longer residence time and lower chlorine residual [43, 56].

Temperature and pH are the other factors significantly influence DBP levels due to their impacts on DBP formation and degradation. Temperature is one of the most important parameters affecting the fate of HAAs. High temperature usually increases the kinetics of both chemical and biological reactions. For example, Rodriguez and colleagues (2004) found that HAAs are stable in winter (cold water) conditions but are degradable in summer

(warm water) conditions [57].

2.2.3.1 Abiotic degradation of DBPs

The abiotic degradation pathways of DBPs mostly include hydrolysis and reductive dehalogenation processes [11]. THMs, HAAs, HNMs, and most brominated DBPs are susceptible to abiotic reductive dehalogenation [25, 30, 95-97] while HANs and HAcAms

20 are more susceptible to hydrolysis [80, 98, 99]. Reductive dehalogenation is a process by which many halogenated DBPs undergo in water to form less halogenated DBPs. One of the most powerful reductant is zero valent iron Fe (0) which is capable of transforming a wide variety of organic compounds including halogenated DBPs because of its large specific surface area and high reductive capacity [31]. It has been demonstrated that the reduction of these compounds by iron can occur via hydrogenolysis (replacement of a halogen by hydrogen) or reductive α-elimination (when a carbon is multiply halogenated) or a combination of the two pathways [97]. With respect to HAAs, it has been reported that they react via sequential hydrogenolysis with the formation of di and mono haloacetic acids, and acetate as the end product of reaction [100]. Arnold et al. (2010) reported rapid degradation of TBAA and concomitant rise of DBAA as the product. Similarly, with the reduction of DBAA, MBAA appeared, and the disappearance of MBAA was accompanied by the production of acetate. These are consistent with the degradation process via sequential hydrogenolysis from TBAA to acetate [101]. If reductive α-elimination occurred, MBAA or acetate should have been detected at early stages of TBAA disappearance, which did not happen in this study. A proposed pathway for degradation of

TBAA using iron powder is as follows:

Figure 2.2 : Proposed abiotic degradation pathway for TBAA

The degradation of TCAA follows a similar pathway, however, reaction rate is much slower than that of TBAA and MCAA is the end product of the process [31].

Hozalski et al. (2001) examined reactions of four trihaloacetic acids including trichloroacetic acid (TCAA), tribromoacetic acid (TBAA), chlorodibromoacetic acid

(CDBAA), and bromodichloroacetic acid (BDCAA), with Fe (0) in a series of batch experiments. They observed that all compounds readily reacted with Fe (0), bromine was preferentially removed over chlorine, and TBAA was the only compound completely dehalogenated to acetic acid. Halogen mass balances were 95-112%, and carbon mass balances were 62.6-112%. The pseudo-first-order rate constants for trihaloacetic acid degradation were as follows: BDCAA (10.6 ± 3.1 h -1) > CDBAA (1.43 ± 0.32 h -1) ≈ TBAA

(1.41 ± 0.28 h -1) >> TCAA (0.08 ± 0.02 h -1) [29].

Fe (0) is also capable of degrading HNMs and THMs through a similar pathway as that of HAAs. Regarding HNMs, Pearson et al (2005) investigated the reaction pathways and kinetics of three HNMs (trichloronitromethane [TCNM], dichloronitromethane

[DCNM], and chloronitromethane [CNM]) with zero valent iron. All three compounds reacted rapidly in the presence of Fe (0) (1.8-4.4 g/l) with methylamine (MA) as the final product. They found that degradation of TCNM and DCNM proceeded via the parallel reaction pathways of hydrogenolysis and α-elimination. For example, for TCNM, 60.7 ±

8.7% of reaction proceeded via hydrogenolysis and 39.3 ± 6.4% via α-elimination. The observed pseudo–first-order reaction rate constants were (2.75 ± 0.42) h-1, (2.25 ± 0.34) h-

1, (1.02 ± 0.29) h-1, and (4.46 ± 0.46) h-1 for TCNM, DCNM, CNM, and nitromethane

(NM), respectively [97]. Xiao et al. (2014) studied the application of nano zero valent iron supported on activated carbon (NZVI/AC) for the removal of THMs. The removal

22 efficiency of all THMs was higher than 70% within 30 min at the optimal NZVI/AC dosage of 1.8 g/l. The reaction rate followed the order of CHBr 3 > CHBr 2Cl > CHBrCl 2 > CHCl 3 with pseudo-first-order kinetics constants of 10.99, 1.47, 1.27, and 1.24 h -1, respectively, in the first 15 minutes with high correlation coefficient (R 2) [30]. The reduction of DBPs by iron minerals may also play an important role in determining the fate of these compounds in distribution systems as corroding iron pipes create high surface area iron oxides that can also catalyze reductive dehalogenation reactions [25]. The corroding iron generates ferrous iron Fe(II). Adsorption of Fe 2+ onto an iron oxide surface catalyzes the destruction of disinfectants, and surface bound Fe 2+ is a potent reductant capable of promoting dehalogenation of DBPs when adsorbed to iron oxide surfaces [10]. Chun et al

(2005) tested the abiotic degradation of both regulated (TCM, TCAA) and emerging DBPs

(trichloroacetonitrile, TCAN; dichloroacetonitrile, DCAN; trichloronitromethane, TCNM;

1,1,1-trichloropropane, 1,1,1-TCP; trichloroacetaldehdye hydrate, TCAh) to figure out the kinetics and pathways of the degradation by Fe(II) in the presence of synthetic goethite and magnetite. They found that TCNM was degraded via reduction while TCAN, 1,1,1-TCP, and TCAh were transformed via both hydrolysis and reduction. TCM and TCAA were unreactive. Pseudo-first-order reductive dehalogenation rates were influenced by DBP chemical structure and identity of the reductant. They observed that TCNM (8.05 ± 0.67) h-1 had the highest and TCAh (4.5 ± 1.8) ×10 -4 h-1 had the lowest degradation rates.

Additionally, Fe(II) bond to iron minerals had greater reactivity than either aqueous Fe(II) or structural Fe(II) present in magnetite. For example TCNM degradation rate constant was

(8.05 ± 0.67) h -1 for Fe(II)/magnetite versus (3.63 ± 0.23) h -1 for Fe(II) and (0.05 ± 0.01) h-1 for magnetite [28].

Hydrolysis is another chemical pathway that may lead to DBP losses in a distribution system. HANs and HAcAms undergo base-catalyzed hydrolysis with the overall reaction following first order kinetics [80, 102]. Glezer et al (1999) studied the hydrolysis of all 9 HANs at pH values 8.7, 7.2, and 5.4. They observed that stability of

HANs depends on their chemical structure and on pH. The HAN's are most stable in weak acidic media (less than 50% were degraded after 4 d at pH 5.4, while at pH 8.7 some of the compounds such as TCAN could not be detected after 24 h). They found that trihalo- substituted compounds were the least stable and the most sensitive to pH changes. For example, TCAN hydrolysis rate constants were 1.4 h -1 at pH 8.7 while it was 0.54 h -1 at pH 7.2 and 0.007 h -1 at pH 5.4. Mono- and di-substituted HANs are very stable in non- basic media and only after 48 h some decrease in their concentrations could be observed.

These authors also demonstrated that the decrease in concentrations of HANs is accompanied by appearance of HAcAms. They also proposed that further hydrolysis of

HAcAms under basic conditions yielded the corresponding HAAs [98]. Figure 2.3 displays the proposed mechanism for degradation of DCAN.

Figure 2.3: Proposed mechanisms for the degradation of DCAN [80]

2.2.3.2 Biological degradation of DBPs

2.2.3.2.1 Biological degradation of HAAs

HAA concentrations in drinking water distribution systems can vary due to numerous factors such as source water quality and age, disinfectant type and concentration, temperature, etc. [43]. Although not consistently, some water utilities have observed decreases in HAA concentrations with the decrease of residual disinfectant and long residence time in the distribution system [103]. The observed loss of HAAs could be due to either abiotic or biological degradation. However, abiotic processes are not likely to be important due to slow reactions at environmental pH and temperature values [53]. Abiotic degradation processes also need specific combination of conditions to achieve desired

HAA degradation. For example, a catalytic metal (e.g. palladium) should be added to zero valent iron for obtaining high percentages of MCAA degradation [104]. Therefore, the loss of HAA in drinking water distribution system is mostly attributed to microbial degradation.

Aerobic microbial degradation is advantageous over abiotic degradation as it results in mineralization of DBPs whereas abiotic degradation results in the formation of other DBPs such as lesser halogenated HAAs [105]. There are two potential mechanisms for the biodegradation of HAAs: hydrolysis-oxidation and reductive dehalogenation [10]. Since the effluent from drinking water treatment plants usually contains 8 to 10 mg/L of dissolved oxygen, suspended bacteria and the biofilm on the pipe walls are exposed to aerobic conditions. Thus, the hydrolysis-oxidation pathway is more likely to occur in most systems

[106]. The hydrolysis-oxidation pathway involves the substitution of halogen atom by a hydroxyl group which is catalyzed by enzymes called α-halocarboxylic acid dehalogenase

[107, 108]. Figure 2.4 exhibits the schematic potential biodegradation pathway for MCAA.

Figure 2.4: Schematic potential biodegradation pathway for MCAA (adopted from [53])

Halocarboxylic acid dehalogenases catalyze the initial step in the biodegradation pathway of HAAs. Genes encoding these α-halocarboxylic acid dehalogenase are grouped in two phylogenetically unrelated classes of genes called dehI and dehII [109]. These two groups are differentiated by the mode of action of corresponding enzymes on the target substrate. The dehalogenase degradation mechanism of dehI genes involves the attack of the water molecule on the substrate (HAA) to displace the halide ion through direct nucleophilic attack (Figure 2.5a). Regarding the dehalogenation mechanism of dehII

(Figure 2.5b), a carboxylate group, from an aspartate or a glutamate unit on the enzyme, acts as the nucleophile and attacks the alpha carbon of the substrate to release the halide ion. This results in the formation of an ester intermediate, which is then subsequently hydrolyzed by an attack of water on the carbonyl carbon [49, 110]. Additionally, the two groups of halocarboxylic acid dehalogenases have stereospecificity towards optically

27 active substrates such as 2-monochloropropionic acid (2MCPA). Thus, the group I of halocarboxylic acid dehalogenases is active with both L- and D-isomers of 2MCPA, while the other group is only active with the L-isomer [53]. Moreover, the dehalogenases from group II are members of the haloacid dehalogenase (HAD) superfamily and are structurally more closely related to other enzymes like phosphatases and epoxidases [109].

Figure 2.5 : Proposed mechanisms for enzymatic dehalogenation of HAAs (a)

dehI genes (b) dehII genes –Source: [49, 110]

A second type of HAA biodegradation pathway was proposed by Weightman et al.

(1992). TCAA was degraded by bacterial isolates as the sole carbon source; however, the final products of the haloalkanoic dehalogenase enzymatic pathway were not detected and dehalogenation was determined to be unlikely. TCAA and other trihalogenated acetic acids can be decarboxylated under proper conditions to form chloroform. Chloroform in protonic, alkaline conditions reacts to form dichlorocarbene, which hydrolyses to formic acid or carbon monoxide with hydrochloric acid [106]. Pseudomonas carboxydohydrogens was found to be responsible, solely or as a member of a bacterial cometabolic community, for 28 catalyzing the initial decarboxylation of TCAA and allowing carboxytroph growth on

TCAA. The authors suggested further studies are needed to confirm decarboxylation as a first step within the degradation pathway [111].

The kinetics and mechanism of HAA biodegradation have been studied by several research groups. McRae et al. (2004) was the first group to perform batch experiments using bacterial enrichment cultures from a wastewater treatment plant to evaluate HAA biodegradation kinetics at low HAA concentrations like those found in surface water and drinking water systems (<< 1 mg/l). Batch biodegradation tests were conducted in serum bottles containing a single HAA (MCAA, MBAA, and TCAA) and microorganisms obtained from enrichment cultures maintained on either MCAA or TCAA as the sole source of carbon and energy. They observed that MCAA culture could degrade both

MCAA and MBAA with pseudo-first order rate constants of 1.06×10 -2 and 1.13×10 -2 L

(mg protein) -1d-1, respectively. The pseudo-first order rate constant for TCAA degradation by the TCAA culture was 6.52×10 -3 l (mg protein) -1d-1. The TCAA culture was also able to degrade MCAA with the rate accelerating as incubation time increased. Additionally, the results of a community structure analysis, using denaturing gradient gel electrophoresis

(DGGE) of the PCR-amplified 16S rRNA gene fragments, showed no bacteria corresponded to HAA degrading bacteria cultivated on HAA-supplemented agar plates

[13]. In a similar experiment, Zhang et al. (2009) used ten biomass samples (i.e., tap water, distribution system biofilms, and prechlorinated granular activated carbon filters) from nine different DWDSs to evaluate the biodegradation of HAAs. Each biomass sample was fed with MCAA, DCAA, and TCAA separately (a total of thirty enrichment cultures were inoculated). HAA degraders were successfully enriched from GAC and distribution system

29 samples but rarely from tap water samples. Eight HAA degrading isolates, all members of the phylum proteobacteria, were isolated including Burkholderia glathei , several Afipia species, and Methylobacterium fujisawaense . Monohalogenated acetic acids were rapidly degraded by all isolates. A closely related trend was observed for DCAA and TBAA.

However, DBAA and TCAA were degraded by only three isolates at lower degradation rates in comparison with other HAAs [12]. HAA degradation by biofilm was also examined in a few research studies. Pluchon et al. (2013) studied DCAA and TCAA degradation by biofilm in a full-scale distribution system by considering the influence of several factors such as retention time, seasonal variation of water temperature, and pipe diameter. It was observed that seasonal variations had a major effect on HAA degradation as the quantity of biomass was lower by 1 to 2 logs in the spring and winter compared to that of fall.

Additionally, larger pipe diameters led to a decrease in HAA removal efficiencies [112].

In another study, Bayless and Andrews (2008) investigated the ability of drinking water biofilm in a bench scale glass bead column to remove six HAAs. They observed that

MCAA and MBAA were readily degradable. DCAA, DBAA, and BCAA were degradable to a lesser extent than mono-halogenated HAAs. TCAA was not removed biologically and the order of biodegradability by biofilm was found to be MBAA > MCAA > BCAA >

DCAA > DBAA > TCAA [113]. There are also studies available for the determination/detection and enumeration of haloacetic acid degrading bacteria in DWDS using dehalogenase genes. Leach et al. (2009) developed a PCR-based technique for detection of bacteria capable of degrading HAAs in DWDS. They used published degenerate PCR primers [109] to determine if water samples are positive for deh genes.

54% positive tap water samples indicated that DWDS may harbor HAA degrading bacteria.

Despite being useful for detecting dehII genes, degenerate primer sets were not sufficiently specific for q-PCR. Therefore, new primers were designed using isolated strains from wastewater enrichment cultures to amplify dehII genes. The dehII genes were grouped into two contigs with similar gene sequences. The first group of alignments consisted of isolated strains of Pseudomonas spp. and Ultramicrobacterium sp. The second set of alignments included Xanthobacter sp. and Afipia sp. Primer sets were designed to be specific for each indicator strain. The developed primer sets were effective in directly amplifying dehII genes from tap water samples [105]. In another study, Grigorescu et al. (2012) used terminal restriction fragment length polymorphism (tRFLP) of PCR-amplified haloacid dehalogenase gene fragments to determine haloacetic acid-degrading bacterial communities in DWDS. They observed substantial similarities among the tRFLP patterns of dehI and dehII gene fragments in drinking water samples obtained from three different cities (Minneapolis, MN; St Paul, MN; Bucharest, Romania) and from one biologically- active granular activated carbon filter (Hershey, PA). For dehI genes the dominant fragment in the tRFLP profiles matched the pattern from an Afipia sp. In contrast, the dominant fragment in the tRFLP profiles of dehII genes did not match any of the previously characterized dehII gene fragments which means the organism that harbors the most prominent dehII gene in drinking water has yet to be identified [114].

2.2.3.2.2 Biological degradation of THMs

In contrast to the biodegradation of HAA species, most trihalomethanes (THMs) are not biodegradable. TCM is not degraded by aerobic biofilms. It has also been reported that there is no degradation of BDCM, DBCM, TBM [113]. Since THMs are more oxidized

31 than oxygen, their direct biodegradation is not thermodynamically favorable under aerobic conditions [10]. However, cometabolic biodegradation of THMs is possible by ammonia oxidizing bacteria (Figure 2.6).

Figure 2.6 Potential THM Co-metabolic Degradation Pathway under Aerobic

Conditions – adapted from [115]

Wahman et al. (2006) reported degradation of low concentrations (25–450 μg/l) of four THMs commonly found in treated drinking water by three mixed-culture nitrifier sources. They observed that THM degradation rate constants (K THMs ) increased with increasing THM bromine-substitution with TBM>DBCM>BDCM>TCM. They also found that a decrease in temperature resulted in a decrease in both ammonia and THM

32 degradation rates with ammonia degradation rates being more severely affected. The significant effect of temperature indicates that seasonal variations in water temperature should be a consideration for technology implementation [116]. The findings of this research were similar to another study conducted by the same authors on cometabolism of

THMs by Nitrosomonas europaea . Nitrosomonas europaea was shown to degrade THMs at low concentrations (50–800 μg/l) with degradation rate constants of 0.23, 0.2, 0.15, and

0.1 liters/mg/day for TBM, DBCM, BDCM, and TCM, respectively. Similar to the THM degradation rates, product toxicity, measured by transformation capacity (T c), increased with increasing THM bromine substitution. Because both the rate constants and product toxicities increase with increasing THM bromine substitution, THM speciation in water will be an important consideration for drinking water treatment processes. This means that although a water sample may be kinetically favored based on THM speciation the toxicity of the resulting THM may prohibit stable treatment process performance [117].

2.2.3.2.3 Biological degradation of N-DBPs

In the case of other DBPs, HNMs, NDMA, and HANs are also susceptible to biodegradation [49, 118-121]. Castro et al (1983) reported biodehalogenation of TCNM by

Pseudomonas Putida sp. that have been isolated from soil. The proposed degradation pathway entails three successive reductive dehalogenation steps to NM (Figure 2.7). In the first step, the disappearance of TCNM occurred which was accompanied by a rise in concentration of DCNM. After 60 min the concentration of DCNM began to fall off while

CNM and more slowly NM started to produce.

Figure 2.7 : Proposed degradation pathway for TCNM [118]

The degradation rate constants were K TCNM > K DCNM > K CNM . In addition, it was observed that highly water soluble substances such as small peptide is produced by a non-enzymatic reaction of TCNM with live or dead cells [118]. In another study, Kawamoto and Urano

(1990) investigated the biodegradation of 10 principle organochlorines including chloropicrin by aerobic activated sludge to predict their fate in the environment. The batch experiments were carried out in 70 mL serum bottles containing inorganic nutrient medium

(oxygen free medium for anaerobic tests) and 100 μg/L of chloropicrin as target substrate with both inoculated (30 mg/L cell protein concentration) and non-inoculated (control) runs at 20 ˚C. They found chloropicrin is biodegradable both aerobically and anaerobically, and its biodegradation rate can be expressed by the first order equation. Complete degradation of chloropicrin was observed within 11 hours and the biodegradation rate constant was 1.5 d-1 [122]. Sharp et al (2005) investigated aerobic biodegradation of NDMA by axenic bacterial strains. They realized that bacteria expressing monooxygenase enzymes have the capability to degrade NDMA. Specifically, the induction of the soluble methane monooxygenase (sMMO) expressed by Methylosinus trichosporium OB3b, the propane monooxygenase (PMO) enzyme of Mycobacterium vaccae JOB-5, and the toluene 4- monooxygenases found in Ralstonia pickettii PKO1 and Pseudomonas mendocina KR1 resulted in NDMA degradation by these strains. In contrast, M. trichosporium OB3b expressing the particulate form of MMO, Burkholderia cepacia G4 expressing the toluene

2-monooxygenase, and Pseudomonas putida mt-2 expressing the toluene sidechain monooxygenase were not capable of NDMA degradation [120]. Patterson et al (2012) studied the fate of NDMA in recycled water after recharge in to anaerobic aquifer. They reported that anaerobic conditions of the aquifer provided a suitable environment for the biodegradation of NDMA with first-order kinetics. Biodegradation of NDMA under anaerobic conditions was reported via a denitrosation pathway in which NDMA was converted to the intermediate dimethylamine (DMA) and nitrite. Aerobic degradation was also occurred close to the recharge bore during injection of aerobic recycled water. Under aerobic conditions, an NDMA hydroxylation was a prominent degradation mechanism

[121] The potential for bacterial degradation of certain HANs was also examined by

Barbeau et al (2006). Their proposed degradation mechanism for the HANs was similar to that proposed for HAAs. However, unlike HAAs biodegradation, the final product of

HANs biodegradation would not be oxalic acid but rather it might be cyanide. Considering

DCAN as a possible substrate, a likely reaction scenario might be represented according to figure 6. HAN biodegradation was checked using three different isolates ( Xanthobacter autotrophicus , Burkholderia and Sphingomonas ). It was found that X. autotrophicus is more effective at degrading the HANs than either of the two isolates. The order of reactivity for X. autotrophicus was found to be MCAN > DCAN > TCAN whereas the Burkholderia and Sphingomonas isolates followed a different pattern which was DCAN > MCAN.

Unlike X. autotrophicus , Burkholderia and Sphingomonas isolates were ineffective in using TCAN as a suitable substrate [49].

Figure 2.8 : Proposed reaction scheme for DCAN biodegradation (adopted from

[49])

Chapter 3

Research Objectives

The objective of this study is to investigate both abiotic and biological degradation of DBPs in distribution systems. For abiotic degradation, emphasis will be placed on application of response surface methodology (RSM) to mathematically model and evaluate the removal of HAAs from aqueous solutions using iron powder. The focus of biological degradation is to investigate how water distribution system conditions will influence removal efficiency of DBPs. Additionally, emerging N-DBPs (HAN and HNM) biodegradation mechanisms and kinetics will also be investigated. The specific objectives of this study are listed below:

(I) Investigation on (HAA) degradation by iron powder: Application of response surface methodology.

Several studies were previously conducted for degradation of DBPs using iron powder [29, 30, 100, 104]. Despite valuable information obtained from these studies, the effect of main experimental factors (e.g. dose, pH, reaction time, DBP speciation, etc) is not well understood. Optimization of these factors is needed to achieve better removal efficiencies. Traditional, one-factor at a time, approaches have been widely used for optimization of this process. However, these methods are time consuming and require a large number of experiments to determine optimum levels [123]. The limitations of traditional methods can be avoided by optimizing all affecting parameters collectively by statistical experimental design such as RSM [124]. In this chapter, RSM will be used to generate statistical models for evaluating the removal of six HAAs (TCAA, DCAA,

MCAA, TBAA, DBAA, and MBAA) from aqueous solutions using iron powder. The relationship between removal efficiency and three quantitative variables (initial pH, iron dosage in solution, and reaction time) is determined by obtaining a second order polynomial equation for each HAA using the Box Behnken Design (BBD) method of RSM.

The obtained models provide a good prediction of removal efficiency for each HAA at other levels of these factors.

(II) Understanding the impacts of water distribution systems conditions on biodegradation of HAAs.

HAA biodegradation, its kinetics and mechanism using bacterial enrichment cultures and isolates from different water systems have been studied extensively by other researchers [13, 105, 107, 125]. However, there is still a relatively poor understanding of the effect of water distribution system conditions on HAA biodegradation [12]. For example, in the case of water pH, it has been reported that biodegradation of HAA led to a release of hydrochloric acid (HCl) and a drop in pH. As HCl accumulates and pH proceeds to drop, there was a resistance to further dehalogenation. Moreover, other sources of carbon such as humic substances are likely to be present in distribution systems which could affect

HAA degradation rates. Another factor that may also need to be accounted for is phosphate which is added to water distribution systems as a corrosion inhibitory compound.

Phosphorous can change both structure and function of microbial communities in drinking water systems and, therefore, may influence biodegradation of HAAs. Finally, in spite of the fact that chlorine is added to water distribution system to prevent any possible microbial activity, biodegradation of HAAs has been observed in some studies in the presence of low

38 residual chlorine. The first goal of current study is to address the knowledge gap regarding the influence of water distribution system conditions (residual chlorine, pH, total organic carbon [TOC], and phosphorous) on the biodegradation of five HAAs (MCAA, DCAA,

TCAA, MBAA, and DBAA) by conducting a series of batch tests. Secondly, quantitative reverse transcription polymerase chain reaction (RT-qPCR) is used to monitor how these water distribution system conditions affect the relative expressions of dehalogenase genes of bacteria.

(III) Investigation on biodegradation of emerging nitrogenous DBPs

In contrast to extensive understanding of abiotic degradation of emerging N-DBPs, their degradation kinetics and mechanisms, and degradation by-products by different techniques such as zero valent iron or iron corrosion by-products [25, 26], advanced oxidation processes [126], etc; the biodegradation of some groups of N-DBPs such as

HANs and HNMs is not well understood. There are only a very few studies available referring to potential biodegradation HANs and HNMs. Furthermore, in these studies the biodegradation mechanisms, kinetics and by-products are not clearly identified. To address the current knowledge gap in this objective, we will fully examine biodegradation of HANs and HNMs in a set of batch experiments by measuring the removal rates of these compounds under water distribution system conditions, identifying the produced compounds as the result of their degradation, and clarification of the degradation mechanism. Additionally, bacteria capable of degrading each compound will be identified.

DCAN and TCNM will be considered as target substrates for HANs and HNMs, respectively. DCAN was selected as target HAN since DCAN tends to form at higher

39 concentrations compared to other HAN species especially in algae-impacted water (rich in proteinaceous material) upon chlorination. Similar to DCAN, TCNM was selected as the target HNM since it has been reported that among all HNM species commonly found in drinking water, TCNM is the most frequently occurring one followed by CNM, DCNM,

BDCNM, and BCNM. Moreover, TCNM is used in some agricultural lands as a soil fumigant because of its broad biocidal and fungicidal properties which means it may be a source of contamination for water resources.

Chapter 4

Investigation on Haloacetic acid (HAA) degradation by Iron powder: Application of response surface methodology

4.1 Introduction

Haloacetic acids (HAAs) are a major group of disinfection by-products (DBPs) that are characterized as having one to three halogen atoms on the carboxylic acid chain.

Primarily, they are formed upon the addition of chlorine to water and wastewater for disinfection purposes [12, 96]. Although chlorinated HAAs are predominate, brominated ones are also produced in water systems containing bromide because hypochlorous acid

(HOCl) reacts with bromide to form hypobromous acid (HOBr). HOBr then reacts with

NOM in a similar mechanism as HOCl [104]. HAAs exist at ng/l to µg/l concentrations in surface water, µg/l concentrations in drinking water distribution systems, and µg/l to mg/l concentrations in treated wastewater [13]. Trichloroacetic acid (TCAA) and dichloroacetic acid (DCAA) are hepato-carcinogenic in laboratory animals, and monochloroacetic acid

(MCAA) is phytotoxic and was used until the late 1980s as an herbicide. In comparison, bromide-substituted HAAs exhibit higher relative toxicity [12, 13]. Because of potential adverse and detrimental human health effects, the U.S. Environmental Protection Agency

(EPA) promulgated the disinfectants and disinfection by-product rule. This rule imposes the maximum contaminant level (MCL) of 60 µg/l for the sum of five HAA species including mono, di, and tri-chloroacetic acids (MCAA, DCAA, and TCAA), mono, and di-

41 bromoacetic acids (MBAA and DBAA) in drinking water [105, 114] As a result of the

EPA’s regulation, it is necessary to know the fate and removal methods of HAAs in water and wastewater systems. In previous studies, researchers reported biotic degradation techniques to control the level of DBPs in water systems [12, 13, 53, 94, 105, 113, 114,

125]. However, there are a few studies regarding the abiotic degradation of DBPs, especially studies using metallic ions [29, 30, 100, 104]. Zero valent iron Fe(0) is a reductant capable of transforming a wide variety of compounds including halogenated aliphatic compounds because of its high specific surface area and high reductive capacity

[30, 97]. It has been demonstrated that the reduction of these compounds by iron can occur via hydrogenolysis (replacement of a halogen by hydrogen) or reductive α-elimination

(when a carbon is multiply halogenated) or a combination of the two pathways [97]. With respect to HAAs, it has been reported that they react via sequential hydrogenolysis with the formation of di and mono haloacetic acids, and acetate as the end product of reaction

[29, 100]. Arnold et al., 2010 reported rapid degradation of TBAA and concomitant rise of

DBAA as the product. Similarly, with the reduction of DBAA, MBAA appeared, and the disappearance of MBAA was accompanied by the production of acetate. These are consistent with the degradation proceeding via sequential hydrogenolysis from TBAA to acetate (Eq 4.1) [101]. If reductive α-elimination occurred, MBAA or acetate should have been detected at early stages of TBAA disappearance, which did not happen in this study.

→ → → (4.1)

The degradation of TCAA follows a similar pathway, however, reaction rate is much slower than that of TBAA and MCAA is the end product of the process. The efficiency of HAA removal using iron particles can be affected by various experimental

42 factors such as initial pH, iron particle dosage in the solution, reaction time, initial HAA concentration, temperature, etc [29, 100, 104]. Therefore, the optimization of these factors is needed to achieve better removal efficiencies. Traditional one-factor at a time approaches have been widely used for optimization of a process, however, such methods ignore the possible interaction between different variables. Moreover, these methods are time consuming and require a large number of experiments to determine optimum levels [123].

Additionally, since these methods do not generate any mathematical model, they do not provide any prediction for process efficiency at other levels of the factors. The limitations of traditional methods can be avoided by optimizing all the affecting parameters collectively by statistical experimental design such as response surface methodology (RSM)

[124]. RSM is a collection of mathematical and statistical methods which are used for designing experiments, building models, analyzing the effects of several factors, and searching the optimum conditions for specified responses. Many research groups applied this method for investigating the removal of different pollutants [127-132]. However, currently, there is no study available for optimizing abiotic HAA degradation by iron powder using this statistical technique.

Accordingly, the aim of this study is to apply RSM to mathematically model and evaluate the removal of six HAAs (TCAA, DCAA, MCAA, TBAA, DBAA, and MBAA) from aqueous solutions using iron powder with the consideration of removal efficiency as

RSM responses. The relationship between removal efficiency and three quantitative variables (initial pH, iron dosage in solution, and reaction time) is determined by obtaining a second order polynomial equation for each HAA using the Box Behnken Design (BBD) method of RSM.

4.2 Materials and Methods

4.2.1 Materials

The following chemicals were purchased and used as received: TCAA (99.5%,

Fisher Scientific) DCAA (99+%, Sigma Aldrich), MCAA (99+%, Sigma Aldrich), TBAA

(99%, Acros Organics), DBAA (99.7%, Supelco), and MBAA (99+%, Fluka). Micro scale iron powder (99%, 70 mesh) was obtained from the Acros Organics and was kept in an anaerobic chamber to avoid further oxidation. Aqueous HAA stock solutions were prepared by dissolving the proper amount of HAAs in deionized water.

4.2.2 Batch Experimental procedure

Batch experiments were performed by diluting freshly prepared 200 mg/l HAA stock solution with deionized water to the required concentration of 300 µg/l in a 125 ml glass bottle. The initial pH of the solution was adjusted to the desired level using H 2SO 4 and NaOH (0.1N, 1N). Then the appropriate amounts of iron powder were added to each bottle, with a total sample volume of 100 ml. For each set of samples, there was a control sample which was prepared without iron powder. The bottles were then loaded on to a rotator to be mixed around their longitudinal axes at 40 rpm at room temperature for the specified reaction time. At the end of each specified time, samples were taken out of the rotator and filtered through polycarbonate membrane filters [Milipore, USA] with a pore diameter of 0.22 µm to remove iron powder and preparation of samples for the analytical phase. All the experiments were conducted with duplicated samples. Additionally, HAA degradation kinetics were determined in a series of experiments under selected conditions

(initial pH of 3, initial concentration of 300 µg/l and iron dosage of 1.1 g/l).

4.2.3 Analytical Methods

Analysis of six HAAs were carried out using a gas chromatograph (GC) (Shimadzu,

Japan, GC-2010 plus) with dual electron capture detectors (ECD) coupled with DB-1 (30 m, 0.25 mm, 1 µm) and DB-5 (30 m, 0.25 mm, 0.25 µm) capillary columns (Agilent, USA).

HAAs were recovered by liquid/liquid extraction with methyl tert-butyl ether (MTBE), followed by methylation with acidic methanol based on the EPA method 552.2 with small modifications [35, 133, 134]. A scanning electron microscope (SEM) equipped with energy dispersive X-ray spectroscopy (EDS) (Quanta 3D FEG Dual Beam Electron Microscope,

FEI, USA) was used to characterize the iron powder for its elemental constituents and morphological information. The crystalline structure of the iron powder was determined by x-ray diffraction (XRD) analysis using a Rigaku Ultima III high resolution X-ray diffraction (Rigaku Company, Japan). Data points were collected over the 2 θ range of 10–

90° with a step size of 0.03 at room temperature.

4.2.4 Experimental design and data analysis

In this study, BBD, which is one of the two most common RSM designs, was used to design the experiments and evaluate the HAAs degradation. The BBD places experimental points on the edge-centers of the bounding box of the input factor and employs quadratic models to design experiments [132, 135]. The effects of three experimental factors including initial pH (X 1), iron dosage in the solution (X 2), and reaction time (X 3) were investigated in this study. The selection of these factors and their levels was based on previous studies [29, 100, 104]. Prior to designing the experiments, preliminary tests were also conducted to determine a narrower range of factors. From the results, it was

45 observed that, for example, at acidic initial pH values, higher degradation of HAAs was possible while no degradation was observed at basic initial pH. Therefore, based on results from the preliminary tests and previous studies, the levels of three experimental factors were selected and presented in Table 4.1. For statistical calculations, the levels of three factors X i [X 1 (initial pH), X 2 (iron dosage), and X 3 (reaction time)] were coded as x i, according to the Eq. (4.2): xi = (X i-X0)/ ∆X (4.2)

Where X0 is the value of X i at the center point and ∆X presents the step change. The equally spaced values of x i are designated as -1 (low), 0 (central point), and 1 (high).

Table 4.1. Experimental range and levels of the test factors Ranges and Levels Variables, Unit Factors -1 0 1 Initial pH X1 3 5 7 Iron dosage (g/l) X2 0.2 1.1 2 Reaction time (hr) X3 1.5 3.5 5.5

Experimental data were analyzed using MiniTab v 16.0 software. A multiple regression analysis was performed to obtain removal efficiency function coefficients by fitting data to a second order polynomial model. The generalized second order polynomial model which also includes linear terms in the response (Y i) surface analysis is given by Eq (4.3):

Y = b + ∑ bx + ∑ b x + ∑ ∑ b xx (4.3)

Where Y i is the percentage of HAA removal, b 0 the constant coefficient, b i the linear coefficients, b ii the quadratic coefficients, b ij the interaction coefficients, and x i and x j are the coded values of the factors. An equation with several factors is obtained primarily by some of the main effects and low-order interactions. It can be assumed that the higher order interactions are small relative to the low-order interactions. Therefore, only two way

46 interactions have been considered in the present work [136]. According to the BBD design, fifteen experiments were required for each HAA species in randomized order to find a model. The reliability of the fitted polynomial model was justified through analysis of variance (ANOVA) with 95% confidence level, the coefficient of R 2, and residual plots.

R2 values explain how much variability in the observed response values can be interpreted by the experimental factors and their interactions [137].

4.3 Results and Discussions

4.3.1 Characterization of Iron powder

The SEM images of virgin iron powder, Fe (0), before the experiment (in 10 µm- scale) and after the experiment (in 500 nm-scale) are illustrated in Figure 4.1(a, b). As it can be seen, the sizes of iron particles are much smaller in Figure 4.1(b) compared to that of Figure 4.1(a) which implies that aggregated particles were disaggregated into smaller ones in the reaction. This observation is mainly due to the particles collision and abrasion in a rotating a tumbler. Lower initial pH values may also influence the size of particles in the mixture as acidic conditions help to remove oxide layer from particles. Smaller particles have a higher surface area and reactivity which is beneficial for process performance. The results of EDS analysis from an average of scanned points showed that Fe (98.4%) and O

(1.5%) are the major elements of iron powder and that all the other possible impurities including Cu, Mn, C, and Ni, are below the detection limit of the instrument. Figure 4.1(c) displays the EDS spectrum of iron powder. Figure 4.1(d) exhibits the XRD diagram of iron powder before and after reaction. According to literature, the sharp peak at the 2 θ of 44.6

0 in iron powder before reaction indicates the presence of Fe . The Fe 3O4 peak was also

47 detected but the intensity was smaller than that of Fe 0 which means Fe 0 is the major constituent of iron powder [30, 104]. After the reaction one peak was observed at the 2 θ of

30, attributable to iron (oxy)(hydr)oxides. The formation of this compound alongside with the detection of smaller peaks of Fe 0 show the reductant role of Fe 0 in the dehalogenation reaction.

(a) (c)

Iron Powder after Reaction Fe Iron Powder before Reaction

Fe 3O4 Fe

Fe 3O4 RelativeIntensity

Fe FeO(OH) Fe O Fe 3O4 3 4 Fe

0 10 20 30 40 50 60 70 80 90 Two-Theta

(b) (d)

Figure 4.1. SEM/EDS and XRD images for iron powder characterization analysis

(a) SEM image of virgin iron particles before reaction, (b) SEM image of iron particles

after reaction, (c) EDS spectrum of iron particles, (d) XRD analysis of iron powder

before and after reaction

4.3.2 Development of regression model equations

As mentioned in section 4.2.4, fifteen experiments are required to obtain a model

for each HAA species. Therefore, a total number of ninety tests were conducted for

different combinations of the factors using statistically designed experiments. The

experimental design matrix together with the maximum actual and predicted HAA removal

efficiencies are listed in Table 4.2(a) for chlorinated HAAs (TCAA, DCAA, and MCAA)

and Table 4.2(b) for brominated HAAs (TBAA, DBAA, and MBAA). Actual HAA

removal efficiencies are the results of the conducted experiments with different

experimental factors. Predicted HAA removal efficiencies are the best fit from the model.

Figure 4.2 (a, b) shows the comparison between the actual and predicted values of HAA

removal efficiency. All the points are distributed relatively close to the regression line. The

graph confirms that the predicted values are in good agreement with the observed ones.

Table 4.2. RSM design and its observed and predicted removals (%)

(a) Chlorinated HAAs

Iron TCAA Removal (%) DCAA Removal (%) MCAA Removal (%) Initial Reaction dosage Observed Predicted Observed Predicted Observed Predicted pH (X 1) time (X 3) (X 2) -1 0 -1 20.2 20.4 2 3 0 -0.2 -1 -1 0 4.2 8.4 0 0.7 0 0.1 -1 1 0 39.5 36.5 16.3 14.1 2 2.4 -1 0 1 30.7 29.3 8.5 9 4 3.7 0 -1 -1 0 -4.5 0 -1.7 0 0.1 0 1 -1 0 2.8 0 1.2 0 -0.2 0 0 0 1.5 0.5 1 0.3 0 0.4 0 0 0 0 0.5 0 0.3 0 0.4 0 0 0 0 0.5 0 0.3 1.2 0.4 0 -1 1 0 -2.8 0 -1.2 0 0.2 0 1 1 6.3 10.8 4.5 6.2 3.5 3.4 1 0 -1 0 1.4 0 -0.5 0 0.3 1 -1 0 0 3 0 2.2 0 -0.4 1 1 0 0 -4.2 0 -0.7 0 -0.1 1 0 1 2.5 2.2 0 -1 0 0.2

(b) Brominated HAA

Iron TBAA Removal (%) DBAA Removal (%) MBAA Removal (%) Initial Reaction dosage Observed Predicted Observed Predicted Observed Predicted pH (X 1) time (X 3) (X 2) -1 0 -1 95.4 88 60.9 59.3 10.1 10.3 -1 -1 0 55.8 59.9 27.2 37 6.3 8.3 -1 1 0 100 104 98.8 96.7 24.2 23.9 -1 0 1 100 99.2 100 94 27 25.1 0 -1 -1 0 3.3 0 -8.1 0 -2.2 0 1 -1 8.2 11.6 2.3 6 0 0.1 0 0 0 20 21.7 3 4.9 3.3 3.2 0 0 0 21.2 21.7 6.1 4.9 2.2 3.2 0 0 0 24 21.7 5.7 4.9 4.2 3.2 0 -1 1 5.3 1.9 3 -0.7 0 -0.1 0 1 1 41 37.7 24.8 33 8.6 10.8 1 0 -1 0 0.8 0 6 0 1.9 1 -1 0 0 -4.1 0 2.1 0 0.3 1 1 0 0 -4.1 0 -9.7 0 -2 1 0 1 6.8 14.2 4.2 5.8 0 -0.2

100

MCAA 80 DCAA TCAA 60

0 Predicted HAA Removal Efficiency (%) Efficiency Removal HAA Predicted 0 20 40 60 80 100 Actuall HAA Removal Efficiency (%)

(a)

100

MBAA 80 DBAA TBAA 60

0 Predicted HAA Removal Efficiency (%) Efficiency Removal HAA Predicted 0 20 40 60 80 100 Actual HAA Removal Efficiency (%)

(b)

Figure 4.2: The actual and predicted response plots of HAA removal efficiency (%) (a) Chlorinated HAAs (b) Brominated HAAs

The coefficients of response function (Eq. 2), P and t values, for HAAs removal efficiencies are also presented in Table 3(a) for chlorinated HAAs and Table 3(b) for brominated ones.

Table 4.3. Estimated regression coefficients for HAA removals (%) in coded units

(a) Chlorinated HAAs

TCAA Removal (%) DCAA Removal (%) MCAA Removal (%) Term Coefficient t P Coefficient t P Coefficient t P Constant 0.50 0.181 0.864 0.33 0.271 0.798 0.40 1.164 0.297 x1 -11.51 -6.790 0.001 -3.35 -4.439 0.007 -0.75 -3.563 0.016 x2 5.20 3.067 0.028 2.60 3.446 0.018 0.69 3.266 0.022 x3 2.41 1.423 0.214 1.37 1.822 0.128 0.94 4.454 0.007 2 x1 11.10 4.447 0.007 2.62 2.360 0.065 0.11 0.363 0.731 2 x2 -0.67 -0.270 0.798 1.12 1.009 0.359 -0.01 -0.040 0.969 2 x3 1.75 0.701 0.514 -0.33 -0.296 0.779 0.49 1.573 0.176 x1x2 -8.82 -3.680 0.014 -4.07 -3.819 0.012 -0.50 -1.680 0.154 x1x3 -2.0 -0.834 0.442 -1.62 -1.523 0.188 -1.0 -3.359 0.020 x2x3 1.57 0.657 0.540 1.12 1.054 0.340 0.87 2.939 0.32

(b) Brominated HAAs

TBAA Removal DBAA Removal MBAA Removal Term Coefficient t P Coefficient t P Coefficient t P Constant 21.73 5.526 0.003 4.93 0.908 0.406 3.23 2.416 0.060 x1 -43.05 -17.78 0 -35.35 -10.62 0 -8.45 -10.31 0 x2 11.01 4.572 0.006 11.95 3.590 0.016 3.31 4.041 0.010 x3 6.19 2.569 0.050 8.60 2.584 0.049 3.19 3.889 0.012 2 x1 27.07 7.636 0.001 30.17 6.158 0.002 5.76 4.773 0.005 2 x2 -9.85 -2.780 0.039 -3.58 -0.731 0.498 -1.36 -1.133 0.309 2 x3 1.74 0.492 0.643 6.17 1.259 0.263 0.28 0.235 0.824 x1x2 -11.05 -3.244 0.023 -17.87 -3.797 0.013 -4.47 -3.860 0.012 x1x3 0.55 0.161 0.878 -8.72 -1.853 0.123 -4.22 -3.645 0.015 x2x3 6.87 2.018 0.100 4.87 1.036 0.348 2.15 1.855 0.123

The second order polynomial equations for HAA removal efficiencies in terms of coded factors are given by Eqs (4.4-4.9) for TCAA, DCAA, MCAA, TBAA, DBAA, and MBAA, respectively.

2 2 2 Y (TCAA %) = 0.5 – 11.51 x 1 + 5.2 x 2 + 2.41 x 3 + 11.1 x 1 – 0.67 x 2 + 1.75 x 3 – 8.82 x 1x2 –

2 x 1x3 + 1.57 x 2x3 (4.4)

2 2 2 Y (DCAA %) = 0.33 – 3.35 x 1 + 2.6 x 2 + 1.37 x 3 + 2.62 x 1 + 1.12 x 2 – 0.33 x 3 – 4.07 x 1x2 –

1.62 x 1x3 + 1.12 x 2x3 (4.5)

2 2 2 Y (MCAA %) = 0.4 – 0.75 x 1 + 0.69 x 2 + 0.94 x 3 + 0.11 x 1 – 0.01 x 2 + 0.49 x 3 – 0.5 x 1x2 – x1x3 + 0.87 x 2x3 (4.6)

2 2 2 Y (TBAA %) = 21.73 – 43.05 x 1 + 11.01 x 2 + 6.19 x 3 + 27.07 x 1 – 9.85 x 2 + 1.74 x 3 – 11.05 x1x2 + 0.55 x 1x3 + 6.87 x 2x3 (4.7)

2 2 2 Y (DBAA %) = 4.93 – 35.35 x 1 + 11.95 x 2 + 8.6 x 3 + 30.17 x 1 – 3.58 x 2 + 6.17 x 3 – 17.87

53 x1x2 – 8.72 x 1x3 + 4.87 x 2x3 (4.8)

2 2 2 Y (MBAA %) = 3.23 – 8.45 x 1 + 3.31 x 2 + 3.19 x 3 + 5.76 x 1 – 1.36 x 2 + 0.28 x 3 – 4.47 x 1x2

– 4.22 x 1x3 + 2.15 x 2x3 (4.9)

From Table 4.2 it can be seen that with the exception of MCAA, which negligible degradation was observed in, other HAAs showed different levels of degradation (up to

100%). This preliminary finding is in accordance with previous studies [29, 100, 101, 104].

P-value is a useful statistical term to confirm the significance of each factor in Table 4.3.

At 95% confidence level, the P-values that are less than 0.05 indicate the significant influence of these factors. As it is observed in Table 4.3, initial pH (x 1 for TCAA, DCAA,

MCAA, TBAA, DBAA and MBAA), iron dosage (x 2 for TCAA, DCAA, MCAA, TBAA,

DBAA, and MBAA), reaction time (x 3 for MCAA, TBAA, DBAA, and MBAA), square

2 2 terms (x 1 for TCAA, TBAA, and DBAA, MBAA and x 2 for TBAA), and interaction terms (x 1x2 for TCAA, DCAA, TBAA, DBAA, MBAA and x 1x3 for MCAA and MBAA) are significant. All the other terms including linear, square, and interaction terms were found to be insignificant in HAA degradation.

4.3.3 Regression model validation

The adequacy of regression models was evaluated through ANOVA and the obtained results are presented in Table 4.4 (a) for chlorinated HAAs and Table 4.4 (b) for brominated HAAs.

Table 4.4. Analysis of variance (ANOVA) for HAA removal efficiencies (%)

(a) Chlorinated HAAs

TCAA Removal (%) DCAA Removal (%) MCAA Removal (%) Source DF Seq Adj Adj F- Seq Adj Adj F- Seq Adj Adj F- P P P SS SS MS value SS SS MS value SS SS MS value Regression 9 2128 2128 236 10.3 0.05 270.8 270.8 30.1 6.6 0.026 24.3 24.3 2.7 7.6 0.019

Linear 3 1323 1323 19.2 0.023 159 159 53 11.6 0.011 15.3 15.3 5.1 14.4 0.007 441 Square 3 467 467 156 6.8 0.105 29.8 29.8 9.9 2.2 0.208 0.9 0.9 0.3 0.9 0.521 Interaction 3 337 337 112 4.9 0.155 82 82 27.3 6 0.041 8.1 8.1 2.7 7.6 0.026 Residual 5 115 115 23 22.8 22.8 4.5 1.8 1.8 0.35 error Lack-of-fit 3 113.5 113.5 37.8 50.4 0.07 22.1 22.1 7.4 22.1 0.044 0.8 0.8 0.3 0.6 0.69 Pure error 2 1.5 1.5 0.75 0.7 0.7 0.3 1 1 0.5 Total 14 2243 293.6 26

(b) Brominated HAAs

TBAA Removal (%) DBAA Removal (%) MBAA Removal (%) Source DF Seq Adj Adj F- Seq Adj Adj F- Seq Adj Adj F- P P P SS SS MS value SS SS MS value SS SS MS value Regression 9 20017 20017 2224 47.9 0 16967 16967 1885 21.3 0.002 1145 1145 116 26.1 0.002 Linear 3 16103 16103 5367 115.6 0 11731 11731 3910 44.1 0.001 740 740 247 45.9 0 Square 3 3236 3236 1078 23.2 0.002 3558 3558 1186 13.4 0.008 134 134 45 8.3 0.022 Interaction 3 678 678 226 4.9 0.060 1677 1677 559 6.3 0.037 170 170 57 10.5 0.013 Residual 5 232 232 46.4 443 443 88 26.9 26.9 5.4 error Lack-of-fit 3 223 223 74.5 17.7 0.054 437 437 146 51.3 0.019 24.9 24.9 8.3 8.3 0.11 Pure error 2 8.4 8.4 4.2 5.7 5.7 2.8 2 2 1 Total 14 20250 17410 1072

The ANOVA results showed that low probability values (P-values of 0.05, 0.026,

0.019, 0, 0.002, and 0.002 for TCAA, DCAA, MCAA, TBAA, DBAA, and MBAA, respectively) and F-values (10.3, 6.6, 7.6, 47.9, 21.3, and 26.1 for TCAA, DCAA, MCAA,

TBAA, DBAA, and MBAA, respectively) of regression model equations verify that second-order polynomial models are well fitted to experimental results. Furthermore, high

R2 values (R 2 ≥ 0.9) of 0.95 (TCAA), 0.92 (DCAA), 0.93 (MCAA), 0.99 (TBAA), 0.97

(DBAA), and 0.97 (MBAA) express a strong correlation between the actual and predicted values. Another suitable way to evaluate the adequacy of models is to use residual values

(difference between the actual and predicted response value). Residuals are considered as elements of variation unexplained by the fitted model and then it is expected that they occur based on normal distribution [137]. The plots of residuals versus fitted values, are illustrated in Figure 4.3 (a-f) for chlorinated and brominated HAAs. The precision of the model fits was investigated with the plot of residuals versus fits. For a model to be valid, no series of increasing or decreasing points and a predominance of positive or negative residuals should be found [136]. All the plots in Figure 4.3 revealed that the developed models are precise enough to describe HAAs removal efficiencies using RSM technique.

10 10

5 5

0 0 Residual Residual

-5 -5

-10 -10 -10 0 25 50 75 100 -10 0 25 50 75 100 Fitted Value Fitted Value (a) (d)

10 10

5 5

0 0 Residual Residual

-5 -5

-10 -10 -10 0 25 50 75 100 -10 0 25 50 75 100 Fitted Value Fitted Value

(b) (e)

10 10

5 5

0 0 Residual Residual

-5 -5

-10 -10 -10 0 25 50 75 100 -10 0 25 50 75 100 Fitted Value Fitted Value

Figure 4.3 Residuals versus fitted plots for HAAs removal efficiencies (a) TCAA, (b)

DCAA, (c) MCAA, (d) TBAA, (e) DBAA, (f) MBAA

4.3.4. 3D Surface Plots for Evaluating Effects of Experimental Factors

on HAA Degradation

In this study, initial solution pH, iron dosage (g/l), and reaction time (hr) were

considered as major experimental factors. After the completion of statistical analysis using

RSM, it was found that initial pH and iron powder dosage have significant effects on the

HAA removal processes (Table 4.5). To better demonstrate the effects of these two

identified factors on HAA degradation, three-dimensional surface plots of HAA removal

efficiency (%) as functions of the initial pH and iron dosage at the reaction time of 3.5 hrs

are shown in Figure 4.4 (a-f) for all 6 HAAs.

a d

AA Removal Efficiency (%) Efficiency Removal AA CAA Removal Efficiency (%) Efficiency Removal CAA

T TB

Initial pH Initial pH

b e

AA Removal Efficiency (%) Efficiency Removal AA CAA Removal Efficiency (%) Efficiency Removal CAA DB D Initial pH Initial pH

c f

Efficiency (%) Efficiency

AA Removal Removal AA CAA Removal Efficiency (%) Efficiency Removal CAA M MB

Initial pH Initial pH

Figure 4.4. Three dimensional surface plots of HAA removal efficiency (%) as function

of initial pH and iron dosage at the reaction time of 3.5 hrs (a) TCAA, (b) DCAA, (c)

MCAA, (d) TBAA, (e) DBAA, and (f) MBAA

As it is seen in figure 4.4, the effect of initial pH and iron dosage on the removal efficiencies of all tested HAAs is important. For example, in the case of initial pH, the removal efficiency of TCAA improves from 0% at initial pH of 7 to 39.5% at initial pH value of 3 after 3.5 hours (at iron dosage of 2 g/l). Similarly, the removal efficiency of TCAA increased from almost 4% at iron dosage of 0.2 g/l to 39.5% at iron dosage of 2 g/l at initial pH of 3 and reaction time of 3.5 hrs. The reduced HAA removals with the increase of initial pH value can be attributed to the existence of fewer numbers of H + in the solution with higher initial pH value. Each HAA molecule has a halogen group (-Cl or –Br) that could be replaced by an H atom. Dehalogenation of HAAs consumes H + and increases pH that can be a reason for having lower degradation efficiency [30]. The final pH values were 5.5,

6, and 7 for the experiments conducted at initial pH values of 3, 5, and 7, respectively. In the case of iron dosage, increasing the amounts of iron particles in the solution would provide more surface area for the reaction between HAA and Fe, and enhance degradation efficiency. However, the increased addition of iron dosage cannot enhance removal efficiency unboundedly due to large amount of hydrogen generation in the solution from

Fe, which reduces the mass transfer of HAA from solution to Fe surface and then decreases the degradation of HAAs. These results are in compatible with the findings of other researchers as they also observed differences in DBP removal efficiencies at different pH and iron dosage levels. Chao et al., 2008 reported that degradation of MCAA using iron bimetallic particles at pH 9 was 71% of that at pH 2.98 [104]. In a similar study for trihalomethane (THM) removals using activated carbon/nano zero valent iron (NZVI/AC),

Xiao et al., 2014 reported that THM removal efficiency decreases severely in alkaline conditions as there are more negatively charged hydroxyl ions in aqueous solution, which

60 leads to electrostatic repulsion between THMs and NZVI/AC particles. They also observed degradation efficiencies at different levels of NZVI/AC dosage (0.2 - 2.2 g/l). Their results revealed that increased addition of NZVI/AC from 0.5 g/l to 1.8 g/l could improve degradation. However, THMs removal changed little when NZVI/AC addition was higher than 1.8 g/l [30]. Besides initial pH and iron dosage, reaction time was also found to influence all HAA degradation (except TCAA and DCAA) based on the P-values (0.214 and 0.128 for TCAA and DCAA, respectively) from RSM analysis results presented in table 4.3. Generally, it is assumed that at longer time intervals, higher numbers of HAAs have the possibility of reacting with Fe particles and as a result, losing their halogen atom via hydrogenolysis. However, in the case of chlorinated HAAs, reaction rate is much slower compared to that of brominated ones [100]. Also with the passage of time, Fe (0) can be oxidized in aqueous solution which led to decreased active surface sites and less adsorption capacity of iron particles. Moreover, as the reaction proceeded, there were insufficient H + ions in the solution to accept the electrons to generate H atoms [30].

Therefore, the degradation of chlorinated HAAs (TCAA and DCAA) are not positively affected by reaction time. For instance, the removal efficiency of DBAA increased from

60% for reaction time of 1.5 hrs to 100% for reaction time of 5.5 hrs at the initial pH of 3 and the iron dosage of 1.1 g/l while for DCAA removal efficiency increased from 2% to

8% in similar condition. Figure 4.5 shows the three-dimensional surface plots of HAA removal efficiency (%) as functions of the initial pH and reaction time at the iron dosage of 1.1 g/l for all six HAAs. Figure 4.6 shows the three-dimensional surface plots of HAA removal efficiency (%) as functions of the iron dosage and reaction time at the initial pH of 5 for all six HAAs.

a d ency ency

Removal Efficiency TBAA TCAA Removal Effici Removal TCAA

Iron dosage Iron dosage

e b

DBAA Removal Efficiency Removal Efficiency DBAA DCAA Removal Efficiency Efficiency Removal DCAA Iron dosage Iron dosage

c f

MBAA Removal Efficiency Efficiency Removal MBAA

MCAA Removal Efficiency Efficiency MCAA Removal Iron dosage Iron dosage

Figure 4.5 Three dimensional surface plots of HAAs removal efficiency (%) as function of initial pH and reaction time at iron dosage of of 1.1 g/l (a) TCAA, (b) DCAA, (c) MCAA, (d) TBAA, (e) DBAA, and (f) MBAA

a d

TBAA Removal Efficiency Removal Efficiency TBAA TCAA Removal Efficiency Efficiency Removal TCAA Iron dosage Iron dosage

b e

DCAA Removal Efficiency Efficiency Removal DCAA Removal Efficiency DBAA Iron dosage Iron dosage

c f

MBAA Removal Efficiency Efficiency Removal MBAA MCAA Removal Efficiency Efficiency MCAA Removal Iron dosage Iron dosage

Figure 4.6 Three dimensional surface plots of HAAs removal efficiency (%) as function of iron dosage and reaction time at initial pH of 5 (a) TCAA, (b) DCAA, (c) MCAA, (d) TBAA, (e) DBAA, and (f) MBAA

Another major observation in this study is the order of HAA degradation that is

TBAA>TCAA, DBAA>DCAA, and MBAA>MCAA. This can be explained through different dissociation energies of C-X bonds (280 kj/mol for C-Br and 397 kj/mol for C-

Cl). Hence, C-Br is more easily broken than C-Cl and reacts better with atomic H [30].

Moreover, it can be seen in figure 4.4 that the degradation of DCAA and MBAA is lower than TCAA, TBAA, and DBAA. Zhang et al., (2004) also reported this observation in their study and concluded that the degradation of TBAA, DBAA, and TCAA is mass transfer limited (mass transfer of HAAs to the surface of Fe is slower than reactions at the surface), while the degradation of DCAA and MBAA is partially mass transfer limited (mass transfer of HAA to the surface of Fe is comparable to the reaction of HAA at the surface) in the presence of oxygen in the solution. Additionally, for MCAA, the same authors reported

199 days as the half-life of MCAA (t 1/2 ) at the iron loading of 2.4 g/l, which implies that the degradation of MCAA by Fe is reaction limited (reaction of HAA on the surface is slower than the mass transfer of HAA to the surface of Fe) [100]. Therefore, as it is displayed in figure 4.4 (c), changing the experimental factors is not enough, and these factors must be accompanied by other techniques such as adding small amounts of catalytic metal (e.g., palladium, copper, nickel) in combination with iron to achieve desired MCAA degradation [29, 104].

4.3.5 Kinetics of HAA Degradation by Iron Powder

The rate of HAA degradation using iron powder can be expressed by pseudo-first- order kinetic model according to Eq. (4.10):

= − (4.10)

Eq. (4.10) can be arranged by simple integration to give

= (4.11)

Where Ct, C 0, and K obs are the HAA concentration at any time t, initial HAA concentration, and kinetics constants, respectively. In the present work, a series of experiments were conducted for all tested HAAs except MCAA (which did not show considerable degradation) under selected conditions (initial pH of 3, initial concentration of 300 µg/l and iron dosage of 1.1 g/l). Figure 4.7(a) shows the degradation of the HAAs and figure

4.7(b) displays the plots of ln(C t/C 0) versus time. The slopes of these plots are reaction rates that are 0.062, 0.016, 0.068, 0.93, and 2.73 hr -1 for TCAA, DCAA, MBAA, DBAA, and TBAA, respectively. These kinetic constants are in consistent with results reported by

Hozalski et al., 2001 [29].

100

40 TCAA

Remaining HAA (%) HAA Remaining DCAA 20 TBAA DBAA MBAA

0 0.0 1.5 3.0 4.5 6.0 7.5

Time (hr)

(a)

-1

TCAA -2 2

) Y=-0.0624 X - 0.0673, R =0.914 0 DCAA /C t -3 Y=-0.0168 X, R 2=0.987 MBAA Ln (C Ln 2 -4 Y=-0.0686 X + 0.0108, R =0.946 DBAA Y=-0.932 X + 0.224, R 2=0.945 -5 TBAA Y=-2.737 X + 0.167, R 2=0.935 -6 0.0 1.5 3.0 4.5 6.0 7.5

Time (hr)

(b)

Figure 4.7: HAA degradation kinetic at selected conditions (initial pH of 3, iron dosage

of 1.1 g/l and initial HAA concentration of 300 µg/l) (a) HAA degradation over time (b)

Pseudo-first order kinetic plots for degradation of HAAs

4.3.6 Evaluation of developed HAA removal models using data from literatures

The accuracy of HAA removal models in predicting the abiotic degradation of

HAAs using iron powder was examined by comparison with the obtained results in previous studies. For example, Hozalski et al. [29] observed the abiotic TCAA removal (5-

10%) in samples containing 100 μM TCAA at initial pH 3.6-4.1, iron dosage 14 g/L, and reaction time of 2 hrs. The predicted TCAA removal using the developed model for TCAA

66 was calculated to be 5% at the same experimental conditions. In another study, Zhang et al. [100] reported 3% DCAA removal in sample containing 45 μM of DCAA at initial pH

7.5, iron dosage 2.4 g/L, and reaction time of 5 hrs. The predicted DCAA removal by substitution of the same experimental conditions in the obtained model for DCAA removal was calculated to be 1.5%. These comparisons display that the developed models for HAA abiotic degradations are in good correlations with the results of HAA removals under the conditions (initial pH, iron dosage, and reaction time) that were not tested to generate these models.

4.4. Conclusions

In this study, response surface methodology was used as an experimental design tool to evaluate the effect of three main experimental factors, and their relative interactions and contribution to the removal of six HAAs by iron powder. To achieve this goal, initial pH (x 1), iron dosage (x 2), and reaction time (x 3) were considered as main parameters. The results showed that initial pH and Iron dosage are significant linear factors for all HAAs

(P-values <0.05), whereas, reaction time is significant for brominated ones. Additionally, x12 (for TCAA, TBAA, DBAA, MBAA), x 22 (for TBAA), x 1x2 (for TCAA, DCAA, TBAA,

DBAA, and MBAA), and x 1x3 (for MCAA and MBAA) are statistically significant square and interaction terms. According to ANOVA test results, for all HAAs, the obtained models represent acceptable R 2 values (>0.9), which indicates the accuracy of polynomial models. From kinetic study it was observed that all HAA degradation can be well explained using pseudo first order models with a very high correlation. In addition, reaction rate constants (0.016-0.062 hr -1 for chlorinated HAAs and 0.068-2.73 hr -1 for brominated HAAs)

67 indicate faster degradation of brominated HAAs in comparison with chlorinated HAAs.

Finally, it can be concluded that iron powder can be applied for the treatment of HAAs in aqueous solutions. RSM is a powerful statistical technique for the evaluation, optimization of a process and can provide accurate models for the prediction of HAA degradation.

Chapter 5

Understanding the impact of water distribution system conditions on the biodegradation of haloacetic acids and expression of bacterial dehalogenase genes

5.1 Introduction

Haloacetic acids (HAAs) are the second most prominent class of disinfection by- products (DBPs) that are characterized as having one to three halogen atoms on the carboxylic acid chain. Monochloroacetic acid (MCAA), dichloroacetic acid (DCAA), and trichloroacetic acid (TCAA) are dominant compounds in most drinking water systems [12].

However, increasing levels of bromide in raw water could shift HAA formation to more brominated species [138]. Human exposure to HAAs has shown cytotoxic, genotoxic, mutagenic and teratogenic impacts on a variety of cells. Due to the concern over detrimental health effects, the US Environmental Protection Agency (EPA) promulgated the disinfectants and disinfection byproduct rule. At present, the maximum contaminant level is 60 µg/L for the sum of five HAAs (HAA 5) including mono, di, and tri-chloroacetic acids (MCAA, DCAA, and TCAA), and mono and di-bromoacetic acids (MBAA and

DBAA) in drinking water [114, 139].

HAA concentrations in drinking water distribution system (DWDS) can vary due to numerous factors such as water quality and age; type and concentration of disinfectants; and organic matter, etc [105]. Some water utilities have observed decreases in HAA

69 concentrations with the decrease of residual disinfectant and long residence time in the distribution system extremities [43, 103]. Several studies reported the abiotic degradation of HAAs in the presence of zero valent iron [31, 104] or iron minerals [28] or by advanced oxidation processes [96]. However, these processes are not likely to be significant due to either slow reactions at common pH and temperature in water systems or the necessity of specific combinations of conditions such as adding catalytic metal (e.g. palladium) to iron to achieve desired HAA degradation [53]. Therefore, the loss of HAA in DWDS is mostly attributed to microbial degradation as it has been reported that HAAs are biodegradable under aerobic conditions via a hydrolysis-oxidation pathway [44]. The HAA biodegradation pathway involves the substitution of a halogen atom by a hydroxyl group which is catalyzed by α-halocarboxylic acid dehalogenase enzymes [107]. Genes encoding

α-halocarboxylic acid dehalogenases are grouped in two phylogenetically -unrelated classes of genes identified as dehI and dehII [109]. dehI and dehII dehalogenases are differentiated by the mechanism of action on the target substrate. The dehII dehalogenase pathways involve a nucleophilic attack which results in a covalent ester-enzyme link between an aspartate residue and the dehalogenated substrate, while dehI dehalogenases do not form any ester bond with the substrate [49].

So far, several research groups have studied HAA biodegradation kinetics and mechanisms using bacterial enrichment cultures from different sources [12, 13, 107, 125,

140]. There are also studies on the detection and enumeration of HAA degrading bacteria using deh genes [105, 114]. In spite of valuable information obtained from these previous works, there remains a relatively poor understanding of the effect of DWDS conditions on

HAA biodegradation. For example, with regard to pH, it has been reported that

70 biodegradation of HAA results in a release of hydrochloric acid (HCl) and pH drop, which increases a resistance to further dehalogenation [141]. The presence of other organic carbon sources (e.g. humic substances) [12] and/or nutrients (e.g. phosphate from corrosion inhibitors) [142] can change both structure and function of microbial communities in drinking water systems which may also influence biodegradation of HAAs.

Therefore, the goal of the study is to address the knowledge gap regarding the influence of DWDS conditions (residual chlorine, pH, total organic carbon (TOC), and phosphorous) on the biodegradation of HAA 5. A series of batch microcosm tests were conducted to determine biodegradation kinetics and collected biomass was used for real time quantitative reverse transcription polymerase chain reaction (RT-qPCR) analyses to monitor how these DWDS conditions affect the relative expressions of bacterial dehalogenase genes for enzyme activity.

5.2 Materials and Methods

5.2.1 Chemicals

The following reagent grade chemicals were used to prepare HAA stock solutions for media in culturing bacteria and batch degradation experiments: TCAA (99.5%, Fisher

Scientific) DCAA (99+%, Sigma–Aldrich), MCAA (99+%, Sigma–Aldrich), TBAA (99%,

Acros Organics), DBAA (99.7%, Supelco), and MBAA (99+%, Fluka). TOC and phosphate stock solution was prepared using humic acid (Sigma-Aldrich) and NaH 2PO 4

(99+%, Fisher Scientific), respectively.

5.2.2 Bacterial enrichment and isolation

A completely mixed master culture reactor (500 ml) was prepared with biomass collected from a local water utility (filter effluent and water distribution system). Culture was kept in the inorganic mineral medium containing 0.03 g MgSO 4, 1.96 g

Na 2HPO 4.7H 2O, 0.37 g KH 2PO 4, 0.5 g NH 4Cl, 0.0006 g CaCl 2, and 0.1 ml of SL7 trace mineral solution (ATCC, USA) per liter of DI water. The biomass was enriched with mixture of HAA 5 (1 mM total concentration) as the sole carbon source. The master culture reactor was re-spiked with HAA upon depletion of HAA concentration. In addition to re- spiking HAA, half the volume of reactor media (250 ml) was replaced with fresh media.

Heterotrophic plate count (HPC) was used to quantify bacterial populations in the reactor.

After reaching a steady state condition in the master culture reactor, biomass was removed, washed, and preserved in 20% glycerol at -80°C for further application in the batch biodegradation experiments. Bacteria responsible for HAA degradation were isolated on agar plates supplemented with HAA 5 as the sole source of carbon. Agar plates were prepared using powder agar (1.4%) and phosphate mineral medium (25mM) to provide buffer capacity. A pH indicator (5 mg/L bromocresol purple) was also added to the mineral buffer, which yields a purple color at pH ≥ 6.8 and yellow color at pH ≤ 5.7. The mineral medium, pH indicator, and agar were blended and autoclaved. Filter sterilized HAA mixture was added to a final concentration of 10 mM after the mineral medium had cooled.

Bacteria mixtures were spread on agar plates and incubated at room temperature. HAA degraders were identified as colonies that caused a distinct color shift from purple to yellow in the agar which occurs during HAA degradation. For purification, these colonies were streaked again (up to two times) on fresh agar plates. HAA degradation ability of these

72 isolates was further verified by incubating each purified isolate in mineral medium spiked with mixture of 5 HAAs (1 mM) and observing a decrease in HAA concentration over time

[12].

5.2.3 Batch biodegradation tests

Batch microcosm tests were performed to determine the HAA biodegradation rate under varying conditions of DWDSs. Biodegradation tests were conducted separately for each HAA compound because the degradation of di- and tri-halogenated HAAs produces single halogenated HAA which interfere in the calculation of biodegradation rates. Initially, a set of experiments were conducted using MCAA as the target HAA at four levels for each

DWDS condition (pH levels of 6, 7, 8.5, and 9.5; TOC at 0, 0.5, 1.5, and 3 mg/L; phosphate at 0, 1, 2, and 3.5 mg/L) reported in previous studies [12, 143-147]. MCAA biodegradation test results were used to determine two levels that best represented the influence of each

DWDS condition for the biodegradation tests of HAA 5. In addition to the tested conditions for DWDS, previous studies also reported that HAA biodegradation would occur when chlorine residuals are lower than 0.3 mg/L and HPC is higher than 10 4 /ml [94]. Thus residual chlorine levels (0 and 0.2 mg/L) were considered to evaluate the impact of chlorine on HAA biodegradation.

Before starting each set of HAA biodegradation tests, 5 ml of frozen enriched culture was thawed and washed 3 times with mineral buffer solution. After inoculation in medium containing 1mM of HAA 5 it was incubated at room temperature overnight to reactivate the biomass. After reactivation, the biomass was washed 3 times with and resuspended in GAC filtered tap water to an O.D 600 of 0.1 [12]. To confirm the bacteria

73 cell number in inoculum, total protein content of inoculum was also quantified by

Coomassie Protein Assay Kit (ThermoFisher, MA, USA). Final concentration of bacterial cells in each test tube was 104 CFU/ml (0.2 µg/ml cell protein). Biodegradation experiments were done by diluting a freshly prepared HAA stock solution with granular activated carbon filtered tap water to obtain the working concentration of 200 μg/L.

Enriched biomass (~10 4 CFU/ml) was added to the prepared HAA solutions with different

DWDS conditions: residual chlorine, pH, PO 4 and TOC. The working volume of samples was 45 ml with 5 ml air headspace. Abiotic controls (samples without biomass) were also prepared to monitor any possible non-biological HAA loss. Samples were then incubated in a rotary tumbler mixed at 40 rpm at room temperature for up to 72 hrs. At each time point (12, 24, 48, and 72 hrs), samples were taken from the tumbler and were centrifuged at 5000 rpm for 10 minutes. The settled biomass was stored at -20 °C for later use in RT- qPCR to monitor deh gene activities and the supernatant was used for HAA analysis. All experiments were conducted with duplicated samples.

5.2.4 Analytical methods

Analysis of five HAAs was conducted using a gas chromatograph (GC) (Shimadzu,

Japan, GC-2010 plus) with dual electron capture detectors coupled with DB-1 (30 m, 0.25 mm, 1µm) and DB-5 (30 m, 0.25 mm, 0.25 µm) capillary columns (Agilent, USA). HAAs were recovered by liquid/liquid extraction with methyl tert-butyl ether, followed by methylation with acidic methanol based on the EPA method 552.2 [35, 133]. The analysis of residual chlorine and phosphate was done by spectrophotometer (DR 2800, Hach, USA).

TOC analyzer (TOC-V, Shimadzu, Japan) was used for the analysis of TOC.

5.2.5 Bacterial genomic DNA extraction, RNA isolation and reverse transcription

5.2.5.1 Genomic DNA extraction

Bacterial genomic DNA was extracted according to the protocol developed by

Maloy [148]. For DNA extraction, a bacteria pellet was re-suspended in 467 µl TE buffer by repeated pipetting in a 2 ml micro-centrifuge tube. Following resuspension, 30 µl of 10% sodium dodecyl sulfate (SDS) (99+%, Fisher Scientific) and 3 µl of proteinase k (20 mg/ml,

Thermo Scientific) were added to the solution. The mixture was incubated for 1 hr at 37 °C.

After incubation equal volume (500 µl) of phenol/chloroform/isoamyl alcohol (25:24:1,

99+%, Fisher Scientific) was added and mixed gently to avoid shearing the DNA by inverting the tube until the phases were completely mixed. The mixture was centrifuged at

12000 rpm for 10 min. The upper aqueous phase was transferred to a new tube, an equal volume of phenol/chloroform/isoamyl alcohol was added and the mixture was centrifuged for the second time. Again the upper aqueous phase was transferred to a new tube, 1/10 volume 3M sodium acetate (99%, Sigma-Aldrich) and 0.6 volume isopropanol were added and mixed gently until DNA precipitated. After decanting the supernatant, the precipitated

DNA was washed using 70% ethanol for 30 sec and centrifuged at 10000 rpm for 10 min.

Finally DNA was resuspended in at least 200 µl TE buffer and stored at -20 °C for further analyses.

5.2.5.2 RNA isolation

TRIzol reagent (Thermofisher, MA, USA) was used for the isolation of high quality total RNA according to manufacturer’s instruction. The collected bacterial cells were lysed

75 in 1 ml Trizol (Thermo Fisher Scientific, MA, USA) at room temperature for 5 min. To achieve phase separation, 0.2 ml chloroform (99+%, Sigma-Aldrich) was added, the mixture was shaken for 15 sec and allowed to stand for 5 min at room temperature. Then the mixture was centrifuged at 10000 rpm for 15 min at 4 °C. The colorless upper aqueous phase (containing RNA) was transferred to a clean tube, and mixed with 0.5 ml of isopropanol to precipitate RNA. Finally, RNA was obtained after a 5 min incubation at room temperature, a 10 min centrifugation at 10,000 rpm at 4 °C, and washed with 75% ethanol.

5.2.5.3 cDNA synthesis

Moloney Murine Leukemia Virus Reverse Transcriptase (M-MLVRT, Promega,

WI, USA) was used in cDNA synthesis according to manufacturer’s instruction. To produce cDNA from the RNA template by reverse transcription, a mixture of 2 μg RNA and 1 μg random primers was prepared and incubated at 70 °C for 5 min to melt secondary structure within the template. After a brief cooling on ice, 200 units M-MLV-RT (Promega,

WI, USA), 25 units RNasin Ribonuclease Inhibitors (Promega, WI, USA), 2 mM dNTP mix (Thermo Fisher Scientific, MA, USA), 50 mM Tric-HCl (pH 8.3), 75 mM KCl, 3 mM

MgCl 2, 10 mM DTT and water were added to the mixture to a final volume of 25 µl followed by a 60 min incubation at 42 °C.

5.2.6 Dehalogenase gene expression using RT-qPCR

For amplification of dehI and dehII genes, the universal deh primers (Table 5.2)

[109] were used as follows: reaction mixture (total volume 25 µl) including 12.5 µl of

SYBR Green reaction mix (Bio- Rad, USA), 2.5 µl of each forward and reverse primer (10

µM, IDT Technologies, IA, USA), 1 µl sample cDNA, and 6.5 µl of sterile distilled water.

The PCR thermal profile for the quantification of dehI group was: 94°C for 2 min; 20 cycles of 92°C for 20 s, 70°C for 30 s (-1 degree per cycle), 75°C for 30s; then 20 cycles of 92°C for 20 s, 51°C for 30s, 75°C for 30s; and the final extension at 75°C for 7 min. For dehII gene group the thermal profile was: 94°C for 10 min; 36 cycles of 94°C for 45 s, 55°C for

1 min, 75°C for 45s; and finally 75°C for 7 min. 16S rRNA was used as the reference gene in this study. For amplification of 16S rRNA gene, forward (F357) and reverse (R519) primers (Table 5.1) were used as suggested by Vieira et al [149]. The same mixture conditions and thermal protocols (as dehII gene) were used for amplification of reference genes, however primer concentrations (1 µM) were employed to avoid formation of primer dimers. qPCR was conducted using MyiQ single color Real-Time PCR Detection System

(Bio-Rad, CA, USA).

Table 5.1 deh and reference gene primers for q-PCR

Gene Primer Product Reference Sequence name name size (bp) dehI ACGYTNSGSGTGCCNTGGGT ForI 230 [109] dehI AWCARRTAYTTYGGATTRCCRTA dehI revI dehI ACGYTNSGSGTGCCNTGGGT ForI 504 [109] dehI revII SGCMAKSRCNYKGWARTCACT dehII TGGCGVCARMRDDARRCTBGARTA dehII For 422 [109] dehII rev TCSMADSBRTTBGASGANACRAA 16S F357 CTCCTACGGGAGGCAGCAG 200 [149] rRNA R519 GWATTACCGCGGCKGCTG

5.3 Results and Discussions

5.3.1 Biodegradation of HAA 5 under selected DWDS conditions

To evaluate the impact of different water pH, TOC and phosphate levels on HAA biodegradation, a set of experiments were conducted using MCAA which is known to be more easily biodegradable [12, 113]. Based on the observed results, two levels that better demonstrate the impact of each DWDS condition were then tested for the HAA 5 biodegradation and degradation kinetic calculations.

5.3.1.1 Effect of pH

The pH range for water systems is typically between 6 and 9.5 depending on type of source water (e.g. surface water, groundwater) and water quality parameters (e.g. disinfectant type, alkalinity, etc.) [143, 144]. In order to cover this range, pH values of 6,

7, 8.5 and 9.5 were considered for MCAA biodegradation tests. MCAA degradation results are displayed in Figure 5.1(a). At basic pH levels, a higher MCAA removal was observed in samples with pH 8.5 in comparison with those of pH 9.5 (e.g. 87.7% versus 66.4% at 48 hrs). Regarding acidic and neutral pH levels, it was observed that MCAA removal efficiency improved with the increase of pH from 6 to 7 (e.g. 63.7% versus 77.5% at 48 hrs). In addition to lower removal efficiency, acidic pH values may cause problems such as corrosion of metal pipes in DWDS. Since the EPA also regulated the desired pH level between 6.5-8.5, pH values 7 and 8.5 were selected for further investigation in HAA 5 biodegradation tests. Figure 5.1 (b, c) shows the biodegradation of chlorinated and brominated HAAs.

100 a pH = 6 pH = 7 pH = 8.5 80 pH = 9.5

Remaining MCAA (%) MCAA Remaining 20

0 12 24 48 72 Time (hr)

100 β b α MCAA (pH = 8.5) α MCAA (pH = 7) α α α DCAA (pH = 8.5) 80 α DCAA (pH = 7) α TCAA (pH = 8.5) TCAA (pH =7) 60 α α α α α β 40 α α

Remaining HAA (%) HAA Remaining α β 20 α α α α αα 0 12 24 48 72 Time (hr)

α 100 c MBAA (pH = 8.5) α MBAA (pH = 7) α DBAA (pH = 8.5) 80 α α DBAA (pH = 7) α

60 α β α 40 α Remaining HAA (%) HAA Remaining 20 α β α β α α 0 12 24 48 72 Time (hr) Figure 5.1 : HAA removal efficiency under the effect of water pH (a) MCAA (b) chlorinated HAAs (c) brominated HAAs (TOC = 0 mg/L, PO 4 = 0.5 mg/L, residual chlorine

= 0 mg/L) Values not followed by a common letter are statistically different for each HAA

(P < 0.05)

As can be seen in this figure, the biodegradation of all HAAs was higher at pH 8.5 than pH 7 at all reaction times. For example, at pH 8.5, DCAA removals at 12, 24, 48, and

72 hrs were 33.6%, 68.8%, 89.3%, and 100%, while pH 7 resulted in 26.6%, 62.5%, 77.1%, and 88.9% removal, respectively. Under pH 8.5, complete degradation of all HAAs was observed by 72 hrs, while there were remaining HAAs available even after 72 hrs at pH 7.

Additionally, the results of statistical analysis using unpaired t-test (P < 0.05) revealed that with the exception of TCAA, at multiple time points, there are significant differences between HAA removals at pH 7 and 8.5. A proposed mechanism involving direct hydrolysis of the acyl function by water to give a corresponding hydroxylchloroacetate can be used to explain the faster biodegradation of HAAs at a pH of 8.5. This proposed

80 mechanism leads to an increase in chloride concentration during HAA degradation together with a decrease in pH [150]. As pH dropped below the optimum value (pH 8.5), there was a resistance to further dehalogenation. Several previous studies reported that the optimum pH values for enzymatic dehalogenation are 8-8.5. For example, Rehm and Heinz described the degradation of DCAA by Xanthobacter autotrophicus via a haloacid dehalogenase enzyme known as GJ-10. They observed maximum enzyme activity at pH 8 and as pH dropped below 8, the DCAA degradation slowed down [141]. In another study,

Keuning et al. [151] investigated the hydrolytic dehalogenation of haloalkanes including brominated and iodinated species. They observed the maximum enzymatic activity at pH of 8.2.

5.3.1.2 Effect of TOC

TOC is a crucial factor for DBP formation and fate in DWDSs. While, the role of

TOC as a major DBP precursor is extensively studied, the role of TOC in the biodegradation of HAAs is not well understood yet. In this study, first, the effect of different TOC levels (0, 0.5, 1.5, and 3 mg/L) was investigated on the biodegradation of

MCAA (Figure 5.2a). It was observed that the increase of TOC level from 0 to 0.5 mg/L did not affect MCAA degradation and for samples with higher TOC concentration (1.5 mg/L), MCAA degradation slightly increased (~10%) in comparison to that of TOC free samples. However, enhanced degradation (~25%) was observed for sample with 3 mg/L

TOC (24 hrs reaction time). Considering notable difference in MCAA degradation between

TOC free samples and samples with the highest level of TOC (3 mg/L), these 2 levels were selected for further biodegradation experiments of the HAA 5. Figure 5.2 (b, c) displays

81 how the removal of chlorinated and brominated HAAs is affected in the presence (3 mg/L) and absence (0 mg/L) of TOC; all HAAs were degraded in TOC containing samples within

48 hrs, whereas for TOC free samples none of the HAAs were completely degraded.

Moreover, the observed removal efficiencies for all the HAAs were higher in samples with

3 mg/L TOC except for MCAA and TCAA at 12 hrs. The result of statistical analysis also exhibited that with the exception of TCAA (48 hr) and MBAA (12 hr), there is a significant difference in HAA removal efficiencies between 0 and 3 mg/L TOC concentration levels

(P<0.05). In addition, the results of HPC (Figure 5.3a) show a higher number of bacteria in samples with 3 mg/l TOC. Therefore, it can be concluded that presence of other carbon sources might have a positive effect on the removal of HAAs. This observation is contradictory to our primary assumption as a decreasing trend was expected in HAA degradation due to the potential ability of HAA degrading bacteria to consume TOC as an additional source of carbon. The improved degradation of HAAs might be attributed to other biodegradation mechanisms such as co-metabolism. In this pathway the consumption of organic carbon (humic acid) by some bacteria may increase the production of α- halocarboxylic acid dehalogenase enzymes. Previous studies indicated potential biodegradation of halogenated organic compounds by co-metabolism in the presence of other sources of organic carbon or nitrogen [116, 152]. For example, Liu et al. [152] observed an accelerated degradation of tri- and tetrachlorophenol by co-metabolism when other chlorinated phenols were present as co-metabolite. To further investigate the impact of TOC on HAA biodegradation, the expression of dehalogenase genes of bacteria was also quantified. The results of deh gene expression will be discussed in section 5.3.2.2.

100 a TOC = 0 mg/L TOC = 0.5 mg/L TOC = 1.5 mg/L 80 TOC = 3 mg/L

(%) MCAA Remaining 20

0 12 24 48 72 Time (hr)

100 β b MCAA (TOC = 0 mg/L) α MCAA (TOC = 3 mg/L) α DCAA (TOC = 0 mg/L) 80 α β DCAA (TOC = 3 mg/L) α TCAA (TOC = 0 mg/L) TCAA (TOC = 3 mg/L) 60 β β α α 40 β α

Remaining HAA (%) HAA Remaining α 20 α β β β α 0 12 24 48 72 Time (hr)

100 c MBAA (TOC = 0 mg/L) α α MBAA (TOC = 3 mg/L) 80 DBAA (TOC = 0 mg/L) α DBAA (TOC = 3 mg/L) α 60 β

α 40 β Remaining HAA (%) HAA Remaining 20 α α β β β 0 12 24 48 72 Time (hr)

Figure 5.2 : HAA removal efficiency under the effect of TOC (a) MCAA (b) chlorinated

HAAs (c) brominated HAAs (pH= 8.5, PO 4 = 0.5 mg/L, residual chlorine = 0 mg/L) Values not followed by a common letter are statistically different for each HAA (P < 0.05)

5 4 TOC (0 mg/L) 3 TOC (3 mg/L)

Log HPC (CFU/mL) HPC Log 2

1 a 0 0 12 24 36 48 60 72

Time (hr)

4 PO 4 (0.5 mg/L) 3 PO 4 (3.5 mg/L)

(CFU/mL) HPC Log 2

1 b 0 0 12 24 36 48 60 72

Time (hr)

8 Residual chlorine (0 mg/L) 7 Residual chlorine (0.2 mg/L)

4 3

HPC(CFU/mL) Log 2 1 c 0 0 12 24 36 48 60 72 Time (hr)

Figure 5.3 : HPC results under different water distribution system condition: (a) TOC (b)

PO 4 (c) Residual chlorine

5.3.1.3 Effect of Phosphate

Orthophosphate is commonly used as a corrosion inhibitor in DWDSs due to its affinity to form a stable phosphate-metal layer which limits metal corrosion [145]. It has been observed that the addition of phosphate (1-3 mg/L) reduced iron leaching and corrosion rate (66%-90%) in DWDSs [146]. However, the addition of phosphate may induce biological instability in DWDSs since phosphate can provide an additional nutrient for bacteria growth and increase total biomass density [147]. It has been demonstrated that phosphate addition changes the microbial community structure with increased diversity which can influence the operational characteristics of biological systems and their ability to degrade different contaminants [142]. In this study efforts were made to elucidate how phosphate addition affects the biodegradation of HAA 5. Figure 5.4(a) shows the removal efficiencies of MCAA under different phosphate concentrations (0.5, 1, 2, and 3.5 mg/L).

There was negligible difference in MCAA removals in samples with phosphate concentrations of 0.5, 1, and 2 mg/L. However, for a sample containing 3.5 mg/L of phosphate, the observed degradation was higher to some extent (~10-15% higher at 24 and

48 hrs) than that of a sample with 0.5 mg/L phosphate. Therefore, 0.5 and 3.5 mg/L were further considered for HAA 5 biodegradation tests. Figure 5.4 (b, c) shows biodegradation of chlorinated and brominated HAAs at phosphate levels of 0.5 and 3.5 mg/L. As shown in the figure, the addition of phosphate did not initially improve HAA biodegradation. At

12 hrs, the removal efficiency of all HAA 5 was similar at both phosphate concentrations.

However, at 24 hrs, the removal efficiency was slightly improved for MBAA, DBAA, and

DCAA in samples containing higher concentration of phosphate (5.6%, 6.7%, and 9.1% increase, respectively). A similar trend was observed for MCAA and DCAA at 48 hrs as

86 their removal efficiency was slightly higher in samples with 3.5 mg/L phosphate concentration. The results of HPC (Figure 5.3b) also showed that the addition of phosphate is marginally influential on bacterial growth as the number of culturable colonies was not significantly higher in samples containing 3.5 mg/L of phosphate. However, the result of statistical analysis showed no significant difference between HAA removal efficiencies at

0.5 and 3.5 mg/L levels (except TCAA at 12 hr). Our results are consistent with what has been reported by other researchers regarding the effect of phosphate addition on bacterial activity. Appenzeller et al [145] investigated the effect of phosphate addition on bacterial growth in drinking water. They found no relationship between bacterial growth and phosphate concentrations (0.1 to 2 mg/L) in their batch tests. Gouider et al [147] also reported that biofilm development in DWDS is not affected by 1 mg/L phosphate addition.

100 PO = 0.5 mg/L a 4 PO = 1 mg/L 4 80 PO 4 = 2 mg/L

PO 4 = 3.5 mg/L 60

(%) MCAA Remaining 20

0 12 24 48 72 Time (hr)

100 α MCAA (PO = 0.5 mg/L) b α 4 MCAA (PO = 3.5 mg/L) β αα 4 80 α α DCAA (PO 4 = 0.5 mg/L) α DCAA (PO 4 = 3.5 mg/L) TCAA (PO = 0.5 mg/L) 60 4 TCAA (PO 4 = 3.5 mg/L) α αα 40 α α

(%) HAA Remaining α 20 α α α α 0 12 24 48 72 Time (hr)

100 c MBAA (PO 4 = 0.5 mg/L) α α MBAA (PO 4 = 3.5 mg/L) 80 DBAA (PO = 0.5 mg/L) α 4 α α α DBAA (PO 4 = 3.5 mg/L) 60

α 40 α Remaining HAA (%) HAA Remaining 20 α α α α 0 12 24 48 72 Time (hr)

Figure 5.4 : HAA removal efficiency under the effect of PO 4 (a) MCAA (b) chlorinated

HAAs (c) brominated HAAs (pH= 8.5, TOC = 0 mg/L, residual chlorine = 0 mg/L) Values not followed by a common letter are statistically different for each HAA (P < 0.05)

5.3.1.4 Effect of residual chlorine

In spite of the fact that chlorine is added to DWDS to prevent any possible microbial activity, biodegradation of HAAs has been observed in some studies in the presence of low residual chlorine [43, 94]. In this study, the biodegradation of HAAs was compared in the presence and absence of low residual chlorine (0.2 mg/L). Figure 5.5 (a, b) exhibits the biodegradation of chlorinated and brominated HAAs under the effect of 0.2 mg/L residual chlorine. In contrast to field study results reported by other researchers, no degradation trend was observed in the presence of residual chlorine even at very low concentration (0.2 mg/L). Moreover, no colonies formed after addition of chlorine to the samples (Figure

5.3c). Unlike our batch experiment results, it has been reported that the HPC in the DWDS can vary between 10 3-10 7 CFU/cm 2 in established biofilm on the internal surface of pipes

[153, 154] and 10-10 3 CFU/ml in the bulk water phase under low residual chlorine levels

[94, 155]. The contradiction between our laboratory HAA biodegradation results and field

DWDS studies might be attributed to the presence of biofilm as the dominant bacterial life form. In DWDS, biofilm can provide shelter for bacteria, protecting them from inactivation by disinfectants like chlorine, especially at low residual concentrations [38]. Further study is needed to understand the role of biofilm on HAA formation and degradation using simulated DWDSs with well-grown biofilm.

100 a

80 MCAA (No chlorine) MCAA (0.2 mg/L chlorine) DCAA (No chlorine) 60 DCAA (0.2 mg/L chlorine) TCAA (No chlorine) TCAA (0.2 mg/L chlorine) 40 Remaining HAA (%) HAA Remaining 20

0 12 24 48 72 Time (hr)

100 b

80 MBAA (No chlorine) MBAA (0.2 mg/L chlorine) 60 DBAA (No chlorine) DBAA (0.2 mg/L chlorine)

(%) HAA Remaining 20

0 12 24 48 72 Time (hr)

Figure 5.5 : HAA removal efficiency under the effect of residual chlorine (a) chlorinated

HAAs (b) brominated HAAs (pH = 8.5, TOC = 0 mg/L, PO 4 =0.5 mg/L)

5.3.1.5 HAA biodegradation kinetics

Biodegradation kinetics of HAA 5 were calculated under tested DWDS conditions.

For each HAA, the biodegradation rates were normalized by the initial biomass concentration expressed as mg protein/L. The normalized rates and initial concentrations were fit to a pseudo-first order model according to the following equation:

= − (5.1)

Where X, C HAA , and k are the biomass concentration (mg/L of cell protein), HAA concentration (µg/L), and pseudo-first order reaction rate constant, respectively [13]. The result of HAA biodegradation kinetic calculations are presented in Table 5.2. From the table, the highest degradation rate constant (34.5 L/(mg protein) -1 × d-1) was observed in

DCAA biodegradation test with 3 mg/L TOC (pH 8.5, 0.5 mg/L PO 4) and the lowest rate

(2.41 L/(mg protein) -1×d-1) was observed in the MCAA biodegradation experiment with adjusted pH of 7 (0.5 mg/L PO 4, 0 mg/L TOC). Additionally, for each HAA, degradation kinetics were calculated under different DWDS conditions, which demonstrates the impact of these factors on HAA biodegradation. For example, MCAA degradation kinetics were

3.73 and 2.41 L/(mg protein) -1×d-1 at pH 8.5 and pH 7, respectively, which shows that pH changes affect MCAA degradation rate at the same PO 4 and TOC levels. Previous studies reported that the relative order of HAA biodegradability is affected by the type of bacteria.

For example, the order of biodegradation was MBAA > MCAA > DCAA for Nocardia

398 bacterium and DCAA > TCAA > MCAA > MBAA for Pseudomonas 409 [113]. For mixed species used in this study, the overall order of biodegradability was MBAA >

MCAA for mono-halogenated HAAs and DCAA > DBAA for di-halogenated HAAs. In contrast to the biodegradation of mono and di-halogenated HAAs where our observation is

91 consistent with previous findings, the biodegradation of TCAA showed a different trend.

Previous studies reported no or limited TCAA biodegradation at much lower rates in comparison with other HAAs [12, 113], while we observed notable TCAA biodegradation at various rates under different DWDS conditions. For example, at pH 8.5, 0.5 mg/L PO 4, and 0 mg/L TOC, the TCAA degradation rate constant was (7.95 L/(mg protein) -1×d-1) whereas MCAA, DCAA, MBAA, and DBAA degradation rates were 3.73, 5.73, 7.39, and

6.75 (L/(mg protein) -1d-1), respectively. The order of biodegradability might be due to not only the type of bacteria but also different conditions in DWDSs based on what has been observed in the current study.

Table 5.2 HAA biodegradation kinetics for HAA 5 under DWDS conditions

Water distribution system conditions HAA 5 degradation kinetics (L/(mg protein ×day) TOC PO Residual chlorine pH 4 MCAA R2 DCAA R2 TCAA R2 MBAA R2 DBAA R2 (mg/L) (mg/L) (mg/L) 8.5 0 0.5 0 3.73 0.93 5.73 0.99 7.95 0.97 7.39 0.89 6.75 0.99 7 0 0.5 0 2.41 0.96 3.64 0.98 8.99 0.94 4.08 0.94 5.66 0.98 8.5 0 3.5 0 4.84 0.90 10 0.97 8.45 0.95 10.01 0.88 8.50 0.99 8.5 3 0.5 0 16.2 0.87 34.5 0.84 7.01 0.91 7.24 0.92 19.8 0.91

5.3.2 Monitoring bacterial dehalogenase gene expressions under different

DWDS conditions

5.3.2.1 Detection of dehalogenase genes and specificity of deh primers for gene expression analysis

In order to confirm whether HAA degrading bacteria were present in our samples, a PCR-based method using primer sets published by Hill et al. [109] was used to amplify the extracted DNA for detection of dehalogenase genes from bacterial cultures. It was observed that all mixed and isolated bacterial strains contain dehII genes. Figure 5.6 shows the amplicons of dehII genes (422 bp) on the agarose gel (lane 9 for mixed strains and lane 11 for isolated strain) while no amplicon of dehI genes was detected for either mixed strains or isolated one (lanes 3,4,6, and 8). The authentication of dehII genes was further confirmed by conducting the PCR on two samples (1 DNA and 1 cDNA) and sequencing the PCR products. The results of protein BLAST data base analysis exhibited the presence of dehII gene product, L-2-haloacid dehalogenase, in both DNA and cDNA samples. According to the mechanism proposed by Kurihara et al. [110] on HAA degradation, the L-2 haloacid dehalogenase initiates the first step of HAA biodegradation by the nucleophilic attack of a specific carboxylate group associated with the active site in the dehalogenase enzyme resulting in the formation of an ester intermediate. Since only the dehII gene was detected, this gene became the focus of our gene expression study.

Additionally HAA degrading bacteria were identified by sending the extracted DNA from isolated bacteria to Functional Bioscience, Inc. (Madison, WI, USA) for the sequencing of the 16S rRNA gene using universal primers 27F and 1492R. The isolated bacteria were

94 identified as Ralstonia solanacearum and Ralstonia pickettii using BLAST database.

500 bp

Figure 5.6: PCR amplification products obtained from mixed and isolated bacteria species.

Lanes 1, 7 DNA ladder; Lanes 2,5,10 DNA free negative controls ( dehI and dehII primers);

Lanes 3, 4 ( dehI ForI , dehI RevI ); Lanes 6, 8 ( dehI ForI , dehI RevII ); Lane 9 (mixed template, dehII For , dehII rev ); Lane 11 (isolated template, dehII For , dehII rev )

For the quantitative analysis of dehII genes, it is necessary to ensure that the developed assay is not prone to false positive results or primer dimer formation as they greatly reduce amplification efficiency of target and reference genes by competing for reaction components during amplification [105]. Therefore, dehII and reference gene primers were tested on extracted cDNA samples to determine their specificity. Figure 5.7 shows the amplicons obtained from two cDNA samples using dehII and reference gene primers. As can be seen in the figure (lanes 2,3,7 and 8), there is no non-specific product or primer-dimer detectable from PCR reaction using dehII primers. Regarding reference

95 gene primers, in spite of detecting an amplicon at the correct size of 200 bp, the formation of primer dimers was also visible (lanes 4,5,9,10 and 11). The melt curve graph (Figure

5.8) shows a single peak for the template sample and no peak for cDNA free sample, which implies the specificity of dehII primers for gene expression. However, a peak was observed in the melt curve analysis for cDNA free sample containing reference gene primers, which can be attributed to primer dimers. To overcome the issue of primer dimer formation, 10 fold diluted primers (1 µM) were used to amplify cDNA in the case of reference genes.

1 2 3 4 5 6 7 8 9 10 11 12

422 bp

200 bp Primer dimer

Figure 5.7 : PCR results for checking specificity of primers: amplification products for mixed and isolated bacteria species “Lane 1 DNA free negative controls ( dehII gene primers); Lane 11 DNA free negative controls (reference gene primers); Lanes 6,12 DNA ladder; Lanes 2,3 ( dehII gene primers-mixed bacteria); Lanes 7,8 ( dehII gene primers- isolated bacteria); Lanes 4,5 (reference gene primers-mixed bacteria); Lanes 9,10

(reference gene primers-isolated bacteria)

160 DNA free negative control (dehII primers) 140 DNA free negative control (reference gene primers) Template sample (dehII primers) 120 Template sample (reference gene primers) 100 80 60 40 -d(RFU)/d(T) 20

-20

-40 40 50 60 70 80 90 100 Temperature (Celcius)

Figure 5.8: melt curve analysis using real time PCR

5.3.2.2 Relative expression of dehII genes using q-PCR

In microbial ecology, real time RT-PCR is used to measure the number of copies of a gene of interest in a community or an environmental sample. This technique is highly sensitive and allows quantification of rare transcripts and small changes in gene expression.

Generally, two quantification methods have been developed and are widely used in real time RT-PCR. (I) A quantification based on the relative expression of a target gene versus a reference gene. Relative quantification using ∆∆ CP method is the most common method used in gene expression analysis. It determines the gene expression ratio of a target gene in a sample compared to a control, normalizing with the expression ratio of a reference gene [156]. (II) An absolute quantification, based either on an internal or an external calibration curve. For using such a calibration curve, the methodology has to be highly validated and the identical amplification efficiencies for standard material and target cDNA

97 must be confirmed. The generation of stable and reliable standard material is very time consuming and it must be precisely quantified [157]. To avoid the time consuming and highly expensive design and production of standard materials, as well as optimization and validation of a calibration curve based quantification model, relative quantification of dehII genes (fold-change in the expression of the gene of interest) is used in this study [158-161]

The Delta-delta method (PE Applied Biosystems) was used for calculating relative expression of dehII genes according to equation 5.2 [157, 158].

= 2[∆ ∆] → = 2∆∆ (5.2)

Where CP is defined as the point at which the fluorescence rises appreciably above the background, ∆CP sample is CP sample ( dehII gene) – CP sample (reference gene) and ∆CP control is CP control ( dehII gene) – CP control (reference gene).

The outcome of real time PCR for each sample at selected DWDS conditions was compared to that of control (pH of 8.5, 0 mg/L TOC, and 0.5 mg/L PO 4) to calculate the relative gene expression ratios (fold changes). CP, ∆CP values and calculated relative gene expression ratios are listed in table 5.3. Figure 5.9 displays the relative expression of dehII genes of samples at 12, 24, and 48 hrs.

Table 5.3 Relative dehII gene expression calculation for different DWDS conditions (pH,

PO 4 and TOC) in comparison with control samples using the delta-delta method

Water distribution Time (hr) Ref gene Ct dehII gene Ct ∆Ct ∆∆ Ct 2-∆∆ Ct Ratio(avg) Std factor 6.01 30.82 24.81 0 1 12 1 0 4.82 29.7 24.88 0 1 pH=8.5, PO =0.5 4 7.3 30.5 23.2 0 1 mg/L, TOC=0 24 1 0 5.34 31.83 26.49 0 1 mg/L 8.24 30.11 21.87 0 1 48 1 0 8.24 30.89 22.65 0 1 6.27 31.66 25.39 0.58 0.67 12 0.86 0.19 5.51 30.32 24.81 -0.07 1.05 pH=7, PO =0.5 4 7.24 30.31 23.07 -0.13 1.09 mg/L, TOC=0 24 0.91 0.18 6 32.96 26.96 0.46 0.73 mg/L 8.82 31.46 22.64 0.77 0.59 48 0.68 0.08 8.82 31.86 23.04 0.39 0.76 6.31 31.38 25.07 0.26 0.84 12 0.66 0.18 4.41 30.36 25.95 1.07 0.48 PO 3- =3.5mg/L, 4 7.14 29.61 22.47 -0.73 1.66 pH=8.5, 24 1.49 0.17 6.43 32.52 26.09 -0.4 1.32 TOC=0mg/L 9.04 30.77 21.73 -0.14 1.1 48 1.57 0.47 9.04 30.67 21.63 -1.02 2.03 6.05 31.04 24.99 0.18 0.88 12 0.96 0.08 4.69 29.53 24.84 -0.04 1.03 TOC=3 mg/L, 6.57 28.57 22 -1.2 2.3 pH=8.5, 24 2.76 0.46 6.3 31.11 24.81 -1.68 3.21 PO =3.5mg/L 4 10.39 32.59 22.2 0.33 0.8 48 1.75 0.95 10.9 31.61 21.22 -1.43 2.69

3.5 a) Control β b) pH 3.0 c) PO α 4 d) TOC 2.5 α 2.0 α

gene expression gene 1.5

dehII α α α α α α 1.0 α β

Relative 0.5

0.0 12 24 48

Time (hr)

Figure 5.9 : Relative expression of dehII genes under different DWDS conditions (a)

Control: pH= 8.5, PO 4 = 0.5 mg/L, TOC= 0 mg/L, (b) pH impact: pH= 7, PO 4= 0.5 mg/L,

TOC= 0 mg/L, (c) PO 4 impact: PO 4= 3.5mg/L, pH= 8.5, TOC= 0 mg/L, (d) TOC impact:

TOC= 3 mg/L, pH= 8.5, PO 4 = 0.5 mg/L. Values not followed by a common letter are statistically different from control (P < 0.05)

As illustrated in figure 5.9, the expression ratio of dehII genes in samples with pH

7 (0.86, 0.91, and 0.68 at 12, 24, and 48 hr, respectively) was down-regulated compared to control (pH 8.5). These results are in accordance with what has been observed in HAA biodegradation tests under different pH values (HAA removal efficiencies at pH 7 were lower than pH 8.5 in all the experiments) which verifies that pH 8.5 is more effective for

HAA biodegradation. With regard to TOC, the dehII gene expression ratio of samples containing 3 mg/L of TOC to control were 0.96, 2.76, and 1.75 at 12, 24, and 48 hr,

100 respectively, which is consistent with the results of HAA biodegradation tests. With the exception of MCAA and TCAA at 12 hr, the HAA removal efficiencies were higher in samples with 3 mg/L of TOC compared to those of 0 mg/L TOC. The smaller gene expression ratio of 0.96 correlates to lower MCAA and TCAA removal efficiencies at 12 hrs. Regarding the effect of different phosphate concentrations, it was observed that dehII gene expression ratio at 3.5 mg/L phosphate increased with time (0.66, 1.49 and 1.57, at

12, 24, and 48 hrs, respectively) in comparison with control (0.5 mg/L). The lower ratio at

12 hrs supports slower degradation of MCAA, TCAA, and MBAA. Additionally, significance of differences between control and each DWDS condition at 12, 24, and 48 hrs was analyzed using unpaired t-test (P < 0.05). As it is shown in figure 6, the gene expression ratios of TOC at 24 hrs and pH at 48 hrs are statistically different from control.

However, the gene expression ratio for PO 4 (3.5 mg/L versus control) was not found to be statistically different at any of the reaction times (p ≥0.05) despite an apparent increase between 12 and 24 hrs.

5.4 Conclusion

In this study, the influence of DWDS conditions (pH, chlorine, TOC, and PO 4) was examined on the biodegradation of HAAs. It was found that low basic pH (8.5) and high

TOC (3 mg/L) improved the HAA removals. A slight improvement was observed with the increased PO 4 concentration (3.5 mg/L). It was also observed that HAAs are not biodegradable in the presence of residual chlorine even at very low concentrations of 0.2 mg/L. Overall, the order of mean HAA degradation rates was di > mono > tri-halogenated

HAAs. From the gene expression test, it was observed that all bacterial strains contain dehII

101 genes. The results of relative dehII gene expression were consistent with the results of

HAA biodegradation tests. It was observed that the relative gene expression was lower for experiments at pH 7 in comparison with pH 8.5. However, it was elevated for tests at 3 mg/L TOC and 3.5 mg/L phosphate concentration when compared to those of TOC free and 0.5 mg/L phosphate.

102

Chapter 6

Biodegradation of dichloroacetonitrile and chloropicrin by multi-species bacteria from a water distribution system

6.1 Introduction

In recent years, because of population growth and increasing water demands, drinking water providers have been forced to treat water sources that are influenced by wastewater effluents and/or algal blooms, both of which are known to be key sources of nitrogenous disinfection by-products (N-DBPs) precursors [75, 81, 162]. Unfortunately conventional water treatment processes exhibited poor N-DBP precursor removal efficiencies. It has been reported that there is a potential for enhanced N-DBP formation due to the reaction of chlorine with increased levels of reactive nitrogen in drinking water distribution systems (DWDSs) [76, 163]. Moreover, some water treatment plants are utilizing alternative disinfectants (e.g. chloramine) rather than chlorine to reduce the formation of regulated DBPs. It has been observed that some of these alternative disinfectants reduce trihalomethanes (THMs) and haloacetic acids (HAAs) formation but promote the formation of N-DBPs [77]. Haloacetonitriles (HANs), halonitromethanes

(HNMs), and haloacetamides (HAcAms) are among the most important halogenated N-

DBPs [52, 164, 165]. The results of a 2000-2002 US survey showed that HANs, HNMs and HAcAms occurred at the highest concentrations of 14, 10 and 7.4 μg/L, respectively

[46]. For HANs studies have reported that dichloroacetonitrile (DCAN) tends to form at higher concentrations compared to other HAN species especially in algae-impacted water

103

(rich in proteinaceous material) upon chlorination. Additionally, the basic fractions of extracted NOM have been found to produce the highest levels of DCAN [76, 77, 164].

Dichloroacetamide (DCAcAm) was found to be the most prominent species of HAcAms which can be formed through either hydrolysis of DCAN or hydrolysis and subsequent chlorination of cyanoacetic acids [46, 77]. Among all different HNM species commonly found in drinking water, trichloronitromethane (TCNM) is the most frequently occurring one followed by chloronitromethane (CNM) and dichloronitromethane (DCNM) [6, 77].

Moreover, TCNM is used in some agricultural lands as a soil fumigant because of its broad biocidal and fungicidal properties which means it may be a contaminant present in water resources [118]. Although N-DBPs typically form at lower concentrations than THMs and

HAAs, they are of particular concern because of their higher genotoxicity and cytotoxicity

[166-170]. For example, it has been reported that halonitromethanes (HNMs) are 82.6 times more cytotoxic and 67.2 times more genotoxic than their analogous HAAs [89].

Therefore, understanding the fate and potential degradation pathway of these compounds in DWDSs should be given high priority.

In contrast to an extensive understanding of abiotic degradation of emerging N-

DBPs, their degradation kinetics and mechanisms, and degradation by-products in DWDSs by different processes such as reduction by iron corrosion products [25, 26], hydrolysis [80,

102], advanced oxidation processes [126], etc., the biodegradation of most N-DBPs is not well understood. There are only a few studies available referring to potential biodegradation of HNMs and HANs [49, 118, 122]. For example, Castro et al. (1983) reported biodehalogenation of TCNM by Pseudomonas putida G-786 that had been isolated from soil. This bacterium contains a high concentration of the enzyme P-450 cam

104 which is capable of cleaving carbon-halogen bonds. The proposed degradation pathway entails three successive reductive dehalogenation steps to nitromethane (NM) [118]. In another study, HAN biodegradation was checked by Baribeau et al. (2006) using three different isolates ( Xanthobacter autotrophicus , Burkholderia , and Sphingomonas ). It was found that X. autotrophicus is more effective in degrading the HANs than either of the other two isolates. The proposed degradation mechanism for the HANs involves the substitution of a halogen atom by a hydroxyl group (hydrolysis-oxidation pathway) under aerobic conditions [49]. Unfortunately, in these studies the biodegradation kinetics and by- products are not clearly identified. Therefore, the goal of this study is to address the current knowledge gap regarding the biodegradation of HANs and HNMs under the effect of different water pH values. DCAN and TCNM were considered as model compounds for

HANs and HNMs, respectively. A series of batch microcosm tests were conducted to determine biodegradation kinetics, identifying the produced compounds as a result of their degradation, and confirmation of the degradation pathways. Additionally the types of bacteria capable to degrading each compound were isolated and identified by sequencing the 16S rRNA bacterial gene.

6.1 Materials and methods

6.2.1 Chemicals

The following reagent grade chemicals were used to prepare stock solutions for media in culturing bacteria and batch degradation experiments: dichloroacetonitrile (98+%,

Alfa Aesar) trichloronitromethane (99+%, Sigma–Aldrich), trichloroacetic acid (99.5%,

Fisher Scientific) dichloroacetic acid (99+%, Sigma–Aldrich), monochloroacetic acid

105

(99+%, Sigma–Aldrich), dibromoacetic acid (99.7%, Supelco), and monobromoacetic acid

(99+%, Fluka).

6.2.2 Bacterial enrichment and isolation

In this study, the collected biomass from a local water utility was enriched with mixture of DBPs in a completely mixed master culture reactor (Please refer to the section

5.2.2 for full explanation of bacterial enrichment). The enriched biomass was then used in the batch biodegradation experiments. Bacteria responsible for N-DBP degradation were isolated on agar plates supplemented with DCAN or TCNM as the sole source of carbon.

Agar plates were prepared using powder agar (1.4%) and phosphate mineral medium

(25mM) to provide buffer capacity. The mineral medium and agar were blended and autoclaved. Filter sterilized DCAN or TCNM solutions were added to a final concentration of 10 mM after the mineral medium had cooled. Bacteria mixtures were spread on agar plates and incubated at room temperature. N-DBP degraders were identified as colonies that grew on agar plates during DCAN and TCNM degradation. For purification, these colonies were streaked again (up to two times) on fresh agar plates. The N-DBP degradation ability of these isolates was further verified by incubating each purified isolate in mineral medium spiked with each N-DBP (1 mM) and observing a decrease in concentration over time. For DCAN and TCNM degrading bacteria identification, bacterial genomic DNA was extracted from isolated bacteria using DNeasy Power Soil kit (Qiagen,

Germany) according to the manufacturer’s instruction. The extracted DNA from isolated bacteria was sent to Functional Bioscience, Inc. (Madison, WI, USA) for the sequencing of the 16S rRNA gene using universal primers 27F and 1492R.

106

6.2.3 Batch biodegradation tests

Batch microcosm tests were performed to measure the DCAN and TCNM biodegradation efficiencies at different water pH (6, 7.5, and 9). Before starting each set of

N-DBP biodegradation tests, frozen enriched culture was thawed and washed with mineral buffer solution and incubated in medium containing DBP mixture to reactivate the biomass as explained in section 5.2.3. Biodegradation experiments were conducted by diluting a freshly prepared DCAN and TCNM stock solutions (to obtain the working concentration of 100 μg/L) with granular activated carbon filtered tap water (TOC free water). Enriched biomass (~10 4 CFU/ml) was added to the prepared N-DBP solutions. The total volume of samples was 55 ml without air headspace to avoid any potential volatilization of DBPs.

Abiotic controls (samples without biomass) were also prepared to monitor any possible non-biological decay of N-DBPs through other processes such as hydrolysis. Samples were then incubated at room temperature in a rotary tumbler mixed at 40 rpm for up to 96 hrs.

At each time point (5, 10, 15, 22.5, 30, 48, 72, and 96 hrs for DCAN and 6, 12, 24, 48, 72 hrs for TCNM), samples were taken from the tumbler and were centrifuged at 5000 rpm for 10 minutes. The supernatant layer was used for N-DBP analysis. All experiments were conducted with duplicated samples.

6.2.4 Analytical methods

Analysis of DCAN and TCNM was conducted using a gas chromatograph (GC)

(Shimadzu, Japan, GC-2010 plus) with dual electron capture detectors coupled with DB-1

(30 m, 0.25 mm, 1µm) and DB-5 (30 m, 0.25 mm, 0.25 µm) capillary columns (Agilent,

USA). DCAN and TCNM were recovered by liquid/liquid extraction with methyl tert-butyl

107 ether (MTBE) based on the EPA method 551.1. HAAs were quantified according to the

EPA 552.2 method by liquid/liquid extraction with MTBE, followed by methylation with acidic methanol [35, 133]. The analysis of N-DBPs degradation by-product was done using

GC-MS (HP 5988, USA).

6.3 Results and Discussions

6.3.1 Biodegradation of DCAN

It has been reported that water pH plays a crucial role in the fate of HANs in

DWDSs. The stability of HANs decreases and the hydrolysis rate increases with the increase of water pH [98]. The typical range of pH for drinking water systems is between

6 and 9 depending on water quality parameters (e.g. alkalinity) and type of source water

(surface water or groundwater). Therefore, to cover the influence of water pH on the removal of DCAN, pH values of 6, 7.5, and 9 were considered in the biodegradation tests.

In addition to measuring DCAN removal by conducting experiments, a chemical kinetic model developed by Yu and Reckhow (2015) was used for quantitative predictions of HAN hydrolysis depending on pH (Eq. 6.1).

(6.1) ln [ ] = [] − + [ ]

Where [HAN] 0 is the initial concentration of DCAN, K H2O is the neutral hydrolysis rate

-4 -1 3 - constant (1.68×10 h for DCAN), K OH is the basic hydrolysis rate constant (5.6×10 M

1h-1 for DCAN), and [OH -] is the concentration of hydroxide ion (M -1) in the solution [102].

Figure 6.1 (a-c) displays the removal of DCAN (from biodegradation and hydrolysis) under different pH values. As can be seen in Figure 6.1 (a, b), there is a considerable difference in the remaining DCAN concentrations between samples with biomass and abiotic ones

108 especially at 48 and 96 hrs reaction times. For example, the percentage of remaining DCAN at 96 hrs was 31%, 82.9% at pH 6 and 15.6%, 62.4% at pH 7.5 for samples with and without biomass, respectively. The concentrations of remaining DCAN using empirical equation were 97.7% and 83% at pH 6 and 7.5. Net DCAN biodegradation (Table 6.1) at each pH

(6, 7.5) was calculated by subtracting the remaining DCAN concentrations in samples with biomass from abiotic samples (DCAN abiotic sample – DCAN biomass sample ). The analysis of net

DCAN biodegradation results using unpaired T-test showed no statistical difference (P- value ≥ 0.05) at these pH values. Therefore, it can be concluded that DCAN is biodegradable at both acidic and neutral pH values, however, there is no significant difference between them. The HPC analysis also displayed an increasing trend at both pH values which can be attributed to the consumption of DCAN and its decay by-products (e.g.

DCAA) by bacteria as the sole source of carbon in the solution.

100 6.5

80 6.0

60 a 5.5

40 5.0

Log HPC (CFU/mL) HPC Log

Remaining DCAN (%) DCAN Remaining Biodegradation (pH=6) 20 4.5 Experimental hydrolysis (pH=6) Theoretical hydrolysis (pH=6) Log HPC(CFU/mL) 0 4.0 0 12 24 36 48 60 72 84 96

Time (hr)

109

100 7.0

6.5 80

6.0 60 b 5.5

40 5.0

Biodegradation (pH=7.5) (CFU/mL) HPC Log Remaining DCAN (%) DCAN Remaining 20 Experimental hydrolysis (pH=7.5) 4.5 Theoretical hydrolysis (pH=7.5) Log HPC (CFU/mL) 0 4.0 0 12 24 36 48 60 72 84 96

Time (hr)

100 7.0 c Biodegradation (pH=9) Experimental hydrolysis (pH=9) 6.5 80 Theoretical hydrolysis (pH=9) Log HPC (CFU/mL)

6.0 60 5.5 40 5.0

Log HPC (CFU/mL) HPC Log Remaining DCAN (%) DCAN Remaining 20 4.5

0 4.0 0 12 24 36 48 60 72 84 96

Time (hr)

Figure 6.1 : DCAN removal efficiency under the effect of pH (a) pH = 6 (b) pH = 7.5 (c) pH = 9 (PO 4 = 0.5 mg/L, TOC = 0 mg/L, residual chlorine = 0 mg/L)

110

In contrast to pH 6 and 7.5, the hydrolysis (abiotic control) and biodegradation curves almost overlapped at pH 9 (Fig 6.1c). This observation implies no potential biodegradation of DCAN at pH 9. The percentage of net DCAN biodegradation is almost negligible ( ≤ 4%) as presented in Table 6.1. This phenomenon means hydrolysis is the major mechanism of DCAN degradation at basic pH values. The increased number of colonies (HPC results) can be attributed to the biodegradation of DCAA as the end product of DCAN hydrolysis. For example, Reckhow et al. (2001) observed complete hydrolysis of 50 μg/L DCAN in sample with pH 9 in less than 40 hrs in the absence of chlorine, however, the hydrolysis efficiency was below 40% in sample with pH 7.5 even after 70 hrs reaction time [80]. In another study Glezer et al. (1999) reported significant removal of

DCAN (>80%) via hydrolysis at pH 8.7, while DCAN was almost stable at pH 5.7 (<10% was degraded via hydrolysis) after 96 hrs [98]. Additionally, the degradation kinetics of

DCAN at pH 6, 7.5, and 9 was calculated by normalizing the biodegradation rates with initial biomass concentration. The normalized rates and initial concentrations were fit to a pseudo-first order model as presented in section 5.3.1.5. The result of DCAN abiotic and biodegradation kinetic calculations are presented in Table 6.2. From the table, the abiotic degradation rate constant at pH 9 (0.150 h-1) is considerably higher than those of 6 and 7.5

(0.0034 and 0.0048 h -1) which implies expedited hydrolysis of DCAN at basic pH values.

This phenomenon can be explained by the role of the hydroxide ion (OH -) in DCAN hydrolysis process. As can be seen in Figure 2.3, the increased concentration of OH -1 ion accelerates the conversion of DCAN to DCAcAm and eventually DCAA. From the calculated biodegradation rate constants, there is no statistically significant difference at pH 6 and 7.5 (1.4 versus 2.2 L/(mg protein×day)) which shows water pH gradually

111 influences biodegradation of DCAN.

112

Table 6.1 : DCAN degradation at different pH values

Remaining DCAN (%)

Time pH = 6 pH = 7.5 pH = 9

(hr) abiotic biomass Net biodegradation abiotic biomass Net biodegradation abiotic biomass Net biodegradation

sample sample (%) sample sample (%) sample sample (%)

0 100 100 0 100 100 0 100 100 0

2.5 ------76.1 72 4.1

5 96.9 90.8 6.1 95.2 91.5 3.7 50.5 48.1 2.4

10 93.9 89.3 4.6 94.8 91.4 3.4 26.4 25.3 1.1

15 92.6 86.2 6.4 90.8 78.7 12.1 13.5 14.1 -0.6

22.5 89.9 85.2 4.7 88.3 82 6.3 5 5.1 -0.1

30 87.8 82.1 5.7 80.7 74.9 5.8 4.3 1.5 2.8

48 84.4 67.4 17 77.1 60.9 16.2 0 0 0

96 82.9 31 51.9 62.4 15.6 46.8 - - -

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Table 6.2 : Abiotic and biological degradation kinetics for DCAN and TCNM at different water pH

Abiotic Degradation Kinetics (h -1) Biodegradation Kinetics (L/(mg protein×day)) Water pH DCAN R2 TCNM R2 DCAN R2 TCNM R2

6 0.003 0.96 0.011 0.95 1.40 0.95 8.0 0.95

7.5 0.005 0.98 0.019 0.99 2.20 0.93 7.3 0.97

9 0.150 0.99 0.027 0.96 - - 6.0 0.96

114

To further investigate the DCAN degradation mechanism, another set of experiment was completed with samples containing a high concentrations of DCAN

(≈2000 μg/L, for better detection of end products) at water pH 6. DCAA, a well-known end product of DCAN hydrolysis [80, 102], and remaining DCAN were measured in samples with and without biomass. As displayed in Figure 6.2, with degradation of DCAN over time, DCAA is produced at an increasing trend in biomass containing samples until it reaches plateau (mainly due slow hydrolysis of DCAN at pH 6 and simultaneous biodegradation of DCAA and DCAN by active micro-organisms). The huge difference between the concentrations of produced DCAA in samples with biomass and abiotic ones

(≈3-4 times higher in biomass samples) indicates that hydrolysis of DCAN is not the only pathway in DCAA formation as the biodegradation of DCAN also ends in the formation of DCAA. Therefore, it might be concluded that the mechanism of DCAN biodegradation is similar to DCAN hydrolysis with the formation DCAA as the end product. This conclusion is contradictory to the proposed degradation pathway in literature which indicated final product of HANs biodegradation would not be oxalic acid but rather it might be cyanide. DCAN degrading bacteria were identified by sequencing the 16S rRNA genes of isolated bacteria. DCAN degraders were identified as Leifsonia xyli , Leifsonia aquatic , and Pimelobacter simplex using BLAST database.

115

2500 100

2000 80 Remaining DCAN ( µg/L) g/L)

Remaining DCAN-control ( µg/L) g/L) µ DCAA formation ( µg/L) µ 1500 60 DCAA formation-control ( µg/L)

1000 40 DCAA formation ( formation DCAA Remaining DCAN ( DCAN Remaining 500 20

0 0 0 12 24 36 48 60 72 84 96

Time (hr)

Figure 6.2 : DCAN degradation and DCAA formation (pH = 6, initial DCAN concentration

≈ 2000 μg/L)

6.3.2 Biodegradation of TCNM

The potential biodegradation of TCNM was also investigated in this study. Similar to DCAN biodegradation tests, a set of experiments were conducted using samples with and without biomass at different water pH values (6, 7.5, and 9). Figure 6.3 (a-c) exhibits the trend of TCNM removal (in biodegradation and abiotic control samples) at the specified pH values. As can be seen in the Figure 6.3 (a, b), the percentage of remaining TCNM is much lower in the biodegradation samples in comparison with abiotic ones especially after

12 hrs reaction times at pH 6 and 7.5. For example, the percentage of remaining TCNM at

24 hrs was 20.8%, 85.3% at pH 6 and 23.3%, 67.4% at pH 7.5 for samples with and without biomass, respectively. The results of statistical analysis using unpaired T-test also showed a significant difference (P-value<0.05) in TCNM removals at pH 6. Figure 6.3(c) exhibits 116 the removal of TCNM at pH 9. Unlike degradation of DCAN where hydrolysis was the only removal mechanism, the biodegradation of TCNM was observed at pH 9 especially at 24 and 48 hrs reaction times. However, the observed removal efficiencies were lower compared to those of pH 6 and 7.5. For example, the remaining TCNM was 46.6% in biodegradation and 67.5% in abiotic samples at 24 hrs and at pH 9. The analysis of net

TCNM biodegradation results (net TCNM biodegradation was calculated in the same way as DCAN and presented in Table 6.3) also demonstrated a significant difference between pH 6 and 9 (P-value 0.021), and pH 7.5 and 9 (P-value 0.041). Additionally, the outcome of HPC (figure 3) displayed an increasing trend in bacterial growth at all pH values with pH 9 showing lower numbers than those of 6 and 7.5 at all sampling points. Therefore, it can be concluded that TCNM is biodegradable at all pH values and the order of degradation is pH 6 > pH 7.5 > pH 9. Degradation kinetic of TCNM at pH 6, 7.5, and 9 was calculated and fitted to a pseudo-first order model as presented in Table 6.2. As can be seen in the table, similar to DCAN, TCNM is more stable at acidic and neutral pH values in comparison with basic ones. The abiotic degradation rate constants were 0.011, 0.019, and

0.027 hr -1 at pH 6, 7.5, and 9, respectively. In contrast to abiotic degradation, it was observed that TCNM is better biodegradable at lower pH values and the biodegradation kinetic constants are 8, 7.3, and 6 [L/(mg protein×day)] at pH 6, 7.5, and 9, respectively.

This observation may be explained by the fact that through reductive dehalogenation of

TCNM, each chlorine atom is replaced with a hydrogen atom from water. This results in an increased concentration of hydroxide ion and higher pH values in the solution. Therefore, it can be concluded that lower initial pH values are beneficial for TCNM reductive dehalogenation as more hydrogen ions are available to be replaced with chlorine [118].

117

100 7.0 Biodegradation (pH = 6) Ctrl (pH = 6) 6.5 80 Log HPC (CFU/mL)

6.0 60 a 5.5 40 5.0

Log HPC (CFU/mL) HPC Log Remaining TCNM (%) TCNM Remaining 20 4.5

0 4.0 0 12 24 36 48 60 72 Time (hr)

100 7.0 Biodegradation (pH = 7.5) Ctrl (pH = 7.5) Log HPC (CFU/mL) 6.5 80

6.0 60 b

5.5 40 5.0 Log HPC (CFU/mL) HPC Log

(%) TCNM Remaining 20 4.5

0 4.0 0 12 24 36 48 60 72

Time (hr)

118

100 7.0 Biodegradation (pH = 9) Ctrl (pH = 9) 6.5 80 LOG HPC (CFU/mL)

6.0 60 C 5.5 40 5.0 Log HPC (CFU/mL) HPC Log Remaining TCNM (%) TCNM Remaining 20 4.5

0 4.0 0 12 24 36 48 60 72

Time (hr)

Figure 6.3: TCNM removal efficiency under the effect of pH (a) pH = 6 (b) pH = 7.5 (c) pH = 9 (PO 4 = 0.5 mg/L, TOC = 0 mg/L, residual chlorine = 0 mg/L)

119

Table 6.3 : TCNM degradation at different pH values

Remaining TCNM (%)

Time pH = 6 pH = 7.5 pH = 9

(hr) abiotic biomass Net biodegradation abiotic biomass Net biodegradation abiotic biomass Net biodegradation

sample sample (%) sample sample (%) sample sample (%)

0 100 100 0 100 100 0 100 100 0

6 94.4 82.1 12.3 90.8 78.2 12.6 90.7 84.7 6

12 91.5 62.1 29.4 84.5 61.8 22.7 80.8 75.8 5

24 85.3 20.8 64.5 67.4 23.3 44.1 67.5 46.6 20.9

48 58 0 58 40.3 0 40.3 27.2 9.5 17.7

72 19.5 0 19.5 0 0 0 0 0 0

120

There are only few previous studies available regarding the biodegradation of

TCNM [118, 122]. It has been reported that TCNM is biologically degradable through reductive dehalogenation with the formation of DCNM as the major decay end-product.

To investigate TCNM degradation pathway, another set of experiments was conducted using samples containing ~2000 μg/L of TCNM. Both TCNM and DCNM were detected and measured in samples with biomass and abiotic control ones as displayed in Figure 6.4.

With degradation of TCNM over time, DCNM is produced at an increasing trend (from 0 to ~100 μg/L in abiotic sample and from 0 to ~900 μg/L in biodegradation sample). The huge difference between the concentrations of produced DCNM in samples with biomass and abiotic ones ( ≈9 times higher in biomass samples) indicates that biodegradation is the dominant degradation method for TCNM and reductive dehalogenation is the major degradation mechanism. TCNM degrading bacteria were identified by sequencing the 16S rRNA genes of isolated bacteria using TCNM supplemented agar plates. TCNM degraders were identified as Paenarthrobacter aurescens TC1 , and Pseudomonas aeruginosa PAO1 using BLAST database.

121

2000 1000

1750 875

1500 750 g/L) g/L) µ µ 1250 625

1000 Remaining TCNM ( µg/L) 500 Remaining TCNM-control ( µg/L) 750 DCNM ( µg/L) 375 DCNM-control ( µg/L) 500 250 DCNM formation ( formation DCNM Remaining TCNM ( TCNM Remaining 250 125

0 0 0 12 24 36 48 60 72 Time (hr)

Figure 6.4 : TCNM degradation and DCNM formation (pH = 6, initial TCNM concentration ≈ 2000 μg/L)

6.4 Conclusion

In this study, the potential biodegradation of DCAN and TCNM was investigated under the effect of different water pH values by conducting several sets of batch experiments. Regarding DCAN, it was found that DCAN is biodegradable at pH 6 and 7.5, however, the influence of pH on biodegradation is not significant (P-value > 0.05). Due to instability of DCAN at pH 9, all DCAN was degraded via hydrolysis at this pH and abiotic and biodegradation curves almost overlapped. It was also observed that DCAN degradation through both hydrolysis and biodegradation produces different levels of DCAA as the end- product. Therefore, it might be interpreted that pathway of DCAN biodegradation is similar to DCAN hydrolysis. TCNM was found to be biodegradable at all tested pH values with

122 pH 6 showing the highest removal rates and pH 9 the lowest ones. The results of statistical analysis also showed significant differences in TCNM biodegradation (P-value<0.05) between pH 6 and 9, and pH 7.5 and 9. TCNM biodegradation pathway includes the formation of considerable amounts of DCNM (as TCNM degradation by-product) which demonstrates that reductive dehalogenation is the major degradation mechanism.

123

Chapter 7

Conclusion and future recommendations

7.1 Conclusion

The main objectives of the study were to investigate potential degradation of DBPs

(HAAs, DCAN, and TCNM) under different conditions in water distribution systems. In detail, the outcome of the first objective of the study demonstrated the abiotic degradation of HAAs using iron powder under different environmental conditions. It also showed the applicability of RSM for designing experimental parameters and developing models to predict the abiotic removal of six HAAs. Initial pH, HAA concentration, and reaction time were the factors considered in this study at different levels. The results showed that initial pH and reaction time are significant linear factors for all HAAs (P-values <0.05), whereas, reaction time is only significant for brominated ones. It was also observed that all brominated HAAs have a higher degradation rates than their chlorinated counterparts.

According to ANOVA test results, all developed models represent acceptable R 2 values

(>0.9) and can be used to accurately predict HAA removals.

The results of second objective reflects the role of water distribution system conditions (pH, TOC, PO 4, residual chlorine) on the biodegradation of HAA 5 and expression of bacterial dehalogenase genes. It was found that Low basic pH and high concentrations of TOC enhanced HAA biodegradation, whereas, with the increased PO 4 concentration a slight improvement was obtained for HAA biodegradation. It was also observed that the presence of residual chlorine even at a very low concentration hinders

124

HAA biodegradation. The overall order of mean HAA degradation rates was di > mono > tri-halogenated HAAs. The results of gene expression analysis also displayed an increased expression of dehII genes for tests at pH 8.5, 3 mg/L of TOC, and 3.5 mg/L of phosphate concentration. These observations are in good correlation with the results of HAA removal experiments where the degradation efficiency improved with the increase of pH from 7 to

8.5, TOC from 0 to 3 mg/L, and PO 4 from 0.5 to 3.5 mg/L.

Regarding the third objective, the biological degradation of emerging N-DBPs

(DCAN and TCNM) were examined by multi-species bacteria from a water distribution system under the effect of different water pH values in batch experiments. In the case of

DCAN, its biodegradability was confirmed at pH 6 and 7.5, however, no biodegradation was observed at pH 9 as almost all of the chemical was degraded via hydrolysis. The order of hydrolysis for DCAN was pH 9 >>> pH 7.5 > pH 6. DCAA was the end product of

DCAN removal for both hydrolysis and biodegradation with higher concentrations of

DCAA being produced from DCAN biodegradation. The obtained results display the major role of biological processes for the removal of DCAN in water systems indicating the similarity of degradation mechanisms for both biodegradation and hydrolysis. TCNM biodegradability was also verified at all tested pH values and the order of biodegradability was pH 6 > pH 7.5 > pH 9. DCNM was identified as the major end product of TCNM biodegradation. Moreover abiotic degradation of TCNM was detected in all samples, however, the kinetics of abiotic degradation was much lower than biological degradation.

The order of abiotic degradation was pH 9 > pH 7.5 > pH 6. Similar to TCNM biodegradation, DCNM was identified as the major end product of abiotic degradation. The formation of DCNM as the result of TCNM decay illustrates reductive dehalogenation as

125 degradation mechanism for TCNM removal.

7.2 Future Recommendations

Recommended topics for future studies based on this work are as follows:

I. In the current study, only the initial pH, iron dosage, and reaction time were

considered in developing second order polynomial models for the prediction

of HAA removals. Although these models provide insightful information for

the abiotic degradation of HAAs, there are several other parameters, such as

co-existing ions, temperature, addition of catalytic metals, etc. that may

drastically affect HAA degradation efficiency. It is suggested to design a new

set of experiments that include the other influential factors on HAA

degradation. The generated model would be more precise in predicting HAA

removals.

II. In this study, zero valent iron, which is not available in real water distribution

systems was used for the abiotic degradation of HAAs. The experiments may

be repeated using iron particles from water distribution systems to confirm the

capability of developed models in predicting the fate of HAAs in real water

systems.

III. In our batch biodegradation experiments, the presence of residual chlorine

even at a very low concentration, hindered bacterial activity. However, in

water distribution systems, the presence of biofilm as the dominant bacterial

life form protects bacteria from inactivation by disinfectants like chlorine,

especially at low residual concentrations. Therefore, further study is

126

recommended to understand the role of biofilm on HAA formation and

degradation using simulated DWDSs with well-grown biofilm.

IV. In the current study, only the influence of water pH was investigated on the

biodegradation of emerging N-DBPs. Similar to HAAs, other water

distribution system conditions (e.g. TOC/TN, PO 4, residual chlorine, etc.) may

also have significant effects on process efficiency. Therefore, it would be a

good idea to examine how other parameters might affect the biodegradation

of N-DBPs.

V. Since dehalogenation enzymes may play a crucial role on the biodegradation

of N-DBPs such as TCNM, it is recommended to conduct another study to

identify the expression of bacterial dehalogenase genes in the biodegradation

of N-DBPs under water distribution system conditions. A change in the

expression of deh genes of N-DBP degrading bacteria may provide another

proof regarding the degradation of N-DBPs via reductive dehalogenation

process.

VI. It is recommended that further studies being conducted to incorporate the DBP

biodegradation data from this study in to abiotic kinetic models that have been

developed by other researchers to more accurately predict the stability of

DBPs in drinking water. The updated kinetic models might be used by water

utility engineers and operators to optimize the amount of a required

disinfectant for maintaining the biological stability of drinking water, as well

as reducing the concentrations of produced DBPs to meet the required

standards.

127

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